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Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET), International Conference on

作者： Ravi Kumar Manikant Kumar Shweta Kumari Ashish Kumar Mithilesh Kumar Rai Vishal Mourya Electronics and Communication Engineering Muzaffarpur Institute of Technology Muzaffarpur Leather Technology Muzaffarpur Institute of Technology Muzaffarpur Information Technology Muzaffarpur Institute of Technology Muzaffarpur Computer Science Engineering Muzaffarpur Institute of Technology Muzaffarpur National Institute of Electronics and Information Technology Patna

ISBN: (数字)9798331528614

ISBN: (纸本)9798331528621

Underwater image enhancement has been employed in a variety of areas, such as terrain scanning, underwater microscopic detection, telecommunication cables, mine detection, and autonomous underwater vehicles. However, underwater imagery suffers from strong scattering, noise, color distortion, and absorption from artificial light sources, causing haziness, image blur, and a greenish or bluish texture. To address these issues, the proposed work introduces a new enhancement approach based on Contrast Limited Adaptive Histogram Equalization (CLAHE) used for images taken underwater. The proposed method employs CLAHE to adaptively enhance local contrast while constraining the amplification of noise and artifacts. The fusion method is proposed to enhance the complementary advantages of different color spaces in YIQ and HSI color models. The YIQ color space separates intensity from chrominance, allowing for independent processing of luminance information, which is crucial for preserving image details and contrast. The proposed algorithm improves low-contrast underwater images with intact color and a natural appearance with reliability according to the results of the qualitative analysis. The proposed method outperforms the state-of-the-art methods in terms of image performance measures like GMSD, VSI, MCSD, and PSNR.

关键词： Visualization Translation Image color analysis Absorption Scattering Distortion Reflection Reliability Colored noise Image enhancement

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Automated Metal Waste Segregation Using IoT-Connected Robotics Using Convolutional Neural Networks for Efficient Sorting 1

Automated Metal Waste Segregation Using IoT-Connected Roboti...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Vimaladevi, S. Sekar, Satheeshkumar Pradeepa, H. Mubarakali, Azath Sendhilkumar, N.C. Murugan, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Commerce Chennai India Mphasis Corporation ChandlerAZ United States Thiagarajar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Madurai India King Khalid University College of Computer Science Saudi Arabia Sri Indu College of Engineering and Technology Department of Electronics and Communication Engineering Hyderabad 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)9798350376517

Sustainable environmental practices depend on effective waste management. Combining Convolutional Neural Networks (CNNs) with Internet of Things (IoT)-connected robots, a new method for automatic metal trash sorting is presented in this research. By automatically detecting and separating metallic items from mixed waste streams, our technology aims to improve the efficiency of trash sorting operations. Robotic arms with sensors to collect data in real-time and CNNs models trained to identify different metal items make up the suggested system. The sorting processes are carried out with perfect coordination and adaptive decision-making due to the IoT integration, which allows the robots system to connect with a centralized control unit. By showcasing the function of CNNs in facilitating reliable metal classification it provides a comprehensive design of the integrated system. The results of the experiments show that our method can efficiently and precisely detect and sort metal waste. IoT connection also allows for remote monitoring and control, which lets user optimize sorting tactics in real-time and make modifications as needed. Our suggested approach provides an encouraging solution to the problem of automating the segregation of metal trash, which may lead to better and more long-term waste management in both industrial and municipal sectors. © 2024 IEEE.

关键词： Robotic arms

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Development of an Instrumented Glove for the Characterization of Human Wrist Tremor 7

Development of an Instrumented Glove for the Characterizatio...

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7th International Conference on electronics, Communication and Aerospace technology, ICECA 2023

作者： Lakineni, Prasanna Kumar Dhana Sony, J. Sharma, Ruchi Chitra, M. Jose Anand, A. Anchitaalagammai, J.V. GITAM School of Technology Department of Computer Science and Engineering GITAM University Visakhapatnam India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Biomedical Engineering Chennai600062 India JECRC Foundation Department of AI&DS Rajasthan Jaipur302022 India Rajalakshmi Institute of Technology Dept of ECE Tamilnadu Chennai India KCG College of Technology Karapakkam Department of Electronics and Communication Engineering Chennai600097 India Velammal College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Madurai India

ISBN: (纸本)9798350340600

It is common for human beings to present some diseases and disorders as they age, due to neuromusculoskeletal disorders. One of the problems that can appear is the human tremor. There are numerous strategies for tremor attenuation. Among the most common methods, the use of medications, local surgical procedures, electrical stimulation and the use of active orthoses stand out. In this context, robotic rehabilitation is of interest to science more and more. Among the methods for inhibiting tremor, active orthoses are the most efficient and non-invasive means. With that in mind, and also in terms of their quality of life, studies on orthoses to suppress tremor were carried out. Due to the complexity of the device, the present study aims to develop and evaluate an instrumented glove, properly constructed to characterize human wrist tremor in patients affected with such dysfunction. To this end, the basic operating principle of the active orthosis is presented, seeking to highlight more specifically the instrumented glove with its motion and muscle actuation sensors. © 2023 IEEE.

