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检索条件"主题词=Signal Processing Algorithms"

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Deep Learning Based Transformer Anomaly Detection

Deep Learning Based Transformer Anomaly Detection

引用

Artificial Intelligence and Power Engineering (AIPE), International Conference on

作者： Xiaomin Zhou Haiwen Chen Kaijing Han Faculty of Engineering The University of Sydney Sydney Australia Faculty of Engineering XiHua University Sichuan China

ISBN: (数字)9798350388244

ISBN: (纸本)9798350388251

The detection of transformer abnormalities is a critical aspect of substation safety and reliability. Traditionally, this task relies on human judgment, where duty officers assess transformer conditions based on audible cues during routine patrols. However, this method are labor-intensive and fail to provide real-time anomaly detection, leaving substations vulnerable during monitored periods. Additionally, inspection robots currently equipped with cameras lack audio processing capabilities, further limiting their effectiveness in identifying transformer anomalies. To address these limitations, this paper proposes a deep learning -based algorithm for real-time transformer anomaly detection. By deploying a voice processing device near transformers, audio signals are continuously collected and transmitted to a server for analysis. On the server side, a deep learning model leverages speech processing techniques to detect abnormalities with high accuracy in real time. The proposed method offers significant advantages over traditional approaches. Unlike manual inspections, it eliminates dependency on the presence and expertise of duty officers, achieving higher real-time performance. Moreover, the system’s classification accuracy of 97% substantially reduces the reliance on human labor and the associated costs. This study demonstrates the potential of integrating deep learning with voice signal processing to enhance the efficiency and reliability of transformer monitoring systems.

关键词： Deep learning Accuracy Substations signal processing algorithms Manuals Transformers Prediction algorithms Real-time systems Servers Anomaly detection

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Enhanced Vital Sign Monitoring in Multi-Target Environments: A FMCW Radar Approach with Blind Source Separation

Enhanced Vital Sign Monitoring in Multi-Target Environments:...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jinyu Yin Zongjie Cao Bin Pan Shu Lv Zongyong Cui School of Information and Communication University of Electronic Science and Technology of China Chengdu P.R.China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This paper presents an innovative approach for detecting human vital signs in multi-target scenarios using Frequency-Modulated Continuous-Wave (FMCW) radar within a Multiple-Input Multiple-Output (MIMO) framework. Recognizing the limitations of traditional contact-based vital signs monitoring methods, especially for sensitive groups such as infants or burn victims, our study proposes a non-invasive, radar-based solution. We develop a Linear Mixed Model (LMM) to address the challenges posed by multi-target, multi-path aliasing interference, employing Blind Source Separation (BSS) model and FastICA algorithms for signal separation and extraction. The algorithm’s effectiveness is validated through simulation experiments, where we successfully distinguish and analyze vital signs, such as heart and respiration rates, from phase-modulated Intermediate Frequency (IF) signals. The IWR6843AOP MIMO radar, operating in the 60-64GHz band, is utilized for data collection. Our results demonstrate the potential of this methodology in accurately identifying vital signs in complex, multi-target environments. Furthermore, the study suggests enhancements to the existing signal processing flow, highlighting areas for future research in real-time vital signs monitoring using FMCW radar.

关键词： MIMO radar signal processing algorithms Radar detection Adaptive filters Radar Blind source separation Real-time systems

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Arc Sensing with DBSCAN for Digital Inverter Welding Machine

Arc Sensing with DBSCAN for Digital Inverter Welding Machine

引用

International Conference on Control, Automation and Systems ( ICCAS)

作者： Hee-Jun Kim Do-Hyung Jeon Hyung-Jin Kim Doo-Jin Choi Jeong-Ho Kim Won-Suk Kim Samsung Heavy Industries Co. Ltd. Geoje Republic of Korea The Department of Computer Engineering Pusan National University Busan Republic of Korea School of Mechanical Engineering Pusan National University Busan Republic of Korea Center for Artificial Intelligence Research Pusan National University Busan Republic of Korea

In the shipbuilding industry, welding automation using welding robots often relies on arc sensing techniques due to spatial limitations. This study proposes a new arc sensing algorithm, DBSCAN (Density-Based Spatial C... 详细信息

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

关键词： Automation Service robots Welding Shipbuilding industry signal processing algorithms Robot sensing systems Weaving Sensor systems Sensors Reliability

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Fingerprint-Spectrum-Based Synchronization in Asynchronous Perceptive Mobile Networks

Fingerprint-Spectrum-Based Synchronization in Asynchronous P...

