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2nd International Conference on Smart technologies for Smart Nation, SmartTechCon 2023

作者： Raman, Ramakrishnan Meenakshi, R. Ramya, R. Kaul, Meenakshi Jayaprakash, S. Srinivasan, C. Maharashtra Pune India Department of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India Symbiosis Law School Uttar Pradesh Noida India Maharashtra Pune India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Monitoring and controlling magnetic field intensity is essential in industrial settings for the reliable and secure functioning of equipment, systems, and other components. The Internet of Things (IoT) is advancing technology solutions for industrial monitoring in magnetic fields. A Raspberry Pi works as the main processing unit of the system and is equipped with a Wi-Fi module for wireless communications. Connecting a Hall Effect sensor to the Raspberry Pi through the proper interfaces may take magnetic field measurements in real-time. The Hall Effect sensor sends its readings to the Raspberry Pi, which then processes and analyzes them. A connected display then shows the processed data, giving the users or operators rapid visual feedback. Wi-Fi allows uninterrupted data transfer to be sent to the cloud or other remote monitoring systems. Magnetic field strength measurements may be taken with great precision using the Hall Effect sensor. Regarding processing and analyzing data, the Raspberry Pi proves to be a flexible and adaptable platform. The data on the magnetic field may be seen in real-time and monitored on the screen. Wi-Fi allows users to connect from afar, share information, and use cloud-based features and storage. By facilitating continuous data gathering, remote access, and enhanced analytics, an IoT-based method improves monitoring capabilities. The technology can identify irregular magnetic field patterns, sending real-time alarms or messages to facilitate preventative maintenance and safety checks. The suggested approach provides a scalable and reasonably priced means of tracking magnetic fields in manufacturing settings. It improves manufacturing safety, efficiency, and productivity by providing users with real-time information and enabling data-driven decision-making. © 2023 IEEE.

关键词： Hall Effect sensor IoT Magnetic field monitoring Raspberry Pi Real-time

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A Hybrid Wireless Image Transmission Scheme with Diffusion 24

A Hybrid Wireless Image Transmission Scheme with Diffusion

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Niu, Xueyan Wang, Xu Gunduz, Deniz Bai, Bo Chen, Weichao Zhou, Guohua City University of Hong Kong Department of Computer Science Hong Kong Huawei Technologies Co. Ltd. Theory Lab Central Research Institute 2012 Labs China Imperial College London Department of Electrical and Electronic Engineering London United Kingdom Huawei Technologies Co. Ltd Ran Research Department Wn China

ISBN: (纸本)9781665496261

We propose a hybrid joint source-channel coding (JSCC) scheme, in which the conventional digital communication scheme is complemented with a generative refinement component to improve the perceptual quality of the reconstruction. The input image is decomposed into two components: the first is a coarse compressed version, and is transmitted following the conventional separation based approach. An additional component is obtained through the diffusion process by adding independent Gaussian noise to the input image, and is transmitted using DeepJSCC. The decoder combines the two signals to produce a high quality reconstruction of the source. Experimental results show that the hybrid design provides bandwidth savings and enables graceful performance improvement as the channel quality improves. © 2023 IEEE.

关键词： Diffusion

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Analysing Technical and Economic Dimensions using AI Techniques 4

Analysing Technical and Economic Dimensions using AI Techniq...

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4th IEEE International Conference on Advance Computing and Innovative technologies in engineering, ICACITE 2024

作者： Tarun, G. Husain, Saif O. Al-Saidi, Imad Al-Din Hussein Vats, Satvik Ali, Rabei Raad Jawad, Ahmed Qusay Abdtawfeeq, Tareq Hafdhi Hindustan Institute of Technology and Science Department of Computer Applications India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Alkunooze University College Computer Engineer Department Basra Iraq Department of Cse Graphic Era Hill University Uttarakhand Dehradun248007 India Graphic Era Deemed to Be University Uttarakhand Dehradun248007 India National University of Science and Technology College of Technical Engineering DhiQar64001 Iraq Kut University College Department of Electrical Engineering Technologies Wasit Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

