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检索条件"任意字段=Conference on Advanced Signal Processing Algorithms"

共 3500 条记录，以下是401-410 订阅

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Secrecy Energy Efficiency Maximization for RSMA-Based UAV Communication with Multiple Colluding Eavesdropperse

Secrecy Energy Efficiency Maximization for RSMA-Based UAV Co...

引用

IEEE International conference on Information Communication and signal processing (ICICSP)

作者： Shuzhen Liu Yi Wang Ziheng Sheng Zhiyu Huang Hongwen Yu Zhichao Sheng School of Communication and Information Engineering Shanghai University Shanghai China Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China The School of Civil and Environmental Engineering UNSW Sydney Australia School of Civil and Environmental Engineering UNSW Sydney Australia

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

This paper explores a rate splitting multiple access (RSMA)-based unmanned aerial vehicle (UAV) communication system aimed at enhancing secrecy energy efficiency. An UAV communicates with ground users while multiple colluding eavesdroppers intercept the signals. The secrecy energy efficiency is optimized by designing the RSMA precoding matrix and UAV trajectory, subject to constraints on UAV energy consumption and data requirements. To address the complexity of coupled variables, an iterative algorithm combining the Dinkelbach algorithm and block coordinate descent method is proposed. Simulation results indicate that the secrecy performance of the proposed algorithm outperforms that of the benchmark schemes.

关键词： Simulation Precoding signal processing algorithms Benchmark testing signal processing Autonomous aerial vehicles Energy efficiency Trajectory Iterative methods Optimization

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Digital Modulation Identification in Spatial Modulation MIMO Systems

Digital Modulation Identification in Spatial Modulation MIMO...

引用

International conference on Image and signal processing and their Applications (ISPA)

作者： Dalil Beknadj Hakim Tayakout Mohamed Tounsi Fatma Zohra Bouchibane Elhocine Boutellaa LIMed Laboratory University of Bejaia Bejaia Algeria Center for Development of Advanced Technologies (CDTA) Algiers Algeria LAMOS Laboratory University of Bejaia Bejaia Algeria Institute of Electrical Engineering and Electronics University Mhamed Bougara Boumerdes Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Cognitive radio (CR) is a game-changing technology that allows devices to adjust transmission and/or reception parameters in real time, improving wireless communication efficiency. Digital modulation identification (DMI) plays a crucial role in in CR, wireless communications, and spectrum management, providing adaptive and efficient signal processing. In this paper, we introduce a DMI approach designed for spatial modulation (SM) within the context of wireless multiple input multiple output (MIMO) technologies. In this study, we use the higher order statistics (HOS) as feature-based digital modulation identification (FB-DMI) to assess the performance of frequently utilized classifiers namely, k-nearest neighbor (KNN), and decision tree (DT). To train these classifiers, several configurations of the SM-MIMO network were evaluated, using the zero forcing (ZF) equalizer. Experiments show that KNN outperforms DT in identifying modulation schemes across all SNR levels.

关键词： Resistance Digital modulation signal processing algorithms Interference Robustness Real-time systems Object recognition

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Continuous and Non-Contact Extraction of Respiratory signal and Rate from Thermal Video

Continuous and Non-Contact Extraction of Respiratory Signal ...

引用

International conference of signal processing and Intelligent Systems (ICSPIS)

作者： Maryam Saidi Mohammad Rezaei Maryam Beigzadeh Amin Mohammadian Abdolaziz Azizi Lari Bioelectric Department Electrical Engineering Faculty Shahrood University of Technology Shahrood Iran Biosignals processing Group Research Center for Development of Advanced Technologies Tehran Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

Achieving a simple, non-contact, non-invasive, and non-disruptive method for measuring and monitoring vital signs, including respiratory signals, is crucial in this field of research. This paper aims to implement algorithms for extracting respiratory signals and breathing rates from thermal facial images. Unlike many previous studies, where the algorithm was validated under resting conditions, this paper designs a protocol to capture a diverse range of respiratory activity datasets in varying conditions. Data were collected from six healthy individuals, with thoracic and abdominal contact-based respiratory signals used as reference signals. processing steps, including robust facial tracking in thermal images and precise selection of target areas around the nose is implemented to extract respiratory signal and rates from the thermal video. The effectiveness of the proposed computational method is assessed through both quantitative and qualitative metrics, considering a variety of breathing patterns beyond just resting conditions

关键词： Training Protocols Correlation Target tracking Shape Neural networks Nose Infrared imaging Feature extraction Recording

