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7th International Conference on Advances in Biomedical engineering, ICABME 2023

作者： Daou, Roy Abi Zeid Geha, Elio Aoun, Youssef Hayek, Ali Boercsoek, Josef Olmedo, Jose Javier Serrano Université la Sagesse Biomedical Engineering Department Furn El Chebbak Beirut Lebanon Mart Learning Education and Research Center Chananir Lebanon Lebanese German University Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 Hamm59063 Germany University of Kassel Faculty of Electrical Engineering /Computer Science Department of Computer Architecture and System Programming Germany Universidad Politecnica de Madrid Lab of Bioinstrumentation and Nanomedicine Centro de Tecnologia Biomedica Spain

ISBN: (纸本)9798350325850

Chronic back pain can present a serious health concern, with symptoms that can significantly affect an individual's well-being, mobility, and overall quality of life over an extended period. While chronic back pain may manifest suddenly in some cases, it often develops gradually and persists for weeks, and in untreated cases, it can linger for years. Hence, the utilization of assistive devices such as wearable posture-monitoring vests can offer valuable assistance and guidance to users. This research paper is dedicated to the development of a system for detecting, diagnosing, and correcting poor posture, specifically leaning posture. The vest is designed to provide users with visual, auditory, and tactile cues to help them address this issue, thereby reducing the risk associated with leaning. Additionally, an integrated electrical box has been designed to consolidate all components directly onto the main board in a secure enclosure. This box also displays the daily count of instances where the user has leaned. This system is characterized by its electrical safety, portability, compactness, comfort, and affordability. A comprehensive analysis of the system's performance has been conducted with a meticulous evaluation of accuracy. Each component of the system has undergone successful testing, and the system as a whole is currently in the testing phase. The results of these tests have indicated a lack of faulty errors and have demonstrated outstanding accuracy and detection rates. Over 100 individuals of varying ages, genders, and BMI categories were involved in testing, with each person wearing the device for an average of six hours. The accuracy rate achieved was 98.85%, with an average of 54.35 instances of poor posture detected per participant. © 2023 IEEE.

关键词： Wearable technology

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Cyber Attack Detection and Classification for Integrated On-board Electric Vehicle Chargers subject to Stochastic Charging Coordination

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Transportation Research Procedia 2023年 70卷 44-51页

作者： Ali Arsalan Laxman Timilsina Behnaz Papari Grace Muriithi Gokhan Ozkan Phani Kumar Christopher S. Edrington Department of Automotive Engineering Clemson University Greenville 29607 U.S. The Holcombe Department of Electrical and Computer Engineering Clemson University Clemson 29631 U.S Distribution A. Approved for public release distribution unlimited. (OPSEC 7474)

Cyber-physical system (CPS) of EV on-board chargers is connected to an IOT-based communication network for coordinated control, which is highly vulnerable to cyber-attacks. This charging coordination control incorporating hundreds of EVs and associated charging sessions, feed in a stochastic reference input to energy management system (EMS) of on-board EV chargers. Hence, under these varying operating conditions, a pure data-driven-based detection model can experience a disturbance detection failure. Therefore, a model predictive control (MPC) based machine learning (ML) network, integrated with a residual based training data pre-processing is proposed in this paper. This MPC based ML approach can effectively detect a tempered response while addressing the aleatory behaviour of cooperative control with enhanced disturbance detection accuracy. The proposed model utilizing various system level signals can also efficiently classify a normal condition, cyber-attack, and a physical fault. The superior performance of the proposed approach is validated by using different case study scenarios of training datasets.

关键词： Integrated on-board chargers (IOBCs) cyber-attack MOSFET fault, neural networks model predictive control direct power control

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Initial Observations from Field Testing of a Digital Participatory Tool to Improve Water Security in Rural India 24

Initial Observations from Field Testing of a Digital Partici...

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2024 International Conference on Information and Communication Technologies and Development, ICTD 2024

作者： Mehta, Shivani A Dutt, Aila Tannirkulam, Ajay Singh, Akshay Pratap Verma, Aman Singha, Anamitra Sreenidhi, Ananda Nandi, Ananjan Ankit Kumar, Ankit Dabli, Atharv Athira, P. Ramachandran, Balakumaran Bansal, Chahat Sheth, Chintan Sanjaybhai Dsouza, Craig Kumar, Deepak Sharma, Dharmisha Harshita Singh, Jaskaran Dadheech, Kapil Agrahari, Ksheetiz Krishna, Om Prasad, Pooja Radhakrishnan, Priyadarshini Sardana, Ramita Bhabak, Rittwick Bodavula, Ruptirumal Sai Vishnubhatla, Saketh Haldar, Samitha Agrawal, Sanjali Maurya, Shiv Prakash Kumari, Shruti Siddharth, S. Kumari, Sukriti Vishnu, S. Seth, Aaditeshwar School of Information Technology Indian Institute of Technology Delhi Delhi New Delhi India Gram Vaani Delhi New Delhi India Magasool Tamil Nadu Chennai India Department of Computer Science and Engineering Indian Institute of Technology Delhi Delhi New Delhi India Civil Engineering Indian Institute of Technology Palakkad Kerala Palakkad India Well Labs Karnataka Bengaluru India School of Public Policy Indian Institute of Technology Delhi Delhi New Delhi India Indian Institute of Technology Delhi Delhi New Delhi India

