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Adaptive Self-healing Routing for Heterogeneous Ambient Backscatter Wireless Sensor Networks 18th

Adaptive Self-healing Routing for Heterogeneous Ambient Bac...

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18th international conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Guo, Huanyu Gao, Hong Yang, Donghua Harbin Institute of Technology Harbin China Zhejiang Normal University Jinhua China

ISBN: (纸本)9783031714665

This paper investigates the adaptive and self-healing routing problem in heterogeneous ambient backscatter wireless sensor networks (AB-WSNs). In such networks, battery-less nodes harvest energy only from ambient RF signals generated by dedicated RF sources and communicate with each other by backscattering these signals, leading to nodes having different communication ranges, referred to as heterogeneous links. Consequently, routing in heterogeneous AB-WSNs suffers from intermittent and irregular connections, dynamic topology, heterogeneous links and ultra-low-power designs of battery-less nodes. To address these issues, we first introduce a multi-agent network model, and concurrently, we present a heterogeneous multi-hop channel competition mechanism to avoid collisions under heterogeneous links. We then model the routing problem as a Markov decision process, enabling battery-less nodes to employ reinforcement learning approaches. Finally, we propose a policy-based learning algorithm that allows each node to learn the optimal routing policy and perform adaptive and self-healing routing. Furthermore, to solve sparse reward issues arising from collision avoidance efforts, we simultaneously integrate the learning from demonstration and prioritized experience replay mechanisms into our proposed learning algorithms. We analyze the convergence of proposed learning algorithm and evaluate its convergence and efficiency through extensive experiments. The experiment results also validate the efficiency of the proposed mechanisms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Markov processes

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Dynamic Computation Offloading and Resource Allocation for Multi-access Edge computing Networks 5th

Dynamic Computation Offloading and Resource Allocation for M...

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5th EAI international conference on 6G for Future Wireless Networks, 6GN 2022

作者： Wang, Meng Shi, Shuo Wang, Xi School of Electronic and Information Engineering Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Network Communication Research Centre Guangdong Shenzhen518052 China

ISBN: (纸本)9783031360107

This paper studies the computation offloading and resource allocation of Multi-access Edge computing Networks (MEC) networks under stochastic task arrivals and imperfect channle state conditions (CSI). We aim to minimize the energy consumptions of all mobile users (MUs) while satisfying the stability of all MUs’ queues by optimizing the transmit power, computational frequency and offloading ratio. The problem is modeled as a a long-term average stochastic optimization problem which cannot be solved trivially. To address this, we firstly apply the Lyapunov framework to transform the long-term average optimization problem into a series of per-slot optimization subproblems, and then an iterative optimization method is proposed to decompose the perslot optimization subproblem into three toy problems. The simulation results demonstrate the convergence and performance of the proposed algorithm. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Computation offloading

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Deep Learning Sea Surface Object Detection Method Based on Infrared Image 7

Deep Learning Sea Surface Object Detection Method Based on I...

引用

7th international conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Qu, Yang Wu, Xiaojun Zhou, Jinghui School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen China School of Information Technology and Engineering Taizhou Vocational & Technical College Taizhou China

ISBN: (纸本)9781839539848

To improve the performance of an existing deep learning object detection framework, an infrared image and deep learning based marine target detection method is presented by introducing the Global Attention Mechanism (GAM) and Weighted Bi-directional Feature Pyramid Network (BiFPN). Dataset creation is initiated, and an image fusion-based data augmentation method is employed to expand the dataset using publicly available datasets. Ablation experiments are conducted to measure and confirm the improvements in network detection performance. The trained model is then implemented on an embedded computing platform to assess the practicality of the infrared image marine target detection scheme. Through comprehensive experimentation, comparison, and analysis, the improved algorithm demonstrates faster convergence speed during training compared to the original algorithm. Furthermore, it exhibits enhancements in accuracy, recall, and mean average precision to varying degrees. As a result of these improvements, the trained model achieves performance metrics exceeding 96% precision and recall, as well as a detection frame rate surpassing 25 FPS. © The Institution of Engineering & technology 2023.

