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arXiv 2024年

作者： Chen, Haipeng Yang, Yuheng Lyu, Yingda College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Public Computer Education and Research Center Jilin University China

Human skeleton-based action recognition has long been an indispensable aspect of artificial intelligence. Current state-of-the-art methods tend to consider only the dependencies between connected skeletal joints, limiting their ability to capture non-linear dependencies between physically distant joints. Moreover, most existing approaches distinguish action classes by estimating the probability density of motion representations, yet the high-dimensional nature of human motions invokes inherent difficulties in accomplishing such measurements. In this paper, we seek to tackle these challenges from two directions: (1) We propose a novel dependency refinement approach that explicitly models dependencies between any pair of joints, effectively transcending the limitations imposed by joint distance. (2) We further propose a framework that utilizes the Hilbert-Schmidt Independence Criterion to differentiate action classes without being affected by data dimensionality, and mathematically derive learning objectives guaranteeing precise recognition. Empirically, our approach sets the state-of-the-art performance on NTU RGB+D, NTU RGB+D 120, and Northwestern-UCLA datasets. © 2024, CC BY-SA.

关键词： Probability density function

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Optimizing Energy Efficiency in Electric Vehicles Through Fuzzy Logic and Neural Network Algorithms

Optimizing Energy Efficiency in Electric Vehicles Through Fu...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： M Devi Siva Satya Sreedhar P S. Geetha Priya M. Amutha D. Kanagajothi R.Kalaivani Sri Research Scholar Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Information Technology Seshadri Rao Gudlavalleru Engineering College Gudlavalleru Andhra Pradesh India Department of computer science and Engineering R. M. D. Engineering College Kavaraipettai Chennai India Department of ECE Sona College of Technology Salem Tamil Nadu India Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R& D Institute of Science and Technology Avadi Chennai India Department of computer science Arunai Engineering College Tiruvannamalai Tamil Nadu India

The introduction of electric vehicles (EVs) has represented a significant step forward in the development of environmentally sustainable transportation. The study addresses the critical need to enhance energy management systems in electric vehicles (EVs) by overcoming the limitations of traditional algorithms. Existing systems, which rely on rule-based controllers and basic predictive models, have poor flexibility and precision when it comes to dynamically optimizing energy efficiency. To revolutionize energy optimization in EVs, the proposed approach uses a hybrid model that combines fuzzy logic and neural networks. It tackles limits through a comprehensive analysis, the design of a fuzzy logic-based controller, and the development of a neural network model trained on historical data. TensorFlow-implemented simulation results in Python validate the reliability of the model by closely matching predicted energy usage in a range of driving scenarios. Real-world experiments with an instrumented EV prototype highlight the proposed system's advantages even further, giving noteworthy results—92% energy efficiency, 25 mile range extension, and 95% predicted accuracy. These findings support the system's ability to greatly improve energy efficiency and range extension in electric vehicles, therefore transforming sustainable transportation.

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Advanced Modulation and Coding Schemes for Increasing Throughput in Satellite Communications

Advanced Modulation and Coding Schemes for Increasing Throug...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Dinesh Goyal H S Shreenidhi Vijaya N. Aher A. Amudha Yuvraj Parmar V. Ashok Quantum University Research Center Quantum University School of Engineering and Technology Computer Science and Engineering JAIN (Deemed to be University) Bangalore Karnataka E&TC Vishwakarma Institute of Technolog Pune India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Centre for Interdisciplinary Research for Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Advanced modulation and coding schemes (AMCS) are techniques used to enhance the records charge and throughput in satellite to for pc communications links with the aid of taking benefit of multiplexing, errors correction, and forward error-correction techniques. AMCS are designed to beautify records transmission from the satellite to the receiving station, as well as from the ground station to the satellite. When a signal is sent through a satellite hyperlink, a top restriction to its statistics charge is reached due to elements which include sign-to-noise ratio and the quantity of interference detected. With a proper combination of modulation and coding strategies, but, this upper restrict can be prolonged, accordingly improving throughput and reliability. AMCS use modulation techniques to encode records whilst coding techniques are used to manipulate and verify information integrity between the source and the vacation spot. Modulation strategies permit records signals to skip thru a transmission channel, together with a satellite to for pc link, through converting the traits of the signal consisting of the amplitude, frequency, or section. Coding techniques make sure that records may be reliably transmitted over the hyperlink by using adding redundant information to the authentic message. Such redundant statistics is used to detect and accurate any errors inside the facts that could occur during the transmission technique.

