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A Target Tracking Agorithm Based on em Method with Inaccurate Based Stations

A Target Tracking Agorithm Based on em Method with Inaccurat...

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2023 IEEE international conference on Signal Processing, Communications and computing, ICSPCC 2023

作者： Zhang, Chun Gao, Jingjie Hua, Fei Gao, Yuhai Tian, Kunkun Jiang, Xingchen School of Information Engineering Chang'An University Xi'an China Xi'An Aeronautics Computing Technology Research Institute Xi'an China

ISBN: (纸本)9798350316728

Aiming at the difficulty of target tracking caused by inaccurate base stations and the performance degradation of target tracking algorithms, a target tracking algorithm based on Expectation-Maximization(EM) method is proposed. Considering the inaccuracy of the base station locations, the algorithm sets the target state as a hidden variable and the base station location as an adjustable parameter, then jointly estimates them by EM method. In E-step, the joint posterior probability of the target under each base station is established, and the target state under each base station is estimated by particle filter. In M-step, a joint likelihood function based on the target and base station is established and the position of the base station is estimated by maximizing the joint likelihood function. Finally, based on the minimum mean square error criterion, the estimation results of each base station are fused to obtain the final solution of the target. The simulation results show that the algorithm shows excellent tracking performance and convergence in the environment of inaccurate base station locations. © 2023 IEEE.

关键词： Base stations

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Optimal Resource Allocation for 6G UAV-enabled Mobile Edge computing with Mission-Critical Applications

Optimal Resource Allocation for 6G UAV-enabled Mobile Edge C...

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2023 IEEE international conference on Metaverse computing, Networking and Applications, MetaCom 2023

作者： Van Huynh, Dang Li, Yijiu Masaracchia, Antonino Hoang, Trang Duong, Trung Q. Queen's University Belfast United Kingdom Ho Chi Minh City University of Technology Vietnam National University Viet Nam

ISBN: (纸本)9798350333336

This paper considers unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) for the sixth generation (6G) networks with mission-critical applications. In particular, the UAVs serve as mobile edge servers to execute offloaded tasks from constrained Internet of things (IoT) devices. A low complexity optimisation algorithm is proposed to deal with the latency minimisation problem of task execution. More specifically, the solution jointly optimises bandwidth allocation and task offloading probability subject to quality-of-service constraints and energy budget of devices. Numerical results validate the effectiveness of the proposed algorithm in terms of convergence behaviour and impacts of various system parameters. © 2023 IEEE.

关键词： Internet of things

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FedCSA: Boosting the convergence Speed of Federated Unlearning under Data Heterogeneity 21

FedCSA: Boosting the Convergence Speed of Federated Unlearni...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Wang, Zhen Alghazzawi, Daniyal M Cheng, Li Liu, Gaoyang Wang, Chen Cheng, Zeng Yang, Yang Hubei University Key Laboratory of Intelligent Sensing System and Security Ministry of Education School of Artificial Intelligence Wuhan China King Abdulaziz University Jeddah Saudi Arabia Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350329223

The exponential growth of the Internet and the widespread availability of personal data have raised significant concerns regarding personal privacy and societal security. In response, the 'right to be forgotten' has emerged as a fundamental principle, empowering individuals to request the removal of their personal data. Though relieving the privacy concerns at a certain degree by doing so, Artificial Intelligence (AI) models, trained by personal data, still suffers from privacy leakage risks. Therefore, machine unlearning attracts great attention from both industry and academia, aiming at erasing the risks of privacy leakage of AI models. However, existing machine unlearning methods primarily operate within centralized frame-works, limiting their applicability to decentralized Federated Learning (FL) settings where data is decentralized and not directly accessible. Additionally, current federated unlearning methods only approximate data deletion, leaving a potential risk of data leakage. To address these issues, this paper presents FedCSA, The FedCSA approach adopts a divide-and-conquer strategy by clustering and slicing the users' data. Subsequently, submodels are trained on each individual slice, and their aggregation yields the global model. During the unlearning task, only the corresponding sub-model undergoes retraining, significantly reducing training time while ensuring data privacy and achieving precise data deletion. Extensive experiments are conducted on real-world datasets, including MNIST, Fashion-MNIST, and CIFAR-10, to evaluate the performance of FedCSA. The results demonstrate that FedCSA outperforms state-of-the-art machine learning methods, such as retrain and SISA, in terms of efficiency and global model performance. Our code is publicly available at https://***/ZhenWang9/FedCSA. © 2023 IEEE.

