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2023 International Conference on computer, Electronics and electrical engineering and their Applications, IC2E3 2023

作者： Sayeedurrahman Sabir, Bushra Khan, Rashid Ahmed Masood, Dawood Nat, Aslam Aligarh Muslim University Z.H. College of Engineering & Technology Department of Electrical Engineering Aligarh202002 India National Taiwan University of Science and Technology Department of Electrical Engineering Taipei10607 Taiwan Sapienza University of Rome Department of Computer Control Management Engineering 00185 Italy

ISBN: (纸本)9798350338003

Solar energy is a clean and simple method of electricity generation. This electricity is utilized for various purposes, ranging from modest electrical gadgets and cars to major enterprises, satellites, and space exploration. The title of this project, "Solar tracking system with light-dependent resistors and a stepper motor controlled by a microcontroller"alludes to the project's purpose, which is to harvest as much solar energy as possible from a solar panel throughout the day using a specific Sun tracking mechanism. To do this, a mechanism was developed that tracks the sun's position and rotates the solar panel correspondingly so that it receives maximum sunlight at all times of the day. This is accomplished by mounting a stepper motor and a couple of light-dependent resistors (LDRs) on the solar panel, which adjusts its orientation based on the location of the sun. This project has three primary components: the solar panel, the stepper motor, and the LDRs. Microcontroller, sensor, and stepper motor. For this aim, an Arduino UNO microcontroller has been utilized. It accepts an output signal from a sensor as input, processes it, and controls stepper motors based on the supplied program. A stepper motor is attached to the shaft on which a panel is positioned to raise and lower the panel. Since maximal solar rays always strike the surface of the solar panel module, it is possible to obtain maximum power output, increasing its total efficiency as compared to the static panel. © 2023 IEEE.

关键词： Microcontrollers

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Ising Solver using Weight Profile of Memristor Crossbar Array for Combinatorial Optimization

Ising Solver using Weight Profile of Memristor Crossbar Arra...

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International Electron Devices Meeting (IEDM)

作者： Kyuree Kim Sangwook Youn Jinwoo Park Hyungjin Kim Department of Electrical and Computer Engineering Inha University Incheon Korea Department of Semiconductor Engineering Division of Materials Science and Engineering Hanyang University Seoul Korea

ISBN: (数字)9798350365429

ISBN: (纸本)9798350365436

In this work, a weight profile design is presented for efficient Ising solver system based on a Hopfield neural network (HNN) using $32\times 32$ memristor crossbar array. It utilizes device noise in the probabilistic decision process of simulated annealing for binary current inputs. By implementing 0 and 1 weight matrix across various conductance states in the crossbar, we experimentally solve an unweighted max-cut problem. It is confirmed that higher noise levels, concentrated in high resistance states, enable more efficient convergence to the minimum point of the HNN energy function. This approach effectively exploits intrinsic noise, reducing external hardware overhead and demonstrating feasibility for optimization problems.

关键词： Resistance Noise Memristors Hopfield neural networks Simulated annealing Probabilistic logic Hardware Electron devices Optimization Noise level

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Image Processing Techniques for Detecting Steel Defects with MATLAB and Machine Learning 1

Image Processing Techniques for Detecting Steel Defects with...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dankan Gowda, V. Mary, P. Arockia Ramanan, S.V. Kumar, N.M.G. Kumar, Pullela Svvsr Abdullah, Ibrahim Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Ppg Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Mohan Babu University Sree Vidyanikethan Engineering College Department of Electrical and Electronics Engineering Andhra Pradesh Tirupathi India Aditya College of Engineering and Technology Department of Computer Science & Engineering Andhra Pradesh Surampalem India Al-Turath University College Department of Computer Science Baghdad Iraq

ISBN: (纸本)9798350367720

This paper focuses on the detection of surface defects in the steel manufacturing industry since these defects inhibit the quality of the manufactured products and the efficiency of the manufacturing processes. Conventional inspection techniques require a extensive amount of work to be done at the inspection sites and perhaps associated with several kinds of errors. The current research work describes an automated system for determining the defects in the steel using image processing and integrating it with computational intelligence in MATLAB. The proposed method consists of pre-processing where the images are enhanced, feature extraction where characteristics of the defects are obtained and classification utilizing the machine learning models. The efficiency of the proposed approach is proved using experiments on the dataset of steel images and a substantial increase in the precision of the defects' recognition compared to other techniques. Thus, the' findings of the apply demonstrate that the use of image processing and machine learning can offer a strong system for real time defect identification in steel production. © 2024 IEEE.

