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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： V. Syambabu K. Kalaiselvi S. Derisha Mahil Hemalatha S Badi Alekhya N. Kanagavalli Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of CSE(AIML) S.A.Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of AIML R.M.D Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

This research paper introduces a sophisticated arm movement tracking system, leveraging a multi-sensor fusion approach for enhanced precision and versatility. The comprehensive system comprises various components, including a dedicated cabinet, six Leap Motion sensors, six microcomputers, a Kinect depth camera, a KVM switcher, a router, and flex sensors. In addition to the aforementioned components, the integration of flex sensors enhances the system's capability to capture nuanced movements and gestures of the human arm. Flex sensors contribute valuable data to the multi-sensor fusion, further refining the arm movement tracking and providing a comprehensive understanding of the intricacies involved. By quantifying the Root Mean Square Error (RMSE) for each filtering technique, including the data from flex sensors, we gained valuable insights into the accuracy and precision achieved by Particle Filtering in refining data from the specified angles. This approach ensures a thorough validation of Particle Filtering's superior performance when compared to Existing filtering Methods. The systematic evaluation of RMSE serves as a robust method for assessing the effectiveness of Particle Filtering, affirming its potential as a high-performing solution for precise angle estimation and tracking within the context of the presented arm movement tracking system that incorporates flex sensors.

关键词： Flexible printed circuits Systematics Filtering Tracking Refining Switches Sensor fusion

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Maneuver at Micro Scale: Steering by Actuation Frequency Control in Micro Bristle Robots∗

Maneuver at Micro Scale: Steering by Actuation Frequency Con...

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2020 IEEE International Conference on Robotics and Automation, ICRA 2020

作者： Hao, Zhijian Kim, DeaGyu Mohazab, Ali Reza Ansari, Azadeh School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30308 United States Foundation for the Advancement of Sciences Humanities Engineering and Arts Fashea RichmondBC Canada

ISBN: (纸本)9781728173955

This paper presents a novel steering mechanism, which leads to frequency-controlled locomotion demonstrated for the first time in micro bristle robots. The miniaturized robots are 3D-printed, 12 mm × 8 mm × 6 mm in size, with bristle feature sizes down to 400 μm. The robots can be steered by utilizing the distinct resonance behaviors of the asymmetrical bristle sets. The left and right sets of the bristles have different diameters, and thus different stiffnesses and resonant frequencies. The unique response of each bristle side to the vertical vibrations of a single on-board piezoelectric actuator causes differential steering of the robot. The robot can be modeled as two coupled uniform bristle robots, representing the left and the right sides. At distinct frequencies, the robots can move in all four principal directions: forward, backward, left and right. Furthermore, the full 360 2D plane can be covered by superimposing the principal actuation frequency components with desired amplitudes. In addition to miniaturized robots, the presented resonance-based steering mechanism can be applied over multiple scales and to other mechanical systems. © 2020 IEEE.

关键词： Robots

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Cloud-enabled high-altitude platform systems: Challenges and opportunities

arXiv

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arXiv 2021年

作者： Mershad, Khaleel Dahrouj, Hayssam Sarieddeen, Hadi Shihada, Basem Al-Naffouri, Tareq Alouini, Mohamed-Slim The Department of Computer and Communications Engineering Arts Sciences & Technology University Beirut Lebanon The Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

Augmenting ground-level communications with flying networks, such as the high-altitude platform system (HAPS), is among the major innovative initiatives of the next generation of wireless systems (6G). Given HAPS quasi-static positioning at the stratosphere, HAPS-to-ground and HAPS-to-air connectivity frameworks are expected to be prolific in terms of data acquisition and computing, especially given the mild weather and quasi-constant wind speed characteristics of the stratospheric layer. This paper explores the opportunities stemming from the realization of cloud-enabled HAPS in the context of telecommunications applications and services. The paper first advocates for the potential physical advantages of deploying HAPS as flying data-centers, also known as super-macro base stations. The paper then presents the merits that can be achieved by integrating various cloud services within the HAPS, and the corresponding cloud-type applications that would utilize the HAPS for enhancing the quality, range, and types of offered services. The paper further sheds light on the challenges that need to be addressed for realizing practical cloud-enabled HAPS, mainly, those related to the high energy, processing power, quality of service (QoS), and security considerations. Finally, the paper discusses some open issues on the topic, namely, HAPS mobility and message routing, HAPS security via blockchain and machine learning, artificial intelligence-based resource allocation in cloud-enabled HAPS, and integration with vertical heterogeneous networks. © 2021, CC BY.

