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检索条件"任意字段=IEEE International Conference on Embedded and Real-Time Computing Systems and Applications"

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Low-Power MIPS32 Processor Design with Cache Implementation Using Clock Gating Techniques on FPGAs

Low-Power MIPS32 Processor Design with Cache Implementation ...

引用

Machine Learning and Autonomous systems (ICMLAS), international conference on

作者： Prasanth Varasala Babulu Kaparapu Kamaraju Maddu Department of ECE Pragati Engineering College Surampalem Andhrapradesh India Department of ECE Jawaharlal Nehru Technological University Viziayanagaram Vizianagaram Andhrapradesh India Department of ECE Seshadri Rao Gudlavalleru Engineering College Gudlavalleru Andhrapradesh India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This paper presents the design and implementation of a low-power MIPS32 processor with an integrated cache system, leveraging clock gating techniques to optimize power efficiency on Field-Programmable Gate Arrays (FPGAs). Clock gating is employed to reduce dynamic power consumption by selectively disabling the clock signal in inactive functional units, thereby minimizing unnecessary switching activity. The proposed architecture incorporates an optimized cache memory hierarchy to enhance performance while maintaining energy efficiency. Experimental results demonstrate significant reductions in power consumption without compromising processing speed., making this design well-suited for power-constrained applications such as embedded systems and IoT devices.

关键词： Performance evaluation Power demand Machine learning Voltage Switches Energy efficiency real-time systems Table lookup Field programmable gate arrays Clocks

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EfficientSleepNet: A Novel Lightweight End-to-End Model for Automated Sleep Staging on Single-Channel EEG

EfficientSleepNet: A Novel Lightweight End-to-End Model for ...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Fei Wang Zekun Zheng BangShun Hu Xiaodong Yang Maolin Tang Haiyun Huang School of Artificial Intelligence South China Normal University Foshan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Sleep staging is critical for evaluating sleep quality and regulating sleep patterns. While deep learning has shown potential for automatically scoring sleep stages from raw signals, many existing models are overly complex, computationally intensive, and rely on future information, limiting their use in real-time monitoring and widespread applications. To address these challenges, we propose EfficientSleepNet, a lightweight architecture for sleep staging based on single-channel EEG. EfficientSleepNet integrates depthwise separable convolutions, grouped convolutions, channel reordering, and a novel channel attention mechanism. Compared to previous models, EfficientSleepNet significantly reduces both parameter count and complexity. We conducted extensive evaluations on two public datasets, SleepEDF-20 and SleepEDF-78, achieving accuracies of 84.4% and 80.8%, respectively, with only 83.8K parameters. These findings demonstrate that EfficientSleepNet substantially reduces model complexity and parameter count, offering strong potential for real-time sleep staging applications.

关键词： Sleep Convolution Computational modeling Signal processing algorithms Computer architecture Brain modeling Feature extraction Electroencephalography real-time systems Complexity theory

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A Computer Vision Approach for Autonomous Cars to Drive Safe at Construction Zone

A Computer Vision Approach for Autonomous Cars to Drive Safe...

引用

international Image Processing, applications and systems conference (IPAS)

作者： Abu Shad Ahammed Md Shahi Amran Hossain Roman Obermaisser Chair of Embedded Systems University of Siegen Siegen Germany

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

To build a smarter and safer city, a secure, efficient, and sustainable transportation system is a key requirement. The autonomous driving system (ADS) plays an important role in the development of smart transportation and is considered one of the major challenges facing the automotive sector in recent decades. A car equipped with an autonomous driving system (ADS) comes with various cutting-edge functionalities such as adaptive cruise control, collision alerts, automated parking, and more. A primary area of research within ADAS involves identifying road obstacles in construction zones regardless of the driving environment. This paper presents an innovative and highly accurate road obstacle detection model utilizing computer vision technology that can be activated in construction zones and functions under diverse drift conditions, ultimately contributing to build a safer road transportation system. The model developed with the YOLO framework achieved a mean average precision exceeding 94% and demonstrated an inference time of 1.6 milliseconds on the validation dataset, underscoring the robustness of the methodology applied to mitigate hazards and risks for autonomous vehicles.

关键词： YOLO Computer vision Computational modeling Roads Urban areas Robustness Smart transportation Automobiles Autonomous vehicles Automotive engineering

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Enhancing Software Defect Prediction:Integrating SAINT with Ant Colony Optimization and Explainable AI

Enhancing Software Defect Prediction:Integrating SAINT with ...

