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检索条件"主题词=Signal Processing Algorithms"

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Coherent Integration Detection of Maneuvering Targets Using Non-Uniformly Resampling Trilinear Autocorrelation Function and KT

Coherent Integration Detection of Maneuvering Targets Using ...

引用

International Conference on signal processing Proceedings (ICSP)

作者： Liang Qiao Hongmeng Chen Ming Li Lei Zuo National Key Laboratory of Radar Signal Processing Xidian University Xi’an China Beijing Institute of Radio Measurement Beijing China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

The presence of range migration (RM) and Doppler migration (DM) leads to the degradation of performance in coherent integration detection of maneuvering targets. This article proposed a coherent integration detection method for third-order maneuvering targets based on the non-uniformly resam-pling trilinear autocorrelation function (NRTAF) and keystone transform (KT). Firstly, The NRTAF is introduced to address DM, as well as second-order and third-order RM, while first-order RM is eliminated using KT. Subsequently, the energy of signal is accumulated through fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT). The proposed algorithm completely avoids searching for motion parameters, resulting in lower computational complexity. It can effectively suppress cross terms, making it suitable for multi-objective scenarios. Additionally, it is appropriate for high-speed target scenarios. The effectiveness of the algorithm has been confirmed through simulation and measured data.

关键词： Resistance Parameter estimation Fast Fourier transforms Noise signal processing algorithms Transforms Doppler effect Autocorrelation Computational complexity Detection algorithms

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Research on Internet of Things signals Acquisition and processing based on Edge Computation

Research on Internet of Things Signals Acquisition and Proce...

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Computer Engineering and Intelligent Control (ICCEIC) International Conference on

作者： Xiaolong Zhang Wenheng Qiu Huaping Yang Aohua Hou College of Electrical Engineering and New Energy China Three Gorges University Hubei Yichang China Faculty of Automotive and Traffic Engineering Hubei University of Arts and Sciences Hubei Xiangyang China

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

The proliferation of Internet of Things (IoT) devices has led to the generation of vast quantities of data, placing considerable strain on conventional cloud computing infrastructure. Edge computing has emerged as a viable solution to address this challenge. In this study, we propose an Internet of Things (IoT) signal processing framework that is combined with distributed edge nodes. The objective is to improve data transmission efficiency and real-time performance. Firstly, in consideration of the constrained resources of edge devices, we have devised lightweight preprocessing algorithms, including frequency domain filtering based on Fourier transform and a denoising algorithm based on wavelet transform, with the objective of reducing the noise and redundant information present in the original signal. Subsequently, the adaptive compressed sensing technology was employed to compress the preprocessed signal, which resulted in a notable reduction in the bandwidth requirement for data transmission. Subsequently, in order to fully utilise the computing capabilities of edge nodes, we introduce a multi-task learning collaborative computing model. The model permits the sharing of computing tasks between multiple edge nodes, facilitates data collaboration between edge nodes through federated learning, and reduces computing redundancy and communication overhead. The experimental phase employs the Google Edge TPU Dataset to evaluate the dataset pertinent to the Internet of Things. The results indicate that the proposed methodology exhibits a notable enhancement in data processing latency and transmission efficacy in comparison to the conventional approach.

关键词： Cloud computing Adaptation models Computational modeling Collaboration signal processing algorithms Bandwidth Data processing Internet of Things Data communication Edge computing

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Geomagnetic Matching Aided Unmanned Surface Vehicle Cooperative Positioning Method

Geomagnetic Matching Aided Unmanned Surface Vehicle Cooperat...

引用

IEEE International Conference on signal processing, Communications and Computing (ICSPCC)

作者： Hanzhang Shi Chengkai Tang Lingling Zhang Zhe Yue Yangyang Liu Zesheng Dan School of Electronics and Information Northwestern Polytechnical University Xi'an China School of Marine Science and Technology Northwestern Polytechnical University Xi'an China School of Surveying and Land Information Engineering Henan Polytechnic University Jiaozuo China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

Due to the complex maritime environment, satellite navigation is easily interfered by weather, radio, etc., and the error of inertial navigation will accumulate rapidly with the increase of sailing time, the existing cooperative positioning methods cannot meet the accuracy and stability of Unmanned Surface Vehicle (USV) clusters to realize rescue at sea. Aiming at the above problems, this paper proposes a Geomagnetic Matching Cooperative Positioning Method for Unmanned Surface Vehicle Based on Geometric Information (GMCP-GI) for USV clusters based on geometric information. Simulation results show that the proposed g GMCP-GI for USVs can realize the cooperative positioning of USV clusters and can effectively improve the positioning accuracy and stability.

关键词： Sea surface Accuracy Simulation Satellite broadcasting signal processing algorithms signal processing Stability analysis Autonomous vehicles Surface treatment Meteorology

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Optimization on a Generalized Multi-Order Complex Quadratic Form with Constant-Modulus Constraints

Optimization on a Generalized Multi-Order Complex Quadratic ...

