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检索条件"任意字段=ACS International Conference on Computer Systems and Applications"

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Enhancing Four-Class Motor Imagery Detection Through Advanced Feature Extraction Techniques 21

Enhancing Four-Class Motor Imagery Detection Through Advance...

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2024 IEEE/acs 21st international conference on computer systems and applications, AICCSA 2024

作者： Souissi, Jihen Karmani, Sourour Belwafi, Kais Djemal, Ridha National Engineering School of Sfax Sfax University ATMS Laboratory ENIS Tunisia Higher Institute of Applied Sciences and Technology of Sousse University of Sousse University of Monastir Tunisia Laboratory of Electronics and Microelectronics FSM Tunisia University of Sharjah Computer Engineering Department CCI Sharjah United Arab Emirates Sfax University National Engineering School of Sfax ATMS Lab. Electrical Engineering Department United Arab Emirates

ISBN: (纸本)9798331518240

Brain-computer interface (BCI) technology has great potential in control, communication, and neurological diagnostics by interpreting EEG signals. This paper introduces a novel method for extracting distinguishing features from EEG signals using a Common Spatial Patterns (CSP) model, enhanced with a Phase Space Reconstruction model and followed by an autoregressive (AR) model. Initially, EEG signals are denoised using an IIR Butterworth filter before feature extraction. Two classifiers, Linear Discriminant Analysis (LDA) and Multi-Layer Perceptron (MLP), are employed to differentiate features across four motor imagery (MI) tasks. For a four-class MI task, combining spatial and temporal-frequency domain features, LDA achieves an accuracy of 67.40%, while MLP reaches 64.78%. LDA yields precision and recall scores of 67.40% and 67.20%, respectively, for the F1-score, whereas MLP records precision, recall, and F1-scores of 65.36%, 64.78%, and 64.28%, respectively. The proposed architecture is evaluated using the BCI Competition IV set 2a dataset, proving its effectiveness in EEG signal classification for BCI applications. © 2024 IEEE.

关键词： Frequency domain analysis

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Simple ARQ Protocol for Reliable Transport in LowPANs 21

Simple ARQ Protocol for Reliable Transport in LowPANs

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2024 IEEE/acs 21st international conference on computer systems and applications, AICCSA 2024

作者： Chahed, Manel Meddeb, Aref Boufaied, Amine Higher Institute of Computer Science and Communication Technologies of Sousse University of Sousse NOCCS Research Laboratory Sousse Tunisia University of Sherbrooke Faculty of Engineering Sherbrooke Canada Higher Institute of Computer Science and Communication Technologies of Sousse University of Sousse MARS Research Laboratory Sousse Tunisia

ISBN: (纸本)9798331518240

Due to the surge in IoT devices, numerous protocols have been proposed to meet their needs. UDP is commonly used for IoT because of its simplicity, low latency, minimal overhead, and low energy consumption. In contrast, TCP is less suitable for IoT due to its higher resource demands, complexity, and greater energy consumption, which are challenging for small devices with limited resources. Therefore, often, reliability is provided by upper-layer protocols, mainly by the applications themselves. In fact, classical IoT applications such as sensing, identification and actuating generate multiple copies of data due to the hardware redundancy and periodic updates. Examples of such applications include, agricultural, environmental, traffic, and healthcare monitoring. UDP and TCP protocols may be inadequate for IoT applications requiring reliable, real-time communication. In critical situations like battlefields or disasters, sensors might only send a few messages before being destroyed. Therefore, the network itself must ensure reliability in these scenarios. Moreover, smart textiles are more and more integrating sensory devices and require reliable transmissions. In fact, with the very stringent resource constraints on one hand, and the requirements to respect the Specific Absorption Rate (SAR) of human bodies, on the other hand, re-transmissions and power must be kept at their lowest levels. In this paper, we propose a simple Automatic Request (SARQ) transport protocol that uses acknowledgments and a retransmission mechanism. Through a realistic simulation setup using Contiki motes in cooja simulator, we show that our protocol exhibits slightly higher energy consumption and resource requirements than UDP, but far less than TCP. Conversely, we show that our protocol exhibits 99% Packet Delivery Ratio (PDR), while UDP and TCP exhibit 74% and 99% PDR, respectively. © 2024 IEEE.

关键词： Smart textiles

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Revealing Dynamic EEG-ECG Connectivity in Temporal Lobe Epilepsy: Insights from Pre-Ictal and Inter-Ictal Periods 21

Revealing Dynamic EEG-ECG Connectivity in Temporal Lobe Epil...

