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A Low-Profile, Size-Reduced, High Gain, Circularly Polarized Anti-Jam Global Positioning System Antenna Array

Progress In Electromagnetics Research C 2024年 139卷 79-86页

作者： Evans, Grant Nemec, Jack Obi, Victor Lim, Sungkyun Department of Electrical and Computer Engineering Georgia Southern University StatesboroGA30460 United States

A high gain, circularly polarized array antenna is proposed with low profile and compact size using T-shaped top loading, t-matching, and a reflector. Composed of 4 individual elements, the array has a −3-dB impedance bandwidth of 1.39% (1.564–1.586 GHz) and a 3-dB axial ratio beamwidth of 79◦ (−42◦ –37◦) in measurement. The front-to-side ratio of the total realized gain pattern is 27.1 dB, and the front-to-back ratio is 25.5 dB. The peak realized gain is 11.0 dBi in the forward (+z) direction. The proposed antenna is a good candidate for functioning as an anti-jam antenna in global positioning system, helping to block out jamming signals coming from the horizontal (90◦ and 270◦) planes. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Global positioning system

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Diagnosis of Autism Spectrum Disorder by Imperialistic Competitive Algorithm and Logistic Regression Classifier

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computers, Materials & Continua 2023年第11期77卷 1515-1534页

作者： Shabana R.Ziyad Liyakathunisa Eman Aljohani I.A.Saeed Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl Kharj16274Saudi Arabia Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah41411Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl Kharj16274Saudi Arabia

Autism spectrum disorder(ASD),classified as a developmental disability,is now more common in children than ever.A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection of autism in *** can seek professional help for a better prognosis of the child’s therapy when ASD is diagnosed under five *** research study aims to develop an automated tool for diagnosing autism in *** computer-aided diagnosis tool for ASD detection is designed and developed by a novel methodology that includes data acquisition,feature selection,and classification *** most deterministic features are selected from the self-acquired dataset by novel feature selection methods before *** Imperialistic competitive algorithm(ICA)based on empires conquering colonies performs feature selection in this *** performance of Logistic Regression(LR),Decision tree,K-Nearest Neighbor(KNN),and Random Forest(RF)classifiers are experimentally studied in this research *** experimental results prove that the Logistic regression classifier exhibits the highest accuracy for the self-acquired *** ASD detection is evaluated experimentally with the Least Absolute Shrinkage and Selection Operator(LASSO)feature selection method and different *** Exploratory Data Analysis(EDA)phase has uncovered crucial facts about the data,like the correlation of the features in the dataset with the class variable.

关键词： Autism spectrum disorder feature selection imperialist competitive algorithm LASSO logistic regression random forest

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Adaptive Cyber Defense Technique Based on Multiagent Reinforcement Learning Strategies

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Intelligent Automation & Soft Computing 2023年第6期36卷 2757-2771页

作者： Adel Alshamrani Abdullah Alshahrani Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia

The static nature of cyber defense systems gives attackers a sufficient amount of time to explore and further exploit the vulnerabilities of information technology *** this paper,we investigate a problem where multiagent sys-tems sensing and acting in an environment contribute to adaptive cyber *** present a learning strategy that enables multiple agents to learn optimal poli-cies using multiagent reinforcement learning(MARL).Our proposed approach is inspired by the multiarmed bandits(MAB)learning technique for multiple agents to cooperate in decision making or to work *** study a MAB approach in which defenders visit a system multiple times in an alternating fash-ion to maximize their rewards and protect their *** find that this game can be modeled from an individual player’s perspective as a restless MAB *** discover further results when the MAB takes the form of a pure birth process,such as a myopic optimal policy,as well as providing environments that offer the necessary incentives required for cooperation in multiplayer projects.

关键词： Multiarmed bandits reinforcement learning multiagents intrusion detection systems

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Absorb and Repel: Pseudo-Label Refinement for Intra-Camera Supervised Person Re-Identification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2884-2896页

作者： Li, Wei Chen, Chuyi Huang, Kaizhu Southeast University School of Instrument Science and Engineering Nanjing210096 China Duke Kunshan University Department of Electrical and Computer Engineering Kunshan215316 China

Person re-identification (ReID) aims to identify pedestrian images with the same identity across non-overlapping camera views. Intra-camera supervised person re-identification (ICS-ReID) is a new paradigm that trains a model using only intra-camera labels, thus reducing the cost of inter-camera identity association. Pseudo-label-based clustering algorithms perform well in the unsupervised ReID task, whereas they inevitably generate noisy pseudo labels through clustering, especially in the early training stage. Given this, we propose an unsupervised pseudo-labeling method to help in the semi-supervised ICS-ReID task. This method improves the clustering results by reassigning pseudo labels for the training data and consists of two modules, Absorb and Repel. The Absorb module aims to group all data with the same intra-camera identity into one cluster. The Repel module ensures that images under the same camera view but with different identities do not appear in the same cluster. Both modules are independent yet complementary to reduce the error rate of pseudo labels generated in each epoch. To our knowledge, this is the first attempt to refine pseudo labels for ICS-ReID. Our method is a simple, nonparametric, and effective strategy that can be easily integrated into existing clustering-based unsupervised ReID tasks. Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art ICS-ReID approaches on three large-scale benchmark person ReID datasets. © 2020 IEEE.

