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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Guotao Chen Zhanjiang University of Science and Technology Zhanjiang China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

The specific content of this design revolves around speech signal processing utilizing MATLAB, with the Discrete Fourier Transform (DFT) as its cornerstone algorithm. DFT facilitates the discretization of signals in both the digital and frequency domains, enabling the manipulation of discrete signals by general-purpose computers. Subsequently, noise signals are introduced, and appropriate filters are employed to suppress these noises, translating digital signal processing theories into practical applications. This project bridges the gap between theoretical knowledge and real-world usage, demonstrating the efficacy of DFT in enhancing signal quality.

关键词： Computers Frequency-domain analysis Discrete Fourier transforms Noise signal processing algorithms Low-pass filters Speech processing MATLAB signal design Information systems

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A Deep Learning-Based Algorithm for Multi-Feature processing and signal Detection

A Deep Learning-Based Algorithm for Multi-Feature Processing...

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International Conference on Computer and Communications (ICCC)

作者： Haoran Ji Lei Wang Shuhao Zhang Chi Yang Wenjie Zhou Cong Peng College of Electrical Information Engineering Hunan University Changsha China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

In the field of signal detection, traditional signal processing algorithms encounter challenges in effectively integrating multiple features for joint detection. To address this issue, we propose a multi-feature fusion detection algorithm. This algorithm processes various signal features, including matched filtering results, Short-Time Fourier Transform (STFT) features, and wavelet transform features. For the matched filtering results, we employ a Long Short-Term Memory (LSTM) network, while a convolutional neural network (CNN) is utilized for processing time-frequency domain features. After processing multiple features and performing a weighted summation of the results, the algorithm yields the final detection outcome. Simulation results indicate that the algorithm demonstrates a high level of detection accuracy.

关键词： Wavelet transforms Time-frequency analysis Filtering Simulation signal processing algorithms Feature extraction Convolutional neural networks Detection algorithms signal detection Long short term memory

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An Explanation of Deep MIMO Detection From a Perspective of Homotopy Optimization

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2023年 4卷 108-116页

作者： Shao, Mingjie Ma, Wing-Kin Liu, Junbin Chinese Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

Since the work of detection network (DetNet) by Samuel, Diskin and Wiesel in 2017, deep unfolding for MIMO detection has become a popular topic. We have witnessed significant growth of this topic, wherein various forms of deep unfolding were attempted in the empirical way. DetNet takes insight from the proximal gradient method in terms of the use of the network structure. In this paper, we endeavor to give an explanation of DetNet-in a fundamental way-by drawing connection to a homotopy optimization approach. The intuitive idea of homotopy optimization is to gradually change the optimization landscape, from an easy convex problem to the difficult MIMO detection problem, such that we may follow the solution path to find the optimal MIMO detection solution. We illustrate that DetNet can be interpreted as a homotopy method realized by the proximal gradient method. We also illustrate how this interpretation can be extended to the Frank-Wolfe and ADMM variants of realizing the homotopy optimization approach, which result in new DetNet structures. Numerical results are provided to give insights into how these homotopy-inspired DetNets and their respective non-deep homotopy methods perform.

关键词： MIMO communication Optimization signal processing algorithms signal processing Detectors Deep learning Convergence Deep MIMO detection deep unfolding homotopy optimization proximal gradient Frank-Wolfe ADMM

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A Proposed Quantum Framework for Low-Complexity Quantum Simulation and Spectrum Estimation of Hankel-Patterned Systems

IEEE TRANSACTIONS ON QUANTUM ENGINEERING

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IEEE TRANSACTIONS ON QUANTUM ENGINEERING 2023年第1期4卷 1页

作者： Laskar, Mostafizur Rahaman Dutta, Amit Kumar Indian Inst Technol Kharagpur GS Sanyal Sch Telecommun Kharagpur 721302 India

The structured matrix completion problem (SMCP) is ubiquitous in several signal processing applications. In this article, we consider a fixed pattern, namely, the Hankel-structure for the SMCP under quantum formalism. By exploiting its structure, a lower-gate-complexity quantum circuit realization of a Hankel system is demonstrated. Further, we propose a quantum simulation algorithm for the Hankel-structured Hamiltonian with an advantage in quantum gate-operation complexity in comparison with the standard quantum Hamiltonian simulation technique. We show its application in eigenvalue spectrum estimation for signal processing applications. An error bound associated with this proposed quantum evolution is proposed with the consideration of spectrum estimation and measurement uncertainty. Numerical results are reported adopting random matrix theory in its fold to evaluate the efficacy of the proposed architecture and algorithm for large-dimensional systems, including an example application in delay estimation for ranging operations in a wireless communication system.

