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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Abiodun, Opeyemi Anuoluwa Oyinloye, Oghenerukevwe E. Thompson, Aderonke F. Olowoyo, Paul Imoize, Agbotiname Lucky Sur, Samarendra Nath Computer Science Department The Federal Polytechnic Ekiti State Ado-Ekiti360211 Nigeria Computer Science Department Ekiti State University Ekiti State Ado-Ekiti360211 Nigeria Cyber Security Department Federal University of Technology Ondo State Akure350106 Nigeria Federal Teaching Hospital Ekiti State Ido-Ekiti Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India

ISBN: (纸本)9789819764648

Biometric authentication scheme has been widely adopted for authentication purpose due to its uniqueness, universality, and distinctiveness. However, research has shown that these schemes are not necessarily more secured, especially with issues of coercion proliferating the cyberspace. Also, available techniques on repudiation of biometric features for authentication have not adequately explored this exciting topic. Integrating emotional state into the authentication scheme helps to mitigate coercion. This paper presents a framework for emotion as a way of biometric authentication scheme. An emotion classification model was developed by training an emotion classifier brainwave signal from eight subjects under normal state and duress using KNN machine learning algorithm. The authentication scheme grants access to users who pass both verification phases. The emotion classification model achieved an accuracy of 93.6% on the full feature set, while the reduced-feature set, as a result of feature selection, produced an improved accuracy of 94.15%. Hence, the emotion classification model can be integrated into existing biometric authentication system for improved security of critical user information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biometrics

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Deep Learning-Powered Edge Analytics for IoT-based Sensor Networks 2

Deep Learning-Powered Edge Analytics for IoT-based Sensor Ne...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Karthikeyan, M. Saravana Subramani, Ponnusamy Selvam, M. Panneer Sworna Kokila, M.L. Vijayalakshmi, R. Sundaravadivel, P. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Physics Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India School of Computing SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331505745

The rapid expansion of loT-based sensor networks has necessitated the development of efficient edge analytics frameworks to process vast amounts of data in real time while minimizing computational overhead. Deep learning-powered edge computing has emerged as a transformative approach, enhancing data processing capabilities at the network edge while addressing constraints related to latency, bandwidth, and energy consumption. This study introduces a novel algorithm, Lightweight AI Model Compression for Energy-Efficient IoT Edge Devices (LiteEdgeAI), designed to optimize deep learning models for resource-constrained edge environments. The proposed method employs quantization, pruning, and knowledge distillation techniques to achieve significant reductions in model size and computational complexity while maintaining high inference accuracy. To validate the efficiency of LiteEdgeAI, a comparative simulation analysis was conducted against existing state-of-the-art algorithms using key performance metrics such prediction accuracy, energy efficiency and computational complexity. Experimental results demonstrated that LiteEdgeAI outperformed baseline models. The findings highlight the potential of LiteEdgeAI in enabling scalable, low-power, and real-time edge intelligence for IoT-based sensor networks. © 2025 IEEE.

关键词： Deep learning

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Hybrid Convtranslstm for Spatio-Temporal Classification: Identifying Early Parkinson's Disease from Gait Patterns

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SSRN 2025年

作者： Mahali, Muhammad Izzuddin Avian, Cries Putro, Nur Achmad Sulistyo Prakosa, Setya Widyawan Leu, Jenq-Shiou Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan Department of Electronics and Informatics Engineering Faculty of Engineering Universitas Negeri Yogyakarta Indonesia Department Electrical Engineering Faculty of Engineering Universitas Brawijaya Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Indonesia

Parkinson's disease (PD) is a prevalent neurodegenerative disorder that affects motor function and impacts millions worldwide. Early diagnosis and precise staging of PD are critical for effective management and timely intervention. Traditional gait analysis focuses on lower limb movement to differentiate between moderate and advanced PD stages;however, integrating upper and lower limb dynamics offers a more comprehensive approach to distinguishing early-stage PD from *** study introduces a novel spatio-temporal model that integrates CNN, Transformer, and BiLSTM layers to classify PD based on the Hoehn and Yahr (H&Y) score. The architecture captures both spatial and temporal dependencies in gait patterns, with a CNN serving as the primary spatial feature extractor, utilizing a temporal-focused kernel, and augmented by a Transformer to enhance temporal feature learning. Additionally, a BiLSTM module is incorporated to effectively capture complex spatio-temporal gait dynamics across all modules. For data augmentation, sliding windows of 500, 800, and 1200 samples were employed, and the model's performance was rigorously assessed through 3-fold and 5-fold cross-subject *** included accuracy, F1-score, recall, precision, and a voting-based confidence score, while t-SNE visualization provided insights into spatio-temporal feature differentiation. The model demonstrated high performance, achieving optimal accuracy of 0.90 and an average confidence score of 0.97 with a 1200-sample window in 5-fold validation. These findings highlight the potential of a spatio-temporal approach incorporating upper and lower limb characteristics in gait analysis as a robust method for PD multi-class classification, supporting early and effective PD diagnosis from raw IMU signals. © 2025, The Authors. All rights reserved.

