tw0122
3 months ago
Hey at least WIMI has more substantial news then HOLO.
HOLO joins an organization and pumps 2000 percent hilarious but good money maker. Recent News here you would think this could be a 5000 percent winner lol..
Beijing, Feb. 05, 2024 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that a multi-view representation learning algorithm is to deal with the data stream clustering problem. The multi-view representation learning algorithm can provide an effective solution to the data stream clustering problem. The multi-view representation learning algorithm is a method of learning and fusing data from multiple views to obtain a more comprehensive representation. In data stream clustering, multiple views can be used to represent different aspects of the data stream, such as time series view, spatial view, etc., and each view can provide different information.
By learning the features of each view, the potential patterns and structures of the data are discovered and fused to improve the accuracy and stability of data stream clustering for better understanding and analyzing the data stream. Currently, multi-view representation learning algorithms have been widely used and their prospects are very promising. For example, in the financial field, it can be used for customer segmentation and so on. In the medical field, it can be used for disease diagnosis, patient monitoring, etc. In the field of e-commerce, it can be used for user behavior analysis, product recommendation and so on.
The multi-view representation learning algorithm is able to synthesize information from multiple views to provide a more comprehensive description of the data. Different views provide different features and perspectives, and by combining them, a more accurate and comprehensive representation of the data can be obtained. Since the multi-view representation learning algorithm can utilize information from multiple views, it can provide a richer representation of the data. By fusing multiple views, the algorithm can capture more details and correlations in the data, thus improving the data representation. Multi-view representation learning algorithms can effectively improve the clustering performance of data. By synthesizing information from multiple views, the algorithm can reduce the shortcomings of individual views and improve the accuracy and stability of clustering as a whole. The multi-view representation learning algorithm can better handle noise and outliers in the data, making the clustering results more reliable. The multi-view representation learning algorithm can adapt to different types of data. Since different views can contain different types of features, the multi-view representation learning algorithm can flexibly handle situations with different data types. This makes the algorithm more versatile and adaptable when dealing with multiple data.
It can be seen that multi-view representation learning algorithms have the advantages of synthesizing multi-view information, enhancing data representation, improving clustering performance and adapting to different data types. These advantages make multi-view representation learning algorithms have the potential to be widely used in data clustering tasks.
The dataset, including data from multiple views, is first collected. Pre-processing the data, including data cleaning, feature extraction, and data transformation. Then the data is learned using the multi-view representation learning algorithm to obtain multiple-view representations of the data. The learned multiple views are then clustered to obtain multiple clustering results. The multiple clustering results are integrated to get the final clustering results.
The multi-view representation learning algorithm can be categorized into matrix decomposition-based methods, deep learning-based methods, graph-based methods, etc. Matrix decomposition-based methods can represent multiple views of the data as a matrix, and then use matrix decomposition to learn the data. Deep learning-based methods can utilize models such as deep neural networks to learn the data and get a more accurate representation. Graph-based methods can utilize the ideas of graph theory to learn from the data and get a more comprehensive representation.
The multi-view representation learning algorithm can effectively deal with the data stream clustering problem by jointly learning multiple-view representations and combining them with traditional clustering algorithms. Its core idea is to utilize the information provided by different views to capture the intrinsic structure of the data so as to improve the accuracy and stability of clustering.
In the future, with the continuous development of big data and artificial intelligence technology, the multi-view representation learning algorithm will be applied in more fields. Meanwhile, with the continuous optimization and improvement of the algorithm, its accuracy will be further improved.
About WIMI Hologram Cloud
WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.
subslover
5 months ago
WiMi Developed Explainable Artificial Intelligence (XAI)-based fNIRS Neuroimage Classification
BEIJING, Dec. 7, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it developed explainable artificial intelligence (XAI)-based fNIRS neuroimage classification, bringing a breakthrough in the development of BCI technology. By combining the latest AI technology and BCI parsing, this system is expected to bring advances in BCI technology.
WiMi's XAI-based fNIRS neuroimage classification system consists of several key modules that work together to process, analyze, and interpret data for accurate brain activity classification and interpretation. The system architecture is designed to improve classification accuracy and interpretability, and to ensure the accuracy and utility of the system. The system includes a data preprocessing module for filtering, denoising and normalizing the raw fNIRS data to improve the accuracy of subsequent data analysis.
WiMi's XAI-based fNIRSfNIRS neuroimage classification system employs two key classification modules, i.e., a one-dimensional sliding-window-based convolutional neural network (CNN) and a long short-term memory (LSTM) neural network. These two modules are used to classify different types of brain activity patterns respectively, thus improving the applicability and generalization ability of the system. To address the need for interpretation of model outputs, the system introduces an interpretability module, which employs the machine learning interpretability tool SHapley Additive exPlanations (SHAP) for interpreting the outputs of CNN models. By interpreting the model input variables, the system is able to identify the features that contribute the most to the classification of a particular brain activity, helping researchers to gain insight into the association between brain activity patterns and external device control.
Through these methods and techniques, the system is able to efficiently transform fNIRS data into interpretable classification results. The preprocessing of the data, the application of CNN and LSTM models, and the SHAP interpretation module together form the core of the system, enabling it to improve the accuracy of brain activity classification and provide researchers with interpretable results.
WiMi's XAI-based fNIRSfNIRS neuroimage classification system shows good application prospects and potential. In real brain-controlled robots, prosthesis control and virtual reality scenarios, the system's high-precision classification results provide reliable support for device control and offer new possibilities for the application of BCI technology in medical rehabilitation and virtual reality.
The research and application of WiMi's XAI-based fNIRSfNIRS neuroimage classification system brings new insights to the field of brain science. By parsing brain activity patterns through the interpretation module, the system reveals for researchers the association and mechanism of action between functional regions of the brain, and promotes the development of the entire field of brain science. These important results show that the XAI-based fNIRSfNIRS neuroimage classification system not only improves the classification accuracy of brain activities, but also brings new perspectives to the development and application of BCI. It is foreseeable that it will promote the development and popularization of BCI in the future, and bring a revolutionary change to the interaction between humans and machines.
About WIMI Hologram Cloud
WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.
Safe Harbor Statements
This press release contains "forward-looking statements" within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as "will," "expects," "anticipates," "future," "intends," "plans," "believes," "estimates," and similar statements. Statements that are not historical facts, including statements about the Company's beliefs and expectations, are forward-looking statements. Among other things, the business outlook and quotations from management in this press release and the Company's strategic and operational plans contain forward-looking statements. The Company may also make written or oral forward-looking statements in its periodic reports to the US Securities and Exchange Commission ("SEC") on Forms 20-F and 6-K, in its annual report to shareholders, in press releases, and other written materials, and in oral statements made by its officers, directors or employees to third parties. Forward-looking statements involve inherent risks and uncertainties. Several factors could cause actual results to differ materially from those contained in any forward-looking statement, including but not limited to the following: the Company's goals and strategies; the Company's future business development, financial condition, and results of operations; the expected growth of the AR holographic industry; and the Company's expectations regarding demand for and market acceptance of its products and services.
Further information regarding these and other risks is included in the Company's annual report on Form 20-F and the current report on Form 6-K and other documents filed with the SEC. All information provided in this press release is as of the date of this press release. The Company does not undertake any obligation to update any forward-looking statement except as required under applicable laws.
https://c212.net/c/img/favicon.png?sn=CN87879&sd=2023-12-07 View original content:https://www.prnewswire.com/news-releases/wimi-developed-explainable-artificial-intelligence-xai-based-fnirs-neuroimage-classification-302008619.html
SOURCE WiMi Hologram Cloud Inc.
Back to News Headlines
Other Financial Information
Recent News & Disclosure Filings
Recent SEC Filings
StockpickerCAPC
10 months ago
WIMI innovatively develops a humanoid robot control system.
Source
https://cj.sina.com.cn/articles/view/7651844612/1c815e20402001gn7w?from=finance
June 21, 2023
It is understood that, as a world-leading artificial intelligence company, the research team of WIMI (NASDAQ: WIMI) has developed a brain-computer interface (BCI) based on a head-mounted display (HMD) through extensive research in experiments. Controlled humanoid robot interactive control system. This type of interaction controls the robot to interact with the environment and humans through Steady State Visual Evoked Potentials (SSVEP). In this solution, the robot's embedded camera provides real-time feedback, and the stimulus feedback is integrated into the HMD display.
In this research, WIMI's research on the humanoid robot control system based on the head-mounted display (HMD) through the brain-computer interface (BCI) demonstrated a new interaction of the BCI-controlled humanoid robot based on the head-mounted display. It provides a more natural and intuitive way to control the robot. Experimental results show that the control system can provide precise control signals, and users have a very good interaction experience with it. This control method has potential and can be used in many scenarios that require precise control, such as medical care, education, entertainment and other fields.
Humanoid robots have achieved rapid development on the basis of continuous changes in control methods and artificial intelligence technologies, and the conditions for commercialization are increasingly mature. WIMI has increased its investment in this field, which shows that it attaches great importance to and is optimistic about humanoid robots. WIMI's navigation assistance solution for BCI-controlled humanoid robots based on head-mounted displays provides users with a novel interaction method that can improve the robot's operating efficiency and interactive experience. In the future, it can bring a more human-like interactive experience and a wider range of application scenarios, and can interact or work collaboratively with people in a designated area.
epilogue
Humanoid robot research started from the imitation of bipedal walking and expanded to the research and development of artificial intelligence. Since the development of the humanoid robot industry chain, the upstream includes raw materials and core components (with a high proportion of value), and the midstream is system integration and body manufacturing. Downstream is subdivided application scenarios (To B & To C), such as education, logistics and mobile, health care and inspection, etc.
Robots are similar to the smart phone industry chain, and are expected to reproduce the changes in the development stage of smart phones. In addition, under the influence of comprehensive factors such as policy support, global aging, and technological progress, the robot industry has fully met the conditions for entering a period of rapid growth. This may be the reason why many high-tech companies attach importance to and take action to enter the market. In general, in recent years, various humanoid robot products have been launched one after another, with different functions and application scenarios. Coupled with the optimism of WIMI, it will undoubtedly further accelerate the development of this field.
StockpickerCAPC
10 months ago
WIMI (NASDAQ:WIMI) Develops Artificial Intelligence Natural Language Generation (NLG) System to Optimize Content Marketing.
Source
https://cj.sina.com.cn/articles/view/7651844612/1c815e20402001glii?from=finance
June 13, 2023
In recent years, with the popularization of the Internet and the acceleration of digital transformation of enterprises, SEO (Search Engine Optimization) has become a key technology for enterprises to improve brand awareness and expand customer base. SEO needs to rely on high-quality website content, and handcrafted content is undoubtedly time-consuming, laborious and inefficient. In order to solve this problem, WIMI developed "Natural Language Generation SEO" to solve this problem. WIMI uses NLG technology to perform semi-automated search engine optimization (SEO) methods for customers' website landing pages, helps customers draft content to support content marketing, and thus greatly reduces the cost of SEO projects and improves ROI (Return on Investment). With the advancement of natural language generation (NLG) technology, technologies such as digital voice assistants and chatbots are developing rapidly. More and more businesses are adopting natural language generation (NLG) technology to support content marketing and optimize SEO results.
The specific implementation process of this semi-automatic method is as follows:
- First, input the website pages that need SEO into the system, and the system will automatically generate basic content. These will be simple descriptions that tell what the page says in terms of relevance and grammar. SEO experts will then review and revise the generated content to ensure its relevance and accuracy.
- Next, use NLG technology to generate more detailed and unique content for the page. During this process, the system analyzes keywords and target audience, and generates highly readable and high-quality content.
- Finally, an SEO expert double-checks and revises the text to ensure its quality and relevance.
WIMI can quickly create high-quality SEO content by using natural language to generate SEO, while avoiding the high cost and low efficiency of manual SEO content. Apart from this, adopting NLG technology also helps to create more attractive and unique content, which is crucial for improving website ranking and attracting more users.
WIMI uses the most advanced NLG semi-automated method and proves that content writing machines can create unique, human-like SEO content. By comparing with traditional human-written SEO text, it was found that modified machine-generated text was almost indistinguishable from text authored by SEO professionals in many human perception dimensions, and SEO content generated using NLG outperformed professional in search engine rankings content created by professionals, while also dramatically reducing the production costs associated with content marketing and increasing ROI.
The technical implementation process of using natural language generation (NLG) technology to support content marketing and optimize SEO results can be divided into the following steps:
Data collection and preparation:
Collect data related to specific fields, including keywords, industry terms, product descriptions, etc. This data will be used as input for training NLG models and generating SEO content.
NLG model training:
Train an NLG model using machine learning and natural language processing techniques. Commonly used methods include neural network-based models such as recurrent neural networks (RNN) or Transformer models. During training, the model learns language patterns, grammar rules, and contextual understanding.
Content generation and optimization:
Use the trained NLG model to generate SEO content. According to pre-set rules and goals, input relevant information and keywords, and the NLG model will generate texts that meet SEO requirements. Generated text can include titles, descriptions, body text, and more.
SEO Content Evaluation:
The generated SEO content is evaluated to ensure its quality and readability. Grammar, spelling, and keyword usage accuracy checks can be done using natural language processing techniques and SEO tools. In addition, human evaluation can be utilized to measure the readability of content and how well it fits the target audience.
Optimization and modification:
According to the evaluation results, optimize and modify the generated SEO content. It can be adjusted for specific keywords to ensure that the content matches the requirements of the search engine algorithm. This process may require several iterations until the resulting SEO content is of the desired quality and effect.
SEO Content Publishing and Tracking:
Publish optimized SEO content to relevant web pages or articles, and track its performance in search engine rankings. Evaluate the effectiveness of NLG-generated SEO content for SEO by monitoring ranking changes and traffic growth, and make necessary adjustments and improvements.
However, it’s worth noting that despite the remarkable results of natural language generation SEO in improving efficiency and reducing costs, there are still some challenges and limitations. The development of NLG technology requires continuous research and innovation to improve the accuracy of language understanding, the quality of data training, and the ability to protect privacy. In addition, manual editing and optimization of generated content is still necessary to ensure its quality and consistency with the brand image.
With the continuous advancement of technology, WIMI's NLG system will become more intelligent and realistic, capable of generating more accurate and personalized SEO content. However, challenges include the accuracy of language understanding, the quality of data training, and privacy protection. Further research and innovation will be key to advancing the application of NLG technology in the field of content marketing.
With the continuous advancement and development of technology, natural language generation SEO is expected to become an important tool in the field of content marketing. Not only does it improve your business' competitiveness and search engine rankings, it also improves user experience and saves time and resources. Leveraging natural language generation SEO techniques to support content marketing and optimize SEO results will become an essential strategy for businesses looking to succeed in the digital age. WIMI holographic natural language generation SEO will continue to be committed to innovation and optimization to provide customers with more efficient and high-quality content marketing solutions. With the continuous development of NLG technology, it will become an important tool in the field of content marketing, helping enterprises to achieve greater success in the highly competitive market.
StockpickerCAPC
10 months ago
WiMi to Develop A Multimodal Information Fusion Detection Algorithm Based on GANs.
Source
https://finance.yahoo.com/news/wimi-develop-multimodal-information-fusion-120000958.html?guccounter=1
June 12, 2023
WiMi Hologram Cloud Inc. (NASDAQ: WIMI), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it is developing a multimodal information fusion detection algorithm based on generative adversarial networks(GANs). The multimodal information fusion detection algorithm is a method to improve detection accuracy and robustness by fusing data from different sensors or modalities using a GAN. It is implemented by training two neural networks, a generator and a discriminator, where the generator is responsible for generating false data samples, and the discriminator is responsible for distinguishing between accurate and inaccurate data. The two networks compete with each other for learning until the generator can produce sufficiently realistic data, and the discriminator cannot differentiate between true and false.
In multimodal information fusion detection, data from different sensors or modalities, such as image, sound, and text, can be fused and processed to obtain more comprehensive and accurate detection results. The generator uses local detail features and global semantic features to extract source image details and semantic information. Perceptual loss is added to the discriminator to make the data distribution of the fused image consistent with the source image, which improves the accuracy of the fused image. The fused features enter the interest pool network for coarse classification, the generated candidate frames are mapped to the feature map, and finally, the fully connected layer completes the target classification and localization.