Artificial intelligence holds significant potential for addressing various real-world challenges in fields like healthcare and transportation. Nevertheless, existing avenues for disseminating AI models, such as Github repositories, academic project websites, and commercial AI marketplaces, exhibit certain limitations, including inadequate monetization methods, model traceability, and auditability. This paper proposes a novel approach to AI diffusion through a decentralized online marketplace, offering guidelines that encompass technical, economic, and regulatory considerations. The study explores potential solutions to challenges within these domains and conducts a comparative analysis of current AI marketplaces, revealing a predominance of centralized commercial platforms featuring a limited model selection. © 2024 IEEE.

关键词： Marketplaces

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Modular Cyber-Physical System for Smart Industry: A case study on energy load disaggregation

Modular Cyber-Physical System for Smart Industry: A case stu...

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2023 International Conference on Control, Automation and Diagnosis, ICCAD 2023

作者： Alves, Jorge Sousa, Pedro Matos, Barbara Mendes, Jerome Souza, Francisco Matias, Tiago University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Coimbra3030-290 Portugal Oncontrol Technologies Coimbra3000-108 Portugal Radboud University Dept. Analytical Chemistry & Chemometrics AJ Nijmegen6525 Netherlands

ISBN: (纸本)9798350347074

Constant changes have marked the industry, and cyber-physical systems have offered great potential in Industry 4.0 on production processes, allowing the integration of computational intelligence and real-time processing. In this context, this paper proposes a modular hardware system for industrial applications according to a plug & play concept. The proposed system comprises a processing module (mPP) and specific expansion modules (mEXs). The mPP is responsible for administrating the network/communication. It collects data from the mEXs, extracts features, processes the features through artificial intelligence, and interacts with the production process. The mEXs comprise sensors, conditioning circuits, and an analog/digital converter. They are responsible for acquiring field signals and their transmission to the mPP. The proposed system is designed for operating with electrical protection, allowing reliable operation of the mPP and mEXs. For evaluating and demonstrating the proposed system in a real scenario, we applied it in a case study for non-intrusive energy load disaggregation. © 2023 IEEE.

关键词： Cyber Physical System

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Ultra-compact and broadband polarization-insensitive mode-order converting power splitter

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Chinese Optics Letters 2024年第3期22卷 36-41页

作者：陈浩琪姚润葵汪鹏君符强陈伟伟戴世勋孔德军林健金涛李军戴庭舸杨建义 Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China College of Electrical and Electronic Engineering Wenzhou UniversityWenzhou 325035China College of Science and Technology Ningbo UniversityNingbo 315211China Laboratory of Infrared Materials and Devices The Research Institute of Advanced TechnologiesNingbo UniversityNingbo 315211China Department of Information Science and Electronics Engineering Zhejiang UniversityHangzhou 310027China

A polarization-insensitive mode-order converting power splitter using a pixelated region is presented and investigated in this *** TE_(0)and TM_(0)modes are injected into the input port,they are converted into TE_(1)and TM_(1)modes,which evenly come out from the two output *** finite-difference time-domain method and direct-binary-search optimization algorithm are utilized to optimize structural parameters of the pixelated region to attain small insertion loss,low crosstalk,wide bandwidth,excellent power uniformity,polarization-insensitive property,and compact *** results reveal that the insertion loss,crosstalk,and power uniformity of the fabricated device at 1550 nm are 0.57,-19.67,and 0.094 d B in the case of TE polarization,while in the TM polarization,the relevant insertion loss,crosstalk,and power uniformity are 0.57,-19.40,and 0.11 d *** a wavelength range from 1520 to 1600 nm,for the fabricated device working at TE polarization,the insertion loss,crosstalk,and power uniformity are lower than 1.39,-17.64,and 0.14 *** the case of TM polarization,we achieved an insertion loss,crosstalk,and power uniformity less than 1.23,-17.62,and 0.14 dB.

