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检索条件"主题词=Signal Processing Algorithms"

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LDPC Decoding Based on Hybrid Stochastic Computing

LDPC Decoding Based on Hybrid Stochastic Computing

引用

signal processing, Communications and Engineering Management Conference (ISPCEM), International

作者： Yali Li Zhisong Bie School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Compared with traditional computing methods, stochastic computing (SC) is simpler in circuit implementation. The Belief Propagation (BP) or Min-Sum (MS) algorithms used in traditional Low Density Parity Check (LDPC) decoding require a large amount of circuits for the calculation of check nodes (CNs). In this paper, decoding is completed on a decoding structure that combines the advantages of the Offset Min-Sum (OMS) algorithm and SC. CN information is calculated in the probability domain, and variable node (VN) information is calculated in the Log Likelihood Ratio (LLR) domain. It is proposed to use the gold sequence combined with a synchronizer to generate a positively correlated sequence, and combine the Noise-Dependent Scaling (NDS) algorithm to obtain a positively correlated random number generator to complete the conversion from LLR to a positively correlated bit stream. The positively correlated bit stream is input into an AND gate to find the minimum value of the input bit streams. Simulation results show that the bit error rate curve of the proposed method is close to the OMS algorithm.

关键词： Power demand Simulation Bit error rate signal processing algorithms signal processing Probability Parity check codes Decoding Synchronization Binary sequences

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Comparative Study on Reference signal for Data Selection algorithms in PEM-AFC Framework

Comparative Study on Reference Signal for Data Selection Alg...

引用

IEEE International Symposium on Intelligent signal processing and Communication Systems (ISPACS)

作者： Yu-Xiang Weng Ying-Ren Chien Felix Albu Dept. of Electrical Engineering National Ilan University NIU Yilan Taiwan

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

In this paper, we explore an acoustic feedback cancellation (AFC) algorithm that integrates the correntropy-based data-selective algorithm within the prediction error method (PEM). The data selective approach compares the performance of using the error signal $e(n)$ and the pre-whitened error signal $e_{p}(n)$ as data samples. It is shown that using the error signal provides superior system performance and audio quality compared to the pre-whitened error signal, while closely approximating the performance of the original algorithm.

关键词： Communication systems System performance signal processing algorithms signal processing Prediction algorithms Approximation algorithms Acoustics Multiple signal classification Speech processing

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Exploiting Bias in Reinforcement Learning for Task Allocation in a Mobile Edge Computing System

Exploiting Bias in Reinforcement Learning for Task Allocatio...

引用

International Conference on signal processing and Communications, SPCOM

作者： Satyadev Badireddi Raunakk Banerjee Parth Hiren Shah Arghyadip Roy Mathematics Department IIT Guwahati Guwahati India Science and Technology Facilities Council Didcot UK ECE Department University of California San Diego California USA MFSDSAI IIT Guwahati Guwahati India

ISBN: (数字)9798350350456

ISBN: (纸本)9798350350463

In an Internet-of-things (IoT) network, incoming tasks at IoT devices can be executed locally or at a Mobile Edge Computing (MEC) server. The problem of minimizing the total discounted delay can be formulated as a Markov Decision Process (MDP) problem. Since the arrival rate of the incoming tasks is typically unknown, Reinforcement Learning (RL) methods that converge to the optimal solution, can be adopted. Q-learning (QL) and Double Q-learning (DQL) are two popular RL algorithms. However, the overestimation and the underestimation biases prevalent in QL and DQL algorithms, respectively, may help and hurt the learning process, depending on the stochasticities of various task scheduling strategies. In this paper, we propose a novel adaptive RL algorithm that switches to the appropriate Q-learning variant depending on the IoT network's stochasticity. Simulation results are presented to evaluate the efficacy of the proposed algorithm over other state-of-the-art solutions.

关键词： Q-learning Multi-access edge computing Simulation signal processing algorithms signal processing Scheduling Delays

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Domesticated Cat Activity Detection Using Deep Learning Technique

Domesticated Cat Activity Detection Using Deep Learning Tech...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Md Ariful Islam Mozumder Rashadul Islam Sumon Shah Muhammad Imtiyaj Uddin Hee-Cheol Kim Institute of Digital Anti-Aging Healthcare Inje University Gimhae Republic of Korea