关键词： Web services

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Deep Learning-based Detection of Greek Locations and Landmarks: A Qualitative Analysis of Weakly Supervised Classification and Supervised Detection

Deep Learning-based Detection of Greek Locations and Landmar...

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International Conference on Mathematics and computers in Sciences and in Industry (MCSI)

作者： Eleftherios Trivizakis Thomas Kassiotis Nikolaos Oikonomou Ioannis Karatzanis Ioannis Stefanis Leoni Chondromatidou Anastasia Manou Athena Panagiotidou Ioannis Papadopoulos Kyriakos Mamoukaris Manolis Tsiknakis Kostas Marias Computational BioMedicine Laboratory (CBML) Foundation for Research and Technology Hellas (FORTH) Heraklion Greece White Fox S.A Athens Greece ICT and Technical Operations Directorate National Centre of Audiovisual Media and Communication Athens Greece ICT and Technical Operations Directorate National Centre of Audiovisual Media and Communication (EKOME) Athens Greece Department of Electrical & Computer Engineering Computational BioMedicine Laboratory (CBML) Foundation for Research and Technology Hellas (FORTH)/Hellenic Mediterranean University Heraklion Greece

The LoockMe platform integrates a wide array of artificial intelligence models that perform natural language processing and deep learning image analysis to assist audiovisual professionals in identifying the best locations for producing content. The latest iteration of the LoockMe dataset consists of images of Greek locations or landmarks with thousands of points of interest annotated and labeled by a diverse group of raters. Transfer learning was employed to adapt several YOLO models originally trained on the COCO dataset. The platform can label and annotate input images of Greek landscapes to identify suitable locations to film audiovisual content. The deep learning-based object detection model achieved high performance with a mAP score of 0.62. Additionally, the presented qualitative analysis sheds light on the real-world performance of the proposed detection model. The LoockMe artificial intelligence-powered platform aims to significantly accelerate location scouting for audiovisual production in Greece.

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Stable optical beats in laser chaos near the threshold level for THz waves generation 16

Stable optical beats in laser chaos near the threshold level...

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Terahertz, RF, Millimeter, and Submillimeter-Wave technology and Applications XVI 2023

作者： Kuwashima, Fumiyoshi Jarrahi, Mona Cakmakyapan, Semih Morikawa, Osamu Shirao, Takuya Iwao, Kazuyuki Kurihara, Kazuyoshi Kitahara, Hideaki Furuya, Takashi Wada, Kenji Nakajima, Makoto Tani, Masahiko Department of Electrical and Electronic Engineering Fukui University of Technology 3-6-1 Gakuen Fukui910-8505 Japan Electrical and Computer Engineering Department University of California Los Angeles 66-147E Engineering IV 420 Westwood Plaza Los AngelesCA90095 United States Liberal Arts Japan Coast Guard Academy Wakabacho 5-1 Kure Hiroshima737-8512 Japan School of Education University of Fukui 3-9-1 Bunkyo Fukui910-8507 Japan Research Center for Development of Far-Infrared Region University of Fukui 3-9-1 Bunkyo Fukui910-8507 Japan Department of Physics and Electronics Osaka Metropolitan University 1-1 Gakuen-cho Naka-ku Osaka Sakai599-8531 Japan Institute of Laser engineering Osaka Univ. 2-6 Yamadaoka Osaka Suita565-0871 Japan

ISBN: (纸本)9781510659452

Stability of optical beats in a chaotically oscillating laser is compared to that of a free-running continuous-wave laser using a highly efficient plasmonic photomixer (Antenna). Numerical calculations were also performed for the multimode semiconductor laser with optical delayed feedback system. As a result, fundamental results on modes simultaneity in laser chaos is obtained. © 2023 SPIE.

