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Iranian Conference of Biomedical engineering (ICBME)

作者： Ali Darzi Masouleh Fariba Bahrami Ahmad Kalhor School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331529710

ISBN: (纸本)9798331529727

In this research we analyzed EEG signals to classify motor planning and execution phases during a set of motor learning task. We began by preprocessing the EEG data and then displaying topographical plots (topoplots) of different frequency bands of EEG data to classify the phases. Afterward, we tested convolutional neural network (CNN) models, ResNet18 and EfficientNet to further enhance the processing and classification of the EEG signals. The CNN models were examined based on performance measures like accuracy, precision, recall, and F1-score. Primary findings suggested that ResNet-18 and EfficientNet models successfully distinguished the motor planning and execution phases, particularly EfficientNet presented better results. This study highlights the utilization of deep learning models as a potential way to classify EEG signals. This will help us to understand motor learning in humans better. Ultimately, after classifying the phases, we proceeded to classifing different drawn patterns and identify them using these two models. For classifying the phases using ResNet18 and EfficientNet networks, we managed to achieve accuracies of $86.3 \%$ and $89.2 \%$, respectively. Additionally, for pattern classification, accuracies of $\mathbf{8 7. 9 \%}$ for ResNet18 and $\mathbf{9 6. 9 \%}$ for EfficientNet were obtained.

关键词： Deep learning Accuracy Pattern classification Motors Brain modeling Electroencephalography Planning Convolutional neural networks Biomedical engineering

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Length matters:Scalable fast encrypted internet traffic service classification based on multiple protocol data unit length sequence with composite deep learning

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Digital Communications and networks 2022年第3期8卷 289-302页

作者： Zihan Chen Guang Cheng Ziheng Xu Shuyi Guo Yuyang Zhou Yuyu Zhao School of Cyber Science and Engineering Southeast UniversityNanjing211189China Key Laboratory of Computer Network and Information Integration Ministry of EducationNanjing211189China Purple Mountain Laboratories Nanjing211111China

As an essential function of encrypted Internet traffic analysis,encrypted traffic service classification can support both coarse-grained network service traffic management and security ***,the traditional plaintext-based Deep Packet Inspection(DPI)method cannot be applied to such a ***,machine learning-based existing methods encounter two problems during feature selection:complex feature overcost processing and Transport Layer Security(TLS)version *** this paper,we consider differences between encryption network protocol stacks and propose a composite deep learning-based method in multiprotocol environments using a sliding multiple Protocol Data Unit(multiPDU)length sequence as features by fully utilizing the Markov property in a multiPDU length sequence and maintaining suitability with a TLS-1.3 *** experiments show that both Length-Sensitive(LS)composite deep learning model using a capsule neural network and LS-long short time memory achieve satisfactory effectiveness in F1-score and *** to faster feature extraction,our method is suitable for actual network environments and superior to state-of-the-art methods.

关键词： Encrypted internet traffic Encrypted traffic service classification Multi PDU length sequence Length sensitive composite deep learning TLS-1.3

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Privacy Preserving Blockchain with Optimal Deep Learning Model for Smart Cities

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computers, Materials & Continua 2022年第12期73卷 5299-5314页

作者： K.Pradeep Mohan Kumar Jenifer Mahilraj D.Swathi R.Rajavarman Subhi R.M.Zeebaree Rizgar RZebari Zryan Najat Rashid Ahmed Alkhayyat Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Information Technology School of Engineering and TechnologyKebridehar UniversityKebridehar250Ethiopia Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Energy Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Computer Network Department Technical College of InformaticsSulaimani Polytechnic UniversitySulaimaniIraq College of Technical Engineering The Islamic UniversityNajafIraq

