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检索条件"任意字段=IEEE International Conference and Workshops on Engineering of Computer Based Systems"

共 89038 条记录，以下是211-220 订阅

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Feature Selection via Dynamic Graph–based Attention Block in MI–based EEG Signals 13

Feature Selection via Dynamic Graph–based Attention Block i...

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13th international Winter conference on Brain-computer Interface, BCI 2025

作者： Han, Hyeon-Taek Lee, Dae-Hyeok Kwak, Heon-Gyu Dept. of Artificial Intelligence Korea University Seoul Korea Republic of Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798331521929

Brain–computer interface (BCI) technology enables direct interaction between humans and computers by analyzing brain signals. Electroencephalogram (EEG) is one of the non–invasive tools used in BCI systems, providing high temporal resolution for real–time applications. However, EEG signals are often affected by a low signal–to–noise ratio, physiological artifacts, and individual variability, representing challenges in extracting distinct features. Also, motor imagery (MI)–based EEG signals could contain features with low correlation to MI characteristics, which might cause the weights of the deep model to become biased towards those features. To address these problems, we proposed the end–to–end deep preprocessing method that effectively enhances MI characteristics while attenuating features with low correlation to MI characteristics. The proposed method consisted of the temporal, spatial, graph, and similarity blocks to preprocess MI–based EEG signals, aiming to extract more discriminative features and improve the robustness. We evaluated the proposed method using the public dataset 2a of BCI Competition IV to compare the performances when integrating the proposed method into the conventional models, including the DeepConvNet, the M–ShallowConvNet, and the EEGNet. The experimental results showed that the proposed method could achieve the improved performances and lead to more clustered feature distributions of MI tasks. Hence, we demonstrated that our proposed method could enhance discriminative features related to MI characteristics. © 2025 ieee.

关键词： Electroencephalography

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Bi-Linear Alternative Least Squares and Maximum Likelihood based MIMO Communication System 1

Bi-Linear Alternative Least Squares and Maximum Likelihood b...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Jasim, Laith Hussein Manjunatha, B.N. Godavarthi, Sahithi Thamizhkani, B. Sushama, C. The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Doddaballapur India Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India Mohan Babu University Department of Computer Science and Engineering Tirupati India

ISBN: (纸本)9798331529246

In the recent years, the field of wireless communication networks have changed dramatically due to Multiple-Input Multiple-Output (MIMO) radar and communication system. MIMO system uses multiple antennas at both ends, transmitter and receiver. The deployment of huge MIMO technology shows enormous progresses in communication systems with extraordinary improvements in energy and spectral efficiencies. Even though, signal processing and channel estimation become challenging tasks in massive MIMO system. To overcome these challenges in this research proposed Orthogonal Matching Pursuit (OMP) with Bi-Linear Alternative Least Squares (BALS) method for channel estimation and Maximum Likelihood (ML) for signal detection. Initially, Channel State Information (CSI) is considered as input data. Then, channel estimation is performed through OMP with BALS and signal detection is performed using ML. From the results, the proposed BALS model attained better outcomes compared to existing OMP-based Linear Minimum Mean Square Error (LMMSE) in terms of Signal-to-Noise Ratio (SNR) by achieving -10 decibels (dB) correspondingly. © 2025 ieee.

关键词： Mean square error

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A Wearable Paper-based Hybrid Energy Harvester from Human Sweat and Ambient Moisture 38

A Wearable Paper-Based Hybrid Energy Harvester from Human Sw...

引用

38th ieee international conference on Micro Electro Mechanical systems, MEMS 2025

作者： Gao, Yang Choi, Seokheun State University of New York at Binghamton Bioelectronics & Microsystems Laboratory Department of Electrical & Computer Engineering BinghamtonNY United States State University of New York at Binghamton Center for Research in Advanced Sensing Technologies and Environmental Sustainability BinghamtonNY United States

ISBN: (纸本)9798331508890

We present a wearable, paper-based energy harvester that combines a sweat-driven microbial fuel cell (MFC) with an ambient moisture-enabled electric generator (MEG), capable of simultaneously or independently harnessing energy from both human sweat and the environment. Paper stands out as an ideal substrate for wearable applications, offering flexibility and easy integration through straightforward printing methods. Due to the simple design of the MFC and MEG, both devices can be seamlessly integrated into a single sheet of paper. The biocatalyst-embedded anode of the MFC is positioned directly against the skin for optimal sweat absorption, while the hygroscopic region of the MEG faces outward to efficiently capture ambient moisture. At low humidity levels, the energy harvesting is primarily driven by the MFC, while in the absence of sweat, the MEG takes over as the main power source. This dual functionality ensures continuous and sustainable power generation, making the system highly efficient and reliable for long-term wearable applications. © 2025 ieee.

关键词： Microbial fuel cells

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Lowering the Peak to Average Power Ratio by using the PTS method for high-speed application systems

Lowering the Peak to Average Power Ratio by using the PTS me...

