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International Conference on Recent Evolutions in Energy, Drives and E-Vehicles, REED-EV 2022

作者： Gupta, S.K. Thakur, Pragya Kumar, Ritik Bharadwaj, Prakhar Department of Electrical Engineering Chandigarh University Mohali India Department of Computer Science Engineering Chandigarh University Mohali India

ISBN: (数字)9789819707638

ISBN: (纸本)9789819707621

Internet of Things technique is being accepted in all the important domains of human existence such as Medical, Defense, e-governance, services, education, pollution, agriculture, and many more. This paper has reviewed the role of IoT in Agriculture domain. Different applications in the domain of agriculture such as automatic water feeding, determining soil composition, crop selection, and the user interface have been studied considering the technologies offered by IoT and its effects. The paper is a complete package for a user who wants to optimize the crop yield and the profit with the help of IoT technology.... © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： User interfaces

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Identification of nonlinear systems in the frequency domain using two-dimensional Volterra series 10

Identification of nonlinear systems in the frequency domain ...

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10th International Conference on Control, Instrumentation and Automation, ICCIA 2024

作者： Soheili, Masoud Shafieirad, Mohsen Morovati, Tahereh Ronaghi, Erfan Kouhi University of Kashan Department of Electrical and Computer Engineering Kashan Iran Iran University of Science and Technology Department of Electrical Engineering Tehran Iran Shahed University Department of Electrical and Electronic Engineering Tehran Iran

ISBN: (纸本)9798331509972

This paper presents the identification of second-order two-dimensional kernels using the Volterra series model in the frequency domain. The study focuses on nonlinear systems where the input varies over both time and spatial dimensions. The Volterra series, known for its effectiveness in modeling complex nonlinear systems, is extended to two dimensions. The kernels are computed using the least squares method, with the input defined as a product of cosine functions. By simplifying the expressions and applying the Fourier transform, accurate kernel identification is achieved. The results demonstrate the efficacy of this approach in precisely matching the estimated system with the actual system, as evidenced by the minimal identification error. This work contributes to the development of advanced techniques for nonlinear system identification in industrial and scientific applications. © 2024 IEEE.

关键词： Frequency domain analysis

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FAU Hack-a-Thons: An Introduction to Computational and Hardware, Logistical Skills, and Intense Training for Outcomes-Based Learning for Developing Internet of Things Products

FAU Hack-a-Thons: An Introduction to Computational and Hardw...

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2024 ASEE Annual Conference and Exposition

作者： Weinthal, Charles Perry Jaramillo, David Department of Computer & Electrical Engineering & Computer Science Florida Atlantic University Boca RatonFL United States

In preparation for the upcoming FAU Hack-a-Thon, we have implemented extensive support structures to ensure that all participating teams are thoroughly prepared for the competition. This preparation includes the provision of online support from a variety of programming experts from IBM and experienced Hack-a-Thon participants. This support is accessible 24/7 in the three weeks leading up to the event, aimed at ensuring all teams are up to speed and ready to participate. Our proactive approach focuses on encouraging teams to engage deeply with the training materials, consistently reminding them of their capability to successfully compete with adequate preparation. During the event, teams are expected to utilize the skills they have honed in areas such as teamwork, programming, and hardware development to create innovative Internet of Things (IoT) products. The support continues throughout the 24-hour event, with resources readily available to assist teams in overcoming any technical challenges they encounter. This setup aims to simulate real-world problem-solving scenarios that participants are likely to face in their professional careers, thus enhancing their experience and skills in a practical, hands-on manner. The Hack-a-Thon not only focuses on technical skills development but also emphasizes the cultivation of soft skills such as communication, teamwork, and time management. By the end of the event, participants are expected to present their projects to a panel of industry experts and faculty, demonstrating not only their technical prowess but also their ability to communicate their ideas effectively. This comprehensive approach ensures that participants leave the Hack-a-Thon with enhanced skills, increased confidence, and a significant competitive edge in the job market, as many past participants have noted the value of this experience during job interviews. © American Society for engineering Education, 2024.