引用

European signal processing Conference (EUSIPCO)

作者： Chunxuan Shi Yongzhe Li Ran Tao School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

In this paper, we study the problem that minimizes or maximizes a multi-order complex quadratic form with constant-modulus constraints on all elements of the optimization variable. This mathematical problem is commonly encountered in various applications of signal processing, and we term it as the constant-modulus multi-order complex quadratic programming (CMCQP). In general, the CMCQP is non-convex, whose objective typically relates to metrics such as signal-to-noise ratio, Cramer-Ran bound, integrated sidelobe level, etc., and constraints normally correspond to requirements on similarity to desired aspects, peak-to-average power ratio, or constant modulus in practical scenarios. In order to find an efficient solution to the CMCQP, we first reformulate it into an unconstrained optimization problem. Then we devise steepest descent/ascent methods with fast determination on their optimal step sizes. Our contribution also lies in identifying two representative cases for the CMCQP on optimization. The accuracy of our proposed step-size determination is evaluated and the superiority of our proposed algorithms than the existing algorithms is verified.

关键词： Measurement Accuracy signal processing algorithms Europe Peak to average power ratio signal processing Quadratic programming Optimization Standards signal to noise ratio

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Review of Machine Learning Techniques for Enhancing the Performance of OAM FSO Systems

Review of Machine Learning Techniques for Enhancing the Perf...

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Innovations in High Speed Communication and signal processing (IHCSP), International Conference on

作者： Yogesh Kumar Gupta Aditya Goel Manish Kumar Soni Electronics & Communication Engineering MANIT Bhopal India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

In recent years, the advancement of Free Space Optical (FSO) communication systems has significant attention due to their potential for high-speed data transmission. This review examines the integration of ma-chine learning techniques in enhancing the performance and reliability of Orbital Angular Momentum (OAM) FSO systems. By machine learning (ML) algorithms, these systems can effectively mitigate challenges such as atmospheric turbulence, alignment issues, and signal attenuation. The paper highlights various ML methodologies employed in FSO systems, including supervised, unsupervised, and reinforcement learning approaches. It also discusses the comparative advantages of these techniques in optimizing signal quality, reducing error rates, and improving overall system efficiency. Through an in-depth analysis of current research and developments, this review provides insights into the future directions in the field of FSO communications facilitated by ML techniques.

关键词： Wireless communication Technological innovation Reviews signal processing algorithms Reinforcement learning Prediction algorithms Predictive analytics signal detection signal resolution Research and development

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Brain-Controlled Wheelchair Navigation Using Visual Perception in Simulated Indoor Environments

Brain-Controlled Wheelchair Navigation Using Visual Percepti...

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Electrical Engineering Conference (EECon)

作者： Apisaruthan Thanabalasingam D.J.T. Fernando Sobikanth Mahendran A.G.B.P. Jayasekara R.A.R.C. Gopura Department of Electrical Engineering University of Moratuwa Katubedda Sri Lanka Department of Mechanical Engineering University of Moratuwa Katubedda Sri Lanka

ISBN: (数字)9798331505950

ISBN: (纸本)9798331505967

Wheelchair navigation based on the Brain-Computer Interface (BCI) has gained significant attention due to its ease of use and offering support to people with severe paralysis in mobility. Enhancing the comfort of such people to use wheelchairs with more efficiency is essential to make it a practical application. In this research endeavor, a novel system based on Steady State Visually Evoked Potential (SSVEP) is introduced to autonomously guide a wheelchair from its initial location to a predetermined destination point. In this research, four distinct flickering frequencies serve as visual stimuli to represent specific locations within a simulated indoor environment. Electroencephalography (EEG) signals are acquired using a set of six electrodes, and signal preprocessing involves Common Average Referencing (CAR). To classify the EEG signals, a novel approach combines Fast Fourier Transform (FFT) and Power Spectrum Density (PSD) analyses, along with peak detection and thresholding techniques. The experiments were conducted in real-time, involving five subjects, each participating in six trials for every distinct frequency. Remarkably, the algorithm achieved an accuracy rate exceeding 90% for each frequency without prior training. Navigation based on the classified SSVEP frequencies was successfully executed within a simulation environment, using the Robot Operating System (ROS2), resulting in favorable outcomes. This promising result suggests the potential for future implementations through the integration of appropriate hardware and sensors.

关键词： Training Time-frequency analysis Accuracy Navigation Fast Fourier transforms Wheelchairs signal processing algorithms Electroencephalography Real-time systems Classification algorithms

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Abnormal Behavior Modelling of Electricity Market by Artificial Intelligence

Abnormal Behavior Modelling of Electricity Market by Artific...

