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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Adekunle A. Akinrinmade Emmanuel Adetiba Joke A. Badejo Comfort O. Lawal Department of Electrical and Information Engineering (Computer Engineering) College of Engineering Covenant University Covenant University Ota Ogun Nigeria Department of Electrical and Information Engineering College of Engineering Covenant University Ota Ogun Nigeria HRA Institute for Systems Science Durban University South Africa

Face recognition and speaker recognition have gained attention in recent times in the large-scale space. Large-scale face databases such as VGG-Face and VGGFace2 have been created using a semi-automated pipeline and used to develop methods for face recognition achieving state-of-the-art performance. This is also true for speaker recognition with large-scale databases such as VoxCeleb and VoxCeleb2. Howbeit, these two modalities have been treated individually. Although some works have explored the fusion of both modalities, they have played in small-scale space. This work aims at creating a large-scale face and corresponding voice database from YouTube under unconstrained conditions with a size comparable to the earlier mentioned and explores the fusion of both face and voice modalities for recognition in the large-scale space. To this end, a face and corresponding voice database of Nigerians available on YouTube was created for 2,656 Nigerians containing 2,055,169 face images and 195 hours of voice recording using a semi-automated curation pipeline. Convolutional Neural Networks (CNNs) were used to perform face recognition and speaker recognition individually. This was followed by the use of CNN for a combination of both modalities achieving an Equal Error Rate (EER) more than 5 times lower than the best result in the individual cases.

关键词： Video on demand Databases Face recognition Pipelines Speech recognition Feature extraction Speaker recognition

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Sensitivity of DC Network Representation for GIC Analysis

Sensitivity of DC Network Representation for GIC Analysis

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IEEE Texas Power and Energy Conference (TPEC)

作者： Aniruddh Mishra Arthur K. Barnes Jose E. Tabarez Adam Mate Department of Electrical and Computer Engineering University of Texas at Austin Austin TX Information Systems and Modeling Group Los Alamos National Laboratory Los Alamos NM

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

Geomagnetic disturbances are a threat to the reliability and security of our national critical energy infrastructures. These events specifically result in geomagnetically induced currents, which can cause damage to transformers due to magnetic saturation. In order to mitigate these effects, blocker devices must be placed in optimal locations. Finding this placement requires a dc representation of the ac transmission lines, which this paper discusses. Different decisions in this process, including the method of representing the blocking devices, result in significant variations to the power loss calculations. To analyze these effects, we conclude the paper by comparing the losses on a sample network with different modeling implementations.

关键词： Analytical models Sensitivity Substations Power transmission lines Windings Circuit faults Security Reliability Electric fields Saturation magnetization

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An Efficient Medium Access Control Mechanism for Flying Ad-hoc Networks

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computer systems Science & engineering 2021年第7期38卷 47-63页

作者： Muhammad Asghar Khan Fazal Noor Insaf Ullah Sajjad ur Rehman Shibli Nisar Mohaira Ahmad Department of Electronic Engineering ISRA UniversityIslamabad44000Pakistan Department of Computer Science and Information Systems Islamic University of MadinahMadinah400411Saudi Arabia Hamdard Institute of Engineering&Technology Islamabad44000Pakistan Department of Electrical Engineering Namal InstituteMianwali42200Pakistan Department of Electrical Engineering National University of Sciences and Technology(NUST)Islamabad44000Pakistan

The Flying Ad-hoc Networks(FANETs)is characterized by the transition from a single large Unmanned Aerial Vehicle(UAV)to multiple small UAVs connected in an ad-hoc *** high mobility is the core feature of such networks,they are prone to route breaks within the *** issue of connectivity loss can be coped with,to some extent,by making use of omnidirectional *** modification,however,curtails Quality-of-Service(QoS)requirements of networks in terms of bandwidth,media access delay,coverage and ***,directional antennas have advantages over omnidirectional antennas such as improved transmission range,spatial reuse and high ***,its introduction raises location-dependent issues to the Medium Access Control(MAC)*** calls for an efficient MAC protocol that can cater to new directional antenna models and,at the same time,can counter the constraints associated with the dynamic ***,in this article,we consider a UAV interconnection mechanism that lets the UAVs execute the communication tasks using the directional MAC *** technique is advantageous as compared to the approach of utilizing the MAC protocol using omnidirectional *** scheme is being implemented as a case study for Industry 4.0 inventory and traceability applications in the *** modeling and simulation purposes,we use the Optimized Network engineering Tool(OPNET)and aim to seek an evaluation with respect to throughput,media access delay,retransmission attempts and data *** results obtained demonstrate the effectiveness of the proposed scheme.