关键词： Neurodegenerative diseases

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Dual-Stage Interleaved On-board Charger for Electric Vehicle using Snow Geese Algorithm 4

Dual-Stage Interleaved On-board Charger for Electric Vehicle...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Chandrashekhar, A. Mallikarjuna, B. Thiyagesan, M. Umasankar, L. Ezhil, G. Devi, Rukmani Icfai Foundation for Higher Education Faculty of Science and Technology Department of Mechanical Engineering Telangana Hyderabad501203 India Rns Institute of Technology Department of Electrical and Electronics Engineering Karnataka Bengaluru560098 India R.M.K Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai601206 India S. A. Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai600077 India Bannari Amman Institute of Technology Department of English Tamil Nadu Sathyamangalam638401 India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331523923

The growing demand for electric vehicles (EVs) has highlighted the need for efficient and reliable on-board chargers (OBCs) to reduce charging time and enhance battery life. Nevertheless, there are some issues in classic OBC designs: harmonic distortion may become too large, electromagnetic interference increases, and thermal management is not proper;all of which directly affects other elements of the system. As an optimization approach to enhance the performance of the dual-stage interleaved on-board charger (OBC) for electric vehicles (EVs), this work introduces the Snow Geese Algorithm (SGA). The objective of the proposed method is to enhance charging efficiency and minimize cost. The proposed SGA is used to optimize the dual-stage interleaved converter, focusing on reliable performance and effective operation within EV charging systems. The suggested approach is then put into practice on the MATLAB platform and contrasted with a number of current techniques, including Artificial Bee Colony (ABC), Simulated Annealing (SA), Multi-objective Genetic Algorithm (MOGA), and Particle Swarm Optimization (PSO). The proposed SGO method for dual-stage interleaved EV chargers achieves a cost of 1,050.80 and an efficiency of 97.2 %, demonstrating its effectiveness in reducing expenses while maximizing optimization performance in charging systems. © 2025 IEEE.

关键词： Simulated annealing

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Nanostructured Quadric-Confined Quantum Dot–Quantum Well Kesterite Solar Cell: A Pathway for Cost-Effective Photosensitive Devices

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Physica Status Solidi (A) Applications and Materials Science 2025年第0期

作者： Mohanty, Smruti Ranjan Palanisamy, Chandrasekar Sahoo, Sudarsan Routray, Soumyaranjan Department of Electronics and Instrumentation Engineering NIT Silchar Assam Silchar788010 India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara391760 India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai603203 India

Nanostructure kesterite-based solar with a combination of multi-quantum dots (QDs) and multi-quantum well (QW) region is proposed to improve photoabsorption rate and conversion efficiency. The scope of kesterite hybrid-confined structure attracts researchers due to their environment-friendly, encouraging optic-electrical characteristics and carrier confinement properties. The optimal design of a quadric-confined QDQW solar cell (QCQDQW) focuses on nanostructures that improve the open-circuit voltage Voc. This article examines the overall performance of QCQDQW structure by implementing both QD (width 10 nm) and QW (10 nm barrier and 5 nm well width) layers (CZGSe/ (Formula presented.)) considering S/Se composition variations (x = 0.2–0.8). Moreover, a thin 5 nm barrier layer is engineered between QD and QW layers, enhancing effective carrier quantization and collection in solar cell. The remarkable efficiency of 17.37% and Voc of 0.945 V is obtained for three QW regions and five QD layers with the highest Jsc value of 21.06 mA. This study encourages a new direction in quantum-confined solar structure. © 2025 Wiley-VCH GmbH.

关键词： Semiconductor quantum dots

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Design of mmWave Broadband Rotary Joint and 360° Beam-Steering Rotenna Based on Gap Waveguide technology

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IEEE Transactions on Antennas and Propagation 2025年

作者： Farahbakhsh, Ali Zarifi, Davood Mrozowski, Michal Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Microwave and Antenna Engineering Gdansk80-233 Poland Graduate University of Advanced Technology Department of Electrical and Computer Engineering Kerman Iran University of Kashan School of Electrical and Computer Engineering Kashan Iran