关键词： Personnel training

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RouteNow: Ensuring Least Hop Routing towards SDIoT based Backup Hospital Networks

RouteNow: Ensuring Least Hop Routing towards SDIoT based Bac...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Sarkar, Kounteya Misra, Sudip Sonu, Mayank Iit Department of Computer Science and Engineering Kharagpur India Iit Department of Electronics and Electrical Communication Engineering Kharagpur India

ISBN: (纸本)9798350351255

This work proposes routeNow, a routing algorithm designed for wireless Software Defined Internet of Things by dynamic path relaxation based on real-time heuristic that always gives the best least hop path. This is aimed towards providing an alternative backup network for a smart hospital that functions independently on the failure of the primary hospital network. Given a pair of source and destination nodes, a central controller with a global network view first calculates a minimum hop path greedily by minimizing the remaining distance to destination. Hence, the path so found is relaxed dynamically by substituting certain links with other links keeping the hop count constant if a new path is found with a lesser latency than the previous one. For this, the controller maintains a matrix containing real-time delay between neighboring IoT nodes given the current network state and whose values are periodically refreshed using a heuristic function based on actual delay measurements. This enables the controller to dynamically modify routing paths. Simulation analysis of routeNow shows that it formulates routing paths with significant less time even with large networks and by always maintaining a route with least hops, on average a least latency path is also ensured. © 2024 IEEE.

关键词： Internet of things

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ElectroBlock: Reinforcement Learning and Blockchain-based Energy Trade to Optimize Tariffs 7

ElectroBlock: Reinforcement Learning and Blockchain-based En...

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7th Conference on Cloud and Internet of Things, CIoT 2024

作者： Islam, Ridwan Arefin Rezwan-Ul-Islam Shatabda, Swakkhar Islam, Salekul McGill University School of Computer Science Montréal Canada United International University Department of Computer Science and Engineering Dhaka Bangladesh Brac University Department of Computer Science and Engineering Dhaka Bangladesh North South University Department of Electrical and Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9798350356717

Energy prices have increased by more than 62% globally on average, while power companies are trying to provide more affordable energy with different options: fixed-rate, slab-based tariffs, and Time-of-Use pricing. This article aims to combine blockchains, smart contracts, smart grids, energy forecasting through reinforcement learning, and energy trading between closed communities, bringing consumer savings. Our proposed model, ElectroBlock, uses the first 25 days of each month's energy usage history along with seasonal and exogenous factors to forecast the final end-of-month usage. Then with the forecast we can predict which customers are the most likely to be pushed to the next energy consumption slab and be charged at a higher rate for the remaining month. This allows these customers to be buyers, and seamlessly buy energy units from users who would sell, the energy units they are predicted to leave unused by the end of the month. Moreover, trading is done with WalletCoins, at a coin per kilowatt hour, but could also be traded directly for fiat currency. Furthermore, the power company makes a profit on every trade through transaction fees. Hence there is great incentive for both customers and power companies to adopt our proposed model. We integrated Hyperledger Fabric into our ElectroBlock prototype to store all customer data and prevent tampering with WalletCoin and consumption records. Finally, we did a performance analysis, on the actual cost savings based on a real-world dataset;and a scalability test for concurrency and customer bases on our prototype. © 2024 IEEE.

关键词： Sales

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TCOA: Triple-Check Offloading Algorithm for Roadside Units and Vehicular Microclouds in 5G Networks and Beyond

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IEEE Access 2023年 11卷 84985-85001页

作者： Qiu, Bo-Jun Hsieh, Cheng-Ying Chen, Jyh-Cheng Dressler, Falko National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan Tu Berlin School of Electrical Engineering and Computer Science Berlin10623 Germany

Next-generation intelligent transportation systems aim to achieve many cooperative perception and cooperative driving functions that require considerable computational resources. Offloading such tasks via mobile edge computing is considered part of the solution;this approach is currently being studied within the scope of 5G networks and beyond. In the automotive context, such edge systems could be roadside units (RSUs), which can easily be overloaded at peak times. Vehicular microcloud approaches have been proposed to overcome such problems by sharing the computational resources of nearby cars. In this study, we propose an offloading system architecture to enable such offloading in a vehicular microcloud interconnected by a 5G core network. We model the system as a queueing model to derive closed-form solutions for selected performance metrics. Based on our insights, we propose the triple-check offloading algorithm (TCOA) to obtain both the best offloading ratio of the vehicular microcloud to the entire offloading system and the optimal maximum number of the remaining vehicle instances in the vehicular microcloud. Our simulation results show that the proposed TCOA achieves better system performance than four other offloading schemes in terms of cost, response time, service rate, and cost response-time production service rate division (CRPSD). © 2013 IEEE.

