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International Conference on Electronics, Information and Communications (ICEIC)

作者： Jungkook Jo Keonhee Sim Sangwoong Sim Jaehoon Jun dept. of electrical and computer engineering and program in semiconductor convergence Inha University Incheon Republic of Korea dept. of electrical engineering Inha University Incheon Republic of Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

This paper proposes a novel spike generator for processing in memory (PIM) technology. Most of the electronics today utilize a von Neumann architecture. The von Neumann architecture suffers from the separation of memory and processor. This architecture delays data transfer between memory and processor. To overcome the issues, we utilize the spiking neural network (SNN) that combines memory and processor. SNN can be classified into voltage-based, current-based, and time-based architectures, each with its own pros and cons. Time-based SNN suffers from timing issues. To address the timing sensitivity of time-based neuron SNN, this paper proposes a PWM-based SNN. The PWM-based SNN utilizes pulse width-based logic to overcome timing sensitivity.

关键词： Sensitivity Image edge detection Neurons Spiking neural networks Data transfer Generators Delays Logic Next generation networking Clocks

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Unlocking the potential of RNN and CNN models for accurate rehabilitation exercise classification on multi-datasets

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Multimedia Tools and Applications 2025年第3期84卷 1261-1301页

作者： Zaher, Moamen Ghoneim, Amr S. Abdelhamid, Laila Atia, Ayman Software Engineering Program Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Computer Science Department Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Information System Department Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Cairo Ain Helwan11795 Egypt Giza October12451 Egypt

Physical rehabilitation is crucial in healthcare, facilitating recovery from injuries or illnesses and improving overall health. However, a notable global challenge stems from the shortage of professional physiotherapists, particularly acute in some developing countries, where the ratio can be as low as one physiotherapist per 100,000 individuals. To address these challenges and elevate patient care, the field of physical rehabilitation is progressively integrating computer Vision and Human Activity Recognition (HAR) techniques. Numerous research efforts aim to explore methodologies that assist in rehabilitation exercises and evaluate patient movements, which is crucial as incorrect exercises can potentially worsen conditions. This study investigates applying various deep-learning models for classifying exercises using the benchmark KIMORE and UI-PRMD datasets. Employing Bi-LSTM, LSTM, CNN, and CNN-LSTM, alongside a Random Search for architectural design and Hyper-parameter tuning, our investigation reveals the (CNN) model as the top performer. After applying cross-validation, the technique achieves remarkable mean testing accuracy rates of 93.08% on the KIMORE dataset and 99.7% on the UI-PRMD dataset. This marks a slight improvement of 0.75% and 0.1%, respectively, compared to previous techniques. In addition, expanding beyond exercise classification, this study explores the KIMORE dataset’s utility for disease identification, where the (CNN) model consistently demonstrates an outstanding accuracy of 89.87%, indicating its promising role in both exercises and disease identification within the context of physical rehabilitation. © The Author(s) 2024.

关键词： Classification (of information)

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Cascaded multiparameter quantum metrology

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Physical Review A 2025年第1期111卷 012618-012618页

作者： Gregory Krueper Lior Cohen Juliet T. Gopinath Department of Physics University of Colorado Boulder Boulder Colorado 80309 USA Department of Electrical Computer and Energy Engineering University of Colorado Boulder Boulder Colorado 80309 USA Materials Science and Engineering Program University of Colorado Boulder Boulder Colorado 80309 USA

We present an innovative, platform-independent concept for multiparameter sensing where the measurable parameters are in series, or cascaded, enabling measurements as a function of position. With temporally resolved detection, we show that squeezing can give a quantum enhancement in sensitivity over that of classical states by a factor of e2r, where r≈1 is the squeezing parameter. As an example, we have modeled an interferometer that senses multiple phase shifts along the same path, demonstrating a maximal quantum advantage by combining a coherent state with squeezed vacuum. Further classical modeling with up to 100 phases shows linear scaling potential for adding nodes to the sensor. The approach can be applied to remote sensing, geophysical surveying, and infrastructure monitoring.

