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检索条件"主题词=Signal Processing Algorithms"

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Anti-interference Intelligent Decision-making Technology Based on Deep Reinforcement Learning

Anti-interference Intelligent Decision-making Technology Bas...

引用

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

作者： Shuo Ma Haitao Xiao Qinyao Li Linkun Ma The Key Laboratory of Intelligent Network and Network Security Ministry of Education China School of Information and Communication Engineering Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

In recent years, with the rapid development of wireless communication technology in the military field and the proliferation of the number of various types of wireless communication devices, the battlefield electromagnetic environment has become more and more complex, which puts forward higher requirements for the anti-interference ability of communication systems. In order to enhance the reliability and adaptability of communication systems in complex electromagnetic environments, this paper combines Deep Q-Learning (DQN) with Long Short-Term Memory (LSTM), and proposes a communication anti-jamming intelligent decision- making algorithm based on DQN-LSTM, which realizes adaptive frequency use. In this method, dynamic s-greedy strategy and LSTM are utilized to improve the convergence and stability of DQN. Simulation results show better anti-jamming performance compared to traditional anti-jamming methods and traditional DQN decision-making algorithms.

关键词： Wireless communication Heuristic algorithms Decision making signal processing algorithms Interference Deep reinforcement learning Stability analysis Electromagnetics Jamming Long short term memory

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Covariance Denoising with Applications to Adaptive Beamforming

Covariance Denoising with Applications to Adaptive Beamformi...

引用

IEEE International Symposium on Phased Array Systems and Technology

作者： Ruifu Li Danijela Cabric Electrical and Computer Engineering Department University of California Los Angeles

ISBN: (数字)9798350392142

ISBN: (纸本)9798350392159

Adaptive beamforming is of high importance in array signal processing applications that require interference suppression. In practice, minimum variance distortion-less re-sponse (MVDR) is one of the most widely used algorithms for adaptive beamforming. Classical MVDR implementations require processing number of samples larger than the number of elements in the array, whereas existing sample-based methods have high computational complexity. This work introduces computationally efficient sample-based method for MVDR beamforming. Utilizing knowledge of the array manifold, the method first finds a sparse representation of the covariance, which is then used to compute MVDR beamformers. The proposed method has low sample complexity and performs well as long as the number of ele-ments is much greater than number of interfering sources. This feature makes the method a promising candidate for adaptive beamforming with large arrays.

关键词： Phased arrays Array signal processing Sparse approximation Superresolution Noise reduction signal processing algorithms Adaptive arrays Parallel processing Computational complexity signal to noise ratio

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Unveiling Ion Channel Dynamics

Unveiling Ion Channel Dynamics

引用

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

作者： Mulli Likhith Reddy V.J. Renuka Talasila Balaji P. Md. Kalesha Masthan Sirigineedi Dinesh R. Ishwariya Dept of Computer Science and Engineering Amrita school of computing Amrita Vishwa Vidyapeetham Chennai India Dept of Science and Humanities Amrita school of Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

This paper explains how faults or anomalies might be indicated by changes in the initial condition of tissues, particularly the flow of potassium and sodium ions with leaks. Convolutional Neural Networks (CNNs) are becoming more and more capable of being used, even if these mistakes have been found in tissue systems. CNNs are trained to identify patterns in data, much like digital analysts. By providing information regarding both typical and exceptional motor function, we hope to help peo-ple comprehend that things aren't always correct. Our research examines how CNN s can be used to forecast changes in ion channel function, which could indicate important problems with neural properties. With this approach, we want to increase our capacity to detect and treat arthritis early on. By in-tegrating CNN s into fault detection algorithms, we want to pave the way for more ecologically friendly neural diagnostic and therapeutic approaches.

关键词： Training Computational modeling Sodium Biological system modeling signal processing algorithms Ions Data models

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Novel Gradient Sparsification Algorithm via Bayesian Inference

Novel Gradient Sparsification Algorithm via Bayesian Inferen...

