recognition function
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2022 ◽  
Vol 355 ◽  
pp. 03043
Author(s):  
Yushan Zhong ◽  
Yifan Jia ◽  
Liang Ma

In order to cultivate children’s imagination and creativity in the cognitive process, combined with the traditional hand shadow game, a children’s gesture education game based on AI gesture recognition technology is designed and developed. The game uses unity development platform, with children’s digital gesture recognition as the content, designs and implements the basic functions involved in the game, including AI gesture recognition function, character animation function, interface interaction function, AR photo taking function and question answering system function. The game is finally released on the mobile terminal. Players can recognize gestures through mobile cameras, interact with virtual cartoon characters in the game, watch cartoon character animation, understand popular science knowledge, and complete the answers in the game. The educational games can better assist children to learn digital gestures, enrich children’s ways of cognition, expand children’s imagination, and let children learn easily with happy educational games.


Nanomaterials ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 3317
Author(s):  
Zeineb Ayed ◽  
Shiana Malhotra ◽  
Garima Dobhal ◽  
Renee V. Goreham

Acinetobacter baumannii is a remarkable microorganism known for its diversity of habitat and its multi-drug resistance, resulting in hard-to-treat infections. Thus, a sensitive method for the identification and detection of Acinetobacter baumannii is vital. However, current methods used for the detection of pathogens have not improved in the past decades and suffer from long process times and low detection limits. A cheap, quick, and easy detection mechanism is needed. In this work, we successfully prepared indium phosphide quantum dots with a zinc sulphide shell, conjugated to a targeting aptamer ligand, to specifically label Acinetobacter baumannii. The system retained both the photophysical properties of the quantum dots and the folded structure and molecular recognition function of the aptamer, therefore successfully targeting Acinetobacter baumannii. Confocal microscopy and transmission electron microscopy showed the fluorescent quantum dots surrounding the Acinetobacter baumannii cells confirming the specificity of the aptamer conjugated to indium phosphide quantum dots with a zinc sulphide shell. Controls were undertaken with a different bacteria species, showing no binding of the aptamer conjugated quantum dots. Our strategy offers a novel method to detect bacteria and engineer a scalable platform for fluorescence detection, therefore improving current methods and allowing for better treatment.


2021 ◽  
pp. 1-15
Author(s):  
Silvia Ceccacci

Driver behaviour recognition is of paramount importance for in-car automation assistance. It is widely recognized that not only attentional states, but also emotional ones have an impact on the safety of the driving behaviour. This research work proposes an emotion-aware in-car architecture where it is possible to adapt driver’s emotions to the vehicle dynamics, investigating the correlations between negative emotional states and driving performances, and suggesting a system to regulate the driver’s engagement through a unique user experience (e.g. using music, LED lighting) in the car cabin. The relationship between altered emotional states induced through auditory stimuli and vehicle dynamics is investigated in a driving simulator. The results confirm the need for both types of information to improve the robustness of the driver state recognition function and open up the possibility that auditory stimuli can modify driving performance somehow.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zuo Wu

Human motion recognition has an important application value in scenarios such as intelligent monitoring and advanced human-computer interaction, and it is an important research direction in the field of computer vision. Traditional human motion recognition algorithms based on two-dimensional cameras are susceptible to changes in light intensity and texture. The advent of depth sensors, especially the Kinect series with good performance and low price released by Microsoft, enables extensive research based on depth information. However, to a large extent, the depth information has not overcome these problems based on two-dimensional images. This article introduces the research background and significance of human motion recognition technology based on depth information, introduces in detail the research methods of human motion recognition algorithms based on depth information at home and abroad, and analyzes their advantages and disadvantages. The public dataset is introduced. Then, based on the depth information, a method of human motion recognition is proposed and optimized. A moving human body image segmentation method based on an improved two-dimensional Otsu method is proposed to solve the problem of inaccurate and slow segmentation of moving human body images using the two-dimensional Otsu method. In the process of constructing the threshold recognition function, this algorithm not only uses the cohesion of the pixels within the class but also considers the maximum variance between the target class and the background class. Then, the quantum particle swarm algorithm is used to find the optimal threshold solution of the threshold recognition function. Finally, the optimal solution is used to achieve accurate and fast image segmentation, which increases the accuracy of human body motion tracking by more than 30%.