关键词： Interferometers

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Cryptanalysis of Tripartite Authentication Protocol RFID/NFC Based on ECC 24

Cryptanalysis of Tripartite Authentication Protocol RFID/NFC...

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Proceedings of the 2024 5th International Conference on Big Data Economy and Information Management

作者： Min-Shiang Hwang Yung-Ling Chang Cheng-Ying Yang Chia-Chun Wu Department of Computer Science and Information Engineering Asia University Taichung Taiwan The Ph.D. Program in Artificial intelligence Asia University Taichung Taiwan Department of Computer Science University of Taipei Taipei Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen Taiwan

ISBN: (纸本)9798400711862

Recently, Wei & Chen proposed a secure three-party mutual authentication protocol for RFID based on elliptic curve cryptography (ECC). Their approach can allow insecure communication between the reader and the backend, so their approach must achieve mutual authentication between the tag, reader, and backend. In addition, their method also satisfies the essential characteristics of RFID systems, such as confidentiality, anonymity, resistance to tracking attacks, and spoofing attacks. However, this article finds that its method cannot meet the mutual authentication and denial of service attacks.

关键词： ECC

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Hyperspectral Image Analysis Using Maximum Abundance Classification

Hyperspectral Image Analysis Using Maximum Abundance Classif...

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International Conference on Intelligent Control and Information Processing (ICICIP)

作者： Nian Zhang Sougre-Nonma Stephanie Rouamba Wagdy H. Mahmoud Lara A. Thompson Department of Electrical and Computer Engineering University of the District of Columbia Washington D.C. USA Department of Mechanical Engineering Biomedical Engineering Program University of the District of Columbia Washington D.C. USA

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

This paper presents a satellite hyperspectral image processing method that utilizes a maximum abundance classifier to categorize different regions of hyperspectral images into ground truth classes. First, the class names for each endmember and their corresponding columns in the signature matrix are identified, followed by the visualization of their spectral profiles. Abundance maps for the endmembers are then generated using the fully constrained least squares (FCLS) method. Afterward, the maximum abundance classifier is applied, and the resulting classified image is displayed with color-coded pixels. The abundance maps illustrate the spatial distribution of endmembers across the hyperspectral image, where the abundance values of each pixel represent the proportion of each endmember present. By determining the highest abundance value for each pixel and assigning it to the corresponding endmember class, the pixels within the hyperspectral images are classified. Experimental results demonstrate that the proposed MAC method effectively handles mixed pixels. In addition, it can effectively deal with the mixed pixel problem in hyperspectral images because it identifies components by calculating the abundance values for each pixel rather than relying solely on single spectral features.

关键词： Visualization Image segmentation Satellites Graphical models Information processing Hyperspectral imaging Intelligent control Image classification Distribution functions

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Alleviating high PAPR and noise issues for optimized performance in ACO-OFDM-Based VLC systems

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Optics and Laser Technology 2025年 188卷

作者： Zenhom, Yasser A. Hamad, Ehab K.I. Elkarim, Mohammed Abd Elnabawy, Mohamed M. Communication Department Modern Academy for Engineering and Technology Cairo11571 Egypt Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Communication and Computer Engineering Program Faculty of Engineering Nahda University Beni Suef62517 Egypt Communication Engineering Department Giza Higher Institute of Engineering and Technology Giza12913 Egypt

