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检索条件"主题词=Signal Processing Algorithms"

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CWA-YOLO: Rethinking the YOLOv8 Architecture at the Level of Feature Information and Applying it to Substation Equipment Detection

CWA-YOLO: Rethinking the YOLOv8 Architecture at the Level of...

引用

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

作者： Li Zhuyun Xu Chong Yang Yingyi Lai Jiayang Xian Qu Mai Xiaoming Ieiri Yuya Yoshie Osamu China Southern Power Grid Technology Co. Ltd. Guangzhou China Graduate School of Information Production and Systems Waseda University Fukuoka Japan BIWIN Storage Technology Company Limited Shenzhen China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Substations act as bridges connecting power gener-ation with consumption and play crucial roles in electrical grids such as power energy conversion, operational dispatching, and information monitoring. With the rapid development of electrical grids in recent years, the increasing contradiction between the shortage of production staff and the growing workload of inspections has urgently necessitated intelligent recognition algorithms for the condition of indoor substation equipment. This paper proposes a novel indoor substation equipment target detection framework based on YOLOv8, aimed at addressing issues like scale variation, occlusion handling, and real-time performance optimization, thereby enhancing detection accuracy and speed in complex environments. By incorporating the ConvNeXt feature extraction network and BiFPN, combined with the Wise-IoU loss function and adding detection heads for small targets, this approach has improved the detection capabilities for multi-scale targets, particularly in recognizing minute objects. Experimental results with actual field data indicate that the proposed CWA-YOLO model outperforms existing methods in both detection accuracy and speed, with the model mAP0.5 and mAP0.5:0.95 being 0.918 and 0.623 respectively, marking improvements of 6.50% and 11.05% over the YOLOv81 model, 7.49% and 11.85% over YOLOv71, and 9.55% and 15.80% over YOLOv51.

关键词： Substations Accuracy Target recognition signal processing algorithms Object detection Production signal processing Feature extraction Data models Real-time systems

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Performance Analysis of Non-Local Means Denoising on Medical Images and the Impact of Filter Parameter Variation

Performance Analysis of Non-Local Means Denoising on Medical...

引用

International Conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Preena Prasad J Anitha Blesson Biji Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore & SAINTGITS College of Engineering Kottayam India Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Electronics and Communication Engineering SAINTGITS College of Engineering Kottayam India

ISBN: (数字)9798350350845

ISBN: (纸本)9798350350852

In medical imaging, diagnostic accuracy depends on clear images, which are frequently damaged by noise. To address this researchers have developed a number of noise reduction techniques, each with unique benefits and limitations. The effectiveness of NLM (Non-Local Means) denoising in medical images at various noise levels is comprehensively examined in this work. The goal is to understand how key parameters patch size, window size, and smoothing parameter (h) interact under different noise conditions. MSE (Mean Square Error), PSNR (Peak signal-to-Noise Ratio), and SSIM (Structural Similarity Index) metrics are applied in performance evaluation to provide quantitative insights into the efficiency of denoising and structural preservation. Experiments are conducted using a diverse dataset that includes MRI (Magnetic Resonance Images) and CT (Computed Tomography). Throughout the study, consistent patch sizes of 3 and 5, along with a window size of 11 and 15, are used to explore the role of the smoothing parameter for different noise levels and assess denoising efficacy at various noise intensities. This study contributes to understanding the potential of NLM denoising in improving image quality under varying noise situations, emphasizing the significance of deliberate filter parameter selection over random choices for improved denoising in both objective and subjective approaches.

关键词： Wireless communication Smoothing methods PSNR Computed tomography Noise reduction signal processing algorithms Filtering algorithms

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A Novel Non-Invasive Cuff-Less Method for Continuous Blood Pressure Estimation Using Machine Learning and Photoplethysmography

A Novel Non-Invasive Cuff-Less Method for Continuous Blood P...

引用

IEEE Region 10 International Conference TENCON

作者： Ren Xiang Yak Zi Heng Ng Zhi Qing Looi Wei Wang Singapore Polytechnic School of Electrical and Electronic Engineering Singapore Singapore

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

Hypertension requires timely monitoring to prevent complications like strokes and heart failure. This study introduces a non-invasive, cuff-less method for continuous blood pressure (BP) estimation using machine learning (ML) and photoplethysmography (PPG) signals. Our approach reveals BP fluctuations by examining the correlation between arterial BP (ABP) and PPG signals. Validation with standard metrics shows that a 16-second segmentation technique and Support Vector Machine (SVM) algorithm achieve mean absolute errors (MAE) of 10.54 for systolic BP (SBP) and 5.28 for diastolic BP (DBP), with R-squared values of 0.59 and 0.34, respectively. This method offers a practical and superior alternative for BP monitoring in healthcare applications.

