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MoFLeuR: Motion-based Federated Learning Gesture Recognition 4

MoFLeuR: Motion-based Federated Learning Gesture Recognition

引用

4th IEEE International Conference on Human-Machine systems, ICHMS 2024

作者： Jamal Seyedmohammadi, S. Sheikholeslami, S. Mohammad Abouei, Jamshid Mohammadi, Arash Plataniotis, Konstantinos N. Concordia University Inst. Of Information Systems Engineering Montreal Canada University Of Toronto Dept. Of Electrical And Computer Engineering Toronto Canada Yazd University Dept. Of Electrical Engineering Yazd Iran

ISBN: (纸本)9798350315790

Most of the recent works on Human Gesture Recognition (HGR) using motion data rely on gathering a dataset, that faces two major challenges: a) the datasets are originally stored on the smart devices at the end-users, and gathering them in one place is not feasible due to communication limitations, and b) clients are reluctant to share their private data with a central server due to privacy concerns. In this paper, we address these issues and propose a privacy-preserving framework based on Federated Learning (FL) for HGR using motion data. Furthermore, we consider data heterogeneity which have destructive effects on the performance of the global model. Accordingly, we propose a communication and computation-efficient client selection method, mitigating the impact of data heterogeneity in the FL process. In the proposed framework, clients are not requested to share sensitive information about their local datasets with the edge server. Simulation results show that the proposed MoFLeuR algorithm improves the performance of the global model in the presence of different degrees of data heterogeneity, and it outperforms the baseline algorithms in terms of different metrics, namely accuracy, convergence speed, and communication and computation efficiency. © 2024 IEEE.

关键词： Internet of things

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Analysis of the PV Energy Potential in the Bulgarian Danube River Region between Ruse and Silistra 5

Analysis of the PV Energy Potential in the Bulgarian Danube ...

引用

5th IEEE International Conference on Communications, Information, Electronic and Energy systems, CIEES 2024

作者： Gabrovska-Evstatieva, Katerina Stoyanov, Ivaylo Kaneva, Tsvetelina Valov, Nikolay Tsankov, Vladimir Evstatiev, Boris Mihailov, Nicolay University of Ruse Dept. of Computer Science Ruse Bulgaria University of Ruse Dept. of Electric Power Engineering Ruse Bulgaria University of Ruse Dept. of Computer Systems and Technologies Ruse Bulgaria University of Ruse Dept. of Automation and Electronics Ruse Bulgaria

ISBN: (纸本)9798350352863

The goal of the study is to assess and compare the photovoltaic energy potential in the central-eastern part of the Bulgarian Danube region between the cities of Ruse and Silistra. To achieve this the solar radiation and the temperature of the environment should be obtained and analyzed. The goal of the study is to assess and compare the photovoltaic energy potential in the central-eastern part of the Bulgarian Danube region between the cities of Ruse and Silistra. To achieve this the solar radiation and the temperature of the environment should be obtained and analyzed. The goal of the study is to assess and compare the photovoltaic energy potential in the central-eastern part of the Bulgarian Danube region between the cities of Ruse and Silistra. To achieve this the solar radiation and the temperature of the environment should be obtained and analyzed. © 2024 IEEE.

关键词： Solar irradiance

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Classifying Driver Distraction with Textile Electrocardiograms 46

Classifying Driver Distraction with Textile Electrocardiogra...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Pavan, Kaveti Roha, Vishal Singh Igasaki, Tomohiko Karthick, P.A. Pawar, Digvijay S. Ganapathy, Nagarajan Iit Dept. of Biomedical Engineering Telangana Hyderabad India Monash University Dept. of Electrical & Computer Systems Melbourne Australia Kumamoto University Dept. of Biomedical Engineering Kumamoto Japan Nit Dept. of Instrumentation Engineering Tamil Nadu Tiruchirappalli India Iit Dept. of Civil Engineering Telangana Hyderabad India

ISBN: (纸本)9798350371499

Textile sensor-based vital sign assessment plays an important role in continuous monitoring due to its unobtrusive and non-invasiveness. Textile electrocardiography (ECG) sensors allow mental wellbeing assessments in drivers during driving. In this study, we assess the effectiveness of a single-lead ECG obtained from a non-medical-grade ECG shirt for detecting driver distraction due to induced stress. Using ECG shirts, a single-lead ECG (256Hz, 12 bits) is acquired from N=10 healthy volunteers having driving licenses in three distinct driving situations (Baseline, Texting, Calling) in a controlled environment. ECG data is manually checked, and segmented into short durations (10, 30, 60 seconds). These segments are applied to a customized convolution neural network (ccNN). The proposed approach is able to classify the driver's distraction with ccNN yielding a weighted F-Score of 0.65 and an average accuracy of 67.12% on the validation set. Leave-One-Subject-Out Cross-Validation results showed weighted F-Scores ranging from 0.53 to 0.75. Thus, a single-lead, wearable textile ECG provides informative insights into a driver's mental wellbeing. © 2024 IEEE.

关键词： Convolutional neural networks

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A Sequential Pattern Mining Approach for Situation-Aware Human Activity Projection

A Sequential Pattern Mining Approach for Situation-Aware Hum...