ISBN: (纸本)9798400710414

With almost half the Indian population facing water stress [42], likely to be aggravated with climate change [76, 122], an intersecting crises of environment, livelihood, and social justice can worsen the vulnerability of rural communities that are directly dependent upon water resources for their livelihood and sustenance. To address this challenge of rural water security especially for marginalized communities, MGNREGA is a public works and employment generation scheme in India that contributes towards the construction of water related assets such as farm ponds, checkdams, and trenches and bunds, to improve irrigation and climate resilience of rural communities. MGNREGA is a demand-driven scheme that solicits demands for water related assets through the local governance structure in India of village-level community meetings called Gram Sabhas. In many areas, Civil Society Organizations (CSOs) and community based institutions also play a significant role by coordinating participatory processes to build the capacity of rural community members to participate in local governance and demand such assets under MGNREGA. However, through multiple field visits, we observed a need to strengthen local processes with appropriate data and tools to support context-sensitive and evidence-based bottom-up planning. In this study, we adopt an approach of building a digital participatory tool, built on a technology stack that encapsulates different geospatial datasets, to answer key research questions of whether such technology artefacts can improve the participation of rural communities in water security planning and management, through, (1) community empowerment by building a data-driven understanding of water security, (2) ecological sustainability of water resources through scientific planning of assets, and (3) equity led participatory processes to improve fairness in the distribution of assets. We discuss here the design of the tool, Commons Connect, underlying datasets on whic

关键词： Data encapsulation

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Enhanced Multiclass Brain Tumor Detection in MRI Using Similarity-Navigated Graph Neural Networks with Banyan Tree Growth Optimization

Enhanced Multiclass Brain Tumor Detection in MRI Using Simil...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Babu Rao K Pallavi Singh Gargi Shankar Verma L. Mohana Kannan Swaraj Kadam Department of Computer Science and Engineering Mangalore Institute of Technology & Engineering Mijar Karnataka India Department of Public Health Dentistry Saraswati Dental College & Hospital Lucknow Uttar Pradesh India Department of Computer Science and Engineering Krishna's Vikash Institute of Technology Raipur Chhattisgarh India Department of Biomedical Engineering Erode Sengunthar Engineering College Perundurai Tamil Nadu India Department of Electrical Engineering Dr. D Y Patil Institute of Technology Pune Maharashtra India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

The diagnosis of brain tumors is admittedly difficult because of their varied and complicated nature. Therefore, one has to get the right diagnosis and categorization for it to be treated well. This is a situation where identification of multiple classes such as; meningioma, pituitary, glioma and no tumor is crucial in taking the right course of action. Therefore, a new deep learning architecture is suggested in this work to increase the specificity between different types of tumors by detecting brain tumors with high accuracy in MRI scans. The proposed methodology starts with the Cumulative Curve Fitting Approximation (CCFA) preprocessing algorithm to improve the quality of MRI images. Preprocessed images are used as inputs for feature extraction based on geometric algebra transformers. These transformers include geometric relationships within the data, ensuring more distinctive selection. The categorization is further carried out with assistance of a Similarity-Navigated Graph Neural Network (SNGNN) incorporating the Banyan Tree Growth Optimizer (BTGO) model, which is constructed for efficient detection of the subtlest patterns and their connection between classes of brain tumors. It integrates very well in terms of the accuracy shown by its performance metrics. It shows effectiveness in a multiclass scenario of detecting brain tumors, which helps in the precise and early diagnosis and even tailors proper treatment plans.

关键词： Accuracy Algebra Magnetic resonance imaging Brain tumors Brain modeling Transformers Feature extraction Approximation algorithms Graph neural networks Curve fitting

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Recursive Feature Elimination with Cross Validation for Alzheimer's Disease Classification using Cognitive Exam Scores 1

Recursive Feature Elimination with Cross Validation for Alzh...

