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International Conference on Computing, Networking and Communications (ICNC)

作者： Nayreet Islam Akramul Azim Department of Electrical Computer and Software Engineering Ontario Tech University Canada

An embedded real-time system (ERTS) needs to provide continuous service in various dynamic situations. Such service requirements in different situations create the need for the ERTS to monitor its environment at run time, gather knowledge of its situations, and guarantee its timing and operational behavior through self-adaptation. Recent advances in sensor technologies have introduced cameras, lidar, and radar as powerful monitoring tools. However, processing and storing raw sensor streams require significant storage and computational ability. An ERTS is embedded in nature, and therefore, it has limited storage and processing capacity. This paper considers that the ERTS contains an analytics endpoint (edge node). We present an edge computing-based monitoring framework that characterizes environmental situations at run time by identifying events and their properties. We enable the framework to store and process from a significantly reduced dataset by creating a knowledge base. The framework also allows the ERTS to identify resource, performance, and safety constraints in the edge node for each situation. The framework assists the ERTS in adapting to the situations (if the constraints are satisfied) by determining adaptive tasks that need to be triggered with respect to the environmental events. The experimental analysis shows that the framework present in the edge node assists in situation characterization in terms of the identified events and admission of adaptive tasks. The monitoring framework also allows improvement regarding the probability of failure and average response time. We use the earliest deadline first (EDF) scheduling algorithm with and without considering the edge node and perform a comparative schedulability analysis. We demonstrate that overall demand due to the admission of adaptive tasks and situation-driven analytics exceeds available supply, which can be addressed using the proposed edge computing-based framework.

关键词： Laser radar Scheduling algorithms Knowledge based systems Information retrieval Real-time systems Safety Timing

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A Novel Predictive Modelling Approach Towards a Spatiotemporal Traffic Safety Index

A Novel Predictive Modelling Approach Towards a Spatiotempor...

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International Conference on Intelligent Transportation

作者： Sifatul Mostafi Asma Fariha Mohamed El-Darieby Khalid Elgazzar Akramul Azim Electrical Computer & Software Engineering Ontario Tech University Oshawa ON Canada

Traffic safety is now a major concern due to the increase in roadways, traffic, and pedestrians. An index quantifying the traffic safety of a location can help transportation authorities take necessary measures to enhance traffic safety by enforcing Intelligent Transportation System Warrants. All the existing approaches adopt descriptive, inferential, or exploratory data analysis-based statistical techniques to calculate a traffic safety index based on different spatial features of a geographic location. In this paper, we propose a novel predictive modeling approach to calculate a spatiotemporal traffic safety index incorporating both spatial and temporal features based on the National Collision Database Canada from year 1999 – 2019. We conduct Pearson chi-square feature selection and apply feature encoding with categorical embedding. We built a deep learning model based on heterogeneous spatiotemporal features to predict traffic accident severity in terms of human injury and fatality and use it as an indicator for a spatiotemporal traffic safety index. The results show that the proposed methodology can effectively generate a spatiotemporal traffic safety index based on accident severity with an accuracy of 96.26% and surpasses the state-of-the-art approach that has an accuracy of 83%.

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Triage-Bot: An Assistive Triage Framework

Triage-Bot: An Assistive Triage Framework

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Digital Health (ICDH), IEEE International Conference on

作者： Drishti Sharma Elyas Rashno Farhana Zulkernine Eyad El Khodary Max Beninger Ronan Almeida Jing Tao Furkan Alaca Khalid Elgazzar School of Computing Queen’s University Kingston Ontario Canada Electrical Computer and Software Engineering Ontario Tech University Oshawa Ontario Canada

ISBN: (数字)9798350368574

ISBN: (纸本)9798350368581

Prompt delivery of healthcare services is critical for saving lives. Streamlining the collection of patients’ health data through automation can minimize waiting times in emergency departments and enhance access to evidence-based care. We propose an innovative solution, Triage-Bot, which leverages the latest advancements in Artificial Intelligence to elevate the standard of patient care. Triage-Bot is a software as-a-service that allows patients or their caregivers to authenticate themselves by presenting their faces to the camera and also using a registered user ID and password. They can communicate about their health situation through voice, chat, and video conversations securely with the robot service to request medical attention. Moreover, Triage-Bot automatically captures vital signs from facial video. Voice data is converted to text to extract a concise summary of the patient’s health condition, while emotions are detected from both text and video data. The processed patient information is linked to the patients’ Electronic Medical Records to automatically classify the patients into one of the five severity level as defined by the Canadian Triage and Acuity Scale. Currently, we are working on the evaluation of the services by medical professionals.