2021 ◽  
Vol 2078 (1) ◽  
pp. 012053
Author(s):  
Yangfeng Wang ◽  
Tao Chen

Abstract With the rapid development of science and technology, biotechnology has developed rapidly. Among the many biometric technologies, finger vein technology has the characteristics of vitality, portability, and non-replicability, so it is considered to be the most promising biometric technology. However, the accuracy of finger vein recognition is affected by the collection device, the surrounding temperature and the algorithm. The flaws cannot be applied to real life on a large scale. This paper designs a finger vein recognition system based on convolutional neural network and Android, which mainly includes the following three parts. First, the system hardware includes the design of the acquisition device, the selection of the core development board and the display screen. Second, the design of the entire system software architecture is based on the MVVM architecture, which ensures low coupling of the program and is easy for later expansion and maintenance. The software includes collection function, recognition function and administrator function. Finally, a lightweight neural network is proposed for finger vein feature extraction, and proposed a storage method based on MMKV to meet the real-time performance of the system.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qi Liang

In order to realize high-accuracy recognition of aerobics actions, a highly applicable deep learning model and faster data processing methods are required. Therefore, it is a major difficulty in the field of research on aerobics action recognition. Based on this, this paper studies the application of the convolution neural network (CNN) model combined with the pyramid algorithm in aerobics action recognition. Firstly, the basic architecture of the convolution neural network model based on the pyramid algorithm is proposed. Combined with the application strategy of the common recognition model in aerobics action recognition, the traditional aerobics action capture information is processed. Through the characteristics of different aerobics actions, different accurate recognition is realized, and then, the error of the recognition model is evaluated. Secondly, the composite recognition function of the convolution neural network model in this application is constructed, and the common data layer effect recognition method is used in the optimization recognition. Aiming at the shortcomings of the composite recognition function, the pyramid algorithm is used to improve the convolution neural network recognition model by deep learning optimization. Finally, through the effectiveness comparison experiment, the results show that the convolution neural network model based on the pyramid algorithm is more efficient than the conventional recognition method in aerobics action recognition.


2021 ◽  
pp. 004051752110342
Author(s):  
Jeanne Tan ◽  
Li Shao ◽  
Ngan Yi Kitty Lam ◽  
Anne Toomey ◽  
Lan Ge

Artificial intelligence (AI) offers the potential for the development of e-textiles that give wearers a smart and intuitive experience. An emerging challenge in intelligent materials design is hand gesture recognition textiles. Most current research focuses on number gesture recognition via smart gloves, so there is a gap in research that studies contact-less number gesture recognition textiles via computer vision. Meanwhile, there is lack of exploration on the integration of illuminating function and number gesture recognition textiles to improve interactivity by real-time visualizing detection results. In this research, a novel interactive illuminating textile with a touch-less number gesture recognition function has been designed and fabricated by using an open-source AI model. It is used in sync with a polymeric optical fiber textile with illuminative features. The textile is color-changing, controlled by the system's mid-air interactive number gesture recognition capability and has a woven stripe pattern and a double-layer weave structure with open pockets to facilitate integration of the system's components. Also described here is a novel design process that permits textile design and intelligent technology to integrate seamlessly and in synchronization, so that design in effect mediates continuously between the physical textile and the intangible technology. Moreover, this design method serves as a reference for the integration of open-source intelligent hardware and software into e-textiles for enhancement of the intuitive function and value via economy of labor.


2021 ◽  
Author(s):  
Chao E ◽  
Liqiang Dai ◽  
Jin Yu

In this work we computationally investigated how a viral RNA polymerase (RNAP) from bacteriophage T7 evolves into RNAP variants under lab-directed evolution to switch recognition from T7 promoter to T3 promoter in transcription initiation. We first constructed a closed initiation complex for the wild-type T7 RNAP, and then for six mutant RNAPs discovered from phage assisted continuous evolution experiments. All-atom molecular dynamics (MD) simulations up to one microsecond each were conducted on these RNAPs in complex with T7/T3 promoter. Our simulations show notably that protein-DNA electrostatic interactions or stabilities at the RNAP-DNA promoter interface well dictate the promoter recognition preference of the RNAP and variants. Key residues and structural elements that contribute significantly to switching the promoter recognition were identified. Followed by a first point mutation N748D on the specificity loop to slightly disengage the RNAP from the promoter to hinder the original recognition, we found an auxiliary helix (206-225) that takes over switching the promoter recognition upon further mutations (E222K and E207K), by forming additional charge interactions with the promoter DNA and reorientating differently on the T7 and T3 promoter. Further mutations on the AT-rich loop and the specificity loop can fully switch the RNAP-promoter recognition to the T3 promoter. Overall, our studies reveal energetics and structural dynamics details along an exemplary directed evolutionary path of the phage RNAP variants for a rewired promoter recognition function. The findings demonstrate underlying physical mechanisms and are expected to assist knowledge/data learning or rational redesign of the protein enzyme structure-function.


Author(s):  
Xiaoli Lu ◽  
Mohd Asif Shah

Background: Human-computer interaction plays a vital role through Natural Language Conversational Interfaces to improve the usage of computers. Speech recognition technology allows the machine to understand human language. A speech recognition algorithm is used to achieve this function. Methodology: This paper is mainly based on the fundamental theoretical research of speech signals, establishes the HMM model, uses speech collection, recognition, and other methods, simulates on MATLAB, and integrates the recognition system ported to ARM for debugging and running to realize the embedded speech recognition function based on HMM under the ARM platform. Conclusion: The conclusion shows that the HMM-based embedded unspecific continuous English speech recognition system has high recognition accuracy and fast speed.


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