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Autonomous Inspection of Solar Panels and Wind Turbines Using YOLOv8 with Quadrotor Drones

Autonomous Inspection of Solar Panels and Wind Turbines Usin...

引用

International Conference on Mechatronics (ICOM)

作者： Nguyen Lionel Kishore Bingi Rosdiazli Ibrahim Reeba Korah Gaurav Kumar B Rajanarayan Prusty Dept. of Computer Science and Electronics Polytech Dijon Dijon France Dept. of Electrical and Electronics Engineering Universiti Teknologi PETRONAS Seri Iskandar Malaysia Dept. of Electronics and Communication Engineering ACED Alliance University Bangalore India Dept. of Electrical Electronics and Communication Engineering School of Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

This paper introduces an autonomous inspection system for solar panels and wind turbines utilizing Tello drones and the YOLOv8 object detection algorithm. The main objective is to establish an efficient method for identifying and evaluating these renewable energy components' conditions, focusing on detecting issues such as breakage and dust accumulation. The system involves a pair of Tello drones operating as a swarm and connected to a standard router to enable real-time video streaming and data processing. The drones utilize the YOLOv8 algorithm for object detection, and Python programming is employed to manage their operations. The methodology encom-passes establishing reliable communication among the drones, router, and laptop, initializing the drones, capturing real-time video, and utilizing YOLOv8 for object recognition and clas-sification. The paper presents case studies demonstrating the system's effectiveness in detecting and classifying solar panels and wind turbines under varied conditions. While the system exhibits promise in reducing manual inspection labour and enhancing safety, limitations related to image quality suggest that using higher- resolution cameras could further improve its efficiency.

关键词： Image quality Object detection Manuals Inspection Streaming media Real-time systems Wind turbines

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Periocular Biometrics: Advancing into the Future

Periocular Biometrics: Advancing into the Future

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2022 IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2022

作者： Namratha, J. Shahane, Shreya Sanyal, Ipsita Sudhamani, M.J. RNS Institute of Technology Dept. Computer Science Bangalore India Global Academy of Technology Dept. AI&DataScience Bangalore India Indian Institute of Science Dept. Electronic System Engineering Bangalore India

ISBN: (纸本)9781665464901

Secured authentication system is drawing attention since recent days. Human behavioural and physical characteristics are analysed for Biometric authentication system. A survey on periocular biometric authentication system is carried out. Periocular features are promising in the field of biometric authentication system as it can compensate the face and iris biometric trait. The inherent features present in the periocular region makes it suitable for space and time constrained situations. Moreover in the current pandemic situation periocular biometrics have become very prevalent owing to it's contactless and robust authentication process. © 2022 IEEE.

关键词： Neural networks

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Real-Time and Low-Overhead Graph Task Scheduling over Vehicular Computing-Assisted Edge Networks

Real-Time and Low-Overhead Graph Task Scheduling over Vehicu...

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IEEE International Conference on communications (ICC)

作者： Bingshuo Guo Minghui Liwang Fan Yang Seyyedali Hosseinalipour Xianbin Wang Huaiyu Dai Dept. of Information and Communication Engineering Xiamen University Xiamen China Dept. of Control Science and Engineering Tongji University Shanghai China Dept. of Electrical Engineering University at Buffalo-SUNY NY USA Dept. of Electrical and Computer Engineering Western University Ontario Canada Dept. of Electrical and Computer Engineering North Carolina State University Raleigh USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Modern vehicular networks encounter a multitude of computation-intensive tasks that have unique processing topologies represented by graph structures. The integration of edge computing and vehicular networks has provided a unique platform for handling these tasks at the network edge. However, the complex structure of these tasks makes their scheduling and execution challenging. This paper proposes a Vehicular Computing-assisted Edge Network (VCEN) architecture, where graph tasks are scheduled over a Vehicle-Edge Collaborative Cloud (VECC) for parallel execution. Our goal is to obtain feasible mappings between task components and computing nodes in the VECC while minimizing task execution latency and energy consumption. We show that achieving this goal requires solving an NP-hard optimization problem with complex constraints related to task structure and VECC topology. We then propose a fast and lightweight approach for graph task scheduling over VECC that comprises two key phases. In the former phase, we introduce a preprocessing algorithm that reduces the graph task's dimensionality by merging important components and cutting redundant edges. In the latter phase, we deploy a cost-reduction-preferred mapping algorithm to obtain feasible mappings between task components and VECC. Through simulations, we demonstrate our superior performance in different network settings.

关键词： Energy consumption Processor scheduling Network topology Merging computer architecture Real-time systems Topology

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Design of Low Transistor Count Approximate Full Adder with Carry Approximation

Design of Low Transistor Count Approximate Full Adder with C...