关键词： Data privacy

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*** - An Automated Parasitic Egg Detection Model from Microscopic Images of Fecal Smears using Deep Learning Techniques 2

*** - An Automated Parasitic Egg Detection Model from Micros...

引用

2nd IEEE international conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Jaya Sundar Rajasekar, Sakthi Jaswal, Gaurav Perumal, Varalakshmi Ravi, Sasana Dutt, Varun Melmaruvathur Adhiparasakthi Institute of Medical Sciences and Research Melmaruvathur India Indian Institute of Technology IHub & Hci Foundation Himachal Pradesh Mandi India Anna University Madras Institute of Technology Campus Chennai India Iit Mandi School of Computing and Electrical Engineering Himachal Pradesh India

ISBN: (纸本)9798350315905

Intestinal parasitic infestations are one of the leading reasons of morbidity worldwide and has been recognized as one of the most significant causes of diseases by World Health Organization (WHO). The mainstay of diagnosis is through direct manual examination of fecal smears using microscope in the laboratory. However, there are several shortcomings in the manual parasite egg detection like its time-consuming nature, low sensitivity and mandatory requirement of skilled technicians. Utilization of Artificial Intelligence in automation of the laboratory identification of parasite eggs has been proposed in this work. Various Deep Learning models like Yolov8, Detectron2, Inception v3 and YOLOs have been utilized for parasite egg detection task and their performance has been evaluated. Our results indicate that YOLOv8 with SGD optimizer has yielded superior performance, mAP of 0.92 and F1-score of 98%, compared to the other models evaluated and the existing literature. Its high accuracy and fast convergence make it an ideal choice for the parasitic egg detection tasks. © 2023 IEEE.

关键词： Automation

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Cost-Minimized Partial Computation Offloading in Cloud-Assisted Mobile Edge computing Systems

Cost-Minimized Partial Computation Offloading in Cloud-Assis...

引用

2023 IEEE international conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Nowadays, smart mobile devices (SMDs) support various computation-intensive and delay-sensitive applications, e.g., online games, and figure compression. However, SMDs have limited computing resources and battery energy and cannot execute all tasks of the above applications in a real-time manner. Cloud computing provides enormous computing resources and energy that can easily execute tasks offloaded from SMDs. However, could data centers (CDCs) are often located in remote sites, which leads to long transmission time. Small base stations (SBSs) offer high-bandwidth and low-latency services for SMDs, which solves the problem of cloud computing. However, it becomes a challenge to achieve the lowest cost in such a heterogeneous architecture including multiple SMDs, SBSs, and the CDC while meeting delay requirements of tasks. This work proposes a cost-minimized computation offloading strategy to minimize the total cost of the system. A constrained optimization problem is first formulated based on the hybrid architecture. Afterward, a two-stage optimization algorithm called a Lévy flights and Simulated Annealing-based Grey wolf optimizer (LSAG) is developed to optimize the total cost of the system. In the first stage, the optimal edge selection policy is determined given multiple available SBSs. In the second stage, task offloading and resource allocation among SMDs, SBSs, and the cloud are determined. Experiments with real-life tasks prove that LSAG significantly achieves lower cost with faster convergence speed than state-of-the-art peers. © 2023 IEEE.

关键词： cloud computing computation offloading intelligent optimization algorithms Lévy flights Mobile edge computing

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High-Resolution Optical Imaging Incorporated Non-Destructive Morphological Boundary Detection of Fruit Disorders: A Pilot Study 6

High-Resolution Optical Imaging Incorporated Non-Destructive...

