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检索条件"任意字段=2nd International Conference on Signal and Data Processing, ICSDP 2022"

共 3153 条记录，以下是1-10 订阅

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2nd international conference on signal and data processing, icsdp 2022

2nd International Conference on Signal and Data Processing, ...

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2nd international conference on signal and data processing, icsdp 2022

ISBN: (纸本)9789819914098

The proceedings contain 48 papers. The special focus in this conference is on signal and data processing. The topics include: Process Mining-Based Behavioral Modeling of Learners in Self-paced Learning Environment;blockchain Scalability: Solutions, Challenges and Future Possibilities;Solution of Unit Commitment Problems in GAMS Computational Environment;taxonomy and Implications of Machine Learning for Internet of Things: Qualities, Uses and Algorithms;auto Organizer: A Machine Learning-Based Tool for Automatic Organization of Files;twitter Spam Detection Using Different Machine Learning Techniques;on the Role of Perceptual Information in Image Classification;bengali Text Classification Based on Probability Measure;comprehensive Analysis on the Performance of Antenna Design Using Machine Learning Techniques;design and Analysis of Novel Miniaturized Metamaterial Structures for Multiband Applications;hybrid Particle Swarm Optimization Based Deep Learning Model for the Stage Classification of Lung Cancer;Interference Cancellation by Using Viterbi Algorithm for Space Base AIS System;a Stacked Multichannel Feature Map Based U-Net Model for Brain Tumor Segmentation;spatial Attention Gated U-Net Structure for Capsule Endoscopy Image Super-Resolution;feature Engineering and Selection for the Identification of Fake News in Social Media;early Detection of Pathological Myopia in Fundus Images Using Deep Learning;an Investigation on the Extractive Summarization of Kannada Text;rice Plant Leaf Disease Detection—A Comparison of Various Methodologies;combating Fake News with Machine Learning and Deep Learning Methods;Employing Soft Computing-Based GGA-MLP for Hyperparameter Optimization in COVID-19-Infected Lung Image data Classification;Design of Compact UWB MIMO Antenna with High Isolation Using Square Swirl Shape EBG Structure;forecasting with Fuzzy Time Series and Variation.

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Proceedings of the 2nd international conference on signal and data processing 1

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丛书名： Lecture Notes in Electrical Engineering

1000年

作者： K. P. Ray Arati Dixit Debashis Adhikari Ribu Mathew

ISBN: (数字)9789819914104

ISBN: (纸本)9789819914098;9789819914128

This volume comprises the select proceedings of the 2nd international conference on signal & data processing (icsdp) 2022. The contents focus on the latest research and developments in the field of artificial intelligence & machine learning, Internet of things (IoT), cybernetics, advanced communication systems, VLSI embedded systems, power electronics and automation, MEMS/ nanotechnology, renewable energy, bioinformatics, data acquisition and mining, antenna & RF systems, power systems, biomedical engineering, aerospace & navigation. This volume will prove to be a valuable resource for those in academia and industry.

关键词： signal, Image and Speech processing Wireless and Mobile Communication Circuits and Systems Image processing and Computer Vision Energy Systems

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Weak signal Detection Technology in Big data Based on Knowledge Base 24

Weak Signal Detection Technology in Big Data Based on Knowle...

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2nd international conference on Advances in Artificial Intelligence and Applications, AAIA 2024

作者： Zhou, Lei School of Cixi Zhejiang Business Technology Institute Ningbo China

ISBN: (纸本)9798400712883

With the development of science and technology and the progress of data processing technology, big data weak signal detection is playing an increasingly important role in scientific research and engineering application. However, the existing detection systems face three main challenges: high false alarm rate and redundant information problems, difficulty in identifying multi-target dispersed homologous signals, and complex signal detection problems with spatio-temporal dispersion. To solve these problems, this paper proposes a weak signal detection model based on knowledge base, by integrating the data of distributed computing framework and ESOM time series analysis methods into the system design, and adds data filtering and signal analysis and state evaluation engine to the existing detection sensors. The experimental results show that this method not only significantly reduces the systematic false alarm rate, but also effectively improves the detection ability of complex signals, providing a new technical path for weak signal detection in big data environment. © 2024 Copyright held by the owner/author(s).

关键词： Spatio-temporal data

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Research on the detection and analysis of weak signals with low SNR based on the Welch algorithm 24

Research on the detection and analysis of weak signals with ...