关键词： Logic gates Sparse matrices Matrix decomposition Symmetric matrices signal processing Computer architecture signal processing algorithms Hamiltonian simulation Hankel-structured system quantum circuits and systems quantum gates and operators quantum signal processing (QSP)

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On Efficient signal processing Solutions for Multichannel Sensor Array Applications

On Efficient Signal Processing Solutions for Multichannel Se...

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Innovative Engineering Sciences and Technological Research (ICIESTR), International Conference on

作者： Umar Hamid Kashif Waqas Shahid Ali Muhammad Irfan COMSATS University Islamabad Islamabad Pakistan FAST National University Islamabad Pakistan Pakistan Navy Engineering College Karachi Pakistan Northwestern Polytechnical University Xian China

ISBN: (数字)9798350348637

ISBN: (纸本)9798350348644

This paper addresses the efficient implementation of signal processing algorithms tailored for multichannel sensor array systems, with a particular focus on an underwater acoustic surveillance and detection application employing both linear and circular sensor arrays. Real-time processing of data from these arrays necessitates an optimized implementation of signal processing onto digital hardware. The paper delineates the processing requirements of signal processing algorithms suitable for sensor array systems, providing mathematical formulations to compute their processing load. By evaluating linear and circular sensor array configurations, it quantifies the processing load of key algorithms such as beamforming and spectral analysis, aiming to guide the selection of an appropriate processing architecture leveraging Commercial Off-The-Shelf (COTS) processors. The study proposes an implementation approach emphasizing the average time cost of signal processing algorithms using contemporary COTS processors. Results demonstrate that the latest Intel-based multicore processors exhibit comparable efficiency to equivalent Digital signal processing (DSP) processors in effectively implementing signal processing algorithms, thereby facilitating real-time system operation.

关键词： Program processors Costs Array signal processing Multicore processing signal processing algorithms Estimation Digital signal processing Computer architecture Real-time systems Sensor arrays

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Ultrasonic testing signal image processing based on improved set empirical mode decomposition

Ultrasonic testing signal image processing based on improved...

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International Conference on Information and Computer Technologies (ICICT)

作者： Siqi Zhao Yuying Peng Department of Software and Big Data Technology Dalian Neusoft University of Information Dalian City

ISBN: (数字)9798350385625

ISBN: (纸本)9798350385632

In view of the fact that the ensemble empirical modal decomposition (EEMD) method can avoid the modal aliasing phenomenon of the EMD method, it is still lacking in noise removal and retention of effective feature signals, so an ultrasonic nondestructive testing signal processing method combining EEMD and wavelet packets is proposed. In this method, the original signal is first processed by EEMD to ensure that the modal aliasing phenomenon is eliminated. However, this will cause the loss of some characteristic signals, and the lost signals will be retrieved through wavelet packet processing to improve the signal-to-noise ratio. In the process of decomposition signal reconstruction, the correlation coefficient method is used for feature selection, and the effective defect signal with high correlation coefficient is selected for reconstruction, which can not only effectively suppress modal aliasing, but also retain more feature signals. Through the experimental verification of the actual signal, the proposed improved method obtains a better signal-to-noise ratio, a lower root mean square error and a higher correlation coefficient, which is more suitable for processing ultrasonic nondestructive testing signals.

关键词： Correlation coefficient Ultrasonic variables measurement Nondestructive testing signal processing algorithms Wavelet analysis Wavelet packets Acoustics

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ECG signal processing for Arrhythmias Monitoring using Artificial Intelligence Techniques

ECG Signal Processing for Arrhythmias Monitoring using Artif...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),Conference on

作者： Mohammed Amine Bouqentar Mohamed Touil Driss Lamrani Mohamed Amine Mahjoubi Bouchaib Cherradi Abdelhadi Raihani EEIS Laboratory ENSET of Mohammedia Hassan II University of Casablanca Mohammedia Morocco STIE Team CRMEF Casablanca-Settat EEIS Laboratory ENSET of Mohammedia Hassan II University of Casablanca Mohammedia Morocco

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

Rhythmology is one of the most important and sensitive fields in the cardiovascular area, this last deals mainly with the heart rhythm troubles or arrhythmias. The most difficult challenge lies on the location of those troubles in time series, sometimes the cardiologist gives Holter ECG to the patient for 24 or 48 hours depending on each case. After the recording completion time the patient comes again to the hospital in order to get the analysis of his cardiac rhythm. When there is a lot of patients, the delay of results interpretation increases significantly. In the present study, a deep learning model was developed to classify the ECG recordings and predict the cardiovascular abnormalities with two aims. Firstly, to reduce the time of analysis and avoid more delays afterwards. Secondly, to make ECG analysis more accurate. for that purpose, Python programming language was used to develop and simulate our algorithm. The training and validation data was downloaded from physionet ATM database.