关键词： Robot vision systems Speech recognition Passwords Oral communication software Speech processing Electronic medical records

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Finite-difference time-domain methods

NATURE REVIEWS METHODS PRIMERS

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NATURE REVIEWS METHODS PRIMERS 2023年第1期3卷 1-19页

作者： [Anonymous] Department of Electrical and Computer Engineering The Ohio State University Columbus OH USA Department of Electrical and Computer Engineering University of Toronto Toronto Ontario Canada Department of Electrical and Computer Engineering University of Utah Salt Lake City UT USA Department of Electrical Engineering Pohang University of Science and Technology Pohang Korea Department of Electrical and Electronic Engineering The University of Manchester Manchester UK Biomedical Engineering Department Northwestern University Evanston IL USA Department of Electrical and Software Engineering University of Calgary Calgary Canada Department of Electrical and Computer Engineering Purdue University West Lafayette IN USA

This PrimeView highlights how algorithm and hardware develops could expand the application range of finite-difference time-domain methods.

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Towards Digital Twining of Lithium-ion Battery Management Systems: An Extended Kalman Filter for State-of-Charge Estimation in Cloud-platform

Towards Digital Twining of Lithium-ion Battery Management Sy...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Karnehm, Dominic Anekal, Latha Samanta, Akash Neve, Antje Williamson, Sheldon Bundeswehr University Munich Institute of Intelligent Distributed Systems Neubiberg Germany Ontario Tech University Electrical Computer and Software Engineering ONL1G 0C5 Canada

ISBN: (纸本)9798350316445

In practice, extended Kalman filter (EKF) is one of the most popular lithium-ion battery (LIB) state of charge (SOC) estimation algorithms in electric vehicles onboard battery management system (BMS). With the increasing necessity of off-loading the computational burden and data storage capacity of onboard BMS, the application of battery digital twin and cloud-BMS are getting popularity. Therefore, to assess the performance of EKF in terms of accuracy, the computational time, and overall request and execution time, an EKF-based SOC estimation method is developed in the cloud platform of Amazon Web Services (AWS). For a specific battery test data set, the cloud-based EKF demonstrated a mean absolute error of 0.00226 and a request time of 0.028s that are highly satisfactory towards developing a digital twin enabled cloud-based BMS. © 2023 IEEE.

关键词： cloud Computing computational cost electric vehicles intelligent state estimation transportation electrification

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Fast Group Scheduling for Downlink Large-Scale Multi-Group Multicast Beamforming

arXiv

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arXiv 2024年

作者： Zhang, Chong Dong, Min Liang, Ben Afana, Ali Ahmed, Yahia The Department of Electrical and Computer Engineering University of Toronto Canada The Department of Electrical Computer and Software Engineering Ontario Tech University Canada AEricsson Canada

Next-generation wireless networks need to handle massive user access effectively. This paper addresses the problem of joint group scheduling and multicast beamforming for downlink transmission with many active user groups. Aiming to maximize the minimum user throughput, we propose a three-phase approach to tackle this difficult joint optimization problem efficiently. In Phase 1, we utilize the optimal multicast beamforming structure obtained recently to find the group-channel directions for all groups. We propose two low-complexity group scheduling algorithms in Phase 2, which determine the subset of groups in each time slot sequentially and the total number of time slots required for all groups. The first algorithm measures the level of spatial separation among groups and selects the dissimilar groups that maximize the minimum user rate into the same time slot. In contrast, the second algorithm first identifies the spatially correlated groups via a learning-based clustering method based on the group-channel directions, and then separates spatially similar groups into different time slots. Finally, the multicast beamformers for the scheduled groups are obtained in each time slot by a computationally efficient method. Simulation results show that our proposed scheduling methods can effectively capture the level of spatial separation among groups to improve the minimum user throughput over the conventional approach that serves all groups in a single time slot or one group per time slot, and can be executed with low computational complexity. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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Exploring 2D, 3D and Spatial Audio User Interfaces in VR for Reminiscence Therapy 12

Exploring 2D, 3D and Spatial Audio User Interfaces in VR for...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Uribe-Quevedo, Alvaro Arevalo, Camilo Villegas, Julián Sun, Winnie Ontario Tech University Faculty of Business and It OshawaON Canada University of Aizu Department of Computer Science and Engineering Fukushima Aizuwakamatsu Japan Ontario Tech University Faculty of Health Sciences OshawaON Canada