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2024 IEEE/acs 21st international conference on computer systems and applications, AICCSA 2024

作者： Mbarek, Manef Ben Ines, Assali Hamdi, Salah Asma, Ben Abdallah Marcel, Carrere Mohamed Hedi, Bedoui Laboratoire de Recherche Technologie et Imagerie Médicale Faculté de Médecine Monastir Monastir Tunisia Aix Marseille University Institute of Systems Neuroscience INSERM Marseille France

ISBN: (纸本)9798331518240

This study investigates the dynamic causal connectivity between EEG and ECG signals in patients with Temporal Lobe Epilepsy (TLE), emphasizing temporal and frequency analyses during pre-ictal and inter-ictal periods. Employing Granger causality, the research uncovers significant bidirectional causal relations, with a particularly robust causal effect observed in the direction of the brain influencing the heart. Analyzing various EEG frequency bands on the ECG signal across different electrodes in TLE patients reveals distinctive patterns. Results indicate a substantial increase in the causal effect of the EEG on the ECG during the pre-ictal period compared to the inter-ictal period. These findings provide valuable insights into the complex interactions between brain and heart signals in TLE, underscoring the dynamic nature of these interactions. © 2024 IEEE.

关键词： Heart

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Application of Artificial Intelligence Techniques to Detect Water Stress 21

Application of Artificial Intelligence Techniques to Detect ...

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2024 IEEE/acs 21st international conference on computer systems and applications, AICCSA 2024

作者： Dorai, Marwa Abdellaoui, Mehrez Douh, Bouthaina Douik, Ali National Engineering School of Sousse University of Sousse NOCCS Laboratory Tunisia Higher Institute of Computer Science and Communication Technologies University of Sousse Tunisia Higher Institute of Agronomic Sciences of Chott Mariem University of Sousse Tunisia

ISBN: (纸本)9798331518240

Water stress is a critical issue that significantly affects agricultural productivity worldwide, particularly in the context of climate change and diminishing water resources. Conventional methods for detecting water stress, such as visual inspections and ground sensors, often lack the precision, costeffectiveness, and scalability needed for modern agricultural demands. Recent advances in artificial intelligence (AI) offer innovative solutions by leveraging complex data from multiple sources. This research aims to develop a multimodal AI model that integrates satellite imagery, drone data, and ground IoT sensors to improve the accuracy and timeliness of water stress detection. By combining Convolutional Neural Networks (CNNs) for image analysis, machine learning algorithms for sensor data interpretation, and advanced data fusion techniques, the proposed model captures water stress indicators from various perspectives, enhancing sensitivity and specificity. The expected outcomes include significant improvements in precision and responsiveness compared to traditional single-source methods. This approach aims to optimize irrigation, minimize crop losses, and support sustainable water management, providing a valuable tool for precision agriculture. © 2024 IEEE.

关键词： Convolutional neural networks

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Decolonizing Federated Learning: Designing Fair and Responsible Resource Allocation 21

Decolonizing Federated Learning: Designing Fair and Responsi...

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2024 IEEE/acs 21st international conference on computer systems and applications, AICCSA 2024

作者： Vargas-Solar, Genoveva Bennani, Nadia Espinosa-Oviedo, Javier A. Mauri, Andrea Zechinelli-Martini, Jose-Luis Catania, Barbara Ardagna, Claudio Bena, Nicola CNRS Univ. Lyon INSA Lyon CPE UCBL LIRIS UMR5205 Lyon France Univ. Lyon Univ. Lyon 1 UR ERIC EA Villeurbanne3083 France Fundación Universidad de Las Américas Puebla San Andrés Cholula Mexico Università Degli Studi Di Genova Italy Università Degli Studi Di Department of Computer Science Milano Italy

ISBN: (纸本)9798331518240

This position paper explores the challenges, existing solutions, and open issues related to resource allocation in federated learning environments. The focus is on how to allocate resources effectively while adhering to service level objectives (SLOs) and fairness requirements, which include factors such as server location, data provenance, energy consumption, sovereignty, carbon footprint, and economic cost. The goal is to optimise resource distribution across different stages of the federated learning process within a given architecture, ensuring that these fairness criteria are integrated into the allocation strategy. This approach aligns with decolonial methodologies that seek to offer more sustainable and equitable alternatives to the resource-intensive artificial intelligence processes prevalent today. © 2024 IEEE.

