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IEEE Region 10 International Conference TENCON

作者： Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Bangkok Thailand

Wearable smart devices, such as smartphones and smartwatches, offer great potential as platforms for automated human action identification. However, accurately monitoring complex human actions on these devices poses a challenge due to the presence of similarities in patterns across different actions. This occurs when distinct human actions exhibit comparable signal patterns or characteristics. The placement of motion sensors on the body plays a crucial role in detecting human behavior. Typically, wearable sensors placed at the trouser pocket or a similar location are used for this purpose. However, this positioning is not suitable for identifying actions involving manual gestures. To address this, wrist-worn motion sensors are employed to detect these specific behaviors. This study aims to investigate the effectiveness of deep learning models in accurately categorizing complex human actions using sensor data from wrist-worn devices. Nine deep learning models utilizing convolutional neural networks and recurrent neural networks were examined for their identification capabilities. The models were evaluated using the WHARF dataset, a publicly available benchmark dataset for human activity recognition. The investigation revealed that the proposed CNN-BiGRU model outperformed other deep learning models, achieving an accuracy rate of 87.20% and an Fl-score of 84.46%.

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Dynamic Resource Management in Integrated NOMA Terrestrial-Satellite Networks using Multi-Agent Reinforcement Learning

arXiv

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arXiv 2023年

作者： Nauman, Ali Alshahrani, Haya Mesfer Nemri, Nadhem Othman, Kamal M. Aljehane, Nojood O. Maashi, Mashael Dutta, Ashit Kumar Assiri, Mohammed Khan, Wali Ullah Department of Information and Communication Engineering Yeungnam University Korea Republic of Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Science & Art at Mahayil King Khalid University Saudi Arabia Department of Electrical Engineering College of Engineering Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University Po Box 103786 Riyadh11543 Saudi Arabia Department of Computer Science and Information System College of Applied Sciences AlMaarefa University Riyadh11597 Saudi Arabia Department of Computer Science College of Sciences and Humanities- Aflaj Prince Sattam bin Abdulaziz University Aflaj16273 Saudi Arabia University of Luxembourg Luxembourg City1855 Luxembourg

The integration of terrestrial and satellite wireless communication networks offers a practical solution to enhance network coverage, connectivity, and cost-effectiveness. Moreover, in today’s interconnected world, connectivity’s reliable and widespread availability is increasingly important across various domains. This is especially more crucial for applications like the Internet of Things (IoT), remote sensing, disaster management, and bridging the digital divide. However, allocating the limited network resources efficiently and ensuring seamless handover between satellite and terrestrial networks present significant challenges. Therefore, this study introduces a resource allocation framework for integrated satellite-terrestrial networks to address these challenges. The framework leverages local cache pool deployments and non-orthogonal multiple access (NOMA) to reduce time delays and improve energy efficiency. Our proposed approach utilizes a multi-agent enabled deep deterministic policy gradient algorithm (MADDPG) to optimize user association, cache design, and transmission power control, resulting in enhanced energy efficiency. The approach comprises two phases: User Association and Power Control, where users are treated as agents, and Cache Optimization, where the satellite (Bs) is considered the agent. Through extensive simulations, we demonstrate that our approach surpasses conventional single-agent deep reinforcement learning algorithms in addressing cache design and resource allocation challenges in integrated terrestrial-satellite networks. Specifically, our proposed approach achieves significantly higher energy efficiency and reduced time delays compared to existing methods. This research highlights the importance and addresses the need for efficient resource allocation and cache design in integrated terrestrial-satellite networks, paving the way for enhanced connectivity and improved network performance in various applications. Copyright © 2023, The Aut

关键词： Resource allocation

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Free-Weight Exercise Activity Recognition using Deep Residual Neural Network based on Sensor Data from In-Ear Wearable Devices

Free-Weight Exercise Activity Recognition using Deep Residua...

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International Conference on Telecommunications and Signal Processing (TSP)

作者： Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

Human activity recognition (HAR) is a popular study area in the current era of the Internet of Things and artificial intelligence. HAR approaches have been successfully applied in many real-world scenarios, such as sports tracking and assessment and remote healthcare for the elderly. Over the past decade, studies on HAR employing wearable sensors have been explored operating various wearable devices, including smartwatches, smart shoes, and other intelligent wearables. The advancement of sensor technology has directed the combining of inertial sensors into ear-worn devices, making it possible to record physical movements privately. In this study, we proposed a deep residual network named ResNeXt, for identifying free- weight exercises using sensor data from smart in-ear devices. To consider the performance of the proposed model and other deep learning models, we conducted experiments using a publicly available dataset called ERICA, which collected free-weight exercise activities from inertial sensors of both an in-ear device and a dumbbell. Our experimental results demonstrated that the suggested ResNeXt network surpassed other deep learning models, gaining the highest F1-score of 96.67% and 99.75% using inertial data from in-ear sensors and dumbbell sensors, respectively