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6th international conference on Advancements in computing, ICAC 2024

作者： Hakmana, Nethmi Tharushi Kahatapitiya, Nipun Shantha Weerasinghe, Sasmitha Kalupahana, Deshan Silva, Bhagya Nathali Ravichandran, Naresh Kumar Seong, Daewoon Han, Sangyeob Lee, Seung Yeol June, Hee-Young Jeon, Mansik Kim, Jeehyun Wijenayake, Udaya Wijesinghe, Ruchire Eranga Faculty of Engineering Sri Lanka Institute of Information Technology Department of Mechanical Engineering Malabe Sri Lanka Faculty of Engineering University of Sri Jayewardenepura Department of Computer Engineering Nugegoda Sri Lanka Faculty of Computing Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Scientific Instrumentation Korea Basic Science Institute Daejeon Korea Republic of School of Electronic and Electrical Engineering College of IT Engineering Kyungpook National University Daegu Korea Republic of ICT Convergence Research Center Kyungpook National University Daegu Korea Republic of Kyungpook National University Department of Plant Medicine Daegu Korea Republic of Institute of Plant Medicine Kyungpook National University Daegu Korea Republic of Faculty of Engineering Sri Lanka Institute of Information Technology Department of Electrical and Electronic Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

Plant diseases caused by various pathogens, such as fungi, bacteria, and viruses, lead to significant reductions in crop yield and quality. Early detection of these diseases is critical for preventing their spread and optimizing crop yield. Traditional methods of disease detection, while effective, often involve invasive or time-consuming procedures that may not provide real-time insights. This study investigates the use of Swept-Source Optical Coherence Tomography (SS-OCT) for non-destructive, high-resolution imaging to identify morphological abnormalities in fruit specimens affected by diseases. The research focuses on differentiating healthy regions, healthy boundaries, infected boundaries, and fully infected regions in apples. Using a 1310 nm center wavelength laser, the SS-OCT system captured detailed cross-sectional images of fruit tissue, revealing microstructural changes associated with disease progression. A qualitative assessment of the two-dimensional (2D) OCT images highlighted distinct layers in healthy tissues, noticeable structural deformities, and the merging of microstructural layers in the boundary and infected regions. Quantitative depth-profile analysis of axial-direction A-scan profiles further demonstrated the reduction and merging of intensity peaks correlating with tissue abnormalities in infected fruit specimens. These findings underscore the potential of SS-OCT as an effective tool for early disease detection in agricultural products, providing a means for timely intervention and optimized postharvest management. © 2024 IEEE.

关键词： Fruits

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Dendritic Learning in Superconducting Optoelectronic Networks: Elastic Weight Collision at Dynamical Boundaries and Intermittent Validation for Early convergence

Dendritic Learning in Superconducting Optoelectronic Network...

引用

2023 international conference on Neuromorphic Systems, ICONS 2023

作者： O'Loughlin, Ryan Primavera, Bryce Shainline, Jeffrey National Institute of Standards and Technology University of Colorado Boulder United States National Institute of Standards and Technology Boulder United States

ISBN: (纸本)9798400701757

Superconducting Optoelectronic Networks (SOENs) combine photonics and superconductors to instantiate computing systems that approach the fundamental limits of information processing in terms of speed and scalability. Overcoming the engineering challenges of integrating these technologies into one system has consistently been aided by using neuro-inspired architectures. SOENs are natively Spiking Neural Networks (SNNs) of loop neurons, which themselves are comprised of many subsequent dendrites organized into intricate morphologies. Therefore, SOENs at scale may embody highly complex superstructures that demand commensurate learning methods. To that end, we here propose a simple activity-based update rule (the arbor-update) for dendritic learning that is found to successfully classify nine-pixel images with a single neuron. We test two amendments to the arbor-update on a winner-take-all (WTA) mutually inhibitory three-neuron network. Both elastic weight collision with dynamical boundaries and intermittent validation are found to improve convergence time and conditions. The arbor-update and its variants are scalable with SOENs and may even map to other systems. Importantly, all proposed learning methods are expected to be entirely implementable for on-chip learning in SOENs. © 2023 Owner/Author(s).