关键词： Biomedical signal processing

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Development of Smart Tongue using Ionic Polymer Metal Composite for Robotic Applications 23

Development of Smart Tongue using Ionic Polymer Metal Compos...

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6th International Conference on Advances in Robotics, AIR 2023

作者： Das, Suman Bhadra, Subir Bhattacharya, Srijan Chattopadhyay, Subrata Department of Electrical Engineering Mckv Institute of Engineering Howrah India Department of Computer Science and Engineering Mckv Institute of Engineering India Applied Electronics and Instrumentation Engineering Department Rcc Institute of Information Technology Kolkata India Electrical Engineering Department National Institute of Technical Teachers Training and Research Kolkata India

ISBN: (纸本)9781450399807

This paper focus on the development Ionic Polymer Metal Composite (IPMC) based robotic tongue as artificial taste sensor to identify and discriminate between different taste senses. In this study, taste samples are used which denotes five taste senses- Salty, Sweetness, Spicy, Bitterness and Sourness. NafionTM-117 (DuPont) substrate based IPMC due to its hydrophilic nature allow cations like Li+, Na+, K+ etc. diffuse through its perfluorinated polymer membrane penetrating Platinum (Pt)-surface electrode layer on both side of the membrane. Cations of different concentration present in natural and artificial food products. Based on the amount of diffused cations inside the polymer matrix of IPMC, various taste samples are identified. While bending force applied on IPMC strip, mobile cations and fixed anions get polarized inside IPMC polymer membrane and create potential difference in millivolts (mV). The potential difference measured across the Pt-surface electrode of IPMC. Magnitude of the developed emf depends on cationic concentration for different taste samples. Considering taste sense as identification index, bending deformation of IPMC strip has clear correlation with magnitude of emf developed across the Pt-surface electrode. K-nearest neighbor (KNN) based Classification technique is applied in developing the Machine Learning (ML) Model for prediction of different taste senses. The model is trained with real-time dataset having information about output voltage developed across IPMC' vs. taste samples of variable concentration'. Training is done in two phases where the ratio of training data: test data was 70%: 30% and 80%: 20% respectively. It is observed that Prediction accuracy of the learned model increases if the volume of training dataset increases. High prediction accuracy of the developed ML model confirms the competence IPMC based e-tongue technology, applicable for robotic system as artificial taste sensor. © 2023 ACM.

关键词： Machine learning

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Genetic Algorithm with Ant Colony Optimization to Enhance Multi-Hop Routing Efficiency in Wireless Ad Hoc Network 2

Genetic Algorithm with Ant Colony Optimization to Enhance Mu...

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

作者： Sumitra, V. Sumathi, V. Fernandes, J. Bennilo Sasi Kumar, C. R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sri Sairam Engineering College Department of Mathematics Tamil Nadu Chennai India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamil Nadu Virudhunagar India Shree Sathyam College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Salem India

ISBN: (纸本)9798350325799

Wireless Ad Hoc Networks (WANETs) develop multi-hop communication to transmit data packets. The unique features of WANETs make it difficult for mobile nodes to communicate reliably. Most physical routing systems do not consider stable connections during packet transmission to manage high mobility and environmental impediments, which results in increased network latency and packet loss. This paper presents a Genetic with Ant Colony Optimization (GACO) Algorithm to enhance multi-hop routing efficiency in WANET. The GACO is a hybrid model by fusing the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) models. The GACO computes the fitness function to improve the accuracy for selecting the best hop. This approach uses the ACO algorithm to make a multi-hop route by applying parameters like buffer availability, loss rate, and channel load factors. In order to simulate tests of GACO performance, Network Simulator-3 is used. Comparing GACO to the baseline technique, the routing performs better. © 2023 IEEE.

