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检索条件"主题词=Signal Processing Algorithms"

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A Novel Combination of PCA and LSTM for Multi-Carrier Modulation signals Classification

A Novel Combination of PCA and LSTM for Multi-Carrier Modula...

引用

International Conference on Circuits, Communication, Control and Computing

作者： KM Sejal Mohit Dua Department of Computer Engineering National Institute of Technology Kurukshetra India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

Fast communication is essential in today's world, and signal demodulation is a key factor affecting latency. Traditional demodulation methods are complex and time-consuming, making Automatic Modulation Classification (AMC) crucial for modern communication systems, particularly in wireless communication and signal processing. This paper presents a novel approach to classify five types of Multicarrier Modulation (MCM) signals. It also analyzes two subcarrier waveforms for each signal type, resulting in 10 unique MCM signals. This paper is divided into 2 parts -feature extraction from signals and classification of signals using those extracted features. To extract relevant features, Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) are applied. A Long Short-Term Memory (LSTM) network is then used to classify the MCM signals. Our results show that the combination of PCA and RFE. LSTM achieves superior performance in categorizing the MCM signals, making it a promising approach for efficient and accurate AMC. This paper is classifying MCM signals of range - 16dB to 16dB with the accuracy of 91% to 97%.

关键词： Wireless communication Accuracy Scalability signal processing algorithms Feature extraction Real-time systems Long short term memory Principal component analysis Demodulation signal to noise ratio

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A Doppler Estimation Algorithm for Radar Imaging Based on Low-Rank Embedding Theory

A Doppler Estimation Algorithm for Radar Imaging Based on Lo...

引用

International Conference on signal processing Proceedings (ICSP)

作者： Zhuojie Zhou Yueli Li Ke Liu College of Electronic Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Doppler estimation accuracy is vital in Doppler diversity-based forward-looking imaging of the monopulse radar. Although the existing Doppler estimation methods can improve the image resolution in highly squinted regions, the accuracy of Doppler frequency estimation still needs to be refined in cases of target multiplicity and low signal-to-noise ratio (SNR). In this paper, a Doppler estimation algorithm based on matrix low-rank embedding theory is proposed, which utilizes the particular structure and decomposition properties of the Hankel-Toeplitz matrix to transform the Doppler estimation problem to an equivalent matrix rank-constrained recovery problem and combines with the Akaike information criterion (AIC) to estimate the number of targets. The numerical analysis results of point-target simulation show that the proposed algorithm can precisely estimate the Doppler frequency and complex amplitude. Notably, in the case of low SNR and target multiplicity, the accuracy is much higher than that of the traditional algorithm.

关键词： Accuracy Estimation signal processing algorithms Imaging Transforms Radar imaging Frequency estimation Doppler effect Matrix decomposition signal to noise ratio

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Sharpening Minimization Induced Penalties

Sharpening Minimization Induced Penalties

引用

European signal processing Conference (EUSIPCO)

作者： Hiroki Kuroda Department of Information and Management Systems Engineering Nagaoka University of Technology

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

In order to remedy the underestimation of an advanced convex penalty defined via minimization, this paper proposes its nonconvex enhancement while preserving convexity of the associated regularized least-squares model. We first design a generalized Moreau enhanced minimization induced (GME-MI) penalty function by subtracting from the MI penalty its generalized Moreau envelope. Then, we derive an overall convexity condition for the GME-MI regularized least-squares model. Finally, under the overall convexity condition, characterizing the solution set of the GME-MI model with a carefully designed averaged nonexpansive operator, we develop a proximal splitting algorithm which is guaranteed to converge to a globally optimal solution. Numerical examples demonstrate the effectiveness of the proposed approach.

关键词： signal processing algorithms Europe signal processing Minimization Numerical models Optimization

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Footprint Based Person's Weight Prediction with Dual Feature Extraction and Multi-Head Attention Mechanism

Footprint Based Person's Weight Prediction with Dual Feature...

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

作者： Tao Zhang Xiaoxue Han Wenchao Cheng Shenglin Wang Miaoxin Xu Jiaoyang Liu School of Physics And Electronic Technology Liaoning Normal University Dalian China Department of Computer Science City University of HongKong Hongkong China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Footprints refer to traces formed by the interaction between the feet and the ground or other supporting surfaces during activities such as standing or walking, due to the weight of the body and the force of muscle contraction. This paper mainly focuses on the automatic prediction of body weight based on footwear footprints, specifically shoeprints. Firstly, a grayscale shoeprint image dataset is collected and constructed and then transformed into a pseudo-color shoeprint image dataset through pseudo-color transformation. Next, a dual feature extraction module composed of the GoogeLeNet and ResNet50 networks is employed to extract features, and a multi-head attention mechanism is adopted to weigh and fuse the input feature maps. Finally, the output features are processed through two fully connected layers to achieve body weight prediction for shoeprint images. Experimental results show that the proposed algorithm outperforms the state of art algorithms, and the accuracy rates for errors within 3 kg, 5 kg,7kg,8kg, and 10 kg are 36.22%,56.22%,77.30%, 85.41 % respectively.

