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检索条件"主题词=Signal Processing Algorithms"

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Research on Feature Extraction Method for Ship Radiated Noise Based on DBO-VMD

Research on Feature Extraction Method for Ship Radiated Nois...

引用

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

作者： Dandan Zhu Hanlin Gao Chenglong Wu Yaan Li School of Marine Science and Technology Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

In order to effectively extract the frequency characteristics of underwater acoustic signals in complex marine background, a variational mode decomposition algorithm (VMD) based on dung beetle optimization algorithm (DBO) is proposed to extract the radiation noise features of ships. Aiming at the problem that the initial parameters of the VMD algorithm are difficult to set, the dung beetle optimization algorithm is used to select the local minimum sample entropy as the fitness function of the algorithm, and the best combination of the initialized parameter mode number and the penalty factor in the VMD algorithm is searched, so that the performance of the VMD algorithm is optimal, and the corresponding IMF components are obtained by decomposing the ship radiation noise signal. Experiments show that the center frequency of the IMF component with the highest degree of correlation with the original signal are good separability for ships as characteristic parameters.

关键词： Correlation Noise signal processing algorithms Optimization methods Feature extraction Search problems Classification algorithms Data mining Marine vehicles Underwater acoustics

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Research on Model Construction and Algorithm of Intelligent Analysis and Fault Diagnosis of Power Equipment

Research on Model Construction and Algorithm of Intelligent ...

引用

Data Analytics, Computing and Artificial Intelligence (ICDACAI), International Conference on

作者： Longxiang Chen Lihua Li Ning Yang Yang Yang State Grid Corporation Of China Beijing China China Electric Power Research Institute Beijing China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

In this paper, a hierarchical intelligent analysis and fault diagnosis model of power equipment is proposed, which includes data acquisition layer, feature extraction layer, fault diagnosis layer and result output layer. Key parameters such as current and voltage are collected in real time by sensors, and fault-related features are extracted by signal processing technology and machine learning algorithm. In the feature extraction layer, wavelet transform (WT) and principal component analysis (PCA) are used to reduce the dimension of features, so as to fully capture the effective information in fault signals. In the fault diagnosis layer, the deep learning (DL) model is adopted, and a large number of historical fault data are trained to realize high-precision fault identification. Aiming at the shortcomings of existing algorithms, this paper proposes a hybrid fault diagnosis algorithm combining DL and physical model. The algorithm improves the diagnosis accuracy and real-time performance by combining the feature learning ability of DL model and the prior knowledge of physical model. Specifically, a dual-stream network structure is designed. One stream processes the original data, and the other stream processes the data converted by the physical model. Finally, the features of the two streams are weighted and fused at the high level of the network for classification and diagnosis. The experimental results show that the hybrid fault diagnosis algorithm proposed in this paper is superior to other algorithms in accuracy, precision, recall and F1 score, which shows its effectiveness and superiority in dealing with complex fault diagnosis problems. In the experiment with 14,000 training data, 4,000 verification data and 2,000 test data, the accuracy of the algorithm is 0.957, the precision is 0.943, the recall rate is 0.962 and the F1 score is 0.952.

关键词： Fault diagnosis Representation learning Analytical models Accuracy signal processing algorithms Feature extraction Real-time systems Data models Classification algorithms Principal component analysis

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Real Time Monitoring and Prediction of Electromagnetic Compatibility Over Internet of Medical Things Using Kalman Filter Algorithm

Real Time Monitoring and Prediction of Electromagnetic Compa...

引用

International Conference on signal processing and Communication Systems, ICSPCS

作者： Chiedza Hwata Egas José Armando Omar Gatera Gerard Rushingabigwi Celestin Twizere Kambombo Mtonga African Centre of Excellence in Internet of Things (ACEIoT) University of Rwanda Kigali Rwanda Superior Rural School of Rural Development Eduardo Mondlane University Vilankulo Mozambique East Africa’s Regional Centre of Excellence in Biomedical Engineering and E-Health (CEBE) University of Rwanda Kigali Rwanda Department of Information and Communication Technology Rwanda Polytechnic Kigali Rwanda

ISBN: (数字)9798350389630

ISBN: (纸本)9798350389647

The Internet of Medical Things has emerged as a transformative technology in healthcare, facilitating remote monitoring and management of medical devices. However, despite these benefits, there is a need for more vigilance in ensuring electromagnetic compatibility within the Internet of Medical Things networks. It is quite essential to prevent electromagnetic interference as it could compromise device efficacy and consequently patient safety. This study aims to enhance real-time detection and prediction of electromagnetic radiations on the Internet of Medical Things networks by leveraging the predictive capabilities of the Kalman filter. An experiment was conducted over 30 days to evaluate the performance of the algorithm in optimizing electromagnetic compatibility management thus mitigating electromagnetic interference events. The algorithm was implemented and validated with 2500 samples of radiation emissions, with coordinates of the device’s location. The results indicated that the implementation of Kalman filter algorithms could reduce sensor noise by up to 88% of accuracy. This research contributes to advancing electromagnetic compatibility monitoring techniques on the Internet of Medical Things networks, offering valuable implications for the design and implementation of resilient healthcare systems.