关键词： Satellite antennas Satellites Modulation Throughput Turbo codes Encoding Parity check codes Reliability Hypertext systems Uplink

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Automatic Plane Pose Estimation for Cardiac Left Ventricle Coverage Estimation via Deep Adversarial Regression Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4738-4752页

作者： Zhang, Le Bronik, Kevin Piechnik, Stefan K. Lima, Joao A. C. Neubauer, Stefan Petersen, Steffen E. Frangi, Alejandro F. University of Birmingham School of Engineering College of Engineering and Physical Sciences BirminghamB15 2TT United Kingdom University of Oxford Department of Engineering Science OxfordOX1 3PJ United Kingdom Division of Cardiovascular Medicine OxfordOX3 9DU United Kingdom Johns Hopkins University School of Medicine Division of Cardiology BaltimoreMD21287 United States Queen Mary University of London Centre for Cardiovascular Medicine The William Harvey Research Institute LondonE1 1BB United Kingdom Barts Health Nhs Trust Barts Heart Center LondonE1 1BB United Kingdom The University of Manchester Division of Informatics Imaging and Data Science School of Health Sciences The Department of Computer Science School of Engineering ManchesterM13 9PL United Kingdom

Accurate segmentation of the ventricles plays a crucial role in determining cardiac functional parameters such as ventricular volume, ventricular mass, and ejection fraction. However, poor image quality, such as inadequate coverage of the left ventricle (LV) and right ventricle (RV) in cardiac magnetic resonance (CMR) image sequences, can significantly affect the assessment of cardiac function. This study investigates issues related to missing or corrupted imaging planes, which often lead to incomplete ventricle coverage. To address the challenge of estimating ventricle coverage in CMR images regardless of variations in imaging parameters such as device type, magnetic field strength, and protocol execution, we introduce a novel convolutional neural network (CNN) based on adversarial learning. Additionally, we integrate supplementary information (e.g., cross-view image data) as privileged information to enhance the interpretability of our model's predictions and identify potential biases or inaccuracies. This research represents the first attempt to automatically estimate ventricular coverage by identifying missing slices and plane orientations in CMR images using a dataset-agnostic approach. The effectiveness of the proposed model is demonstrated through the evaluation of datasets from three diverse and sizable image acquisition cohorts, demonstrating superior performance compared to existing methods. © 2020 IEEE.

关键词： Image quality

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Advances of wearable silicone rubber-based triboelectric nanogenerators:from manufacturing to application

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International Journal of Extreme Manufacturing 2025年第3期7卷 126-158页

作者： Dianlong Shen Taili Du Fangyang Dong Hu Cai Aziz Noor Xiannan Du Yongjiu Zou Chengkuo Lee Minyi Xu Dalian Key Laboratory of Marine Micro/Nano Energy and Self-Powered Systems Marine Engineering CollegeDalian Maritime UniversityDalian 116026People’s Republic of China State Key Laboratory of Maritime Technology and Safety Dalian 116026People’s Republic of China Collaborative Innovation Research Institute of Autonomous Ship Dalian Maritime UniversityDalian 116026People’s Republic of China Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117576Singapore