关键词： Object detection

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Graph Convolutional Networks Approach for Alzheimer's Disease Stage Classification Using Multimodal Data 13

Graph Convolutional Networks Approach for Alzheimer's Diseas...

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Joint 13th international conference on Soft computing and Intelligent Systems and 25th international Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Jeong, Hyujeong Kang, Do-Young Yun, Yeboon Kang, Jum-Ran Yoon, Min Lee, Gyu-Bin Pukyong National University Dept. of Applied Mathematics Busan Korea Republic of Dong-A University College of Medicine and Medical Center Institute of Convergence Bio-Health Dong-A University Dept. of Nuclear Medicine Busan Korea Republic of Kansai University Faculty of Environmental and Urban Engineering Osaka Japan Pukyong National University Dong-A University College of Medicine and Medical Center Dept. of Applied Mathematics Dept. of Nuclear Medicine Busan Korea Republic of

ISBN: (纸本)9798350373332

With the advancement of medical technology, the global population is aging and this is expected to increase the number of dementia patients. Alzheimer's disease (AD) is the most common cause of dementia and is a heterogeneous, neurodegenerative brain disorder. Due to the lack of a complete cure, early diagnosis is crucial to slow the progression of symptoms. Mild cognitive impairment (MCI), an intermediate state between normal cognition (NC) and dementia, also necessitates early diagnosis because it can progress to AD. In reality, doctors diagnose AD stage by considering multiple types of data such as brain imaging, demographic information, genetic tests, and neuropsychological tests. Therefore, AD stage classification models need to learn these multimodal data. In this study, we proposed applying the graph convolutional network (GCN) to population graphs constructed by using cosine similarity, L1-norm, and L2-norm on multimodal data for AD stage classification. The multimodal data consisted of F -18 florbetaben (FBB) amyloid brain positron emission tomography (PET) images, demographic information, and genetic test, and neuropsychological test data. Different dimensional FBB PET images and non-image data were matched by using a multimodal fusion method utilizing a 3D dense convolutional network. The proposed methods are investigated through cross-validation with support vector machine, random forest, and multilayer percep- tron models. The results suggest that the L 1 -norm leads to better performance of GCN in AD stage classification due to its better robustness against outliers and its ability to account for the heterogeneity of AD compared to cosine similarity and L2 -norm. © 2024 IEEE.

关键词： Positron emission tomography

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Energy Efficient Computation Offloading and Resource Allocation for Satellite Multi-access Edge computing 11

Energy Efficient Computation Offloading and Resource Allocat...

引用

11th international conference on Networks, Communication and computing, ICNCC 2022

作者： Hao, Yuanyuan Zhang, Qian Zheng, Zhong Miao, Zhongyu Zheng, Hanyu Institute of Telecommunication and Navigation Satellites China Academy of Space Technology Beijing100094 China Innovation Center of Satellite Communication System CNSA Beijing100094 China

ISBN: (纸本)9781450398039

In this article, the energy-efficient computation offloading and resource allocation for satellite multi-access edge computing (MEC) is studied. The problem is formulated to minimize the sum energy consumption of users while ensuring the task tolerance latency requirement. The formulated problem is divided into two subproblems, which are solved by exploring the convex structure and applying Lagrangian dual decomposition, respectively. Based on the obtained solutions of the two subproblems, we design an iterative algorithm for satellite MEC, and theoretically analyze its effectiveness, convergence and complexity. Simulation results verify that our proposed algorithm converges fast, and outperforms both full offloading and local computing algorithms. Besides, it is also observed that improving the computing capacities of satellite MEC servers helps to save the energy of users. © 2022 ACM.

关键词： Resource allocation

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A Novel Decentralized Coordination Control Scheme for the Complex Transactive Energy Prosumers

A Novel Decentralized Coordination Control Scheme for the Co...

