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检索条件"主题词=Signal Processing Algorithms"

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Automatic Algorithm Design Assisted by LLMs for Solving Vehicle Routing Problems

Automatic Algorithm Design Assisted by LLMs for Solving Vehi...

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International Conference on signal processing Proceedings (ICSP)

作者： Liang Ma Xingxing Hao Ruoxi Yang Zhilan Hai School of Information Science and Technology Northwest University Xi’an China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Currently, automatic algorithm design for solving combinatorial optimization problems (COPs) is a popular research area, since manual algorithm design typically demands substantial domain-specific knowledge and considerable time cost. In this paper, we propose an automatic algorithm design paradigm, AutoDH, based on deep reinforcement learning (DRL) that takes advantages of both large language models (LLMs) and traditional heuristics for solving the Capacitied Vehicle Routing Problems (CVRPs). In AutoDH, the agent can intelligently select the most cost-effective heuristics from the LLM pool, improvement pool, and perturbation pool, according to the current state, to refine the current solution. The LLM pool consists of three LLMs that can automatically design algorithms to enhance the current solution based on the given prompts. The improvement pool consists of local search heuristics to fine-tune and improve the current solutions. Meanwhile, heuristics in the perturbation pool can significantly perturb the current solutions to escape local optima. Moreover, to ensure the learning efficacy of AutoDH, a reward that integrates the improvement in solution quality, the time cost of heuristic, and the cost of invoking LLMs API is designed. The effectiveness of AutoDH is validated on CVRPs with various scales.

关键词： Costs Perturbation methods Large language models Heuristic algorithms Vehicle routing signal processing algorithms Manuals signal processing Routing Optimization

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Accuracy evaluation of ECG waves detection and segmentation

Accuracy evaluation of ECG waves detection and segmentation

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IEEE Instrumentation and Measurement Technology Conference

作者： Livio D'Alvia Eduardo Palermo Zaccaria Del Prete Sapienza University - DIMA Rome Italy

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Wearable wireless devices are vital in regulating lifestyles and facilitating telemedicine and home healthcare monitoring. Despite abundant low-cost sensor options flooding the market, only a handful have received validation and certification for clinical use. This study offers initial insights from a detailed evaluation of an electrocardiography (ECG) tool designed for patients' self-managed monitoring of cardiovascular diseases. The primary focus of this assessment is on examining the functionality of the MAX30003 integrated circuit and the essential characteristics embedded within its firmware. The study also investigates six cardiac conditions requiring thorough and continual ECG monitoring. To assess accuracy, the experimental setup utilizes a National Instruments USB6001, applying seven distinct signals-Typical, Hyper- and Hypo-calcemia, Hyper- and Hypo-kalaemia, Ventricular and Atrial Tachycardia-under three overlapping and non-overlapping noise sources: white noise, powerline noise, and their combination. Results from the accuracy evaluation demonstrate the integrated circuit's performance, revealing a normalized root mean square error lower than 10% in the worst-case scenario (signal with power line and noise) and an average of 2.5% in the best-case scenario (no noise). Similarly, repeatability and reproducibility are higher overall for TYP ECG. PLN is the noise that least affects repeatability, while white noise is the least attenuated one. Moreover, it achieves a 95% detection rate for P, R, and S peaks, 98% precise segmentation for the QRS complex, and 95% precision for P and S peaks under no-noise conditions. In noise conditions, precise segmentation rates are slightly lower, at 87% for the QRS complex and 91% for P and S-peaks. Preliminary results from healthy subjects align with those from systemic signals, indicating promising segmentation performance. These findings provide crucial insights into the potential effectiveness of the MAX30003 for accura

关键词： Pathology Wireless sensor networks Accuracy signal processing algorithms Electrocardiography White noise Market research

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Wideband Direction-of-Arrival Estimation via Covariance Fitting and Focusing

Wideband Direction-of-Arrival Estimation via Covariance Fitt...

