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检索条件"任意字段=4th International Conference on Signal Processing and Computer Science, SPCS 2023"

共 2398 条记录，以下是1-10 订阅

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A dynamic analysis approach in racket sports 5

A dynamic analysis approach in racket sports

引用

5th international conference on signal processing and computer science, spcs 2024

作者： Jia, Jinghao Qin, Jialong Yu, Changyang School of Computer Science Beijing Institute of Technology Beijing China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Using a combination of the TOPSIS model, time series analysis,Lasso regression, and multivariate linear regression, this study aims to quantify and predict momentum disparities among players and to analyze the momentum fluctuation characteristics of player in rocket games. Focusing on the 2023 Wimbledon Men's Singles and other events, this study initially establishes a momentum assessment model based on match progression, then evaluates the influence of various indicators on momentum-difference and subsequently uncovers players' momentum fluctuation characteristics during *** study has also been validated in other competitions, exhibiting good generalizability, and can be applied to matchesunderdifferent environmentalconditions. In summary,this work not only enhancesunderstandingof momentum but also offers a method for quantifying and predicting performance. the study offers crucial insights for players and coaches to analyze matches, understand individual strengths and weaknesses, and serves as an important reference. © 2025 SPIE.

关键词： Time series

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A Transformer-based framework for non-Cartesian MRI reconstruction 5

A Transformer-based framework for non-Cartesian MRI reconstr...

引用

5th international conference on signal processing and computer science, spcs 2024

作者： Ji, Wuzheng Zhang, Ze Tan, Huiyuan Yang, Wenhui Wang, Hui Liu, Xin Wang, Qiuliang University of Science and Technology of China Anhui Hefei230026 China Ganjiang Innovation Academy Chinese Academy of Sciences Jiangxi Ganzhou341000 China Institute of Electrical Engineering Chinese Academy of Sciences Haidian Beijing100190 China University of Chinese Academy of Sciences Huairou Beijing101408 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Non-Cartesian reconstruction is a crucial technique for accelerating MRI. However, traditional non-Cartesian reconstruction algorithms often result in suboptimal image quality. Recently, deep neural networks have emerged as powerful tools for MRI reconstruction, yet their application to non-Cartesian acquisitions remains underexplored. Transformer-based approaches have shown impressive performance in image super-resolution, prompting us to explore their potential in this domain. To tackle these challenges, this paper introduces a novel framework that combines non-Cartesian image reconstruction techniques with a Transformer-based network. the proposed framework comprises nonuniform Fourier transform, image feature extraction, and image reconstruction modules. To assess the effectiveness of our approach, we performed experiments using the single-coil knee dataset from fastMRI. Compared to other methods, our proposed approach demonstrated a 2.024 dB improvement in PSNR and a 0.117 increase in SSIM under a 4x accelerated radial undersampling condition. © 2025 SPIE.

关键词： Deep neural networks

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Research on modeling and feature extraction of maritime vessel targets based on micro-doppler effect characteristics 5

Research on modeling and feature extraction of maritime vess...

引用

5th international conference on signal processing and computer science, spcs 2024

作者： Cai, Fan Jiang, Yuesong Zhu, Tongbo School of Electronic and Electrical Engineering Minnan University of Science and Technology Quanzhou362700 China School of Electronic and Information Engineering Beihang University Beijing100000 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

the micro-Doppler effect captures the fine motion characteristics of maritime targets, serving as a crucial feature for distinguishing between sea clutter and targets, thereby enhancing radar target detection and recognition capabilities. In this study, a long-term radar echo model for micro-motion targets in sea clutter is established using maritime surveillance radar. By analyzing the morphological differences between constant-velocity targets and micro-motion clutter in Short-Time Fourier Transform (STFT) spectrograms, a sliding window cancellation method based on STFT spectrograms is proposed to remove micro-motion clutter. this method effectively separates target echoes from micro-motion clutter, facilitating the extraction of micro-motion target features. Both simulation and experimental results validate the effectiveness of the proposed method, laying a theoretical foundation for advancing maritime vessel detection and recognition. © 2025 SPIE.

关键词： Surveillance radar

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A novel large kernel two-branch semantic segmentation network 24

A novel large kernel two-branch semantic segmentation networ...