关键词： FANETs QoS Industry 4.0 IEEE 802.11 MAC OPNET mobility models directional antenna

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Latency Optimization in LEO Satellite Communications with Hybrid Beam Pattern and Interference Control

arXiv

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arXiv 2024年

作者： Zhang, Qianqian Hu, Ye Jung, Minchae Department of Electrical and Computer Engineering Rowan University NJ United States Department of Industrial and Systems Engineering University of Miami FL United States Department of Electronics and Information Engineering Sejong University Seoul Korea Republic of

The rapid advancement of low Earth orbit (LEO) satellite communication systems has significantly enhanced global connectivity, offering high-capacity, low-latency services crucial for next-generation applications. However, the dense configuration of LEO constellations poses challenges in resource allocation optimization and interference management, complicating coexistence with other communication systems. To address these limitations, this paper proposes a novel framework for optimizing the beam scheduling and resource allocation in multi-beam LEO systems. To satisfy the uneven terrestrial traffic demand, a hybrid beam pattern is employed to enhance the downlink quality of service and minimize the transmission latency from LEO satellites to ground user terminals. Additionally, a dynamic co-channel interference (CCI) control mechanism is developed to mitigate inter-beam interference within the LEO constellation and limit cross-system interference affecting protected users from other networks. The problem of user-beam-frequency allocation with power optimization is formulated as a mixed-integer dynamic programming model and solved using a low-complexity neural network-based graph generation algorithm. Simulation results show that the proposed approach outperforms the baseline methods of full frequency reuse and single-channel transmission, and highlights the potential for further performance improvement with multi-user transmissions. © 2024, CC BY.

关键词： Frequency allocation

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Incentive Design for Eco-driving in Urban Transportation Networks

Incentive Design for Eco-driving in Urban Transportation Net...

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European Control Conference (ECC)

作者： M. Umar B. Niazi Jung-Hoon Cho Munther A. Dahleh Roy Dong Cathy Wu Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Civil and Environmental Engineering Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign Urbana IL USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Eco-driving emerges as a cost-effective and efficient strategy to mitigate greenhouse gas emissions in urban transportation networks. Acknowledging the persuasive influence of incentives in shaping driver behavior, this paper presents the ‘eco-planner,’ a digital platform devised to promote eco-driving practices in urban transportation. At the outset of their trips, users provide the platform with their trip details and travel time preferences, enabling the eco-planner to formulate personalized eco-driving recommendations and corresponding incentives, while adhering to its budgetary constraints. Upon trip completion, incentives are transferred to users who comply with the recommendations and effectively reduce their emissions. By comparing our proposed incentive mechanism with a baseline scheme that offers uniform incentives to all users, we demonstrate that our approach achieves superior emission reductions and increased user compliance with a smaller budget.

关键词： Transportation Europe Greenhouse gases

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Next Generation Memristor Reservoir Computing

Next Generation Memristor Reservoir Computing

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Metrology for Extended Reality, Artificial Intelligence and Neural engineering (MetroXRAINE), IEEE International Conference on

作者： K. Nikiruy T. Ivanov M. Ziegler D. Rossetti F. Corinto A. Ascoli R. Tetzlaff A. S. Demirkol N. Schmitt Department of Electrical Engineering and Information Technology Micro-and Nanoelectronic Systems TU Ilmenau Ilmenau Germany Department of Electronics and Telecommunications (DET) Politecnico di Torino Turin Italy Faculty of Electrical and Computer Engineering Institute of Circuits and Systems TU Dresden Dresden Germany

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

The nonlinear transformation used in reservoir computing can be effectively replaced by nonlinear vector autoregression (NVAR) for data prediction. In such a method, also known as next generation reservoir computing (NGRC), the input signal consists of a linear part, including several previous data points, and their nonlinear combinations. Here we show that the application of this method to a network with memristive weights (memristors) can be used to predict signals, depending on the nature of the nonlinear functions and the number of memristors. The network allows an accurate prediction of chaotic time series of Mackey-Glass and Duffing oscillators.