引用

2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： D'Aniello, Giuseppe Falcone, Roberto Gaeta, Matteo Rehman, Zia Ur Fortino, Giancarlo University of Salerno Dept. Information and Electrical Engineering and Applied Mathematics SA Fisciano Italy University of Calabria Dept. Computer Engineering Modeling Electronics and Systems Cs Rende Italy

ISBN: (纸本)9781665410205

Human activity prediction has become increasingly prevalent in a plethora of time-critical applications. To realize accurate identification and prediction of human behaviour, we propose a situation-aware wearable computing system. A wearable computing system has the capability to perceive, comprehend and project situations by analyzing the human behavioral patterns in different environments. In particular, this work proposes a situation-aware human activity prediction (SA-HAP) approach based on sequential pattern mining that aims to anticipate future activities and tailor its responses according to situations by analyzing frequent sequential patterns and their correlations to understand how these situations are interrelated. The approach not only improves prediction accuracy but also provide the foundation for a more informed decision-making process, as the projected situations can be explained using the identified behavioral patterns. The approach is compared with other traditional techniques for activity prediction (LSTM and HMM), achieving better performance on the Extrasensory dataset. © 2024 IEEE.

关键词： Prediction models

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PLCD: Policy Learning for Capped Service Mobility Downtime 32

PLCD: Policy Learning for Capped Service Mobility Downtime

引用

32nd International Conference on computer Communications and Networks, ICCCN 2023

作者： Mwasinga, Lusungu J. Raza, Syed M. Le, Duc-Tai Kim, Moonseong Choo, Hyunseung Sungkyunkwan University Dept. of Computer Science and Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of Seoul Theological University Dept. of It Convergence Software Bucheon Korea Republic of

ISBN: (纸本)9798350336184

Service mobility in Multi-access Edge Computing (MEC) paradigm is necessary to provide ultra-Reliable Low Latency Communications for the erratically roaming MEC users. It involves relocation of containerized application services to a strategically selected optimal edge host. During relocation, service containers are unavailable (downtime), resulting in the interruption of ongoing user sessions and increased operational expenses for the network operator. Prolonged service downtime degrades perceived quality of experience for users, and this study handles this problem by proposing a downtime-aware Policy Learning based Capped Downtime (PLCD) service mobility strategy. It exploits Deep Actor-Critic prowess for effectively deciding when and where to relocate a containerized application service while taking user mobility and MEC server resource fluctuations into account. Efficacy of the proposed PLCD strategy is confirmed through simulation experiments, and results indicate over 90% average reduction in service downtime comparing to a baseline scheme. © 2023 IEEE.

关键词： Reinforcement learning

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A Variable Step-Size Hybrid Adaptive Nonlinear Filter for Solar Radiation Prediction 10

A Variable Step-Size Hybrid Adaptive Nonlinear Filter for So...

引用

10th International Conference on systems and Informatics, ICSAI 2024

作者： Xiao, Y. Ma, L. Khorasani, K. Prefectural University of Hiroshima Dept. of Management & Information Systems Hiroshima734-8558 Japan Concordia University Dept. of Electrical & Computer Engineering Montreal Canada

ISBN: (纸本)9798331510138

In this paper, a new hybrid adaptive nonlinear filter (HANF) scheme with variable step sizes (VSS) is proposed for solar radiation prediction. Our methodology consists of a Volterra filter and a functional link artificial neural network (FLANN) filter. The Volterra filter with the first- and second-order kernels is included, that is capable of expressing both linearity and nonlinearity. The FLANN filter can handle the nonlinearity that may be expressed by higher-order exponential terms that the Volterra filter with a limited number of kernels is unable to deal with. The VSSs are introduced in the two filters to allow the HANF to enjoy desirable tracking capability such that the time-varying nonlinearity underlying the nonlinear phenomena can be detected and tracked. The proposed VSS-HANF is applied to a real hourly solar radiation time sequence to confirm its improved prediction performance as compared to its counterpart with fixed step sizes. © 2024 IEEE.

关键词： Adaptive filters

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Optimizing Random Forest Classifier for Detection of Alzheimer's Using OASIS

Optimizing Random Forest Classifier for Detection of Alzheim...

引用

2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Sanjana Reddy, Karri Sai Reddy C, Kishor Kumar Doss, Srinath Lippert, Kari Stanley College of Engineering and Technology for Women Dept. of Computer Science and Engineering Hyderabad India Botho University Faculty of Engineering and Technology Botswana University of South Alabama Dept. of Systems Engineering Alabama United States

ISBN: (纸本)9798331540821

Alzheimer's disease (AD) is a progressive brain disorder impacting behavior, memory, and cognition, with over a million cases reported annually in India. The risk significantly increases beyond age 65. Early diagnosis and treatment can result in better recovery. We propose a predictive model using the Random Forest algorithm and the OASIS dataset for early AD diagnosis, leveraging MRI data, clinical notes, genetic markers, and cognitive test results. Our model was evaluated against several others, including Decision Tree, AdaBoost, SVM, and Logistic Regression. With a 97.3% accuracy and a 2.7% error rate, our Random Forest Classifier o utperformed t he others, demonstrating superior predictive power for early AD diagnosis and potentially improving patient care. © 2024 IEEE.