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1st International Conference of Intelligent Methods, Systems and Applications, IMSA 2023

作者： Freytes, Christian Yaphet Perry Mayrand, Robin Sawada, Luana Okino Yan Liang, Thony Curiel Cid, Rosie E. Burke, Shanna Loewenstein, David Duara, Ranjan Adjouadi, Malek Florida International University Electrical & Computer Engineering MiamiFL United States Florida International University Computing & Information Sciences MiamiFL United States University of Miami Department of Neuropsychology MiamiFL United States Florida International University Public Health and Social Work Miami United States University of Miami Cognitive Neurosciences and Aging MiamiFL United States Mt Sinai Medical Center Wien Center for AD & MD MiamiFL United States Florida International University Electrical & Computer Engineering Miami United States

ISBN: (纸本)9798350335569

Prodromal detection of Alzheimer's Disease(AD) is a substantial challenge in the research community. Among the tools used in AD diagnosis, cognitive exams are standard in most procedures. However, the barrage of cognitive examinations is both time and resource consuming. With the use of Machine Learning, Feature Elimination (FE) can be combined with classification algorithms to determine which cognitive exams are best suited for diagnosis. Using the results of FE, it can be determined if subsections of different composite scores can be combined to create a new enhanced and exhaustive exam. This paper implements a Recursive Feature Elimination with Cross Validation (RFECV) machine learning algorithm to determine which cognitive exams perform best for AD classification tasks. Out of 119 features, an average of 16 features were selected as optimal. These optimal features average 75% Accuracy, 70% Precision, and 75% Recall and an F1 Weighted score of 71% in classification. © 2023 IEEE.

关键词： Decision trees

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Integration of 5G and Motion Sensors for Vehicular Positioning: A Loosely-Coupled Approach

arXiv

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arXiv 2024年

作者： Saleh, Sharief Bader, Qamar Karaim, Malek Elhabiby, Mohamed Noureldin, Aboelmagd Lab Department of Electrical and Computer Engineering Royal Military College of Canada KingstonONK7K 7B4 Canada The Department of Electrical and Computer Engineering Queen's University KingstonONK7L 3N6 Canada Micro Engineering Tech. Inc The Public Works Department Ain Shams University Cairo11566 Egypt

Autonomous vehicles (AVs) are poised to revolutionize the transportation industry by enhancing traffic efficiency and road safety. However, achieving optimal vehicular autonomy demands an uninterrupted and precise positioning solution, especially in deep urban environments. 5G mmWave holds immense potential to provide such a service due to its accurate range and angle measurements. Yet, as mmWave signals are prone to signal blockage, severe positioning errors will occur. Most of the 5G positioning literature relies on constant motion models to bridge such 5G outages, which do not capture the true dynamics of the vehicle. Few proposed methodologies rely on inertial measurement units (IMUs) to bridge such gaps, where they predominantly use tightly coupled (TC) integration schemes, introducing a nonlinear 5G measurement model. Such approaches, which rely on Kalman filtering, necessitate the linearization of the measurement model, leading to pronounced positioning errors. In this paper, however, we propose a loosely coupled (LC) sensor fusion scheme to integrate 5G, IMUs, and odometers to mitigate linearization errors. Additionally, we propose a novel method to design the process covariance matrix of the extended Kalman filter (EKF). Moreover, we propose enhancements to the mechanization of the IMU data to enhance the standalone IMU solution. The proposed methodologies were tested using a novel setup comprising 5G measurements from Siradel's S_5G simulation tool and real IMU and odometer measurements from an hour-long trajectory. The proposed method resulted in 14 cm of error for 95% of the time compared to 1 m provided by the traditional constant velocity model approach. © 2024, CC BY.

关键词： Linearization

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Methods for studying policies and regulations impacting demand response in Dutch wholesale day-ahead power market

Methods for studying policies and regulations impacting dema...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Sugandha Chauhan Sebastiaan Hers Milos Cvetkovic Laurens De Vries Faculty of Technology Policy and Management Delft University of Technology Delft The Netherlands Energy Transition TNO Amsterdam The Netherlands Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

Decarbonisation of the electricity sector has led to the adoption and deployment of a large number of consumer-sited flexible assets. Simultaneously, consumers are becoming increasingly aware of their consumption patterns and are eager to reduce their energy expenses making demand response a significant source of flexibility in energy markets. In this paper, we discuss the policy measures that influence a consumer’s ability to respond to price signals and offer flexibility in the day-ahead market. We propose two methods to quantitatively analyse these policy instruments through their inclusion in market clearing models for the Dutch day-ahead power market. A single-level optimisation model with social welfare maximisation objective can be used to perform a simplified assessment of changes in demand bids due to policy-based financial influences. This model is suitable for studying simple policies such as time-independent taxes but unsuitable for complex policies such as network tariffs and subsidies. A bi-level optimisation model with consumer surplus maximisation on the upper level and social welfare maximisation on the lower level allows more sophisticated modelling of policies but is limited by its scalability and computational complexity. The two methods can be compared on the basis of their ability to incorporate different policy instruments and market design choices, model consumer bidding behaviour, their computational complexity and challenges to implementation.