关键词： Semiconductor lasers

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Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection

arXiv

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arXiv 2025年

作者： Xu, Meiyan Chen, Qingqing Chen, Duo Ding, Yi Wang, Jingyuan Gu, Peipei Pan, Yijie Huang, Deshuang Guo, Jiayang Zhang, Xun Department of Computer Science The Institute of Human Factors Engineering Minnan Normal University Zhangzhou363000 China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing210023 China College of Computing and Data Science Nanyang Technological University Singapore School of Digital Media and Design Arts Beijing University of Posts and Telecommunications Beijing100088 China College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450000 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo315200 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315200 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361005 China

EEG-based fatigue monitoring can effectively reduce the incidence of related traffic accidents. In the past decade, with the advancement of deep learning, convolutional neural networks (CNN) have been increasingly used for EEG signal processing. However, due to the data’s non-Euclidean characteristics, existing CNNs may lose important spatial information from EEG, specifically channel correlation. Thus, we propose the node-holistic graph convolutional network (NHGNet), a model that uses graphic convolution to dynamically learn each channel’s features. With exact fit attention optimization, the network captures inter-channel correlations through a trainable adjacency matrix. The interpretability is enhanced by revealing critical areas of brain activity and their interrelations in various mental states. In validations on two public datasets, NHGNet outperforms the SOTAs. Specifically, in the intra-subject, NHGNet improved detection accuracy by at least 2.34% and 3.42%, and in the inter-subjects, it improved by at least 2.09% and 15.06%. Visualization research on the model revealed that the central parietal area plays an important role in detecting fatigue levels, whereas the frontal and temporal lobes are essential for maintaining vigilance. Copyright © 2025, The Authors. All rights reserved.

关键词： Electroencephalography

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A Two-Stage Approach to Device-Robust Acoustic Scene Classification

A Two-Stage Approach to Device-Robust Acoustic Scene Classif...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Hu Hu Chao-Han Huck Yang Xianjun Xia Xue Bai Xin Tang Yajian Wang Shutong Niu Li Chai Juanjuan Li Hongning Zhu Feng Bao Yuanjun Zhao Sabato Marco Siniscalchi Yannan Wang Jun Du Chin-Hui Lee School of Electrical and Computer Engineering Georgia Institute of Technology GA USA Tencent Media Lab Tencent Corporation China University of Science and Technology of China HeFei China Computer Engineering School University of Enna Kore Italy

ISBN: (纸本)9781728176055;9781728176062

To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convolutional neural networks (CNNs) is proposed. Our two-stage system leverages on an ad-hoc score combination based on two CNN classifiers: (i) the first CNN classifies acoustic inputs into one of three broad classes, and (ii) the second CNN classifies the same inputs into one of ten finergrained classes. Three different CNN architectures are explored to implement the two-stage classifiers, and a frequency sub-sampling scheme is investigated. Moreover, novel data augmentation schemes for ASC are also investigated. Evaluated on DCASE 2020 Task 1a, our results show that the proposed ASC system attains a state-of-the-art accuracy on the development set, where our best system, a two-stage fusion of CNN ensembles, delivers a 81.9% average accuracy among multi-device test data, and it obtains a significant improvement on unseen devices. Finally, neural saliency analysis with class activation mapping (CAM) gives new insights on the patterns learnt by our models.

关键词： Analytical models Image analysis Conferences Signal processing Acoustics Robustness Data models

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A Novel Method for Attendance Marking System Using Hybrid LSTM and RNN Based Networks

A Novel Method for Attendance Marking System Using Hybrid LS...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： E. Manigandan Mohammed Saleh Al Ansari Praveena Nuthakki Bhuvneshwari S G Kala Priyadharshini Muruganantham Ponnusamy Department of Computer Applications Saveetha College of Liberal Arts and Sciences SIMATS Thandalam Tamilnadu India Department of Chemical Engineering College of Engineering University of Bahrain Bahrain Department of CSE Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Mathematics KSR Institute for Engineering and Technology Tiruchengode Tamilnadu India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Indian Institute of Information Technology Kalyani Nadia West Bengal India