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2nd international conference on Communication Networks and Machine Learning, CNML 2024

作者： Wu, Bin Zhang, Wanqing Zhang, Jun Yu, Jing Huangfu, Taoyu Hebei Key Laboratory of Marine Perception Network and Data Processing School of Computer and Communication Engineering Northeastern University at Qinhuangdao Hebei Qinhuangdao China Southeast University Jiangsu Nanjing China

ISBN: (纸本)9798400711688

Submarines are vital for military defense and national security in strategic communication systems. Power Spectral Density (PSD) characterizes signal spectrum frequency dependence and is crucial for analyzing broadband random signals. This study proposes a simplified PSD estimation method using the Welch algorithm for detecting weak signals in noisy environments, particularly in submarine communications. MATLAB simulations demonstrate that the Welch algorithm with a Blackman window function and 512-point overlap improves variance performance and resolution, especially in low signal-to-noise ratio conditions. The results underscore the algorithm’s effectiveness in enhancing signal detection in submarines. © 2024 Copyright held by the owner/author(s).

关键词： MATLAB

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Application of Computer databases in Information Management Systems 24

Application of Computer Databases in Information Management ...

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2nd international conference on Artificial Intelligence, Systems and Network Security, AISNS 2024

作者： He, Dandan Zeng, Weijia Wang, Congwen Dalian University of Science and Technology Liaoning Dalian China People?s Liberation Army No. 91524 Liaoning Dalian China

ISBN: (纸本)9798400711237

This paper discusses the core features of computer database technology and elaborates on its practical strategies in the field of information management. It aims to optimize information management processes, enhance the application efficiency of computer database technology, ensure the accuracy and efficiency of data processing, and promote the overall optimization and upgrading of information management systems. © 2024 Copyright held by the owner/author(s).

关键词： data accuracy

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Hierarchical data-driven Scheduling-Control Framework for Energy-efficient Industrial Flexible Production 24

Hierarchical Data-driven Scheduling-Control Framework for En...

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2nd international conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Jiang, Yuliang Zhu, Shanying Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798400710681

This paper proposes a hierarchical data-driven scheduling-control framework for a multi-machine/mode production system to operate in an energy-efficient manner. At the scheduling layer, the scheduling problem incorporating process dynamics is formulated into a quadratic programming to arrange candidate processing machines/modes and corresponding production objectives. Based on the dynamic linearization model, scheduling results are executed by model-free adaptive control at the control layer. The real-time process output is feedback to the scheduling layer for the next decision-making. The practicality of the proposed framework is validated in a thread-tapping process featuring four distinct operational modes. On average, operating in a single mode maintains output accuracy while saving 14.2% on energy. The multi-mode operation further optimizes energy usage through its capability for dynamic mode switching. © 2024 Copyright held by the owner/author(s).

关键词： Quadratic programming

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Fall Detection Using Sound with FPGA signal Analysis and Machine Learning Integration 2

Fall Detection Using Sound with FPGA Signal Analysis and Mac...

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2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Andre, Mariano Viegas Ozdemir, Ahmet Turan Kavuncuoglu, Erhan Erciyes University Department of Electric and Electronic Engineering Kayseri Turkey Erciyes University Department of Electric and Electronic Engineering KayseriTR-38039 Turkey Gemerek Vocational School Cumhuriyet University Department of Computer Tecnology SivasTR-58840 Turkey

ISBN: (纸本)9798331505745

Falls represent a considerable risk to the well-being of geriatric populations, frequently resulting in injuries or mortality. In response to this pressing concern, we introduce an innovative fall detection apparatus employing acoustic sensors and FPGA (Field Programmable Gate Array) technology to effectively alleviate these hazards. Our system capitalizes on acoustic phenomena, particularly those associated with footstep sounds and falls, which are gathered through routine activities and video documentation to develop a comprehensive model adept at differentiating between fall incidents and non-fall occurrences. The primary attributes encompass the derivation of Mel-frequency cepstral coefficients (MFCC) from the processed auditory signals. To enhance operational efficacy, an FPGA is utilized to execute all data processing functions, inclusive of Fourier transformations and a threshold-based predictive algorithm. This methodology diminishes dependence on conventional computational systems while preserving elevated processing efficiency. The derived features are subsequently employed to formulate a data classification model utilizing a Convolutional Neural Network (CNN) and Random Forest Tree (RFT), which proficiently discriminates between sounds indicative of falls and those not related to falls. © 2025 IEEE.