Visible Light Communication (VLC) is an emerging technology poised to complement radio frequency (RF) communication systems in various high-demand applications. By employing optical orthogonal frequency division multiplexing (O-OFDM) techniques, VLC enhances data transmission rates and minimizes intersymbol interference. Among these techniques, asymmetrically clipped O-OFDM (ACO-OFDM) is notable for its high power efficiency and superior performance. However, a significant challenge in ACO-OFDM-based VLC systems is the high peak-to-average power ratio (PAPR), which forces light-emitting diodes (LEDs) to operate beyond their linear range, leading to signal distortion, power inefficiency, thermal stress, and reduced LED lifespan. Additionally, high PAPR increases receiver complexity and limits system scalability, necessitating effective mitigation strategies for reliable VLC deployment. This study addresses the PAPR challenge by introducing a hybrid reduction strategy that combines precoding with nonlinear companding techniques. The proposed approach attains a PAPR reduction of 6.1855 dB compared to basic ACO-OFDM without degrading bit error rate (BER) performance. Additionally, the study enhances BER performance by integrating two noise mitigation models at the receiver. These models improve system performance by 2.275 dB relative to standard ACO-OFDM while maintaining the same BER level. When applied together, the PAPR reduction strategy and noise mitigation models yield a total PAPR diminution of 6.2611 dB and an enhancement in BER performance of 0.76 dB. A detailed comparative analysis with established methods from the literature confirms the efficiency and robustness of the proposed approach. This systematic evaluation underscores its potential to address key limitations of traditional ACO-OFDM, positioning it as a viable solution for next-generation VLC systems. © 2025 Elsevier Ltd

关键词： Intersymbol interference

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The Robustness of Structural Features in Species Interaction Networks

arXiv

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arXiv 2025年

作者： Fard, Sanaz Hasanzadeh Dolson, Emily Department of Computer Science and Engineering Michigan State University United States Program in Ecology Evolution and Behavior Michigan State University United States

Species interaction networks are a powerful tool for describing ecological communities;they typically contain nodes representing species, and edges representing interactions between those species. For the purposes of drawing abstract inferences about groups of similar networks, ecologists often use graph topology metrics to summarize structural features. However, gathering the data that underlies these networks is challenging, which can lead to some interactions being missed. Thus, it is important to understand how much different structural metrics are affected by missing data. To address this question, we analyzed a database of 148 real-world bipartite networks representing four different types of species interactions (pollination, host-parasite, plant-ant, and seed-dispersal). For each network, we measured six different topological properties: number of connected components, variance in node betweenness, variance in node PageRank, largest Eigenvalue, the number of non-zero Eigenvalues, and community detection as determined by four different algorithms. We then tested how these properties change as additional edges – representing data that may have been missed – are added to the networks. We found substantial variation in how robust different properties were to the missing data. For example, the Clauset-Newman-Moore and Louvain community detection algorithms showed much more gradual change as edges were added than the label propagation and Girvan-Newman algorithms did, suggesting that the former are more robust. Robustness also varied for some metrics based on interaction type. These results provide a foundation for selecting network properties to use when analyzing messy ecological network data. © 2025, CC BY.

关键词： Anthropogenic

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Feasibility Analysis Study on Constructing a Grid Intrusion Detection System Using Semi-supervised Learning Models 2nd

Feasibility Analysis Study on Constructing a Grid Intrusion ...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Tsai, Chia-Wei Lin, Jason Yang, Chun-Wei Loo, Fu-Nie Department of Computer Science and Information Engineering National Taichung University of Science and Technology No.129 Sec.3 Sanmin Rd North District Taichung404 Taiwan Department of Computer Science and Engineering National Chung Hsing University No. 145 Xingda Rd. South District Taichung40227 Taiwan Master Program for Digital Health Innovation China Medical University No. 100 Sec. 1 Jingmao Rd. Beitun Dist. Taichung406040 Taiwan

ISBN: (纸本)9789819777853

In recent years, due to the proliferation of information and communication technology, as well as AI technology, industrial control systems, which were once in a closed network environment, have also integrated related technologies and gradually connected to external networks. Although this can enhance the efficiency of industrial production, it greatly increases the information security threats to industrial control systems. Among them, the information security issues of critical infrastructures are a key issue. If these critical infrastructures are attacked by cyber hackers, it could pose threats that jeopardize the safety of the general population. In light of this, this study focuses on the critical infrastructure of the power grid, proposing a semi-supervised intrusion detection system (IDS) technology by using the one-class classification. It analyzes data from the electricity distribution system to assess whether the system has been attacked. This research employs a power grid dataset to train the proposed model and execute the experiments. The experimental results demonstrate the feasibility of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Using Random Forest and Linear SVM for Dyslexia Classification on Hebb-Williams Mazes