GANs have inherent advantages in image generation, allowing unsupervised fitting and approximation of accurate data distributions. Using generators and discriminators for adversarial purposes allows fused images to retain richer information, and the end-to-end network structure no longer requires the manual design of fusion rules.
The technical process of the GANs-based multimodal information fusion detection algorithm studied by WiMi includes data preprocessing, GANs model training, model testing, result evaluation, and optimization and improvement. Data from different sensors or modalities, such as image, sound, and text, are fused for fusion processing, improving target detection accuracy and robustness. In addition, the end-to-end trained GANs can enhance the complementarity and redundancy between multimodal information features after fusing them to improve the accuracy of target detection and classification based on fused elements.
The multimodal information fusion detection algorithm treats the whole image fusion process as adversarial between a generator and a discriminator. For each modality, a generator and a discriminator can be trained separately. Then, by combining the generated results of multiple modalities, a more accurate and comprehensive detection result can be obtained.
Multimodal information fusion detection algorithm based on GANs is one of the fast-developing research directions in recent years. Much related research has been applied in different fields, such as intelligent surveillance, speech recognition, medical image analysis, industrial inspection, etc.
In the future, WiMi will further explore how to fuse more sensors and modalities to improve the fusion effect and applicability range. At the same time, WiMi will investigate how to adopt more efficient GAN structures and enhance model performance through more effective training methods. In addition, WiMi also considers combining this technique with deep learning to improve the accuracy and robustness of detection further. In conclusion, the multimodal information fusion detection algorithm based on GANs has many application prospects and is a research direction worthy of attention and in-depth study.
StockpickerCAPC
10 months ago
WIMI Holography (NASDAQ: WIMI) launches high-resolution image automatic registration technology based on feature space objects.
Source
https://cj.sina.com.cn/articles/view/7651844612/1c815e20402001glao?from=finance
June 12, 2023
Automatic registration of high-resolution remote sensing images (HRRSIs) has been a challenging problem due to local deformations caused by different shooting angles and lighting conditions. In order to solve this problem, WIMI has proposed a new method based on feature space object (CSO) extraction and matching. First, the CSO and its localization points on the image are automatically extracted using the Mask R-CNN model. Then, each object and its nearest neighbors are encoded by an encoding method based on object category, relative distance and relative orientation. Next, a code matching algorithm is applied to search for the most similar object pairs. Finally, object pairs are filtered by position matching to construct the final control points for automatic image registration. Experimental results show that the proposed method outperforms traditional optimization methods based on local feature points in terms of registration success rate.
With the continuous development of remote sensing technology, the automatic registration of high-resolution remote sensing images (HRRSIs) has been a serious challenge. Different shooting angles and lighting conditions will cause local deformation of the image, which brings difficulties to data processing and analysis. It is reported that WIMI has launched a high-resolution image automatic registration technology based on feature space objects. Provide reliable information support in areas such as monitoring, urban planning, and agricultural management.
The core of this technological innovation is an automatic registration method based on feature-space objects (CSO). Traditional image registration methods usually rely on grayscale registration, transform domain registration or feature point-based registration, but these methods are very sensitive to grayscale, rotation and deformation, and are computationally intensive, making them unsuitable for automatic registration. WIMI holographic technology adopts a brand-new idea, and achieves more accurate registration results by using the Mask R-CNN model to automatically extract CSO and locate its position.
First, the image is scanned using the Mask R-CNN model, and the CSO and its positioning points on the image are automatically extracted. The accuracy and efficiency of this step is based on the accumulation of research and innovation in the field of computer vision by the WIMI Holographic team for many years. Subsequently, each extracted CSO and its nearest neighbors are encoded according to object category, relative distance and relative orientation. The encoded feature vectors provide the basis for subsequent matching.
In order to find the most similar object pair, WIMI Hologram adopts advanced code matching algorithm. The algorithm determines the degree of matching by calculating the similarity between encoded feature vectors. Object pairs with higher similarities are considered as candidates for registration. Further, the initial object pairs are filtered by position matching algorithm to exclude some false matches and obtain more reliable registration results. Through this step, WIMI holographic technology can accurately capture the spatial position relationship in the image, further improving the accuracy and robustness of registration.
According to the data, WIMI's high-resolution image automatic registration technology based on feature space objects has achieved remarkable results in experiments. By testing and comparing multiple data sets, the results show that this technique is significantly better than the traditional optimization method based on local feature points in terms of registration success rate. [color=green]This breakthrough achievement will enable the remote sensing industry to perform data processing and analysis more accurately and efficiently, providing a more reliable basis for decision-making.[/color]
In addition to remote sensing image processing, this technology also has a wide range of application prospects. In the field of urban planning, automatic registration technology based on characteristic spatial objects can help planners better understand urban changes and development trends, so as to formulate more scientific urban development strategies. In terms of environmental monitoring, this technology can provide accurate image registration results, help scientists monitor and evaluate environmental changes, and provide important data support for environmental protection and resource management. In addition, in areas such as agricultural management and disaster monitoring, this technology can also play an important role, providing accurate data analysis and decision support.
At present, WIMI holographic has introduced the high-resolution image automatic registration technology based on feature space objects to the market, and is exploring application scenarios with industry partners. By integrating this technology with existing remote sensing data processing platforms and software, users will be able to easily achieve high-precision image registration, thereby improving the accuracy and efficiency of data analysis. The introduction of high-resolution image automatic registration technology based on feature space objects marks another important breakthrough of WIMI in the field of image processing. The application of this technology will have a huge impact on the remote sensing industry. In the past, image registration required a lot of time and labor, and the results were not necessarily accurate. However, WIMI holographic technology will make the registration process more automated, efficient and accurate, greatly improving the processing efficiency and quality of remote sensing data.
It can be said that WIMI based on the high-resolution image automatic registration technology of feature space objects has solved the long-standing problems in the field of remote sensing image processing. The application of this technology will provide a more reliable and accurate data basis for scientific research and practice in related fields. In addition, WIMI also plans to carry out further research and development work with industry partners. They will work on further optimizing the efficiency and performance of the algorithm, expanding the scope of application of the technology, and developing more application solutions for different fields. This will provide users with more choices and meet the needs of different industries for image processing and data analysis. It provides strong support for accurate data analysis and intelligent decision-making. The successful application of this technology will bring huge economic and social benefits to environmental protection, urban planning, agricultural management and other fields.
StockpickerCAPC
11 months ago
WiMi Hologram Cloud (WIMI) creates a digital holographic AR-HUD solution,
Source
https://www.newstrail.com/wimi-hologram-cloud%EF%BC%88wimi-creates-a-digital-holographic-ar-hud-solution/
June 7, 2023
After years of development, AR has been applied in many fields, such as the introduction of AR filter by Snapchat (SNAP), which adds a new possibility for social entertainment; Baidu (BIDU) map has created a new model of AR innovation and launched the “walking AR navigation” function.
With the continuous improvement of AR technology, a series of new technologies and new trends have emerged and attracted wide attention. For example, AR has also shown great development potential in the automotive field. The emergence of AR-HUD has taken the car driving experience to a new level.
HUD originated in the aviation field, and GM first applied HUD in the automotive field in 1988. As the name suggests, HUD is the projection of important driving information such as speed and navigation onto the windshield, so that the driver can need to see the vehicle information without bowing his head. Its obvious features can better guarantee driving safety. With the update and iteration of the intelligent cockpit, HUD is expected to develop into the core component of intelligent cars with its excellent interactive features of “people and car, people and environment”.
AR-HUD integrates AR function based on HUD, covering a layer of digital images in the real world seen by driving so that the information projected by HUD is integrated with the real driving environment. With the continuous progress of science and technology, more and more automobile manufacturers begin to pay attention to the human-car interaction experience, making the automobile AR-HUD begin to carry richer and more delicate functions and display tasks.
On-board HUD is divided into three main categories
The onboard HUD has experienced three generations of upgrades, continuously optimized imaging quality, continuous increase of information, and greatly enhanced the sense of technology. At present, W-HUD is the mainstream in the market, and AR-HUD is accelerated for mass production.
The first generation is the C-HUD (Combiner HUD), a combined head-up display system. C-HUD uses translucent resin board as the display medium, which has the advantage of convenient installation, but the imaging area is small and the display information is less. Because C-HUD is installed on the vehicle in the form of accessories, it is easy to cause secondary injury to the driver in case of an accident.
The second generation W-HUD (Windshield HUD) windshield head-up display system, which is currently the most widely used HUD, has achieved mass production. The W-HUD uses optical reflection to project driving information onto the front windshield of the car. W-HUD shows a larger range and further projection distance than C-HUD.
The third generation AR-HUD (Augmented Reality HUD) augmented reality head-up display system is a new head-up display technology. Compared with the traditional W-HUD, AR-HUD has a large projection range and more information, which can better combine the data collected by ADAS for scene fusion. Through the superposition of digital images and real scenes, it can enhance the sense of practicality and technology of HUD.
In 2020, the market began to accelerate the iteration of W-HUD to AR-HUD. Because AR-HUD displays increased information compared to W-HUD. AR-HUD collects the data of the external environment through the front-facing radar and camera equipment, calculates the required image and data information through the AR algorithm, and then reflects the image to the windshield through the optical structure in the AR-HUD, and produces a virtual image superimposed on the real object in front of the windshield.
There is no doubt that, as one of the representative features of the intelligent cockpit, AR-HUD is expected to become the final form of HUD in future vehicles. AR-HUD technology has been attracting the attention of the industry. Some institutions expect that the market size of China’s AR-HUD industry will exceed 7 billion yuan in 2023.
Technology is a solid barrier to WiMi
On the eve of the market explosion of AR-HUD, it is reported that WiMi Hologram Cloud (NASDAQ: WIMI) is working on building AR-HUD-related products. Its light field AR-HUD has multi-faceted optical imaging capabilities, which can achieve a better AR fusion effect and a more natural visual experience. In addition, WiMi Hologram Cloud technology solutions are in rapid progress, with uHD key capabilities, to provide rich application scenarios such as instrument information display, AR navigation, safety-assisted driving, night vision/rain and fog enhancement tips, and audio and video entertainment. Project virtual images in different locations in the real world, so that real objects in different positions can visually integrate with HUD virtual images, and may become a dark horse on the track in the future.
WiMi Hologram Cloud As a leading holographic AR application technology provider, fully aware based on AR-HUD technology is gradually become the focus of auto manufacturers, years committed to through independent innovation, positive research, and development, through AR-HUD transmission to holographic AR image display various digital information, an intuitive way to guide driving, improve the intelligent experience, make AR-HUD intelligent cockpit interactive hub, to improve the safety of driving, to help reduce the risk of accident.
With the further development of AR technology, WiMi Hologram Cloud will focus on the core capability of “visual interaction” in the future, while consolidating the dominant position in the field of intelligent automotive electronics and gradually developing products with technological technology such as AR-HUD. It can be said that this is an effort and attempt in emerging industries such as autonomous driving. It is expected to form a perfect human, car, road, and network ecology, and open up a new entrance for the next generation of augmented reality Internet. In addition, WiMi Hologram Cloud will continue to focus on related fields and technologies, and further participate in the larger holographic AR industry track.
To Sum Up
The HUD market is facing a period of rapid development, and many research institutions have given the same view. On-board HUD technology has become an important trend in the automotive industry. Through HUD technology, drivers can get more driving information while maintaining attention and concentration on the road, thus improving driving safety and comfort. With the development and application of AR technology, it is believed that the AR-HUD system can be said to be a major trend in the development of intelligent vehicles in the future, and its addition also makes us full of expectations for future cars.
At present, some people point out that AR-HUD is only a transitional technology to achieve fully autonomous driving in the future. After all, after the realization of autonomous driving, the convenience of AR-HUD head display is also insignificant for users.
StockpickerCAPC
11 months ago
WIMI (NASDAQ: WIMI) develops DPCEngine to improve the efficiency of strategy evaluation through efficient density peak clustering algorithm.
Source
https://cj.sina.com.cn/articles/view/7651844612/1c815e20402001gkoh?from=finance
June 9, 2023
With the rapid development of technologies such as Industry 4.0, CPS (Cyber-Physical System), blockchain, cloud computing, and big data, and the rapid development of today's network and information security, access control has become the key for enterprises to protect data and resource security. The essential. In the context of ever-increasing security requirements for network and information systems, access control plays an important role in the field of network and information security as well as interdisciplinary topics in the Internet of Things. XACML (Extensible Access Control Markup Language) is a standard widely used in the field of access control. When the defined policy set becomes large and complex, the policy evaluation time will increase significantly, and the traditional policy evaluation methods often face performance bottlenecks. Performance for large-scale systems is a challenge. In order to solve this problem, WIMI (NASDAQ: WIMI) has developed DPCEngine, which is an efficient density peak clustering algorithm for improving the performance of strategy evaluation.
It is reported that WIMI WIMI Hologram Institute proposed a policy set clustering method based on the density peak clustering algorithm, which reduces the complexity of policy evaluation by identifying the cluster structure in the policy set. The architecture and algorithm flow of WIMI DPCEngine include key steps such as data preprocessing, density peak clustering, strategy matching and evaluation.
To evaluate the performance and effectiveness of DPCEngine, a real dataset containing a large-scale complex policy set is used for experiments. This dataset contains policies from different domains, covering a variety of access control scenarios. The data set is divided into training set and test set, where the training set is used to build the model of DPCEngine, and the test set is used to evaluate its performance.
WIMI Hologram R&D staff compared DPCEngine with traditional strategy evaluation methods, including methods based on linear search and tree structure.
Two performance metrics are evaluated:
- policy evaluation time and
- matching accuracy.
Policy evaluation time refers to the time required to evaluate an access request, while matching accuracy refers to the consistency between the matching results of DPCEngine and traditional methods.
DPCEngine has a significant performance advantage in policy evaluation time. Compared with traditional methods, DPCEngine can greatly reduce the policy evaluation time, especially when the policy set is large in scale and high in complexity. This is due to the density-peak-based clustering algorithm adopted by DPCEngine, which is able to cluster policy sets into smaller subsets, thereby reducing the search space for evaluation.
In terms of matching accuracy of WIMI's DPCEngine, the experimental results show that there is a high consistency between the matching results of DPCEngine and traditional methods. This shows that DPCEngine does not sacrifice accuracy while improving policy evaluation performance. In addition, we also conducted scalability experiments to evaluate the performance of DPCEngine under different scale policy sets. The results show that DPCEngine can effectively cope with large-scale policy sets and has good scalability. Its working process is shown in the figure.
According to the data, WIMI's DPCEngine, as a strategy evaluation engine based on the density peak clustering algorithm,
has three main functions:
- preprocessing strategy set,
- cluster strategy set and
- matching strategy.
The combined use of these functions can significantly improve the performance and accuracy of policy evaluation.
Preprocessing strategy set:
Before policy evaluation, DPCEngine prepares data by preprocessing strategy sets, making it more suitable for density peak clustering. The preprocessing process includes steps such as data cleaning, feature extraction and data conversion. Ensure data accuracy and consistency by cleaning data to remove redundant, incomplete or erroneous policy information. Avoid negatively affecting the assessment results. The feature extraction process extracts key features from the policy set, such as user roles, resource types, and operation permissions, for subsequent clustering operations. Data transformation converts the strategy set into a data representation suitable for the density peak clustering algorithm, such as a vector or matrix, for cluster analysis.
Cluster policy set:
DPCEngine uses the density peak clustering algorithm to perform cluster operations on the policy set. The density peak clustering algorithm identifies the cluster structure in the policy set by evaluating the density and distance between the policies. The algorithm determines the peak point based on the density and distance between the policies, and divides the policies between the peak points into different clusters . This enables the clustering of a large and complex set of policies into smaller subsets, each cluster representing a set of policies with similar characteristics and behavioral patterns, reducing the time and complexity of policy evaluation. The result of the cluster strategy set is a group of strategy clusters with similar characteristics and behavior patterns. This cluster strategy set method can reduce the time and computational complexity of strategy evaluation, and improve the performance and efficiency of the system.
