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Designing of Smart Home automation Using IoT

Designing of Smart Home Automation Using IoT

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international conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)

作者： H.S. Sridhar Sakcham Somvanshi Mansi Ramsinghani Nimishambha H. C. Electrical and Electronics Department Siddaganga Institute of Technology Tumkur Karnataka India

The Internet of Things (IoT) conceptualizes the idea of remotely connecting and monitoring real-world objects through the Internet. When it comes to our homes, this concept can be suitably incorporated to make them smarter, safer and more automated. Therefore, smart home automation is a topic that is gaining popularity day by day due to its advantages. One can quickly achieve automation by connecting home appliances to the Internet or cloud storage. The cloud computing-based platform helps to connect with the things that surround everyone so that they can easily access anything and everything anytime anywhere in a user-friendly way. This IoT paper focuses on building an innovative wireless system that allows the owner to control the appliances of the place. The most important aspect of this system is to incorporate smooth working of the automated appliances via both the internet and the manual control. The ESP 8266 is a low-cost microcontroller that works on a 3.3V dc supply and has integrated Wi-Fi and Bluetooth modules with built-in TCP/IP networking software. It has 17 programmable GPIOs, enough to control all four relays, and two servo motors for the paper. Bell types switches are used for the manual control of the appliances.

关键词： Wireless communication Home appliances Cloud computing automation Smart homes Human factors TCPIP

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Advanced Battery Defect Detection Using VisionMaster: A Deep Learning Approach to Enhance Accuracy and Efficiency

Advanced Battery Defect Detection Using VisionMaster: A Deep...

引用

Information Science, Parallel and Distributed Systems (ISPDS), international conference on

作者： Xiaoyan Zhang Jingpeng Hu School of Computing Beijing Institute of TechnologyZhuhai Zhuhai China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

VisionMaster algorithm development platform is a powerful machine vision software. The platform aims to provide users with efficient and convenient algorithm tools to quickly build visual applications and solve complex visual detection challenges, further promoting the deep application and development of machine vision technology. In this study, the process of battery defect detection utilizing the VisionMaster algorithm platform is thoroughly examined. The detection workflow is initiated by acquiring 2D color images of batteries, followed by the collection of 3D depth maps. These imaging modalities are integrated to provide a comprehensive assessment of battery quality. The VisionMaster platform's embedded vision algorithms are discussed in detail, highlighting their role in the quality evaluation process. The visualization of the scanning process, the interpretation of feedback from the vision system, and the corresponding responses from the lower-level control systems are *** from both 2D and 3D camera scans are analyzed to illustrate the advantages and limitations of each method. The study demonstrates the core value of the vision detection system in ensuring quality control in battery manufacturing. The integration of VisionMaster with advanced vision algorithms is shown to significantly enhance the accuracy and reliability of defect detection, thereby improving overall production quality.

关键词： Visualization Accuracy Three-dimensional displays Machine vision Software algorithms Batteries Reliability

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An Efficient Method for Seeds Classification and Quality Testing Using Fast R-CNN

An Efficient Method for Seeds Classification and Quality Tes...

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international conference on Inventive Research in computing Applications (ICIRCA)

作者： S. Ashwini B. Srinivasa Rao Anil Shirgire K. R. Kannan T Ch Anil Kumar G. Sivashankar Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India Dr D Y Patil Institute of Technology Pune Maharashtra India Department of ECE Hindusthan Institute of Technology Coimbatore Tamilnadu India Department of Mechanical Engineering Vignan's Foundation for Science Technology and Research Guntur Andhra Pradesh India Department of Computational Intelligence SRM University Chennai Tamilnadu India

Research into agriculture is gaining an increasing attention in recent years. The newest innovations use a variety of computational technology to make agricultural tasks easier. Seed quality is the most important of many factors that determine agricultural output. Further insights into quality manufacturing, seed quality control, and impurity discovery can be gained by proper seed classification. Seeds have traditionally been sorted into different categories based on their appearance (including their color, texture, and shape). This is often done by experts who must visually inspect each sample, which is a laborious and time-consuming process. Seed sorting is a labor-intensive process that can be greatly improved by automating the inspection process. This study presents a classification system for seeds using Fast RCNN, inspired by the success of computational intelligence methods in other image classification challenges. The suggested method includes five stages: wiener filter preprocessing; HE image enhancement; binarization; adaptive watershed segmentation; and Rapid R-CNN model training. The proposed approach outperforms other two models such as CNN and RNN.

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TiV-ODE: A Neural ODE-based Approach for controllable Video Generation From Text-Image Pairs

TiV-ODE: A Neural ODE-based Approach for Controllable Video ...