引用

IEEE Workshop on Machine Learning for signal processing

作者： Ali Bereyhi Ben Liang Gary Boudreau Ali Afana ECE Department University of Toronto Ericsson Canada Ottawa Canada

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

Error accumulation is an essential component of the Top-k sparsification method in distributed gradient descent. It implicitly scales the learning rate and prevents the slow-down of lateral movement, but it can also deteriorate convergence. This paper proposes a novel sparsification algorithm called regularized Top-k (REGTop-k) that controls the learning rate scaling of error accumulation. The algorithm is developed by looking at the gradient sparsification as an inference problem and determining a Bayesian optimal sparsification mask via maximum-a-posteriori estimation. It utilizes past aggregated gradients to evaluate posterior statistics, based on which it prioritizes the local gradient entries. Numerical experiments with ResNet-18 on CIFAR-10 show that at 0.1% sparsification, REGTop-k achieves about 8% higher accuracy than standard Top-k.

关键词： Training Computer aided instruction Machine learning algorithms Distance learning signal processing algorithms Machine learning signal processing Inference algorithms Bayes methods Standards

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Machine Learning Aided Prediction of Pulse Condition in Traditional Chinese Medicine

Machine Learning Aided Prediction of Pulse Condition in Trad...

引用

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

作者： Ziwei Tian Yunfang Jia College of Electronic Information and Optical Engineering Nankai University Tianjin China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

The human body's pulse waveform signals can indicate the overall health state, and the reliability of pulse diagnosis in Traditional Chinese Medicine (TCM) has been well-accepted. With the enhancing healthy consciousness, an intelligent pulse diagnosis is urgently needed in the field of “Big Health”. Herein, a study about prediction of pulse conditions in TCM is conducted by using 8 machine learning (ML) methods, including Gradient Boosting Decision Tree (GBDT), K Nearest Neighbors (KNN), Support Vector Machine (SVM) and so on. Meanwhile, the parameters in 8 ML methods are optimized by Cuckoo Search (CS) and Grid Search, thus there are 16 combinations of algorithms. By these methods, the models for predicting pulse conditions in the signals collected by our self-made wearable pulse detection device are carefully investigated and evaluated. It is found the best over-all performance is obtained by CS-GBDT model, which is demonstrated by the lowest mean squared error (MSE), mean absolute error (MAE), and the highest coefficient of determination (R).

关键词： Support vector machines signal processing algorithms signal processing Prediction algorithms Boosting Stability analysis Decision trees Reliability Medical diagnostic imaging Thermal stability

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DBCTfuse: A Dual-Branch CNN-Transformer feature extraction network for Infrared and Visible Image Fusion

DBCTfuse: A Dual-Branch CNN-Transformer feature extraction n...

引用

International Conference on signal processing Proceedings (ICSP)

作者： Yujing Li Xiaole Ma Xiaoxuan Chen Shaohai Hu School of Computer Science and Technology Beijing Jiaotong University Beijing China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

The process of fusing infrared and visible images necessitates integrating thermal radiation information from infrared image with the edge and texture detail captured by visible images. Most current fusion methods are based on convolutional neural networks, which can effectively extract local features from source images but are somewhat lacking in global features. To address this, we propose a dual-branch feature extraction fusion network based on convolutional neural network and Transformer—DBCTfuse, to effectively synthesize both local details and global contextual information from source images. Comprehensive experiments conclusively demonstrate the superiority of our DBCTfuse method in infrared and visible image fusion, surpassing contemporary state-of-the-art algorithms in both qualitative and quantitative assessments.

关键词： Measurement Visualization Image edge detection signal processing algorithms signal processing Feature extraction Transformers Convolutional neural networks Image fusion Image reconstruction

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A Direct Position Determination Method of Over-the-Horizon Shortwave Emitters Based on 2-Dimensional DOAs

A Direct Position Determination Method of Over-the-Horizon S...

引用

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

作者： Gaoyuan Yang Jiexin Yin Bin Yang Ding Wang PLA Strategic Support Force Information Engineering University Zhengzhou Henan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

This paper investigates the method of Direct Position Determination (DPD) using the Direction of Arrival (DOA) information of Over-the-horizon (OTH) shortwave signals in 3D scenes. We first establish the positioning model of the shortwave signal under the ionospheric virtual height model. Then a DPD method is proposed using 2-dimensional DOAs based on maximum likelihood (ML), which directly locate the emitter in a single step by exploiting the position information embedded in azimuth angles and elevation angles. We use the Monte Carlo method to validate the performance of the proposed algorithm in terms of localization root mean square errors (RMSE). We compare the proposed algorithm with the conventional two-step positioning algorithm as well as Cramer-Rao Bound (CRB). Simulation results verify the superior performance of the proposed algorithm.

