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3rd International Conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Swarnkar, Manini Jatav, Ajay Singh, Gurpinder Swarnkar, Anil Soni, Amit Niazi, Khaleequr Rehman Department of Computer Science Engineering Manipal University Jaipur Rajasthan Jaipur303007 India Department of Electrical Engineering Malaviya National Institute of Technology Jaipur Rajasthan Jaipur302017 India Department of Electrical Engineering Manipal University Jaipur Rajasthan Jaipur303007 India

ISBN: (纸本)9789819784288

This paper introduces a comprehensive investigation into the realm of electric load profile analysis, shedding light on its pivotal aspects and far-reaching implications for effective electricity management. The study places a particular emphasis on essential components such as household consumption segmentation, the identification of peak demand instances, computation of the load factor, and the extraction of consumption trends. These elements collectively contribute to a deeper understanding of power system optimization, underlining their significance in shaping strategies for efficient resource allocation and consumption control. By harnessing the capabilities of Google Colab, a cloud-based notebook environment built upon the Jupyter platform, this research aims to empower both data scientists and machine learning developers. This collaborative digital space facilitates seamless Python code execution while offering a shared platform for real-time cooperation, thus enhancing productivity and streamlining research efforts in load profile analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Charging stations

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Visual Computing Unified Application Using Deep Learning and computer Vision Techniques

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第1期18卷 59-74页

作者： Sowmya, B.J. Meeradevi Seema, S. Dayananda, P. Supreeth, S. Shruthi, G. Rohith, S. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Karnataka Bengaluru India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering M S Ramaiah Institute of Technology Karnataka Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India School of Computer Science and Engineering REVA University Karnataka Bengaluru India Department of Electronics & Communication Engineering Nagarjuna College of Engineering & Technology Karnataka Bengaluru India

Vision Studio aims to utilize a diverse range of modern deep learning and computer vision principles and techniques to provide a broad array of functionalities in image and video processing. Deep learning is a distinct class of machine learning algorithms that utilize multiple layers to gradually extract more advanced features from raw input. This is beneficial when using a matrix as input for pixels in a photo or frames in a video. computer vision is a field of artificial intelligence that teaches computers to interpret and comprehend the visual domain. The main functions implemented include deepfake creation, digital ageing (de-ageing), image animation, and deepfake detection. Deepfake creation allows users to utilize deep learning methods, particularly autoencoders, to overlay source images onto a target video. This creates a video of the source person imitating or saying things that the target person does. Digital aging utilizes generative adversarial networks (GANs) to digitally simulate the aging process of an individual. Image animation utilizes first-order motion models to create highly realistic animations from a source image and driving video. Deepfake detection is achieved by using advanced and highly efficient convolutional neural networks (CNNs), primarily employing the EfficientNet family of models. © 2024 by the authors of this article. Published under CC-BY.

关键词： Animation

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Generalized Two-Point Visual Control Model of Human Steering for Accurate State Estimation1

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ASME Letters in Dynamic Systems and Control 2025年第1期5卷 011004页

作者： Mai, Rene E. Sears, Katherine Roessling, Grace Julius, Agung Mishra, Sandipan Department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Cognitive Science Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

We derive and validate a generalization of the two-point visual control model, an accepted cognitive science model for human steering behavior. The generalized model is needed as current steering models are either insufficiently accurate or too complex for online state estimation. We demonstrate that the generalized model replicates specific human steering behavior with high precision (85% reduction in modeling error) and integrate this model into a human-as-advisor framework where human steering inputs are used for state estimation. As a benchmark study, we use this framework to decipher ambiguous lane markings represented by biased lateral position measurements. We demonstrate that, with the generalized model, the state estimator can accurately estimate the true vehicle state, providing lateral state estimates with under 0.15 m error across participants. However, without the generalized model, the estimator cannot accurately estimate the vehicle's lateral state. © 2024 by ASME.