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Thermal-Aware Channel Capacity

Thermal-Aware Channel Capacity

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IEEE International Symposium on Information Theory

作者： Yeow Meng Chee Tuvi Etzion Kees A. Schouhamer Immink Tuan Thanh Nguyen Van Khu Vu Jos H. Weber Eitan Yaakobi Department of Industrial Systems Engineering and Management National University of Singapore Singapore Department of Computer Science Technion — Israel Institute of Technology Haifa Israel Turing Machines Inc. Rotterdam The Netherlands Science Mathematics and Technology Cluster Singapore University of Technology and Design Singapore Department of Applied Mathematics Delft University of Technology Delft The Netherlands

High temperatures in electronic devices have a negative effect on their performance. Various techniques have been proposed and studied to address and combat this thermal challenge. To guarantee that the peak temperature of the devices will be bounded by some maximum temperature, the transmitted signal has to satisfy some *** this motivation, we study the constrained channel that only accepts sequences that satisfy prescribed thermal constraints. The main goal in this paper is to compute the capacity of this channel. We provide the exact capacity of the channel with some certain parameters and we also present some bounds on the capacity in various ***, we consider the model that multiple wires are available to use and find out the smallest number of wires required to satisfy the thermal constraints.

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A Study on Seizure Detection Performance in an Automated Process by Extracting Entropy Features 5

A Study on Seizure Detection Performance in an Automated Pro...

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5th International Conference on Signal Processing and Information Security, ICSPIS 2022

作者： Parui, Sricheta Samanta, Debasis Chakravorty, Nishant Mansoor, Wathiq Ghosh, Uttam Indian Institute of Technology Advanced Technology Development Centre Kharagpur India Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India Indian Institute of Technology School of Medical Science and Technology Kharagpur India University of Dubai Dubai United Arab Emirates Meharry School of Applied Computational Sciences NashvilleTN United States

ISBN: (纸本)9781665492652

Millions of people of all ages have been diagnosed with epilepsy all over the world. Electroencephalography (EEG), a quantitative component, is vital in identifying and analyzing epileptic seizures. Manual EEG detection takes a long time and has serious consequences. To prevent this circumstance, the world needs alternative detection technologies. For more than a decade, several strategies and procedures have been used to assist medical doctors in detecting technological improvement. Numerous automated seizure identification frameworks that use machine learning approaches have recently been presented to replace traditional methodologies. However, because multichannel EEG data is unpredictable, selecting optimal channels as well as characteristics and classifying them remain unsolved issues. During automatic signaling, the gadget also emits a noisy signal, making detection and prediction challenging. In this paper, we have tested various entropy features and selected the best features to classify seizure patients. First, we preprocessed the data using IMF of the raw signal, and then entropy features were extracted to classify the epileptic patients. The proposed channel selection method, SVM-GA, works well with our framework and successfully removes redundant channels from the data. © 2022 IEEE.

关键词： Electroencephalography

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Deep Residual Neural Network for Aggressive Physical Activity Recognition Using Surface Electromyography Sensors

Deep Residual Neural Network for Aggressive Physical Activit...

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IEEE International Conference on Software engineering and Service sciences (ICSESS)

作者： Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Mathematics Intclligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

Wearable sensors and machine learning have opened new doors for innovative automated systems. Smart wearables like smartwatches and wristbands can efficiently record human movements due to their integrated, compact sensors. This data collection is vital for understanding human behavior, a field known as human activity recognition (HAR), dedicated to classifying actions. Deep learning methods, which autonomously extract intricate features, have shown promise in HAR. Sensor-based HAR is extensively studied in academia and applied across domains like wellness tracking, medical diagnosis, and mobility analysis. However, challenges persist in detecting complex activities. Our study focuses on recognizing aggressive behaviors through surface electromyography (sEMG) sensor data analysis. We introduce ResNeXt, a novel deep neural network, to improve recognition accuracy. Through a series of tests, we evaluate ResNeXt's recognition capabilities and compare its results to other deep learning models. Our experiments reveal that ResNeXt outperforms other models, achieving an impressive 94.06% overall accuracy and an exceptional 97.68% accuracy in distinguishing normal from aggressive actions.

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A Lightweight Deep Residual Network for Recognizing Activities in Daily Living Using Channel State Information

A Lightweight Deep Residual Network for Recognizing Activiti...

