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检索条件"主题词=Signal Processing Algorithms"

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Optimization of Compressive Sensing Reconstruction algorithms Based on Deep Learning

Optimization of Compressive Sensing Reconstruction Algorithm...

引用

Progress in Electromagnetic Research Symposium (PIERS)

作者： Zhe Wang Youyu Zhao Shixi Wen Junjie Feng School of Information and Engineering Dalian University Dalian China School of Physics and Electrical Engineering Liupanshui Normal Universit Liupanshui China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

The theory of compressive sensing (CS) has been widely used in the field of image signal processing, and CS can improve the reconstruction quality of images. While traditional iterative algorithms lead to resource waste due to too many iterations, a deep reconstruction network CSResNet is proposed to save the amount of computation in the paper. Firstly, network training is used to replace the sampling matrix. Secondly, the structure is optimized by adding a residual network to form a deep network to recover the image. Finally, the experiment results are shown to demonstrate the advantages of the algorithm in the paper.

关键词： Training signal processing algorithms signal processing Reconstruction algorithms Iterative methods Image reconstruction Compressed sensing

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Microphone Pair Selection for Sound Source Localization in Massive Arrays of Spatially Distributed Microphones

Microphone Pair Selection for Sound Source Localization in M...

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European signal processing Conference (EUSIPCO)

作者： Bilgesu Çakmak Thomas Dietzen Randall Ali Patrick Naylor Toon Van Waterschoot Dept. of Electrical Engineering (ESAT) STADIUS KU Leuven Leuven Belgium Dept. of Music & Media Institute of Sound Recording University of Surrey Guildford UK Dept. of Electrical and Electronic Engineering Imperial College London London UK

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

In massive distributed microphone arrays, such as in conference systems, the use of all sensors leads to unnecessary energy consumption and limited network life, as some sensors may have a limited contribution to specific estimation tasks, such as sound source localization (SSL). In this work, we propose two pre-processing methods for microphone pair selection in steered response power (SRP) based SSL using massive arrays of spatially distributed microphones. The first method is based on thresholding which results in a selection of broadside microphone pairs, that is, microphone pairs that are oriented broadside to the source direction, and only the signals from those microphones are collected for use in the SRP algorithm. The second method is based on sparseness of the cross-correlation in which the pairs which have a high sparsity factor are selected for use in localization. These methods are further improved by leveraging orientational diversity. Simulations show that we achieve a localization performance only using the selected microphone pairs that is comparable to the performance when all microphones are employed.

关键词： Location awareness Simulation signal processing algorithms Europe Estimation signal processing Microphone arrays Sensor systems Microphones Sensor arrays

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Enhancing Quantum Machine Learning algorithms for Optimized Financial Portfolio Management

Enhancing Quantum Machine Learning Algorithms for Optimized ...

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IEEE International Conference on Intelligent Techniques in Control, Optimization and signal processing (INCOS)

作者： Narinder Kumar Bhasin Sunil Kadyan Kathari Santosh Ramya HP Ravindra Changala B Kiran Bala Amity University Zonal Head Indian Institute of Banking and Finance Delhi India School of Management and Commerce Manav Rachna University Faridabad Haryana India Department of MBA CMR Institute of Technology Bengaluru India Department of Management Studies (MBA) Dayananda Sagar College of Engineering Bangalore India Department of Information Technology Guru Nanak Institutions Technical Campus Hyderabad India Department of AI & DS K.Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Advancements in quantum machine learning offer unprecedented potential to revolutionize financial portfolio optimization, maximizing returns while managing risks efficiently. This study focuses on advancing quantum machine learning algorithms for optimal financial portfolio management, presenting a novel approach implemented in Python that outperforms existing methods. The algorithm's capability to generate such substantial returns over time positions it as a groundbreaking tool for portfolio optimization in the dynamic landscape of financial markets. In the pursuit of enhancing quantum machine learning algorithms, this research focuses on the development and optimization of the QSVM algorithm. Leveraging Python for implementation, the study considers critical factors such as quantum circuit optimization, noise mitigation, and the integration of classical and quantum components to achieve superior results. The achieved portfolio performance over time not only underscores the algorithm's efficacy but also signifies a quantum advantage in financial decision-making. The implementation in Python ensures accessibility and applicability, facilitating the integration of this advanced quantum algorithm into existing financial frameworks. This research contributes to the evolving field of quantum finance, showcasing the potential of quantum machine learning in optimizing financial portfolios. The findings not only validate the superior performance of the proposed QSVM but also highlight the broader implications for the future of financial decision support systems, where quantum algorithms could play a transformative role in enhancing portfolio management strategies. The proposed Quantum Support Vector Machine (QSVM) demonstrates unparalleled success, with a remarkable Portfolio performance over time of 89.65%. This result significantly surpasses existing quantum algorithms, including Quantum Principal Component Analysis (QPCA), Quantum Boltzmann Machines (QBM), and Quantum K

关键词： Support vector machines Machine learning algorithms Quantum algorithm Heuristic algorithms signal processing algorithms Machine learning Quantum circuit

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Partial Discharge Feature Extraction and Diagnosis of Industrial Acoustic Imaging Equipment Based on Time-Frequency Analysis

Partial Discharge Feature Extraction and Diagnosis of Indust...