关键词： Support vector machines Maximum likelihood estimation Machine learning algorithms Heuristic algorithms signal processing algorithms Machine learning Photoplethysmography Biomedical monitoring Wearable devices Monitoring

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Abnormal Traffic Detection Based on Data Augmentation and Hybrid Neural Network

Abnormal Traffic Detection Based on Data Augmentation and Hy...

引用

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

作者： Jing Zang Haoran Li School of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

Aiming at the problem of low detection rate caused by class imbalance and limited detection ability of single model in abnormal traffic detection, an abnormal traffic detection model based on hybrid sampling and deep learning has been proposed. On the basis of using Tomek-Link method to filter noise samples in data, CTGAN technology is used to generate minority samples that conform to the original data distribution, make the samples tend to balance. By combining the advantages of convolutional neural network and bidirectional long short term memory network, the spatial and temporal features of network data are extracted in depth to further improve the overall detection performance of the model. The multi-classification experimental results on the NSL-KDD dataset show that the accuracy and recall of the proposed algorithm. The rates were increased by 12.06% and 11.53%, respectively, which verified the effectiveness of the model.

关键词： Deep learning Noise signal processing algorithms Telecommunication traffic Feature extraction NSL-KDD Data augmentation Internet Data mining Convolutional neural networks

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Carrier Phase Localization with Single Antenna: A Virtual Array Approach

Carrier Phase Localization with Single Antenna: A Virtual Ar...

引用

International Conference on Computer and Communications (ICCC)

作者： Jinghui Han Xiaohui Chen Li Chen Huarui Yin Department of Electronic Engineering & Information Science University of Science and Technology of China Hefei Anhui China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

With the release of 3GPP Release 18, Carrier Phase Positioning (CPP) has emerged as a pivotal technology in 5G positioning. However, the current CPP algorithms typically require the coordination of multiple base stations or antennas for effective localization, which limits their applicability in scenarios where only single-antenna devices are available. To adress this problem, we integrate carrier phase positioning with virtual array technology to propose a novel method that enables accurate localization using single-antenna terminals. Unlike traditional multi-antenna localization approaches, our method eliminates the need for time synchronization and spatial resolution, offering greater flexibility and adaptability across various scenarios. Additionally, we conducted real-world experiments using software-defined radio equipment, and the results confirmed the algorithm's feasibility.

关键词： Phased arrays Location awareness Accuracy Array signal processing signal processing algorithms Parallel processing Autonomous aerial vehicles Synchronization Spatial resolution Software radio

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RGBT Object Tracking Algorithm Based on an Adaptive Feature Fusion Mechanism

RGBT Object Tracking Algorithm Based on an Adaptive Feature ...

引用

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

作者： Zhengting Jiang Yongsheng Qi Inner Mongolia University of Technology School of electric power Hohhot China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

To address the low accuracy and poor robustness of traditional RGBT object tracking algorithms, this paper proposes a new RGBT object tracking algorithm based on an adaptive feature fusion mechanism. Firstly, it enhances the feature extraction of each modality in complex scenes by introducing a spatial attention mechanism. Secondly, an inter-modal adaptive fusion mechanism is introduced to fully utilize the complementary information between the two modalities, enhancing the cross-modal fusion of RGB and infrared features. The algorithm is validated using authoritative object tracking datasets GTOT and RGBT234. The proposed method achieves highly competitive results and more comprehensive tracking performance, effectively solving the interference from similar objects and improving the tracker's robustness.

关键词： Target tracking Accuracy signal processing algorithms Interference signal processing Feature extraction Robustness Real-time systems Object tracking Faces

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On the Impact of Frequency Resolution on Female and Male Speech in DNN-Based Noise Reduction Systems

On the Impact of Frequency Resolution on Female and Male Spe...

引用

International Workshop on Acoustic signal Enhancement (IWAENC)

作者： Maurice Oberhag Yan Zeng Rainer Martin Institute of Communication Acoustics Ruhr-Universität Bochum Bochum Germany

ISBN: (数字)9798350361858

ISBN: (纸本)9798350361865

Neural network-based noise reduction has shown huge potential and superior performance in comparison to traditional statistical signal processing methods. In low-power devices such as hearing aids, however, constraints such as low complexity and algorithmic latency, as well as trade-offs between frequency resolution and algorithmic latency, must be considered. In this study, we vary the frequency resolution of an established low-complexity DNN-based algorithm while keeping the number of network parameters as well as the time resolution fixed. Furthermore, we compare the results to a traditional Wiener filter-based algorithm. We find that the performance of DNN-based noise reduction algorithms is contingent upon the frequency resolution and the fundamental frequency of the speech signal, in a manner analogous to that observed with traditional Wiener filters. When evaluating the performance dependent on the fundamental frequency of the speech signal, we find that the DNN-based noise reduction in female speech benefits more from higher frequency resolution than noise reduction in male speech.