Recently,smart cities have emerged as an effective approach to deliver high-quality services to the people through adaptive optimization of the available *** the advantages of smart cities,security remains a huge challenge to be ***,Intrusion Detection System(IDS)is the most proficient tool to accomplish security in this ***,blockchain exhibits significance in promoting smart city designing,due to its effective characteristics like immutability,transparency,and *** order to address the security problems in smart cities,the current study designs a Privacy Preserving Secure Framework using Blockchain with Optimal Deep Learning(PPSF-BODL)*** proposed PPSFBODL model includes the collection of primary data using sensing ***,z-score normalization is also utilized to transform the actual data into useful ***,Chameleon Swarm Optimization(CSO)with Attention Based Bidirectional Long Short TermMemory(ABiLSTM)model is employed for detection and classification of *** is employed for optimal hyperparameter tuning of ABiLSTM *** the same time,Blockchain(BC)is utilized for secure transmission of the data to cloud *** cloud server is a decentralized,distributed,and open digital ledger that is employed to store the transactions in different methods.A detailed experimentation of the proposed PPSF-BODL model was conducted on benchmark dataset and the outcomes established the supremacy of the proposed PPSFBODL model over recent approaches with a maximum accuracy of 97.46%.

关键词： Blockchain smart city security intrusion detection system chameleon swarm optimization deep learning parameter tuning

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Multiple detectors-based Bayesian inference and statistics analysis for incipient fault detection

Multiple detectors-based Bayesian inference and statistics a...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Wang, Ruixue Zhao, Wenqing Cheng, Chao Zou, Xiaoyu Ji, Hongquan Shandong University of Science and Technology College of Electrical Engineering and Automation Qingdao China Changchun University of Technology College of Computer Science and Engineering Changchun China China University of Mining and Technology School of Mechanical and Electrical Engineering Xuzhou China

ISBN: (纸本)9798350337754

To establish a secure and dependable operational setting for practical industrial processes, it is crucial to detect incipient faults promptly and accurately. In this work, a novel data-driven process monitoring approach is presented for incipient fault detection. First, inspired by the ensemble learning algorithm, multiple Mahalanobis distance (MD) detectors are trained, and corresponding detection indices for each sample are calculated. Second, with Bayesian inference, all the obtained indices are converted to probabilities for samples being faulty. Furthermore, the statistics of probabilities are analyzed by implementing sliding time window technique, ultimately achieving incipient fault detection. Finally, the effectiveness of the proposed strategy is demonstrated by a numerical example and the continuous stirred tank reactor benchmark process. The proposed approach inherits the advantages of ensemble learning, so it has the capability to improve the stability of detection performance. Meanwhile, due to the utilization of Bayesian inference, together with the sliding time window technique, it can better differentiate normal and faulty samples and improve the sensitivity to incipient faults. © 2023 IEEE.

关键词： Fault detection

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Real-Time Automotive Accident Detection Using Unsupervised Machine Learning

Real-Time Automotive Accident Detection Using Unsupervised M...

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East-West Design & Test Symposium (EWDTS)

作者： Reza Baharvand Mahsa Jamshidi Mozhgan Rezaie Manavand Rezgar Sadeghi Zainalabedin Navabi School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331515768

ISBN: (纸本)9798331515775

This paper presents a comprehensive approach to accident detection using supervised and unsupervised learning methods. Our methodology encompasses data gathering, preprocessing, and dimensionality reduction, applied to a highly imbalanced dataset with 99.99% of instances being non-crash and 0.01% of instances being crash. We evaluate various traditional machine learning algorithms, which achieve high accuracy but fail to provide satisfactory precision, a critical metric for our application. To address this, we propose an unsupervised learning method—novelty detection—that effectively identifies crash instances without relying on labels, accurately predicting the crash events based on the provided “Crash Time.” Our results demonstrate the limitations of traditional supervised methods in this context and emphasize the potential of unsupervised learning for improved accident detection.

关键词： Dimensionality reduction Machine learning algorithms Embedded systems Vehicle safety Forestry Real-time systems Classification algorithms Unsupervised learning Anomaly detection Accidents

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List Edge Colorings of Planar Graphs without Non-induced 7-cycles

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Acta Mathematica Sinica,English Series 2025年第3期41卷 1037-1054页

作者： Li Zhang Hajo Broersma You Lu Shenggui Zhang School of Mathematics and Computer Science Yan’an UniversityYan’an716000P.R.China School of Mathematics and Statistics Northwestern Polytechnical UniversityXi’an710129P.R.China Faculty of Electrical Engineering Mathematics and Computer ScienceUniversity of TwenteP.O.Box 2177500 AEEnschedeNetherlands Xi’an-Budapest Joint Research Center for Combinatorics Northwestern Polytechnical UniversityXi’an710129P.R.China