ISBN: (纸本)9798350340181

Reminiscence Therapy (RT) is a non-pharmacological treatment focused on helping individuals with dementia recollect memories through their senses. Traditionally, RT therapy has relied on multimedia, where caregivers choose videos, pictures, and audio files. Since cross-sensory stimuli play an important role during RT, the adoption of Virtual Reality (VR) has spiked as the technology becomes commonplace. Our previous work has focused on prototyping a VR RT tool through co-design and usability testing, utilizing hand tracking for ease of use. In this research, we aim to further understand usability, presence, and cognitive load effects by comparing a 2D Graphical User Interface (GUI), a radio-style 3D User Interface (3DUI), a curved-style 3DUI, and a spatial audio User Interface UI that allows playing, stopping, and adjusting the volume of an audio file. Our preliminary study indicates that a traditional 2D GUI has higher usability, and lower cognitive load, while a curved-style 3DUI with 3D push buttons and a slider for volume around the user has a higher presence than the radio-style 3DUI and the spatial audio UI. © 2023 IEEE.

关键词： Virtual reality

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Design and Implementation of a Web-Based Application for Managing Agricultural Health Products 4

Design and Implementation of a Web-Based Application for Man...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Oluwagbemi, Olugbenga O. Onyeka, Emebo Ishieka, Ikashi S. Wieclaw, Anna Azeta, Ambrose A. Middlesex University Department of Computer Science London United Kingdom Virginia Tech University Department of Computer Science Blacksburg United States Namibia University of Science and Technology Department of Software Engineering Windhoek Namibia

ISBN: (纸本)9798350353266

Caring for the health of food crops and plants can help boost food production and eradicate hunger according to the second United Nations (UN) Sustainable Development Goal (SDG). The procurement of agricultural health products such as fertilizers, fungicides, pesticides, and herbicides are a major problem for an average Nigerian farmer. This results in low crop yield and negatively impacts on food production which eventually causes hunger in the land. Existing methods of procuring these health products have majorly been through offline platforms such as agricultural stores and other relevant stores. This has its disadvantages such as inaccessible roads to stores where such products can be obtained, varying prices, health products out of stock, ignorance in the application of such products. These barriers deter farmers from procuring these health products. In this paper, a survey was conducted on the needs and problems of farmers. The results of the survey informed the design and implementation of an agricultural health Informatics web application for health product management for food crops. The application was designed using unified modelling language class diagram and implemented using Python. SQLite3 serves as the backend database. The agricultural informatics solution will help farmers in Nigeria to easily procure health products for food crops and get the products delivered without stress and worry when fully deployed. This will boost food production, thus eradicating hunger;it will also help in exporting food crops to other countries. © 2024 IEEE.

关键词： Agriculture AI Application Chatbot Farmers Health Informatics

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Steady State Analyses of DC-DC Converters Using MATLAB 3

Steady State Analyses of DC-DC Converters Using MATLAB

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3rd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2024

作者： Surya, Sumukh Chhetri, Ahilya Madhu, S. Williamson, Sheldon Bosch Global Software Technologies Pvt. Limited Bengaluru India Cactus Communications Mumbai India Bnm Institute of Technology Department of Electrical and Electronics Bengaluru India Ontario Tech University Electrical Computer and Software Engineering ONL1G0C5 Canada

ISBN: (纸本)9798350387193

Steady state analysis of a DC-DC converter is extremely important while designing controllers. In this paper, the steady-state analyses of different isolated and non-isolated dc-dc converters, such as Buck, Cuk, and Flyback, operating in continuous conduction mode (CCM) are conducted by solving non-linear algebraic equations using 'fsolve' in MATLAB software tool. In addition, the central processing unit (CPU) execution time and real-time taken by the software to solve these equations are measured. Furthermore, the minimum time required to achieve the steady state condition of different converters is measured. The number of iterations required to solve equations is found to be independent of the switching frequency but dependent on state variables. © 2024 IEEE.

关键词： MATLAB

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A collaborative and distributed task management system for real-time systems

A collaborative and distributed task management system for r...

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International Symposium on Object-Oriented Real-Time Distributed Computing

作者： Maria J. P. Peixoto Akramul Azim Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

This paper discusses the benefits of a distributed and collaborative approach for optimizing real-time intelligent systems with complex task scheduling requirements. We focus on the specific example of implementing car platoons in urban traffic, which requires efficient task mapping and scheduling to maximize efficiency and ensure optimal performance. To meet the demands of a car platoon environment, a collaborative task management system, EDFHC-ML, is proposed for connected autonomous vehicles using edge, fog, and cloud computing. We also evaluated our approach with three others and found that our method had the best performance in executing tasks within the deadline. Our proposed approach is beneficial for developing intelligent systems that require high-performance computing and real-time response.

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