关键词： Cameras

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The BRAM is the Limit: Shattering Myths, Shaping Standards, and Building Scalable PIM Accelerators 32

The BRAM is the Limit: Shattering Myths, Shaping Standards, ...

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32nd IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, FCCM 2024

作者： Kabir, Md Arafat Kamucheka, Tendayi Fredricks, Nathaniel Mandebi, Joel Bakos, Jason Huang, Miaoqing Andrews, David University of Arkansas Department of Electrical Engineering and Computer Science United States University of South Carolina Department of Computer Science and Engineering United States United States

ISBN: (纸本)9798350372434

Many recent FPGA-based Processor-in-Memory (PIM) architectures have appeared with promises of impressive levels of parallelism but with performance that falls short of expectations due to reduced maximum clock frequencies, an inability to scale processing elements up to the maximum BRAM capacity, and minimal hardware support for large reduction operations. In this paper, we propose a 'Standard' set of design objectives for PIM array-based FPGA designs. We then propose a PIM array-based GEMV accelerator architecture as a case study to show the proposed Standard can be realized in practice. The GEMV accelerator serves as existence proof that dispels several myths surrounding what is normally accepted as clocking and scaling FPGA performance limitations. Specifically, the proposed accelerator clocks at the maximum frequency of the BRAM and scales to 100% of the available BRAMs. Comparative analyses show execution speeds over existing PIM-based GEMV engines on FPGAs and achieving a 2.65×-3.2× faster clock. An AMD Alveo U55 implementation achieves a system clock speed of 737 MHz, providing 64K bit serial multiply-accumulate (MAC) units for GEMV operation. © 2024 IEEE.

关键词： Memory architecture

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Powder spreading process monitoring of selective laser melting manufacturing by using a convolutional Takagi–Sugeno–Kang fuzzy neural network

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International Journal of Advanced Manufacturing Technology 2024年第9-10期132卷 4989-5004页

作者： Lin, Chun-Hui Lin, Cheng-Jian Wang, Shyh-Hau Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung411 Taiwan

Process monitoring plays a pivotal role in elucidating the intricate interplay among process, structure, and property in additive manufacturing production. The control of powder spreading affects not only particle adhesion but also rearrangement, both of which are indispensable constituents in the selective laser melting (SLM) process. To develop a novel monitoring process, this study proposed a convolutional Takagi–Sugeno–Kang fuzzy neural network (C-TSKFNN) to assess images with uneven powder distribution. The performance of the model was compared with that of conventional image analysis and state-of-the-art deep learning models. With its fusion layer and TSKFNN design, the C-TSKFNN model retains the distinctive characteristics of input images and minimizes expenditures through trainable parameters. The experimental results demonstrated that the average F1 score of the C-TSKFNN model achieved 97.07% and that it had the lowest standard deviation compared to state-of-the-art deep learning models. The trainable parameters were only 103,506, which is lower than those of models. Defect information was also provided locally through a model interpretation technique, facilitating the monitoring of SLM manufacturing. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Process monitoring

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mLBOA-DML: modified butterfly optimized deep metric learning for enhancing accuracy in intrusion detection system

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Journal of Reliable Intelligent Environments 2023年第3期9卷 333-347页

作者： Prabhakaran, Varun Kulandasamy, Ashokkumar Department of Computer Science and Engineering St.Joseph’s College of Engineering Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

Intrusion detection is a prominent factor in the cybersecurity domain that prevents the network from malicious attacks. Cloud security is not satisfactory for securing the user’s information because it is based on standard protocols. Hence, cloud users cannot fully trust the security offered by cloud service providers. The state-of-the-art techniques create clusters for classes and manually label the unknown classes to detect novel attacks. This notion binds the network traffic associated with each attack together and drifts the similarity between the same attacks. These techniques are often prone to errors and degrade performance. To overcome this drawback, various researchers have developed different intrusion detection system which relies on specific attack patterns to distinguish between normal and abnormal behavior. This paper presents a modified Lagrange interpolated Butterfly optimization algorithm-based deep metric learning (mLBOA-DML) architecture for intrusion detection to detect both host-based and network attacks. DML architecture parameters are optimized utilizing mLBOA algorithm via its global optimization capability for increasing the accuracy of attack prediction. DML algorithm does both feature extraction and classification processes. When evaluated using the UNSW-NB15 and NSL-KDD datasets, the proposed model offers improved accuracy near 99% for both datasets. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Intrusion detection

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Fault-Tolerant Bounded Flow Preservers 35

Fault-Tolerant Bounded Flow Preservers

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35th International Symposium on Algorithms and Computation, ISAAC 2024