关键词： Training Arrhythmia Time series analysis signal processing algorithms Electrocardiography Predictive models Rhythm

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Exploring Predictive Maintenance and signal processing Techniques for Automotive Health Monitoring

Exploring Predictive Maintenance and Signal Processing Techn...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： Amruta Amune Sagar Shahari Soham Kasurde Soham Nimale Soham Surdas Shubham Tayde Department of Multidisciplinary Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Predictive maintenance is one of the most crucial technologies in today's world, having applications in various domains like automotive, aerospace, manufacturing, and so forth. It is a data-oriented approach that helps predict if any machine or associated component with the machine needs maintenance or not. It also helps predict the remaining lifespan of the same. Various machine learning-based algorithms and models are being developed in this domain. S ome of them include Deep-learning-based approaches like Autoencoders, ANN s, CNN s, and so forth, and signal processing models using factors like Mean Squared Error for superimposition of primary signals with appropriate opposite phase signals. This article presents a systematic study of various research works done in the fields specific to the Automotive sector. This study focuses on the problems solved by the authors through their proposed solutions, some of the results and insights, and a brief description of their proposed solution. Approximately 30 research papers were included selectively in this study. This study concludes by suggesting a future direction in the predictive maintenance field based on periodic monitoring, in contrast with the trend of continuous monitoring for early diagnosis.

关键词： Surveys Systematics signal processing algorithms signal processing Prediction algorithms Market research Monitoring

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Low Probability of Interception Radar signals Detection, Comparison of a YOLOv8 Model and a Conventional signal processing Method

Low Probability of Interception Radar Signals Detection, Com...

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IEEE International Conference on Radar

作者： Mazouz Reihan Picheral José Marcos Sylvie Taylor Abigael Bosse Jonathan L2S CentraleSupelec Université Paris-Saclay Gif sur Yvette France DEMR ONERA Universié Paris-Saclay Palaiseau France

ISBN: (数字)9798350362381

ISBN: (纸本)9798350362398

Radar interception plays a critical role in electronic warfare by capturing and analyzing enemy radar emissions. Low Probability of Intercept (LPI) signals pose a significant challenge to conventional detection methods due to their design to minimize detectability. This paper explores the benefit of the YOLOv8 algorithm, a state-of-the-art deep learning model, for detecting LPI radar signals in time-frequency images. A comparative analysis to assess detection performance is conducted with a common sense solution based on energy detector which is the Generalized Likelihood Ratio Test (GLRT) when the signal is unknown. Results demonstrate that YOLOv8 achieves detection with a notable improvement in Peak signal-to-Noise Ratio (PSNR) for every waveform considered.

关键词： Time-frequency analysis Adaptation models PSNR Noise Radar detection signal processing algorithms Radar Detectors Radar imaging Radar signal processing

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Design of Radar signal processing Extended Range Based on Track Algorithm

Design of Radar Signal Processing Extended Range Based on Tr...

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IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Wei Wang Zhen Huang Yitong Shi China Satellite Maritime Tracking and Control Department Jiangyin Jiangsu China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

For ordinary pulse radars with determined output power, their target reflection signal is weak and their operating range is often limited. The coherent radar system accumulates multiple echoes of the target through signal processing, improving the signal-to-noise ratio of the target and thereby increasing the system's operating range. However, in the process of moving object detection, the constant changes in the target's spatial motion trajectory and attitude often lead to changes in Doppler frequency and diffusion of accumulated energy, resulting in low signal-to-noise ratio or signal-to-noise ratio of the target signal. The article introduces a design of a switchable radar extended range signal processing program based on trajectory algorithm, which optimizes the signal tracking process under dynamic target conditions through trajectory algorithm and long-term coherent accumulation, thereby improving the radar operating range.

关键词： Target tracking Heuristic algorithms signal processing algorithms Radar detection Process control Radar tracking Radar signal processing Trajectory Doppler radar signal to noise ratio

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