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检索条件"主题词=Signal Processing Algorithms"

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Design of High-Performance Millimeter-Wave Radar Digital processing Accelerator

Design of High-Performance Millimeter-Wave Radar Digital Pro...

引用

International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Lingbo Ai Zhentao Li Yang Yu Menglin Zeng School of Physical and Electronic Sciences Changsha University of Science and Technology Changsha China Hunan Great-Leo Microelectronics CO.LTD Changsha China

ISBN: (数字)9798350389968

ISBN: (纸本)9798350389975

This research topic aims at designing a high-performance millimeter-wave radar digital processing accelerator. According to the current millimeter-wave radar point cloud generation algorithm, the internal computing components of the accelerator mainly include Fast Fourier Transform(FFT), Constant False Alarm Rate(CFAR), windowing, and other sub-modules. To minimize the interference of the central processor with this accelerator as much as possible, this research designs an operational register that is highly versatile, reusable, and circular. After parameter programming by the user, the accelerator can independently and continuously complete the operation of the point cloud generation algorithm. The experiments show that the accelerator can independently calculate complex sample data of 256*32 size without external interference. After the operations of 2DFFT and ABS, the relative error remains below 10% compared to MATLAB. The computational results of 2D-CFAR are entirely consistent with the simulated results from MATLAB.

关键词： Point cloud compression Microwave technology signal processing algorithms Interference Millimeter wave radar Programming Timing

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Low-light Image Enhancement Algorithm Based on Transformer

Low-light Image Enhancement Algorithm Based on Transformer

引用

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

作者： Zeyu Wang Yan Qi Institute of Information Science and Technology Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

Low light image enhancement is an important task in computer vision. The Low-light images can adversely affect more advanced computer vision tasks such as target tracking, image segmentation, and target recognition due to the presence of large amounts of noise and low contrast. Therefore, research on low-light image enhancement is crucial. To address these issues, this paper delves into the comparison between traditional and non-traditional low-light image enhancement algorithms and has achieved the following results:We abandon the traditional approach based on a priori knowledge of images and instead redesign a new network structure for low-light image enhancement with the existing theoretical foundation for Transformer. The network performs information extraction in the channel dimension of the image through self-attention mechanism and then extracts the shallow feature information of the image through Shallow layer. After fusing these two types of information, more advanced information is extracted to guide the final image enhancement. Experimental results show that the redesigned network structure not only achieves better results but also has improved execution efficiency.

关键词： Knowledge engineering Computer vision Target tracking Target recognition Noise signal processing algorithms Feature extraction Transformers Data mining Image enhancement

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A Deep Optimization Image Deblurring Model Based on Internal and External Information

A Deep Optimization Image Deblurring Model Based on Internal...

引用

Communication, Image and signal processing (CCISP), International Conference on

作者： Yongqun Tan Lingli Zhang School of Mathematics and Statistics Chongqing Jiaotong University Chongqing China Chongqing Key Laboratory of Statistical Optimization and Complex Data Chongqing University of Arts and Sciences Chongqing China Chongqing Key Laboratory of Group & Graph Theories and Applications Chongqing University of Arts and Sciences Chongqing China

ISBN: (数字)9798350356656

ISBN: (纸本)9798350356663

In recent decades, there has been a notable focus on addressing image deblurring problem. Different deblurring algorithms integrate prior information into the image deblurring model, and the effectiveness of the prior information directly determines the performance of the deblurring. This paper presents a deep optimization image deblurring model based on internal and external information, which is joint with total variation, non-local self-similarity and convolutional neural network. The corresponding algorithm of the presented model utilizes half quadratic splitting method to decouple the fidelity and regularization terms, which can be solved through the sub-problems, respectively. Numerical experiments show that the presented algorithm can outperform the existing algorithms both in preserving the edges and assessment indicators (PSNR, RMSE, and SSIM).

关键词： TV Image edge detection Noise signal processing algorithms Color Gray-scale Image restoration Numerical models Convolutional neural networks Optimization

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Judging the Psychological Impact of News Articles on Rehab Patients: A Deep Learning Approach

Judging the Psychological Impact of News Articles on Rehab P...