关键词： Legged locomotion Attention mechanisms Accuracy signal processing algorithms signal processing Muscles Feature extraction Prediction algorithms Resource management Residual neural networks

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Evaluation and Evolution Method of Command and Control Structures in UAV Swarm Operations Based on Potential Energy

Evaluation and Evolution Method of Command and Control Struc...

引用

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

作者： Yujuan Huang Yaoqin Zhu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Based on the analysis of factors influencing the combat mission process, considering heterogeneous nodes, constraint relationships, and various influencing factors, this study proposes a command and control structure evaluation method based on the concept of potential energy in the military operations domain for unmanned swarm combat. The adaptive evolution of the command and control structure for unmanned swarms under dynamic tasks is investigated, where the potential energy reward for stage tasks and the structural adjustment cost constitute the evolution benefits. A mathematical model for the adaptive evolution of the command and control structure is constructed with the objective of maximizing the evolution sequence benefits. An algorithm based on reinforcement learning (PPO) and genetic algorithms (GA) is designed to solve the command and control structure evolution sequence under dynamic tasks. GA is used to search for command and control structures adapted to different stage tasks, while PPO is used to search for the evolution path under stage task transitions.

关键词： Command and control systems Potential energy Heuristic algorithms signal processing algorithms Clustering algorithms Process control Reinforcement learning signal processing Autonomous aerial vehicles Genetic algorithms

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A Study of Recovery Algorithm Adapted to the Pulsed Electro-acoustic Method with Optical Excitation

A Study of Recovery Algorithm Adapted to the Pulsed Electro-...

引用

Power and Electrical Engineering (ACPEE), 5th Asia Conference on

作者： Tianrun Qi Haoyu Gao Hanwen Ren Qingmin Li Yidan Ma Yiqun Ma Tao Xiao North China Electric Power University Beijing China

ISBN: (数字)9798350309638

ISBN: (纸本)9798350309645

An optical excitation-based pulsed electro-acoustic method has been suggested for high-resolution space charge measurements. However, it still requires a suitable charge recovery algorithm to complement it. By applying the electroacoustic pulse method, we were able to identify two elements that contribute to the distortion of the observed waveforms. These components are the combination of the elastic waveform caused by the thermoelastic effect, and the gradient distribution of the potential in the ring electrode. The formation mechanisms of the two aberrations are analyzed individually, and methods for restoring various aberrations are suggested. The proposed methods involve the utilization of virtual electrode distribution for potential compensation and extraction of interference components, respectively. Based on this premise, a recovery algorithm specifically designed for the pulsed electroacoustic method with optical excitation is established. The viability of the enhanced algorithm is confirmed by the outcomes of space charge recuperation in response to laser pulses. The comparison results indicate that the enhanced algorithm exhibits a much higher level of accuracy compared to earlier recovery algorithms. The results can support high temporal and spatial resolution space charge measurements under transient stresses

关键词： Electrodes Electric potential signal processing algorithms Optical distortion Interference Space charge Optical variables measurement

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An Unsupervised Learning Method for Noise Detection in Photoacoustic Gas Measurements

An Unsupervised Learning Method for Noise Detection in Photo...

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Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International Conference on

作者： Ada Fort Matteo Intravaia Marco Mugnaini Marco Bindi Enza Panzardi Valerio Vignoli Department of Information Engineering and Mathematics University of Siena Siena Italy Department of Information Engineering DINFO University of Florence Florence Florence Italy

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

In this work, the authors propose a digital noise detection technique based on an artificial intelligence signal processing method. The proposed technique is intended to be used to detect and identify acoustic interferences such as ambient noise that influence the photoacoustic signal in resonance-based photoacoustic gas sensing systems. Particularly, an unsupervised classification method based on iterative data clustering is implemented to identify and discard samples affected by noisy interferences. Leveraging the inherent characteristics of the acquired photoacoustic gas signal, the algorithm identifies noise events, including glitches and other forms of acoustic interference, from the relevant gas signal. The method employs an iterative clustering technique, where the number of clusters increases with each iteration while adjusting the clustering threshold. As iterations proceed, the threshold decreases, thereby enhancing the system ability to detect noise by excluding more examples from the noise-free class in each step. Experimental validation confirms the feasibility of the proposed algorithm, yielding promising results. This approach presents a viable solution for improving the performance of photoacoustic gas measurement systems in noisy environments, thus contributing to enhanced measurement system resolution.

