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检索条件"主题词=Signal Processing Algorithms"

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Research on Pedestrian Target Detection Method Based on Improved YOLOv8 Algorithm

Research on Pedestrian Target Detection Method Based on Impr...

引用

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

作者： Wei Li Jinsheng Li Fei Xiao Xin Li Smart City R&D Center Intelligent Technology Co. Ltd of Chinese Construction Third Engineering Bureau Wuhan China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

In the field of target detection, particularly pedestrian detection in complex scenarios, accuracy and real-time performance are persistent challenges. To address this issue, this paper introduces an enhanced model named MAM-Guided YOLOv8, which builds upon YOLOv8 by incorporating four distinct attention mechanisms: Local Attention Mechanism (LAM), Global Attention Mechanism (GAM), Efficient Channel Attention (ECA), and Axial Attention Mechanism (AA). Through a meticulously designed attention mechanism fusion strategy, the MAM-Guided YOLOv8 model aims to improve pedestrian detection capabilities in complex environments. Experimental results demonstrate that this model outperforms traditional methods on urban complex scene datasets, effectively balancing high precision with rapid processing demands. The contribution of this paper lies in the innovative integration of multiple attention mechanisms, significantly enhancing pedestrian detection accuracy and providing an effective solution for object detection tasks in practical applications.

关键词： Pedestrians Attention mechanisms Accuracy signal processing algorithms Object detection signal processing Drives Feature extraction Real-time systems Optimization

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Optimal Configuration of Radial Distribution Networks with Stud Krill Herd Optimization

Optimal Configuration of Radial Distribution Networks with S...

引用

signal processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), International Conference on

作者： ChithraDevi SA I. Mahendravarman A. Ragavendiran M.Muthamizh Selvam K. Yamuna Vibin R Dept of Electrical & Electronics Engg. A.V.C. College of Engineering Mayiladuthurai India Dept. of Electrical & Electronics Engg. A.V.C. College of Engineering Mayiladuthurai India Department of EEE QIS college of Engineering and Technology ONGOLE Andra Pradesh Dept. of Electrical and Electronics Engineering K.S.R. College of engineering Tiruchengode Namakkal Department of EEE CMS College of Engineering and Technology coimbatore

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

In radial distribution system, reconfiguration is a mixed-integer nonlinear optimisation problem, which minimises power loss without incurring new costs. The reconfiguration problem in the radial distribution system may be solved by applying the Stud krill herd method, which is suggested in this research for system active power loss minimization and voltage profile improvement. By taking into account various load circumstances, the suggested algorithm's performance is confirmed on IEEE standard test systems with 33 and 69 buses. It is shown how the suggested SKHA compares to current techniques for reconfiguring radial distribution networks. The results were compared with current techniques for reconfiguring radial distribution networks. Our findings show that the SKHA method significantly enhanced voltage profile, reduced power loss, and improved the overall solution quality. Specifically, SKHA was able to reduce real power loss by 33.85% and reactive power loss by 28.45% for the 33-bus system and 56.17% of real power loss and 43.85% of reactive power loss for the 69-bus system.

关键词： Reactive power Costs signal processing algorithms Voltage Distribution networks Krill herd algorithm signal processing

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Combining SIFT and BRISK Descriptors to Improve Image Matching Accuracy

Combining SIFT and BRISK Descriptors to Improve Image Matchi...