关键词： Federated learning

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Resource Allocation and Scheduling of Linear Workflow applications with Ageing Priorities and Transient Failures 19

Resource Allocation and Scheduling of Linear Workflow Applic...

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19th IEEE/acs international conference on computer systems and applications (AICCSA)

作者： Stavrinides, Georgios L. Karatza, Helen D. Aristotle Univ Thessaloniki Dept Informat Thessaloniki Greece

ISBN: (纸本)9798350310085

In distributed environments, applications are usually complex and computationally demanding, having a linear workflow (LW) structure. Additionally, such LW jobs may also have different priorities for processing. This entails the danger of long delays for low priority jobs. Furthermore, transient software failures may occur during the execution of the workload. Consequently, resource allocation, scheduling and fault tolerance are three crucial aspects that should be efficiently and effectively addressed in such environments, in order to achieve good system performance. To this end, in this paper we investigate the resource allocation and scheduling of LW jobs that arrive dynamically in an environment of distributed resources. We consider that the LW jobs have different priorities and that transient software failures may occur during their execution. A novel scheduling technique is proposed, which takes into account the ageing priorities of the LW jobs, as well as the resulting scheduling overhead. We examine the performance of three routing strategies in this framework, under various load cases and different failure probabilities, taking also into account their implementation complexity. The simulation results reveal how each routing strategy is affected in each of the examined scenarios.

关键词： linear workflows resource allocation scheduling distributed resources ageing priorities transient software failures

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Identification Management for Zero Trust Through Network Analysis 21

Identification Management for Zero Trust Through Network Ana...

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2024 IEEE/acs 21st international conference on computer systems and applications, AICCSA 2024

作者： Alshehri, Amal Tufekci, Burak Tunc, Cihan University of North Texas Department of Computer Science & Engineering United States

ISBN: (纸本)9798331518240

Internet of Things (IoT) and Operational Technology (OT) are becoming essential parts of next-generation smart environments, including Industry 4.0 thanks to their capabilities of monitoring and/or controlling industrial devices, processes, and events. It is expected that there will be around 75 billion such devices, especially considering 5G (and 6G in the future) networking. However, security is one of the major concerns, especially because most legacy devices/equipment have little to no security measures applied. And, replacing all the equipment with new ones continuously is not a feasible solution to enforce security measures. One alternative is enforcing Zero Trust Architecture (ZTA) principles in these environments based on the 'never trust, always verify' principle. Applying ZT for an OT environment through its network (by continuously monitoring and checking the activities based on ZTA-based policies) can improve the sustainability and security of the IoT/OT-based environments because no major changes would be needed in the existing infrastructures while improving security. For this reason, we propose an architecture that monitors the network behavior and compares them with the devices' proposed identities (their IP and MAC addresses). We first monitor the network behaviors, create flows, and select the best features. Then, we identify the individual devices using supervised machine learning techniques such as Gradient Boosting Classifier as they are lightweight and fast in detection compared to alternatives. Our results show high accuracy, precision, and recall values in our experiments. Using different scenarios, we evaluated our idea and if a device behaves differently than the proposed behavior, we are able to detect and put a firewall rule that blocks the device network traffic on the gateway. © 2024 IEEE.

关键词： Gateways (computer networks)

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YOLO-Based Detector for Enhancing Workplace Safety: Real-Time PPE Detection and Monitoring 21

YOLO-Based Detector for Enhancing Workplace Safety: Real-Tim...

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2024 IEEE/acs 21st international conference on computer systems and applications, AICCSA 2024

作者： Amri, Souha Ouannes, Laila Sahbani, Anis Ben Amara, Najoua Essoukri Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse Tunisia Enova Robotics S.A. Paris France

ISBN: (纸本)9798331518240

Ensuring the safety of workers in hazardous environments is crucial, given the potential severity of accidents. Indeed, Personal Protective Equipment (PPE), including safety helmets, vests, and boots, is vital in mitigating risks. However, adherence to PPE usage often falls short due to inadequate safety awareness among workers. To address this challenge, we aim to develop a robust safety monitoring system by leveraging advancements in deep learning and object detection technology. Our proposed method uses YOLOv7 and YOLOv8 algorithms for real-time detection and classification of safety equipment, including helmets, vests, and boots. For this aim, we carefully collect and annotate datasets from diverse sources like Roboflow and Kaggle. Then, we accurately evaluate performance metrics such as precision, recall, and speed through rigorous training and testing of various YOLOv7 and YOLOv8 configurations on annotated data batches. By conducting thorough comparative analyses, we aim to identify the most effective model for each PPE category, considering both detection accuracy and computational efficiency. Finally, the object detection system is deployed through a web interface to determine whether a person is protected or not. © 2024 IEEE.