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IEEE International Conference on Software engineering and Service sciences (ICSESS)

The field of human activity recognition (HAR) focuses on predicting human motion and actions by analyzing data from various sensors. HAR tasks can benefit from vision-based and sensor-based approaches, which offer high data quality. However, it's essential to recognize that these methods have drawbacks, such as intrusiveness and privacy concerns. With the increasing prevalence of Wi-Fi devices, Wi-Fi technology has gained popularity for tracking everyday activities, particularly among senior individuals, making it an appealing choice in healthcare applications. Channel State Information (CSI), a crucial attribute of Wi-Fi signals, can be leveraged to recognize different human actions. This study introduces CSI-ResNeXt, a lightweight deep residual network designed to classify human actions using CSI data from daily living scenarios. A series of tests were conducted to evaluate the network's performance, using a standard CSI-HA dataset. The results of the experiments demonstrated that the proposed lightweight deep residual network achieved an impressive accuracy of 99.17% and an F1-score of 99.10%, surpassing the performance of previous deep learning models.

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Smart Aging Technology with IoT and Deep Learning Analytics for Elderly Activity Patterns and Health Outcomes

Smart Aging Technology with IoT and Deep Learning Analytics ...

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International Conference on Communication and Electronics Systems (ICCES)

作者： V.Prasanna Srinivasan Pramod Pandey S. Muthumarilakshmi Jothibabu. Konidhala M. Rajmohan S. Murugan Department of Information Technology R.M.D. Engineering College Kavaraipettai Chennai Tamil Nadu India Symbiosis institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering University of Technology and Applied Sciences- Al Musannah Musanna South Al Batina Oman Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Innovative healthcare solutions for older people have developed from the fast progress of the Internet of Things (IoT) technology and deep learning (DL). This research proposes a smart aging system that uses IoT devices and Long Short-Term Memory (LSTM) networks to track and evaluate geriatric behavior and health. The proposed approach collects real-time health and daily activity data using a wearable, smart home, and ambient sensors. A central platform uses LSTM networks to identify and forecast activity and health variables from collected data. LSTM can capture long-term relationships and patterns in time-series data, making it ideal. By evaluating these patterns, the system may detect abnormal behavior that may indicate health difficulties like falls, chronic diseases, or cognitive impairment. Preliminary findings show that LSTM-based analytics may provide accurate and timely insights to help caregivers and healthcare providers improve health outcomes. By encouraging mobility and preventive health management, the smart aging system improves older quality of life and decreases healthcare costs. To increase predicted accuracy and system dependability, LSTM models will be refined, the dataset expanded, and more health measures will be included.

关键词： Training Accuracy Medical services Predictive models Aging Data models Real-time systems Internet of Things Older adults Long short term memory

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Attention Based Hybrid Deep Learning Network for Locomotive Mode Recognition in Natural Environments Using Wearable Sensors

Attention Based Hybrid Deep Learning Network for Locomotive ...

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Research, Invention, and Innovation Congress (RI2C)

作者： Sakorn Mekruksavanich Ponnipa Jantawong Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

The field of pervasive computing encompasses the significant domain of study known as WS-LMR, which focuses on identifying locomotive modes through wearable sensors. WS-LMR aims to automatically evaluate and interpret individual locomotive actions along with contextual information using wearable sensor data. This technology finds applications in medical surveillance systems and various systems involving interactions between individuals and intelligent wearable devices. However, existing WS-LMR algorithms are often evaluated using data collected under controlled conditions, limiting their effectiveness in uncontrolled real-world environments. This scholarly article addresses the primary objective of identifying human locomotion activities in real-world contexts. To enhance the efficacy of the WS-LMR system, our study proposes the utilization of an attention-based hybrid deep learning network that combines convolutional neural network and long-short term memory networks, referred to as the Att-CNN-LSTM model. We evaluated the performance of the proposed network using the publicly available Bath Natural Environment HAR dataset, a benchmark LMR dataset. Both model training and testing were conducted using a 5-fold cross-validation approach. The Att-CNN-LSTM model achieved the highest accuracy of 94.25 % and an F1-score of 91.47% based on the results obtained from multiple experiments.

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Improving EEG-Based Epileptic Seizures Detection Using Self-Normalizing Neural Network

Improving EEG-Based Epileptic Seizures Detection Using Self-...

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Big Data Analytics and Practices (IBDAP), International Conference on

作者： Sakorn Mekruksavanich Ponnipa Jantawong Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

Epilepsy, a neurological disorder, is typically identified by analyzing electroencephalogram (EEG) signals. However, manually inspecting epileptic seizures is a time-consuming and labor-intensive task. Previous automatic detection algorithms utilizing traditional methods have demonstrated good accuracy in specific EEG classification scenarios but have shown poor performance in others. To tackle this challenge, our study introduces a new deep learning model called the self-normalizing neural network. This model leverages convolutional neural networks and recurrent neural networks to enhance the detection of epileptic seizures using EEG signals. To evaluate our approach, we designed and assessed the proposed detection model using a publicly available Epileptic Seizures Recognition Dataset (ESRD). The experimental findings indicate that the self-normalizing neural network achieves a performance improvement of up to 1.03% accuracy and 1.78% F1-score in epileptic seizure detection, surpassing traditional convolutional neural network approaches.

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