Matching strategy:
DPCEngine uses clustering results for strategy matching. When an access request arrives, DPCEngine compares and matches it with the pre-generated policy cluster. By searching for the most similar policy in each cluster, DPCEngine can quickly determine the policy set that matches the access request. This clustering-based matching method can significantly speed up policy matching and provide accurate matching results. In addition, DPCEngine can also combine other access control technologies and rule engines to further optimize the policy matching process to ensure system security and compliance.
Through the comprehensive use of preprocessing policy sets, cluster policy sets and matching policies, DPCEngine can provide enterprises with efficient and accurate policy evaluation.
In addition, WIMI's (NASDAQ: WIMI) DPCEngine's preprocessing policy set, cluster policy set and matching policy functions cooperate with each other to provide enterprises with an efficient, accurate and scalable policy evaluation performance. By utilizing the density peak clustering algorithm and the clustering structure of policy sets, DPCEngine can achieve fast policy matching in the case of large-scale complex policy sets. This cluster-based approach reduces the time and computational complexity of policy evaluation and improves the performance and efficiency of the system.
The three main functions of DPCEngine have broad application prospects in enterprises.
- First, the preprocessing policy set function can help enterprises process and prepare huge policy data to ensure data quality and consistency. This is crucial for cleaning and transforming the data prior to policy evaluation to improve the accuracy of subsequent clustering and matching.
- Second, the Cluster Policy Set feature enables enterprises to divide large and complex policy sets into relatively smaller policy clusters with similar characteristics. This cluster operation reduces the size and complexity of policy evaluation and improves the performance and efficiency of the system. By grouping similar policies together, enterprises can match access requests more quickly and implement fine-grained management and control over policies.
- Finally, the Match Policy feature allows organizations to compare and match access requests against pre-generated policy clusters. This clustering-based matching method can quickly locate the policy set that matches the access request, improving the speed and accuracy of policy matching. At the same time, DPCEngine can be used in combination with other access control technologies and rule engines to further optimize policy matching results and ensure system security and compliance.
Currently, the preprocessing policy set, cluster policy set and matching policy functions of WIMI DPCEngine enable enterprises to efficiently evaluate access control policies. This policy evaluation engine based on the density peak clustering algorithm has broad application prospects in various industries and fields, and can help enterprises build a robust security protection system and cope with growing security challenges. With the continuous development and improvement of technology, DPCEngine will further improve the performance and accuracy of policy evaluation, and provide reliable support for enterprises to ensure the security of data and resources.
StockpickerCAPC
11 months ago
WiMi Hologram Cloud is Developing A Memristor-based Neural Signal Analysis System for Efficient BCI.
Source
https://finance.yahoo.com/news/wimi-hologram-cloud-developing-memristor-120000124.html
June 8, 2023
WiMi Hologram Cloud Inc. (NASDAQ: WIMI), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it is developing a memristor-based neural signal analysis system to improve signal processing capability, response time and accuracy. Results show a significant improvement in signal processing capability.
The system employs a new computational paradigm closer to how the human brain works, i.e., a memristor-based neural network system. The memristor-based neural network is characterized by high parallelism and low energy consumption. When processing neural signals, the system uses an end-to-end data processing process. That is, the acquired raw signal is directly converted into the final control signal through pre-processing, feature extraction, and classification recognition steps, thus avoiding the frequent data transmission and computation process in traditional architectures and significantly improving the efficiency and accuracy of the system. The memristor arrays can quickly process a large amount of data because they can hold information between each neuron, thus enabling highly parallel processing. This approach is similar to how neurons in the human brain communicate, allowing for more efficient data processing. In addition, memristor-based systems also have higher energy efficiency in storing and reading data, which can significantly reduce power consumption.
Compared with the traditional von Neumann architecture, the system improves brain-computer interface signal processing and linking capabilities hundreds of times. In addition, the system employs innovative hardware architecture and algorithm optimization to efficiently process neural signals from the human brain and convert them into computer-recognizable signals for seamless human-computer connectivity.
The path and manner of implementation of this technology are as follows.
First, an array of memristors needs to be designed and prepared. A memristor is an electronic device that can change its resistance value in response to a voltage and remember previous voltage and current states. A memristor array is a circuit system consisting of many memristors that mimic the synaptic connections between neurons and record the postsynaptic potentials between neurons. The human brain's neural signals then need to be captured. Brain-computer interface technology usually uses electroencephalography (EEG), magnetic resonance imaging (MRI), and other methods to acquire neural signals. These signals are generally weak and require processing, such as signal amplification and filtering, to enhance the strength and accuracy of the signal. After the signal acquisition, data pre-processing is required, including noise removal, filtering, and feature extraction.
These steps can improve the quality and accuracy of the signal and reduce misclassification and interference. The pre-processed neural signals are fed into the memristor array for simulation. In the memristor array, each memristor represents a neuron, and the connection and synaptic strength between them can be regulated employing voltage and current, etc. The memristor array can simulate and record the postsynaptic potentials and signaling between neurons. Finally, the simulation results of neural signals are interpreted and controlled by algorithms and other means. The brain-computer interface system can control external devices such as computers and prostheses by interpreting neural signals such as brain waves and can also realize applications such as human-computer interaction. In the practical application of this system, many details still need to be considered, such as different functional requirements, differences in the source of signal letter acquisition and the environment used, and other detailed adjustments to the application of different scenarios.
WiMi's system adopts the latest technological solution to achieve efficient neural signal processing and analysis by simulating the synaptic connections between neurons through an array of memristors. This technology will lead a new revolution in brain-computer interface technology and bring a more convenient and efficient intelligent interaction experience for human beings. The system also adopts adaptive adjustment algorithms and reinforcement learning algorithms, which can quickly adjust the parameters of the neural network according to the user's operating habits and intentions, thus achieving more accurate control. In addition, the system introduces multimodal sensors and multi-source data fusion technology, which can fuse data from different sensors to improve the accuracy and robustness of the signal.
Artificial intelligence, machine learning, and brain-computer interface technologies are developing rapidly. As an emerging hardware gas pedal with low power consumption, high speed, and high accuracy, memristor arrays have a broad application prospect in neural signal analysis. The neural signal analysis system based on memristor arrays has been verified in the laboratory for several experiments and achieved excellent results. Compared with traditional brain-computer interface technology, WiMi's memristor-based neural signal analysis system has higher efficiency and accuracy and can realize more complex control tasks and interaction modes. The launch of this system will significantly impact healthcare, education, and entertainment, bringing a smarter, more convenient, and more comfortable future for human beings.
StockpickerCAPC
11 months ago
WIMI (NASDAQ: WIMI) Releases Brain-Computer Interface (BCI) Encrypted Anonymizer Software Technology System.
Source
https://finance.sina.com.cn/tech/roll/2023-06-06/doc-imywiftk2574032.shtml
June 6, 2023
Brain-computer interface technology is a cutting-edge technology with great prospects and a broad market application base.
It has unique advantages:
it can access the neural data decoded by the brain in real time. Even sports and other activities are combined to carry out neuromodulation technology. With the rapid development of technology, mobile technology, communication and cloud computing, the brain-computer interface technology has developed rapidly, enabling us to explore things that were unimaginable a few years ago. Application scenarios, the development far exceeds expectations. Currently there are business-oriented (B2B) devices on the market, and consumer-oriented (B2C) commercial devices. Although the development of brain-computer interface technology has brought many opportunities and application prospects, it also brings Here comes some security and privacy concerns.
According to reports, in order to ensure the security and privacy protection of brain-computer interface technology, WIMI (NASDAQ: WIMI) is developing a software system brain-computer interface encrypted anonymizer to ensure user information security and privacy issues.
Brain-computer interface (BCI) technology represents a direct communication link between the brain and external devices. Recent findings demonstrate how electroencephalography (EEG) signals recorded from consumer-grade BCI devices can be used to extract private information about users. With enough computing power, this information can be used by others to infer our memories, intentions, conscious and unconscious interests, and our emotional responses.
This technology has a wide range of applications, from medical devices to gaming and entertainment. However, the use of BCIs raises security and privacy concerns because EEG signals can reveal sensitive information about a user's thoughts, emotions, and memories. Therefore, it is critical to develop techniques that ensure the privacy and security of BCI users.
Research on improving the privacy and security properties of BCI-enabled technologies involves two major steps.
The first step focuses on identifying which components of electroencephalography (EEG) signals can be used to extract private information. After identifying potential vulnerabilities, WIMI's R&D team will quantify the amount of information exposed. Based on the results obtained,
the second step was the development of software encryption tools designed to prevent the possible extraction of users' private information. The "BCI encrypted anonymizer" developed by WIMI is based on signal components, that is, the recorded brain signals can be decomposed into a collection of characteristic signal components in real time. From these components, information corresponding to the user's expected BCI commands can be extracted while filtering out potentially private information.
EEG signals are a combination of different frequencies, each with unique characteristics. The EEG signal components used for private information extraction, the EEG signal components most susceptible to private information extraction are alpha waves and beta waves. Alpha waves are associated with relaxation, while beta waves are associated with cognitive processing and attention. Thus, alpha and beta waves can reveal sensitive information about a user's emotional state, cognitive ability, and attention span.
In order to quantify the amount of information that can be extracted from EEG signals, WIMI WIMI will use the mutual information (MI) method. MI measures the amount of information shared by two variables. In this case, WIMI's R&D team will measure the MI between the EEG signal and the private information that can be extracted from it. Based on MI results, the risk level of extracting private information from EEG signals can be determined.
The BCI encrypted anonymizer developed by WIMI Hologram is a software tool that can prevent the extraction of private information from EEG signals. The tool works by decomposing the EEG signal into a set of characteristic signal components. These components are the building blocks of the EEG signal and can be used to reconstruct the original signal. However, BCI anonymizers filter out components that contain private information, allowing only those needed to control external devices. This technology aims to strengthen the development of closed-loop brain-controlled interfaces, so as to better protect the privacy and security of users. We believe that this technology will provide a safer and more reliable option for future BCI applications.
The brain-computer interface encrypted anonymizer released by WIMI (NASDAQ: WIMI) works in real time without any additional hardware or sensors. It can be integrated into existing BCI systems and customized for different applications. The tool can also be used for offline data analysis to identify potential privacy risks in existing BCI data.
The privacy and security of brain-computer interface technology is critical to ensuring the trust and acceptance of the technology by users in its future development. WIMI's technical solutions can help address security and privacy concerns by identifying EEG signal components that are susceptible to private information extraction and developing software tools to prevent their extraction. The encrypted anonymizer of brain-computer interface can improve the privacy and security of users without compromising the function of brain-computer interface system. This technology could lead to the development of more advanced brain-computer interface systems that provide real-time control of external devices without compromising user privacy. This technical solution can effectively improve the security and reliability of brain-computer interface devices, avoid data leakage and abuse, and provide better user experience and services.
StockpickerCAPC
11 months ago
WiMi Hologram Cloud Launches Its Transformer Algorithm-based WiMi AI Assistant.
Source
https://finance.yahoo.com/news/wimi-hologram-cloud-launches-transformer-120000316.html
June 5, 2023
WiMi Hologram Cloud Inc. (NASDAQ: WIMI), a leading global Hologram Augmented Reality ("AR") Technology provider, today launched an artificial intelligence assistant using an advanced transformer algorithm, called WiMi AI Assistant, which is built up through years of R&D and improvement. WiMi successfully applied the transformer algorithm to natural language processing, providing users with a more intelligent and efficient communication platform.
The WiMi AI Assistant has the following highlights:
- Transformer algorithm:
WiMi AI Assistant uses an advanced transformer algorithm to automatically learn and understand human language and engage in intelligent conversations with humans. The introduction of the transformer algorithm enables the assistant to handle long-distance dependencies and complex contexts better, thus improving overall language understanding.
- Natural Language Processing:
Through deep learning and big data, WiMi AI Assistant understands the user's context and needs and can quickly answer complex questions accurately. In addition, the assistant can learn and improve by itself, continuously optimizing the accuracy and efficiency of question responses.
- Personalized recommendations:
WiMi AI Assistant can provide users with customized information recommendations based on their behavior and interests. Whether it's news, music, or movies, the assistant can precisely meet users' needs and bring them a more personalized experience.
- Multiple applications:
WiMi AI Assistant can be applied in smart homes, intelligent customer service, and many other industries such as healthcare, finance, and education. This has significantly expanded the influence of artificial intelligence.
WiMi Hologram Cloud has always been committed to the R&D and application of innovative technologies. The launch of the WiMi AI Assistant is another significant breakthrough in artificial intelligence. WiMi will continue to increase its investment in R&D to provide users with more high-quality intelligent products and services.
StockpickerCAPC
11 months ago
WIMI (NASDAQ: WIMI) develops a bionic pattern recognition (BPR)-based convolutional neural network (CNN) image classification technology solution.
Source
https://cj.sina.com.cn/articles/view/7651844612/1c815e20402001gjlu?from=finance
June 5, 2023
In recent years, with the continuous development and application of artificial intelligence technology, image classification technology has been widely used in many fields. And with the rise of deep learning, convolutional neural network (CNN) has become the mainstream model for processing image classification tasks. CNNs recognize images by automatically extracting features from them, and classify them using the softmax function. However, due to the limitations of the softmax function, traditional CNN models have some deficiencies in image classification.
It is reported that in order to solve this problem, WIMI has developed a new image classification method that uses a hierarchical structure inspired by the animal visual system to automatically extract features from images. This method combines bionic pattern recognition (BPR) with CNN, which can make full use of the geometric structure of high-dimensional feature space, so as to achieve better classification performance, so it can overcome some shortcomings of traditional pattern recognition. The method has been validated in multiple experiments and, in most cases, achieves higher classification performance than traditional methods.
A Convolutional Neural Network (CNN) is a deep learning model specifically designed to process images. It can automatically extract features from images through convolution and pooling operations, and use fully connected layers for classification. The convolution operation refers to applying a convolution kernel (also known as a filter) to each location on the image and outputting the result as a feature map. The pooling operation refers to downsampling on the feature map to reduce the amount of computation and the risk of overfitting.
In the traditional CNN image recognition classification model, the softmax function is used for classification. The softmax function can convert a set of scores into a probability distribution, where each score represents a confidence score that the image belongs to a certain class. Traditional pattern recognition methods usually use hyperplanes in feature space to segment categories. However, this method has some disadvantages, such as requiring manual feature selection and difficulty in handling nonlinear data. In contrast, bioinspired pattern recognition (BPR) can overcome these problems by performing class recognition on geometric cover sets that are unioned in a high-dimensional feature space.
BPR is a pattern recognition method based on bionics.
Its basic idea is to use biological systems to simulate the processing of sensory information, and regard the pattern recognition process as being carried out in a high-dimensional feature space. In this high-dimensional space, each sample point is regarded as an object rather than a point. Therefore, samples of different classes are distributed in different regions in the high-dimensional feature space, and these regions are called geometric covering sets. Each geometry cover set consists of a set of geometric objects called geometric primitives, such as spheres, cones, polyhedra, etc. With a proper combination of geometric primitives, a coverage set with high classification performance can be constructed to achieve class recognition.
Research shows that WIMI combines BPR and CNN to achieve better image classification effect. Specifically, based on bionic pattern recognition (BPR) convolutional neural network (CNN) image classification, CNN features can be mapped into a high-dimensional feature space, and a geometric coverage set can be constructed in this space, and then the new sample map Go into that space and decide what category it belongs to.
According to the data, WIMI uses a mapping function to map CNN features into a high-dimensional feature space in BPR-based CNN image classification. This mapping function can be a simple nonlinear transformation such as a polynomial transformation or a radial basis function (RBF) transformation. It is also possible to use some more complex function, such as a neural network or a support vector machine (SVM), to learn this mapping function, converting the CNN features into a form that is easier to classify in a high-dimensional feature space.