关键词： Logistic regression

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Selfish Users in Listen-Before-Talk Co-existence of Cellular-Wi-Fi Networks: Counteraction Methods 21

Selfish Users in Listen-Before-Talk Co-existence of Cellular...

引用

21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Saibharath, S. Mishra, Sudeepta Arora, Gazal Sharma, Anamika Hota, Chittaranjan Dept. of Computer Science and Information systems BITS Pilani Hyderabad Campus India Dept. of Computer Science and Engineering IIT Ropar Punjab India

ISBN: (纸本)9783903176553

5G operates in the unlicensed spectrum to expand its capacity. This spectrum provides more uplink and downlink frequency bands in 5G to satisfy ultra-dense and scalability requirements. Wi-Fi is a prominent wireless technology that operates in unlicensed bands. Due to the proximity of cellular and Wi-Fi channels, utilization of both Wi-Fi and 5G New Radio (NR) can cause interference. The co-existence of 5G NR and Wi-Fi devices lacks the means of communication for negotiation and coordination among them. Listen-Before-Talk and Duty-cycling are standard Medium Access Control (MAC) mechanisms to enable co-existence. Due to a lack of coordination, network utilization becomes unfair with the existence of selfish devices. The selfish users could maximize throughput and affect other Wi-Fi and NR users. Here, we investigate the effects of selfish nodes on this Wi-Fi Co-existence with 5G. These nodes disobey exponential backoff to boost their channel acquisition. We study the effect of misbehavior through the Order Gain metric. Through standard CSMA/CA settings with overall channel occupancy of 99.75%, we demonstrate that selfish users profoundly impact fair co-existence. We study the side effects through throughput, channel sensing, and acquisition for various backoff window access patterns. Finally, we go through counteraction methods to overcome the selfish user impact. © 2023 IFIP.

关键词： Wi-Fi

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A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning 58

A Tensor Low-Rank Approximation for Value Functions in Multi...

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58th Asilomar Conference on Signals, systems and computers, ACSSC 2024

作者： Rozada, Sergio Paternain, Santiago Bazerque, Juan Andrés Marques, Antonio G. King Juan Carlos University Dept. of Signal Theory and Communications Madrid Spain Rensselaer Polytechnic Institute Dept. of Electrical Computer and Systems Engineering TroyNY United States University of Pittsburgh Dept. of Electrical and Computer Engineering PittsburghPA United States

ISBN: (纸本)9798350354058

In pursuit of reinforcement learning systems that could train in physical environments, we investigate multi-task approaches as a means to alleviate the need for massive data acquisition. In a tabular scenario where the Q-functions are collected across tasks, we model our learning problem as optimizing a higher order tensor structure. Recognizing that close-related tasks may require similar actions, our proposed method imposes a low-rank condition on this aggregated Q-tensor. The rationale behind this approach to multi-task learning is that the low-rank structure enforces the notion of similarity, without the need to explicitly prescribe which tasks are similar, but inferring this information from a reduced amount of data simultaneously with the stochastic optimization of the Q-tensor. The efficiency of our low-rank tensor approach to multi-task learning is demonstrated in two numerical experiments, first in a benchmark environment formed by a collection of inverted pendulums, and then into a practical scenario involving multiple wireless communication devices. © 2024 IEEE.

关键词： Multi-task learning

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FedStar Caching: Decision Center Assisted Federated Cooperative Edge Caching 4

FedStar Caching: Decision Center Assisted Federated Cooperat...

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4th IEEE International Conference on Human-Machine systems, ICHMS 2024

作者： Gharib, Sirvan Kesargheh, Mohammad Mansour Seyedmohammadi, S. Jamal Hajiakhondi, Zohreh Abouei, Jamshid Plataniotis, Konstantinos N. Yazd University Dept. Of Electrical Engineering Yazd Iran Concordia University Inst. Of Information Systems Engineering Montreal Canada Concordia University Dept. Of Electrical Engineering Montreal Canada University Of Toronto Dept. Of Electrical And Computer Engineering Toronto Canada

ISBN: (纸本)9798350315790

With the explosion of data, Wireless Edge Caching (WEC) has become a promising approach for locally accessing cached contents. Due to the limited storage capacity of local caching devices and the varying preferences of humans for content, it is necessary to predict popular contents. In this article, we will address two distinct objectives: 1) Popularity prediction of the content that should be cached at the edge to effectively utilize edge device memory. 2) Edge devices collaboration for efficient content delivery. Accordingly, we propose an FL-based Star Cooperative Caching (FedStar Caching), utilizing a Star network topology for realizing an efficient cooperation among Femto-cell Access Points (FAPs) using a Decision Center (DC) to enhance power efficiency and delay performance. Considering human sensitivity to privacy, collecting users' data on a central server is not desirable, therefore, we leverage Federated Learning (FL) to alleviate this challenge. Simulation results demonstrate that the proposed method outperforms alternatives in terms of cache efficiency, as well as delay and power consumption. © 2024 IEEE.

关键词： Stars

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