作者： Bansal, Shivam Choudhary, Keerti Dhanoa, Harkirat Wardhan, Harsh Department of Computer Science and Engineering IIT Delhi India Department of Electrical Engineering IIT Delhi India

ISBN: (纸本)9783959773546

Given a directed graph G = (V, E) with n vertices, m edges and a designated source vertex s ∈ V, we consider the question of finding a sparse subgraph H of G that preserves the flow from s up to a given threshold λ even after failure of k edges. We refer to such subgraphs as (λ, k)-fault-tolerant bounded-flow-preserver ((λ, k)-FT-BFP). Formally, for any F ⊆ E of at most k edges and any v ∈ V, the (s, v)-max-flow in H \ F is equal to (s, v)-max-flow in G \ F, if the latter is bounded by λ, and at least λ otherwise. Our contributions are summarized as follows: 1. We provide a polynomial time algorithm that given any graph G constructs a (λ, k)-FT-BFP of G with at most λ2kn edges. 2. We also prove a matching lower bound of Ω(λ2kn) on the size of (λ, k)-FT-BFP. In particular, we show that for every λ, k, n ≥ 1, there exists an n-vertex directed graph whose optimal (λ, k)-FT-BFP contains Ω(min{2kλn, n2}) edges. 3. Furthermore, we show that the problem of computing approximate (λ, k)-FT-BFP is NP-hard for any approximation ratio that is better than O(log(λ−1n)). © Shivam Bansal, Keerti Choudhary, Harkirat Dhanoa, and Harsh Wardhan.

关键词： Polynomial approximation

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Data-driven modeling of power system dynamics:Challenges,state of the art,and future work

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iEnergy 2023年第3期2卷 200-221页

作者： Heqing Huang Yuzhang Lin Yifan Zhou Yue Zhao Peng Zhang Lingling Fan Department of Electrical and Computer Engineering University of Massachusetts LowellLowellMA 01852USA Department of Electrical and Computer Engineering Stony Brook UniversityStony BrookNY 11794USA Interdisciplinary Science Department Brookhaven National LaboratoryUptonNY 11973USA Department of Electrical and Computer Engineering University of South FloridaTampaFL 33620USA

With the continual deployment of power-electronics-interfaced renewable energy resources,increasing privacy concerns due to deregulation of electricity markets,and the diversification of demand-side activities,traditional knowledge-based power system dynamic modeling methods are faced with unprecedented ***-driven modeling has been increasingly studied in recent years because of its lesser need for prior knowledge,higher capability of handling large-scale systems,and better adaptability to variations of system operating *** paper discusses about the motivations and the generalized process of datadriven modeling,and provides a comprehensive overview of various state-of-the-art techniques and *** also comparatively presents the advantages and disadvantages of these methods and provides insight into outstanding challenges and possible research directions for the future.

关键词： Data-driven modeling machine learning model construction parameter identification power system dynamics system identification

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A Smart CIoT with Secure Healthcare Framework Using Optimized Deep Recuperator Neural Network Long Short-Term Memory

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IEEE Internet of Things Journal 2024年第6期11卷 10551-10562页

作者： Narmatha, C. Manimurugan, S. Karthikeyan, P. University of Tabuk Faculty of Computers and Information Technology Tabuk71491 Saudi Arabia Oakland University Department of Electrical and Computer Engineering RochesterMI112345 United States National Chung Cheng University Department of Computer Science and Information Engineering Chiayi62102 Taiwan RV University School of Computer Science and Engineering Bengaluru560059 India

The healthcare system currently relies on the facility to store and process large amounts of health data, supported by efficient management. The Internet of Things (IoT) has driven the growth of Adroit Healthcare, which has vast data processing capabilities and extensive data collection. The consumer IoT (CIoT), also known as the IoT in the context of individual use cases, is dominated by personal healthcare applications. The remarkable expansion of the CIoT can be attributed to the extensive embrace of wearable technology, facilitating everyday monitoring of vital health indicators like blood pressure, heart rate, and respiration rate. However, the CIoT raises concerns about data security, confidentiality, and customer trust. This study proposes an integrating blockchain and deep learning (DL) approach to promote responsible use of CIoT. The study develops an Intelligent IoT (IIoT) and healthcare diagnostic model, named BT-PWO-DRNN-LSTM, using blockchain technology and Prairie Wolf optimization-based deep recuperator neural network (DRNN) and long short-term memory (LSTM). The BT-PWO-DRNN-LSTM approach includes three key processes: 1) encrypted transactions;2) cryptographic hash feature;and 3) medical diagnosis. The PWO technique is used for secure communication of medical images, the neighborhood catalogue disposition (NCD) technique is used in the cryptographic hash feature operation (CHFO), and the DL approach is employed as a classification algorithm for diagnosing disorders. The use of PWO methodology for secure healthcare communication and optimal parameter fine-tuning highlights the originality of the study. The BT-PWO-DRNN-LSTM prototype showed positive results with high sensitivity, specificity, and accuracy during the diagnostic process. © 2014 IEEE.

关键词： Blockchain

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