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12th International Conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Kumar, Munish Kumar, Sandeep Gulhane, Monali Beniwal, Rajender Kumar Nitin Choudhary, Shilpha Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India Department of Cse Pune India Electrical and Electronics Engineering Guru Jambheshwar University of Science and Technology Haryana Hisar India Govt. Polytechnic Department of Computer Engg. Haryana Hisar India Hyderabad India

ISBN: (纸本)9798350369861

In the realm of biometric authentication and forensic analysis, the accurate matching of fingerprints is paramount. Fingerprint displacement, caused by factors such as skin elasticity and pressure during touch, has been a significant challenge in achieving precise fingerprint recognition both during the enrollment and the authentication process. This research presents a technique to address this issue by leveraging Support Vector Machines (SVM) for fingerprint transformation and displacement minimization. The proposed methodology involves the extraction of distinctive fingerprint features and the application of SVM-based algorithms to realign and correct fingerprint distortions resulting from displacement. We analyze the effectiveness of SVM in reducing displacement-induced errors and improving matching accuracy. Experimental results demonstrate the potential of SVM-based fingerprint transformation techniques to significantly enhance the robustness and reliability of fingerprint recognition systems. This research contributes to the ongoing efforts in biometrics and forensic science by providing a practical solution to the problem of fingerprint displacement, with potential applications in identity verification, criminal investigations, and security systems. The findings of this study offer a promising avenue for further research and development in the field of biometric authentication, ultimately leading to more accurate and secure identification methods. © 2023 IEEE.

关键词： Support vector machines

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Thermally robust compact single Bragg grating silicon modulator for photonic computing

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Applied Optics 2025年第16期64卷 4646-4653页

作者： Zhang, Meng Gangi, Nicholas BEGOVIć, Amir Chen, Alexander Liu, Bowen Huang, Z. Rena Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute TroyNY12180 United States

We report, to our knowledge, a novel compact, thermally operated optical modulator with 6-bit resolution. The device comprises a slow-light photonic structure, a Bragg grating with a footprint of 10 µm by 50 µm. This single Bragg-grating modulator (SBG-M) operates based on thermally induced band edge shift of the photonic stop band and demonstrates almost constant normalized mean square error (NMSE) lower than 0.001 from room temperature to 40◦C, while maintaining thermal robustness up to 65◦C limited by the Johnson noise of the on-chip photodetector. The clock frequency, computing accuracy, power consumption, thermal stability, and fabrication-induced spectrum non-ideality are thoroughly studied. The SBG-M achieves a measured lowest NMSE of 5.0 × 10−4, with an average power consumption of 5.29 mW. Compared to other optical devices such as micro-ring resonators and slow-light incorporated Mach–Zehnder modulators, the reported device demonstrates significant advantages in compactness, lower power consumption, and thermal robustness. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Light modulation

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Design of Multi-Robotic System for Navigation in a Tomato Crop Field Using Human Robot Interaction, Robot Operating System and Gazebo

Design of Multi-Robotic System for Navigation in a Tomato Cr...

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electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Balasubramaniyan Chandrasekaran Department of Electrical and Computer Engineering Florida Polytechnic University Lakeland FL

Robotics is an emerging field and finds application in various areas. For applications such as navigation and collecting information in a large agricultural field, it is important to use more than one robot for timely and efficient information gathering. In this paper, an architecture for multi-robot navigation in an agricultural field is presented and the system uses Robot Operating System and Gazebo. This is a work in progress.

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A review of Multisensor Fusion techniques used in the development of Mobile Robotic Systems

A review of Multisensor Fusion techniques used in the develo...

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International Conference on Robotics and Automation engineering (ICRAE)

作者： Balasubramaniyan Chandrasekaran Department of Electrical and Computer Engineering Florida Polytechnic University Lakeland FL

Robotics is a multi-disciplinary field that involves the use of multiple sensors for the application. These sensors can be of the same type or different types and measure various attributes of the environment. In this paper, a review of some of the multi sensor fusion techniques used to combine the data from the sensors for the development of robotic systems is presented.