A graph G is edge-k-choosable if,for any assignment of lists L(e)of at least k colors to all edges e∈E(G),there exists a proper edge coloring such that the color of e belongs to L(e)for all e∈E(G).One of Vizing’s classic conjectures asserts that every graph is edge-(Δ+1)-*** is known since 1999 that this conjecture is true for general graphs withΔ≤*** recently,in 2015,Bonamy confirmed the conjecture for planar graph withΔ≥8,but the conjecture is still open for planar graphs with 5≤Δ≤*** confirm the conjecture for planar graphs withΔ≥6 in which every 7-cycle(if any)induces a C_(7)(so,without chords),thereby extending a result due to Dong,Liu and Li.

关键词： Planar graph list edge coloring edge-k-choosability Combinatorial Nullstellensatz discharging

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A Robust Holomorphic Embedding-Based Method for Fast Tracing of P-V Curves With Limiting Points

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IEEE Transactions on Power Systems 2025年

作者： Zhang, Wen Zhang, Yusi Zhang, Cuiqing Chiang, Hsiao-Dong Wang, Tao Sun Yat-sen University School of Computer Science and Engineering Guangdong Guangzhou510006 China Cornell University School of Electrical and Computer Engineering IthacaNY14853 United States

Existing approaches for addressing the continuation power flow problem with limits are mainly iterative and can face the serious drawbacks, e.g., the divergence, slow convergence, and ignoring or mis-computing a limiting point, especially for large systems. This paper thus proposes a new method called Enhanced Holomorphic Embedding combining Arc-length Parametrization (E-HEAP) for high performance in the continuation power flow computation of large power systems. A novelty of E-HEAP lies in an effective treatment of the generator's physical hard limits and operating constraints tailored for the framework of holomorphic embedding. Another novelty is attributed to the calculations of the eigenvalues of companion matrices to locate the first position of a violating hard limit robustly and efficiently. These novelties turn the task of solving a set of nonlinear equations or optimization problems formulated by the majority of the existing methods into a simple linear algebra (or eigenvalue) problem for small-sized companion matrices to locate the potential limiting points. Several numerical examples are presented to explain this new eigenvalue- based approach to find limiting points. Distinguishing features of the proposed new method in capturing all the potential and first limiting points in comparison with an intuitive iterative scheme employed in existing methods are highlighted. The performance of E-HEAP in numerical robustness and efficiency is examined on power systems with 10,000+ buses and compared with existing methods that apply the predictor-corrector and other techniques. Numerical results show that E-HEAP can robustly and efficiently deal with the large power system voltage stability problems and significantly outperform other existing methods in terms of robust convergence, numerical efficiency, and technical soundness. © 1969-2012 IEEE.

关键词： Numerical methods

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Research on Low-Light Image Enhancement method based on UNet 5

Research on Low-Light Image Enhancement method based on UNet

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5th International Conference on Telecommunications, Optics, and computer Science, TOCS 2024

作者： Zhu, Fuyu Fei, Rong Ji, Chengjie School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shannxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China

ISBN: (纸本)9781510691667

As technology develops continuously, digital images are being applied more and more extensively in daily life. Digital images are mainly divided into two categories: one is the images obtained by photosensitive devices such as cameras, and the other is the images generated by software. Nevertheless, in low - light conditions, the images captured by traditional photosensitive devices typically suffer from issues like low luminance and subpar visual qualities. These problems pose significant challenges to subsequent image processing and practical applications. Thus, the technology of enhancing low - light images has emerged as a crucial research area for the purpose of elevating image quality. This paper puts forward a solution centered around the improved UNet network to address the issue of image enhancement in low - light settings. The UNet network effectively performs image enhancement through feature extraction, feature fusion, and restoration operations. However, the classical UNet can only process single - channel grayscale images, and the loss function it uses has relatively limited effects in low - light image processing. Therefore, this paper adjusts the number of channels of the UNet network so that it can process three - channel RGB images and improves the loss function. We compared the enhancement effects of L1 Loss, VGG Loss, and SmoothL1 Loss respectively, and carried out a quantitative analysis through evaluation metrics such as PSNR and SSIM. Ultimately, through the combination of the three loss functions and their weighted optimization, the enhancement effect of low - light images is notably enhanced. The innovation of this paper lies in expanding the structure of the UNet network to make it adaptable to three - channel image processing, and improving the quality of image enhancement by improving the loss function. This approach is capable of efficiently enhancing the visual impact of low - light images, thereby offering a practical solution for imag