引用

IEEE Pacific Rim Conference on Communications, Computers and signal processing

作者： Sina Ghaffari Kin Fun Li David W. Capson Department of Electrical and Computer Engineering University of Victoria Victoria Canada

ISBN: (数字)9798350362312

ISBN: (纸本)9798350362329

Local descriptor algorithms are foundational in computer vision applications such as image matching and image retrieval. Some local descriptor algorithms extract features containing similar information from images while others extract complementary information. In this work, we investigate the advantages of combining a binary and a non-binary local descriptor algorithm. We propose and compare three methods to combine SIFT and BRISK descriptor algorithms selected because they produce complementary descriptor vectors. First, we propose to combine SIFT and BRISK descriptors using a weighted summation of their individual descriptor distances with learned weights. Our second method converts SIFT into a binary descriptor and concatenates the binary SIFT vector with the BRISK descriptor. The third method is to scale the binary BRISK descriptor vector and concatenate it with the SIFT descriptor. Parameters for combining the descriptors are learned based on the HPatches data set for each of the three methods. Our proposed methods increase the mean Average Precision in the range of 3% to 15.8% over the original BRISK and in the range of 5.9% to 21.8% over the original SIFT algorithm in various evaluation conditions.

关键词： Machine learning algorithms Image matching Neural networks Image retrieval signal processing algorithms Machine learning signal processing Feature extraction Vectors Data mining

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Evaluation of Radiometric Localization Techniques Based on the Intensity of the Received signal

Evaluation of Radiometric Localization Techniques Based on t...

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International Symposium on Instrumentation Systems, Circuits and Transducers (INSCIT)

作者： L. F. B. Alves A. D. C. Silva L. A. M. M. Nobrega G. V. R. Xavier Electrical Engineering Department Federal University of Campina Grande Campina Grande Brazil Electrical Engineering Department Federal University of Sergipe São Cristovão Brazil

ISBN: (数字)9798350350869

ISBN: (纸本)9798350350876

This paper presents an analysis of the efficacy of radiometric localization techniques for partial discharges sources, utilizing received signal strength. Tests in experimental setups with dimensions ranging from $2\times 2\ \mathrm{m}$ to $6\times 4\ \mathrm{m}$ with two different sources to emulate the phenomena were employed: a controlled source and an oil cell. In addition, the envelope detection was evaluated for this purpose. The techniques based on amplitude made the detection and localization of PD sources more practical and feasible. The location errors are around 0.25 m indicates that these techniques are promising and could help to reduce the costs associated with partial discharges monitoring processes.

关键词： Location awareness Partial discharges Transducers Prevention and mitigation signal processing algorithms signal processing Radiometry Sensors Circuit faults Monitoring

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Task-Based Quantizer Design for Sensing With Random signals

Task-Based Quantizer Design for Sensing With Random Signals

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IEEE International Symposium on Information Theory

作者： Hang Ruan Fan Liu School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

In integrated sensing and communication (ISAC) systems, random signaling is employed to convey useful information as well as to sense the environment. Such randomness poses challenges in various components of sensing signal processing. In this paper, we investigate quantizer design for sensing in ISAC systems. Unlike quantizers for channel estimation in massive multiple-in-multiple-out (MIMO) communication systems, sensing in ISAC systems need to deal with random non-orthogonal transmitted signals rather than fixed orthogonal pilots. Considering sensing performance and hardware implementation, we focus on task-based hardware-limited quantization with spatial analog combining. We propose two strategies of quantizer optimization, i.e., data-dependent (DD) and data-independent (DI). The former achieves optimized sensing performance with high implementation overhead. To reduce hardware complexity, the latter optimizes the quantizer with respect to the random signal from a stochastic perspective. We derive the optimal quantizers for both strategies and formulate an algorithm based on sample average approximation (SAA) to solve the optimization in the DI strategy. Numerical results show that the optimized quantizers outperform digital-only quantizers in terms of sensing performance. Additionally, the DI strategy, despite its lower computational complexity compared to the DD strategy, achieves near-optimal sensing performance.