关键词： Image enhancement

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Experiment on Multi-hop LoRa for Tracking Application

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International Journal of Sensors, Wireless Communications and Control 2023年第1期13卷 29-39页

作者： Sagala, Albert Lubis, Ramot Silalahi, Gomgom Ginting, Estika V. Simorangkir, Andreas J. D. Faculty of Informatics and Electrical Engineering Institut Teknologi Del Toba Samosir Indonesia Department of Computer Science Binus University West Jakarta Indonesia

Background: The Internet of Things (IoT) is an embryonic model that converts real-world things (objects) into smarter devices. IoT is applicable to a variety of application domains, including healthcare, smart grid, and agriculture. Low Power Wide Area Network (LPWAN) technology can transmit data over long distances with a low data rate without using the internet. There-fore, this technology is suitable for sending sensor measurement data in Internet of Things (IoT) applications. LPWANs, such as LoRa, use a star topology to reach their coverage to the maximum power that can be transmitted, considering the environmental conditions in the test area and the LoRa parameters used. Whereas for application development in rural areas, there is no internet connection, and often longer coverage is required from the sensor node to the gateway. Objective: This study aims to increase the transmission range from the end node to the gateway by adding an intermediate node. It is used as a relay from the end node to the gateway. Method: Static routing was applied so that any broadcast data from the end node would be for-warded first to the intermediate node and then to the gateway. Results: The LoRa transmission range expanded by changing the topology from peer-to-peer to multi-hop. An intermediate node between the transmitter (end node) and the gateway is available. In the experiment performed, a multi-hop with static routes successfully expanded the distance from 150 meters (single hop) to 250 meters (multi-hop), with a packet delivery ratio of 97%. In this experiment, GPS Ublox Neo was used for the sensor position and was successfully tested on the end node with an accuracy rate of 2.4% for latitude and 0.2% for longitude. Conclusion: The transmission range from the sensor node to the gateway can be extended using static routing by installing an intermediate node between the gateway and the sensor node. The data from the end node is successfully received by the gateway. © 2023 Be

关键词： energy efficiency GPS ublox neo internet of things LoRa tracking LPWAN multi-hop wireless sensor networks

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A ResNet-Based DVFS Regulator for Heterogeneous Multi-Core Mobile Processors

A ResNet-Based DVFS Regulator for Heterogeneous Multi-Core M...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Hu, Shibo Xu, Xinzi Chen, Yuze Qin, Muyun Lian, Yong Zhao, Yang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Xiaomi Mobile Software Co. Ltd. Beijing China York University Department of Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350350821

Dynamic voltage and frequency scaling (DVFS) is commonly used for the balance of performance and power of mobile processors. Conventional DVFS regulators take the processor cores with the same power and performance weight showing their inability in the regulation of heterogeneous processors. This paper proposes a core-aware frequency-power evaluation scheme that utilizes a multilayer perceptron (MLP) to assess the efficacy of DVFS regulation actions. Additionally, a ResNet-based DVFS regulator, trained with the assistance of the MLP, is developed for heterogeneous multi-core processors. Evaluated on Xiaomi 13 Pro powered by a Qualcomm Snapdragon 8 Gen 2, the proposed DVFS approach achieves an up to ×1.47 improvement in FPS Performance Per Watt (FPPW) compared with the Qualcomm's default DVFS governor in video recording scenario. © 2024 IEEE.

关键词： Dynamic frequency scaling

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Fast Blind Recovery of Linear Block Codes over Noisy Channels

Fast Blind Recovery of Linear Block Codes over Noisy Channel...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Wang, Peng Guan, Yong Liang Wang, Lipo Cheng, Peng The University of Sydney School of Electrical and Information Engineering Australia Nanyang Technological University School of Electrical and Electronic Engineering Singapore La Trobe University Department of Computer Science and Information Technology Australia

ISBN: (纸本)9781665475549

This paper addresses the blind recovery of the parity check matrix of an (n, k) linear block code over noisy channels by proposing a fast recovery scheme consisting of 3 parts. Firstly, this scheme performs initial error position detection among the received codewords and selects the desirable codewords. Then, this scheme conducts Gaussian elimination (GE) on a k-by-k full-rank matrix and uses a threshold and the reliability associated to verify the recovered dual words, aiming to improve the reliability of recovery. Finally, it performs decoding on the received codewords with partially recovered dual words. These three parts can be combined into different schemes for different noise level scenarios. The GEV that combines Gaussian elimination and verification has a significantly lower recovery failure probability and a much lower computational complexity than an existing Canteaut-Chabaud-based algorithm, which relies on GE on n-by-n full-rank matrices. The decoding-aided recovery (DAR) and error-detection-&-codeword-selection-&-decoding-aided recovery (EDCSDAR) schemes can improve the code recovery performance over GEV for high noise level scenarios, and their computational complexities remain much lower than the Canteaut-Chabaud-based algorithm. © 2023 IEEE.