关键词： data integration

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An optimized scheme of computer-generated hologram compression with convolutional neural networks and genetic algorithm 2

An optimized scheme of computer-generated hologram compressi...

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2nd international conference on Optical Communication and Optical Information processing, OCOIP 2024

作者： Yao, Yuxuan Yang, Guanglin Laboratory of Signal and Information Processing School of Electronics Peking University Beijing100871 China

ISBN: (数字)9781510688995

ISBN: (纸本)9781510688988

Holography is pivotal in various applications ranging from microscopy to data storage. However, the practical implementation of holography often necessitates compression, which inevitably degrades the quality of the reconstructed images. Addressing this challenge, a scheme of computer-generated hologram compression with convolutional neural networks optimized is proposed. In this scheme, our research investigates the potential of genetic algorithm, a class of evolutionary algorithms inspired by natural selection, to optimize the initial weights of the network. Experimental results show that the scheme is feasible. © 2025 SPIE.

关键词： Convolutional neural networks

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Fall Detection and Alerts in Hearing Aids for Deaf using Generative AI 2

Fall Detection and Alerts in Hearing Aids for Deaf using Gen...

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2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Tripathi, Bharat Lal Saini, Madan Jain, Manish Saxena, Apoorva Pal, Narendra Department of Computer Application Allenhouse Institute of Technology Kanpur India Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab India Department of Computer Application Allenhouse Business School Kanpur India Department of Computer Science & Engineering Axis Institute of Technology & Management Kanpur India Department of Computer Science & Engineering Invertis University Bareilly India

ISBN: (纸本)9798331530389

Individuals with sensorineural hearing loss often experience difficulty comprehending speech when background noise is present. This paper investigates the extent of this problem in various listening scenarios and with different types of background sounds. Additionally, the section explores the contributing factors to this challenge such as reduced audibility, reduced frequency selectivity, loudness recruitment, and dead regions in the cochlea where there are no surviving inner hair cells and or neurons. The sections evaluate different techniques for compensating for the effects of these factors. While signal-processing methods using the output of a single microphone to compensate for reduced frequency selectivity have had limited success, techniques utilizing multiple microphones have proven effective. Amplitude compression has also been effective in compensating for the effects of loudness recruitment, enabling individuals to understand speech across a wide range of sound levels. Next, the paper delves into the technical aspects of implementing fall detection systems in hearing aids, learning algorithms are also investigated for their potential in enhancing fall detection accuracy and minimizing false alarms. Furthermore, the importance of user-friendly interfaces and customization features in ensuring the effectiveness and acceptance of fall detection alerts is also highlighted. The paper also addresses the ethical and privacy considerations associated with fall detection in hearing aids. It emphasizes the need for transparent data handling practices and user consent, while also addressing potential concerns related to user autonomy and intrusion. © 2025 IEEE.

关键词： data privacy

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Cloud Rehabilitation System Based on Automatic sEMG signal processing 2nd

Cloud Rehabilitation System Based on Automatic sEMG Signal P...

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2nd international conference on the Frontiers of Robotics and Software Engineering, FRSE 2024

作者： Pang, Gongpeng Liu, Bo Zhang, Wenli Beijing University of Technology Beijing100124 China

ISBN: (纸本)9789819631605

With the advancement of data analysis and pattern recognition technology, gesture recognition based on electromyographic signals (EMG) has been widely used in the field of rehabilitation therapy and human-computer interaction. Currently, most gesture recognition models use deep learning techniques, but face problems such as data labels needing manual labeling and inaccurate EMG data labels, resulting in low and unstable recognition accuracy. In addition, these systems mainly rely on computing devices, which is not conducive to patients’ rehabilitation at home. Therefore, this paper proposes a cloud-based rehabilitation system based on automatic processing of sEMG signals. The method is based on the acquisition of sEMG data on the surface of the AoYi bracelet, applying an automatic annotation algorithm to the acquired data, and then uploading it to a cloud model (MotionSenseCNN) for training and testing, and ultimately realizing gesture classification. Our network model achieves 90% accuracy on the sparse channel EMG data collected in the experiment. The experimental results show that the system maintains high accuracy in different gesture recognition, enhances the stability and flexibility of gesture recognition, and has significant advantages over other EMG rehabilitation systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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