关键词： Ant colony optimization

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Vehicle Detection in UAV Images Using Data Augmentation and Transfer Learning with Modified YOLOv9

Vehicle Detection in UAV Images Using Data Augmentation and ...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Vyomika Singh Vedant Kashyap Laxman Singh Khangarot Dharmendra Singh Department of Electronics & Communication Engineering Indian Institute of Technology Roorkee Roorkee Computer Science and engineering Graphic Era deemed to be University Dehradun Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

Vehicle detection in UAV images is constrained by the differences in environmental and technical conditions such as target type and size, degree of occlusion, acquisition altitude, illumination, and weather. High levels of false alarms and missed detections are the most prominent challenges faced by traditional detection algorithms, particularly in the case of occluded or tiny targets acquired by UAV. This research sustains such problems by employing data augmentation and transfer learning techniques with an adjustable YOLOv9 model on UAV image dataset. We apply focused data augmentation techniques to enhance model performance over varied use cases, and transfer learning turns out to be effective in adapting the model to work for UAV-based occluded vehicle detection. The targeted improvements in the modified architecture of YOLOv9 enables effective detection of vehicles, including occluded ones with different sizes and orientations, thus, minimizing false alarms. The effectiveness of the proposed approach was experimentally validated, outperforming the existing ones and providing an architecturally and practically adequate solution for vehicle detection on real UAVs.

关键词： Adaptation models Vehicle detection Transfer learning Refining Object detection Autonomous aerial vehicles Data augmentation Data models Optimization Meteorology

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Optimizing Resource Allocation in Mobile Edge Computing for IIoT through Reinforcement Learning and Multiobjective Particle Swarm Optimization 2

Optimizing Resource Allocation in Mobile Edge Computing for ...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Avv, Sudhakar Mohan, M. Maharajan, K. Habelalmateen, Mohammed I. Naga Saranya, N. SR University Department of Electrical and Electronics Engineering Telangana India Nitte Meenakshi Institute of Technology Department of ISE Bengaluru India School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Bangalore India Universiti Tun Hussein Onn Malaysia Parit Raja Faculty of Electrical and Electronic Engineering Johor Batu Pahat Malaysia College of Technical Engineering the Islamic University Department of Computer Technical Engineering Najaf Iraq Saveetha College of Liberal Arts and Science SIMATS Department of Computer Applications Chennai India

ISBN: (纸本)9798350317558

Mobile networks are flexible enough to support a range of resource allocation models and service-based choices in computing domains, which affects both virtual reality and the Industrial Internet of Things (IIOT). Virtual resource management is made easier by the Mobile Edge Computing (MEC) paradigm, which also makes edge connectivity between data terminals and execution in the core network under heavy load possible. Meeting customer requirements efficiently is achieved through thoughtful planning, aided by cognitive agents. User data, incorporating behavioral patterns, is amalgamated to cater to IIOT service types. Neural caching for memory during task execution is made easier by the use of swarm intelligence and reinforcement learning techniques. Prediction strategies optimize business operations and caching to reduce execution delays. Predictive models evaluate performance by taking into account variables like nearby user equipment and mobile edge computing resources. This method's efficacy is demonstrated by a cognitive agent model that manages resource distribution and creates networks of communication to improve service quality. For accurate resource distribution among end users, reinforcement learning techniques - more especially, Multi-Objective Particle Swarm Optimization (MOPSO) algorithms - are used. This includes building cost mapping tables and optimizing allocation in MEC. The proposed method outperforms existing algorithms like Task-Offloading and Resource Allocation Strategy and achieves a better throughput value of 785.32. © 2024 IEEE.

关键词： Communication Industrial Internet of Things Mobile Edge Computing Optimization Quality of Service Reinforcement Learning

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Sustainable Energy Evolution Using IoT-Enhanced Carbon Capture and Storage for Oil Recovery 6

Sustainable Energy Evolution Using IoT-Enhanced Carbon Captu...