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Network Traffic Anomaly Detection Based on Siamese U-Net

Network Traffic Anomaly Detection Based on Siamese U-Net

引用

6th International conference on Intelligent Computing and signal processing (ICSP)

作者： Yupeng Wang Cheng Fang Civil Aviation University of China Tianjin China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

With the widespread of various applications, the internet has become an important part of our daily lives. Serving as a pivotal platform for information exchange and resource sharing, the internet has promoted the development of advanced technologies. However, the number of attacks targeting network security vulnerabilities is also increasing. Attackers threaten the network traffic transmission by some special methods such as tracking target hosts, attacking network systems, and sending malicious packets. In light of these challenges, this paper introduces a network traffic anomaly detection method based on Siamese U-Net. Network traffic data are converted into multiple pairs of data to facilitate the extraction of similarity between samples. Further, leveraging the similarity information, we propose an enhanced Siamese network model for the detection of network traffic anomalies. This method utilizes the representational learning capabilities of U-Net. Testing results on the publicly available CIC-IDS2017 demonstrate that our approach achieves leading accuracy compared to other deep learning models.

关键词： Target tracking signal processing algorithms Telecommunication traffic Computer architecture Transforms signal processing Feature extraction Resource management Anomaly detection Testing

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Adaptive Neural Mode Selection for Optimized D2D Communication in LTE-advanced Networks

Adaptive Neural Mode Selection for Optimized D2D Communicati...

引用

Next Generation Communication & Information processing (INCIP), International conference on

作者： V. Asha S. Sreekanth Ginni Nijhawan Abhishek Kaushik Mohammed Wael Mohammed Department of Computer Applications New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

In LTE-advanced networks, D2D communication comes out as a critical technology to increase the number of connections, decrease delay while improving spectral efficiency. Therefore, this paper focuses on developing an enhanced mode selection algorithm, Adaptive Neural Mode Selector (ANMS), to enhance D2D communication on LTE-advanced network that considers the performance parameters such as throughputs, interference, and energy consumption. The ANMS framework involves machine learning where the receivers adjust the mode of communication to either direct D2D, relay-assisted D2C, or cellular depending on network and the user's preferences. Combining deep learning with conventional signal processing strategies, the ANMS framework learns and foresees the transition of different states of the network for stabilized and effective D2D communication. Cognitive mode selection approach is the one proposed in this paper, and the extensive simulations prove its effectiveness in increasing a network's spectral efficiency, decreasing the interferences, and improving energy consumption compared to conventional methods. These results raise the extremely positive outlook on the role of IMS in enhancing the practical D2D communication in the LTE-advanced context and create the prospect for the much more reliable and faster future wireless networks.

关键词： Deep learning Energy consumption Adaptive systems Spectral efficiency Wireless networks signal processing algorithms Throughput Device-to-device communication Reliability Long Term Evolution

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Artificial Intelligence(AI)-Based algorithms for Bioelectrical signals Applications: A Review

Artificial Intelligence(AI)-Based Algorithms for Bioelectric...

引用

Networking Systems of AI (INSAI), International conference on

作者： Yu Hong Xinhua Zeng Weilong Lin Run Fang Chengsheng Liao Academy for engineering and technology Fudan University Shanghai China Yiwu Research Institute Yiwu China Yiwu Research Institute Fudan University Yiwu China

ISBN: (数字)9798350344660

ISBN: (纸本)9798350344677

In this review, we summarize some main research areas and implementation techniques of bioelectrical signals in resent years from the perspective of AI-based algorithms for analyzing and processing. This study first introduces the development of one-dimensional time-series signal processing. Then this study examines some representative classical signal processing algorithms and thus leads to the limitations of conventional methods. It also reviews how the techniques can help with these problems with the advent of AI and focuses on how AI techniques have advanced the scientific research in the field of bioelectrical signals in recent years. The major signals represented by electroencephalography(EEG), electromyography(EMG), electrooculography(EOG), electrocardiogram(ECG) as well as joint data were selected to demonstrate the importance of that combination. In this sections, we review some of the applications of these bioelectrical signals in recent years for disease diagnosis, rehabilitation of disabled people, and facilitation of human life, and point out in which ways AI has contributed to the application of these signals with what results. Finally, we summarize some unsolved issue and gain insight for future design of biological signals(biosignals).

关键词： Data analysis Reviews signal processing algorithms signal processing People with disabilities Biology Medical diagnosis Artificial intelligence

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A Deep Neural Network for Simultaneous Channel Estimation and signal Detection in MIMO-OFDM-FSO System

A Deep Neural Network for Simultaneous Channel Estimation an...