WIMI Holographic CNN-BPR image classification technology uses proven geometric primitives with high classification performance in high-dimensional feature spaces, such as spheres, cones or polyhedrons, to construct geometric coverage sets. Then, we can use some optimization algorithm, such as genetic algorithm or particle swarm optimization algorithm, to search for the optimal combination of geometric primitives to construct the optimal geometric cover set. Finally, we can use a classifier, such as the K-Nearest Neighbors algorithm or a Support Vector Machine (SVM), to identify the class to which the new sample belongs.
The specific way to realize the image classification method combining BPR and CNN is as follows:
Prepare training and test datasets:
You need to collect a dataset that contains images of many different categories.
This dataset should consist of two parts:
a training dataset and a testing dataset. The training data set is used to train the CNN model, and the test data set is used to test the performance of the classifier.
Train the CNN model to extract image features:
Use the training dataset to train the CNN model and use the model to extract the features of each image. These features will be used to construct a geometric cover set in a high-dimensional feature space.
Mapping CNN features into a high-dimensional feature space:
A mapping function needs to be used to map CNN features into a high-dimensional feature space. This mapping function can be learned using some nonlinear transformation, such as polynomial transformation or RBF transformation, or using more complex functions, such as neural network or SVM.
Build a geometric cover set:
Use some geometric primitives that have been proven to have high classification performance in high-dimensional feature spaces, such as spheres, cones, or polyhedra, to build geometric cover sets. Then, we can use some optimization algorithm, such as genetic algorithm or particle swarm optimization algorithm, to search for the optimal combination of geometric primitives to construct the optimal geometric cover set.
Classify new samples:
Finally, use a classifier, such as K-Nearest Neighbors or SVM, to identify the class to which the new samples belong. We can map the features of a new sample into a high-dimensional feature space, then find the nearest geometric cover set in this space, and finally classify the new sample into the category represented by the cover set.
In addition, WIMI's CNN-BPR image classification technology features a combination of convolutional neural network and bionic pattern recognition, and performs image classification by constructing a geometric coverage set in a high-dimensional feature space. Compared with the current traditional CNN model that uses the softmax function for classification, the softmax function has limited capacity and cannot handle complex classification problems well, such as image classification. In addition, CNN models cannot fully exploit the geometric structure of high-dimensional feature spaces, thus failing to achieve optimal classification performance. And traditional pattern recognition methods usually need to manually select features and classifiers, requiring a lot of manpower and time costs.
By combining BPR and CNN, this technology can overcome some shortcomings of traditional pattern recognition, improve the performance of image classification, and can handle complex image classification problems. This method can overcome some shortcomings of the current traditional pattern recognition in image classification and in most cases, it has higher classification performance than traditional methods. And it can handle complex image classification problems, such as image recognition, object detection and image segmentation.
At present, image classification technology based on convolutional neural network has been widely used in many fields. The method of WIMI hologram combined with bionic pattern recognition can overcome the limitations of traditional pattern recognition methods and improve the accuracy and reliability of image classification. It is believed that with the continuous development and progress of technology, this technology will have wider application and better performance in the future.
StockpickerCAPC
11 months ago
Brain-computer interface major achievements released, WIMI.US has been deeply involved in the development of BCI game models and paradigms.
Source
https://finance.sina.com.cn/tech/roll/2023-06-02/doc-imyvwfif8543218.shtml
June 2, 2023
Last week, Tesla CEO Elon Musk tweeted that the brain-computer interface could solve the biggest bottleneck in human progress. As we all know, Musk launched Neuralink in 2016. He believes that the company's technology can help humans and artificial intelligence achieve a "symbiotic" state. Simply put, people will be able to integrate their brains with computers effortlessly.
When it comes to brain-computer interfaces, the first impression in everyone's mind may be the scene where Musk lets monkeys realize "idea typing". Since then, Musk's Neuralink has been developing brain implants aimed at treating paralysis and blindness, etc. disease. However, what surprised the outside world recently is the team from China.
The world's first "Monkey Brother" interventional brain-computer interface test was successful
Recently, the world's first non-human primate interventional brain-computer interface test was successful in Beijing, which is of great significance to promoting research in the field of brain science and marks that my country's brain-computer interface technology has become an international leader.
The experiment was led by the team of Professor Duan Feng of Nankai University, jointly completed with the General Hospital of the Chinese People's Liberation Army (301 Hospital) and Shanghai Xinwei Medical Technology Co., Ltd., which made breakthroughs in intravascular EEG signal acquisition and interventional EEG signal recognition. Core Technology. Judging from the video data, the post-operative monkey only needs to "think about it" before it can let the robotic arm deliver food into its mouth.
This technology has a wide range of applications in medical, military and other fields. For example, it can help patients with stroke and frostbite recover, and it can even store human thinking, consciousness, and memory in the future. This series of results that sound amazing has already begun to be realized. It seems that the scenes in some sci-fi blockbusters are not so far away from us.
Brain-computer interface refers to the direct connection created between the human or animal brain and external equipment to realize the information exchange between the brain and the equipment.
At present, there are three main types of brain-computer interface, including:
- invasive brain-computer interface,
- non-invasive brain-computer interface and
- interventional brain-computer interface.
Neuralink, founded by "Silicon Valley Iron Man" Musk and a team of scientists, is a representative of a small number of companies engaged in the development of invasive brain-computer interfaces.
In contrast, the interventional brain-computer interface technology used in the Chinese trial is safer. According to the information released by Nankai University, the research team let the interventional EEG sensor pass through the monkey's jugular vein, enter the sagittal sinus, and reach the monkey's motor cortex brain area. After the operation, the team successfully collected and recognized the interventional EEG signals of non-human primates, realizing the active control of the mechanical arm by the animals.
It can be said that the interventional brain-computer interface takes the advantages of both invasive and non-invasive at the same time, while avoiding the disadvantages of both. Judging from the current development, brain-computer interface technology has continuously made breakthroughs, which may first benefit the medical industry. Guotai Junan Securities pointed out that medical health is the main application scenario of brain-computer, and it will gradually penetrate into entertainment, smart home and other fields in the future, becoming an important form of human-computer interaction.
Brain-computer interface technology will empower the metaverse
Brain-computer interface is a complex system and a basic tool for reading and writing neuron dynamic network data. What is even more surprising is that with the sudden emergence of the Metaverse, people have great expectations for the integration and interaction of the virtual world and the real world. Many people in the industry believe that the brain-computer interface is a cutting-edge technology hatched by the Metaverse.
In the future, the brain-computer interface technology will empower the metaverse, and the brain-computer interface is expected to become the next generation of human-computer interaction technology and the ultimate form of the metaverse. At the same time, with the continuous development of the Metaverse, the strong support of national policies, and the continuous exploration of brain science, the brain-computer interface will become the entrance of the next generation of Metaverse after VR and AR, realizing the real Metaverse.
According to the "White Paper on Brain-Computer Interface Standardization" released by the China Electronics Standardization Institute, the potential market for Brain Computer Interface (BCI) technology will soon reach tens of billions of dollars. Another predictive analysis believes that in the next 20-30 years, the commercial application of brain-computer interface will gradually land, which will open up a market worth hundreds of billions of dollars.
The technological race has already begun. Brain-computer interface technology is an important strategic direction for a new round of scientific and technological revolution and industrial transformation. The future industry represented by brain-computer interface has already entered the fast lane, and the formation of breakthrough scientific research results and innovative applications is a strategic need for innovative development.
WIMI Hologram focuses on the field of brain-computer interface and has made great achievements
At present, the field of brain-computer interface has tried to integrate technology in the consumer field, and its application in game interaction is becoming more and more popular. It is understood that WIMI (NASDAQ: WIMI) has started to develop the BCI game model and paradigm based on the brain-computer interface. A game model is designed with a P300 brain-computer interface to explore a feasible and natural game execution experience using electroencephalography (EEG) signals in a real-world environment.
According to reports, the novelty of WIMI's research is reflected in the design of the BCI game and paradigm, which integrates the game rules and the characteristics of the BCI system. In addition, a convolutional neural network (CNN) algorithm is introduced to achieve high accuracy on training samples. This brain-computer interface system not only provides users with a form of entertainment, but also provides more manipulation possibilities for games.
To be sure, WIMI is based on the CNN BCI game model, forming a platform that can satisfy the interests of both healthy and disabled users. For healthy users, brain-computer interface games are mysterious and technical, which increases the charm of the game and is very conducive to the promotion of the game. For disabled users, BCI games provide them with a fair gaming platform, not only allowing them to play games in the same way as healthy users, but also as a functional rehabilitation system to help patients with rehabilitation training. Obviously, WIMI's application of brain-computer interface technology to entertainment games is an important part of promoting BCI technology from the scientific research stage to the actual application market stage.
end
On May 29, Zhao Zhiguo, chief engineer of the Ministry of Industry and Information Technology, introduced that with the joint efforts of the industry, my country has formed a whole industrial chain of brain-computer interface covering the basic layer, technical layer and application layer, and it has been applied in fields such as medical care, education, industry, and entertainment. landing. The Ministry of Industry and Information Technology will take the brain-computer interface as an important direction for cultivating future industrial development, strengthen the exploration of brain-computer interface application scenarios, and accelerate the vigorous development of the brain-computer interface industry.
StockpickerCAPC
11 months ago
WIMI (NASDAQ: WIMI) develops virtual digital human rendering technology based on real-time dynamic simulation.
Source
https://cj.sina.com.cn/articles/view/7651844612/1c815e20402001ghh4?from=finance
May 26, 2023
Virtual digital human refers to a virtual person or avatar created by using computer technology, simulation technology and artificial intelligence technology. Humans interact in various ways such as voice, text and touch. With the rapid development of artificial intelligence, cloud computing, VR/AR and other technologies, virtual digital human technology is also constantly improving, and the virtual digital human industry is currently in a stage of rapid development. More and more application scenarios are emerging, including games, education, medical care, entertainment and other fields. Virtual digital humans have broad application prospects and commercial value. The key technologies of virtual digital human mainly include natural language processing, graphics rendering, multi-modal interaction technology, etc.
The significance of virtual digital human rendering technology to the development of virtual digital human is to improve the fidelity and realism of virtual digital human, so as to make it closer to the expression form of real people, and enhance the user's immersive experience and interactivity. On the one hand, virtual digital human rendering technology can perform fine control in image generation, lighting, shadows, materials, etc., making the appearance and movements of virtual digital human more natural and realistic. On the other hand, virtual digital human rendering technology can be dynamically adjusted according to different scenarios and interaction requirements, thereby increasing the plasticity and personalization of virtual digital human.
It is understood that the research and development team of WIMI (NASDAQ: WIMI) is developing a virtual digital human rendering technology based on real-time dynamic simulation. The virtual digital human rendering technology based on real-time dynamic simulation is a technology for real-time rendering and dynamic simulation of digital characters through computer programs. Using theories and technologies in multiple fields such as 3D computer graphics, dynamics, and biomechanics, real-time calculations and simulated renderings of the appearance, shape, action, and expression of digital human beings are performed to achieve a sense of reality and lifelikeness. A more realistic interactive experience.
The realization of the virtual digital human rendering technology based on real-time dynamic simulation developed by WIMI requires high-precision modeling and dynamic control of digital characters, including the establishment of the skeletal system, the simulation of physical characteristics such as muscles, skin, and hair, and the human body. Calculation of kinematics and aspects of motion capture. At the same time, it is also necessary to use real-time rendering engine, lighting simulation technology, etc. to realize the realistic rendering and dynamic simulation of digital characters in the virtual scene.
The core of virtual digital human rendering technology based on real-time dynamic simulation is real-time dynamic simulation and rendering. The digital human is modeled by computer, and the physical engine is used to simulate the digital human movement, physiological characteristics and the influence of the external environment, and it is rendered in real time to achieve a highly realistic effect.
Specifically, digital human modeling mainly includes:
- human bone structure,
- muscle system,
- skin tissue,
- facial expressions, etc., as well as
- motion capture and motion generation of digital human.
Real-time dynamic simulation uses a physics engine to simulate the physical movement of a digital human, and on this basis calculates the physiological characteristics of the digital human, such as heart rate, breathing, and muscle fatigue, and then simulates the response of the digital human to the external environment. In terms of real-time rendering, the virtual digital human rendering technology uses advanced graphics technologies, such as physically-based ray tracing, material reflection, and panoramic rendering. In this way, a realistic digital human rendering effect can be achieved in a short time, which greatly reduces the pressure on the computer.
According to the data, the virtual digital human rendering technology based on real-time dynamic simulation developed by WIMI has technical advantages such as real-time performance, high fidelity, dynamic interaction and personalized customization. The virtual digital human rendering technology based on real-time dynamic simulation can simulate and render in a real-time environment, so that the movements and expressions of digital human can be displayed in real time.
By simulating details such as human muscles, bones, skin, and hair, it presents subtle and complex movements and expressions more realistically, making digital humans closer to the shape and behavior of real humans, and enhancing the user's sense of immersion and visual experience. It can also be adaptively adjusted according to different scenes and environments, making the performance of the virtual digital human more natural. It can also be customized according to user needs. For example, the appearance, clothing, image, movements and expressions of the virtual digital human can be adjusted to meet personal needs and aesthetics. At the same time, it can also realize the interaction with the user, and respond according to the user's actions and voice.
At present, the virtual digital human rendering technology based on real-time dynamic simulation developed by WIMI Hologram is widely used in games, virtual reality, augmented reality, human-computer interaction, education, advertising and other fields. For example, in film production, the use of virtual digital human rendering technology can significantly improve production efficiency, reduce production costs, and can also create more realistic visual effects for films; in games, this technology is also widely used, making the game experience more Realistic and smooth. In addition, in the field of virtual reality and augmented reality, virtual digital human rendering technology is also widely used, which can achieve a more realistic virtual reality experience and provide users with a more complete sense of immersion. Through the rendering technology of virtual digital human, it can also realize more intelligent human-computer interaction and provide users with a more natural interaction method.
The virtual digital human rendering technology based on real-time dynamic simulation has broad application prospects, and it has become a popular research direction in the fields of computer graphics and artificial intelligence. In the future, with the continuous improvement of related technologies, it will be more widely used and developed.
StockpickerCAPC
11 months ago
WiMi Hologram Cloud Designed A VR-based Remote Collaboration System.
Source
https://finance.yahoo.com/news/wimi-hologram-cloud-designed-vr-120000462.html
May 24, 2023
WiMi Hologram Cloud Inc. (NASDAQ: WIMI), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced the development of a VR-based Remote Collaboration System using communication technologies, and human-computer interaction. The system creates a shared immersive space that can be interacted with, enabling a multi-person remote collaboration work environment that breaks geographical and time constraints and improves collaboration efficiency and productivity.
First, the system uses VR technology to create a shared virtual space and then uses a graphics rendering engine to render the VR scenes to ensure user experience in the virtual environment. The system can transmit the VR scene data of multiple users to the server side for processing and synchronize the actions and operations of different users to the server side through data synchronization and present them on the terminals of other users. Users can transmit voice, gesture, and other data to other users through the network. After recognizing the user's actions and commands, the system can update the state and scene of the virtual world in real-time to achieve collaborative and natural interaction.
In addition, the VR interaction interface enables the assignment and execution of joint tasks and the sharing of resources. Multiple users interact with 3D objects in a collaborative AR environment to understand each other's completed work and ultimately complete collaborative tasks. Immersive collaboration platforms are more expressive than 2D video remote platforms and have the advantage of being able to share common goals accurately. When using a full-body avatar to collaborate realistically in a virtual environment, the sense of physical ownership is enhanced, and immersion is increased.
VR remote collaboration systems can eliminate time and space constraints and improve collaboration efficiency. Users can communicate in real-time in virtual space, share information and resources, and work more efficiently. In addition, virtual reality technology uses stereoscopic vision and seamless interaction to provide a more realistic user experience. This is something that traditional remote collaboration tools cannot include. VR remote collaboration system is an innovative model of modern remote collaboration that can be more widely used in the future.