引用

IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Yan Li Yue Qu Xiaolong Zhang Wenmin Wu International Business School Guangzhou City University of Technology Guangdong China China southern air media company limited Guangdong China Civil Aviation Northwest Air Traffic Control Bureau Gansu Branch Gansu China Faculty of Finance City University of Macau Macao China

ISBN: (数字)9798350377460

ISBN: (纸本)9798350377477

The rapid development of electric power technology has exacerbated issues with anomalies in market transactions, making effective identification and regulation of these anomalies a pressing task. This study proposes a trading behavior analysis model for market players, leveraging a stepwise regression algorithm and introducing a monitoring module based on the ARM Cortex series microcontroller. The model enables deep assessment of transaction data, timely detection of anomalies, and pattern mining for accurate regulatory support. The monitoring module enhances real-time monitoring and dynamic feedback, leveraging the ARM Cortex microcontroller's efficient data processing and low-power characteristics. Experimental validation shows the model's significant advantages in evaluation and error control, effectively identifying anomalies and demonstrating its potential to enhance electricity market regulation.

关键词： Renewable energy sources Microcontrollers signal processing algorithms Pressing Brain modeling Electricity supply industry Data models Regulation Real-time systems Monitoring

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Channel-Equalized Terahertz Compressed Sensing Imaging

Channel-Equalized Terahertz Compressed Sensing Imaging

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International Conference on Infrared and Millimeter Waves

作者： Chun-Yu Kao Li-Hsuan Chiu Chia-Ming Mai Rayko Ivanov Stantchev Yuan-Hao Huang Shang-Hua Yang Institute of Electronic Engineering National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan Department of Physics National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (数字)9798350370324

ISBN: (纸本)9798350370331

Among all terahertz (THz) imaging techniques, compressed sensing imaging shows the potential to perform large area detection with faster acquisition time and capability to be integrated with single pixel THz receiver. However, despite the mentioned advantages, unevenly distributed THz beam and spatial modulated optical pump begin to affect the image system and reduce the reconstruction quality within the utilization of larger field of view. Hence, we establish a THz compressed sensing imaging system with equalization procedure along orthogonal basis pursuit (OMP) algorithm to mitigate these effects. Results from imaging experiments demonstrate the effectiveness of the proposed equalization method in improving image reconstruction, presenting a precise mathematical model simulating and non-ideal phenomenon calibration.

关键词： Terahertz wave imaging signal processing algorithms Optical receivers Optical pumping Optical sensors Optical modulation Time-domain analysis Optical signal processing Compressed sensing Image reconstruction

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Quaternionic multilayer bottleneck autoencoder for color image compression

Quaternionic multilayer bottleneck autoencoder for color ima...

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signal processing algorithms, Architectures, Arrangements and Applications (SPA)

作者： Nick Petrovsky Maxim Vashkevich Department of Computer Engineering Belarusian State University of Informatics and Radioelectronics 6 P. Brovky st Minsk Belarus

ISBN: (数字)9788362065486

ISBN: (纸本)9798350373806

This paper presents a study on the multilayer bottleneck autoencoder applied for thumbnail color image compression task using quaternionic neural nets (QNN). Existing state-of-the-art real-valued autoencoder architectures are weak for reconstruction of images with high-frequency components. However, experiments shows that hypercomplex representation of color and activation functions, gives better results in terms of pixellevel quality metrics PSNR and SSIM. Designed hypercomplex autoencoders are simplest type of the QNN - fully connected quaternionic layers. Investigated one- and two-layer QNN structures for encoder/decoders in compare with real-valued ones provides better PSNR by approx. 2.5 dB and 0.11 SSIM (Real valued: PSNR - 26.36 dB, SSIM - 0.856; Quaternionic: PSNR 31.28 dB, SSIM - 0.951) for 3:1 compression ratio and equal or less number of hyperparameters.

关键词： Measurement Image coding Quantization (signal) Quaternions Neural networks signal processing algorithms Color Nonhomogeneous media Inference algorithms Image reconstruction

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A Lightweight and Anonymous MAKE for LDACS

A Lightweight and Anonymous MAKE for LDACS

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International Conference on Wireless Communications and signal processing (WCSP)

作者： Pengtao Liu Tong Bai Zhiqiang Dan Shujing Wang Zhipeng Wang Yanbo Zhu School of Electronic and Information Engineering Beihang University Beijing China School of Cyber Science and Technology Beihang University Beijing China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

The L-band Digital Aeronautical Communication System (LDACS) plays an important role in ensuring the security of future aeronautical communications infrastructure. However, the establishment of LDACS security design is still complicated because lots of authentication messages need to be transmitted while air-to-ground channel resources are limited. And it lacks anonymous measures to avoid tracking by malicious entities and enhance the security of the authentication process. In contrast to the traditional protocol, we propose a lightweight LDACS Mutual Authentication and Key Establishment (MAKE) protocol with anonymous identity and conduct a security and performance comparison analysis of the protocols in this paper. The analysis results show that the proposed protocol can well achieve the security goals of LDACS, and can well protect the privacy of stations. The number of air-to-ground communication interactions from cell-attachment to mutual authentication has also been reduced to five times, and the total communication cost is 4578 bits.

关键词： Wireless communication Privacy Protocols Costs Authentication signal processing algorithms signal processing Air to ground communication Performance analysis Security

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