关键词： Quantization (signal) signal processing algorithms Channel estimation Integrated sensing and communication Hardware Task analysis Computational complexity

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An Imperceptible and Robust Audio Watermarking Algorithm Based on SNGAN

An Imperceptible and Robust Audio Watermarking Algorithm Bas...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Weili Zhou Jiabei Zhou Shuangyuan Yang School of Informatics Xiamen University Xiamen China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Digital audio watermarking has emerged as a significant research focus in the field of multimedia data hiding in recent years. However, existing deep learning-based watermarking algorithms have primarily concentrated on the image field. Moreover, current audio watermarking techniques mostly rely on traditional methods, exhibiting clear drawbacks in imperceptibility and robustness against common signal processing attacks, particularly Re-sampling and MP3 compression. To address this challenge, this paper introduces a novel digital audio watermarking approach. Leveraging the high-quality generation and outstanding generalization capabilities of Spectrally Normalization Generative Adversarial Network (SNGAN), we apply it for the first time to embed and extract watermarks in the audio domain. Additionally, we incorporate Dense Connections within the generator of the GAN to further enhance its learning capacity. Furthermore, we design a dedicated attack network tailored for common audio attacks to bolster the robustness of our model. Extensive experiments show that our proposed model achieves an advanced level of performance, with an average signal-to-noise ratio (SNR) of 26.59 and an average watermark extraction BER of 0.42%. These results conclusively establish the exceptional imperceptibility and robustness of our model.

关键词： Training Neural networks signal processing algorithms Watermarking QR codes Network architecture Generative adversarial networks

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Variance Score and Pearson Similarity based Unsupervised Feature Selection (VPUFS) for Sample Clustering in Microarray Gene Expression Data

Variance Score and Pearson Similarity based Unsupervised Fea...

引用

Communication, Computing and signal processing (IICCCS), IEEE International Conference on

作者： Sourav Dutta Soumik Banerjee Arghya Nath Ankita Ghosh Chandra Das Shilpi Bose Dept. of CSE NSEC Kolkata India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

The analysis of high-dimensional gene profiles plays a vital part in cancer research, facilitating early detection, ameliorated prognostic, and the development of targeted therapeutic strategies. Grouping of similar data samples based on their gene profiles can be very beneficial for new cancer class identification. However, the high dimensionality and complexity of these gene profile data pose significant computational challenges, necessitating innovative approaches for feature selection and data reduction. This study proposes a novel framework called ‘Variance Score and Pearson Similarity based Unsupervised Feature Selection (VPUFS)’, a feature selection fashion to choose relevant features, for reducing high dimensionality of unlabeled datasets, thereby helping as a preprocessing step to cluster cancer samples effectively. The unsupervised dimensionality reduction framework leverages variance to calculate feature class relevance score, and similarity metrics to identify and retain informative features while eliminating the redundant ones. The proposed framework is rigorously evaluated across multiple gene profiles, such as, Leukemia, Prostate, Breast and Colon cancer samples. Comparative evaluations demonstrate that the proposed methodology outperforms existing feature selection techniques from unlabeled gene profiles, thus showing an enhanced stability and improved classification and clustering accuracies.

关键词： Measurement signal processing algorithms Medical services signal processing Feature extraction Stability analysis Telecommunication computing Prognostics and health management Medical diagnostic imaging Cancer

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Sparsity-Promoting Direction Finding of Superimposed signals Using Nonuniform Arrays: A Variational Bayesian Treatment

Sparsity-Promoting Direction Finding of Superimposed Signals...

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

作者： Jie Yang Jingtao Ma Yasong Ji Guang Liang Wenbin Gong Li Yang Shaanxi Key Laboratory of Underwater Information Technology Northwestern Polytechnical University Xi'an China The Innovation Academy for Microsatellites of CAS The Shanghai Engineering Center for Microsatellites Shanghai China State Key Laboratory of Acoustics Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