The application and market prospect of VR remote collaboration systems is extensive. In the industrial field, this system can help companies to conduct remote collaboration and training, improving productivity and employee skill levels. In the medical area, it can support medical experts in conducting remote consultations and surgical operations and solve the problem of geographical limitation. It can also give students a more prosperous online learning environment and interactive experience to improve teaching quality. And in the market, as people become more and more interested in virtual reality, the application of this technology will become more and more widespread.
StockpickerCAPC
11 months ago
WiMi Hologram Cloud Develops A Human-Robot Interaction System Based on Machine Learning Algorithms.
Source
https://finance.yahoo.com/news/wimi-hologram-cloud-develops-human-120000844.html
May 23, 2023
WiMi Hologram Cloud Inc. (NASDAQ: WIMI), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced the development of an HRI system based on machine learning algorithms. The multi-modal HRI system fuses voice and gestures, which converts the user's voice and gestures into commands that the robot can execute. Using gestures for HRI is an ideal way to do so. This is because gestures express rich semantics and are easy to recognize. Voice interaction based on natural language understanding is the most direct and convenient.
The HRI system, based on machine learning algorithms, uses a combination of gestures and voice to control the robot. Voice is used as a natural interaction method to control the robot, and gestures are used as a complement to voice to improve the accuracy of commands. Combining gestures and speech reduces the disadvantages of using gestures or speech alone and makes communication between humans and robots more natural, efficient, and accurate.
Through voice interaction, the robot understands what people say and communicates with humans with emotion. The human-robot dialogue system presents humanized and intelligent interaction characteristics. Gesture recognition is based on the motion trajectory of human hands and simulates images or syllables according to the change of gestures. Thus, certain meanings or words are formed to express thoughts vividly, allowing the robot to understand and interact with human language.
With the gradual maturity of HRI and the application of voice emotion recognition in people's lives, the need for machine intelligence to understand human emotions has become more urgent.
WiMi's HRI system provides a faster, more efficient, and more diverse interaction experience by fusing multi-modal perceptual information. Using gestures and voice for real-time parallel interaction, visual communication, and voice information are associated and shared in real-time during the interaction. Multiple interaction modes complement each other to form a complete interaction system. The system leads the gradual development of HRI in the direction of intelligence and humanization and helps build a harmonious and natural human-robot environment.
StockpickerCAPC
11 months ago
WIMI (NASDAQ: WIMI) develops a common architecture for a blockchain-based business process management system (BPMS).
Source
https://cj.sina.com.cn/articles/view/1704103183/65928d0f020036d1b?from=finance
May 23, 2023
With the continuous development and application of blockchain technology, the blockchain-based business process management system (BPMS) has gradually become an important part of the digital transformation of enterprises, and more and more enterprises have begun to adopt blockchain in their business process environment chain technology to improve the efficiency and security of its business processes. Blockchain-based business process management systems (BPMS) have become an integral part of this trend.
However, due to the complex requirements for data security and privacy in practice, no single blockchain can capture all use cases. The coexistence of blockchains optimized for various risk tolerances and confidentiality in a multi-chain environment poses serious architectural challenges for blockchain-based business process management systems (BPMS). In order to solve these problems, WIMI (NASDAQ: WIMI) has developed a general architecture for blockchain-based business process management system (BPMS) to support orchestrated multi-chain BPMS.
1. An overview of the general architecture of WIMI's blockchain-based business process management system (BPMS).
Blockchain-based business process management system (BPMS) refers to the management and orchestration of internal or cross-enterprise business processes through blockchain technology, thereby improving the efficiency and security of business processes.
Generally, a blockchain-based BPMS includes the following components:
- On-Chain Protocol,
- Off-Chain Protocol,
- Smart Contract and
- Business Process Orchestration.
However, due to the various complex requirements and constraints of different business processes and environments, no single blockchain can meet all needs. Therefore, WIMI Hologram has developed a general architecture to support orchestrated multi-chain BPMS, which can adapt to different business processes and environments while ensuring data security and privacy protection.
2. The architecture design of WIMI holographic multi-chain environment.
In a multi-chain environment, we need a flexible architecture to support different business processes and environments. To this end, we propose a multi-chain architecture based on process orchestration, which consists of three main components: a business process management system (BPMS) orchestration engine, a blockchain integration layer, and a smart contract layer.
1. Business process management system (BPMS) orchestration engine
The business process management system (BPMS) orchestration engine is the core component of the multi-chain BPMS, which is responsible for mapping business processes to blockchains and integrating different blockchains into the same business process. In the implementation, we use a model based on graph theory to describe business processes to support complex business processes and interactions between processes.
The BPMS orchestration engine provides the following functions:
Visually design and edit business processes:
Support users to use a graphical interface to design and edit business processes, including defining process nodes, transition conditions, and business rules.
Blockchain integration management:
support the integration of different types of blockchains, and provide related interfaces and tools to directly call the functions of blockchains in business processes.
Interaction coordination:
Support interaction and coordination between different business processes to achieve cross-chain transactions and cooperation.
2. Blockchain integration layer
The blockchain integration layer is a bridge connecting the BPMS orchestration engine and the underlying blockchain, which provides interfaces and tools for interacting with different blockchains. In the implementation, we use standardized interface protocols, such as RESTful API, WebSocket, etc., to ensure the scalability and compatibility of the integration.
The blockchain integration layer provides the following functions:
Blockchain node management:
support connection, configuration and management of different blockchain nodes, including public chains, alliance chains and private chains.
Blockchain interaction interface:
Provide interfaces and tools for interacting with different blockchains, so that the functions of the blockchain can be directly called in the business process, such as querying transaction records, creating transactions, etc.
Blockchain data synchronization:
Supports the synchronization of data on the blockchain to the business process management system to achieve more efficient business processes and data analysis.
3. Smart contract layer
The smart contract layer is the core component of the underlying blockchain, which is responsible for executing and managing contract codes, and provides interfaces and tools for interacting with the blockchain. In the implementation, we use smart contract languages, such as Solidity, Vyper, etc., to realize the writing and deployment of smart contracts.
The smart contract layer provides the following functions:
Contract writing and deployment:
Supports writing and deploying contracts using smart contract languages ??to automate business processes.
Contract execution and management:
responsible for executing and managing smart contracts, and providing interfaces and tools for interacting with the blockchain.
Contract upgrade and expansion:
Support contract upgrade and expansion to adapt to different business needs and environmental changes.
The multi-chain architecture of WIMI (NASDAQ: WIMI) process orchestration can flexibly support different business processes and environments, realize cross-chain transactions and cooperation, and provide standardized interfaces and tools to ensure integrated scalability and compatibility. The orchestration engine is the core of the system and is responsible for managing the entire business process. It can interact with the multi-chain blockchain network through smart contracts and ensure the smooth progress of cross-chain transactions on all chains. The orchestration engine also provides advanced features such as automated process monitoring, error handling, and event notification. It can also integrate with external systems, such as CRM and ERP, to collaborate with a business' existing systems.
3. Advantages of WIMI's blockchain-based business process management system (BPMS) general architecture:
Flexibility:
Since our architecture is based on a multi-chain blockchain network, it can be flexibly configured according to different business needs, such as adjusting block size, confirmation time and number of nodes.
Scalability:
Our architecture can be scaled horizontally to hundreds of nodes, thus supporting larger business scale and higher throughput.
Privacy protection:
Each blockchain can have its own access control and authentication mechanism, as well as private smart contracts, thus providing better privacy protection.
Security:
Transactions between multiple blockchains can be mutually verified, reducing the possibility of attacks.
Transparency:
Every transaction can be verified on multiple chains, providing better transparency.
In this architecture, a multi-chain blockchain network is used, which can provide better scalability because each blockchain can be optimized according to its needs, such as adjusting block size and confirmation time. Second, it can provide better privacy protection, since each blockchain can have its own access control and authentication mechanisms, as well as private smart contracts. It can provide greater security because transactions between multiple blockchains can be mutually verified, reducing the possibility of attacks.
WIMI's (NASDAQ: WIMI) blockchain-based business process management system (BPMS) general architecture can be applied to various industries. For example, modern supply chain management requires a high degree of coordination and cooperation among various players, involving multiple organizations and complex transaction processes. Traditional supply chain management often lacks transparency and data sharing, which can lead to delays, increased costs and trust issues. A blockchain-based BPMS can provide a traceable, immutable, and distributed shared ledger, which can help organizations better manage supply chains, reduce delays and costs, and build trust.
At the same time, through the use of smart contracts, many supply chain processes can be automated, improving efficiency and reducing errors. Another industry pain point is financial services. Blockchain technology can provide a safer, more transparent and more efficient transaction platform, which can help banks and financial institutions reduce operating costs, increase transaction speed and reduce fraud. Through the blockchain-based BPMS, data security and privacy can be ensured, and various financial transactions such as payment, transfer, settlement and asset management can be executed through smart contracts. In conclusion, blockchain-based BPMS can be applied in a variety of different industries and fields, improving data security, transparency and efficiency, and helping organizations better manage complex business processes.
StockpickerCAPC
11 months ago
WiMi Hologram Cloud to Vigorously Dig Into Web 3.0 the Spatial Web Technology.
Source
https://finance.yahoo.com/news/wimi-hologram-cloud-vigorously-dig-120000439.html
May 22, 2023
WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, who has invested in research related to virtual digital technology since its early days, today announced its continual exploration of cutting-edge technologies and that it has accumulated a large number of patented technologies and software systems related to Web 3.0.
Web 3.0 is the next-generation Internet that stimulates group intelligence, with applications including blockchain, artificial intelligence, the Internet of Things, and extended reality. These technologies will form the core technology stack for future enterprises and reconstruct new ways of linking, communicating, and collaborating in the digital world.
WiMi has accumulated over 500 intellectual property rights in the Internet of intelligence, personalization, interactivity, and virtualization in the Web 3.0 era. For example, the human-computer interaction method and system based on XR technology, the convenient 3D touch interaction system based on image processing technology, the holographic radar, the reflective holographic display system, the immersive VR device middleware software, the industrial Internet identification security control service platform, the AI-powered IoT chip with fuzzy PID algorithm, the microfluidic diffraction phase holographic imaging chip. These intellectual property rights contain patents, software systems, and chip designs. WiMi has long been involved in Web 3.0, the spatial web. This time, related applications, technologies, and patents will give WiMi a first-mover advantage.
About WIMI Hologram Cloud
WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.
StockpickerCAPC
11 months ago
WiMi Hologram Cloud Develops A Dynamic Gesture Interaction System Based on Image Processing.
Source
https://finance.yahoo.com/news/wimi-hologram-cloud-develops-dynamic-120000644.html
May 18, 2023
WiMi Hologram Cloud Inc. (NASDAQ: WIMI), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced the development of an image processing-based dynamic gesture interaction system. The system is based on multi-touch detection and localization algorithm and gesture tracking and recognition algorithm. The algorithm uses optical multi-touch technology, digital image processing technology, and gesture tracking and recognition technology. The system realizes multi-touch positioning and gesture recognition and is a human-computer interaction system that can steadily, quickly, and accurately acquire multi-touch coordinate information and track and recognize user gestures in real time. It integrates image processing and gesture recognition to achieve efficient HCI.
Image processing refers to digital image processing, which uses computers to accurately interpret images and fully simulate the functions of the human visual system. The interactive touch system based on image processing includes multi-touch detection and positioning module, a multi-touch digital image processing module, and multi-touch gesture tracking and recognition module. Digital image processing includes image transformation, coding and compression, enhancement and recovery, segmentation, description, and classification. The techniques used in the system include image denoising, image feature extraction, edge detection, and algorithms related to motion tracking.
Gesture recognition is an essential 3D interaction tool for virtual environment interaction, and its key technologies are gesture segmentation, gesture analysis, and gesture recognition. The recognition process includes the gesture's motion trajectory and the hand's movement. Gesture recognition aims to recognize human gestures through mathematical algorithms to determine the user's intention. Dynamic gesture recognition is based on a series of continuous hand activities, including hand rotation, shape changes, and motion trajectories.
HCI technology realizes the information exchange between humans and computers, including human-to-computer and computer-to-human information exchange, which is a two-way information exchange process. HCI is developing from the direction of humans adapting to the working way of computers to the direction of computers adapting to the way of human behavior. In terms of interaction, the ideal interaction should be that the user can manipulate the computer freely and directly without using intermediate media and expressions. The gesture is a kind of interaction that conforms to human daily habits and is a very natural and intuitive way of communication. With the increase in computer processing power and the emergence of diverse computing tasks, the role of gestures in human-computer interaction is becoming more apparent.
WiMi's dynamic gesture interaction system uses gestures directly as input devices for computers, eliminating the intermediate medium in traditional HCI. Users can realize simple and direct interactions with the computer. Undoubtedly, this will be a future development trend, and the system has a wide range of application prospects in robotics, games, virtual reality, and other fields.
StockpickerCAPC
11 months ago
WIMI (NASDAQ: WIMI) launched a 3D-CNN-based hologram classification algorithm.
Source
https://t.cj.sina.com.cn/articles/view/1747383115/6826f34b020018pra?from=tech
May 18, 2023
A hologram is a holographic interferogram that records an object. It has very rich optical information and can reconstruct the three-dimensional structure of the object at different angles. It has the characteristics of high pixel density, wide field of view, and deep depth of field. Due to its high similarity to the three-dimensionality of real objects, it is widely used in many fields, including medical imaging, material science, and three-dimensional display technology. Hologram classification is an important technology to extract objects and information in holograms, which can be used to judge the type or state of objects.
It is reported that WIMI (NASDAQ: WIMI) has developed a three-dimensional convolutional neural network (3D-CNN) hologram classification algorithm based on deep learning technology, which uses convolutional neural network technology and computer vision to build a classifier. A technique to classify objects in a hologram. Using a three-dimensional holographic image as input, it captures the shape and spatial characteristics of the target more accurately, extracts feature information through operations such as convolutional layers, pooling layers, and fully connected layers, and screens and optimizes them layer by layer to achieve three-dimensional Fast and accurate automatic identification and classification of objects.
3D-CNN can efficiently extract 3D features at multiple resolutions and combine them to improve classification performance. When training the model, the labeled hologram is used for supervised learning, and the model parameters are optimized through the backpropagation algorithm. The hologram classification technology based on 3D-CNN takes advantage of deep learning to realize fast and accurate classification of holograms by training the neural network model, which provides important technical support for object recognition.
The implementation steps of the algorithm technology include:
- first, feature extraction and preprocessing of the hologram, and transform it into three-dimensional tensor data;
- then, use 3D-CNN to train and learn the features of the hologram, and extract its high-level semantics features;
- finally, a classifier is used to classify the obtained features to realize automatic classification of holograms.
WIMI's 3D-CNN-based hologram classification technology can adapt to the particularity of holograms and better handle the three-dimensional information and wavefront information of holograms. It uses deep neural networks to extract more feature information, thereby Achieve higher accuracy classification. 3D-CNN can use GPU for efficient parallel computing, high training efficiency, and expands with the increase of data size, can process more data and obtain better classification results.
The hologram classification algorithm based on 3D-CNN has wide application and development prospects in many fields. At the same time, its technical principles can also be applied to the classification or processing of other 3D images, which has good promotion value. At present, hologram classification technology based on 3D-CNN has been widely used in the fields of automatic driving, medical image diagnosis, intelligent security, virtual reality and so on.
In automatic driving, hologram classification can identify objects such as vehicles, pedestrians, and traffic lights on the road, thereby helping automatic driving decision-making and realizing functions such as vehicle automatic driving, safety detection, and path planning.
In medical image diagnosis, hologram classification can analyze and diagnose medical images, help doctors make a diagnosis quickly and accurately, and improve the work efficiency of doctors.
In intelligent security, 3D-CNN-based hologram classification technology can be used for character recognition, behavior analysis, etc., to improve monitoring effects and early warning capabilities.
In virtual reality, hologram classification can realize object recognition in the virtual world, thereby enhancing the realism and interactivity of virtual reality.