关键词： Man machine systems

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Deep Learning Approaches for Cataract Detection Using Fundus and Real-Time Naked Eye Imaging

Deep Learning Approaches for Cataract Detection Using Fundus...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Shetty, Chinmai Fernandes, Shainy Bagchi, Susama Malathi, S.Y. Nayak, Subrahmanya Rajesh Debnath, Sanjoy Kumar Department of Information Science and Engineering Nitte India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Kle Institute of Technology Dept of Computer Science and Engineering Hubballi India

ISBN: (纸本)9798350388602

Cataracts are the most common eye disease and the leading cause of blindness worldwide. The eye ailment known as cataracts results in clouded lenses, leading to vision that appears foggy or frosted. People with cataracts often struggle with tasks like reading, driving, or recognizing faces due to the impaired clarity of their vision. Effective treatment and prevention of vision loss depend on the early detection of cataracts. The proposed system is a deep learning-based approach to detect cataracts from the naked eye and fundus images that can help ophthalmologists diagnose cataracts earlier, more accurately, and efficiently. Both fundus and naked eye images are processed by the system to detect cataracts. The system offers a dependable and automated solution for cataract detection, enabling timely diagnosis and treatment. Its performance is on par with that of ophthalmologists, highlighting its potential as an effective and trustworthy tool for automated cataract detection. The proposed model employs two distinct deep learning approaches. For identifying cataracts using naked eye images, a standard CNN architecture was utilized, achieving an accuracy of 94.26%. For detecting cataracts in fundus eye images, a Deep Convolutional Neural Network (DCNN) with the VGG19 architecture was implemented, demonstrating a notable accuracy of 96.78%. This model holds substantial promise, offering a non-invasive and accessible method for cataract screening. It is particularly helpful in environments with limited resources, where access to expert eye care is frequently limited. © 2024 IEEE.

关键词： Convolutional neural networks

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Au/SiC Microfluidic Devices Fabricated by Rapid Laser Cladding for Photocatalytic Degradation of Water Pollutants

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ACS Sustainable Chemistry and engineering 2024年第11期12卷 4486-4496页

作者： Chang, Chun-Hao Lin, Hsiu-Pen Chen, Chien-Wei Lu, Yu-Cheng Hsiao, Fu-He Hsieh, Chi-Hsiang Lin, Bo-Cheng Yu, Bang-Ying Ma, Dai-Liang Kuo, Hao-Chung Tu, Chang-Ching Li, Bor-Ran Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrical and Computer Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Centre for Emergent Functional Matter Science National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrophysics College of Science National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei11492 Taiwan Department of Photonics Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Taisic Materials Corp. Taoyuan320 Taiwan Department of Electrical Engineering National Central University Taoyuan32001 Taiwan Medical Device Innovation and Translation R&D Center National Yang Ming Chiao Tung University Taipei112304 Taiwan

The increasing proliferation of third-generation semiconductor silicon carbide (SiC) products has led to the emergence of SiC powder as an industrial byproduct. To reutilize the SiC efficiently, we propose a facile method for fabrication of photocatalytic plates from reclaimed SiC. Our approach involves affixing the SiC onto a glass surface with laser cladding, followed by sputter deposition of a gold film and laser annealing. The resulting nanogold-coated SiC plate exhibited enhanced photocatalytic activity due to the injection of hot electrons into the SiC via surface plasmon resonance. Unlike chemical reduction, which requires hours for the synthesis, purification, and isolation of Au/SiC, our approach accomplished this in just 30 min. When illuminated, the Au/SiC plate generated electron-hole pairs that formed radicals in the presence of water and oxygen, leading to the decomposition of methylene blue. Furthermore, the Au/SiC plate can be integrated into a microfluidic device for an enhanced degradation rate and efficiency. This research provides a simple and environmentally friendly approach to the production of photocatalytic plates from reclaimed SiC and contributes to sustainable development. © 2024 The Authors. Published by American Chemical Society.