关键词： Performance evaluation Time-frequency analysis Wiener filters Noise signal processing algorithms Speech enhancement Polynomials Complexity theory signal resolution Power harmonic filters

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Comparative Analysis of Wavelet Transform on fPCG signals for Denoising and Peak Extraction

Comparative Analysis of Wavelet Transform on fPCG Signals fo...

引用

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

作者： Megh Mhatre Geetha Narayanan Anjali Deshpande Biomedical Engineering Vidyalankar Instiute of Technology Mumbai India Systems and Control Indian Institute of Technology Bombay Mumbai India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

Fetal Phonocardiogram (fPCG) is a bio-signal obtained from recording the heart sounds from stethoscopes or auditory transducers for auscultations of FHR. Analysis of this data helps to give early diagnosis of any irregularity throughout Fetal development process. However, the analysis of this signal poses many challenges due to noise components like Motion Artifacts and Respiration of the Foetus and Mother, Uterine Contractions, Power Line Interference, Sensor Noise as well as Background Noise. Many approaches have been presented by the researchers to extract characteristic heart sound peaks from these signals to estimate the heart rate and to detect the abnormalities like arrhythmia. In this work, we present the results of application of advanced signal processing techniques such as wavelet transform with adaptive thresholding, to extract the peaks of the fPCG signal. Simulations have been performed on the set of real data from fetus as well as simulated signals available in the resources. Simulation results show that Discrete Wavelet Transform with adaptive thresholding is a better technique for denoising the signal and isolating the heartbeat peaks as compared to other studies discussed in various resources. This study also includes evaluation of various parameters employed in the processes including the verification of peaks by setting up a window of specific time to analyze the peak count within each window for consistent peak extraction results. Various combinations of wavelets with different thresholding techniques have been studied and evaluated for accuracy.

关键词： Wavelet transforms Adaptation models Transducers Finite impulse response filters Simulation Noise reduction Noise signal processing algorithms Stethoscope Wavelet analysis

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Dynamic UAV Flight Simulator Utilizing a Stewart Platform

Dynamic UAV Flight Simulator Utilizing a Stewart Platform

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Iberian Robotics Conference (ROBOT)

作者： Anurag Mukherjee Irfan Khan William Turner Peter Sullivan Chris Mahony Oliver Gilbert John Economou Cranfield University Defence Academy of the UK Shrivenham United Kingdom Richmond Defence Systems Ltd North Lopham United Kingdom

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

This paper focuses on replicating the motion profile of a UAV flying in different conditions on a Stewart platform testbed. The resulting system provides a safe platform to operate indoors reproducing the same signals to those of a UAV flying outdoors. This approach results in more frequent use of all flying abilities while retaining high safety standards. To illustrate the effectiveness of the testbed, experimental results are presented. Six linear actuators are used in the testbed to achieve the desired orientation and position. The desired length of each actuator is calculated via an inverse kinematics algorithm. To validate the algorithm, Inertial Measurement Unit (IMU) data from three aerial platforms of different configurations/size are used. The IMU data captured from flying/hovering the aerial platforms indoors, with and without disturbance, is pre-processed using Fast Fourier Transform (FFT) and used as an input to the Stewart platform.

关键词： Vibrations Actuators Fast Fourier transforms Heuristic algorithms Dynamics signal processing algorithms Autonomous aerial vehicles Usability Robots Drones

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A Hallucination Metric and Correction Scheme for Diffusion-Based Image Restoration

A Hallucination Metric and Correction Scheme for Diffusion-B...

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IEEE Workshop on Machine Learning for signal processing

作者： Daniel Siromani Di You Pier Luigi Dragotti Electrical and Electronic Engineering Department Imperial College London London UK

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

AI hallucination affects image inverse problem solvers using deep generative priors. Such models can produce highly realistic generations which are semantically different from the ground truth or input. The risk of hallucination limits application of these models in areas where data consistency is vital. This work considers hallucination in diffusion-based image restoration models. A metric based on deep perceptual similarity and ArcFace face ID is developed to predict the presence of hallucinated content. Using this metric a system to reduce hallucination during generation is developed. The proposed system adapts the guidance scale and diffusion sampling process based on the hallucination metric computed at each timestep. Using the metric within the DPS algorithm resulted in a significant reduction in hallucinations. Hallucinations were also reduced for INDigo to a lesser degree, with artifacts introduced where the model could not fully eliminate the hallucinated content. With a hallucination metric score and system to avoid hallucinations it is possible to reduce the frequency of hallucinations, favour input consistency over realism when a perfect reconstruction is not possible, and indicate to the user the likelihood of the generation being hallucinated.

关键词： Measurement Adaptation models Machine learning algorithms Inverse problems Computational modeling signal processing algorithms signal processing Image restoration Faces Tuning

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