With the continuous development and deepening of artificial intelligence technology, the application field of hologram classification technology based on 3D-CNN will continue to expand, and its application in intelligent transportation, intelligent medical care, intelligent security, virtual reality and other fields will bring more More convenience and innovation.
However, there are also some challenges in the 3D-CNN-based hologram classification algorithm technology. It faces problems such as difficult data acquisition, high computational complexity, and model parameter optimization, and requires continuous exploration and research on solutions. In the future, WIMI will further study how to improve the performance and efficiency of 3D-CNN-based hologram classification algorithm technology, and continue to expand its application scenarios.
StockpickerCAPC
11 months ago
WiMi Hologram Cloud Builds A 3D Computer Vision-based HCI System.
Source
https://finance.yahoo.com/news/wimi-hologram-cloud-builds-3d-120000863.html
May 17, 2023
WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced the development of a computer vision-based 3D human-computer interaction system. With the help of VR technology, it builds a natural and intuitive 3D interaction environment and presents a 3D user interface. It provides a unified and human-centered interface for users and improves the naturalness and efficiency of HCI.
In a 3D HCI system, vision is the effective input modality. Computer vision is an AI technology that guides the system and interacts with the visual world, recognizing objects from the acquired inputs and providing the final result. Tasks such as acquiring, transforming, and analyzing digital images are included in computer vision to assist humans with recognition tasks and enable interactions. They are evaluated based on training data sets. The technology allows for greater and richer forms of information exchange for human-machine communication and helps to exploit the different cognitive potentials of humans and machines concerning each other.
The primary functions of WiMi's 3D HCI system include human contour extraction, human behavior detection, and 3D HCI in a VR environment. Among them, human contour extraction is the first step of human behavior recognition, and whether the human contour can be extracted accurately directly affects the efficiency of human recognition. Human behavior detection is the key to HCI, and whether the computer can accurately understand human behavior is related to the regular operation of the system. The 3D HCI in VR uses static human action to interact with the virtual reality system.
The determined interaction information is sent to the VR system through image acquisition, image pre-processing, motion target recognition, interaction behavior detection, and other processing. The VR system provides the user with a manipulable 3D environment so that users can feel they are using motion to operate the system. Using human behavior for HCI is also the most direct and convenient way of interaction.
HCI in virtual reality has become closer to people's lives. Its unique features of immersion, realism, and interactivity enable people to be immersed in it and form a computer application environment with interactive functions. In this case, WiMi's computer vision-based 3D HCI system should have broad application prospects in security monitoring, entertainment games, and other fields.
StockpickerCAPC
11 months ago
WIMI (NASDAQ: WIMI) creates optical scanning hologram numerical reconstruction three-dimensional image technology.
Source
https://cj.sina.com.cn/articles/view/7651844612/1c815e20402001gfay?from=finance
May 17, 2023
With the development of computer technology, digital signal processing algorithms have been continuously optimized and improved, such as GPU-based efficient algorithms, multi-level parallel processing, etc., which have greatly improved the reconstruction speed of holographic images. Holographic three-dimensional imaging technology has gradually been widely used in medicine, biological science, material science, physics and other fields due to its unique advantages, such as beam navigation, optical micro-imaging, high-resolution micro-imaging, etc.
It is understood that the research and development team of WIMI has developed digital holography and three-dimensional imaging technology, which is a technology that uses laser technology, digital signal processing and other technical means. A new technology for 3D image detection, analysis, recognition and display. It calculates the three-dimensional structure of the object by recording the amplitude and phase of the object in the interference pattern of the object and the reference wave, then uses digital signal processing to convert the interference pattern into a holographic image, and then converts the holographic image into a three-dimensional image through numerical reconstruction processing technology image, so as to realize the three-dimensional image reconstruction of the object.
The basic principle of numerical reconstruction of three-dimensional stereoscopic image by optical scanning hologram developed by WIMI involves optics, interferometry and digital image processing technology. Its technical process includes hologram recording, digital hologram, calculation of reverse transfer function, digital image processing, reconstruction of hologram, etc. Through these steps, a three-dimensional image with high fidelity and realistic feeling can be obtained, which is helpful to better study and display the shape and structure information of the object.
hologram recording
A laser source is used to generate a coherent beam of light, which is then passed through an object, causing the beam to scatter or reflect. The scattered or reflected light is then optically interfered with a reference beam to form an optical hologram.
digital hologram
Digitally scan the optical hologram, and convert the optical hologram into digital information through digital processing. The digitized holographic image is represented by an array of pixels (dot matrix), and the gray value of each pixel is proportional to the density of the ink sheet.
Calculate the back transfer function
Using computer algorithms to calculate the object information stored in the hologram, this process is called calculating the reverse transfer function. The back transfer function refers to the algorithm that derives the original object field from the hologram. The three-dimensional shape information of the object is obtained, that is, the position coordinates of each point in space and the shape of the object.
digital image processing
In the process of digital image processing, it is necessary to pre-process the digital hologram, such as enhancing contrast, reducing noise, etc., in order to improve the quality of the final reconstructed image.
Three-dimensional reconstruction
The processed hologram is converted into a three-dimensional image through a computer algorithm, thereby completing the process of image reconstruction. A technique based on the principle of diffraction, based on the interaction of the calculated inverse transfer function with the wavefront generated by the light source, resulting in a three-dimensional stereoscopic image.
render image
Use computer graphics algorithms to render three-dimensional stereoscopic images into realistic images. The physical properties of light, such as shadows and reflections, can be simulated during rendering.
WIMI's optical scanning hologram numerical reconstruction three-dimensional image technology has a very high resolution and can achieve microscopic imaging. Moreover, the visualization effect is good, not only can intuitively present the three-dimensional shape of the object, but also provide information about the optical properties of the object, which will provide a powerful tool for scientific research. It will be widely used in biology, medicine, material science and other fields, such as three-dimensional microscopic imaging, medical imaging diagnosis, quantitative analysis and quality inspection of materials, etc.
With the increasing demand for 3D information, the 3D imaging technology represented by optical scanning hologram numerical reconstruction technology will gradually become an important basis for 3D vision, AR/VR and other fields. Therefore, the application prospect of this technology will also show a trend of becoming more and more extensive. On the one hand, with the continuous advancement of science and technology, the emergence of new technologies will provide a broad space for the continuous improvement of the technology. At the same time, the technology itself has many fields that can be further cultivated, such as 3D super-resolution microscopic imaging, 3D surface measurement, 3D printing, etc., which have great application potential.
StockpickerCAPC
11 months ago
WiMi Hologram Cloud Obtained Several Holographic Brain-Computer Interface-Related Patents.
Source
https://finance.yahoo.com/news/wimi-hologram-cloud-obtained-several-120000334.html
May 16, 2023
WiMi Hologram Cloud Inc. (NASDAQ: WIMI), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it had been granted three patents related to holographic BCI. They are A Protective Structure for Holographic BCI, An Angle-Adjustable Holographic BCI Device, and A Holographic Brain-controlled Robot System.
The protective structure of the holographic BCI includes a protective box sited on the holographic BCI. The said protective box consists of two hemispherical shells. The bottom of the two hemispherical shells are rotated and installed close to each other, the bottom is fixedly connected with the first support block, and the bottom wall of the hemispherical shell is fixedly connected with the rubber support block. With the two hemispherical shells, the utility model technology fully protects the holographic BCI from dust. The first splint, the second splint, and two rubber support blocks together can stably clamp holographic BCI of different specifications in the shell. This reduces the risk of damage caused by collision during carrying and makes it easy to take out the holographic brain interface quickly.
The angle-adjustable holographic BCI device includes a body and a wiring socket on the body. A plurality of flexible wires connects the wiring socket to the body. The top of the body is fixedly connected with a return socket. The flexible wires are located in the return socket. The return socket is provided with a multi-angle adjustment mechanism. It includes a rectangular groove on the return socket's left inner wall, with an opening at the top. A rotating shaft is connected to the bottom inner wall of the rectangular groove, and a turntable is fixedly connected to the top of the rotating shaft. This mechanism allows for quick adjustment of the tilt angle and rotation angle of the wiring socket, improving connection accuracy and meeting usage requirements.
The main body of the holographic brain-controlled robot system includes a robot and a wearable helmet-type BCI device. The BCI device has a rectangular card block and a quick-fit cushioned shield mechanism fixedly attached to the top of the device. The right side of the rectangular card block is provided with a slot with openings on both the front and rear sides. The quick-fit cushion shield mechanism is equipped with an arc-shaped plate in active contact with the top of the device, and an arc-shaped shield is provided above the plate. The patented technology has a cushioning shield around the top of the wearable helmet-type brain machine equipment, which can reduce the risk of impact damage to the top. In the subsequent equipment maintenance, users can quickly and easily disassemble the cushion shield mechanism.
BCI is a new communication and control technology between the brain and computers or other electronic devices that do not rely on conventional brain information output pathways (peripheral nerve and muscle tissue). BCI is one of the most active research directions in the field of neural engineering. It is a control system that does not rely on the standard output channels of the peripheral nerves and muscles of the brain. It establishes a direct communication and control channel between the human brain and the computer by collecting and analyzing bioelectrical signals from the human brain. This allows people to express their will or manipulate a device directly through the brain without speaking.
BCI has significant advantages in medical care and broad application prospects in education, artificial intelligence, and entertainment.
According to research institutions, its market size will reach $2.5 billion in 5 years purely from the dimension of devices (EEG/EMG). In several technology areas that BCI will profoundly affect, the market size will reach hundreds of billions of dollars in 5 years. For example, 46 billion dollars in market size in ADHD BCI feedback therapy, 12 billion dollars in brain detection systems, 250 billion dollars in education, and 120 billion dollars in the game industry.
For example, in entertainment, BCI technology is up-and-coming. It can be combined with VR. Without additional peripheral control devices, users can directly control the characters in the game through their minds for a more immersive gaming experience.
WiMi plans to use its existing VR and holographic AR technologies to research and develop a BCI gaming system to help game users gain a more realistic and immersive experience. Overlaying digital content in the real world can "seamlessly" integrate real-world information with virtual-world information. The system applies the virtual knowledge to the real world by simulating and then superimposing the physical information (visual information, sound, taste, touch, etc.) that is difficult to experience within the limited scope of the natural world through technology. The human senses can perceive and gain a sensory experience beyond reality: the natural environment and virtual objects are superimposed on the same image or space in real time.
BCI research has a broad future. It will not only enhance people's understanding of themself but also change the way of life of human beings. As a new way of control and communication, BCI can also be applied to the broader field of brain-computer fusion. With the continuous improvement of technology and the efforts of multidisciplinary integration, BCI will indeed be gradually applied to reality for the benefit of human beings.
StockpickerCAPC
12 months ago
WIMI (NASDAQ: WIMI) develops a log analysis framework for self-driving car test drives.
Source
https://finance.sina.com.cn/tech/roll/2023-05-12/doc-imytperx0170251.shtml
May 12, 2023
The autonomous vehicle (AV) industry is growing rapidly due to improvements in artificial intelligence (AI) technology based on sensing devices. So far, self-driving cars from as many as dozens of companies are driving on real roads for safety and performance testing. Some of them have driven tens of millions of miles, and even billions of miles in simulation. These car markets contain a large number of data sets, including all sensor data and vehicle driving information, which can be used for deep learning and provided for autonomous driving learning. These datasets include not only the vehicle's position, velocity, acceleration, but also image information obtained from cameras, light detection and ranging (LiDAR) sensors, and GPS. The information is very detailed, including not only the own vehicle, but also the information of the surrounding environment of the vehicle, which is very useful for analyzing autonomous driving problems. However, this data is so large that it takes up a lot of storage space and is difficult to analyze. Therefore, there are difficulties in testing various environments.
Based on this, WIMI (NASDAQ: WIMI) has developed a log analysis framework for autonomous vehicle test drives. Based on a formally-specified logging architecture, the framework can organize logs hierarchically, find out the prior relationships between them, and detect algorithmic or implementation errors. Vehicle logs are an essential source for detecting and analyzing events during driving. However, a set of dumped logs is usually mixed and fragmented because they are generated simultaneously by many modules such as sensors, actuators, and programs. This makes it difficult to analyze them for potential errors that may occur due to the complex chain reaction between these modules. In order to solve this problem, WIMI Hologram proposes a logging architecture based on formal specifications, and organizes the logs hierarchically to find out the prior relationship between them. Algorithmic or implementation errors are detected by examining posterior relationships, while metamorphic tests are employed to quantitatively verify formal specifications to uncover potential conflicts that may exist.
According to the information, the system is a log analysis framework for self-driving car test drives, designed to help discover potential errors that may be caused by complex chain reactions between different modules. A major component of the framework is a formal specification-based logging architecture that organizes logs hierarchically to find out their relationships. By examining these relationships, algorithmic or implementation errors can be detected.
However, testing under certain parameters does not necessarily trigger a potential conflict on the relationship. To remedy this shortcoming, the framework employs metamorphic testing to quantitatively verify the formal specification. This can help locate relationship conflicts and help improve the system. In order to verify the effectiveness of the system, the researchers constructed two experimental environments. One experimental environment is based on simulated driving tests, and the other experimental environment is based on driving log analysis techniques of actual vehicles. Both test environments used the implemented autonomous driving system to evaluate the performance and feasibility of the framework.
At present, the log analysis system of WIMI (NASDAQ:WIMI) self-driving car test drive is a logging framework based on formal specifications, which aims to detect and analyze the basic source of events during driving through the analysis of logs.
The technical logic of the system mainly includes the following steps:
Data collection and preprocessing:
First, the system needs to collect data from various sensors of the self-driving car. These data include the vehicle's position, velocity, attitude, acceleration, sensor data, etc. Since vehicle logs are usually mixed and fragmented since they are generated simultaneously by many modules (such as sensors, actuators, and programs), preprocessing is required for the next step of analysis.
Hierarchical organization of logs:
The system organizes the collected data hierarchically according to formal specifications. This specification includes the formal description of vehicle and environmental parameters, traffic regulations, control algorithms, and the formal definition of the execution process. Through this hierarchical organization, a priori relationship between data can be found, and then algorithm or implementation errors can be detected in subsequent analysis.
Posterior relation detection:
The system detects algorithmic or implementation errors by examining the posterior relation. By analyzing hierarchically organized logs, the system can find a posteriori relationships and detect potential errors by comparing expected and actual outcomes. If there are bugs, the bugs need to be fixed and retested and validated.
Metamorphic tests:
tests performed in the case of certain parameters (so-called oracle tests) do not necessarily trigger potential conflicts on relationships. In systems, this is remedied by employing metamorphic tests to quantitatively verify the formal specification. Variation testing is a systematic approach to varying input data to test the behavior of a system under different conditions. With this approach, the system can more fully verify its formal specification and discover potential errors and flaws.
Application example:
Finally, the market application of the system is mainly for test driving and testing of autonomous vehicles. By using the system, developers can gain a more complete understanding of how autonomous vehicles behave under different conditions and detect potential errors and flaws. This can greatly improve the safety and performance of self-driving cars and promote the development of self-driving car technology.
It can be said that WIMI Hologram provides a novel method to analyze the logs generated during the test drive of self-driving cars and help to find possible errors. It uses advanced logging techniques and testing methods to provide useful tools and techniques for those developing self-driving cars.
For the large amount of data generated when self-driving cars are tested in a real road environment, it needs to be coordinated and analyzed through a certain technical framework. Among them, the key technologies include data acquisition, data storage, data preprocessing, data analysis, etc. First of all, data acquisition requires the acquisition of vehicle driving data through a variety of sensors, including visual sensors, lidar, millimeter-wave radar, inertial navigation systems, etc., to obtain information about the surrounding environment of the vehicle and the state of the vehicle. Second, data storage requires efficient storage and management of acquired data. Distributed file systems and database technologies are usually used to ensure high reliability and scalability of data. At the same time, the data needs to be stored in a certain format to facilitate subsequent data preprocessing and analysis.