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Devices for Integrated Circuit (DevIC)

作者： Chirayush Chakraborty Atanu Kundu Sukumar Nandi Dept. of Electronics & Communication Engineering Heritage Institute of Technology Kolkata India Dept. of Computer Sc & Engg Indian Institute of Technology Guwahati Guwahati India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

In many digital signal processing (DSP) applications, such as image and speech processing, slight inaccuracies in output data are often tolerable for human interpretation. This may be exploited with approximate computing which allows for reduced computational complexity by using circuits that are intentionally designed to be less precise. By adopting approximate circuits, which require fewer transistors than their exact counterparts, designers can achieve more efficient hardware implementations. In this work, we explore a full adder design that leverages transmission gates to approximate the carry output using the primary inputs A and B. This approach effectively reduces the transistor count and propagation delay from the traditional 28T in accurate full adders to either 16T or 20T, depending on the level of approximation employed.

关键词： Integrated circuits Accuracy Approximate computing Digital signal processing Logic gates Hardware Transistors Speech processing Adders Propagation delay

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Integrating Random Forest and Support Vector Regression Models for Optimized Energy Consumption Evaluation in Cloud Computing Data Centers 3

Integrating Random Forest and Support Vector Regression Mode...

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Shaik, Nazeer Kumar, B. Vinoth Bhati, Anita Kumar, Sandeep Singh, Jagendra Sahu, Dinesh Prasad SRIT-Autonomous Department of Computer Science Engineering Anantapur India Ksr Institute for Engineering and Technology Dept. of Electronics and Communication Engineering Tiruchengode India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350342338

This study proposes a novel approach for energy consumption evaluation in cloud computing data centers. The immense energy consumption of these data centers presents significant environmental and economic challenges. Traditional evaluation methods often fail to provide accurate estimations due to the complex dynamics of cloud computing environments. The research introduces an integrated model, combining Random Forest Regression and Support Vector Regression, along with a Gradient Boosting Regressor for iterative training of regression parameters. We measure the model's performance using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), replacing traditional relative deviation measures. The proposed approach aims to provide more accurate and efficient energy consumption evaluations, offering potential benefits in energy management, operational costs reduction, and minimizing environmental impact. © 2023 IEEE.

关键词： Cloud Computing Energy Consumption Evaluation Gradient Boosting Regressor Random Forest Regression Support Vector Regression

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Real-Time Object Detection with IOT Using a Smart Cart

Real-Time Object Detection with IOT Using a Smart Cart

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International Conference on Ubiquitous Information Management and communication (IMCOM)

作者： Muhammad Omer Sardar Jaffar Ali Syed Muhammad Raza Duc-Tai Le Hyunseung Choo Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Dept. of AI System Engineering Sungkyunkwan University Suwon Korea Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea College of Computing and Informatics Sungkyunkwan University Suwon Korea

With Industry 4.0's technological advancements, the convenience and affordability of Artificial Intelligence (AI) and IoT have increased. However, customers still face long waiting times in payment queues during the smart shopping experience. Current solutions rely on expensive sensor-based RFIDs or simple barcodes, making them impractical and limited in scalability. This paper proposes an economically smart solution with two cameras on NVIDIA JETSON NANO for realtime object monitoring and automated shopping. communication between modules is facilitated by TCP/IP and HTTP protocols. By utilizing the YOLO v7, real-time monitoring is achieved. This innovative approach revolutionizes the customer experience by providing a seamless interface and time-saving features. Additionally, it offers potential integration with e-commerce businesses and payment gateways, further enhancing the overall customer and vendor experience.

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IRS Controlling Method With Machine Learning Based on Position Information

IRS Controlling Method With Machine Learning Based on Positi...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Yoshihiko Tsuchiya Norisato Suga Kazunori Uruma Masaya Fujisawa Dept. of Information and Computer Technology Tokyo University of Science Tokyo Japan Dept. of Information and Communication Engineering Shibaura Institute of Technology Tokyo Japan Dept. of Computer Science Kogakuin University Tokyo Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

Intelligent Reflecting Surfaces (IRS) is attracting attention for wireless communications at high-frequency band. IRS can control radio propagation by reflecting radio waves and shifting their phase. As one of the methods to control IRS, location-based methods have been proposed. However, to achieve highly accurate IRS control by the methods, the assumed propagation model must match the actual propagation environment. This study proposes a method to control the IRS from location information adapted to the propagation environment by using machine learning. Numerical experiments confirm that the proposed method can appropriately control IRS regardless of the propagation environment.

关键词： Wireless communication Adaptation models Accuracy Surface waves Machine learning Radio propagation Numerical models Consumer electronics

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Explainable Classification of Macular Degeneration Using Deep Learning 20

Explainable Classification of Macular Degeneration Using Dee...