关键词： Photointerpretation

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Design and Analysis of Deep Learning based Water Potability Prediction

Design and Analysis of Deep Learning based Water Potability ...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Arumugam, M. Vijayakumar, P. Kannan, A.C. Velmurugan, J. Arun, G. Rohith Bhat, C. Kongu Engineering College Department of Computer Technology - Pg Perundurai India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai638060 India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul624622 India Erode Sengunthar Engineering College Department of Artificial Intelligence and Data Science Thuduppathi638057 India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350300857

According to the United Nations, having access to clean water for consumption is a fundamental human right. General Assembly in 2010. The importance of safe drinking water cannot be overstated. Unsafe drinking water access is a significant public health concern, especially in poor nations. In many regions of the world, waterborne illnesses including cholera, typhoid fever, and dysentery are important causes of illness and death. These diseases are especially dangerous for children, who are more vulnerable to their effects. In addition, a lack of access to clean drinking water can result in other health issues like stunted growth and malnutrition. Access to safe drinking-water is also a key component of effective policy for health protection at all levels, whether it is at the national, regional, or local level. Policies aimed at improving access to safe drinking-water can include measures such as water quality testing, water treatment, and the provision of clean water sources. Investments in water supply and sanitation can have a positive economic impact, especially in areas with inadequate access to clean drinking water. The costs of implementing the treatments may be outweighed by the reductions in unfavorable health impacts and medical expenses that may result from these investments. In conclusion, having access to clean drinking water is crucial for encouraging wellness, reducing waterborne illnesses, and raising standard of living in general. The input to the DNN could be the various water quality parameters, such as pH, hardness, chloride content, and total dissolved solids, while the output would be a binary classification indicating whether the water is potable or not. The DNN would need to be trained on a large and diverse dataset of water samples to accurately classify new samples. © 2023 IEEE.

关键词： Potable water

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Reconfigurable Holographic Surface: A New Paradigm for Ultra-Massive MIMO

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IEEE Transactions on Cognitive Communications and networking 2025年

作者： Di, Boya Zhang, Hongliang Han, Zhu Zhang, Rui Song, Lingyang Peking University School of Electronics China University of Houston Department of Electrical and Computer Engineering HoustonTX United States Kyung Hee University Department of Computer Science and Engineering Seoul Korea Republic of The Chinese University of Hon g Kong School of Science and Engineering Guangdong Shenzhen China National University of Singapore Department of Electrical and Computer Engineering Singapore Singapore Peking University Shenzhen Graduate School School of Electronic and Computer Engineering China

Evolving from massive multiple-input multiple-output (MIMO) in current 5G communications, ultra-massive MIMO emerges as a seminal technology for fulfilling more stringent requirements of future 6G communications. However, widely-utilized phased arrays relying on active components make the implementation of ultra-massive MIMO in practice increasingly prohibitive from both cost and power consumption perspectives. In contrast, the development of reconfigurable holographic surface (RHS) provides a new paradigm to solve the above issue without the need of costly hardware components. By leveraging the holographic principle, the RHS serves as an ultra-thin and lightweight surface antenna integrated with the transceiver, which is a promising alternative to phased arrays for realizing ultra-massive MIMO. In this paper, we provide a comprehensive overview of the RHS, especially the RHS-aided communication and sensing. We first describe the basic concepts of RHS, and introduce its working principle and unique practical constraints. Moreover, we show how to utilize the RHS to achieve cost-efficient and high-performance wireless communication and sensing, and introduce the key technologies. In particular, we present the implementation of RHS with a wireless communication prototype, and report the experimental measurement results based on it. Finally, we outline some open challenges and potential future directions in this area. © 2015 IEEE.

关键词： Antenna phased arrays

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