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Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach

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IEEE Transactions on Wireless Communications 2023年第3期22卷 1808-1822页

作者： He, Hengtao Wang, Rui Jin, Weijie Jin, Shi Wen, Chao-Kai Li, Geoffrey Ye Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong Hong Kong Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2BX United Kingdom Southeast University National Mobile Communications Research Laboratory Nanjing210096 China

Millimeter-wave (mmWave) communications have been one of the promising technologies for future wireless networks that integrate a wide range of data-demanding applications. To compensate for the large channel attenuation in mmWave band and avoid high hardware cost, a lens-based beamspace massive multiple-input multiple-output (MIMO) system is considered. However, the spatial-wideband effect in wideband mmWave systems makes channel estimation very challenging, especially when the receiver is equipped with a limited number of radio-frequency (RF) chains. Furthermore, the real channel data cannot be obtained before the mmWave system is used in a new environment, which makes it impossible to train a deep learning (DL)-based channel estimator using real data set beforehand. To solve the problem, we propose a model-driven unsupervised learning network, named learned denoising-based generalized expectation consistent (LDGEC) signal recovery network. By utilizing the Stein's unbiased risk estimator loss, the LDGEC network can be trained only with limited measurements corresponding to the pilot symbols, instead of the real channel data. Even if designed for unsupervised learning, the LDGEC network can be supervisingly trained with the real channel via the denoiser-by-denoiser way. The numerical results demonstrate that the LDGEC-based channel estimator significantly outperforms state-of-the-art compressive sensing-based algorithms when the receiver is equipped with a small number of RF chains and low-resolution ADCs. © 2002-2012 IEEE.

关键词： Channel estimation

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A Hybrid based Kolmogorov-Arnold Network for Intrusion Detection Systems in IoT

A Hybrid based Kolmogorov-Arnold Network for Intrusion Detec...

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2024 IEEE Middle East Conference on Communications and Networking, MECOM 2024

作者： Amouri, Amar Rahhal, Mohamad Mahmoud Al Bazi, Yakoub Alaparthy, Vishwa Teja Fotopoulos, Georgios Abu Dhabi Polytechnic Academic Support Department Abu Dhabi United Arab Emirates King Saud University College of Computer & Information Science Riyadh Saudi Arabia University of South Florida Electrical Engineering Department TampaFL United States

ISBN: (纸本)9798350376715

In recent years, machine learning models have become increasingly prevalent across various fields, particularly in intrusion detection. This trend is especially noticeable in the Internet of Things (IoT) domain, where the intricate network of interconnected devices requires sophisticated defence mechanisms against potential threats. To tackle this challenge, researchers are turning to deep learning techniques, harnessing the power of artificial intelligence (AI) to secure IoT environments. We introduce a novel Intrusion Detection System (IDS) based on the Kolmogorov-Arnold Network (KAN). This system combines the efficiency of Long Short-Term Memory (LSTM) networks, which excel in handling time series data, with the adaptability of KAN, thanks to its learnable activation functions. To the best of the authors' knowledge, this is the 1st manuscript utilizing KANs as a core algorithm for intrusion detection. Our experimental results highlight the high detection accuracy of the hybrid IDS, achieving over 98% accuracy in distinguishing between malicious and normal instances. Additionally, we provide a comparative analysis with conventional Multi-Layer Perceptron (MLP) networks, evaluating performance metrics such as Precision, Recall, and F1-score. Our findings underscore the potential of a hybrid IDS approach in enhancing the security of IoT networks. © 2024 IEEE.

关键词： Network intrusion

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Investigation of a New Stator Permanent Magnet with the effects of Flux-Reversal and Flux-Switching

Investigation of a New Stator Permanent Magnet with the effe...

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Power Electronic and Drive Systems and Technologies Conference (PEDSTC)

作者： Mohammad Reza Sarshar Mohammad Amin Jalali Kondelaji Mojtaba Mirsalim Seyed Hamid Montazeri Department of Electrical Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology (MIT) Cambridge USA

ISBN: (数字)9798331533946

ISBN: (纸本)9798331533953

This article studies a new stator-permanent magnet (PM) motor with flux-switching (FS) and flux-reversal (FR) effects synergies. The proposed structure benefits from the splitting stator pole PMs and consequent-pole FRPM, which reduces the PM utilization compared to the conventional ones. Also, the proposed structure is optimized by a random search multiobjective algorithm to attain the structure with the highest output torque and minimum torque ripple. Additionally, the proposed motor is compared to a conventional FSPM to investigate the advantages of the main motor. Using finite element analysis (FEA) studies, the flux density distribution results show that the FR effect lowers the saturation risk compared to FSPM. Also, the back-EMF generation of the proposed motor has improved by 135% compared to its benchmark. Correspondingly, fullload results show that the proposed topology has enhanced the average torque by 75% and deteriorated the ripple by about 55% compared to FSPM. Therefore, the proposed structure offers a profitable for high torque density applications.