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20th IEEE India Council International Conference, INDICON 2023

作者： Khose, Sahil Ghosh, Ankita Kamath, Yogish S. Kuzhuppilly, Neetha I. R. Kumar, J. R. Harish Manipal Institute of Technology Manipal Academy of Higher Education Dept. of Information and Communication Technology Manipal India Manipal Institute of Technology Manipal Academy of Higher Education Dept. of Computer Science and Engineering Manipal India Kasturba Medical College Manipal Academy of Higher Education Dept. of Ophthalmology Manipal India Manipal Institute of Technology Manipal Academy of Higher Education Dept. of Electrical and Electronics Engineering Manipal India

ISBN: (纸本)9798350305593

Age-related macular degeneration is one of the leading causes of vision loss in individuals. This paper proposes a fundus image-based analysis and automatic grading of AMD using deep learning techniques. We utilize data points from three different datasets and perform augmentation and data sampling to equalise the data distribution. We apply deep learning by proposing EfficientNet-B3 for training a three-class classification model which categorizes the fundus images as having no AMD, mild AMD or severe AMD. The results obtained are evaluated on various metrics and we obtain 93.6% accuracy. We also analyse the trained model by visualising the information learned by it using class activation map algorithms. The accuracy of the proposed method outperforms the majority of the existing methods. The confidence in the proposed model is further increased by the CAM algorithms which indicate that the model predicts the output based on the presence of retinal lesions. © 2023 IEEE.

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An Enhanced Optimization of Machine Learning Model in Prostate Cancer Detection 5

An Enhanced Optimization of Machine Learning Model in Prosta...

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5th IEEE International Conference on Computing, Power, and communication Technologies, IC2PCT 2024

作者： Yennapusa, Haritha Ramakrishnan, Rakesh Muniandi, Balakumar Gupta, Ketan Logeshwaran, J. Gla University Dept. of Computer Engineering and Applications Mathura India University of the Cumberlands Department of Information Technology Union CityCA United States Lawrence Technological University Department of Electrical and Computer Engineering SouthfieldMI United States University of the Cumberlands Department of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350383522

the present study goals to optimize the overall performance and accuracy of a device-mastering model for prostate most cancers detection. Prostate most cancers is a malignancy which regularly leaves little to no clue of its presence. Early detection is, therefore, the important thing to a hit remedy. Gadget mastering models are increasingly more being applied in fitness care for prognosis and diagnosis. However, those fashions frequently require good sized quantities of records and well-crafted machines to gain ultimate accuracy. The observe proposed a more desirable Optimization of machine mastering (EOML) method to enhance the accuracy of a prostate cancer detection version. First, a gadget learning model changed into educated the usage of publicly available records from the cancer Genome Atlas. These facts set become preprocessed, and a feature extraction and choice method were executed the usage of a classical ensemble function selection approach. After that, a pass-validation method become used to optimize the version further. Eventually, an ensemble gaining knowledge of approach became adopted to enhance the model's accuracy and performance further. The ensemble getting to know technique blended the predictions of some of device getting to know models to create a more robust and dependable version. © 2024 IEEE.

关键词： Application Ensemble Learning Machine-Learning Publicly Technique

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Development of a OTA with DFVF for a 50nm VLSI Technology Application

Development of a OTA with DFVF for a 50nm VLSI Technology Ap...

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IEEE India Conference (INDICON)

作者： T.C. Manjunath Hareesh Kumar Rajani Rai B. S. Krishna Narayanan Padmavathi M. Pavithra G. Swapnil S. Ninawe Department of Electronics & Communication Engineering Dean (Research - R & D) Rajarajeswari College of Engineering Bangalore Karnataka Dept. of Electronics & Communication Engineering RV Institute of Technology & Management Bangalore Karnataka Dept. of Electronics & Communication Engineering Vivekananda College of Engineering and Technology Puttur South Kanara Karnataka Dept. of Computer Science & Engineering MVJ College of Engineering Bangalore Karnataka Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Bangalore Karnataka India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

This research paper presents the design and simulation of Multiple Path Fully Operational Transconductance Amplifiers (OTAs) with Dual Flipped Voltage Follower, targeted for VLSI-based signal processing applications. Leveraging 50nm CMOS technology and Cadence tools, the proposed OTA architecture offers enhanced performance and versatility in signal processing tasks. The design incorporates multiple paths for signal processing, enabling improved bandwidth, gain, and linearity while minimizing common-mode noise. The inclusion of Dual Flipped Voltage Follower enhances the OTA's stability and dynamic range, ensuring reliable operation in high-speed VLSI systems. Through extensive simulation studies, the paper demonstrates the effectiveness of the proposed OTA design in meeting the stringent requirements of modern signal processing applications. This research contributes to the advancement of VLSI design methodologies, offering a promising solution for high-performance signal processing tasks in nanoscale CMOS technologies. The simulation results from the cadence tool shows the effectiveness of the methodology that is being presented in this research article.

关键词： Simulation Noise Linearity Voltage Very large scale integration CMOS technology Stability analysis Nanoscale devices Reliability Transconductance

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