Wearable bioelectronic devices are rapidly evolving towards miniaturization and multifunctionality,with remarkable features such as flexibility and ***,achieving a sustainable power supply for wearable bioelectronic devices is still a great *** nanogenerators(TENGs)provide an efficient solution by converting irregular,low-frequency bioenergy from the human body into electrical *** sustainably powering wearable bioelectronics,the harvested electrical energy also carries rich information for human body *** this conversion process,the choice of material plays a crucial role in affecting the output performance of the *** various materials,silicone rubber(SR)stands out due to its exceptional plasticity,flexibility,comfortability and other favorable ***,with appropriate treatment,SR can achieve extreme functionalities such as high robustness,good stability,self-healing capabilities,rapid response,and *** this review,recent advances in wearable SR-based TENGs(SR-TENGs)are systematically reviewed with a focus on their application in different parts of the human *** that the manufacturing method of SR-TENGs largely determines its output performance and sensitivity,this paper introduces the design of SR-TENGs,including material selection,process modulation,and structure ***,this article discusses the current challenges in the SR-TENG fabrication technology and potential future directions,aiming to promote the effective development of SR-TENGs in biomechanical energy harvesting and self-powered sensing applications.

关键词： extreme function silicone rubber triboelectric nanogenerators wearable bioelectronics energy harvesting self-powered sensing fabrication methods

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Non-Classical Quantum State Generation using Superconducting-Nanostrip Photon-Number Resolving Detector with Optical Sampling

Non-Classical Quantum State Generation using Superconducting...

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2024 Frontiers in Optics, FiO 2024

作者： Endo, Mamoru Takahashi, Kazuma Nomura, Takefumi Sonoyama, Tatsuki Miki, Shigehito Yabuno, Masahiro Terai, Hirotaka Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Nehra, Rajveer Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology Japan Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States Department of Physics University of Massachusetts Amherst AmherstMA01003 United States College of Information and Computer Science University of Massachusetts Amherst AmherstMA01003 United States

Superconducting-nanostrip photon detectors with optical sampling method now function as true photon-number resolving detectors in real-time without multiplexing. We applied this technique for quantum state generation ... 详细信息

关键词： Quantum computers

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Quality of Service Factor based Unfailing Route Formation in Wireless Sensor Network

Quality of Service Factor based Unfailing Route Formation in...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： S. Yuvarani A. Gayathri K.Jayasakthi Velmurugan V. Meenakshi S Sadhana C. Srinivasan Department of Computer Applications Thanthai Hans Roever College (Autonomous) Perambalur Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India Department of Computer Science and Engineering Jeppiaar Engineering College Chennai Tamil Nadu India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science Engineering (Cyber Security) Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Research & Development Division Vee Eee Technologies Solution Pvt Ltd Chennai Tamil Nadu India

Nowadays, several analytical methods exist to make unfailing route formation. However, it is still challenging work because the wireless medium is unreliable. Sensor nodes are utilized for widespread distribution. Thus, security issues greatly affect wireless sensor networks (WSN). The Quality of Service Factor-based Unfailing Route (QFUR) Formation in WSN is introduced to solve these issues. In this approach, Quality of Service (QoS) factors like sensor node delay, packet drop, and residual energy is verified to determine whether the sensor node is normal. The value of the QoS factor is less than the threshold for that node to be a normal sensor node in the WSN. This approach is serious to notice and separate the compromised nodes from evading misinformation through the falsified data inserted by the opponent over compromised nodes. The Reinforcement Learning (RL) algorithm computes the reward value based on node energy utilization, hop count, dropped rate, and delay. The simulation outcomes demonstrate a lesser delay and lesser packet loss in the network. Furthermore, it improves the opponent node's detection and minimizes the false negative ratio in the WSN.

关键词： Wireless communication Wireless sensor networks Computational modeling Packet loss Quality of service Reinforcement learning Delays

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research on the Mechanism and Suppression Methods of Commutation Failure in High-Voltage Direct Current Transmission Systems

Research on the Mechanism and Suppression Methods of Commuta...

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China International Electrical and Energy Conference (CIEEC)

作者： Ming Li Xiaolin Shen Xuanlang Li Yiqi Liu State Grid Economic and Technology Research Institute Co. Ltd. Beijing China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

High-voltage Direct Current (HVDC) transmission technology has obvious advantages in power transmission, so it has been developed rapidly. Commutation failure is one of the most common faults in HV AC-DC hybrid transmission systems, which can cause DC lock and power oscillation. This paper takes commutation failure as the research object analyzes its causes and main influencing factors and puts forward corresponding suppression strategies, which are significant for the safe and stable operation of HVDC transmission systems.