引用

4th international conference on signal processing and Communication Technology, SPCT 2024

作者： Zhang, Xinrui Fu, Peng School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798400710636

For semantic branching in a two-branch network structure, it is crucial to quickly improve the feeling field, in addition, the feature fusion interaction of two-branching needs to take into account the structural and multi-scale information fusion of features under different feeling fields. In this paper, in order to cope with this challenge, we propose to accumulate the receptive field in dense convolution using separable large kernel convolution through an adaptive semantic extraction module, and design a selectable large kernel attention module for the problem of mismatch of receptive field of feature information. Finally, this paper designs a multiscale feature extraction module that fuses local and global information, and uses large kernel dense separable convolution in the module with the ability to extract high-dimensional semantics serially and capture multiscale in parallel. © 2024 Copyright held by the owner/author(s).

关键词： Semantic Segmentation

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CodGRU: Covariate gated attention GRU for short term runoff forecasting 24

CodGRU: Covariate gated attention GRU for short term runoff ...

引用

4th international conference on signal processing and Communication Technology, SPCT 2024

作者： Cao, Jiahao Xiao, Tong Cao, Zhe School of Computer Science and Engineering Chongqing University of Science and Technology Chongqing China Shenzhen Branch of China Academy of Urban Planning and Design Guangdong Shenzhen China

ISBN: (纸本)9798400710636

Runoff simulation and forecasting are critical for effective watershed management, yet their complexity and nonlinear characteristics pose significant challenges. the generation of runoff is the result of the interaction between multiple factors such as precipitation, soil moisture, topography, and human activities, making this dynamic process extremely complex and difficult to model accurately. Traditional models are often based on an understanding of these natural processes but may fall short when dealing with complex real-world scenarios. To address these issues, this study has developed a dynamic gated feature selection model with attention mechanisms (CodGRU), specifically designed for multi-watershed hourly runoff forecasting. By dynamically adjusting the weights of input variables and selecting the most important features, CodGRU can better capture the key factors influencing runoff, thereby improving the accuracy of forecasting. the test results of five watersheds on the real dataset show that the CodGRU model performs excellently in multi-watershed hourly-scale forecasting, outperforming the existing mainstream models and providing reliable support for water resource management and disaster forecasting and mitigation. © 2024 Copyright held by the owner/author(s).

关键词： Soil moisture

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Predictive Analysis of Diabetes Data in Healthcare System Using Machine Learning Algorithms 4

Predictive Analysis of Diabetes Data in Healthcare System Us...

引用

4th international conference on Robotics, Electrical and signal processing Techniques, ICREST 2025

作者： Al Arafat, Kazi Abdullah Al Amin Rahman, Md Siddique, Sumaya Roni, Mahmudur Rahman Parvez, Imtiaz Atish Dipankar University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh American International University Department of Computer Science and Engineering Dhaka Bangladesh Utah Valley University Department of Computer Science 800 W University Pkwy OremUT84058 United States

ISBN: (纸本)9798331530938

Diabetes, influenced by factors like high blood pressure, aging, obesity, and poor lifestyle choices, has become a significant health issue, increasing the risk of heart disease, kidney disease, stroke, and other serious conditions. Traditional diagnostic methods in hospitals can sometimes be misleading, underscoring the need for big data analysis in healthcare. this approach can uncover hidden patterns in diabetes-related data, enabling more accurate predictions of outcomes. this paper aims to classify large diabetes datasets to predict the disease's behavior using various attributes. A machine learning model with sixteen classifiers, fine-tuned through k-fold cross-validation, was developed to ensure optimal results, with accuracy measured by F1 score, precision, and recall. © 2025 IEEE.

关键词： Adversarial machine learning

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Silent Speech Interfaces: Non-Invasive Neuromuscular signal processing for Assistive Communication 4

Silent Speech Interfaces: Non-Invasive Neuromuscular Signal ...

引用

4th IEEE international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Khan, Sallar Ali, Khawaja Usman Qadri, Ali Raza Ali Rizvi, Maisam Technological University Dublin School of Informatics and Cybersecurity Dublin Ireland Sir Syed University of Engineering and Technology Department of Computer Science & Information Technology Karachi Pakistan

ISBN: (纸本)9798331518882

Our research demonstrates a discrete speech system that empowers those afflicted with speech impairments to convey ideas by internally articulating words without producing audible sound. this method harnesses the minimal movements involved in articulation, making it less arduous than conventional vocalization, especially for individuals facing communication challenges. Silent Speech Interfaces (SSIs) form the foundation of our research, enabling the decoding of neuromuscular signals associated with internal speech articulation to facilitate non-invasive and effective communication for individuals with speech impairments. this research entails the utilization of a multi-electrode configuration to capture subtle, low-voltage electrical signals positioned on the surface area of the subject's neck. these signals emerge as a consequence of neural activation within the speech articulator muscles, signifying the internal articulation of words within the oral cavity, accompanied by minimal physical movement. Subsequent to signal acquisition, a comprehensive analysis of the data is conducted using a biosensing board (Cyton), facilitated by the implementation of the OpenBCI GUI interface and visualization via Python plotting tools. © 2025 IEEE.