关键词： Silicon carbide

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A Compositional Simulation Framework for Testing Adversarial Robustness of Deep Neural Networks

A Compositional Simulation Framework for Testing Adversarial...

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2023 Design and Verification Conference and Exhibition Europe, DVCon Europe 2023

作者： Nader, Youssef Maher Hatab, Mostafa Lotfy Ghaleb, Mazen Mostafa Bakr, Safia Medhat Awaad, Tasneem A. AlGanzouri, Ahmed Abdelsalam, Mohamed El-Kharashi, M. Watheq The Department of Computer and Systems Engineering Faculty of Engineering Ain Shams University Cairo Egypt Department of Electrical and Computer Engineering Faculty of Engineering & Computer Science University of Victoria Victoria Canada Siemens EDA Cairo Egypt

ISBN: (纸本)9783800762064

Deep neural networks (DNN) have reached impressive performance in computer vision, making them a natural choice for object detection problems in automated driving. However, DNNs used for object detection are known to be highly vulnerable to adversarial attacks. Even small changes to the input such as adding a customized noise pattern that remains invisible to the human eye, can stimulate silent prediction errors. In this study, we present a compositional simulation framework for testing the adversarial robustness of DNNs used in object detection. We demonstrate our framework with a comprehensive case study of a speed-sign detector model with two different adversarial attacks. © 2023 Design and Verification Conference and Exhibition Europe, DVCon Europe

关键词： Object detection

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A Universal Virtual Reality Training Platform for Microscale Robotic Cell Injection in Biomedical Research and Practice

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SN computer science 2024年第8期5卷 1134页

作者： Hafeez, Rubab Amjad, Zohaib Waheed, Sadia Hasan, Osman Sharif, Muhammad Imran Ulhaq, Anwaar Department of Computer Science University of Wah Wah Cantt 47010 Pakistan School of Electrical Engineering and Computer Science NUST Islamabad 44320 Pakistan Department of Computer Science Kansas State University Manhattan 66506 United States Manna Institute School of Engineering and Technology Central Queensland University Brisbane QLD Australia

Micro-robotic cell injection is a widely used procedure in cell biology where a small quantity of biological material is inserted into a cell using an automated or semi-automated micro-robotic system. Given its micro-scale nature, cell injection using a semi-automated micro-robotic system requires extremely high precision, hand-eye coordination, and depth perception for maneuvering the micro-pipette. As the cell injection is a laborious work, there is a dire need of rigorous training and a great deal of experience using live specimens and high-end microscopes. To facilitate this training, we have presented a paper in the past about modular virtual reality (VR) with basic simulation modules, here, the present work deals with the modelling of advanced training modules and experimenting of VR simulator. The proposed simulator includes: (i) Advanced training modules to train users on complete injection/suction procedures;(ii) The performance evaluation of the users using three metrics, i.e., (i) time, (ii) trajectory, and (iii) positioning accuracy is also integrated within the simulator;(iii) An experimental set-up has been developed for the face, content, and construct validity of the VR simulator. Finally, the effectiveness of the proposed simulator is validated by experts from the relevant domains and can be used for various applications like drug delivery, injection procedures and in-vitro fertilization. The experimental results demonstrated that a Virtual cell injection training simulator is feasible for the training of cell injection procedures and depicted that the injection success rate is 100% after using VR simulator. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adherent cells Cell injection Collision modeling Micro-robotic Simulator validity VR simulation

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A Comparative Analysis of Swarm Algorithms for Enhancing Communication in Drone Networks 15

A Comparative Analysis of Swarm Algorithms for Enhancing Com...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Chowdary, Chava Sriya Ruthwik, Dovala Sai, Kancharla Nitish Rishik, Suthapalli Sampath, Nalini Anand, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