引用

Communication, Computing and signal processing (IICCCS), IEEE International Conference on

作者： Amit Khan Dipankar Majumdar Bikromadittya Mondal Dept. of Information Technology RCC Institute of Information Technology Kolkata India Dept. of Computer Sc & Engineering RCC Institute of Information Technology Kolkata India Dept. of Computer Sc & Engineering B.P. Poddar Institute of Mgmt & Tech. Kolkata India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

Sentiment detection and analysis from review comments of products, blogs and other social media communication is a common practice. Lot of research work have been proposed and established in the domain. While the above may prove to be effective for sales and marketing challenges but the same effort for the sentiment analysis of news articles is scarce. But surprisingly, this can definitely establish as a potentially efficient instrument for medical domain judging the suitability of a news article for individuals with disturbed health condition like depression, heart diseases or mental retardation having risk of committing suicides. To address the same, this article presents a novel approach harnessing the potential of Machine Learning (ML) and Deep Learning (DL) to detect the sentiment of a news article so as to measure its impact on the individual readers. The proposed work uses various supervised ML algorithms such as SVM, Naïve Bayes’, Logistic Regression, Random Forest and Ensemble Method for assessing the inherent sentiment of news articles. Additionally, DL based Transformer model specifically BERT has also been employed to enhance the performance. Obtained results with various performance metrics clearly depict our model’s excellent performance.

关键词： Deep learning Support vector machines Sentiment analysis Social networking (online) Reviews Computational modeling signal processing algorithms signal processing Transformers Telecommunication computing

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Pareto-Optimal Waveform Design for Multi-User and Multi-Target MIMO-ISAC Systems

Pareto-Optimal Waveform Design for Multi-User and Multi-Targ...

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Acoustics, Speech, and signal processing Workshops (ICASSPW), IEEE International Conference on

作者： Peng Wang Yashuai Cao Wanli Ni Dongsheng Han School of Electrical and Electronic Engineering North China Electric Power University Beijing China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (数字)9798350374513

ISBN: (纸本)9798350374520

In this paper, we propose a multi-objective optimization-based waveform design for multi-user and multi-target MIMO-ISAC system. Specifically, by striking a trade-off between communication and sensing (C&S) objectives, the waveform with different performance preferences can be flexibly design. Due to the existence of multi-user interference, we consider the constructive interference (CI) mechanism during precoding. To tackle the conflict constraints in ISAC systems, we propose a weighted Chebyshev-based transformation scheme to re-frame the C&S trade-off problem as a Pareto-optimal problem, then develop a successive convex approximation algorithm to solve it. Simulation results reveal the trade-off relation between C&S performances, which provides insights for the flexible waveform design under difference C&S performance preferences in multi-user and multi-target ISAC systems.

关键词： Simulation Precoding Conferences signal processing algorithms Interference Chebyshev approximation signal processing

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Impulse Radio UWB Radar-based Respiration Rate Estimation Method Under Clutter Environments

Impulse Radio UWB Radar-based Respiration Rate Estimation Me...

引用

International Conference on signal processing and Information Security (ICSPIS)

作者： Asit Kumar Panda M. Sabarimalai Manikandan Department of Electrical Engineering Indian Institute of Technology Palakkad Palakkad Kerala INDIA

ISBN: (数字)9798350368673

ISBN: (纸本)9798350368680

This paper presents an effective real-time IR-UWB radar-based respiration rate (RR) estimation method by exploring a simple clutter removal technique. The proposed method consists of radar matrix generation, clutter removal, bin selection, band-pass filter, Fourier magnitude computation, dominant spectral peak determination, and RR computation. The performance of the method is evaluated using the radar signals of 12 subjects collected at different distances (0.3, 0.5, 1, and 1.5 meters) from the radar locations and with their ground-truth RR values. The RR estimation method with clutter removal demonstrates accuracy improvement with reductions in mean absolute error (MAE) by 39.33%, standard deviation (SD) by 39.77%, root mean square error (RMSE) by 39.54%, and Bland-Altman Ratio (BAR) by 44.07%. Evaluation results further demonstrate that the proposed method improves the RR estimation accuracy with 46.02% for the estimation error (EE) range of 0-2 breaths per minute (BrPM). The method reduces estimation error for 65.22% of the segments with the clutter removal algorithm (from 670 to 233 segments for the EE range greater than 10 BrPM).

关键词： Estimation error Accuracy Radar clutter signal processing algorithms signal processing Real-time systems Clutter Root mean square Standards Monitoring

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A Method for Calibration of RIS Array Element Errors Based on Dynamic Reconstruction of Time-Domain Electromagnetic Response

A Method for Calibration of RIS Array Element Errors Based o...

引用

International Conference on Communication Technology (ICCT)

作者： Wuding Liu Ming Yi Keming Ma Lilong Hou Information Engineering University Zhengzhou China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

The performance of Reconfigurable Intelligent Surfaces (RIS) as an innovative technology for enhanced communication is constrained by errors in their array elements. The communication system assisted by RIS is characterized by its large array number and the use of a single receiving channel. To achieve the multichannel transmission required for RIS calibration, existing methods require the use of auxiliary calibration sources or receivers equivalent in number to the RIS elements. This makes conventional methods for calibrating positional inaccuracies of array elements unsuitable. In addition, the effect of reflection coefficient inaccuracies on performance is equally significant. This paper proposes a method for jointly calibrating the errors of RIS array elements. The method is based on dynamically reconstructing the electromagnetic response in the time domain to address the problem. This method uses the programmability and flexibility of RIS to virtualize a physical single-channel receiving system into a logical multichannel signal processing system. At the receiver end, the signal amplitude and phase information equivalent to that of a conventional multi-channel array are recovered. This facilitates the estimation of fixed errors in each element of the RIS. Simulation outcomes validate the enhanced performance of our proposed algorithm.

