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SSRN 2024年

作者： Cheng, Chang Parthiban, Rajendran Kalavally, Vineetha Wang, Xin Department of Mechanical Engineering School of Engineering Monash University Malaysia Subang Jaya47500 Malaysia Department of Electrical and Computer Systems Engineering School of Engineering Monash University Malaysia Subang Jaya47500 Malaysia Department of Electrical and Computer Systems Engineering Faculty of Engineering Monash University Clayton3800 Australia

Unharvested palm fruit bunch (PFB) ripeness detection is crucial for efficient palm oil production as labor costs rise and worker shortages grow. Challenges remain under varying illumination, affecting detection performance. Although the deep learning-based color constancy model shows promise, the limitation lies in the lack of data. This paper addresses these issues by proposing a hybrid color correction method that combines the learning-based and the physical-based color constancy technique, then trains with YOLOv8 to enhance unharvested PFB detection performance. Specifically, the captured images and their corresponding simultaneous spectra are used to generate the ground truth images, then map the images to other ambient spectra to train the color constancy model, and finally, train the YOLOv8 with the corrected images for ripeness detection. Results demonstrate a 1.5% improvement in the mean Average Precision (mAP) @0.5 of YOLOv8, increasing from 88.2% to 89.7% after applying the proposed hybrid color correction method. For each ripeness category, the mAP@0.5 for unripe and ripe bunches increased by 1.7% and 1.8%, respectively. Underripe bunches exhibit the most increment in mAP of 2.1%, while the overripe bunch gives the least increase of 0.4%. The qualitative results show that the model has enhanced the ability to distinguish subtle differences in PFB ripeness. Consequently, it advances the effectiveness and reliability of PFB ripeness detection in practical palm oil production scenarios, especially for unripe to ripe bunches wherein color gives the most information. This research demonstrates potential in object detection tasks that incorporate illumination-based color correction methods. © 2024, The Authors. All rights reserved.

关键词： Wages

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Design and Development of a Microcontroller Based Fingerprint-Attendance System 4

Design and Development of a Microcontroller Based Fingerprin...

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4th International Conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Mbengwa, Kaloso Samikannu, Ravi Rohini, R. Begam, K.Maruliya Kanimozhi, G. Yahya, Abid Botswana International University of Science and Technology Department of Electrical and Communications Systems Engineering Palapye Botswana Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Ece Chennai India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tiruchengode India Periyar Maniammai Institute of Science and Technology Department of Electrical and Electronics Engineering Thanjavur India Center for Smart Grid Technologies School of Electrical Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798331529635

In the modern era of rapidly advancing technology, organizations across various sectors are continuously seeking innovative solutions to enhance operational efficiency, streamline processes, and improve overall productivity. One area that significantly impacts these goals is the management of employee/student attendance. Traditional manual attendance tracking methods often lead to inaccuracies, inefficiencies, and increased administrative overhead. Recognizing this challenge, this proposal is to bring forth developing a microcontroller-based implementation of a Fingerprint-Based Attendance System. The system uses the power of biometric authentication to improve how attendance is recorded and managed. Firstly, this system provides real-time attendance tracking, reducing the delay in data processing and enabling immediate access to attendance records. Secondly, the proposed system ensures a high level of accuracy in attendance recording. Thirdly, this system promotes enhanced security. © 2024 IEEE.

关键词： Microcontrollers

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A Multi-Lane Platooning Paradigm with ETSI DCC

A Multi-Lane Platooning Paradigm with ETSI DCC

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IEEE Vehicular Networking Conference (VNC)

作者： Huy Nguyen Suryansh Sharma R Venkatesha Prasad Falko Dressler Networked Systems Group TU Delft The Netherlands School of Electrical Engineering and Computer Science TU Berlin Germany

ISBN: (数字)9798350362701

ISBN: (纸本)9798350362718

Smart autonomous vehicles can cooperatively drive as platoons offering benefits like enhanced safety, traffic efficiency, and fuel conservation. While traditionally platoons have followed a single-lane, train-like structure they face challenges when scaling that include communication range limitations and lane-change difficulties. In this article, we propose a new paradigm of multi-lane platoons that spreads platoons across multiple lanes. We explore the characteristics of multi-lane platoons particularly focusing on communication parameters. Additionally, we propose a cross-layer mechanism to seamlessly integrate this concept within the existing communication standard, ETSI. Our work significantly enhances platoon communication performance in mixed traffic scenarios and we propose optimizations to improve its effectiveness.