关键词： Deep learning

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25th international conference on Internet Computing and IoT, ICOMP 2024, and 22nd international conference on Embedded systems, Cyber-physical systems, and applications, ESCS 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

25th International Conference on Internet Computing and IoT,...

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25th international conference on Internet Computing and IoT, ICOMP 2024, and 22nd international conference on Embedded systems, Cyber-physical systems, and applications, ESCS 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031859229

The proceedings contain 33 papers. The special focus in this conference is on Internet Computing and IoT. The topics include: Malware Detection in the IoT Home Network;IoT-Based Analysis of Environmental and Motion Data for Comfort and Energy Conservation in Optimizing HVAC systems;improving Critical Controls Using IoT and computer Vision;A Data-Driven Driving Under the Influence (DUI) Detection, Notification and Prevention System Using Artificial Intelligence and Internet-Of-Things (IoT);understanding User Interactions with IoT Process Models: A Demographic Perspective;Advancing IoT Process Modeling: A Comparative Evaluation of BPMNE4IoT and Traditional BPMN on User-Friendliness, Effectiveness, and Workload;Threat Detection Using MLP for IoT Network;harnessing Social Robotics and the Internet of Things to Reduce the Risk of Older Adults Developing Hypothermia and Dehydration;re/Imagining Smart Home Automation Framework in the Era of 6G-Enabled Smart Cities;smart Roadway Monitoring: Pothole Detection and Mapping via Google Street View;Energy-Efficiency Modeling for AI applications on Edge Computing;optimizing Wireless Sensor Network Node Placement Using Bacterial Foraging Optimization;The Vital Role of Small and Marginal Farmer in Future of Our Climate: Democratization of Machine Learning, Artificial Intelligence, and Dairy Cow Necklace Sensors in Achieving the UN Climate Change Goals (COP21) and the Paris Agreement;autonomous Driving Prototype with Raspberry Pi by Using Image Processing Technology;towards Implementation of Privacy-Preserving Federated Learning Aggregation Using Multi-key Homomorphic Encryption;advancing Nursing Education Through Virtual Reality Training: A Revolutionary Approach to Ensuring Patient Safety;multiDrone Simulator An Open Source Multi-plataform Tool to Use in Tests of Optimized Flight of Group of Drones;Revolutionizing Multiplayer Gaming: A Deep Dive into VisionXO, a 3D Multiplayer Tic-Tac-Toe Game.

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Deep Learning and Kinect Skeleton-based Approach for Fall Prediction of Elderly Physically Disabled 19

Deep Learning and Kinect Skeleton-based Approach for Fall Pr...

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19th IEEE/acs international conference on computer systems and applications (AICCSA)

作者： Nouisser, Raoudha Jarraya, Salma Kammoun Hammami, Mohamed Univ Sfax Fac Econ & Management MIRACL Lab Sfax Tunisia King Abdulaziz Univ Fac Comp & Informat Technol MIRACL Lab Jeddah Saudi Arabia Univ Sfax MIRACL Lab Fac Sci Sfax Tunisia

ISBN: (纸本)9798350310085

Falls are considered one of the most severe health problems, especially among older people with physical disabilities. To ensure the security of the elderly, it is necessary to predict fall before it happens. This paper proposes a computer vision method for fall prediction among physically disabled elderly. Within our approach, we propose a novel implementation of Encoder-Decoder ConvLSTM (Convolutional Long Short-Term Memory). This work includes three parts: Data Acquisition, Data Preprocessing, and Data Analysis. Starting by acquiring skeleton streams using the Kinect camera. Then, applying preprocessing techniques: extracting skeleton features and selecting key frames. Finally, the analysis step consists of predicting the next frames and classifying them. In case of a predicted fall, an alert will be launched. To evaluate our approach, we use the FallFree dataset that covers all fall types and scenarios of people using canes and others without any mobility aid. Our method achieves an accuracy of 99.64%.

关键词： Elderly fall prediction computer vision Deep learning Skeleton Data Kinect Autoencoder ConvLSTM

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