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European signal processing Conference (EUSIPCO)

作者： Xunmeng Wu Zai Yang Zhiqiang Wei Robert Schober Zongben Xu School of Mathematics and Statistics Xi'an Jiaotong University China Institute for Digital Communications Friedrich-Alexander-University Erlangen-Nü rnberg (FAU) Germany

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

This paper studies the problem of direction-of-arrival (DOA) estimation for multiple wideband source signals using a uniform linear array. We propose a covariance $\boldsymbol{f}$ itting and $f$ ocusing-based estimation method (COFFEE). Specifically, we develop a wideband covariance fitting criterion and formulate a corresponding bilevel optimization problem by introducing focusing operations on covariance matrices. We show that the upper-level problem for covariance fitting can be cast as a rankconstrained semidefinite programming (SDP) and the lower-level problem for the focusing matrices has a closed-form solution. We develop an alternating direction method of multipliers algorithm for the rank-constrained SDP. We further propose an alternating iterative algorithm to solve the overall bilevel optimization problem. Numerical results demonstrate that COFFEE outperforms existing methods.

关键词： Direction-of-arrival estimation Fitting Focusing signal processing algorithms Estimation signal processing Iterative methods Covariance matrices Wideband Optimization

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Energy Saving Temperature Control of UAV Nest Inverter Air Conditioning Based on Variable Air Volume and DE Algorithm

Energy Saving Temperature Control of UAV Nest Inverter Air C...

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Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Weihao Zhang Wenbin Wu Zhuolei Chen Meiyan Yu Hui Dong State Grid Fujian Electric Power Research Institute Fuzhou China State Grid Electric Power Space Technology Co. Ltd. Beijing China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

The current temperature control model of UAV nest inverter air conditioning energy saving is mostly set to a single scheduling form, and the control efficiency is relatively low, resulting in a decline in the final energy consumption optimization ratio. Therefore, the design and practical verification of the temperature control method of UAV nest inverter air conditioning energy saving based on variable air volume and DE algorithm are proposed. According to the current measurement, the dynamic balance adjustment of the air volume and frequency of variable frequency air conditioning is carried out first. Based on this, the change trend of the thermal load of the UAV nest is analyzed. On this basis, combined with variable air volume and DE algorithm, the efficiency of control is strengthened, and the energy-saving temperature control model of UAV nest inverter air conditioning with variable air volume and DE algorithm is established, and the multi-level real-time balance correction method is used to achieve temperature control processing. The test results show that compared with DFT central air conditioning control method and deep learning intelligent energy-saving air conditioning control method, the energy consumption optimization ratio finally obtained by the variable air volume and DE calculation UAV machine nest variable frequency air conditioning energy-saving temperature control method is relatively high, which indicates that this time, with the help and support of variable air volume and DE algorithm, the designed energy-saving temperature control method of UAV nest inverter air conditioning is more flexible and changeable, and its own temperature control effect has been further improved, and the results have been verified in practice.

关键词： Air conditioning Energy consumption Heuristic algorithms Energy conservation signal processing algorithms Autonomous aerial vehicles Inverters Temperature control Frequency measurement Optimization

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Half-Split ResUNet Denoiser Based Deep Unrolling for Photon Limited Image Deblurring

Half-Split ResUNet Denoiser Based Deep Unrolling for Photon ...

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International Conference on signal processing and Communications, SPCOM

作者： Koyyada Dinesh Kumar Sujit Kumar Sahoo School of Electrical Sciences Indian Institute of Technology Goa Ponda India

ISBN: (数字)9798350350456

ISBN: (纸本)9798350350463

Photon-limited deblurring is a complex and demanding problem encountered in various applications where low-light conditions prevail. The scarcity of photons in such situations leads to the introduction of shot noise, resulting in a degradation of image quality. Solving this problem with Neural networks often involves constructing models empirically, making the behavior of the underlying architecture challenging to comprehend. A recent technique known as algorithm unrolling has enabled the connection of iterative algorithms with neural networks, where the Convolutional Neural Network (CNN) acts as a denoiser. This paper introduces a reduced parameter denoiser to enhance image quality and preserve finer details or avoid over-smoothing of the image during reconstruction. As a result, the unrolled model surpasses existing deblurring methods for improving image quality in low-light conditions. The proposed denoiser reduces the number of parameters by a factor of 3.84 and preserves the finer details while reconstructing. Our model improves computational efficiency and storage requirements compared to the state-of-the-art.