This paper develops a general variational Bayesian algorithm for obtaining sparse solutions to direction-of-arrival (DOA) estimation tasks utilizing nonuniform arrays. In this context, we focus on a fully Bayesian treatment, also known as maximum a posteriori (MAP) estimation using priors that induce sparsity in such estimates. We introduce a hierarchical form of the multivariate K prior to model the sparsity of the unknown signal coefficients, and use this hierarchy to establish variational representations of latent variables in probabilistic graphical model. We then discuss how the parameters within this framework can be estimated in an iterative procedure, where we interpolate the observed data to a virtual uniform linear array (ULA) in order to enhance the achievable degrees-of-freedom (DOF). The proposed algorithm iterates back and forth, using the current parameter estimates to interpolate the observed data better and then increase the likelihood of the next parameter estimates. Experimental results with synthetic signals are used for illustrating the superior performance of the proposed approach as compared with other state-of-the-art DOA estimation algorithm.

关键词： Estimation error Direction-of-arrival estimation Graphical models Estimation signal processing algorithms Probabilistic logic Inference algorithms Bayes methods Arrays Iterative methods

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Reduction of Stochastic Interference in Receivers Operating in the Very/Extremely Low-Frequency Nonlinear Channels

Reduction of Stochastic Interference in Receivers Operating ...

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

作者： Sumou Hu Hui Xie Feng Liu College of electronic Engineering Naval University of Engineering Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

The transmission of underwater very/extremely low frequency (VLF/ELF) electromagnetic waves is typically affected by background noise originating from its components or seawater, along with the presence of atmospheric noise superimposed on it. This study presents a variable step size robust mixing norm (RMN) algorithm to suppress such mixed noise in VLF/ELF nonlinear channel receivers. The conventional RMN algorithm, which is based on a fractional low-order moment (FLOM) criterion, experiences brief failures when confronted with high-intensity non-Gaussian atmospheric noise interference. By leveraging the nonlinear characteristics of the Sigmoid function and adjusting the instantaneous error $\boldsymbol{e}[\boldsymbol{t}]$ of the RMN algorithm through the tilt parameter $\boldsymbol{\lambda}$ , the robustness of the RMN is enhanced. The effectiveness of the proposed algorithm is validated through simulation experiments, where it is compared with the traditional RMN algorithm and the kernel least mean square (KLMP) algorithm.

关键词： Simulation Software algorithms Noise signal processing algorithms Interference Receivers Software Robustness Steady-state MATLAB

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Bearing Fault Diagnosis Based on VMD-KPCA Fusion Algorithm for Vibration and Sound signals

Bearing Fault Diagnosis Based on VMD-KPCA Fusion Algorithm f...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Yuanhao Xiong Zhaoyang Tian Shuping Yan Yongqaing Xiao Yubin Zhang Fengtao Wang College of Mechanical Engineering Anhui Polytechnic University Wuhu China Mechanical design department Efte Intelligent Equipment Co. LTD Wuhu China College of Digital Technology Engineering Ningbo University of Finance and Economics Ningbo China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

Traditional signal processing methods are often unable to fully reflect the operating status of bearings when dealing with vibration and sound signals, resulting in insufficient accuracy of fault detection. The single signal is easily disturbed by the surrounding environment, which affects the judgment of equipment reliability. In this paper, a fusion algorithm of sound and vibration signals based on variational modal decomposition (VMD) and kernel principal component analysis (KPCA) is proposed for bearing fault diagnosis. The VMD algorithm decomposes the signals efficiently to reduce the phenomenon of aliasing, while the KPCA enhances the feature extraction capability through the kernel function. The algorithm fuses the features of vibration and acoustic signals, which can realize complementary information and improve diagnostic accuracy and robustness. In this study, by analyzing the signals of normal and multiple fault states, efficient processing of data and accurate diagnosis are realized. The experimental results show that the VMD-KPCA algorithm proposed in this multi-proposed paper demonstrates superior robustness while improving the fault recognition rate, and has good prospects for practical application. Future research can further explore the optimization of this algorithm to meet the demands of industrial practical applications.

关键词： Vibrations Fault diagnosis Accuracy signal processing algorithms signal processing Robustness Acoustics Kernel Optimization Principal component analysis

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