关键词： integrated optics optical waveguide polarization-insensitive property mode-order converting power splitter

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Temperature Dependent Schottky Contact Behavior of Iron (Fe) Doped $\beta$-Ga2O3

Temperature Dependent Schottky Contact Behavior of Iron (Fe)...

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IEEE Southeastcon

作者： Makhluk Hossain Prio Durga Gajula Lavanya Muthusamy Rackley Wren Jacob Leach Goutam Koley Department of Electrical and Computer Engineering Clemson University Clemson USA Institute for Electronics and Nanotechnology Georgia Institute of Technology Atlanta USA Kyma Technologies Massachusetts USA

This research investigates the temperature dependent conductivity of Fe-doped bulk $\beta{-}$ Ga203 substrate, aiming for potential applications in high-temperature scenarios and high-power and radio frequency (RF) devices. To compensate unintentional n-type conductivity and excessive free carrier densities in single-crystal substrates, intentional incorporation of Fe, acting as a deep acceptor or electron trap, is employed in $\beta{-}$ Ga203 crystals. We focused on the behavior of a heavily iron-doped bulk p-Ga203 substrate, featuring a lateral Schottky-ohmic interdigitated finger device, examined at both ambient and elevated temperatures. As the temperature rose, the deep acceptor Fe progressively released trapped electrons, thereby enhancing the conductivity of the lateral Schottky-ohmic interdigitated finger device. This observed phenomenon effectively offsetted the insulating properties inherent in the heavily doped bulk $\beta$ .Ga203 substrate. The findings contribute to the understanding of Fe-doped $\beta$ .Ga203 substrates characteristics for high-temperature applications, facilitating the ongoing development of high-power devices.

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Efficient Data Compression on Healthcare Images Using Improved Hadamard Transform Based Filter

Efficient Data Compression on Healthcare Images Using Impro...

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International Conference on Intelligent Systems and Networks, ICISN 2024

作者： Thi, Thu Nguyen Haridoss, G. Rubia, Jency Dilipkumar, S. Chowdhury, Subrata Nguyen, Anh Phuong Duc, Tan Tran Faculty of Electronic Engineering Hanoi University of Industry Hanoi10000 Viet Nam Department of Electronics and Communication Engineering M.A.M College of Engineering and Technology Tiruchirappalli India Department of Electronics and Communication Engineering Vel Tech University Chennai India Department of Analytics SCOPE Vit Vellore Vellore India Department of Computer Sciences and Engineering Sreenivasa Institute of Technology and Management Studies AP Chittoor India Foreign Language Specialized School University of Languages and International Studies Hanoi Viet Nam Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi Viet Nam

ISBN: (纸本)9789819755035

The Hadamard transform is an ancient method for efficient data compression, especially for medical image data. Thus, the proposed work consists of analyzing the filter performance using a novel Hadamard transform to apply data compression. The parametric evaluation has been employed for factors such as PSNR, compression time, and compression ratio. For the application purpose, the proposed modified Hadamard transform is applied for the encryption and decryption process and retrieves the original image. The experimental analysis reveals that the proposed Novel Hadamard Transform gives the best performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Compression ratio Data compression and decompression Hadamard transform Vector Merging Adder PSNR

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Improvement of Task Scheduling with Energy Efficiency including Fault Tolerant with Resilient Computing System in Parallel and Distributed Communication Network 2

Improvement of Task Scheduling with Energy Efficiency includ...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Kumar, Vijay Madhuri, P. Yamini, R. Sravani, Palla Muthukumar, K. Graphic Era Hill University Department of Physics Dehradun India Hyderabad501401 India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India Raghu Engineering College Department of Computer Science and Engineering Andhra Pradesh Visakhapatnam531162 India Sri Krishna College of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350325799