引用

International conference on advanced Infocomm Technology (ICAIT)

作者： Zhou Luxia Cao Minghua Zhang Yue Wang Rui Zhang Xingyu Wang Huiqin Lanzhou University of Technology School of Computer and Communication Lanzhou China

ISBN: (数字)9798350391497

ISBN: (纸本)9798350391503

The complex receiver and limited performance of Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) Free Space Optical (FSO) communication systems are addressed by employing a Deep Neural Networks (DNN) for channel estimation and detection tasks. The DNN learns the mapping relationship between the atmospheric turbulence channel's complex characteristics and the signals through extensive data processing, enabling joint channel estimation and detection. Experimental results demonstrate that the proposed method exhibit enhanced robustness, superior generalization performance compared to traditional detection algorithms.

关键词： OFDM Noise Channel estimation Artificial neural networks Receivers Robustness MIMO signal detection Optimization MIMO communication

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An Enhanced Velocity Measurement of Coherent Lidar Based on Time-Domain Compensation and Phase Compensation

An Enhanced Velocity Measurement of Coherent Lidar Based on ...

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Asia Communications and Photonics conference and Exhibition (ACP)

作者： Xianzhuo Li Nan Ye Junjie Zhang Yingchun Li Zhiyong Lu Honghui Jia Haoming Yuan Wenzhong Liu Kun Chen Key Laboratory of Specialty Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Key Laboratory of Space Laser Transmission and Detection Technology Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai China

We propose an enhanced speed detection method that reduces computational resource. Measurements of targets at 18.5 km show that the average speed detection probability increases by 49.17%, and the average speed mean s... 详细信息

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

关键词： Laser radar Phase measurement signal processing algorithms Radar detection Mean square error methods Optical variables measurement Radar signal processing Velocity measurement Time-domain analysis Photonics

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D-3DLD: Depth-Aware Voxel Space Mapping for Monocular 3D Lane Detection with Uncertainty

D-3DLD: Depth-Aware Voxel Space Mapping for Monocular 3D Lan...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Nayeon Kim Moonsub Byeon Daehyun Ji Dokwan Oh Samsung Advanced Institute of Technology

The estimation of 3D lanes from monocular RGB images is a fundamentally ill-posed problem. Previous studies have assumed that all lanes are on a flat ground plane. However, we argue that the algorithms based on this assumption have difficulty in detecting various lanes in actual driving environments. Contrary to previous approaches, we expand rich contextual features from an image domain to a 3D space by utilizing depth-aware voxel mapping. In addition, we determine 3D lanes based on voxelized features. We design a new lane representation combined with uncertainties and predict the confidence intervals of 3D lane points using Laplace loss. Experimental results show that the proposed method achieves state-of-the-art detection accuracy on three challenging datasets, including two real-world datasets, and significantly outperforms existing methods with reasonable computation load.

关键词： Three-dimensional displays Uncertainty Lane detection Roads signal processing algorithms Estimation signal processing

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Enhancing Urban Accessibility: Comparative Analysis of Intelligent Traffic signal Systems in Major Global Cities

Enhancing Urban Accessibility: Comparative Analysis of Intel...

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International conference on advanced Technologies for Communications, ATC

作者： Min-kai Hsiao Chien-Min Chen School for Creative Studies Quanzhou University of Information Engineering Quanzhou China Software Institute/Quanzhou University of Information Engineering Quanzhou China International School of Finance Fundan University Shanghai China

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

As urbanization intensifies, cities worldwide are facing more complex traffic management challenges. The advent of Intelligent Transportation Systems (ITS) and Internet of Things (IoT) technology offers unparalleled opportunities to improve urban traffic light systems' effectiveness and availability. This paper compares smart traffic signal reforms in Tokyo, Copenhagen, Stockholm, San Francisco, and Hamburg, analyzing their effects on urban accessibility. The study examines three primary perspectives. First, it assesses the influence of intelligent traffic systems on the accessibility of special groups, such as disabled and socially disadvantaged populations, by making precise and responsive adjustments to traffic signals. Second, it investigates enhancements in smart signal control within multi-modal transportation systems, focusing on how algorithms and real-time data processing improve coordination and efficiency. Lastly, it evaluates the overall effectiveness of real-time traffic management implementation.

关键词： Urban areas Transportation Focusing Data processing Real-time systems Internet of Things

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