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

作者： Zuyang Cao Kaiqiang Zhang Junkang Bao Hangzhou Crysound Electronics Co. Ltd. Hangzhou Zhejiang China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

Based on the project framework of “High end Testing Equipment and Systems - Key Technology Research and System Development of Industrial Acoustic Imager”, this study proposes a time-frequency analysis based method for extracting and diagnosing partial discharge characteristics of industrial acoustic imager. The method achieves efficient extraction and accurate diagnosis of partial discharge features by in-depth analysis of the time-frequency characteristics of acoustic signals and combining advanced signal processing techniques. In the detection accuracy experiment, the detection accuracy of normal signals reached 92 0 /0, and the recall rate of partial discharge signals was 95%. From the experimental data conclusion, it can be seen that the time-frequency analysis method performs well in terms of detection accuracy and is suitable for partial discharge detection and diagnosis in complex industrial environments.

关键词： Partial discharges Time-frequency analysis Accuracy Ultrasonic imaging signal processing algorithms Feature extraction Acoustics Robustness Real-time systems Testing

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Residual Domain Super-Resolution Generative Adversarial Networks

Residual Domain Super-Resolution Generative Adversarial Netw...

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IEEE Workshop on Multimedia signal processing

作者： Nelson Francisco Julien Le Tanou MediaKind Southampton UK MediaKind Rennes France

ISBN: (数字)9798350387254

ISBN: (纸本)9798350387261

Generative Adversarial Networks (GANs) deliver significant perceptual quality improvements over super-resolution methods using simple reconstruction losses. By combining adversarial training with perceptual loss functions, they avoid the over-smoothing resulting from the convergence towards the average of all plausible solutions. However, the likelihood of mode collapse if generator and discriminator learning rates are not carefully tuned or the training data does not represent the entire solution space, often introduces unpleasant artifacts that can compromise the overall quality of the upscaled images. We propose a new GAN architecture trained on the statistical distribution of a residue resulting from subtracting a spatial interpolation of the low-resolution (LR) image from the high-resolution (HR) ground-truth, instead of directly learning the pixel distribution of the HR image conditioned to the LR input. Since the statistical distribution of this residue is easier to model, the probability of mode collapse and consequent introduction of artifacts are mitigated, resulting in an algorithm competitive with much more computationally complex diffusion or transformer models.

关键词： Visualization Computational modeling Superresolution Statistical distributions signal processing algorithms Generative adversarial networks Transformers Data models Spatial resolution signal resolution

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A Deep Residual Network-Based Model for Image Steganography Analysis

A Deep Residual Network-Based Model for Image Steganography ...

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

作者： Fei He Renchao Qin Keqiang Tan Lan Zhang Jianlin Long Jialin Duan Suonan Li School of Computer Science and Technology Southwest University of Science and Technology Mianyang Sichuan China Sichuan ShuTian Information Technology Co. Ltd. Yaan Sichuan China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Steganalysis is the process of detecting and analyzing steganography, which is widely utilized in fields such as network security, copyright protection, military, and intelligence. To address the challenges of existing steganalysis models, particularly their difficulty in capturing subtle steganographic information and the low detection rates for adaptive steganography algorithms, this paper proposes an image steganalysis model based on deep residual networks. In the preprocessing stage, the model captures residual features by combining fixed high-pass filters with learnable filters. During feature extraction, feature enhancement is performed between two sets of depthwise separable convolutions to improve the signal-to-noise ratio of the steganographic image. Four improved residual blocks are employed as the backbone network, with the inclusion of the ECA attention mechanism to further enhance the accuracy of steganalysis. The model was tested on the BOSSBase-v1.01 and BOWS2 datasets. Experimental results demonstrate that the model exhibits strong detection performance across different embedding rates of various steganography algorithms, particularly in the detection of the WOW algorithm. For the merged dataset of BOSSBase-v1.01 and BOWS2 with an embedding rate of 0.4bpp, the detection rate reached 95.0%, verifying the effectiveness of the proposed model.

关键词： Adaptation models Steganography Analytical models Filters Attention mechanisms Accuracy Convolution signal processing algorithms Feature extraction Residual neural networks

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Optimized Liver Tumor Segmentation: Integrating U-Net with Graph Cut algorithms

Optimized Liver Tumor Segmentation: Integrating U-Net with G...