引用

international conference on Big Data and Smart computing (BIGCOMP)

作者： Sriman Mohapatra Eunjeong Ju Jeonghwa Lee Duksan Ryu Department of Software Engineering Jeonbuk National University Jeonju South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Software Defect Prediction (SDP) enhances software quality by identifying error-prone modules early in development. While deep learning models like SAINT improve prediction accuracy, their complexity limits interpretability. This study evaluates SAINT against traditional models (XGBoost, Random Forest, CatBoost) and addresses interpretability using Ant Colony Optimization (ACO) for feature selection and hyperparameter tuning, along with LIME for explaining predictions. Performance metrics, including Prediction Detection (PD), Prediction False Alarm (PF), Balance, and Fault Identification Rate (FIR), demonstrate that SAINT outperforms traditional models in accuracy. The integration of SAINT, ACO, and LIME provides a robust, transparent framework for real-world SDP applications.

关键词： Deep learning Ant colony optimization Accuracy Software quality Predictive models Feature extraction Software systems Software reliability Tuning Random forests

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Steerable Differential Polynomial Beamforming

Steerable Differential Polynomial Beamforming

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Feng Jiang Huawei Chen College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Conventional steerable differential beamformers are usually only applicable to microphone arrays with regular planar geometries. Recent years have seen the interest in the design of steerable differential beamformers for microphone arrays with an arbitrary planar geometry. However, the existing steerable differential beamformers for arbitrary planar arrays need to repeatedly recalculate their beamformer weight vectors when the steering angle changes, which may affect their real-time implementation. In this paper, we propose a steerable differential polynomial (SDP) beamformer design approach based on the Farrow structures by using the Jacobi-Anger expansion and least-squares method. The SDP beamformers are applicable to arbitrary planar microphone arrays, and can realize continuous 360-degree mainlobe steering in real time without the need of updating beamformer weight vector, which is very useful for time-critical applications such as hearing assisting devices. Simulation results are also reported to demonstrate the effectiveness of the proposed SDP beamformers.

关键词： Geometry Jacobian matrices Array signal processing Simulation Microphone arrays real-time systems Vectors Polynomials time factors Speech processing

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Spatio-Temporal Mixed Graph Neural Controlled Differential Equations with Adaptive Connection Sampling for Irregular Multivariate time Series Anomaly Detection

Spatio-Temporal Mixed Graph Neural Controlled Differential E...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xudong Jia Wei Peng Chiran Shen Baokang Zhao Peng Xun College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multivariate time series data often demonstrate sparse and irregular characteristics in real-world signal processing applications, making anomaly detection challenging. This paper introduces STMG-AD, a spatio-temporal mixed graph neural controlled differential equation method with adaptive connection sampling, designed specifically for anomaly detection in irregular multivariate time series. By integrating causal graphs with graph attention networks and employing adaptive connection sampling coupled with Monte Carlo dropout, STMG-AD can enhance the robustness and accuracy of anomaly detection. Experiments on various real-world datasets demonstrate its superiority over existing methods in detecting anomalies in irregular multivariate time series.

关键词： Uncertainty Monte Carlo methods Adaptive systems Accuracy time series analysis Differential equations Signal processing Robustness Speech processing Anomaly detection

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Research on the Application of Chinese Language Database in Intelligent Communication System

Research on the Application of Chinese Language Database in ...

引用

Power Electronics, Computer applications (ICPECA), ieee international conference on

作者： Sun Luyao Liu Qian Niu Jin Shandong Vocational and Technical University of Engineering Jinan China Biobase Jinan China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

This study proposes an innovative Chinese language information extraction algorithm, which combines Bi-LSTM and CRF, aiming to improve the processing accuracy and efficiency of Chinese language in intelligent communication system. Bi-LSTM captures contextual information in sentences through two LSTM networks, forward and reverse, to solve the shortcomings of traditional LSTM in sequence modeling; The CRF makes the optimization of the label sequence output by Bi-LSTM, and the precision of named entity recognition, word segmentation and syntax analysis can be improved greatly. Experimental results show that the accuracy of the system based on this algorithm in the named entity recognition task is 92.4%, which is more than 6% higher than the traditional HMM model. At the same time, the model shows higher robustness and lower error rate in the processing of long texts and complex semantic relationships. On multiple test data sets, the F1 score and recall rate of the algorithm are also greatly improved, and the inference time is kept within the range required for real-time communication.

