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2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023

作者： Bozkaya, Elif Bilen, Tugce Erel-Ozcevik, Muge Ozcevik, Yusuf National Defence University Turkish Naval Academy Department of Computer Engineering Istanbul Turkey Turk Telekom Istanbul Turkey Manisa Celal Bayar University Department of Software Engineering Manisa Turkey

ISBN: (纸本)9798350302523

With the recent surge in the Internet of Things (IoT) devices and applications, computation offloading services in Mobile Edge Computing (MEC) have provided the significant potential to upcoming 6G networks for a better Quality of Service (QoS). However, IoT devices are typically resource and energy-constrained, so this challenge can be compensated by incorporating energy-efficient approaches into the solution. Digital Twin is a candidate technology to reshape the future of the industry and energy-efficiently manage tremendous growth in data traffic at the network edge. Thus, we propose a Digital Twin Edge Network (DTEN) architecture for energy-aware task scheduling. More specifically, we formulate an energy optimization problem and identify a set of computation strategies to minimize both the task processing time and energy consumption. Due to being NP-hard, we compare it by Warehouse Location Problem (WLP) and solve it with the genetic algorithm-based approach in an energy and time-efficient manner. To achieve these, we present our digital twin-assisted energy-aware task scheduling algorithm by using both real-time and historical data in virtualization and service layers. After this, IoT devices can compute their tasks locally or offload to the edge/cloud server with the assistance of digital twins of the physical assets. Simulations are carried out to show the superiority of the proposed energy-aware task scheduling algorithm in terms of the task processing time and consumed energy in DTEN. © 2023 IEEE.

关键词： Genetic algorithms

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LiDAR-based Real-Time Object Detection and Tracking in Dynamic Environments

arXiv

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arXiv 2024年

作者： Du, Wenqiang Beltrame, Giovanni MISTLab the Department of Computer Engineering and Software Engineering Polytechnique Montreal Montreal Canada

In dynamic environments, the ability to detect and track moving objects in real-time is crucial for autonomous robots to navigate safely and effectively. Traditional methods for dynamic object detection rely on high accuracy odometry and maps to detect and track moving objects. However, these methods are not suitable for long-term operation in dynamic environments where the surrounding environment is constantly changing. In order to solve this problem, we propose a novel system for detecting and tracking dynamic objects in real-time using only LiDAR data. By emphasizing the extraction of low-frequency components from LiDAR data as feature points for foreground objects, our method significantly reduces the time required for object clustering and movement analysis. Additionally, we have developed a tracking approach that employs intensity-based egomotion estimation along with a sliding window technique to assess object movements. This enables the precise identification of moving objects and enhances the system's resilience to odometry drift. Our experiments show that this system can detect and track dynamic objects in real-time with an average detection accuracy of 88.7% and a recall rate of 89.1%. Furthermore, our system demonstrates resilience against the prolonged drift typically associated with front-end only LiDAR odometry. All of the source code, labeled dataset, and the annotation tool are available at: https://***/MISTLab/lidar dynamic objects *** © 2024, CC BY.

关键词： Object detection

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CLPiDA: A Contrastive Learning Approach for Predicting Potential PiRNA-Disease Associations

CLPiDA: A Contrastive Learning Approach for Predicting Poten...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Hu, Xiaowen Zhang, Yuanpeng Zhang, Jiaxuan Deng, Lei Central South University School of Computer Science and Engineering Changsha China Xinjiang University School of Software Urumqi China University of California Department of Electrical and Computer Engineering San DiegoCA United States

ISBN: (纸本)9798350337488

Piwi-interacting RNAs (piRNAs) function as critical regulators, safeguarding genome stability through mechanisms like transposable element repression and gene stability maintenance, while also being associated with various disease pathways. Developing computationally efficient methods to predict piRNA-disease associations is vital for enhancing disease-specific drug discovery while managing costs. In this study, we present CLPiDA, a novel method for predicting potential piRNA-disease associations. CLPiDA begins by computing gaussian kernel similarities for piRNA-piRNA and disease-disease pairs to establish initial embeddings for piRNAs and diseases. Subsequently, it employs a parameter-sharing online and target network, along with data augmentation techniques, to create a contrastive learning framework. This facilitates the generation of embeddings for piRNAs and diseases using piRNA-disease association pairs. Furthermore, CLPiDA employs a cross-prediction approach to determine association scores for specific piRNAs and diseases. Notably, CLPiDA introduces a novel approach by excluding negative samples, thereby avoiding the introduction of false negatives and enhancing its reliability. Comparative experiments validate CLPiDA's superiority in terms of performance over existing methods. Case studies underscore CLPiDA's efficacy as a valuable tool for predicting piRNA-disease associations, providing valuable insights for biological experiments. The data and source code for CLPiDA are available at https://***/altriavin/CLPiDA. © 2023 IEEE.