引用

International Conference on Communications and signal processing

作者： K. Kishore Anthuvan Sahayaraj Aniket Laad Abhinav Mishra S Prithika Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

This paper presents an innovative approach to liver tumor segmentation, combining the strengths of U-Net architecture with Graph Cut refinement. This paper focuses on enhancing the precision of segmentation in computed tomography (CT) scans, crucial for accurate diagnosis and treatment planning in liver cancer cases. Our methodology utilized the U-Net model for initial segmentation, owing to its efficiency in handling complex biomedical images. To further refine these results, we integrated the Graph Cut algorithm, which significantly improved the accuracy of tumor boundary delineation. We validated our approach using the LiTS17 dataset, comprising diverse CT scans with varying tumor characteristics. Our results demonstrated a Dice Coefficient of 96.4 and an Accuracy of 99.4, outperforming several existing segmentation methods. This fusion of deep learning and optimization algorithms not only provided superior segmentation results but also highlighted the potential of combining different computational techniques for enhanced performance in medical image analysis.

关键词： Deep learning Image segmentation Image analysis Computed tomography Computational modeling signal processing algorithms Liver

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RD Imaging Algorithm Based on the Waveform with Costas-FSK and 4-PSK for Synthetic Aperture Radar

RD Imaging Algorithm Based on the Waveform with Costas-FSK a...

引用

Data Science and Computer Application (ICDSCA), IEEE International Conference on

作者： Ni Yang School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

Synthetic aperture radar (SAR) is an important representative of modern radar technology, which are widely used in the field of target detection, location and imaging. In order to prevent the signal transmitted by active radar from being detected and intercepted by the enemy, the radar waveform needs to have the characteristics of low interception. In this paper, the imaging performance of a complex encoded waveform in range doppler (RD) imaging algorithm is studied. By adding frequency symbol keying (FSK) based on Costas encoding and phase symbol keying (PSK) to the single frequency signal, the waveform has the characteristics of low interception with frequency hopping and high randomness. At the same time, due to the characteristic that RD algorithm's azimuth matching filter is not affected by signal waveform, it still maintains good target detection and imaging performance based on this waveform.

关键词： signal processing algorithms Imaging Radar detection Symbols Frequency shift keying Object detection Radar imaging Encoding Doppler radar Synthetic aperture radar

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Joint Optimization of Cooperative Caching and Recommendation in Wireless Edge Networks

Joint Optimization of Cooperative Caching and Recommendation...

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

作者： Yutian Ye Quan Yu Wuhan University of Technology

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Edge caching is an efficient solution for alleviating backhaul congestion by pre-caching popular contents at edge nodes. Due to the heterogeneity of user requests, the benefits of edge caching are diminished. The recommendation system is applied to the edge caching systems since it can reshape the user request behavior. To maximize the quality of user experience, this paper jointly designs the strategies of edge cooperative caching and recommendation. The recommendation-driven user request behavior is first modelled as a Markov chain. The joint optimization framework of edge cooperative caching and recommendation is then established. Since the formulated joint optimization problem is a non-convex non-linear mixed integer programming problem, a heuristic algorithm is proposed based on the idea of alternating optimization, which decomposes the original problem into two sub-problems and solves the two sub-problems iteratively. The simulation results show that the proposed algorithm outperforms the baseline policies.

关键词： Wireless communication Cooperative caching Simulation signal processing algorithms signal processing User experience Polynomials Delays Optimization Recommender systems

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Machine Learning based approach for Myocardial Infarction Prediction using real time ECG signals

Machine Learning based approach for Myocardial Infarction Pr...

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Intelligent Systems and Machine Learning Conference (ISML)

作者： Shilpa Sondkar Sharv Apte Aneesh Deshmukh Paritosh Chavan Dept. Of Instrumentation and Control Engineering VIT Pune India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

This project focuses on utilizing machine learning techniques to identify and detect abnormal cardiac conditions through the analysis of Electrocardiogram (ECG) signals. With cardiovascular diseases being a significant global health concern, the need for precise diagnostic tools is paramount. The primary objectives of this study involve identifying specific cardiac conditions, including Myocardial Infarction, Arrhythmias and discerning normal cardiac conditions. Real time ECG signals were taken from Symbiosis hospital and research center pvt. Limited hospital in Satara. The methodology encompasses the acquisition of diverse ECG datasets, preprocessing to enhance signal quality, and feature extraction to characterize different cardiac conditions effectively. Support vector machines (SVMs), logistic regression, kNNs, and XGBoost these machine learning techniques are employed to develop robust classification models. The anticipated outcomes of this research aim to provide accurate models capable of early detection, enabling prompt interventions and, ultimately, enhancing patient outcomes within the domain of cardiovascular health.

关键词： Support vector machines Machine learning algorithms Accuracy signal processing algorithms Machine learning Cardiac arrest Electrocardiography Myocardium Prediction algorithms Real-time systems

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