关键词： Image quality Computational modeling Noise Neural networks signal processing algorithms Computer architecture Convolutional neural networks

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Optimizing Job Matching Through Automated Resume Screening with NLP and Machine Learning

Optimizing Job Matching Through Automated Resume Screening w...

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Intelligent signal processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： CH Sai Surya Glvs Kiriti K Satya Saketh P Sri Charan A. Anjali 2021IMT-024 Information Technology ABV-IIITM Gwalior 2021IMT-035 Information Technology ABV-IIITM Gwalior 2021IMT-050 Information Technology ABV-IIITM Gwalior 2021IMT-072 Information Technology ABV-IIITM Gwalior Information Technology ABV-IIITM Gwalior

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

Resume screening determines a candidate's qual-ification for a position based on their education, experience, and other resume information. Candidates are only called for interviews if their resume matches the job description. Manual resume examination is time-consuming and burdensome, prompting companies to use applicant tracking systems to shortlist candidates based on their skills. The primary cause of rejection is often a mismatch between the job role and the applicant's skill set. Job seekers must understand which job categories align with their skill sets. This study employs natural language processing (NLP) and machine learning (ML) techniques to evaluate applicants in real time. Methods such as tokenization, stemming, lemmatization, TF-IDF, and string matching are used to identify keywords. Subsequently, ML models are applied to determine the most suitable role for each applicant. Four models-SVM, logistic regression, random forest, and k-nearest neighbors-are compared for accurate classification, with SVM outperforming the other benchmarks. This project aims to revolutionize the resume screening process by integrating advanced technologies and refining our methodologies.

关键词： Support vector machines Training Logistic regression Accuracy Resumes Refining signal processing algorithms Training data Nearest neighbor methods Random forests

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A Real-Time Multiple Traffic Violation Detection System Using DeepSORT

A Real-Time Multiple Traffic Violation Detection System Usin...

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Artificial Intelligence and signal processing (AISP), INDIA, International Conference on

作者： Renuka Devi S M A Manasa Karkala Deepti Y. Rakesh Kumar ECE Department G Narayanamma Institute of Technology & Science(for women) Hyderabad TS India ECE Department G Narayanamma Institute of Technology & Science Hyderabad TS India ECE Department Vasavi College of Engineering Hyderabad TS India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

As population growth accelerates, the number of vehicles on the roads increases, leading to rise in road accidents. A significant cause of these accidents is traffic rule violations, such as speeding and wrong-way driving. To address this issue, an advanced real-time system is needed to detect traffic rule violations using CCTV cameras. The proposed traffic signal violation detection system automates the monitoring process, performing tasks such as speed and wrong-way driving detection without human intervention. The system focuses on three primary tasks: vehicle detection, vehicle tracking, and violation detection. Vehicles are first located using the YOLOv3 deep learning method. The Kalman filter combined with DeepSORT is then used to track the detected vehicles, assigning each a unique ID. algorithms for wrong-way driving and speed violations are subsequently applied to identify rule-breaking vehicles. This method ensures high efficiency and accuracy in detecting vehicles that violate traffic rules.

关键词： YOLO Deep learning Accuracy Road accidents Roads signal processing algorithms signal processing Real-time systems Kalman filters Monitoring

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Improved mmWave signal Mode Decomposition and Time-frequency Domain Conversion Algorithm

Improved mmWave Signal Mode Decomposition and Time-frequency...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Mengyun Zhou Haiwen Liu Xinyi Gu Huiting Zhang Shaofei Wang School of Information and Communications Engineering Xi’an Jiaotong University Xi’an Shaanxi China School of Information Engineering Dalian University Dalian Liaoning China Beijing University of Chinese Medicine Beijing China

ISBN: (数字)9798350351019

ISBN: (纸本)9798350351026

In this study, A complete signal processing algorithm of 60 GHz millimeter wave radar echo is designed. The improved ICEEMDAN adaptive modal decomposition algorithm is used to obtain the time domain map of the chest fluctuation, and the improved all-phase FFT (ApFFT) and fast Fourier transform are used to obtain more multi-dimensional images. Through the introduction of adaptive noise, the improvement of Fourier transform window and the experimental adjustment of parameters, the accuracy of the algorithm reached 95.3% in the real experiment, and it can well realize long-term detection and recognition of various lesions such as accelerated heart rate, weakened breathing and so on.