关键词： contrastive learning graph convolutional network piRNA-disease association

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Secure Energy Efficiency for ARIS Networks With Deep Learning: Active Beamforming and Position Optimization

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IEEE Transactions on Wireless Communications 2025年第6期24卷 5282-5296页

作者： Wang, Dawei Wang, Zijun Zhao, Hongbo Zhou, Fuhui Alfarraj, Osama Yang, Weichao Mumtaz, Shahid Leung, Victor C. M. Northwestern Polytechnical University School of Electronics and Information Shaanxi Xi’an710072 China Beihang University School of Electronic and Information Engineering Beijing100191 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing210000 China King Saud University Community College Computer Science Department Riyadh11437 Saudi Arabia Nottingham Trent University Department of Computer Science NottinghamNG1 4FQ United Kingdom Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen528060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Incorporating an active reconfigurable intelligent surface on an autonomous aerial vehicles (AAVs), denoted as an aerial reconfigurable intelligent surface (ARIS), introduces a novel dimension for secure transmissions. Given the constraint of limited battery capacity in AAVs, energy management emerges as a key challenge within AAV networks. In response, we propose a secure energy efficiency (SEE) transmission scheme for ARIS networks, where active ARIS is strategically deployed to enhance information security. In addition, a SEE optimal problem is formulated by considering the imperfect wiretap channel state information to optimize the active beamforming vector and the ARIS position. For this non-convex problem, we first reformulate the fractional SEE objective into an equivalent form and subsequently decompose it into two distinct subproblems: optimizing the AAV’s position and designing the active beamforming. For the AAV’s position optimization, we propose a sophisticated deep deterministic policy gradient algorithm that enables the AAV to autonomously determine the optimal ARIS position through a self-learning strategy. Regarding beamforming design, we transform this aspect into a quadratic constrained quadratic programming problem and design an alternating direction multiplier method to optimize the reflection coefficient. Subsequently, an alternating optimization algorithm is proposed to synergistically solve these subproblems. Empirical simulations validate our proposed scheme, indicating an improvement in SEE of up to 47.2%. This significant improvement underscores the efficacy of the proposed ARIS-assisted secure transmission scheme in enhancing both security and energy efficiency in AAV networks. © 2002-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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MRIAD: A Pre-clinical Prevalence Study on Alzheimer’s Disease Prediction Through Machine Learning Classifiers 4th

MRIAD: A Pre-clinical Prevalence Study on Alzheimer’s Dise...

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4th Joint International Conference on Deep Learning, Big Data and Blockchain, DBB 2023

作者： Loba, Jannatul Mia, Md. Rajib Mahmud, Imran Mahi, Md. Julkar Nayeen Whaiduzzaman, Md. Ahmed, Kawsar Department of Software Engineering Daffodil International University Dhaka Bangladesh School of Information Systems Queensland University of Technology Brisbane Australia Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada

ISBN: (纸本)9783031423161

Alzheimer’s disease (AD) is a neurological illness that worsens with time. The aged population has expanded in recent years, as has the prevalence of geriatric illnesses. There is no cure, but early detection and proper treatment allow sufferers to live normal lives. Furthermore, people with this disease’s immune systems steadily degenerate, resulting in a wide range of severe disorders. Neuroimaging Data from magnetic resonance imaging (MRI) is utilized to identify and detect the disease as early as possible. The data is derived from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) collection of 266 people with 177 structural brain MRI imaging, DTI, and PET data for intermediate disease diagnosis. When neuropsychological and cognitive data are integrated, the study found that ML can aid in the identification of preclinical Alzheimer’s disease. Our primary objective is to develop a model that is reliable, simple, and rapid for diagnosing preclinical Alzheimer’s disease. According to our findings (MRIAD), the Logistic Regression (LR) model has the best accuracy and classification prediction of about 98%. The ML model is also developed in the paper. This article profoundly, describes the possibility to getting into Alzheimer’s disease (AD) information from the pre-clinical or non-preclinical trial datasets using Machine Learning Classifier (ML) approaches. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Dis...

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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Karthikeyan, D. Nivasini, R. Raja, Kiruba Thangam Madhivadhani, D. Sivakumar, B. Krishna Priya, A.T.R. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India Iitm Research Park Guvi Geek Networks Tamilnadu Chennai India Vit University School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Vellore India Kings Engineering College Department of Electronics and Communication Engineering Sriperumbudur Chennai India Rohini College of Engineering and Technology Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350393378

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer's disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment Information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Wide-ranging parameter extraction of Lithium-ion Batteries to Estimate State of Health using Electrochemical Impedance Spectroscopy 1

Wide-ranging parameter extraction of Lithium-ion Batteries t...