引用

IEEE Workshop on signal processing Advances in Wireless Communications (SPAWC)

作者： Xiao-Yang Wang Shaoshi Yang Mingyang Chen Christos Masouros School of Information and Communication Engineering Beijing University of Posts and Telecommnuications Beijing China Department of Electronic and Electrical Engineering University College London London UK

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

Asynchronous radio transceivers introduce significant range and velocity ambiguity, posing challenges for accurate positioning and velocity estimation in passive sensing perceptive mobile networks (PMNs). To address this issue, we propose the Cross-Multipath Cross-Correlation (CMCC) algorithm, a joint carrier frequency offset (CFO) and time offset (TO) estimation algorithm that supports passive sensing in both non-line-of-sight (NLOS) and line-of-sight (LOS) environments. The CMCC algorithm treats the delay-Doppler spectrum of signals reflected from static objects as an environment-specific "fingerprint spectrum" that exhibits a cyclic shift property as the CFO and/or TO change. By analyzing the number of cyclic shifts, CMCC efficiently estimates both CFO and TO. Additionally, we provide a simplified version of CMCC to reduce computational complexity. Simulation results demonstrate the performance advantages of our algorithms across various configurations.

关键词： Wireless communication Time-frequency analysis Radio transceivers Simulation signal processing algorithms Estimation Line-of-sight propagation Fingerprint recognition signal processing Synchronization

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Performance Characterization of Sparse Array Angle Finding algorithms for Automotive Radars

Performance Characterization of Sparse Array Angle Finding A...

引用

IEEE National Conference on Radar

作者： Ryan H. Wu Jun Li Binbin Shi NXP Semiconductors USA

ISBN: (数字)9798350329209

ISBN: (纸本)9798350329216

This study introduces two novel coherent-signal single-snapshot sparse array high resolution angle finding (HRAF) algorithms: the Binary Search Matching Pursuit (BSMP) and the Bayesian Linear Regression with Cauchy Prior (BLRC). To provide a comprehensive and quantitative evaluation of these HRAF algorithms, we develop four key performance indicators (KPls): the probability of target separation, the 95th percentile direction-of-arrival (DoA) estimation error, the 95th percentile number of spurious targets, and the 95th percentile power of spurious targets. The analysis conducted in this study not only highlights the strengths and potential applications of the BSMP and BLRC algorithms but also sets a new benchmark for evaluating various HRAF algorithms in the context of advanced automotive radar systems.

关键词： Direction-of-arrival estimation Array signal processing Spaceborne radar Key performance indicator signal processing algorithms Matching pursuit algorithms Radar

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Energy Efficient Image Transmission Over Semantic Communication Enabled UAV Swarm Networks

Energy Efficient Image Transmission Over Semantic Communicat...

引用

International Conference on Wireless Communications and signal processing (WCSP)

作者： Yixiao Feng Yining Wang Bo Zhang Huadong Dai Academy of Military Science Beijing China Advanced Institute of Big Data Beijing China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In this paper, a semantic communication framework is proposed for unmanned aerial vehicle (UAV) swarm formation flight. In the studied model, a leader extracts semantic information from the aerial image and transmits it to each follower using limited transmission power. The borders of obstacles are referred to semantic information of the aerial image. Based on the received semantic information, each follower reconstructs semantic maps for trajectory planning so as to maintain formation and avoid obstacles. We use the metric of Intersection over Union (IoU) to evaluate the effectiveness of the proposed semantic communication network. The problem is formulated as an optimization problem whose goal is to maximize the sum of IoUs of all followers. To solve this problem, a semantic information selection and power allocation (SISPA) algorithm based on reinforcement learning (RL) framework is proposed. The proposed SISPA algorithm enables the leader to evaluate the importance of semantic information, and, thus efficiently allocating transmission power and selecting important semantic information to improve the sum of IoUs. Simulation results demonstrate that, compared with the traditional wireless communication networks without semantic communication techniques, the proposed approach yields a 17.4% improvement in terms of the sum of IoUs.

关键词： Measurement Trajectory planning Simulation Wireless networks signal processing algorithms signal processing Semantic communication Autonomous aerial vehicles Resource management Optimization

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Adaptive Hybrid FFT: A Novel Pipeline and Memory-Based Architecture for Radix-2k FFT in Large Size processing

Adaptive Hybrid FFT: A Novel Pipeline and Memory-Based Archi...