Using Random Forest and Linear SVM for Dyslexia Classificati...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Padmanabh Kaushik Yih-Choung Yu Lisa Gabel Department of Electrical and Computer Engineering Lafayette College Easton PA USA Department of Psychology and Neuroscience Program Lafayette College Easton PA USA

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

Dyslexia is a learning disability that negatively impacts an individual's ability to read, write, spell, and sometimes speak. It results in difficulties in recognizing and decoding words and patterns, despite normal level of education and intelligence. Studies have shown that early detection of dyslexia is vital for improving learning abilities in young children. Many virtual platforms exist for diagnosing and rehabilitating dyslexia; however, most require tests that measure reading skills. Since developing reading capabilities can delay the detection of dyslexia, a gaming platform based on Hebb-Williams mazes has been developed. This platform does not rely on reading skills and can diagnose dyslexia in young children. This paper presents a machine learning driven approach using two algorithms - Random Forest and Linear SVM - to classify reading abilities based on data from participants performing virtual maze tasks, which are indicative of symptoms of dyslexia. Results from this study indicate that it is possible to predict dyslexia with up to 95% accuracy based on a participant's performance in virtual gaming environments.

关键词： Support vector machines Radio frequency Accuracy Dyslexia Prediction algorithms Boosting Classification algorithms Pattern recognition Random forests Synthetic data

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A review of hybrid EEG-based multimodal human–computer interfaces using deep learning: applications, advances, and challenges

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Biomedical engineering Letters 2025年 1-32页

作者： Lee, Hyung-Tak Shim, Miseon Liu, Xianghong Cheon, Hye-Ran Kim, Sang-Gyu Han, Chang-Hee Hwang, Han-Jeong Department of Electronics and Information Engineering Korea University 2511 Sejong-ro Jochiwon-eup Sejong-si30019 Korea Republic of Department of Artificial Intelligence Tech University of Korea Siheung Korea Republic of Department of Computer Information Korea University 2511 Sejong-ro Jochiwon-eup Sejong-si30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong Korea Republic of

Human–computer interaction (HCI) focuses on designing efficient and intuitive interactions between humans and computer systems. Recent advancements have utilized multimodal approaches, such as electroencephalography (EEG)-based systems combined with other biosignals, along with deep learning to enhance performance and reliability. However, no systematic review has consolidated findings on EEG-based multimodal HCI systems. This review examined 124 studies published from 2016 to 2024, retrieved from the Web of Science database, focusing on hybrid EEG-based multimodal HCI systems employing deep learning. The keywords used for evaluation were as follows: ‘Deep Learning’ AND ‘EEG’ AND (‘fNIRS’ OR ‘NIRS’ OR ‘MEG’ OR ‘fMRI’ OR ‘EOG’ OR ‘EMG’ OR ‘ECG’ OR ‘PPG’ OR ‘GSR’). Main topics explored are: (1) types of biosignals used with EEG, (2) neural network architectures, (3) fusion strategies, (4) system performance, and (5) target applications. Frequently paired signals, such as EOG, EMG, and fNIRS, effectively complement EEG by addressing its limitations. Convolutional neural networks are extensively employed for spatio-temporal-spectral feature extraction, with early and intermediate fusion strategies being the most commonly used. Applications, such as sleep stage classification, emotion recognition, and mental state decoding, have shown notable performance improvements. Despite these advancements, challenges remain, including the lack of real-time online systems, difficulties in signal synchronization, limited data availability, and insufficient explainable AI (XAI) methods to interpret signal interactions. Emerging solutions, such as portable systems, lightweight deep learning models, and data augmentation techniques, offer promising pathways to address these issues. This review highlights the potential of EEG-based multimodal HCI systems and emphasizes the need for advancements in real-time interaction, fusion algorithms, and XAI to enhance their adaptability, interpreta

关键词： Convolutional neural networks

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