关键词： Wireless communication Cross layer design Roads Focusing Regulation Safety Proposals

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Constructive Function Approximation with Local Models

Constructive Function Approximation with Local Models

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Mediterranean Conference on Control and Automation (MED)

作者： Christos N. Mavridis Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

We introduce a constructive function approximation approach as a general tool, particularly useful in adaptive and data-driven methods for perception and control. The key idea is to estimate of a collection of simple local models as opposed to a single and complex regression model trained in the entire input space. We use principles from the Online Deterministic Annealing (ODA) optimization framework to construct an adaptive partition of the input space, which enables the introduction of local function approximation models within each subset of the partition. We show that both the partitioning and the local model training algorithms are stochastic approximation algorithms that operate online, and with the same observations, as part of a two-timescale stochastic approximation scheme. This process constitutes a heuristic method to gradually increase the complexity of the function approximation framework in a task-agnostic manner, giving emphasis to regions of the input space where the regression error is high. As a result this framework has inherent explainability properties, and is suitable for continuous learning applications where regression improvement without retraining from scratch is crucial. Simulation results illustrate the properties of the proposed approach.

关键词： Training Adaptation models Simulation Stochastic processes Aerospace electronics Approximation algorithms Partitioning algorithms

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EEG-based Emotion Recognition using Graph Attention Network with Dual-Branch Attention Module 46

EEG-based Emotion Recognition using Graph Attention Network ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Li, Cheng Pun, Sio Hang Li, Jia Wen Chen, Fei University of Macau State Key Laboratory of Analog and Mixed-Signal Vlsi China Southern University of Science and Technology Department of Electrical and Electronic Engineering Shenzhen China Guangdong Polytechnic Normal University School of Computer Science Guangzhou China

ISBN: (纸本)9798350371499

EEG reveals human brain activities for emotion and becomes an important aspect of affective computing. In this study, we developed a novel approach, namely DAM-GAT, which incorporated a dual-branch attention module (DAM) into a graph attention network (GAT) for EEG-based emotion recognition. This method used the GAT to capture the local features of emotional EEG signals. To enhance the important EEG features for emotion recognition, the proposed method also included a DAM that calculated weights considering both channel and frequency information. Additionally, the relationship between EEG channels was determined using the phase-locking value (PLV) connectivity of corresponding EEG signals. Based on the SEED datasets, the proposed approach provided an accuracy of up to 94.63% for emotion recognition, demonstrating its impressive performance compared with other existing methods. © 2024 IEEE.

关键词： Emotion Recognition

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Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System

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Multimedia Tools and Applications 2024年第21期83卷 59887-59913页

作者： Abualigah, Laith Ahmed, Saba Hussein Almomani, Mohammad H. Zitar, Raed Abu Alsoud, Anas Ratib Abuhaija, Belal Hanandeh, Essam Said Jia, Heming Elminaam, Diaa Salama Abd Elaziz, Mohamed Abd Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science Wenzhou-Kean University Wenzhou China Department of Computer Information System Zarqa University P.O. Box 13132 Zarqa Jordan School of Information Engineering Sanming University Sanming365004 China Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha12311 Egypt Computer Science Department Faculty of Computer Science Misr International University Cairo12585 Egypt Faculty of Computer Science & Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt

With the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection System (IDS) has surged. The IDS serves as a critical bastion within the security framework, a significance further magnified in wireless networks where intrusions may stem from the deluge of sensor data. This influx of data, however, inevitably taxes the efficiency and computational speed of IDS. To address these limitations, numerous strategies for enhancing IDS performance have been posited by researchers. This paper introduces a novel feature selection method grounded in Support Vector Machine (SVM) and harnessing the innovative modified Aquila Optimizer (mAO) for Intrusion Detection systems in Wireless Sensor Networks. To evaluate the efficacy of our approach, we employed the KDD'99 dataset for testing and benchmarking against established methods. Multiple performance metrics, including accuracy, detection rate, false alarm rate, feature count, and execution time, were utilized for assessment. Our comparative analysis reveals the superiority of the proposed method, with standout results in terms of feature reduction, detection accuracy, and false alarm mitigation, yielding significant improvements of 11%, 98.76%, and 0.02%, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Network security

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Low-dimensional Feature Enhanced Camouflaged Object Detection

Low-dimensional Feature Enhanced Camouflaged Object Detectio...