关键词： Bridges Thyristors Harmonics suppression HVDC transmission High-voltage techniques Hybrid power systems Oscillators

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EEG Right & Left Voluntary Hand Movement-based Virtual Brain-computer Interfacing Keyboard Using Hybrid Deep Learning Approach

arXiv

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arXiv 2024年

作者： Paneru, Biplov Thapa, Bipul Paneru, Bishwash Sapkota, Sanjog Chhetri Department of Electronics and Communication Engineering Nepal Engineering College Affiliated to Pokhara University Bhaktapur Nepal Department of Computer and Information Sciences University of Delaware Newark United States Department of Applied Sciences and Chemical Engineering Institute of Engineering Pulchowk Campus Affiliated to Tribhuvan University Lalitpur Nepal Nepal Research and Collaboration Center Nepal

Brain-machine interfaces (BMIs), particularly those based on electroencephalography (EEG), offer promising solutions for assisting individuals with motor disabilities. However, challenges in reliably interpreting EEG signals for specific tasks, such as simulating keystrokes, persist due to the complexity and variability of brain activity. Current EEG-based BMIs face limitations in adaptability, usability, and robustness, especially in applications like virtual keyboards, as traditional machine-learning models struggle to handle high-dimensional EEG data effectively. To address these gaps, we developed an EEG-based BMI system capable of accurately identifying voluntary keystrokes, specifically leveraging right and left voluntary hand movements. Using a publicly available EEG dataset, the signals were pre-processed with band-pass filtering, segmented into 22-electrode arrays, and refined into event-related potential (ERP) windows, resulting in a 19x200 feature array categorized into three classes: resting state (0), 'd' key press (1), and 'l' key press (2). Our approach employs a hybrid neural network architecture with BiGRU-Attention as the proposed model for interpreting EEG signals, achieving superior test accuracy of 90% and a mean accuracy of 91% in 10-fold stratified cross-validation. This performance outperforms traditional ML methods like Support Vector Machines (SVMs) and Naive Bayes, as well as advanced architectures such as Transformers, CNN-Transformer hybrids, and EEGNet. Finally, the BiGRU-Attention model is integrated into a real-time graphical user interface (GUI) to simulate and predict keystrokes from brain activity. Our work demonstrates how deep learning can advance EEG-based BMI systems by addressing the challenges of signal interpretation and classification. By providing a comparative analysis of multiple models and implementing a real-time application, this research highlights the feasibility and reliability of BMIs in improving accessibility fo

关键词： Graphical user interfaces

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Exploring the Potential of AI-Driven Systems for Automating Data Science

Exploring the Potential of AI-Driven Systems for Automating ...

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Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Adarsh Srivastava Ashu Katyal Srisainath R Pranali S. Kshirsagar Akhilendra Pratap Singh A Rajesh Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India Department of Computer Science Engineering Faculty of Engineering and Technology Bangalore Karnataka India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

this paper discusses the capacity of artificial intelligence (AI)-pushed structures for automating records technological know-how duties including records cleaning, function engineering, and version improvement. We argue that those systems provide a promising opportunity to automate the hard work-extensive and time-ingesting venture of data technological know-how, enabling records scientists to recognition on the more creative factors of the paintings. We begin by using offering a brief evaluation of AI and its capacity packages after which study the existing AI-driven data technology gear on the market. We then describe a few revolutionary applications of AI and discuss their capability to improve existing facts technology approaches. Ultimately, we talk the demanding situations and barriers of AI-driven structures and advocate approaches to address them. We finish by using emphasizing the importance of responsibly integrating AI into the statistics science field and highlighting the capacity of collaboration between AI and facts technological know-how specialists.

关键词： Gears Collaboration Data science Cleaning Artificial intelligence Task analysis Information technology

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