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1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022

作者： Anekal, Latha Samanta, Akash Williamson, Sheldon Ontario Tech University Department of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9798350398069

To ensure effective and safer utilization of Lithium-Ion Batteries (LIB), accurate State Estimation particularly State of Health (SOH) is extremely essential, especially in automotive applications. Accurate information about SOH not only plays a crucial role in health-conscious battery management while at the same time helps to prognose battery fault and replacement. Battery SOH cannot be measured directly. Therefore, typically equivalent models and data-driven estimation techniques are used to obtain SOH through external measurements. Feature vectors required for SOH estimation can be very effectively extracted by Electrochemical Impedance Spectroscopy (EIS). Still, existing EIS-based methods have several limitations, issues, and challenges. Thus, a comprehensive review of state-of-the-art EIS-based SOH estimation techniques is presented in this paper to expedite the research effort toward developing a more advanced and practical EIS method for accurate and real-time SOH estimation. © 2022 IEEE.

关键词： Lithium-ion batteries

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GPT-Based Models Meet Simulation: How to Efficiently use Large-Scale Pre-Trained Language Models Across Simulation Tasks

GPT-Based Models Meet Simulation: How to Efficiently use Lar...

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2023 Winter Simulation Conference, WSC 2023

作者： Giabbanelli, Philippe J. Miami University Department of Computer Science & Software Engineering 501 East High Street OxfordOH45056 United States

ISBN: (纸本)9798350369663

The disruptive technology provided by large-scale pre-trained language models (LLMs) such as ChatGPT or GPT-4 has received significant attention in several application domains, often with an emphasis on high-level opportunities and concerns. This paper is the first examination regarding the use of LLMs for scientific simulations. We focus on four modeling and simulation tasks, each time assessing the expected benefits and limitations of LLMs while providing practical guidance for modelers regarding the steps involved. The first task is devoted to explaining the structure of a conceptual model to promote the engagement of participants in the modeling process. The second task focuses on summarizing simulation outputs, so that model users can identify a preferred scenario. The third task seeks to broaden accessibility to simulation platforms by conveying the insights of simulation visualizations via text. Finally, the last task evokes the possibility of explaining simulation errors and providing guidance to resolve them. © 2023 IEEE.

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FEW-SHOT LEARNING AS CLUSTER-INDUCED VORONOI DIAGRAMS: A GEOMETRIC APPROACH 10

FEW-SHOT LEARNING AS CLUSTER-INDUCED VORONOI DIAGRAMS: A GEO...

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10th International Conference on Learning Representations, ICLR 2022

作者： Ma, Chunwei Huang, Ziyun Gao, Mingchen Xu, Jinhui Department of Computer Science and Engineering University at Buffalo United States Computer Science and Software Engineering Penn State Erie United States

Few-shot learning (FSL) is the process of rapid generalization from abundant base samples to inadequate novel samples. Despite extensive research in recent years, FSL is still not yet able to generate satisfactory solutions for a wide range of real-world applications. To confront this challenge, we study the FSL problem from a geometric point of view in this paper. One observation is that the widely embraced ProtoNet model is essentially a Voronoi Diagram (VD) in the feature space. We retrofit it by making use of a recent advance in computational geometry called Cluster-induced Voronoi Diagram (CIVD). Starting from the simplest nearest neighbor model, CIVD gradually incorporates cluster-to-point and then cluster-to-cluster relationships for space subdivision, which is used to improve the accuracy and robustness at multiple stages of FSL. Specifically, we use CIVD (1) to integrate parametric and nonparametric few-shot classifiers;(2) to combine feature representation and surrogate representation;(3) and to leverage feature-level, transformation-level, and geometry-level heterogeneities for a better ensemble. Our CIVD-based workflow enables us to achieve new state-of-the-art results on mini-ImageNet, CUB, and tiered-ImagenNet datasets, with ∼2%-5% improvements upon the next best. To summarize, CIVD provides a mathematically elegant and geometrically interpretable framework that compensates for extreme data insufficiency, prevents overfitting, and allows for fast geometric ensemble for thousands of individual VD. These together make FSL stronger. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Computational geometry

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Surface EMG Signal Analysis using Hand-Crafted Features for Detection and Classification of GTC seizures 2

Surface EMG Signal Analysis using Hand-Crafted Features for ...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Naveed, Maryam Gul Khawaja, Sajid Usman Akram, Muhammad National University of Sciences and Technology Department of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665498197

Epileptic seizures with the risk of sudden unexpected death in epilepsy affect the quality of life. Nearly, one-fourth of the individuals suffer from seizures that cannot be treated with medications. Due to the high-level possibility of injuries and complications, generalized tonic-clonic seizures have a considerable contribution to unexpected death. These generalized tonic-clonic seizures activity need to be detected and identified through brain and muscle activity, heart rates, and EMG signals. In this paper, we propose a framework for distinguishing normal activity from seizure activity along-with its categorization. Proposed framework focuses on extraction of multiple sEMG hand-crafted features with the time and frequency domain analysis. The proposed methodology for sEMG signals and for GTC class detection has been tested using multiple classifiers including KNN, SVM and ensembles. The obtained results have shown 10% improvement in classification over the state-of the-art approaches available in literature. © 2022 IEEE.

关键词： Feature extraction

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