关键词： Recovery

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Unpaired remote sensing image super-resolution with content-preserving weak supervision neural network

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science China(Information sciences) 2023年第1期66卷 324-325页

作者： Jie WU Runmin CONG Leyuan FANG Chunle GUO Bob ZHANG Pedram GHAMISI College of Electrical and Information Engineering Hunan University Institute of Information Science Beijing Jiaotong University College of Computer Science Nankai University Department of Computer and Information Science University of Macau Helmholtz-Zentrum Dresden-Rossendorf Helmholtz Institute Freiberg for Resource Technology Institute of Advanced Research in Artificial Intelligence (IARAI)

Dear editor,Image super-resolution(SR) uses image processing methods to increase the resolution of the image without increasing any hardware cost. Remote sensing images with high spatial resolution contain rich imagery information, which can be utilized for object detection, building segmentation, building extraction, change detection, and so on.

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UNDERSTANDING RESILIENCE OF AGRICULTURAL SYSTEMS: A SYSTEMATIC LITERATURE REVIEW 24

UNDERSTANDING RESILIENCE OF AGRICULTURAL SYSTEMS: A SYSTEMAT...

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24th International Conference on engineering Design, ICED 2023

作者： Boahen, Samuel Oviroh, Peter Ozaveshe Austin-Breneman, Jesse Miyingo, Emmanuel W. Papalambros, Panos Y. Department of Mechanical Engineering Kwame Nkrumah University of Science and Technology Pmb Kumasi Ghana Department of Mechanical Engineering University of Michigan Ann Arbor United States Department of Mechanical Engineering Science University of Johannesburg South Africa Department of Electrical and Computer Engineering Makerere University Kampala Uganda

Resilience is a widely studied concept that is a key objective in the design and development of sustainable systems. This is especially true for the agricultural systems critical to food production, economic viability, and sustainability of our communities, as farmers seek to meet increasing demand in the face of shocks such as climate change and natural disasters. Although there is a rich body of work examining resilience, there is limited understanding of how the concept of resilience should be tailored for agricultural systems. This study seeks to address this gap by performing a systematic literature review of 50 papers selected from SCOPUS using the PRISMA protocol. A summary of research topics and characteristics by geographical region is presented. The paper also categorizes the types of shocks studied and the corresponding response methods. Results suggest that the focus of resilience research changes by region, which may indicate that design strategies and objectives should also differ by region. Furthermore, the work identifies a need for more simulation-based quantitative research into the impact of resilience. © The Author(s), 2023. Published by Cambridge University Press.

关键词： Sustainable development

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Uncertainty-Aware Deep Neural Representations for Visual Analysis of Vector Field Data

arXiv

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arXiv 2024年

作者： Kumar, Atul Garg, Siddharth Dutta, Soumya The department of Computer Science and Engineering The Electrical Engineering department IIT Kanpur India

The widespread use of Deep Neural Networks (DNNs) has recently resulted in their application to challenging scientific visualization tasks. While advanced DNNs demonstrate impressive generalization abilities, understanding factors like prediction quality, confidence, robustness, and uncertainty is crucial. These insights aid application scientists in making informed decisions. However, DNNs lack inherent mechanisms to measure prediction uncertainty, prompting the creation of distinct frameworks for constructing robust uncertainty-aware models tailored to various visualization tasks. In this work, we develop uncertainty-aware implicit neural representations to model steady-state vector fields effectively. We comprehensively evaluate the efficacy of two principled deep uncertainty estimation techniques: (1) Deep Ensemble and (2) Monte Carlo Dropout, aimed at enabling uncertainty-informed visual analysis of features within steady vector field data. Our detailed exploration using several vector data sets indicate that uncertainty-aware models generate informative visualization results of vector field features. Furthermore, incorporating prediction uncertainty improves the resilience and interpretability of our DNN model, rendering it applicable for the analysis of non-trivial vector field data sets. © 2024, CC BY.

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K-means Clustering for Large Data: Anomaly Detection in Supervisory Control and Data Acquisition Systems

K-means Clustering for Large Data: Anomaly Detection in Supe...

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2023 Congress in computer science, computer engineering, and Applied Computing, CSCE 2023

作者： Stanford, Corwin Tanner, April Jackson State University Department of Mathematics & Statistical Sciences United States Jackson State University Department of Electrical & Computer Engineering and Computer Science United States

ISBN: (纸本)9798350327595

K-means is a common unsupervised method for partitioning low dimensional datasets. However, it is usually not used for higher dimensional datasets, such as those found in Supervisory Control and Data Acquisition (SCADA) systems. Here we examine its application to a large dataset for the purposes of detecting cyber attacks using anomaly-based intrusion detection in the Battle of the Attack Algorithms (BATADAL) dataset. Additionally, dimensionality reduction using Principal Component Analysis (PCA) is examined as a method of improving anomaly detection performance. Emphasis is placed on methods for selecting parameters without using information obtained from examination of attack labels. © 2023 IEEE.

关键词： BATADAL cyberattacks machine learning operational technology SCADA systems

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