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2023 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2023

作者： Wang, Xin Zhang, Zhao Xu, Junfeng School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Anhui Hefei230601 China

ISBN: (纸本)9781510667662

Camouflaged Object Detection (COD) is a challenging vision task that aims at locating objects having high similarity in appearance with its background. Existing COD methods are highly dependent on the high-dimensional feature of camouflaged object while neglecting details within the low-dimensional feature. In this paper, we highlight low-dimensional information to enlarge the discrepancies between objects and their surroundings. We propose a CNN-based network ShallowNet with enhanced low-dimensional feature to retain more differences in the process of encoding. The proposed approach shows enhanced effectiveness both qualitatively and quantitatively compared with existing COD methods regarding various datasets. © 2023 SPIE.

关键词： Object detection

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Fast Iterative Multi-Site Hosting Capacity Analysis for Distribution systems with Search Space Pruning

Fast Iterative Multi-Site Hosting Capacity Analysis for Dist...

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IEEE General Meeting Power& Energy Society

作者： Kishan Prudhvi Guddanti Shiva Poudel Monish Mukherjee Alok Kumar Bharati Xiaoyuan Fan Yang Weng Pacific Northwest National Laboratory USA School of Electrical and Computer Engineering Arizona State University USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Interconnection studies for distributed energy re-sources (DERs) are a time-intensive process, primarily due to the necessity of solving a large number of powerflow scenarios. Hosting capacity analysis (HCA) is a time-consuming aspect of interconnection studies that is divided into single-site HCA (SHCA) and multi-site HCA (MHCA). For computation effi-ciency, big data nature, and lucidity, the industry seeks iteration-based solutions for HCA despite not maximizing the total DER hosting capacity (DERHC), as MHCA does. In this work, we present a fast, iterative method for MHCA with increased computational efficiency by eliminating the need to solve a multitude of powerflows, thus making iterative solutions feasible. The proposed approach guarantees a global optimal solution and a faster variant to achieve a near-optimal solution. The results are demonstrated on select locations in the IEEE 123-bus system for community-scale interconnection studies. We highlight the benefits of skipping the need to solve millions of powerflows while maximizing the grid's DERHC.

关键词： Industries Upper bound Big Data Planning Distributed power generation Computational efficiency Iterative methods Load flow Convergence Capacity planning

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Evolution of Photonics Integrated Circuits for Microwave Photonics Applications

Evolution of Photonics Integrated Circuits for Microwave Pho...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Jianping Yao Microwave Photonics Research Laboratory School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

Integrated microwave photonics to study the use of photonic integrated circuits (PICs) to implement microwave photonic subsystems and systems have been heavily studied for the last few years. In this talk, the evolution of PICs in microwave photonics applications will be discussed with an emphasis on the use of different materials systems to implement photonic integrated microwave photonic systems.

关键词： Optical losses Microwave integrated circuits Costs Masers Photonic integrated circuits Laser stability Microwave circuits

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Strategic Electric Distribution Network Sensing via Spectral Bandits

Strategic Electric Distribution Network Sensing via Spectral...

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IEEE Conference on Decision and Control

作者： Samuel Talkington Rahul Gupta Richard Asiamah Paprapee Buason Daniel K. Molzahn School of Electrical and Computer Engineering Georgia Tech Atlanta GA USA Los Alamos National Laboratory

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Despite their wide-scale deployment and ability to make accurate high-frequency voltage measurements, communication network limitations have largely precluded the use of smart meters for real-time monitoring purposes in electric distribution systems. Although smart meter communication networks have limited bandwidth available per meter, they also have the ability to dedicate higher bandwidth to varying subsets of meters. Using this capability to enable real-time monitoring from smart meters, this paper proposes an online bandwidth-constrained sensor sampling algorithm that takes advantage of the graphical structure inherent in the power flow equations. The key idea is to use a spectral bandit framework where the estimated parameters are the graph Fourier transform coefficients of the nodal voltages. The structure provided by this framework promotes a sampling policy that strategically accounts for electrical distance. Maxima of sub-Gaussian random variables model the policy rewards, which relaxes distributional assumptions common in prior work. The scheme is implemented on a synthetic electrical network to dynamically identify meters exposing violations of voltage magnitude limits, illustrating the effectiveness of the proposed method.

关键词： Meters Voltage measurement Bandwidth Smart meters Real-time systems Mathematical models Sensors Random variables Communication networks Load flow

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