关键词： Phased arrays Array signal processing System performance signal processing algorithms Receivers Reconfigurable intelligent surfaces Encoding Calibration Time-domain analysis Electromagnetics

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On the Performance Bounds of Steganographic Polar Codes

On the Performance Bounds of Steganographic Polar Codes

引用

International Conference on Wireless Communications and signal processing (WCSP)

作者： Haocheng Fu Yun Cao Xiaolei He Chinese Academy of Sciences Institute of Information Engineering Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Steganographic coding is a fundamental technique in adaptive steganography, and the introduction of polar codes has significantly enhanced its security. However, the performance of steganographic polar codes has primarily been evaluated through experiments, and their construction has remained some-what heuristic. This paper theoretically presents an optimal model for steganographic polar codes based on a analysis of their performance bounds, by examining the relationship between polar code decoding and steganographic coding based on polar codes. Although this model can only be constructed approximately, numerical simulations indicate that the proposed optimal construction offers improved security performance compared to classic codes.

关键词： Wireless communication Analytical models Steganography signal processing algorithms signal processing Encoding Decoding Numerical models Security Polar codes

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Machine Learning-based Spectrum Sensing Techniques for Optimized Spectrum Utilization in Cognitive Radio Networks

Machine Learning-based Spectrum Sensing Techniques for Optim...

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Advancement in Technology (ICONAT), International Conference for

作者： Pavan Chaudhary Rajesh Gupta H Malathi Maharishi University of Information Technology Uttar Pradesh India Department of Management Sanskriti University Mathura Uttar Pradesh India Department of Biotechnology and Genetics School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

the usage of cognitive radios has enabled dynamic spectrum to get the right of entry to spectrum-congested regions. It has revolutionized how spectrum is allotted with the aid of turning the conventional static spectrum allocation into a dynamic and opportunistic method. Gadget studying-based totally spectrum sensing techniques have been advanced to permit optimized spectrum usage with better accuracy in cognitive radio networks. Those strategies use machine learning algorithms to learn the behaviors of spectrum assets and adaptively replace sensing strategies by intelligently deciding on the maximum suitable spectrum. This manner permits faster and more correct sensing in comparison to traditional spectrum sensing strategies. Furthermore, various fashions were proposed for specific spectrum sensing functionalities together with signal category, spectrum prediction, and function extraction. Those fashions are employed that allow you to maximize the spectrum assets utilization in cognitive radio networks. Furthermore, diverse processes have been proposed with the purpose of improving the overall performance of spectrum sensing techniques. Those consist of the usage of deep mastering fashions, exploiting heterogeneous facts sources, and using ensemble getting-to-know strategies. These approaches remember the complexity of the environment and permit extra correct and reliable choices. To conclude, device learning-based spectrum sensing techniques preserve a fantastic capacity for optimized spectrum utilization in cognitive radio networks. Consequently, they constitute a vital step forward closer to the improvement of green and reliable verbal exchange structures.

关键词： Performance evaluation Accuracy Noise signal processing algorithms Turning Sensors Complexity theory Cognitive radio Reliability Resource management

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Waveform Design for Multi-Carrier Multiuser MIMO Joint Communications and Sensing

Waveform Design for Multi-Carrier Multiuser MIMO Joint Commu...

引用

IEEE Workshop on signal processing Advances in Wireless Communications (SPAWC)

作者： Mohammad Hatami Nhan Nguyen Markku Juntti Centre for Wireless Communications University of Oulu Finland

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

We consider a joint communications and sensing (JCAS) system that operates as a monostatic MIMO radar while simultaneously providing downlink communications service to multiple single-antenna users. We address the joint optimization of transmit waveform design and subcarrier allocation. Specifically, we focus on maximizing the minimum radar signal-to-interference-plus-noise ratio (SINR) while ensuring communications SINR and power constraints. To tackle this challenging problem, we decompose it into two subproblems by using alternating optimization. Subsequently, we employ a successive convex approximation (SCA) algorithm to find an effective solution for the resulting nonconvex waveform design problem. The numerical results indicate that the proposed method converges and show the tradeoff between radar and communications performances.

关键词： Wireless communication MIMO radar signal processing algorithms Radar Interference signal processing Sensors Resource management Optimization signal to noise ratio

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