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Cats are one of the most well-known pet species worldwide. Cat owners take great care to ensure their feline companions' safety. Continuous observation of cats' behavior can help to assure their welfare. Due to the substantial benefits of sensor technology in the recent era, studies about activity detection have become very prominent. Applications for automated cat monitoring include monitoring and surveillance systems that accurately identify pets using deep learning methods for classifying pet activities. We can now use cutting-edge ways to get better results thanks to the technological revolution. Recently, Long Short-Term Memory (LSTM) has worked as a cutting-edge methodology for anomaly detection in manufacturing environments, sensor-based health monitoring systems, and signal processing-based systems. As compared to other models employed in the study for categorizing various types of activities, the CNN-LSTM architecture is seen to be the most effective. Additionally, it was discovered that all models outperformed their counterparts in data from the accelerometer, gyroscope, and magnetometer. The goal of this study is to use multi-axis sensor devices to identify ten pet behaviors, such as walking, running, eating, jumping, resting, etc. The approach for gathering data for this study involved 10 cats of various breeds, sexes (male=6, female=4), ages (age = 43), and the cats are small, medium, and large-sized in a safe setting. Following the data collection, the stages of data synchronization and data preparation were taken into consideration to eliminate unnecessary data from the dataset. We employed the class-weight technique to solve the dataset's imbalance issues. We used the class weight method to apply the LSTM algorithm. 99.45% training and 96.23% validation accuracy were displayed by the model using the class weight technique. The LSTM method will be useful for tracking cat behavior and for real-time activity monitoring.

关键词： Deep learning Training Cats Accuracy Magnetometers signal processing algorithms Data models Classification algorithms Long short term memory Wearable sensors

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Performance Analysis of Distributed Quantization Aware LMS Algorithm with Quantized Measurements

Performance Analysis of Distributed Quantization Aware LMS A...

引用

International Symposium on Wireless Communication Systems (ISWCS)

作者： Alireza Danaee Rodrigo C. de Lamare Vitor H. Nascimento Department of Communications Engineering University of Bremen Germany Centre for Telecommunications Studies Pontifical Catholic University of Rio de Janeiro Brazil Department of Electronic Systems Engineering University of São Paulo Brazil

ISBN: (数字)9798350362510

ISBN: (纸本)9798350362527

This paper analyzes the mean-square performance of a distributed quantization-aware framework for adaptive Internet of Things (IoT) networks where agents use coarsely quantized measured data. Smart agents in the IoT networks contain many sensors to interact with the physical world by using analog-to-digital converters (ADCs). The distributed quantization-aware Least Mean Square (DQA-LMS) algorithm that can learn parameters in an energy-efficient fashion using measurements quantized by low-resolution ADCs with few bits is investigated. The DQA-LMS algorithm uses a bias compensation strategy to further improve its performance. We carry out a statistical analysis of the DQA-LMS algorithm and derive an expression for the mean square deviation (MSD). Numerical results show that the proposed analytical formulas are very accurate to predict the performance of the DQA-LMS algorithm.

关键词： Wireless communication Quantization (signal) Parameter estimation signal processing algorithms Energy measurement Prediction algorithms Performance analysis

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Vehicle Small Target Detection Algorithm Based on Improved YOLOv7

Vehicle Small Target Detection Algorithm Based on Improved Y...

引用

signal processing and Intelligent Computing (SPIC), International Conference on

作者： Dawei Yang Ming Cui Junchen Liu Shuhui Li Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

In response to the intelligence of modern transportation, the demand for vehicle detection has increased dramatically, and vehicles on urban roads often appear in the form of small targets in the video. However, encountering challenges such as limited image resolution, subtle target features, and a narrow field of view, which collectively hinder the effective detection of small objects, we have refined a YOLOv7-Tiny-based algorithm specifically tailored for small target detection. As a result, we have enhanced a small target detection approach utilizing the YOLOv7-Tiny framework to address the difficulties encountered in detecting minute *** CBAM attention mechanism is introduced into YOLOv7-Tiny, and the loss function is optimized using the NWD strategy. Finally, it is tested on the traffic camera detection dataset and compared with other target detection algorithms, and the improved network model can achieve 0.737 of mAP@0.5, which is 1.2 percentage points better than the baseline. The detection effect is better than other classical networks.

关键词： Location awareness Image resolution Vehicle detection Roads Refining signal processing algorithms Transportation Object detection Cameras Feature extraction

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Dry Power Transformer Fault Diagnosis Using Deep Learning: A Review

Dry Power Transformer Fault Diagnosis Using Deep Learning: A...

引用

Sustainable Power & Energy (ICSPE), International Conference on

作者： Priyanka Tiwari Shweta Singh Vinod Kumar Singh Dept of Electrical Engineering Maharishi University of Information Technology Lucknow UP India Dept. of Electrical Engineering SR Group of Institutions Jhansi UP India

ISBN: (数字)9798350366051

ISBN: (纸本)9798350366068

Dry power transformers to be most effectively used in industrial, commercial, and even utility applications. They are ideal for both low voltage and medium ones hence assuring comfort in providing the needs of utility company. In this article, we summarize the applications of deep learning on vibration-based diagnosis of deformed core detection in dry power transformer based on systematic review from related literature. The perspective of data-driven, that is to say integrating various technologies related with deep learning into traditional on-the-spot fault diagnosis, especially vibration analysis and acoustic signal processing technology; infrared thermal imaging technique coupled with typical diagnostic sinkhole transformation matrix algorithm by using neural network computing. Next, we consider the impact of emerging research in these areas on transformer reliability and discuss several examples from recent literature that show how intelligent systems can be used to improve this.