关键词： Magnetic flux Torque Switches Stators Motors Permanent magnets Power electronics Topology Torque measurement Saturation magnetization

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Stream-Based Ground Segmentation for Real-Time LiDAR Point Cloud Processing on FPGA

arXiv

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arXiv 2024年

作者： Zhang, Xiao Huang, Zhanhong Antony, Garcia Gonzalez Jachimczyk, Witek Huang, Xinming Department of Electrical and Computer Engineering Worcester Polytechnic Institute MA01609 United States The Computer Vision and Autonomous Vehicles Team The MathWorks MA01760 United States The Department of Electrical and Computer Engineering Worcester Polytechnic Institute MA01609 United States

This paper presents a novel and fast approach for ground plane segmentation in a LiDAR point cloud, specifically optimized for processing speed and hardware efficiency on FPGA hardware platforms. Our approach leverages a channel-based segmentation method with an advanced angular data repair technique and a cross-eight-way flood-fill algorithm. This innovative approach significantly reduces the number of iterations while ensuring the high accuracy of the segmented ground plane, which makes the stream-based hardware implementation possible. To validate the proposed approach, we conducted extensive experiments on the SemanticKITTI dataset. We introduced a bird’s-eye view (BEV) evaluation metric tailored for the area representation of LiDAR segmentation tasks. Our method demonstrated superior performance in terms of BEV areas when compared to the existing approaches. Moreover, we presented an optimized hardware architecture targeted on a Zynq-7000 FPGA, compatible with LiDARs of various channel densities, i.e., 32, 64, and 128 channels. Our FPGA implementation operating at 160 MHz significantly outperforms the traditional computing platforms, which is 12 to 25 times faster than the CPU-based solutions and up to 6 times faster than the GPU-based solution, in addition to the benefit of low power consumption. Copyright © 2024, The Authors. All rights reserved.

关键词： computer graphics equipment

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A Survey of Path Planning and Navigation in Multi-Robotic Systems

A Survey of Path Planning and Navigation in Multi-Robotic Sy...

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Advanced engineering and Technologies (ICONNIC), International Conference on

作者： Balasubramaniyan Chandrasekaran Department of Electrical and Computer Engineering Florida Polytechnic University Lakeland FL

Robotics is a interdisciplinary field and has seen a lot of development recently. Applications utilize multiple robots to achieve the tasks, so the concept of multi-robots is gaining popularity. Navigation and Path planning become an important topic for multi-robotic systems. In this paper, a review of some of the navigation and path planning used in multi-robot systems is presented. This is a work in progress and the next step of the research is to use this survey to develop a customized multi-robotic system for navigation in a crop field.

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WIP: Federated Learning for Routing in Swarm Based Distributed Multi-Hop Networks

WIP: Federated Learning for Routing in Swarm Based Distribut...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Martha Cash Joseph Murphy Alexander Wyglinski Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA

Unmanned Aerial Vehicles (UAVs) are a rapidly emerging technology offering fast and cost-effective solutions for many areas, including public safety, surveillance, and wireless networks. However, due to the highly dynamic network topology of UAVs, traditional mesh networking protocols, such as the Better Approach to Mobile Ad-hoc Networking (B.A.T.M.A.N.), are unsuitable. To this end, we investigate modifying the B.A.T.M.A.N. routing protocol with a machine learning (ML) model and propose implementing this solution using federated learning (FL). This work aims to aid the routing protocol to learn to predict future network topologies and preemptively make routing decisions to minimize network congestion. We also present an FL testbed built on a network emulator for future testing of the proposed ML aided B.A.T.M.A.N. routing protocol.

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