关键词： Learning systems

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An Efficient Method for Solving a Least Square Problem over Mixed Constraints 2

An Efficient Method for Solving a Least Square Problem over ...

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2nd international conference on Applied Statistics, Computational Mathematics, and Software Engineering, ASCMSE 2023

作者： Luo, Jinzhi College of Mathematics and Computing Science Hunan University of Science and Technology Hunan Xiangtan411201 China

In this paper, we consider solving the generalized Sylvester equation AXB + CYD = E under mixed constraints {X T = X, L1 ≤ X ≤ U1,Y T = Y, L2 ≤ Y ≤ U2 }, where A, C ∈ Rm×n , B, D ∈ Rn×m, E ∈ Rm×m... 详细信息

ISBN: (数字)9781510668195

ISBN: (纸本)9781510668188

In this paper, we consider solving the generalized Sylvester equation AXB + CYD = E under mixed constraints {X T = X, L1 ≤ X ≤ U1,Y T = Y, L2 ≤ Y ≤ U2 }, where A, C ∈ Rm×n , B, D ∈ Rn×m, E ∈ Rm×m and L1,U1, L2 ,U2 ∈ Rn×n are suitable given matrices, and the • is the Frobenius norm. An effective iterative algorithm is presented to solving the proposed problem and the relevant convergence properties of the algorithm are proved. Numerical examples are performed to demonstrate the algorithm is efficient. © 2023 SPIE.

关键词： Iterative methods

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Deep Reinforcement Learning-based Partial Task Offloading in High Altitude Platform-aided Vehicular Networks 13

Deep Reinforcement Learning-based Partial Task Offloading in...

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13th international conference on Information and Communication technology convergence, ICTC 2022

作者： Nguyen, Tri-Hai Truong, Thanh Phung Dao, Nhu-Ngoc Na, Woongsoo Park, Heejae Park, Laihyuk Seoul National University of Science and Technology Department of Computer Science and Engineering Seoul Korea Republic of Viettel Network Viettel Group Hanoi Viet Nam Sejong University Department of Computer Science and Engineering Seoul Korea Republic of Kongju National University Division of Computer Science and Engineering Cheonan Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Compared with traditional terrestrial access networks, a mobile edge computing-enabled aerial access network is a potential paradigm for performing complicated computations by offloading tasks to edge servers. To this end, high altitude platforms employed in the stratosphere to offer extensive coverage and powerful computing capabilities are regarded as a crucial component of an aerial access network. In this work, we investigate a computation offloading issue in a high altitude platform -aided vehicle network, where a ground base station is either overload or not accessible. Non-orthogonal multiple access is employed to enhance the transmission rate. We model the problem as a Markov decision process due to the complexity of the vehicular network and computation-intensive, latency-sensitive tasks. Then, we develop a deep reinforcement learning-based intelligent offloading strategy to minimize all vehicles' total energy consumption and task execution latency. © 2022 IEEE.

关键词： Reinforcement learning

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Grey Wolf Optimizer for enhancing local search based on population 9

Grey Wolf Optimizer for enhancing local search based on popu...

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9th international conference on Computer and Communications, ICCC 2023

作者： Gao, Cen Duan, Xiaohui Li, Xiaoxiao Chinese Academy of Sciences Shenyang Institute of Computing Technology Shenyang110168 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798350317251

To improve the convergence rate and avoid early maturity when using Grey Wolf Optimizer (GWO) to solve the Traveling Salesman Problem (TSP), a Grey Wolf Optimizer for enhancing local search based on population (GWO-LSP) has been *** grey wolf is divided into populations, and the GWO is iterated within the population and cross-genetically inherited between the populations to avoid falling into local optimum solutions,which improves the quality of the algorithm to solve the TSP problem. Meanwhile, the position update step of the grey wolf optimizer is optimized and improved, so that the iteration within populations and cross genetics between populations can be processed in parallel and the iteration speed is improved. By conducting on the TSPLib dataset experiments, the results show that the improved algorithm has the advantages of high solution quality and fast solution speed. © 2023 IEEE.

关键词： Traveling salesman problem

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