关键词： Time-frequency analysis Accuracy Fast Fourier transforms Noise signal processing algorithms Millimeter wave radar Radar imaging Radar signal processing Time-domain analysis Monitoring

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MixDACM and WMF-DAWHampel-Based FMCW Radar for Vital Sign Detection

MixDACM and WMF-DAWHampel-Based FMCW Radar for Vital Sign De...

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Robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Wei Xiong Xi Ma Lin Xia Fei Xia College of Automation Engineering Shanghai University of Electric Power Shanghai China Fengxian Power Supply Co State Grid Shanghai Electric Power Co Shanghai China School of Mechanical Engineering Tongji University Shanghai China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

In the field of life sign detection, the use of 77GHz Frequency Modulated Continuous Wave (FMCW) millimeterwave radar is being explored for non-contact monitoring of vital signs. This technology is challenged by interference from respiratory harmonics and clutter, which can result in abnormal phase values. An advanced algorithm for FMCW radar life sign signal detection is introduced in this study, combining first-order and second-order Difference of Arbitrary-Constant Multiplication (MixDACM) for phase unwrapping with a Windowed Median Filter-based Adaptive Weighted Hampel (WMFDAWHampel) method for managing outliers. The methodology involves utilizing the intermediate frequency signal from the FMCW radar to locate the target accurately. Subsequently, the MixDACM technique is applied for phase unwrapping, followed by the WMF-DAWHampel filter to address any residual abnormal phase values. Band-pass filters are then used to isolate the respiratory and heartbeat signals, facilitating the estimation of breathing and heart rates. Experimental results indicate that the integration of MixDACM and WMF-DAWHampel methods significantly improves phase unwrapping quality and outlier management. The integration of advanced signal processing techniques results in precise respiratory and heartbeat signal extraction, achieving estimation errors of 8.810% and 3.325%, respectively. Results from the study affirm the effectiveness of the proposed algorithm in boosting the precision of life sign detection through FMCW radar technology.

关键词： Band-pass filters Accuracy Heart beat Radar detection signal processing algorithms Radar Filtering algorithms Heart rate detection Harmonic analysis Power harmonic filters

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ISAR imaging of complex moving targets based on coherent accumulation type non-uniform sampling third-order autocorrelation function

ISAR imaging of complex moving targets based on coherent acc...

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International Conference on Computer Engineering and Applications (ICCEA)

作者： Zongyang Sun Tong Zhang Kangfa Xu Qiheng Song Aviation Key Laboratory of Science and Technology on Airborne and Missle-Borne Computer Xi’an Aeronautics Computing Technique Research Institute AVIC School of Computer Science Northwestern Polytechnical University Xi’an China Aviation Key Laboratory of Science and Technology on Airborne and Missle-Borne Computer Xi’an Aeronautics Computing Technique Research Institute AVIC Xi’an China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

ISAR imaging algorithm based on Coherent accumulation type non-uniform sampling third-order autocorrelation function (CANS-TAF) is proposed for complex moving targets. CANS-TAF defines a new non-uniform sampling third-order autocorrelation function (NTAF), which enables signal energy to accumulate coherently in the slow time delay two-dimensional data domain and improves the noise resistance performance of CPS parameter estimation. And the implementation process of this algorithm only requires complex multiplication, Scaled Fourier Transform (SFT) and Complete the Fast Fourier Transform (FFT) operation. It is a new non search parameter estimation algorithm that does not require tedious parameter search processes or the use of non-uniform Fourier transform. The final simulation experimental results demonstrate the superiority of the proposed algorithm in terms of Noise immunity.

关键词： Resistance Parameter estimation Fast Fourier transforms signal processing algorithms Imaging Resists Radar

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