关键词： 5G mobile communication systems

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Design of a Model-Free Controller based on Predictive Functional Control

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2024年第1期144卷 15-20页

作者： Ashida, Yoichiro Katayama, Masaru Department of Electrical Engineering and Computer Science National Institute of Technology Matsue College 14-4 Nishi-Ikuma Shimane Matsue690-8518 Japan

Intelligent-PID (i-PID) control proposed by Fliess is a simple control algorithm. The controller is designed based on ultra-local model, and consisted of PID type controller and derivatives of reference signal and controlled variables. Authors have considered discrete i-PID controller and its properties, and one of the result was that PD controller was sufficient in discrete time domain. In discrete i-PID controller, PD type controller is still required to ensure convergence of controlled error to zero, and PD type parameters affects convergence property, fast-response property and stability. In this paper, predictive functional control (PFC) based on an ultra-local model is proposed. By introducing of PFC, controlled variable converge to reference value without PD type controller. User-specified parameters of the proposed controller is smaller than the conventional i-PID controller. Characteristics of the proposed controller are compared with discrete i-PID controller by numerical examples. © 2024 The Institute of electrical Engineers of Japan.

关键词： Controllers

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Optical Flow Estimation in Dense Foggy Scenes with Domain-Adaptive Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第6期4卷 1777-1788页

作者： Yan, Wending Sharma, Aashish Tan, Robby T. National University of Singapore Electrical and Computer Engineering Department Singapore Singapore Agency for Science Technology and Research Singapore Singapore

Estimating optical flow in dense foggy scenes is a challenging task. The basic assumptions for computing flow such as brightness and gradient constancy become invalid. To address the problem, we introduce a semisupervised deep learning method that can learn from real fog images without requiring the corresponding optical flow ground-truths. Our method is a multitask network, integrating the domain transformation and optical flow networks in one framework. The domain transformation is performed between foggy and clean domains. Under our semisupervised training strategy, we train our method in a supervised manner with a pair of synthetic fog images, their corresponding clean images and optical flow ground-truths. Subsequently, given a pair of real fog images and a pair of clean images that are not corresponding to each other (unpaired), in the next training batch, we train our network in an unsupervised manner. The supervised and unsupervised training processes are alternated iteratively. Since our method relies on unsupervised learning for real data, we show that it can be used for test-time training. We show in our experiments that performing test-time training improves the results further on our test data. Extensive experiments show that our method outperforms the state-of-the-art methods in estimating optical flow in dense foggy scenes. © 2020 IEEE.

关键词： Optical flows

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An 840 V-to-120 V Radiation-Tolerant Flying Capacitor Multilevel Converter for Space Robotics

An 840 V-to-120 V Radiation-Tolerant Flying Capacitor Multil...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Krause, Elisa Coday, Samantha Horowitz, Logan Pilawa-Podgurski, Robert C.N. University of California Department of Electrical Engineering and Computer Sciences BerkeleyCA United States Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA United States

ISBN: (纸本)9798350376067

High-performance, high conversion ratio power electronics are necessary to enable robotics in future space exploration. This work utilizes the flying capacitor multilevel topology to build a lightweight, efficient converter for these applications. The design is optimized to minimize the mass of the passive components, and a stacked printed circuit board layout is used to further reduce the size. Radiation-tolerant part equivalents are utilized to emulate the performance of a space-rated prototype, and GaN HEMTs are used to further improve the converter's resiliency towards radiation effects. The design approach is verified through an 840 V, 600 W, 8-level FCML hardware prototype. The design achieves a peak efficiency of 95.4% and a power density of 4.92 kW/kg. © 2024 IEEE.

关键词： Gallium nitride

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A Robust End-to-End CNN Architecture for Efficient COVID-19 Prediction form X-ray Images with Imbalanced Data

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Informatica (Slovenia) 2023年第7期47卷 115-126页

作者： Oraibi, Zakariya A. Albasri, Safaa Department of Computer Science College of Education for Pure Sciences Unviersity of Basrah Basrah Iraq Electrical Engineering Department College of Engineering Mustansiriyah University Baghdad Iraq

The world witnessed big changes in 2019 when a new virus called coronavirus affected the lives of hundreds of millions of individuals and led to huge disruptions in healthcare systems. Early prediction of this virus was a top priority to limit its damage and save countless lives. Many advanced artificial intelligence technologies like deep learning have used chest X-ray images for this task. In this paper, a new CNN architecture is introduced to classify chest X-ray images. The new model is applied on a 256 × 256 × 3 input image and consists of six convolutional blocks. In addition, we improve the performance of our model by adding regularization techniques, including batch normalization and dropout. We tested our model using an imbalanced COVID-19 dataset of 5000 COVID and Non-COVID images. Four metrics were used to test the new model: sensitivity, specificity, precision, and F1 score. In experiments, we achieved a sensitivity rate of 97%, a specificity rate of 99.32%, a precision rate of 99.90%, and F1 score of 97.73% despite being provided with fewer training images. In conclusion, we proposed a light deep learning model capable of achieving high prediction accuracy that outperformed the best deep learning methods in terms of specificity and achieved high sensitivity result. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Coronavirus

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