关键词： Deep learning Vibrations Fault diagnosis Neural networks signal processing algorithms Transformer cores Power system reliability Thermal analysis Power transformers Systematic literature review

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The impact of inverse matrix computation on the results of the Capon algorithm

The impact of inverse matrix computation on the results of t...

引用

Telecommunications Forum (TELFOR)

作者： Nebojša Kolarić Miloš Bjelić Nataša Ćirović School of Electrical Engineering Belgrade Belgrade Serbia

ISBN: (数字)9798350391060

ISBN: (纸本)9798350391077

algorithms for estimating the direction of signal arrival are crucial in various applications, particularly in acoustics. They are essential for accurately locating sources of both desired and undesired sound signals. This research focuses on the performance analysis of the Capon algorithm using an approximated inverse covariance matrix. Instead of using direct methods for matrix inversion, iterative methods are employed, potentially leading to shorter processing times and reduced memory usage. Furthermore, the performance of these iterative methods is compared to the direct Cholesky method in terms of computational operations.

关键词： Sensitivity Program processors Heuristic algorithms signal processing algorithms Approximation algorithms Telecommunications Performance analysis Iterative methods Sparse matrices Covariance matrices

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Identification of Stochasticity by Matrix-Decomposition: Applied on Black Hole Data

Identification of Stochasticity by Matrix-Decomposition: App...

引用

International Conference on signal processing and Communications, SPCOM

作者： Chakka Sai Pradeep Neelam Sinha International Institute of Information Technology Bangalore India Center for Brain Research Indian Institute of Science Bangalore India

ISBN: (数字)9798350350456

ISBN: (纸本)9798350350463

Timeseries classification as non-stochastic (structured) or stochastic (lacking structure) helps understand underlying dynamics, in several domains. One of the novel contributions of this work is to utilize the well-known Principal Component Analysis (PCA) for identifying structured behavior or lack thereof, in a timeseries. For classification, we propose a two-legged matrix decomposition-based algorithm utilizing two complementary techniques (SVD and PCA). SVD-Ieg performs topological analysis (Betti numbers) on singular vectors containing temporal information, leading to SVD-Iabel. Parallely, temporal-ordering agnostic PCA is performed hierarchically on the timeseries, computing proposed Eigen-ratio based features that quantify structure, on progressively shorter time-windows, to obtain PCA-Iabel. For a given timeseries, if SVD-Iabel and PCA-label concur, then the label is retained; else deemed “Uncertain”, requiring further investigation. The proposed methodology is illustrated on publicly available data of black hole GRS 1915+105, obtained from RXTE satellite, with average length of 25000 datapoints, across 12 temporal classes. Comparison of obtained results with those in literature using traditional and deep-learning based methods are presented. Concurrence in labels is shown in 11 temporal classes; the one temporal class deemed “Uncertain” turns out to be differently labelled using yet another deep-learning based approach, warranting further investigation into its characteristics.

关键词： Legged locomotion Satellites Heuristic algorithms signal processing algorithms signal processing Vectors Inference algorithms

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A HVDC System Fault Diagnosis Method Based on PSO-LightGBM Algorithm

A HVDC System Fault Diagnosis Method Based on PSO-LightGBM A...

引用

International Conference on Vision, Image and signal processing (ICVISP)

作者： Zhanjun Huang Maning Zhang Jun Xie Yiting Wang School of Aeronautics Northwestern Polytechnical University Xi'an China National Key Laboratory of Aircraft Configuration Design Xi'An China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Due to the complexity of their structure and the particularity of their application environments, aircraft High-Voltage Direct Current (HVDC) systems are prone to faults, with inter-module failures complicating fault diagnosis. To address this issue, a fault diagnosis method for HVDC systems has been developed. Feature extraction methods were designed for the rectifier, BUCK converter, and inverter, respectively, with the sum-to-amplitude ratio of signals selected as a feature for the rectifier; multi-scale skewness was proposed for the BUCK converter; and the ratio of the signal's average to peak absolute value was chosen for the inverter. Subsequently, the PSO-LightGBM algorithm was proposed, which employs the LightGBM algorithm for classification and utilizes a particle swarm algorithm to optimize the parameters of the LightGBM, establishing the optimal model. The experimental results demonstrate that the proposed method can accurately achieve fault diagnosis in HVDC systems.

关键词： Fault diagnosis Buck converters HVDC transmission Atmospheric modeling signal processing algorithms Rectifiers Feature extraction Inverters Classification algorithms Aircraft

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