关键词： Digital signal processing

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A Detailed Analysis of Machine Learning Algorithm Performance in Heart Disease Prediction 4

A Detailed Analysis of Machine Learning Algorithm Performanc...

引用

4th international conference on Robotics, Electrical and signal processing Techniques, ICREST 2025

作者： Rabbi, Mohammad Ali Rijon, Rokibul Hasan Akhi, Sharmin Sultana Hossain, Alomgir Jeba, Samiha Maisha International University of Business Agriculture and Technology Dept of Computer Science and Engineering Bangladesh Monroe University Dept of Computer Science United States

ISBN: (纸本)9798331530938

Human deaths are natural events, but heart disease remains a leading cause of mortality worldwide. Predicting heart disease using machine learning can significantly benefit global health, though it presents clinical challenges requiring high accuracy and efficiency. Many countries face a shortage of cardiovascular experts and a high rate of misdiagnoses, which can be mitigated by accurately determining the stage of coronary disease and supporting medical decisions with automated patient records. this paper addresses the prediction of coronary disease based on input attributes using data mining techniques. the results showed that the CatBoost Classifier achieved the highest accuracy at 87.2%, followed closely by the Ridge Classifier at 86.3%. Other models, such as XGBoost and Gradient Boosting, performed similarly, achieving 84.0%, while the RandomForest Classifier had a slightly better performance with an accuracy of 85.6%. © 2025 IEEE.

关键词： Adversarial machine learning

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Adaptive Traffic signal Control System Using Object Detection Based Approach 4

Adaptive Traffic Signal Control System Using Object Detectio...

引用

4th international conference on Robotics, Electrical and signal processing Techniques, ICREST 2025

作者： Khan, Irfan Hasan, Mehedi Islam, Shamsul Reza, Suman Reza, Tuhin Efaz, Erteza Tawsif Faridpur Engineering College Department of Computer Science & Engineering Faridpur Bangladesh Louisiana State University Division of Electrical & Computer Engineering Baton RougeLA United States

ISBN: (纸本)9798331530938

In Bangladesh, most four-legged intersections use a static-timed or manually-controlled traffic signaling approach, which creates irrefutable congestion and requires human involvement. this paper proposes an adaptive traffic control system utilizing the YOLO object detection framework within CNNs to address this issue. the system captures and frames images using cameras facing every road at a roundabout to detect and count vehicles in real-time for providing lights based on the traffic conditions, prioritizing emergency vehicles such as ambulances, firetrucks, and police-car while also maintaining queues. It sets the signal duration considering road condition. Using a dataset of 10,462 images across 12 classifications of automobiles and a transfer learning approach in YOLOv8, the model achieved a mAP50 score of 0.90. this automated method reasonably enhances traffic flow management by dynamically adjusting signal lights without human intervention. © 2025 IEEE.

关键词： Adaptive control systems

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A Novel Emotion Recognition System Based on Non-Invasive EEG signals 4

A Novel Emotion Recognition System Based on Non-Invasive EEG...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Jin, Hulin Jin, Zhiran Kim, Yong-Guk Li, Kun School of Computer Science and Technology Anhui University Hefei230031 China Foothill Preparatory School Temple CityCA91780 United States Sejong University Department of Computer Engineering Seoul05006 Korea Republic of School of Electronic and Information Engineering Anhui University Hefei230031 China

ISBN: (纸本)9798331523923

Emotion recognition is an important scenario for EEG applications, which can assist in the stable analysis of EEG information. In this study, the novel emotion recognition system based on non-invasive EEG signals is proposed. In the signal acquisition and the processing stage, this study adopted a standardized EEG signal acquisition process, combined with artifact space reconstruction technology to remove signal noise, laying the foundation for accurate analysis process. In the EEG data feature extraction stage, this study effectively captures the randomness and complexity characteristics of the EEG signals through an algorithm that combines differential entropy and power spectral density. Subsequently, at the emotion recognition level, this study designed and trained a deep neural network model, including four convolutional layers, four maximum pooling layers and a fully connected layer, to extract multi-level features and complete the emotion data classification. the experiment was verified on a self-collected EEG dataset, and 10 groups of comparative tests were conducted on the Accuracy and Precision indicators, with the traditional algorithm as the comparison object. the results show that the system has good performance in emotion recognition accuracy. © 2025 IEEE.

关键词： Brain computer interface

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