引用

IEEE international conference on Robotics and automation (ICRA)

作者： Yucheng Xu Nanbo Li Arushi Goel Zonghai Yao Zijian Guo Hamidreza Kasaei Mohammadreza Kasaei Zhibin Li School of Informatics University of Edinburgh UK College of Information and Computing Sciences University of Massachusetts-Amherst US Department of Electrical and Computer Engineering Boston University US Department of Artificial Intelligence Bernoulli Institute University of Groningen The Netherlands Department of Computer Science University College London UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Videos capture the evolution of continuous dynamical systems over time in the form of discrete image sequences. Recently, video generation models have been widely used in robotic research. However, generating controllable videos from image-text pairs is an important yet underexplored research topic in both robotic and computer vision communities. This paper introduces an innovative and elegant framework named TiV-ODE, formulating this task as modeling the dynamical system in a continuous space. Specifically, our framework leverages the ability of Neural Ordinary Differential Equations (Neural ODEs) to model the complex dynamical system depicted by videos as a nonlinear ordinary differential equation. The resulting framework offers control over the generated videos’ dynamics, content, and frame rate, a feature not provided by previous methods. Experiments demonstrate the ability of the proposed method to generate highly controllable and visually consistent videos and its capability of modeling dynamical systems. Overall, this work is a significant step towards developing advanced controllable video generation models that can handle complex and dynamic scenes.

关键词： Computer vision Computational modeling Ordinary differential equations Aerospace electronics Mathematical models Nonlinear dynamical systems Image sequences

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A Next-Gen Approach to Streamlined Canteen Management Systems using Mesa Framework

A Next-Gen Approach to Streamlined Canteen Management System...

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international conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Advik Narendran Anantha Hothri Inuguri Manyam Yashaswini Srinidhi Sundaram Manju Khanna Department of Computer Science and Engineering Amrita School of Computing Bangalore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

A next-generation canteen management system is proposed that employs intelligent software agents to streamline various tasks, such as order processing and customer service, within canteens. The system automates verification through ID card scanning and OTP authentication, ensuring secure and efficient operations. By integrating agents to manage orders and provide real-time updates, the system enhances the dining experience by reducing manual effort and improving service speed. This smart solution, designed to cater to institutional needs, provides seamless control over canteen processes while enhancing user satisfaction through a modern, technology-driven approach.

关键词： Hands Customer services Authentication process control Manuals Software agents Real-time systems Computational efficiency Information technology Next generation networking

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Efficient High-Level Synthesis of Approximate computing Circuits via Multi-fidelity Modeling 14

Efficient High-Level Synthesis of Approximate Computing Circ...

引用

14th IEEE international conference on ASIC, ASICON 2021

作者： Li, Yingqi Yang, Fan Yan, Changhao Zeng, Xuan Fudan University State Key Lab of Asic System 201203 China

ISBN: (纸本)9781665411493

High-Level Synthesis (HLS) of approximate computing circuits generates circuits based on functional units such as adders and multipliers. An approximate library containing approximate functional units is firstly built, and then an algorithm is employed to search in the library, solving the binding problem of HLS and finally generating an approximate circuit that meets design specifications. In this paper, we propose a discrete-input and multi-fidelity Bayesian optimization (BO) approach to solve this design space exploration problem. Since the input of the algorithm is discrete functional units in the approximate library, we modify the kernel function of Gaussian process (GP) model in BO. In addition, we adopt a multi-fidelity strategy to speed up the optimization process, which is proved to be effective and efficient according to our experimental results. © 2021 IEEE.

关键词： High level synthesis

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Deep neural network algorithm for MPPT control of double diode equation based PV module

Deep neural network algorithm for MPPT control of double dio...

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1st international conference on Innovative Technology for Sustainable Development (ICITSD)

作者： Leelavathi, M. Kumar, V. Suresh Thiagarajar Coll Engn Madurai Tamil Nadu India