引用

International Symposium on Parallel and Distributed processing with Applications, ISPA

作者： Fangyu Zhao Chunhua Xiao Zhiguo Wang Xiaohua Du Bo Dong College of Computer Science Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education China Sichuan Huacun Zhigu Technology Co. Ltd. Chengdu China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In the field of digital signal processing, the fast Fourier transform (FFT) is a fundamental algorithm, with its processors being implemented using either the pipelined architecture, well-known for high-throughput applications but weak in hardware utilization, or the memory-based architecture, designed for area-constrained scenarios but failing to meet stringent throughput requirements. Therefore, we propose an adaptive hybrid FFT, which leverages the strengths of both pipelined and memory-based architectures. In this paper, we propose an adaptive hybrid FFT processor that combines the advantages of both architectures, and it has the following features. First, a set of radix-2 k multi-path delay commutators (MDC) units are developed to support high-performance large-size processing. Second, a conflict-free memory access scheme is formulated to ensure a continuous data flow without data contention. Third, We demonstrate the existence of a series of bit-dimension permutations for reordering input data, satisfying the generalized constraints of variable-length, high-radix, and any level of parallelism for wide adaptivity. Furthermore, the proposed FFT processor has been implemented on a field-programmable gate array (FPGA). As a result, the proposed work outperforms conventional memory-based FFT processors by requiring fewer computation cycles. It achieves higher hardware utilization than pipelined FFT architectures, making it suitable for highly demanding applications.

关键词： Program processors Fast Fourier transforms Memory architecture Pipelines signal processing algorithms Throughput Hardware Delays Commutators Field programmable gate arrays

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High-Resolution Imaging Capability of Large-Scale LEO Satellite Constellations

High-Resolution Imaging Capability of Large-Scale LEO Satell...

引用

IEEE International Conference on Communications (ICC)

作者： Lhamo Dorje Xiaohua Li Yu Chen Nihal A Poredi Department of Electrical and Computer Engineering Binghamton University Binghamton NY USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

There has been a great interest in deploying large-scale Low-Earth Orbit (LEO) satellite constellations for wireless communications. This paper shows that LEO satellite constellations developed for communication purposes can be exploited for ground target imaging applications, where a unique advantage is to achieve super-high imaging resolutions that are not achievable via other imaging techniques. A new imaging algorithm is developed for this novel integrated sensing and communication (ISAC) application based on delay-sensitive signal processing and irregular sensing data exploitation. Imaging performance is analyzed. Simulations with the practical SpaceX Starlink satellite orbital data are conducted to verify both the new algorithm and the analysis results. This paper demonstrates that while the resolution of conventional satellite imaging is limited to sub-meters, the new method can potentially use only a small set of LEO satellites to achieve sub-centimeter resolution.

关键词： Wireless communication Satellite constellations Satellites Spaceborne radar Low earth orbit satellites Imaging signal processing algorithms

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A New Track Initiation Method for HFCSSMR

A New Track Initiation Method for HFCSSMR

引用

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

作者： Mengxiao Zhao Huilin Mu Qiushi Chen Xiaolong Hu Air Force Engineering University Air Defense and Antimissile School Xi'an China Air Force Engineering University Information and Navigation School Xi'an China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

High Frequency Combined Sky and Surface- Wave Monostatic Radar (HFCSSMR) is a new type of over-the-horizon radar which utilizes a combined propagation mode of sky and surface waves for target detection. The radar may receive four different propagation modes' echoes from a single target without considering the ionospheric stratification. When the one-point track initiation algorithm is applied, all the propagation modes need to be initiated, and the amount of computation is extensive. This article proposes a new track initiation method in HFCSSMR to solve this problem, which first uses the prior information of ionospheric height to determine the propagation mode of echoes, and then initiates the state estimation and covariance for the determined modes rather than all modes. The simulation results show that this method can achieve effective propagation mode judgment, so as to reduce the amount of calculation of target tracking in HFCSSMR.

关键词： Target tracking Surface waves Simulation Measurement uncertainty signal processing algorithms Radar detection Radar Radar tracking Trajectory State estimation

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Towards Road Anomaly Detection for Autonomous Vehicle System Using Deep Learning Technique

Towards Road Anomaly Detection for Autonomous Vehicle System...

引用

Intelligent signal processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： Prachi Agrawal Deepak Kumar Dewangan Amrendra Singh Yadav Department of CSE RGPV University Bhopal India Department of CSE ABV-IIITM Gwalior India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

Safety on road is still the top priority, despite the development of intelligent as well as autonomous car technologies brought about by recent advances in automobile technology. A move towards vision-based sensors and deep learning approaches for better scene interpretation has been prompted by the high costs and patchy performance of conventional sensors like radar and LiDAR for the safety of vehicles. It is crucial to detect road irregularities accurately, like potholes and speed bumps Convolutional Neural Networks (CNNs) are optimized for improved anomaly detection in this research study, the accuracy and performance of the model have been improved by carefully regulating the batch size and experimenting with various optimizers a dataset of 1900 labeled photos was used to test the CNN architecture which was modeled around LeNet-5 the adam optimizer was used for fine-tuning resulting in a detection accuracy of 94.2% and using common assessment matrices, the suggested model's performance is verified. This optimized approach enhances safety and reliability in autonomous driving systems.

关键词： Deep learning Accuracy Roads signal processing algorithms Safety Convolutional neural networks Automobiles Intelligent sensors Autonomous vehicles Anomaly detection

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