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6th International Conference on Energy, Power and Environment, ICEPE 2024

作者： Valli, K. Vijaya Naga Swathi, G. Lumina Julie, R. Saravanan, S. Sujatha, S. Malarvizhi, E. Srkr Engineering College Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Cmr Technical Campus Department of Computer Science and Engineering Telangana Hyderabad India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Management Studies Tamil Nadu Chennai India Muthayammal Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Namakkal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350379297

The global energy environment is shifting toward sustainability and carbon neutrality. Internet of Things (IoT) technology with Carbon Capture and Storage (CCS) in Enhanced Oil Recovery (EOR) may reduce carbon emissions and optimize hydrocarbon recovery. This paper examines IoT-enabled CCS for EOR and carbon-neutral oil and gas operations. Real-time data capture and analysis using IoT technologies optimize CCS operations. Installing IoT sensors and devices across the EOR and CCS infrastructure lets operators monitor CO2 injection rates, reservoir pressure, temperature, and fluid composition. The CCS process is precisely controlled using data, improving carbon capture efficiency and lowering operating costs. Remote monitoring and management of carbon storage sites using IoT in CCS for EOR improves environmental sustainability. The collected CO2 is securely and efficiently contained, reducing leakage and environmental effects. Preventing CCS infrastructure problems with IoT-driven predictive maintenance ensures long-term operational dependability. Securely storing collected CO2 offsets oil and gas emissions and contributes to carbon neutrality. EOR process optimization with IoT technologies may boost hydrocarbon recovery, making the switch to greener energy sources easier. IoT-enabled CCS for EOR has the potential to produce carbon-neutral oil and gas operations. The system improves CCS efficiency and sustainability using real-time data, remote monitoring, and predictive maintenance. Integration of IoT with CCS reduces carbon emissions and promotes the industry's worldwide sustainability objectives. © 2024 IEEE.

关键词： Carbon capture and utilization

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Cloud-Based Foot Pressure Analysis for Diabetic Care Using ANN 5

Cloud-Based Foot Pressure Analysis for Diabetic Care Using A...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Pradeepa, H. Amarnath, Raveendra N. Sivakumar, V.G. Thamizhamuthu, R. Manikandan, G. Bhuvaneswari, G. Thiagarajar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Madurai India Mphasis Campbell Dr MelissaTX United States Vidya Jyothi Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus-Chengalpattu Tamil Nadu Chennai India Rmk Engineering College Department of Artificial Intelligence and Data Science RSM Nagar Tamil Nadu Chennai India Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350389609

It introduces a new method for caring for diabetic feet combines smart pressure monitoring for the feet with wearable gear that is cloud-based and uses artificial neural networks (ANNs). It is essential to recognize diabetic foot ulcers early and monitor them continuously to avoid complications. Wearable pressure sensors record data on the user's foot pressure in real time make up the proposed system. ANNs trained to detect patterns recommend a possible risk of ulceration by analyzing this data once it is transferred to a cloud-based platform. By using cloud-based architecture, healthcare practitioners can remotely monitor patients and intervene as necessary. Clinical trials show the technology improves diabetic foot care by detecting irregularities in pressure distribution and lowering the risk of ulcers and their sequel. Improved patient outcomes and decreased healthcare expenses are achieved via the preventive treatment of diabetic feet made possible by this system, which makes use of breakthroughs in wearable technology and artificial intelligence (AI). © 2024 IEEE.

关键词： Remote patient monitoring

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Convergent Approaches to AI Explainability for HEP Muonic Particles Pattern Recognition

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Computing and Software for Big Science 2023年第1期7卷 1-18页

作者： Maglianella, Leandro Nicoletti, Lorenzo Giagu, Stefano Napoli, Christian Scardapane, Simone Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Rome Italy Department of Physics Sapienza University of Rome Rome Italy Institute for Systems Analysis and Computer Science Italian National Research Council Rome Italy Department of Computational Intelligence Electronics and Telecommunications Czestochowa University of Technology Czestochowa Poland Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome Italy

Neural networks are commonly defined as ‘black-box’ models, meaning that the mechanism describing how they give predictions and perform decisions is not immediately clear or even understandable by humans. Therefore, Explainable Artificial Intelligence (xAI) aims at overcoming such limitation by providing explanations to Machine Learning (ML) algorithms and, consequently, making their outcomes reliable for users. However, different xAI methods may provide different explanations, both from a quantitative and a qualitative point of view, and the heterogeneity of approaches makes it difficult for a domain expert to select and interpret their result. In this work, we consider this issue in the context of a high-energy physics (HEP) use-case concerning muonic motion. In particular, we explored an array of xAI methods based on different approaches, and we tested their capabilities in our use-case. As a result, we obtained an array of potentially easy-to-understand and human-readable explanations of models’ predictions, and for each of them we describe strengths and drawbacks in this particular scenario, providing an interesting atlas on the convergent application of multiple xAI algorithms in a realistic context. © 2023, The Author(s).

关键词： Explainable Artificial Intelligence High-energy physics Intrinsically interpretable Decision Trees Saliency maps methods Tracing gradient descent

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