Data preprocessing includes data cleaning, denoising and feature extraction. In autonomous vehicle testing, the acquired data is noisy because sensors may be disturbed by various factors. Therefore, it is necessary to denoise the data to ensure the accuracy of subsequent data analysis and visualization. In addition, through feature extraction, key information in the data can be extracted for subsequent data analysis. Data analysis requires the analysis of data through techniques such as machine learning and deep learning. Among them, analysis methods based on statistics, methods based on machine learning and methods based on deep learning are included. Through data analysis, evaluation results on the performance and safety of self-driving cars can be obtained, as well as optimization suggestions. Through the realization of the above technical logic, the test data of self-driving cars can be coordinated and analyzed in an all-round way to provide support for the performance and safety of self-driving cars.
Self-driving cars are an important part of future transportation, and their market prospects are huge. According to market research firm forecasts, by 2025, the global autonomous vehicle market will exceed $200 billion. At present, self-driving car technology has received extensive attention from many automakers and technology companies, including Tesla, Google, Uber, etc. These companies are constantly advancing their self-driving car technology and investing heavily in research and development. WIMI (NASDAQ: WIMI) will also continue to promote the development of software and data analysis technology related to self-driving cars, making contributions to the development of the industry. greater contribution.
StockpickerCAPC
12 months ago
Leading the new breakthrough of global light field display technology, WIMI has consolidated the "technical foundation" with technological innovation.
Source
https://t.cj.sina.com.cn/articles/view/1765776051/693f9ab30200119vx?from=tech
May 12, 2023
As we all know, Metaverse, as an integrated innovation and integrated application integrating Internet, big data, cloud computing, artificial intelligence, blockchain, virtual reality (VR), augmented reality (AR) and other technologies, is developing rapidly. At present, technology giants such as META, Sony, and Samsung are successively releasing related VR/AR terminal products. At the same time, the rise of the metaverse industry has also promoted the progress of light field display (holographic projection) and VR/AR near-eye display technology.
Principle and Prospect of Light Field Display Technology
According to the definition, a light field is a complete representation of a collection of light rays in space. Collecting and displaying a light field can visually reproduce the real world, and describes the collection of light rays centered on the human eye. The light field display is to show the light in all directions of the optical scene, including the light emitted at various distances, so it is also called a holographic display.
The research on light field technology is mainly divided into two aspects, including light field acquisition and light field display. The light field acquisition technology is relatively more mature, and has basically reached the level where it can be used in some To B fields. Light field collection is mainly to provide 3D digital content. One collection can be popularized and used. This does not require individual consumers to complete it, and it is generally completed by a team. Therefore, the hardware cost, volume, and power consumption of the light field acquisition system are more acceptable.
In contrast, light field display is a To C product, and individual users are extremely picky in terms of cost, volume, power consumption, and comfort. The development of light field display prototypes has been completed in many universities and scientific research institutions. On the road to commercialization and practicality, the biggest challenge at present lies in the miniaturization and low power consumption of light field display devices.
Looking at the field of display technology, in recent years, the transformation of display technology has intensified. With the advent of the digital economy era, display technology will surely be deeply integrated with IC, 5G communications, artificial intelligence, and the Internet of Things. The Ministry of Science and Technology has always attached great importance to the innovation and industrialization of display technology, promoted technological innovation in the new display industry, continued to provide technical support for national key research and development plans, and shifted to "leading the global display" technology development.
Global industry giants are stepping up their layout
Undoubtedly, light field display technology is the next-generation display technology that is currently attracting attention, and can be widely used in exhibitions, education, medical/health, industrial design and architecture, entertainment media, and metaverse digital asset visualization. Although light field display technology is still a cutting-edge technology that is still being explored globally, global industry giants are stepping up their deployment.
Previously, Google (GOOGL.US) announced at the 2021 I/O conference that it is developing the "Starline project" that combines light field hardware and software. Starline uses 3D imaging to capture users, real-time data compression to pass real-time There is a network to transmit a large amount of result data, and it is possible to create a face-to-face experience with real people without additional depth-sensing equipment. Near-eye light field display glasses released by Nvidia (NVDA.US), and light field VR helmets being developed by Douglas Lanman team of Oculus under Meta (META.US).
In addition, it is reported that WIMI, as a leading company that has been cultivating light field display technology for many years, has always adhered to the development and forward-looking research vision, insisted on technological innovation, improved its innovative research and development capabilities, and enhanced its technical reserves. At the same time, the content ecological layout has been improved, and benchmark cooperation cases and content ecological partners have been established in multiple industries. WIMI is committed to empowering the Metaverse field and other industries through self-developed light field display technology and its full-link ecology of products, software, and application development, and accelerating the comprehensive development of the digital economy.
At present, WIMI's light field display technology can make the display interface itself have a sense of depth and three-dimensionality, and collect natural light completely and truly. Then, through a special optical display structure (micro-projection array, micro-lens array, micro-mirror array and other specific solutions), each beam of light is projected in a predetermined direction to simulate real natural light, so as to achieve a real depth of field effect. virtual image. It is reported that WIMI can perform with real people and virtual people at the same time through the light field display technology, and realize the interaction with the audience from multiple angles.
Continue to develop cutting-edge technology, WIMI holographic light field display technology empowers technological innovation, products have broad application prospects in advertising media, exhibitions, digital people, smart cities and many other scenarios, which is very in line with WIMI holographic hard technology The investment logic of empowering large industries. At the same time, the R&D team of WIMI has all kinds of talents from well-known universities, focusing on the research of 3D display technology and light field holographic display technology, constantly breaking through key process technologies and promoting product applications, which has further enhanced the technical strength of WIMI. It can be expected that WIMI will continue to improve product performance, dig deep into industry needs, and better apply technology to all walks of life.
epilogue
According to industry analysts, the light field naked-eye 3D industry will also usher in a new round of blowout development. Industry reports indicate that the global 3D display market size is expected to reach USD 413.13 billion by 2030, with a CAGR of 19.0% from 2023 to 2030. In this process, opportunities and challenges coexist. WIMI has long been committed to the R&D and application promotion of light field display technology. In the future, it will generate huge commercial value in various vertical industries and is expected to become an industry leader.
StockpickerCAPC
12 months ago
WIMI Expands the Imagination Space of AIGC+ Digital Humans.
Source
https://t.cj.sina.com.cn/articles/view/1765776051/693f9ab30200119pb?from=tech
May 11, 2023
It is understood that with the continuous iteration and development of digital human technology, more and more companies have gradually realized that if a technology is to be widely applied to all walks of life, lowering the threshold and inclusiveness are the only way to go. Based on this, WIMI, as the world's leading AI solution provider, has developed a data-driven virtual digital human construction technology for the public.
According to public information, this technology quickly collects and processes the action data of real people to generate action data sets, and uses machine learning algorithms for model training and optimization to generate high-fidelity virtual digital human models, thus providing low-cost Efficient solution.
It is reported that the key to the data-driven virtual digital human construction technology researched by WIMI is to collect data to obtain information such as real human body characteristics, shapes and actions. Data acquisition equipment includes 3D scanners, cameras, motion capture systems, etc., which can collect real human bodies from multiple angles and record information such as their shapes and movements. In addition, its digital human has an intelligent question-and-answer function, which can interact and communicate with the audience in real time, improve the flexibility, functionality and authenticity of the digital human, and enhance the audience's sense of experience.
Relying on this, the WIMI Holographic team has developed a product application that uses AI technology to quickly generate a digital human image. As the link point technology between artificial intelligence and the metaverse, the WIMI Holographic Digital Human has been widely used in various applications. Scenes. Such as intelligent digital customer service, digital human live broadcast, cultural tourism digital human, digital human marketing, etc., and can create new impetus and new economic growth points. WIMI will continue to create and optimize digital human technology and related services, and provide high-quality and efficient digital human service solutions for the generative AI technology development industry, animation film and television industry, pan-entertainment live broadcast industry, and e-commerce industry.
epilogue
In recent years, with the continuous development and maturity of digital human technology and AI technology, digital human has been more and more widely used to provide users with intelligent services. In particular, the IDC report pointed out that AI digital humans are gradually upgraded under the promotion of AIGC. AI digital humans can already be used in entertainment-oriented scenarios such as delivery of goods and live broadcasts, as well as in education, medical care and other professional fields. In the future, generative AI will gradually infiltrate from the C-side to the B-side. With the blessing of AIGC, digital humans will usher in new breakthrough windows in terms of manufacturing efficiency, usage scenarios, multi-modal interaction, and analysis and decision-making. Then you might as well look forward to it.
StockpickerCAPC
12 months ago
Explanation: AIGC AI-Generated Content
A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT.
Recently, ChatGPT, along with DALL-E-2 and Codex,has been gaining significant attention from society. As a result, many individuals have become interested in related resources and are seeking to uncover the background and secrets behind its impressive performance. In fact, ChatGPT and other Generative AI (GAI) techniques belong to the category of Artificial Intelligence Generated Content (AIGC), which involves the creation of digital content, such as images, music, and natural language, through AI models. The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace. AIGC is achieved by extracting and understanding intent information from instructions provided by human, and generating the content according to its knowledge and the intent information. In recent years, large-scale models have become increasingly important in AIGC as they provide better intent extraction and thus, improved generation results. With the growth of data and the size of the models, the distribution that the model can learn becomes more comprehensive and closer to reality, leading to more realistic and high-quality content generation. This survey provides a comprehensive review on the history of generative models, and basic components, recent advances in AIGC from unimodal interaction and multimodal interaction. From the perspective of unimodality, we introduce the generation tasks and relative models of text and image. From the perspective of multimodality, we introduce the cross-application between the modalities mentioned above. Finally, we discuss the existing open problems and future challenges in AIGC.
https://arxiv.org/abs/2303.04226
Nature - AI-Generated Content
https://www.nature.com/search?q=AI-Generated%20Content&order=relevance
The agency predicts that the AIGC market space will exceed one trillion yuan, and the commercial application of WIMI Hologram will open a new era
https://t.cj.sina.com.cn/articles/view/1765776051/693f9ab3020010t1e?from=tech
Accelerate industrial layout,WIMI Holographic AIGC Drives the Underlying Innovation of the Metaverse to Empower the Future of Technology
https://xueqiu.com/8520530702/249834504
AI-generated content helping redefine industry
http://www.china.org.cn/business/2022-12/13/content_85009779.htm
Tech giants explore AI opportunities
http://www.china.org.cn/business/2023-02/07/content_85092578.htm
China's digital economy a new growth engine to drive modernization
http://www.china.org.cn/business/2023-04/28/content_85259709.htm
Metaverse frenzy cools as ChatGPT gains steam
http://www.china.org.cn/business/2023-03/08/content_85153040.htm
China ushers in golden era of AI, chatbots.
http://www.china.org.cn/business/2023-02/17/content_85111668.htm
Big coffee point of view?AIGC and causal inference of two-way empowerment
https://t.cj.sina.com.cn/articles/view/1765776051/693f9ab30200117v7?finpagefr=p_104_js
Agency Forecast AIGC Market More Than Trillion; WiMi’s Applications Ushered In A New Era
https://www.newstrail.com/agency-forecast-aigc-market-more-than-trillion-wimis-applications-ushered-in-a-new-era/
Intensive policies promote the AIGC industry to "let a hundred flowers bloom", and WIMI has stepped up efforts to become the focus of the industry
https://t.cj.sina.com.cn/articles/view/1765776051/693f9ab30200115rr?from=tech
The heroes stand together and embrace AIGC tightly, and WIMI Hologram (WIMI.US) actively pays attention to the application development of related scenarios
https://finance.sina.com.cn/jjxw/2023-03-16/doc-imykzspz4582337.shtml
WIMI Hologram Cloud focuses on AIGC field for the AI new era.
https://investorshub.advfn.com/boards/read_msg.aspx?message_id=171854403
StockpickerCAPC
12 months ago
WIMI (NASDAQ: WIMI) develops 3D computer-generated hologram technology based on deep learning.
Source
https://cj.sina.com.cn/articles/view/1704103183/65928d0f0200360s0?from=finance
May 11, 2023
With the gradual maturity of 3D printing technology, 3D computer-generated technology has become another way for people to explore the digital world. In this field, hologram technology is a very promising technology. A hologram is an image that uses the principle of light wave interference to achieve a 3D effect. In a hologram, when light passes through an object, interference fringes will be generated, and the information of these fringes will be recorded, and then the 3D effect of the object will be presented through the reflection and refraction of light. Hologram technology can present real 3D objects optically, which can make people feel the actual existence of objects in space, giving people a visual effect of interlacing virtual and real.
Nowadays, due to the continuous development of deep learning technology, 3D computer-generated holograms based on deep learning have become a reality.
It is understood that WIMI (NASDAQ: WIMI) has developed a 3D computer-generated hologram technology based on deep learning. Deep learning can realize automatic object recognition and three-dimensional modeling by training neural networks. At the same time, it can also optimize the light field information, thereby improving the quality and resolution of the hologram. First, WIMI uses deep learning algorithms to analyze the 3D model, extracts the depth information, and then turns the depth image into a hologram after a series of optical processing. The technical process of WIMI 3D computer-generated hologram based on deep learning covers data preparation, model construction, deep learning model training, hologram generation and display, etc.
? Data preparation
To generate a hologram, first the data of the 3D object needs to be prepared. Usually, we can use 3D scanners or manual modeling to obtain data of 3D objects. In this process, attention needs to be paid to the accuracy and resolution of the data. The higher the accuracy of the data, the clearer the resulting hologram, and the higher the resolution, the more details can be presented.
? Model building
After preparing the data of the 3D object, the next step is to build the model. Model building is a key step in converting 3D objects into holograms. In this process, 3D modeling software is required. During the model building process, attention needs to be paid to the details of the model's geometry and texture maps. These factors can affect the quality and effect of the final hologram produced.
? Deep learning model training
After preparing the data of the 3D object and building the model, the next step is to train the deep learning model. Deep learning models are the key technology for converting 3D models into holograms. In this process, we can use deep learning models such as convolutional neural networks (CNN). In the process of model training, a large amount of data needs to be used for training to improve the accuracy and stability of the model.
? Hologram generation
After the training of the deep learning model is completed, the hologram can be generated next. In this process, we need to input the 3D model into the deep learning model, and then render the output into a hologram. During the hologram generation process, it is necessary to pay attention to the setting of the light source and the adjustment of the hologram. These factors affect the visual quality and realism of the resulting hologram.
? Hologram display
Finally, the resulting hologram needs to be displayed. In the process of displaying holograms, light sources and specific projection equipment, such as holographic projectors, are required. During the presentation, it is necessary to pay attention to the position of the light source and the intensity of the light, as well as the setting and calibration of the projection equipment. These factors affect the look and feel and clarity of the final hologram.
At present, the deep learning-based 3D computer-generated hologram technology of WIMI Hologram Research has broad application prospects, including virtual reality, augmented reality, medical imaging and other fields. In virtual reality, holograms can be used to present scenes and objects with 3D effects, allowing users to feel a real sense of presence. In augmented reality, holograms can be used to augment objects in a reality scene, giving users a deeper understanding of the 3D structure of the object. In medical imaging, holograms can be used to present the 3D structure of medical images and help doctors better diagnose conditions.
StockpickerCAPC
12 months ago
WIMI (NASDAQ: WIMI) develops an optical scanning holographic reconstruction algorithm based on convolutional neural network.
Source
https://cj.sina.com.cn/articles/view/7651844612/1c815e20402001gdrc?from=finance
May 10, 2023
With the continuous development of science and technology, optical scanning holographic imaging technology has increasingly become a popular technology in the research field. The technique creates a holographic image of a three-dimensional object by recording the interference pattern of light scattered by the object. However, due to its high complexity and large amount of data processing needs, this technology still has many challenges. To solve these problems, in recent years, deep learning techniques have been applied to optical scanning holographic reconstruction to improve the quality and speed of reconstructed holographic images.
Deep learning is a machine learning method based on neural networks, which can automatically learn features and rules from data and be used for tasks such as classification, prediction and generation. In optical scanning holographic reconstruction, deep learning can be used to optimize the reconstruction algorithm and improve the quality of reconstructed holographic images.