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2023 international conference on Energy, Power, Environment, Control, and computing, ICEPECC 2023

作者： Ikram, Muhammad Ullah, Zakir Nasir, Muhammad Ahmed, Salman Marwat, Safdar Nawaz Khan University of Engineering and Technology Dept. of Computer Systems Engineering Peshawar Pakistan University of Engineering and Technology Dept. of Electrical Power Engineering Peshawar Pakistan Allama Iqbal Open University Deptment of Physics Islamabad Pakistan

ISBN: (纸本)9781665476010

The significant penetration of renewable energy sources (RES) in the smart grid (SG) provides a new landscape for researchers to develop an optimal energy management model. The households with RES become prosumers to provide the surplus energy in the local community. The coordination and control of such RES prosumers are important to estimate the available energy in the system and the total demand required. In this paper, we proposed a decentralized coordination control approach for transactive energy systems (TES). The proposed TES is capable of coordinating and controlling a complex network of prosumers with limited control information. The consensusability and graph-theoretic schemes are used for prosumer RES nodes. In the proposed model, energy trading among large-scale prosumers has been demonstrated. The algorithm is capable to provide the total surplus energy available across the network and total demand in an autonomous fashion. The coordination control among prosumers is achieved through a computationally efficient approach without sharing the prosumer's profile to preserve the security and privacy of the prosumers. To validate the proposed scheme, we perform extensive simulations on decentralized 450 prosumers. The resiliency of the proposed scheme is confirmed through dynamic and contingent communication topologies for energy trading ratios, cumulative returns, and convergence ratios. The cumulative returns improved from 0.447% to 24.8% while the energy trade among the prosumers recorded from -0.89 to 2.09. The final return is enhanced from -1.95% to 22.3% due to internal and external coordination control of nonlinear loads. © 2023 IEEE.

关键词： Multi agent systems

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Computation Offloading with Bus-Mounted Edge Servers Based on Dung Beetle Optimization in Internet of Vehicles 12

Computation Offloading with Bus-Mounted Edge Servers Based o...

引用

12th international conference on Advanced Cloud and Big Data, CBD 2024

作者： Deng, Xia Bao, Yubin Chen, Shuhong Chang, Le School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China School of Automation Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9798331511074

In Internet of Vehicles (IoV), edge computing alleviates congestion in the backbone network and significantly reduces the delay by deploying servers close to users. However, the existing fixed-site and fixed-capacity edge server deployment cannot effectively cope with the spatiotemporal dynamics of IoV end users, i.e., vehicles on road. Therefore, this paper proposes leveraging the inherent mobility of public buses to carry edge servers, and cooperate with fixed-site edge servers to provision elastic joint task offloading service for 1oV end users. We first build a multi-objective optimization model with fixed-site and bus-mounted servers to minimize the user delay, energy consumption, and payment cost. Then, a computation offloading strategy based on the Dung Beetle optimization algorithm is proposed. Taking into account the varying tolerance for delay among different tasks, a preprocessing method is adopted to set the offloading ratio for each task, thereby reducing the search space and accelerating the convergence speed. Furthermore, the maximum acceptable delay constraint is transformed into an unconstrained problem through the penalty function method. Experimental results verify that the proposed computation offloading algorithm outperforms various classical intelligent optimization algorithms in terms of the overall cost. © 2024 IEEE.

关键词： Computation offloading

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Yuwathi: Early Detection of Breast Cancer and Classification of Mammography Images Using Machine Learning 7

Yuwathi: Early Detection of Breast Cancer and Classification...

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7th IEEE international conference for convergence in technology, I2CT 2022

作者： Munasinghe, Suranga Mahesh Diddugoda, Devindi Fernando, Dinuwan Bhagya Weerasinghe, Lokesha Weerathunga, Ishara Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9781665421683