One of the most promising renewable energy sources (RES) is photovoltaic energy system. It generates power by using a photovoltaic (PV) module. The solar illumination and temperature value are the main parameters for the PV module because a small change can affect the entire power generation. The PV module is based on the double diode equation for extracting unknown parameter values and tracking maximum power point using basic analytical methods (Newton Raphson and Lambert W-function), particle swarm optimization (PSO) and artificial neural network (ANN) algorithms are performed. Then, a deep neural network (DNN) algorithm is proposed in this work for alleviating the disadvantages of the existing methods. Particularly, the DNN algorithm is proposed for reducing the mismatching power loss. Firstly, two-parameter values retrieve from a specific location at NASA open-access source for the year 2020. Then, the parameter values optimization through two analytical methods such as Newton Raphson (NR) and Lambert-W function (W-function) methods. Based on these analytical methods, the characteristic curve is executed. It is compared one among one and also the analytical methods evaluates with soft computing algorithms. The soft computing algorithm represents particle swarm optimization and the artificial neural network. Both the analytical methods and the soft computing algorithms compare for the extraction of the unknown parameter value for the PV module. Then, the existing algorithm is compared with advanced soft computing algorithms. In that deep neural network, the algorithm is implemented for the maximum power point tracking (MPPT) process. The DNN algorithm contains two inputs, three hidden layers and one output. The input neuron is defined with the voltage and current value and the hidden layer contains the sigmoid activation function. The result evaluates in terms of percentage of error for maximum power point (PEmpp). Copyright (C) 2022 Elsevier Ltd. All rights rese

关键词： Deep neural network Soft computing PV module Newton Raphson Lambert-W function Maximum power point tracking

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Developing Virtual Reality Applications in Medical Education for Osteotomy Knee Surgery

Developing Virtual Reality Applications in Medical Education...

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international conference on computing and Networking Technology (ICCNT)

作者： N Subash M Mallegowda S. Rajarajeswari Alisha Ahmed Department of CSE Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Virtual reality (VR) has emerged as a promising tool in medical education, offering immersive and interactive experiences for trainees to learn complex surgical procedures. This paper explores the development of VR applications specifically tailored for medical education in osteotomy knee surgery. By leveraging VR technology, medical students can engage in realistic simulations of the surgical procedure, enhancing their understanding of anatomy, surgical techniques, and decision-making processes in a controlled and risk-free environment. The VR applications for osteotomy knee surgery providing a comprehensive educational experience. Through a combination of realistic graphics, and interactive scenarios, VR facilitates experiential learning and skill acquisition, ultimately improving the competency. Moreover, the scalability and accessibility of VR platforms offer opportunities for widespread adoption in medical training programs, addressing the growing demand for innovative and effective educational tools in healthcare.

关键词： Training Solid modeling Adaptation models Technological innovation Musculoskeletal system Scalability Surgery process control Virtual reality Medical services

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Enhancing Automated Production through Real-Time Vision Monitoring

Enhancing Automated Production through Real-Time Vision Moni...

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international conference on computing and Networking Technology (ICCNT)

作者： Shreya Chakraborty Manish Srivastava Suparna Ghosal Department of optometry ARKA JAIN University Jamshedpur Jharkhand India Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Management School of Mangement - PG JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Computerized manufacturing methods contain the usage of automated machines and device to offer gadgets or services with out the direct involvement of human beings. these procedures can considerably grow performance and productiveness; but, additionally, they come with their set of challenges. One such task is the want for real-time tracking of the manufacturing line to ensure that the machines are functioning well and producing merchandise. real-time imaginative and prescient monitoring is a technology that mixes pc imaginitive prescient and synthetic intelligence to continuously display screen and observe photos or video images from the manufacturing line. The system may be educated to understand and find abnormalities or defects in the products and any anomalies inside the production method. By imposing actual-time vision tracking, producers can quickly become aware of and address any issues at the manufacturing line, decreasing downtime and growing generic productiveness.

关键词： Performance evaluation Productivity Accuracy IEEE merchandise process control Streaming media Real-time systems Manufacturing Monitoring

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Gradient-Free High-Order Accelerated Self Optimizing control of processes with Time Delay

Gradient-Free High-Order Accelerated Self Optimizing Control...

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international conference on Industrial Artificial Intelligence (IAI)

作者： Justus Nwoke Jairo Viola YangQuan Chen Mechatronics Embedded Systems and Automation (MESA) Lab University of California Merced Merced CA USA

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

A self-optimizing controller (SOC) adds an upper optimization layer to closed-loop systems, enhancing self-awareness and improving performance during repetitive tasks. However, most SOC optimization algorithms use only the current and previous cost function evaluations to determine the next optimal parameters (first-order optimization methods). This paper introduces a high-order self-optimizing controller (HOSOC) that accelerates the optimization algorithm's convergence by utilizing the history of optimal parameters within a sliding window. A modified version of the Globalized Constrained Nelder Mead optimization method is implemented to account for the past centroids within a memory window to compute the upcoming solution. The algorithm is tested on a general delay-aware First-Order plus Dead Time (FOPDT) system with varying lag, balanced, and delay time dynamics. Results show that the high-order SOC controller speeds up convergence and meets the desired performance specifications effectively.

关键词： Heuristic algorithms Delay effects Optimization methods process control Cost function Delays History Closed loop systems Artificial intelligence Convergence

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