The introduction of drone technology has transformed a variety of businesses, from surveillance and monitoring to delivery services. However, effective communication among drones is critical for guaranteeing smooth operations, particularly in tough areas. This research study provides a comparative analysis of swarm intelligence algorithms for improving communication in drone networks. The study focuses on comparing the performance of various swarm algorithms in terms of communication count, with impediments represented by forest density ranging from 0 to 50 percent. Performance indicators, notably communication counts, are used to evaluate each algorithm’s success in facilitating drone communication. The major performance statistic is communication count, which is defined as the number of drone interactions. The study tries to determine the most effective swarm intelligence algorithm for drone communication in forested situations by examining how each method’s communication count changes as forest density increases. The discoveries will help to construct stable and communication-efficient drone networks for a variety of applications including forestry, search and rescue, and environmental monitoring. Understanding the impact of forest density on communication and the efficiency of various swarm algorithms provides a way to optimize drone network communication strategies for real-world circumstances. ©2024 IEEE.

关键词： Drones

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A Robotic Arm Vehicle using Voice Recognition for Physically Challenged People 2

A Robotic Arm Vehicle using Voice Recognition for Physically...

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2nd International Conference on Advances in Computation, Communication and information Technology, ICAICCIT 2024

作者： Nannda, Jia Dora, Lokareddy Venkanna Gupta, Tanvi Chouhan, Aryan Verma, Anmol Department of Computer Science and Engineering Manav Rachna International Institute of Research and Studies India Department of Computer Science and Engineering Manav Rachna International Institute of Research and Studies Faridabad India Department of electrical and electronics engineering Manav Rachna International Institute of Research and Studies India

ISBN: (纸本)9798331541217

In Today's world, all the work is automated by robots. The Robots have become a significant part of today's world, improving many people's lives, especially those who are physically challenged. This research paper presents a unique method for helping people with physical disabilities: A robotic arm vehicle using voice recognition for physically challenged people. Two remarkable features of the robot are its design and its adaptability in handling objects accurately. The comparative research demonstrates improved performance, such as faster job completion and increased grasping efficiency, when compared to older methods. Additionally, by integrating AI, the robotic arm gains the flexibility needed to operate in real-world environments. This finding might lead to more autonomous and effective assistive technology for personal support and healthcare applications. © 2024 IEEE.

关键词： Robotic arms

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Autonomous Vehicle Communication with Parrot-Optimized Orientation-Aware Graph Neural Network for Next-Generation IoT Connectivity 4

Autonomous Vehicle Communication with Parrot-Optimized Orien...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Mercy, Immaculate Nainwal, Ankita Balasubramani, R. Nandankar, Praful V. Parikh, Swapnil Taqui, Syed Noeman Department of Computer Applications Vallam Tamil Nadu Thanjavur613403 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Department of Information Science and Engineering Karnataka Nitte574110 India Government College of Nagpur Department of Electrical Engineering Maharashtra 441108 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat Waghodia India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331540364

Effective communication between autonomous vehicles is essential for ensuring their safe and efficient operation. This research introduces Parr-OrAGNet, a novel approach that addresses the limitations of traditional communication methods in IoT automotive networks. Parr-OrAGNet integrates Macro-Micro Depth Normalization (MaMiDN) for accurate data normalization, Orientation-Aware Graph Neural Network (OrAGNet) for enhanced neural network modeling, and Parrot Optimizer (ParrOpt) for optimization. This combined approach effectively reduces packet loss, communication overhead, and latency. The evaluation results demonstrate significant improvements in communication efficiency, with a 97% reduction in packet loss and a 95% decrease in communication overhead. Additionally, Parr-OrAGNet achieves exceptional performance metrics in terms of accuracy, precision, recall, and computational efficiency. This research contributes to the advancement of autonomous vehicle communication by establishing a new benchmark for performance and scalability. Parr-OrAGNet offers a promising solution for addressing the challenges of IoT automotive networks and paving the way for the realization of fully autonomous driving. © 2024 IEEE.

关键词： Macroinvertebrates

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