关键词： Location awareness Solid modeling Accuracy Direction-of-arrival estimation Three-dimensional displays Time difference of arrival Simulation signal processing algorithms signal processing Root mean square

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A Speech Dataset for Snoring Detection in Sleep Based on Deep Learning

A Speech Dataset for Snoring Detection in Sleep Based on Dee...

引用

Smart World Congress (SWC), IEEE

作者： Xiaobo Zhao Bing Wei Hengyu Wang Jiaqi Shi Jingyu Zou School of Cyberspace Security Hainan University Haikou China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is a common sleep disorder that affects the health and quality of life of millions of people worldwide. Traditional methods for diagnosing OSAHS, such as polysomnography (PSG), are widely recognized but have drawbacks such as high cost, operational complexity, and inconvenience to patients. Snoring sound detection methods based on deep learning offer a low-cost and convenient alternative for OSAHS diagnosis. However, these methods often require large amounts of data for training, and the lack of datasets severely hinders the development of snoring recognition. In this study, we aim to construct a comprehensive snoring audio dataset to support research and diagnosis of OSAHS. This dataset includes four categories: non-snoring, normal snoring, abnormal snoring, and breathing sounds. We utilize four signal processing algorithms—Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), and Mel-Frequency Cepstral Coefficients (MFCC)—to extract features from the audio. These extracted features are then evaluated using 12 state-of-the-art (SOTA) classification models. Experimental results demonstrate that this dataset can achieve high accuracy across most classification models. We believe this dataset can provide valuable reference and support for related research.

关键词： Deep learning Training Accuracy Continuous wavelet transforms signal processing algorithms signal processing Feature extraction Sleep apnea Speech processing Optimization

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TGD: Topology-Enhanced Framework for Graph Drawing

TGD: Topology-Enhanced Framework for Graph Drawing

引用

International Congress on Image and signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Peidong Chen Qingming Zhang Desen Yang Jian Gou School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Topological structure refers to the geometric properties of an object that remain invariant under continuous transformations. In the task of graph drawing, accurately capturing topological structures ensures that the layout of the graph aligns more closely with its inherent characteristics and relationships, thereby generating aesthetically pleasing layouts. Previous work has demonstrated that by optimizing models using graph topology-related loss functions, it is possible to learn sequences of operations that translate a graph into a layout. To further explore the model's ability to capture graph topological information, the enhanced topological representation framework for graph drawing, called TGD, is proposed in this paper. This framework employs Structural Deep Network Embedding (SDNE) with in-degree centrality encoding and spatial encoding during the graph preprocessing stage to enrich node embedding features, thereby enhancing the topological representation of the graph. By jointly optimizing multiple topology-related loss functions, the framework reduces the noise sensitivity of individual loss functions, allowing the model to better learn graph structure information and balance the overall layout optimization. Upon completion of the model training, it can quickly draw the input graph and achieve good visual representations. Experimental results show that TGD performs excellently in various graph layout tasks and has advantages over existing layout algorithms.

关键词： Training Visualization Biological system modeling Layout Urban areas signal processing algorithms Graph drawing Encoding Topology Optimization

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A Multi Target Tracking Algorithm Based on Acoustic Feature Correlation

A Multi Target Tracking Algorithm Based on Acoustic Feature ...

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

作者： Xueli Sheng Yan Wang Bingyu Shi Yichuan Zhu Linna Wan National Key Laboratory of Underwater Acoustic Technology Harbin Engineering University Key Laboratory for Polar Acoustics and Application of Ministry of Education(Harbin Engineering University) Ministry of Education College of Underwater Acoustic Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

This paper proposes a joint probability data association based on acoustic feature correlation (JPDA-AF) to address the issue of poor target tracking stability caused by low acoustic positioning accuracy. This algorithm utilizes the correlation information between the measured values and the acoustic characteristics of the tracked target, sets up dual gate detection, and weights the joint probability density of JPDA. The simulation analysis and experimental data processing results show that this method can effectively improve the stability of target tracking, enhance the integrity of tracking trajectory, reduce the number of false targets, and estimate the number of targets more accurately.

关键词： Target tracking Correlation Accuracy signal processing algorithms Acoustic measurements Acoustics Stability analysis Frequency measurement Trajectory Numerical stability

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