关键词： Noise signal processing algorithms Clustering algorithms Acoustic measurements Acoustics Classification algorithms Sensors Noise measurement Artificial intelligence signal resolution

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Rolling Bearing Fault Detection Method Using Subtraction-Average-Based Optimizer and Enhanced Bi-LSTM

Rolling Bearing Fault Detection Method Using Subtraction-Ave...

引用

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

作者： Yuzhi Ma Yinhai Lu Yaowei Ji Meixuan Li Jiahui Liu Lei Tang School of Electronic Engineering Harbin Institute of Information Engineering Harbin China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

For rolling bearing fault diagnosis., extracting the features which can represent current signal is vital. However.,its large reconstruction error and complex signal sources bring challenges to fault diagnosis. Therefore., a new rolling bearing fault diagnosis method based on Variational Mode Decomposition (VMD) optimized by Subtraction-Average-Based Optimizer (SABO) and enhanced Bi-directional Long Short-Term Memory (BiLSTM) is proposed in this paper. Firstly., VMD is used to decompose the bearing signal into different Intrinsic Mode Functions (IMFs)., and then SABO is used to find the best IMF to represent the bearing signal. Then., BiLSTM is used to extract the timing features., and make the final decision. Finally., the feasibility of the proposed method is verified on the dataset of Western Reserve University. The results show that the method proposed in this paper can effectively classify the bearing signal. Compared with several methods., the performance of this proposed method is better than those of other methods.

关键词： Fault diagnosis Employee welfare Support vector machines Recurrent neural networks Accuracy Rolling bearings signal processing algorithms Feature extraction Timing Long short term memory

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Implementation of optimized technique for enhancing EEG signal Quality by improving signal to Noise Ratio

Implementation of optimized technique for enhancing EEG sign...

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Intelligent algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Subhra Chakraborty Raja Raman Ramanujam Upadhayay Rahul Department of Electronics and Communication Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Optimized technique for enhancing EEG signal quality by improving the signal-to-noise ratio (SNR). EEG signals are inherently noisy due to various factors, such as the presence of biological and environmental artifacts, and low amplitude of brain signals. The proposed technique involves the application of advanced signal processing algorithms, including wavelet denoising, independent component analysis (ICA), and adaptive filtering. These algorithms are applied sequentially to reduce noise and improve the SNR of EEG signals. The proposed technique was tested on real EEG data and showed a significant improvement in the quality of the EEG signals. The results demonstrate that the optimized technique can effectively enhance the SNR of EEG signals and improve their quality, which could have important implications in clinical and research applications of EEG.

关键词： Brain Filtering Noise reduction signal processing algorithms Wavelet analysis Electroencephalography Recording Reliability Noise measurement signal to noise ratio

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An Efficient Cluster-Based Asynchronous Activity Detection Algorithm for Cell-Free Massive MIMO Systems

An Efficient Cluster-Based Asynchronous Activity Detection A...

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International Conference on Wireless Communications and signal processing (WCSP)

作者： Chenhao You Ziheng Li Tong Wang Lin Gao Yufei Jiang School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In this paper, we propose an efficient cluster-based activity detection algorithm for asynchronous grant-free random access (GFRA) cell-free massive multiple-input multiple-output (CF-mMIMO) systems. To address the uncertain delay challenge in the asynchronous transmission scenario, we aim to enhance the accuracy of active user devices detection and meanwhile reducing the computational complexity of the detection process by utilizing an efficient cluster-based block coordinate descent (BCD) activity detection algorithm. This challenge is heightened in the context of ultra-reliable low-latency communications (URLLC), where precise and timely detection of active users is crucial. To enhance the efficiency of the detection process, we investigate an active set strategy to reduce the number of potential active user devices requiring identification. The accuracy of active user device detection for asynchronous GFRA in CF-mMIMO systems is significantly enhanced by the implementation of the proposed cluster-based BCD algorithm with active set. Simulation results demonstrate that the proposed algorithm achieves superior detection performance with lower computational complexity compared to the traditional algorithms.

关键词： Wireless communication Performance evaluation Accuracy Simulation signal processing algorithms Clustering algorithms Ultra reliable low latency communication signal processing Detection algorithms Computational complexity

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