引用

2025 international conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Kanjilal, Pushpendu Kumar, Arun Nanthaamornphong, Aziz Bhowmick, Soumitra Techno India University Department of Electronics and Communication Engineering Kolkata India New Horizon College of Engineering Department of Electronics and Communication Engineering Baengaluru India Prince of Songkla University Phuket Thailand

ISBN: (纸本)9798331513894

Orthogonal Time-Frequency Space is a promising modulation waveform for beyond-5G or 6G communication systems: superior robustness in high mobility and multipaths is achieved from the delay Doppler domain;however, one severe challenge of this OTFS was its high peak-to-average ratio of power, constraining the linearity of efficient power amplifiers at the transmitter which degrades further the overall performance of the entire system. This paper presents a new application of the Partial Transmit Sequence (PTS) method in reducing the PAPR for OTFS systems, which will ensure compatibility with future wireless networks that have stringent requirements. The PTS is a distortion less technique that divides the input signal into sub-blocks and applies optimized phase factors to reduce the peak power of the signal. It will help to eliminate computational complexity existing with traditional PTS and incorporates enhanced optimization techniques like heuristics algorithm, reduced space searching, hence ensuring a dramatic PAPR reduction with complexity that is minimized. Simulation analysis proved the ability of this proposed work for achieving efficient results in lowering the PAPR with relatively little effect on the bit error rate performances. The results demonstrate the feasibility of PTS-based PAPR reduction to further improve the energy efficiency, spectral efficiency, and reliability of OTFS systems, thereby making it an attractive solution for beyond 5G communication scenarios. The numerical results reveals that the proposed PTS obtain an energy saving of 25% and reduce the PAPR by 3.9 dB to 5.8 dB. © 2025 ieee.

关键词： 5G mobile communication systems

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Stream Processing and Real Time Analytics in the Era of Data engineering 1

Stream Processing and Real Time Analytics in the Era of Data...

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1st international conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Chintala, Sathishkumar Plano TX United States

ISBN: (纸本)9798331518578

The prevalence of increased streaming of data revolutionises organisations approach with stream processing and real time analytics through actionable and immediate insights in the generated data streams. Advancements in data engineering drives the stream processing and real time analytics. The present study explores the transformative impact of real time analytics by examining the major components in stream processing and real time analytics such as visualisation tools, in-memory storage, stream processing and data ingestion. It enables the decision-making using data, optimised operations and personalised user experience. The study examines the multinational retail corporations and video streaming services for data engineering. The major advantages are competitive and operational efficiencies enhanced customer experience and rapid insights on stream processing. The tremendous potential is highlighted for implementation of real time analytics and stream processing with integration complexities, skill gaps, privacy and security concerns, systems scalability, enhanced data quality and management of data volume. Stream processing engine is used for higher throughput and lower processing latency based on workload. © 2025 ieee.

关键词： Data streams

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The Power of PDF-to-Audio Summaries in Modern Learning 4

The Power of PDF-to-Audio Summaries in Modern Learning

引用

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

作者： Kawde, Dhanshri Mendhe, Sameer Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Sawangi Wardha442001 India

ISBN: (纸本)9798331523923

PDF -to-audio based summarization technology is designed to enhance information accessibility and efficiency by transforming long written reports into short, audible summaries. AI, NLP, and TTS technologies that have developed recently have made it possible to extract main points without losing context. Yet, current systems have various shortcomings such as inability to process intricate PDF layouts like tables, charts, and annotations, contextual summarization inaccuracies, robotic or unhuman speech synthesis, and limited customization. Furthermore, the absence of multilingual support and easy integration into various platforms hinders wider adoption. These issues are addressed in this paper with a better approach that employs sophisticated NLP methods, context-sensitive summarization algorithms, and adaptive TTS systems. Our goal is to create a strong, user-oriented solution that provides greater summarization accuracy, personalized speech output, and improved accessibility for a variety of users, such as professionals, students, and the disabled. © 2025 ieee.

关键词： Web Design

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Electroencephalography-based Human Emotion Detection using Hybrid ABC-F-LSTM Model 1

Electroencephalography-based Human Emotion Detection using H...

引用

1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Kaur, Gaganjot Gupta, Meenu Kumar, Rakesh Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798331529246

The advancement of human-computer interfaces (HCIs) has significantly impacted accurate emotion detection using electroencephalography signals. Existing methods unveil several limitations, including feature redundancy and reduced estimation accuracy for new users when utilizing pre-trained models. Therefore, this study emphasizes improved EEG-based emotion recognition by developing a Swarm Intelligence (SI) based model for feature selection. The proposed model integrates a feature extractor, feature selector, and label classifier interface. It employs time-domain (TD) and time-frequency domain (TFD) analyses to establish fundamental information for emotion recognition. Then, an Artificial Bee Colony (ABC) algorithm will be used to select the most discriminative features from the brainwave data. The Long short-term memory (LSTM) is utilized to train and classify emotions. Another swarm-based algorithm, the firefly algorithm, is used for the tuning of hyperparameters of a classification model. The proposed approach demonstrates a significant improvement in accuracy over ABC-LSTM, LSTM, DNN, RF, and NB at 8.71%, 12.7%,20.2%, 22.7%, and 20.1%, respectively. The proposed hybrid ABC-F-LSTM model with precision 96.39, a recall of 98.86, an F-measure of 97.65 and an accuracy of 92.03% performed best against state-of-the-art classifiers and represents a very effective strategy for EEG-based emotion classification. © 2025 ieee.