关键词： Performance evaluation Accuracy Time series analysis Memristors Reservoir computing Neural engineering Ordinary differential equations Vectors Next generation networking Oscillators

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Comparative Analysis of AlexNet, GoogLeNet, VGG19, ResNet50, and ResNet101 for Improved Plant Disease Detection Through Convolutional Neural Networks

Comparative Analysis of AlexNet, GoogLeNet, VGG19, ResNet50,...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Atoshe Islam Sumaya Shamim Forhad Md Al Rafi Hamdadur Rahman Muhibul Haque Bhuyan Qazi Tareq Electrical and Electronic Engineering Brac University Dhaka Bangladesh Electrical and Electronic Engineering American International University-Bangladesh Dhaka Bangladesh Information Technology Washington University of Science and Technology (WUST) Virginia USA Management of Information Systems International American University Los Angeles California Electrical and Computer Engineering Concordia University Montreal Canada

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

To meet the increasing need for food from a growing global population, it is imperative to make progress in agricultural technology, namely in the area of disease detection. This will help reduce the need for pesticides and improve the overall health of crops. The issue at hand is the need for accurate and effective identification of plant diseases to enhance food production. This research tackles this problem by using cutting-edge computational neural networks (CNNs) on pre-existing agricultural datasets to assess and compare their effectiveness. Existing research does not show the precise and detailed performance of the latest CNN models in agricultural data sets. We addressed this issue by examining five Convolutional Neural Network (CNN) models: AlexNet, GoogLeNet, VGG19, ResNet50, and ResNet101. This approach involved meticulous training and assessment of these models to compare their precision, loss, duration of training, and complexity of the model. ResNet 101 outperformed all other models with an accuracy rate of approximately $\mathbf{97\%}$. Nevertheless, the practical implementation of this technology is hindered by its intricate model complexity and demanding resource needs, which make it unsuitable for low-power applications. GoogLeNet demonstrated superior performance in terms of accuracy, model complexity, training time, and other evaluation measures.

关键词： Training Performance evaluation Plant diseases Analytical models Accuracy Computational modeling Time measurement Complexity theory Convolutional neural networks Residual neural networks

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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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Key-Independent Image Encryption using DNA Encoding and Chaotic Dynamics

Key-Independent Image Encryption using DNA Encoding and Chao...

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International Conference on Communications and Signal Processing

作者： V Maharaja Vannia Moorthi Megha R Krishna Gopika Anil Kumar V Thanikaiselvan S Subashanthini Rengarajan Amirtharajan School of Electronics Engineering Vellore Institute of Technology Vellore India School Of Computer Science Engineering And Information Systems Vellore Institute of Technology Vellore India School of Electrical & Electronics Engineering SASTRA Deemed University Thanjavur

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Privacy violations are common in our technology-driven world, where almost everyone interacts with internet-connected electronic devices. To address this, cryptography has emerged, concealing data during transmission to deter hackers. Cryptography involves basic mathematical operations, exchanging data based on a shared key known only to the sender and receiver. However, this method suffers from key dependency at the receiver’s end, requiring constant key knowledge. To overcome this issue, a novel image encryption method has been introduced in this paper that is independent of the key on the receiver side. This method leverages techniques such as DNA Encoding, Chaotic Dynamics, and Key Hiding Mechanisms to increase the security of the sent image. The proposed method has been tested experimentally, and the results demonstrate its effectiveness with entropy values exceeding 7.995 and correlation coefficient values below 1% for the vertical, horizontal, and diagonal directions. Overall, the proposed image encryption scheme offers a promising solution for protecting sensitive image data in various applications, combining advanced encryption techniques with key management enhancements to ensure confidentiality and integrity.

关键词： Correlation coefficient Privacy Image coding Chaotic communication computer hacking DNA Receivers

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Taxonomy of Proactive Detection Methods of Drunk Driving for Enhancing Traffic Safety

Taxonomy of Proactive Detection Methods of Drunk Driving for...

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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Richard Preston Swiley Razan Alsulieman Ahmed Sherif Mohamed Elsersy Kasem Khalil School of Computing Sciences and Computer Engineering University of Southern Mississippi MS USA Computer and Information Systems Dept. Higher Colleges of Technology AlAin Abu Dhabi UAE Electrical and Computer Engineering Department University of Mississippi MS USA

ISBN: (数字)9798331530471

ISBN: (纸本)9798331530488

Drunk driving continues to be a substantial public health issue, leading to a multitude of accidents and deaths on a global scale. Conventional techniques for identifying drunk driving, such as breathalyzers and field sobriety tests, tend to be reactive rather than proactive. This paper surveys the existing methods for detecting drunk driving to enhance traffic safety. The technologies studied can proactively detect impaired driving behaviors by combining machine learning (ML) approaches with real-time data processing. The study outlines various classifications based on measured metrics and driving behavior.

关键词： Surveys Visualization Technological innovation Accuracy Face recognition Taxonomy Road safety Sensor systems Driver behavior Vehicles

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