关键词： Instruments Computational modeling Scalability Optimization models Europe Elasticity Power markets Demand response Computational complexity Optimization

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Analytical Approximation for ATTR with Respect to Node Removals

Analytical Approximation for ATTR with Respect to Node Remov...

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International Workshop on Design of Reliable Communication Networks (DRCN)

作者： Fenghua Wang Jinyi Zou Robert E. Kooij Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands Unit ICT Strategy and Policy Netherlands Organisation for Applied Scientific Research (TNO) Den Haag The Netherlands

ISBN: (数字)9798350348972

ISBN: (纸本)9798350348989

We propose an analytical approach to approximate the average two-terminal reliability (ATT R) for graphs where a fraction of the nodes is removed. The approximation is based on the generating function of the network's degree distribution under random node removals and stochastic degree-based node removals. Through validation on synthetic graphs, including Erdos Renyi random graphs and Barabasi-Albert graphs, as well as four real-world networks from the Internet Topology Zoo, we observe that the analytical method effectively approximates the average two-terminal reliability under random node removals for synthetic graphs. In the case of real-world graphs under random and stochastic degree-based node removals or synthetic graphs under stochastic degree-based node removals, the analytical ap-proximation yields reasonably accurate results when the fraction of removed nodes is small, specifically less than 10%, provided that the initial analytical approximation closely aligns with the real ATT R values.

关键词： Reliability engineering Communication networks Internet topology

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Crowdsourcing and Bus Telematics for Promoting Fuel Efficiency and Eco-Driving Practices on the EDSA Busway

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Transportation Research Procedia 2025年 82卷 404-423页

作者： Noriel Christopher Tiglao Nestor Michael Tiglao Erris Sanciangco Mark Angelo Tacderas National College of Public Administration and Governance University of the Philippines Diliman 1101 Quezon City Philippines Department of Electrical and Computer Engineering University of Maryland College Park Maryland 20742 USA SafeTravelPH Mobility Innovations Organization Inc 1103 Quezon City Philippines

Crowdsourcing through telematics and mobile platforms has been heavily researched for transport applications in the last decade, falling in the realm of intelligent transport systems (ITS). The value of crowdsourced data lies in enabling the participation of the key stakeholders for a human-centric, user-oriented, and reliable information system. On the other hand, there is a need for robust systems for public transport telematics. This study demonstrates the power of crowdsourced open data for promoting fuel efficiency and eco-driving practices on bus operations through the pilot implementation of a real-time smartphone-based bus monitoring system for the EDSA Busway. The platform is integrated with the SafeTravelPH public transport crowdsourcing app. Through collaboration with operators and drivers, the prototype collected real-time data on vehicle operation, passenger boarding and alighting and vehicle occupancy feeds. An innovative approach in capturing vehicle operating parameters, including engine RPM, speed, and fuel levels, is implemented using image processing and artificial intelligence (AI) techniques. Various fuel efficiency indices that can provide sound bases for the evaluation of bus driving behavior and fuel-efficient bus operations are presented.

关键词： Fuel efficiency Eco-driving Crowdsourcing Artificial Intelligence ITS

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Multi-Objective Transmission Expansion: An Offshore Wind Power Integration Case Study

arXiv

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arXiv 2023年

作者： Khanal, Saroj Graf, Christoph Liang, Zhirui Dvorkin, Yury Ünel, Burçin The Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States The Departments of Electrical and Computer Engineering Department and Civil and Systems Engineering Johns Hopkins University BaltimoreMD21218 United States The Institute for Policy Integrity New York University School of Law New YorkNY10012 United States

Despite ambitious offshore wind targets in the U.S. and globally, offshore grid planning guidance remains notably scarce, contrasting with well-established frameworks for onshore grids. This gap, alongside the increasing penetration of offshore wind and other clean-energy resources in onshore grids, highlights the urgent need for a coordinated planning framework. Our paper describes a multi-objective, multistage generation, storage and transmission expansion planning model to facilitate efficient and resilient large-scale adoption of offshore wind power. Recognizing regulatory emphasis and, in some cases, requirements to consider externalities, this model explicitly accounts for negative externalities: greenhouse gas emissions and local emission-induced air pollution. Utilizing an 8-zone ISO-NE test system and a 9-zone PJM test system, we explore grid expansion sensitivities such as impacts of optimizing Points of Interconnection (POIs) versus fixed POIs, negative externalities, and consideration of extreme operational scenarios resulting from offshore wind integration. Our results indicate that accounting for negative externalities necessitates greater upfront investment in clean generation and storage (balanced by lower expected operational costs). Optimizing POIs could significantly reshape offshore topology or POIs, and lower total cost. Finally, accounting for extreme operational scenarios typically results in greater operational costs and sometimes may alter onshore line investment. Copyright © 2023, The Authors. All rights reserved.

关键词： Investments

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