Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into *** article reviews the relevant literature on Chinese Sign Language Re...
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Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into *** article reviews the relevant literature on Chinese Sign Language Recognition(CSLR)in the past 20 *** Markov Models(HMM),Support Vector Machines(SVM),and Dynamic Time Warping(DTW)were found to be the most commonly employed technologies among traditional *** from the rapid development of computer vision and artificial intelligence technology,Convolutional Neural Networks(CNN),3D-CNN,YOLO,Capsule Network(CapsNet)and various deep neural networks have sprung *** Neural Networks(DNNs)and their derived models are integral tomodern artificial intelligence *** addition,technologies thatwerewidely used in the early days have also been integrated and applied to specific hybrid models and customized identification *** language data collection includes acquiring data from data gloves,data sensors(such as Kinect,LeapMotion,etc.),and high-definition ***,facial expression recognition,complex background processing,and 3D sign language recognition have also attracted research interests among *** to the uniqueness and complexity of Chinese sign language,accuracy,robustness,real-time performance,and user independence are significant challenges for future sign language recognition ***,suitable datasets and evaluation criteria are also worth pursuing.
In this work, we developed and enhanced an artificial intelligence (AI)-centered hardware framework. This framework integrates the Nvidia Jetson Nano processing unit with a Depth AI camera. Our primary goal was to cre...
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Forecasting retail sales often requires various number of products from different stores. Existing deep or machine learning techniques fall short of producing accurate classification results because of overfitting and...
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The efficient implementation of the Advanced Encryption Standard(AES)is crucial for network data *** paper presents novel hardware implementations of the AES S-box,a core component,using tower field representations an...
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The efficient implementation of the Advanced Encryption Standard(AES)is crucial for network data *** paper presents novel hardware implementations of the AES S-box,a core component,using tower field representations and Boolean Satisfiability(SAT)*** research makes several significant contri-butions to the ***,we have optimized the GF(24)inversion,achieving a remarkable 31.35%area reduction(15.33 GE)compared to the best known ***,we have enhanced multiplication implementa-tions for transformation matrices using a SAT-method based on local *** approach has yielded notable improvements,such as a 22.22%reduction in area(42.00 GE)for the top transformation matrix in GF((24)2)-type S-box ***,we have proposed new implementations of GF(((22)2)2)-type and GF((24)2)-type S-boxes,with the GF(((22)2)2)-type demonstrating superior *** implementation offers two variants:a small area variant that sets new area records,and a fast variant that establishes new benchmarks in Area-Execution-Time(AET)and energy *** approach significantly improves upon existing S-box implementations,offering advancements in area,speed,and energy *** optimizations contribute to more efficient and secure AES implementations,potentially enhancing various cryptographic applications in the field of network security.
The connection between manufacturers and retailers in the retail industry, as well as supply chain management, depend heavily on sales forecasting. The efficiency of traditional methodologies and methods for completin...
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Edge computing nodes undertake an increasing number of tasks with the rise of business ***,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical *** study prop...
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Edge computing nodes undertake an increasing number of tasks with the rise of business ***,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical *** study proposes an edge task scheduling approach based on an improved Double Deep Q Network(DQN),which is adopted to separate the calculations of target Q values and the selection of the action in two networks.A new reward function is designed,and a control unit is added to the experience replay unit of the *** management of experience data are also modified to fully utilize its value and improve learning *** learning agents usually learn from an ignorant state,which is *** such,this study proposes a novel particle swarm optimization algorithm with an improved fitness function,which can generate optimal solutions for task *** optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition *** proposed algorithm is compared with six other methods in simulation *** show that the proposed algorithm outperforms other benchmark methods regarding makespan.
Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Ae...
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Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.
In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems,...
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In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.
3D vision recognition offers a significantly more robust tool for achieving machine cognition compared to traditional 2D vision techniques. However, similar to the vulnerabilities present in 2D vision, many 3D vision ...
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Road rage detection in India's chaotic traffic conditions demands robust, real-time solutions. This paper proposes a 3D CNN-based system that leverages transfer learning from a violence detection model (98 % accur...
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