关键词： Performance evaluation Electromagnetic interference signal processing algorithms Internet of Medical Things Prediction algorithms Electromagnetic compatibility Real-time systems Safety Kalman filters Remote monitoring

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A Low-Complexity Orthogonal Matching Pursuit Algorithm Based on Multi-Scale Multi-Lag Underwater Acoustic Channels

A Low-Complexity Orthogonal Matching Pursuit Algorithm Based...

引用

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

作者： Jiaqi Du Yongsheng Yan Xiangxiang Li School of Marine Science and Technology Northwestern Polytechnical University Xi'an Shaanxi China Key Laboratory of Ocean Acoustics and Sensing Ministry of Industry and Information Technology Northwestern Polytechnical University Xi'an Shaanxi China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

This paper studies the estimation of parameter for wideband underwater acoustic (UWA) channel with multi-scale and multi-lag (MSML) characteristics, within the framework of the OFDM system. Based on the Orthogonal Matching Pursuit (OMP) reconstruction model, the high computational complexity issue caused by over-parameterized dictionaries during channel estimation is addressed. A low-complexity OMP algorithm based on independent stepwise search is proposed. This algorithm leverages the MSML characteristics of UW A channels to concentrates the computation cost waste problem of inner in critical regions, thereby solving the computational waste problem of the OMP algorithm, which requires repeated inner product calculations in each iteration. This provides a low-complexity, high-performance channel estimation solution for underwater OFDM communication.

关键词： Costs OFDM Search methods Matching pursuit algorithms signal processing algorithms Channel estimation Estimation signal processing Underwater acoustics Wideband

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Gridless Channel Estimation for MmWave Systems with Non-Ideal Pulse Shaping Filters

Gridless Channel Estimation for MmWave Systems with Non-Idea...

引用

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

作者： Yu-Tai Chiew Yuan-Pei Lin Inst. of Electrical and Control Engr. National Yang Ming Chiao Tung Univ Hsinchu Taiwan

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

In this paper, we consider channel estimation for wideband millimeter-wave (mmWave) systems with non-ideal pulse shaping filters. The use of non-ideal pulse shaping filters complicates the channel estimation problem. In the literature, such a problem has been addressed using oversampling or on-grid methods. We show that gridless estimation can be achieved for a orthogonal frequency-division multiplexing (OFDM) system with non-ideal pulse shaping filters. By using an appropriately chosen subset of subcarriers of the OFDM system, the estimation of a wideband mmWave channel is converted to a harmonic retrieval problem, which can be solved efficiently using gridless algorithms. Simulations are given to show that the proposed method enjoys high estimation accuracy with low complexity.

关键词： OFDM Millimeter wave technology Channel estimation Estimation signal processing algorithms Low-pass filters Pulse shaping methods Millimeter wave communication Wideband Power harmonic filters

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Reconfigurable Intelligent Surface Assisted Low-Cost DOA Estimation Using A Single Antenna

Reconfigurable Intelligent Surface Assisted Low-Cost DOA Est...

引用

European signal processing Conference (EUSIPCO)

作者： Yu Feng Ye Tian Hua Chen Wei Liu Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China School of Electronic Engineering and Computer Science Queen Mary University of London U.K.

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

In this paper, a challenging direction-of-arrival DOA estimation problem is addressed in a non-line-of-sight (NLOS) scenario, with the aid of the reconfigurable intelligent surface (RIS). Different from traditional methods using sensor arrays, the proposed one can achieve low-cost DOA estimation with a single receiving antenna, which is motivated by the fact that the phase shifts on RIS elements can realize multiple measurements of the reflected signals. Under such configurations, a two-stage approach for high-efficiency RIS phase shift and robust covariance matrix reconstruction is first developed in the presence of phase shift errors. Then, satisfactory DOA estimation is achieved by applying the MUSIC algorithm. Simulation results are provided to demonstrate the superiority and effectiveness of the proposed method.

关键词： Antenna measurements Direction-of-arrival estimation Simulation Estimation signal processing algorithms Receiving antennas Reconfigurable intelligent surfaces Multiple signal classification Matrix decomposition Covariance matrices

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Digital Modulation Identification in Spatial Modulation MIMO Systems

Digital Modulation Identification in Spatial Modulation MIMO...

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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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Optimizing Loss Functions in Conditional GANs for Astronomical Image Deconvolution: A Comparative Study at Varied Epochs*

Optimizing Loss Functions in Conditional GANs for Astronomic...