It is understood that the research and development team of WIMI (NASDAQ: WIMI) is developing an optical scanning holographic reconstruction algorithm based on convolutional neural network, which processes interference images through convolutional neural network (CNN) to generate high-quality holographic images. The convolution kernel is used to extract the features in the interference image, and then these features are used to generate the holographic image. Compared with traditional reconstruction algorithms, the use of CNN can reduce noise and artifacts, and improve the resolution and sharpness of reconstructed images. In addition, convolutional neural networks can speed up the reconstruction process through parallel processing and optimization algorithms. While achieving high-quality holographic reconstruction, it can also reduce computational complexity and data requirements.
According to the data, WIMI Hologram's convolutional neural network-based optical scanning holographic reconstruction algorithm uses the convolutional neural network model to perform end-to-end learning and reconstruction of optical scanning holographic images. Specifically, firstly, the collected optical scanning holographic image is fed into the convolutional neural network model as input, and then the convolutional neural network model automatically extracts high-level features from the input holographic image, and through backpropagation The algorithm continuously adjusts the network parameters to minimize the reconstruction error, and finally, outputs a high-quality holographic image reconstruction result. Its technical process includes steps such as data acquisition, data preprocessing, convolutional neural network model training, model testing, result evaluation, and optimization and improvement.
Compared with traditional holographic image processing algorithms, WIMI's optical scanning holographic reconstruction algorithm based on convolutional neural network can automatically extract features, learn more complex and advanced features from data, and obtain higher quality reconstruction results , and the whole reconstruction process is carried out in an end-to-end framework, which simplifies the process and improves the efficiency of holographic reconstruction.
The optical scanning holographic reconstruction algorithm based on convolutional neural network is a very potential technology, which has important application value and has broad application prospects in digital holographic imaging, medical image analysis, three-dimensional object recognition and other fields. It is believed that with the continuous development and improvement of technology, it will be applied in more fields and bring more convenient and efficient services to people.
In the future, WIMI will also study how to combine the convolutional neural network-based optical scanning holographic reconstruction algorithm with other computer vision and image processing technologies to achieve more comprehensive and accurate data analysis and image processing. In addition, it will also consider applying this technology to actual production, such as industrial testing, non-destructive testing and other fields, in order to achieve more intelligent and efficient production management.
StockpickerCAPC
12 months ago
A spark can start a prairie fire Web3.0 set off a whirlwind, WIMI Hologram (WIMI.US) took advantage of the opportunity to explore the potential track.
Source
https://cj.sina.com.cn/articles/view/1704103183/65928d0f020035zca?from=finance
May 10, 2023
Web3.0, also known as the decentralized Internet, is an Internet architecture based on blockchain technology, which aims to break the centralized Internet model and return data control to users.
The realization of Web3.0 requires the support of many different technologies and standards, including blockchain, distributed ledger, cryptography, smart contract, decentralized storage, etc. These technologies and standards will play an important role in the future development of the Internet and promote the further development and realization of Web3.0.
Try first, Hong Kong establishes Web3.0 Association
Not long ago, the Web3.0 conference in Hong Kong made many people in the industry see the long-lost lively scene, intensive meetings, crowds from all over the world, and the platform of the Chief Executive of the Special Administrative Region, Li Jiachao, like a prosperous scene of a bull market.
At the beginning of this year, the Hong Kong Special Administrative Region established the "Cyberport Web3.0 Base" to better gather global Web3.0 start-ups and talents. In addition, in order to seize the opportunity of Web3.0, the Hong Kong Special Administrative Region Government announced a new "Financial Budget" to allocate 50 million Hong Kong dollars to Cyberport to accelerate the development of Hong Kong's Web3.0 ecosystem, especially to promote cross-sector business cooperate.
However, most importantly, the establishment of the Web3.0 Association in Hong Kong is committed to promoting the enrichment of Web 3.0 application scenarios and promoting the integration of Web 3.0 into life. As one of the world's financial centers, Hong Kong's dynamics have attracted worldwide attention, and its frequent efforts to promote Web3.0 have once again ignited this field. The development of Web 3.0 is now at the golden starting point. This disruptive technology can change many original business operation models and create more new opportunities at the same time.
We must know that since this year, all regions have focused on stimulating the economy, and Web 3.0 is a brand new concept, and Web 3.0 is a historical necessity. Therefore, under the current overall situation, Hong Kong has given unprecedented support to the development of Web3.0, paying equal attention to supervision and promotion, and took the lead in igniting the spark of Web3.0.
Grasping the opportunity of growth, WIMI takes advantage of the opportunity to take off
Although the Web 3.0 industry has just emerged and has not yet formed a large-scale and mature business model, the exploration and enthusiasm for the Web 3.0 industry is quite high. It is worth noting that there are also many listed companies in the capital market that have deployed in the Web3.0 field. Among them, WIMI.US, known as the "Forerunner in the Web3.0 Era", has advanced technology and seized this growth opportunity , to accelerate digital transformation, enhance its own competitiveness and resilience, and lay a solid foundation for future enterprise development.
From a technical point of view, Web 3.0 is not only an iteration of previous technologies, but also an integration of multiple technologies, including 5G, VR, AR, MR, blockchain, chips, cloud computing, and edge computing. It is reported that WIMI has shown its talents, involving multiple Web 3.0 core technologies including 5G, AR, semiconductors, cloud computing, etc., and has made a qualitative leap in recent years, further improving the underlying technology of Web 3.0. At the same time, WIMI has firmly grasped the advantages of talents, introduced a large number of R&D personnel, established a high-quality Web 3.0 technical team, iteratively produced high-quality products, and accumulated a positive reputation.
In addition, Web 3.0 may be an indispensable infrastructure for the Metaverse in the future. The emergence of Web 3.0 is expected to accelerate the digital living space built by the Metaverse. Judging from the current situation, Web3.0 is not only an intelligent Internet, but also a three-dimensional holographic Internet, which provides users with unprecedented interactivity and a high sense of immersion and participation. According to PricewaterhouseCoopers' prediction, the economy related to Metaverse will usher in substantial growth, and the market size is expected to increase from US$5 million to US$1.5 trillion in the next ten years, with a compound growth rate of 253%.
With the prevalence of the metaverse concept, in terms of the metaverse, WIMI has prepared for a rainy day, allowing the virtual and real worlds to continue to merge. The construction of digital scenes is accompanied by changes in technology and content production methods. actions in other fields. In the future, WIMI will deepen its main business field, and at the same time, will join hands with multiple partners in the industry chain to gather industry strength and provide a good impetus for the sustainable development of Web3.0.
end
From the perspective of the market's promotion of the industry, according to the "Global Web3.0 Technology Industry Ecological Development Report (2022)" data released by the Institute of Information and Communications Technology, the global Web3.0 project investment is booming. As an epoch-making technology, Web 3.0 has won the favor of capital, and industry leaders are optimistic about the track of Web 3.0, which will surely become the next outlet.
With the continuous increase of capital support for Web3.0 and the vigorous promotion of enterprises, the development of Web3.0 is expected to further accelerate, and the application scenarios will continue to expand. Undoubtedly, the era when Web 3.0 can show its talents has come, and as long as you can seize the opportunity on the Web 3.0 track and find the right position, you may be able to overtake in a corner.
StockpickerCAPC
12 months ago
Hong Kong Web3.0 Festival: WiMi Hologram Cloud(WIMI) Grasps The Golden Chance.
Source
https://www.newstrail.com/hong-kong-web3-0festival-wimi-hologram-cloud%EF%BC%88wimi-grasps-the-golden-chance/
May 8, 2023
Recently, the “2023 China Hong Kong Web3.0 Carnival” jointly organized by Wanxiang Blockchain Lab and HashKey Group was solemnly opened in the Hong Kong Convention and Exhibition Center, China. During the four-day agenda, the top people, technologies, projects and capital of the global Web3.0 industry will gather here to discuss the core issues of various Web3.0 tracks, such as regulatory policies, Web3.0 infrastructure, Metaverse, and share the development trends and challenges of the industry.
Internet revolution that full of technology
Web3.0 is in the spotlight of the market, so why say Web3.0 is the third generation of the Internet era? In a narrow sense, users in the Web1.0 stage can only passively browse the web, Web2.0 stage users rely on the platform to create and spread information, and Web3.0 stage users can manage data more independently and gain value in Internet content creation.
From a broad point of view, Web3.0 through the design of new technical agreement and construction of new infrastructure, make the Internet more decentralized, more secure, allowing users to master their own digital identity and digital assets, is the first time in the digital world created real oriented data elements approval, circulation, trading economic system.
With the development of the Internet, the decentralized and creator-oriented network will become the mainstream. Web3.0 is an open and decentralized Internet model, which has a revolutionary change power. This is the inevitable product of the development of the Internet to a certain stage, but also the inevitable result of social and cultural progress.
WiMi Hologram Cloud Seizes the Web3.0 development opportunities
Web3.0 is a sea of stars, and all this is in its infancy. However, this puts forward high requirements for enterprises to build a development environment, which is not an opportunity, but also a challenge. In fact, the Metaverse holographic AR first share – WiMi Hologram Cloud (NASDAQ: WIMI) early in the field, with the development trend of the Internet and national development strategy research and forward-looking layout, a number of frontier scientific research and technical cooperation, and integration of various resources, for the future development of Web3.0 provide powerful product iteration and service innovation.
Industry analysis, behind the germination of Web 3.0 is the continuous maturity of human cutting-edge technologies such as blockchain and artificial intelligence. Given the potential of the industry in the future, WiMi Hologram Cloud is excited about Web3.0 and taking action to embrace technological change. The company is committed to developing AIGC based on artificial intelligence, big data and high computing power. The explosion of AIGC brings new opportunities for it. It has launched new 5G communication products integrating AIG C and immersive experience based on 5G + AI.
It is worth mentioning that WiMi Hologram Cloud has a good foundation for AI services:
- leading algorithm capabilities,
- massive data resources, and
- broad AI applications.
In the future, WiMi Hologram Cloud will continue to develop technology research and development in AI vision, natural language processing and other fields, continue to invest in computing power, and anchor the development of “Web3.0 ecological innovation”. WiMi Hologram Cloud The ecological foundation of Web3.0 goes first, and its development and paving can better meet the upgrading of the base layer, extension layer and application layer.
To sum up
Web 3.0 will be used widely in the future. Web 3.0 can promote the development of sustainable cities, and urban planning based on Web 3.0 can make better use of urban space resources, improve the transportation system, improve urban energy efficiency, reduce urban pollution, and so on. At the same time, as an epoch-making technology, Web 3.0 has gained the favor of capital. With the continuous increase of capital support for Web3.0, and the strong promotion of enterprises in cities and WiMi Hologram Cloud represented by Hong Kong, the development of Web3.0 is expected to further accelerate, and the application scenarios are expected to continue to expand.
StockpickerCAPC
12 months ago
WiMi Hologram Cloud Made Innovation In Its 6G Tech Development.
Source
https://www.newstrail.com/wimi-hologram-cloud-made-innovation-in-its-6g-tech-development/
May 6, 2023
It is learned that the 25th Institute of the Second Institute of Aerospace Science and Industry of China has completed the first real-time wireless transmission communication experiment of terahertz orbital angular momentum in China, achieving a major technological breakthrough and providing important guarantee and support for the development of 6G in China.
Important breakthroughs in 6G technologies
It is reported that this experiment uses the high-precision spiral phase plate antenna to realize four different beam modes in the 110GHz frequency band, and completes 100 Gbps wireless real-time transmission in the 10GHz transmission bandwidth, maximizing the bandwidth utilization.
Terahertz communications, as a key technology to realize the 6G vision, has been recognized by the global communications industry. Earlier, the Federal Communications Commission (FCC) voted unanimously to open the “terahertz” spectrum for future 6G network services for innovators to test 6G technology.
Compared with 5G, the communication capacity of 6G will be more than 10 times that. The development of 5G to 6G will be a process from the interconnection of everything to the “intelligent connection of everything, digital twin”, and finally realize the three-dimensional linkage of the physical world, virtual world, and people. Today, when 5G technology is just popularized, China has taken the lead in achieving key technological breakthroughs in 6G technology, which will undoubtedly bring new vitality to the global communication revolution.
The Ministry of Industry and Information Technology is accelerating 6G development and exploring market opportunities
The Ministry of Industry and Information Technology said that the ministry will focus on the information and communication industry to improve and provide a solid support for high-quality economic and social development. One is to consolidate the base of the digital facilities. Firstly, China will study and introduce policies and measures to promote the coordinated development of new information infrastructure and promote the integrated development of new infrastructure.
Second, China will accelerate innovation in the technological industries. It will accelerate breakthroughs in key core technologies such as 6G, optical communication, and quantum communication, and step up research and development of cutting-edge technologies such as artificial intelligence, blockchain, and digital twinning. Third, The team will deepen the application of industrial integration. They will vigorously promote the integrated application of 5G and other next-generation information and communication technologies in vertical industries, information consumption, and people’s livelihood.
Judging from the latest progress, after 5G, China still leads the world in 6G technology. In this new era, 6G technology will lead the global communication revolution and bring more convenience and intelligence to human life.6G will drive the communication industry chain to have a higher degree of scientific research breakthroughs, greater capital expenditure, and broader market prospects.
Research firm Market Research Future expects the global 6G market to exceed $340 billion in 2040, with a compound annual growth rate of 58.1%. It believes that China will be one of the world’s largest 6G markets, with nearly 50 percent of global 6G patent applications coming from China, ranking first.
WiMi advances the age of 6G
Although it is still in the early research stage of 6G technology, with the deepening of the development of the 6G industry, satellite Internet, high-speed wireless transmission, network, and data center energy-saving sectors are expected to meet new development opportunities. It is understood that WiMi Hologram Cloud (NASDAQ: WIMI), a technology company with deep technology foundation advantages, has long accelerated the layout of humanoid robots, 5G, universe, quantum computing, and other cutting-edge fields, under the call of the Ministry of Industry and Information Technology, comprehensively promote the 6G application technology research and development, will undoubtedly be a piece of the pie when the arrival of the 6G wave.
6G is the endogenous integration of communication, computing, perception, artificial intelligence, blockchain, security, satellite, and other technologies. It will become the commanding point in the digital economy and play a very strong driving role in the development of digital industrialization and industrial digitalization. Therefore, WiMi’s 6G technology will promote the development of the digital economy and provide new development opportunities for all walks of life. For example, in the field of education, 6G technology will improve the quality of online education, and WiMi allows more people to enjoy high-quality educational resources. In the transportation field, 6G technology will make self-driving cars a reality, bringing more convenience for people to travel.
It is worth noting that, aiming at the hot demand of 6G communication and following the forefront of international communication technology, WiMi’s research and development team is studying 6G intelligent holographic radio technology. It is reported that 6G intelligent holographic radio realizes the reconstruction of electromagnetic space through interferometry and computing holographic technology. On the one hand, it realizes the full space-time-frequency multiplexing of wireless communication with higher resolution, and on the other hand, it realizes the fusion of wireless communication imaging and perception. WiMi’s 6G intelligent holographic radio technology will help build a new network of the intelligent and efficient interconnection of people, machines, and things, and finally, realize the beautiful vision of “all things and digital twin”.
To sum up
6G technology is the next generation of wireless communication technology after 5G, and its transmission speed and network connection stability will be faster and more stable than 5G. At present, China is leading the world in 5G construction, and the progress of 6G research and development has led the world. Faced with the huge market space and economic benefits, WiMi upholds the original aspiration as a rock and moves forward vigorously to build a win-win digital future.
To sum up, China’s key technological breakthroughs in 6G technology mark China’s leading position in the global communications revolution. With this new achievement, 6G technology will bring new vitality to the global communication revolution and bring more convenience and intelligence to human life. So look forward to the arrival of a new era of 6G technology and contributing China’s strength to the global communications revolution.
Level up your trading with our powerful tools and real-time insights all in one place.
INV4
4 days ago
da_stock_analyst
1 month ago
glenn1919
1 month ago
Monksdream
1 month ago
nowwhat2
2 months ago
WallStreetMyWay
3 months ago
Hot Features
Premium