In parallel and distributed communication networks, task scheduling is essential for attaining the best system performance. Innovative ways that may intelligently distribute computing resources while minimizing energy consumption and maintaining system resilience are required due to the rising need for energy efficiency and fault tolerance. This system, offers a unique approach to job scheduling in parallel and distributed communication networks that integrate genetic algorithms (GA) and machine learning (ML).Using ML approaches, proposed method analyses historical task execution data to discover patterns that may be applied to task execution time prediction in various computing systems. As a result, this can assign tasks to resources effectively, maximize resource usage, and cut down on execution time as a whole. In addition, using predefined objectives like energy efficiency and fault tolerance, GA is used to find the best task-resource assignments. This applies resilient computing approaches to the job scheduling procedure to overcome problems with fault tolerance. This prevents the execution of crucial operations from being hampered by system faults or resource shortages by utilizing fault detection and recovery technologies. This system can adapt dynamically to shifting network conditions through the combination of ML, GA, and resilient computing, which raises the entire system's reliability and availability. © 2023 IEEE.

关键词： Machine learning

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Real-time Agrochemical Management Solutions using Cloud Computing and K-Nearest Neighbors Algorithm 8

Real-time Agrochemical Management Solutions using Cloud Comp...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Sivanesan, T.M. Mohankumar, N. Shibu, N.V. Nandagopal, V. Rajasekaran, B. Srinivasan, S. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Nagpur Campus Maharastra Pune India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India Mohan Babu University School of Engineering Department of Electrical and Electronics Engineering Andhra Pradesh Tirupathi India Vinayaka Missions Kirupananda Variyar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Salem India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350367904

Optimizing pesticides to ensure crop health and productivity while limiting environmental effect is one of the many issues faced by modern agriculture. In this research, it provides a cloud-based, K-Nearest Neighbors (KNN) algorithm-based real-time agrochemical management system. The proposed method allows for effective data processing and analysis by using the computing power of cloud platforms. Environmental factors, crop type, soil properties, and historical data are used to inform the KNN algorithm's prediction of appropriate pesticide application rates. To provide the most current insights and suggestions, the system incorporates real-time data from a variety of sources, including sensors, satellites, and weather predictions. Enhanced crop yields, decreased costs, and less environmental impact may result from farmers making informed decisions about the use of agrochemicals via continuous monitoring and analysis. The system can be easily deployed and adjusted to many agricultural contexts because of the scalability and flexibility of cloud computing. This method provides a viable strategy for improving pesticide management practices, which in turn promotes sustainable farming and increases food safety. © 2024 IEEE.

关键词： Smart agriculture

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MMAQ: A Multi-Modal Self-Supervised Approach For Estimating Air Quality From Remote Sensing Data

MMAQ: A Multi-Modal Self-Supervised Approach For Estimating ...

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IEEE International Conference on Image Processing

作者： G. F. Angelis A. Emvoliadis A. Drosou D. Tzovaras Centre for Research & Technology Hellas Information & Technologies Institute Thessaloniki Greece Department of Electrical & Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Air quality intensifies climate change through global pollution, particularly affecting lower and middle-income countries that lack local ground pollutant monitoring networks. While atmospheric pollutant satellite measurements offer a broad view, their coarse spatial resolution limits detailed air pollution insights, resulting in unmonitored regions and information gaps. Furthermore, satellites generate extensive data that is often challenging to directly correlate with ground air pollution stations. To overcome this, we propose a multimodal self-supervised approach that learns from diverse satellite sources for air pollution monitoring. More specifically, aside from incorporating a combination of multi-spectral and spectral modalities (Sentinel-2 & 5P products), we also leverage tabular land cover data. Their integration into multimodal self-supervised learning is highlighted, employing a novel augmentation scheme that results in more resilient embeddings. The proposed approach integrates a self-supervised redundancy reduction loss in a multi-modal fashion, capturing both inter-modal and intra-modal correspondences. Furthermore, an adaptive loss weighting mechanism is introduced to effectively combine different multi-modal losses. Our approach’s efficacy is showcased in the air pollution prediction task, exhibiting a noteworthy improvement of up to 17% compared to existing methods. Furthermore, in our experiments, the applicability of our approach in other environmental tasks is also exhibited.

关键词： Satellites Atmospheric measurements Land surface Termination of employment Self-supervised learning Air pollution Sensors

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