关键词： Frequency domain analysis

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TruChain: A Blockchain-based Access Control to Improve the Security of Smart Water Grid systems 17

TruChain: A Blockchain-based Access Control to Improve the S...

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17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Mondal, Susmita Chakraborty, Suchetana Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798331531195

Water is a significant resource in day-to-day life, and it usually requires technological association for comprehensive management. Smart Water Grids (SWG) typically use cyberphysical systems (CPS) to monitor several parameters associated with water quality and distribution, leveraging the possibility of performing remote operations. For robust architecture, remote operations rely on application-specific sensors, communication networks, edge control, and cloud computation. Needless to say, the centralized control of edge and cloud infrastructure is prone to attacks and failures, such as unauthorized access or single-point-of-failure. We propose TruChain, a Blockchain-based access control mechanism to eliminate the probability of access takeover and aggregate quorum-based validation on sensory data for transparent provenance reliability. We also provide access revocation to strengthen security by restricting access to sensitive data stored in a privately distributed cloud. We believe our solution has empirical decentralization and a verifiable authorization method by maintaining a tamper-evident, auditable trail of interactive events, building trust without third-party intervention. © 2025 ieee.

关键词： Cloud security

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Interference-based Reliability and Capacity Analysis for ieee 802.11 Broadcast Ad-Hoc Networks on the Highway 11

Interference-Based Reliability and Capacity Analysis for IEE...

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11th international conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2025

作者： Li, Zhijuan Wu, Xintong Li, Xiaokun Ma, Xiaomin Department of Computer and Big Data Heilongjiang University Harbin150080 China Postdoctoral Program of Heilongjiang Hengxun Technology Co. Ltd. Harbin150090 China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China College of Science and Engineering Oral Roberts University TulsaOK74171 United States

ISBN: (纸本)9789897587450

Interference is a critical factor that degrades wireless network performance. In ieee 802.11 wireless broadcast networks, hidden terminals and concurrent transmissions are the primary sources of interference due to the carrier sense multiple access (CSMA) mechanism. Therefore, analyzing the signal-to-interference-plus-noise ratio (SINR) distribution is essential for evaluating network performance, whose derivation relates to the transmission probabilities of hidden terminals and concurrent transmissions. In this study, we utilize the existing semi-markov process (SMP) model to calculate these transmission probabilities. Subsequently, we employ the Laplace transform to analyze the SINR distribution in ieee 802.11 broadcast ad-hoc networks on highways. based on the derived SINR distribution, we further evaluate the reliability and capacity. This approach can be readily extended to two-dimensional (2D) or three-dimensional (3D) scenarios by employing d-dimensional (1 ≤ d ≤ 3, dD) point process. Experimental results demonstrate that the proposed model achieves high accuracy under small to medium interference ranges. Additionally, the analysis remains highly accurate for receivers within 70 meters, even in scenarios with large interference ranges. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Markov processes

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GRU based Multiview Summarisation to obtain fast effective Key-frame

GRU Based Multiview Summarisation to obtain fast effective K...

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2025 international conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Kandaswamy, Vijay Anand Joshua Kumaresan, S. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331513894

This paper proposes a novel approach to video summarisation by introducing a feature variance model that enhances video frame extraction. In typical video record aggregation systems, a variety of image processing tasks, such as acquisition, preprocessing, segmentation, and feature extraction, should be carried out with high precision to guarantee seamless system performance. Among these, feature extraction is essential since it breaks down large amounts of visual data into more manageable, informative vectors, and the success of the system is directly impacted by how effectively this process functions. Widely Recurrent Neural Networks (RNNs) are used in supervised video summarisation processes to obtain a model for the temporal dependencies between video frames. RNN's ability to learn long-range memory in the video is limited, and CNN often focusses on local information while losing long-term knowledge when the input sequence is lengthy. Therefore, a Gated Recurrent Unit based Neural Network (GRU-NN) for video summarisation is proposed, to classify them into keyframes and non-keyframes. Keyframes are selected for summarisation, while non keyframes are discarded or reduced. We explore various keyframe extraction techniques, analyse the performance of the proposed summarisation model, and discuss the results. Finally, we offer insights into the model's potential improvements and its broader applications in video summarisation tasks. © 2025 ieee.

关键词： Image segmentation

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