According to the World Health Organization's (WHO) data and records, breast cancer is one of the most common diseases among women. As a result of the mutations of the genes within a cell, the cell starts growing uncontrollably and rapidly. Such a condition is known as cancer. Cancer tumors can be categorized into two major categories, benign and malignant. However, there is no existing solution in practice to automate early breast cancer identification and risk prediction using medical images (Mammograms). This paper discusses automating breast cancer detection, breast density identification, risk prediction, and solution suggestion using machine learning, image processing, and computer vision techniques. All the mentioned features can be accessed using the application "YUWATHI", and a user can take advantage of this application by using a smartphone also a web application. The objectives of the present study are mammographic mass detection without user intervention, identifying pectoral muscles and removing them, training a machine learning model to identify the future risk of breast cancers by obtaining clinical reports from the OCR application and suggesting solutions for the above problems using a computer-aided diagnosis (CADx) system that helps doctors to make decisions swiftly. The algorithms used for breast cancer detection, breast density classification, and future breast cancer risk prediction are Convolutional Neural Network (CNN), CNN and Logistic Regression with the accuracies 97.32%, 71.97% 74.76%, respectively. © 2022 IEEE.

关键词： Image classification

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An Integrating Computational Approach Review to Analyse the Biological Functions

An Integrating Computational Approach Review to Analyse the ...

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2023 international conference on Advancement in Computation and Computer Technologies, InCACCT 2023

作者： Kimothi, Sanjeev Joshi, Pooja Shukla, Sunil Kumar, Rajiv Ahmad, Ishteyaaq Memoria, Minakshi University Department of Physics India Swami Rama Himalayan University Dept. of Computer Science Dehradun India COER University School of Computing Roorkee India Uttaranchal Institute of Technology Uttaranchal University Dehradun India Uttaranchal University Uttaranchal School of Computing Sciences Dehradun India

ISBN: (纸本)9798350396485

In present study the fractal theory has been reviewed in the context of bio-functional and biomedical complex systems. The chaotic approach is a critical component of the theoretical framework and can be used in analyzing complex biological structures such as chromatin structures. Fractality is a metric of complexity in biological functions;it is an indicator of the complication level of the self-similar structure, while chaos is a sort of dynamic behavior that usually produces totally arbitrary patterns. Fractal measurements in vivo could be used to predict the efficiency of painful therapy. The fractal technique can be used to assess carcinogenesis, tumor progression, chemoprophylaxis, and treatment with the convergence of modern sensing techniques in nano-scale spectroscopic techniques, which is a prospective biomarker. The mathematical principles of fractals and chaos in biological systems are presented in the context of the condition of health treatment and their significance. Fractality in different biological functions including the heart has now been investigated and measured the dosing quantity with chaos and fractal level. As excessive amounts of chaos and fractal complexity are harmful to biological predictions. For biological applications, chaos analysis may be advantageous. This paper is a review which highlights the fractal and chaos theories for biological functions and biomedical systems. The focus will be to explore biological functions, due to its computational machine learning-based demands and capability in mathematical complexity. © 2023 IEEE.

关键词： Fractals

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Location Representations for Accelerating the Training of Next POI Recommender System

Location Representations for Accelerating the Training of Ne...

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2022 ACM international Joint conference on Pervasive and Ubiquitous computing and the 2022 ACM international Symposium on Wearable Computers, UbiComp/ISWC 2022

作者： Xia, Qingxin Hara, Takahiro Maekawa, Takuya Graduate School of Information Science and Technology Osaka University Osaka Japan

ISBN: (纸本)9781450394239

With the widespread use of mobile devices, many next Point-Of-Interest (POI) recommender systems train users' data on their devices to provide personalized recommendation services, which requires the algorithms to have an efficient convergence rate. Besides, POI location, an important feature in improving the POI recommendation performance, has been represented in various ways, such as encoding locations with distinctive IDs (e.g., different areas with different IDs). However, the positional relationship of a POI of interest and other POIs is ignored, which is crucial to predicting the next POI. In this study, we propose describing a POI's location representation as the positional relationship with other POIs. Based on the idea that users prefer to visit closer regions, our approach transfers the distance interval of POIs to the similarity of the location representations, thus the possibility of a user visiting a POI can be revealed from the representation similarities. The proposed location representations that contain relative location information of all POIs can be applied to initialize POI recommendation models to accelerate model convergence. Experiments on our approach show that the proposed method on a sequential recommendation model improves Hit@10 by 4%, and the convergence rate (average regret before epoch@150) increases 4% with a 10% Hit@10 improvement. © 2022 Owner/Author.

关键词： Location

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