关键词： Training Analytical models Accuracy Communication systems Databases Semantics Hidden Markov models Bidirectional long short term memory Named entity recognition Conditional random fields

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ML-Driven real-time Emotion and Stress Level Monitoring

ML-Driven Real-Time Emotion and Stress Level Monitoring

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), ieee conference on

作者： Bharti Parate Diksha Mathankar Richa Khandelwal Prashant Dwivedy Department of Electronics and Computer Science Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Electronics Engineering Ramdeobaba University Nagpur India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The growing global prevalence of stress-related disorders has necessitated the development of advanced, noninvasive technologies for real-time stress detection. Traditional stress monitoring methods often require physiological sensors, which, while accurate, can be intrusive and impractical for everyday use. In this paper, we present an innovative approach that leverages deep learning and facial emotion recognition to detect stress levels dynamically and non-invasively. Our system is based on a Convolutional Neural Network (CNN) trained using the FER-2013 dataset, which contains a wide variety of facial expressions. The model is capable of classifying seven core emotions such as anger, disgust, fear, happiness, sadness, surprise, and neutral with high accuracy. Each of these emotions is then mapped to corresponding stress levels (low, moderate, or high), allowing for real-time stress evaluation through facial expressions alone. A key feature of our proposed system is its user-centric design, which includes a real-time visual feedback mechanism. This interface displays stress levels continuously as they are detected, making the system suitable for various practical applications such as mental health monitoring, stress management in workplace environments, and personal well-being tracking. The non-invasive nature of this approach eliminates the need for cumbersome sensors and facilitates its use in dynamic, everyday environments. To validate the effectiveness of the system, extensive experiments were conducted to assess both emotion recognition accuracy and the reliability of stress-level detection. The model achieved a significant l e vel o f p e rformance, d e monstrating t h e v i ability of using facial expressions as a surrogate for physiological stress markers. Additionally, we discuss potential real-world applications, where this system could be integrated into wearable devices or smartphone applications, providing continuous, unobtrusive stress monitoring. The

关键词： Deep learning Emotion recognition Accuracy Employment Anxiety disorders Human factors real-time systems Convolutional neural networks Biomedical monitoring Monitoring

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Research on Optimization Algorithm and Simulation Model of On-Board Battery Energy Management

Research on Optimization Algorithm and Simulation Model of O...

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Power Electronics, Computer applications (ICPECA), ieee international conference on

作者： Yinquan Hu Chongqing Vocational Institute of Engineering Chongqing China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

This paper proposes an on-board battery energy management optimization algorithm based on deep reinforcement learning (DRL). By adjusting the battery charging and discharging strategy in real time, it optimizes energy usage and prolongs battery life. The algorithm models the battery charging and discharging process as a Markov decision process (MDP) and designs an adaptive reward function, which can dynamically optimize battery performance under different driving environments. Experimental simulation results show that the system using this optimization algorithm has significant advantages in energy efficiency, charging cycle and battery life compared with the traditional PID control algorithm. In various driving scenarios such as simulated urban roads and highways, the optimization algorithm improves energy efficiency by 12.6%, reduces charging cycles by 15%, and prolongs battery life by 18%. These experimental results verify the application potential of deep reinforcement learning in on-board battery energy management and demonstrate its advantages and feasibility in practical applications.

关键词： PI control Heuristic algorithms Simulation Battery management systems Deep reinforcement learning Energy efficiency Batteries Safety PD control Optimization

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DITHER: Digital Twin for Energy Estimation of Heterogeneous Swarm of Robots

DITHER: Digital Twin for Energy Estimation of Heterogeneous ...

引用

Annual ieee systems conference

作者： Omar Hammami Chunyu ZHANG U2IS ENSTA Paris Palaiseau France

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

The increasing adoption of Unmanned Aerial Vehicles (UAVs) in industrial applications such as construction inspection and infrastructure monitoring necessitates the development of efficient path planning and energy management strategies. This paper presents DITHER, a Digital Twin system designed for energy consumption profiling and management in UAV-based construction inspection tasks. The proposed system integrates real-time data acquisition, energy consumption profiling, and path optimization using a hybrid approach combining path planning and energy efficiency models. Experiments conducted with a DJI Tello UAV show that the digital twin framework facilitate UAV operational in complex inspection tasks by balancing energy consumption and path length. The proposed system can be extended to support multi-UAV collaborative operations in future research.

关键词： Autonomous aerial vehicles Inspection Digital twins Energy consumption Robot kinematics Optimization Batteries Path planning Robot sensing systems Service robots

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