引用

IEEE International Conference on signal processing, Informatics, Communication and Energy Systems (SPICES)

作者： Vishnu Vasudev M.V Rajesh Electronics & Communication Engineering College of Engineering Chengannur APJ Abdul Kalam Technological University Kerala India Electronics & Communication Engineering College of Engineering Poonjar Kerala India

ISBN: (数字)9798350376135

ISBN: (纸本)9798350376142

This study explores the optimization of loss functions in conditional Generative Adversarial Networks (cGANs), specifically the pix2pix model, for the deconvolution of astronomical images obtained from the Hubble Space Telescope. The objective is to mitigate the effects of atmospheric distortion, commonly referred to as the "seeing" effect. We systematically evaluated various combinations of Mean Absolute Error (MAE), Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Binary Cross-Entropy (BCE) with logits as loss functions for the generator. These were tested at different training epochs—10, 20, 30, 40, and 50. Our results indicate that a specific weighting of BCE with logits, MAE, and SSIM—1, 1, and 50, respectively—significantly enhances the performance, yielding higher Peak signal-to-Noise Ratio (PSNR) and SSIM values post 30 epochs. In addition, we compared our cGAN-based method with conventional techniques, including Wiener deconvolution, Richardson-Lucy algorithm, and Total Variation (TV) deconvolution. Our proposed method demonstrated superior clarity and detail retention.

关键词： Training Deconvolution PSNR TV Atmospheric modeling signal processing algorithms Telescopes Generative adversarial networks Indexes Optimization

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Enhancing EEG signals Classification through Spatio-Temporal Feature Fusion in Dense Connection Neural Networks

Enhancing EEG Signals Classification through Spatio-Temporal...

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International Conference on signal processing Proceedings (ICSP)

作者： Mengxue Liu Wenhui Guo Jingyuan Chen Bingfeng Zhang Yanjiang Wang College of Control Science and Engineering China University of Petroleum (East China) Qingdao China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Brain-computer interface (BCI) technology based on electroencephalogram (EEG) has attracted significant interest, particularly in EEG signal interpretation, pattern recognition, and brain activity classification, which are considered promising research avenues. Nonetheless, the classification of target signals using EEG continues to pose substantial challenges regarding the performance and interpretability of human brain signals. To address these issues, this study proposes a novel dense connection convolutional neural network with spatio-temporal feature fusion to classify brain visual images. Drawing inspiration from visual attention and brain memory mechanisms, a densely connected module is incorporated to mitigate the vanishing gradient issue and facilitate effective training of deep networks. EEG signals are subsequently encoded and stored along both temporal and spatial dimensions. To leveraging the unique properties of EEG signals, a bidirectional gated recurrent unit network is employed to derive temporal features, while spatial features are extracted using a two-dimensional mixed dilated convolution module. Ultimately, the extracted spatio-temporal features are concatenated and classified. The findings confirm the feasibility and effectiveness of the proposed model.

关键词： Training Visualization Convolution Time series analysis Pattern classification signal processing algorithms Feature extraction Brain modeling Electroencephalography Biological neural networks

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Apnea Aware: An Intelligent Sleep Monitoring and Intervention System for Obstructive Sleep Apnea

Apnea Aware: An Intelligent Sleep Monitoring and Interventio...

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IEEE International Conference on signal processing, Informatics, Communication and Energy Systems (SPICES)

作者： Archana Aniyan Liya Sam Sereen Sabu Soorya S Pai Department of Electronics and Communication Adi Shankara Institute of Engineering and Technology Ernakulam India

ISBN: (数字)9798350376135

ISBN: (纸本)9798350376142

Sleep apnea is a serious condition where breathing is more than once hindered or halted for at slightest 10 seconds amid rest. There are three fundamental sorts of sleep apnea: Obstructive Sleep Apnea (OSA), Central Sleep Apnea (CSA), and Complex Sleep Apnea. OSA, the most common type, is regularly caused by loosening up of throat muscles, driving to trachea blockage. This condition can lead to different wellbeing complications such as heart issues, stroke, anxiety, and depression. Elements like corpulence, liquor ingestion, and smoking contribute to its advancement. Early discovery is significant for convenient treatment. This paper presents a reasonable sleep monitoring framework for recognizing OSA, utilizing SPO2, heart rate, body position changes, and snoring occasions, coordinates with machine learning for progressed precision. The framework incorporates a web interface for patient and doctor to access, calculating the Apnea-Hypopnea Index (AHI) to classify cases as Hypopnea, Apnea, or Normal.

关键词： Training Machine learning algorithms Accuracy signal processing algorithms Sleep apnea Classification algorithms Wearable devices Trachea Random forests Monitoring

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