The high penetration of converter-based renewable energy systems in microgrids confront absence of rotational masses from synchronous generators may lead to a shortage of inertia, which may lead to steeper frequency a...
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This paper presents a centralized model based on metaheuristics to solve the problem of optimal Electric Vehicle (EV) charge scheduling in multiple parking lots. A centralized optimization model using two-level partic...
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Electric Vehicles (EV) are increasingly popular due to environmental benefits but can strain the power grid. Optimizing EV charging in large parking lots is a challenge. This study uses a metaheuristic approach on a g...
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Rapid development of renewable energy systems is causing considerable reduction of inertia and damping in the power system networks. This adds difficulty to frequency control. This paper suggests a novel method of fre...
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The advancement of YOLO and similar computer vision models represents a technological era that could revolutionize numerous aspects of day-to-day life, including traffic management. This study explores the feasibility...
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Small squarers are needed in real-life applications. Field Programmable Gate Arrays (FPGAs), like the modern 7-series Xilinx ones, come with DSP blocks that contain 25x18 bits hardwired multipliers. For practical reas...
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Equipment health monitoring (EHM) techniques are increasing in their ability to accurately diagnose defective equipment. This increase in capability comes with an increase in computational complexity. For these techni...
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Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid *** and timely diagnosis of these disorders is...
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Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid *** and timely diagnosis of these disorders is crucial for effective treatment and patient *** research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection *** forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this *** ensemble learning,random forest,adaptive boosting,and bagging classifiers are *** effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature *** experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,*** enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical *** significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed *** research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.
This Innovative Practice Category Full Paper presents the remote implementation of an embedded systems capstone project for computerengineering students. A capstone project is a feature of most undergraduate programs...
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ISBN:
(纸本)9798350351507
This Innovative Practice Category Full Paper presents the remote implementation of an embedded systems capstone project for computerengineering students. A capstone project is a feature of most undergraduate programs in computerengineering. Such a project is usually meant to expose students to the development of a large system from conceptualization to its final implementation, involving substantial design and development of hardware and software components. In our university, students were given the opportunity to work on a cutting-edge problem focused on healthcare - 'Designing a wearable device that automatically detects human activities'. It is an area where a large amount of research is ongoing, and hundreds of scientific papers are published every year. In addition, students had to evaluate and adopt techniques from existing literature and adapt them to meet the problem requirements. Students equipped themselves with state-of-the-art hardware, Bio-signal processing, machine learning, power optimization and secure communications to design the wearable. Thus, the project reinforced their knowledge of fundamentals, while exposing them to a problem with no obvious solution. Through the capstone project, students are able to better appreciate the relevance of the various components in the computerengineering curriculum to large-scale computerengineering projects. Students are organized into teams of six to execute the project. The COVID-19 pandemic resulted in the university migrating the teaching online for all courses. This was particularly challenging to implement for the capstone project as one of the key requirements is for the members to work together and subsystems to interact with each other. A course refresh resulted in a credits update and this provided a unique opportunity for the teaching team to re-design the project with core Industry 4.0 technologies such as hardware acceleration, remote processing, networking, remote analytics, and secure protoc
This paper provides a comprehensive analysis of the security-centric performance of the Lightweight Cryptography (LWC) algorithms ASCON, TinyJambu, and Xoodyak, finalists in the NIST standardization process, through t...
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