The suggested system uses face detection and identification algorithms to automate the attendance marking and management processes. Face recognition refers to the process of identifying a person by their distinctive facial traits. At the moment, face recognition software is the most rapidly developing field in IT. This suggested system seeks to replace manual attendance tracking with an automated system that uses facial recognition technology to keep track of which students are present in the classroom at any given time. The primary goal of this endeavor is to create an automatic, user-friendly system for recording and managing attendance. The suggested method involves analyzing data, selecting features to employ, and assessing the model's efficiency. The suggested method employs Gaussian blur, segmentation, and scaling for preprocessing. PCA and LDA are used for feature selection and extraction, respectively. The LSTM-KNN hybrid method is used for model training. When compared to LSTM and KNN, two existing approaches, the proposed methodology performs exceptionally well.

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Routing on the Shortest Pairs of Disjoint Paths

Routing on the Shortest Pairs of Disjoint Paths

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IFIP Networking Conference

作者： Pé ter Babarczi Gá bor Ré tvá ri Lajos Ró nyai Janos Tapolcai Department of Telecommunications and Media Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Department of Algebra Institute for Computer Science and Control Institute of Mathematics Budapest University of Technology and Economics E&#x00F6 tv&#x00F6 s Lorand Research Network Budapest Hungary

Recent trends point towards communication networks will be multi-path in nature to increase failure resilience, support load-balancing and provide alternate paths for congestion avoidance. We argue that the transition from single-path to multi-path routing should be as seamless as possible in order to lower the deployability barrier for network operators. Therefore, in this paper we are focusing on the problem of routing along the shortest pairs of disjoint paths between each source-destination pair over the currently deployed link-state routing architecture. We show that the union of disjoint path-pairs towards a given destination has a special structure, and we propose an efficient tag encoding scheme which requires only one extra forwarding table entry per router per destination. Our numerical evaluations demonstrate that in real-world topologies usually only 4 bit tags are sufficient in the packet headers to route on the disjoint path-pairs. Finally, we show that our tags automatically encode additional paths beyond the shortest pair of disjoint paths, including the shortest paths themselves, which enables incremental deployment of the proposed method.

关键词： Focusing Tagging Routing Data structures Market research Load management Encoding

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A Conceptual Exploration of Generative AI-Induced Cognitive Dissonance and its Emergence in University-Level Academic Writing

arXiv

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arXiv 2025年

作者： Seran, Carl Errol Tan, Myles Joshua Toledo Karim, Hezerul Abdul AlDahoul, Nouar Biology Program College of Arts and Sciences University of St. La Salle Bacolod Philippines Department of Natural Sciences College of Arts and Sciences University of St. La Salle Bacolod Philippines Faculty of Education University of the Philippines Open University Los Baños Philippines Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Department of Epidemiology College of Public Health and Health Professions College of Medicine University of Florida GainesvilleFL United States Department of Chemical Engineering College of Engineering and Technology University of St. La Salle Bacolod Philippines Department of Electronics Engineering College of Engineering and Technology University of St. La Salle Bacolod Philippines Yo-Vivo Corporation Bacolod Philippines Faculty of Engineering Multimedia University Persiaran Multimedia Cyberjaya Malaysia Department of Computer Science Division of Science New York University Abu Dhabi Abu Dhabi United Arab Emirates

The integration of Generative Artificial Intelligence (GenAI) intouniversity-level academic writing presents both opportunities and challenges,particularly in relation to cognitive dissonance (CD). This work explores howGenAI serves as both a trigger and amplifier of CD, as students navigateethical concerns, academic integrity, and self-efficacy in their writingpractices. By synthesizing empirical evidence and theoretical insights, weintroduce a hypothetical construct of GenAI-induced CD, illustrating thepsychological tension between AI-driven efficiency and the principles oforiginality, effort, and intellectual ownership. We further discuss strategiesto mitigate this dissonance, including reflective pedagogy, AI literacyprograms, transparency in GenAI use, and discipline-specific task *** approaches reinforce critical engagement with AI, fostering a balancedperspective that integrates technological advancements while safeguarding humancreativity and learning. Our findings contribute to ongoing discussions on AIin education, self-regulated learning, and ethical AI use, offering aconceptual framework for institutions to develop guidelines that align AIadoption with academic values. © 2025, CC BY.

关键词： Generative adversarial networks

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