This work presents the design and fabrication of a groove gap waveguide-based rotary joint and a rotenna (rotary antenna) with 360° mechanical beam-steering for wideband mmWave applications. The proposed rotary joint incorporates two WR-22 to groove gap waveguide transitions, connected back-to-back with an innovative transformer. A key advantage of gap waveguide technology is its ability to operate without physical contact between the rotary joint's components, allowing the rotor to rotate freely near the stator with an air gap, while wave leakage is suppressed. By replacing common coaxial connectors with standard waveguide flanges and introducing a novel transformer between the rotor and stator, we have successfully developed a wideband rotary joint suitable for mmWaves. To demonstrate the practical applicability of this concept, we designed a rotenna with 360° mechanical beam-steering. This design addresses the common limitations of electronically steerable antennas, such as limited steering range, gain degradation, and high cost and complexity. To validate the concept, prototypes of the proposed rotary joint and rotenna were fabricated and measured. The measured and simulated results show good agreement. The rotary joint exhibits a 44.4% fractional bandwidth over the frequency range of 35 to 55 GHz with an insertion loss below 0.68 dB. Additionally, the engineered rotenna demonstrates a peak realized gain of 13 dBi with no scan loss over the full 360° steering range. © 1963-2012 IEEE.

关键词： Steerable antennas

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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A Novel RIS-Empowered Base Station: A Practical Implementation and Experimental Validation

A Novel RIS-Empowered Base Station: A Practical Implementati...

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IEEE Conference on Wireless Communications and Networking

作者： Je Hyeon Park Suhyun Kim Muhammad Miftahul Amri Nguyen Minh Tran Dong In Kim Kae Won Choi Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea Faculty of Electronics and Telecommunications VNU - University of Engineering and Technology Hanoi Vietnam

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

This article presents a novel reconfigurable intelligent surface (RIS)-integrated base station (BS) by deploying an RIS very close to the base station antennas (BAs), within its radiative near-field range. We propose a practical algorithm to maximize the performance of uplink communications with the proposed RIS-integrated BS while maintaining reasonable complexity. Furthermore, we have implemented a testbed to experiment with the proposed RIS-integrated BS-based uplink multiple-input-multiple-output (MIMO) system. The experimental data validates the performance of the proposed RIS-integrated BS-based uplink MIMO system.

关键词： Base stations Barium Reconfigurable intelligent surfaces MIMO Data models Complexity theory Uplink Antennas

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Extremum Encoding for Joint Baseband Signal Compression and Time-Delay Estimation for Distributed Systems

Extremum Encoding for Joint Baseband Signal Compression and ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Amir Weiss Yuval Kochman Gregory W. Wornell Faculty of Engineering Bar-Ilan University School of Computer Science and Engineering The Hebrew University of Jerusalem Research Laboratory of Electronics Massachusetts Institute of Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The ubiquitous time-delay estimation (TDE) problem becomes nontrivial when sensors are non-co-located and communication between them is limited. Building on the recently proposed "extremum encoding" compression-estimation scheme, we address the critical extension to complex-valued signals, suitable for radio-frequency (RF) baseband processing. This extension introduces new challenges, e.g., due to unknown phase of the signal of interest and random phase of the noise, rendering a naïve application of the original scheme inapplicable and irrelevant. In the face of these challenges, we propose a judiciously adapted, though natural, extension of the scheme, paving its way to RF applications. While our extension leads to a different statistical analysis, including extremes of non-Gaussian distributions, we show that, ultimately, its asymptotic behavior is akin to the original scheme. We derive an exponentially tight upper bound on its error probability, corroborate our results via simulation experiments, and demonstrate the superior performance compared to two benchmark approaches.

关键词： Radio frequency Baseband Upper bound Statistical analysis Noise Estimation Rendering (computer graphics) Encoding Sensors Speech processing

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Rotor Poles Analysis of a Novel Wound Field Flux Switching Machine with π - Shaped Stator 33

Rotor Poles Analysis of a Novel Wound Field Flux Switching M...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Raafi, Rafiullah Shirani, Habiburahman Ullah, Wasiq Khan, Wasiullah Akuru, Udochukwu Bola Khan, Faisal Kabul Polytechnic University Faculty of Electromechanics Dept. of Electrical Engineering Kabul Afghanistan Kabul University Faculty of Engineering Dept. of Electrical and Electronics Engineering Kabul Afghanistan Kabul Polytechnic University Vice Chancellor of Research and Journals Kabul Afghanistan Comsats University Islamabad Abbottabad Campus Dept. of Electrical and Computer Engineering Abbottabad Pakistan Tshwane University of Technology Pretoria West Campus Dept. of Electrical Engineering Pretoria South Africa

ISBN: (纸本)9798331535162

In this paper, the performance of various rotor pole configurations in a novel π-shaped stator wound-field flux switching machine (WFFSM) is investigated to address concerns with cost and environmental concerns associated with permanent magnet machines. By employing the finite element analysis (FEA) method, the study focuses on the no-load, on-load and overload performance characteristics of the proposed machines based on four different rotor poles-13P, 14P, 16P and 17P. The 14P rotor configuration emerges as superior machine, showcasing highest torque, lowest torque ripple and best overload capability. The future study will cover full optimization of all four machines with experimental validation. © 2025 IEEE.

关键词： Stators

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