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Stationary, continuous, and discrete wavelet-based approach for secure medical image transmission

Research on Biomedical engineering 2023年第1期39卷 167-178页

作者： Sayah, Moad Med Redouane, Kafi Med Amine, Khaldi Electrical Engineering Laboratory Department of Electronics Faculty of Sciences and Technology University of Kasdi Merbah Ouargla30000 Algeria Computer Science Department Faculty of Sciences and Technology University of Kasdi Merbah Ouargla30000 Algeria

Purpose: Watermarking is one of the techniques used to protect multimedia data, and images in particular, from malicious attacks by inserting a signature into these images. However, traditional watermarking schemes encounter limitations for sensitive images such as medical images. Methods: In this work, a robust and blind watermarking approach is proposed to secure medical images exchanged in telemedicine. Marking medical images allows precise authentication of the patient and avoids confusing scans and thus avoiding diagnostic errors that can have serious consequences. In this approach, the frequency content of the image is acquired using three different transforms. Schur decomposition is then applied to the low-frequency sub-bands. Finally, the obtained upper triangular matrix values are modifier to integrate the watermark bits. Result and conclusion: Imperceptibility and robustness experimental results show that the proposed methods adequately maintain a significant quality of watermarked images and are remarkably robust against several conventional attacks. Indeed, the average peak signal-to-noise ratio (PSNR) obtained is 44.90 dB which proves that the integration process generates few distortions to the original image. For the robustness, the obtained results show that the watermark is resistant to the most common attacks used in watermarking with a normalized cross correlation rate higher than 0.9 for the majority of the attacks. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering.

关键词： Discrete wavelet transforms

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Study of Ocean Pattern's and Ocean Bed Using Applications of IOT 2

Study of Ocean Pattern's and Ocean Bed Using Applications of...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Vishwanath, K. Reddy, Rachit Roy, Sritama Vellore Institute of Technology Department of Computer Science Chennai600127 India School of Electronics Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9798350328202

The ocean plays an integral role for influencing Earth's climate and sustaining diverse ecosystems as oceans covers almost 70 % of the earth's surface. It is important to monitor and control the ocean biodiversity through the advanced IoT technologies. The internet of Underwater things (IoUT) is an emerging technology which can connect the underwater objects as well as environment. IoUT is directly related with the navigation, disaster prediction, smart monitoring and intelligent system. Understanding the dynamics of the ocean is crucial for various applications, including weather forecasting, maritime operations, and environmental conservation. This research paper presents an in-depth analysis of an Ocean buoy Monitoring System that utilizes temperature, humidity, and geolocation data collected from buoys to predict ocean patterns, tides, and marine life. The system combines real-time sensor measurements with advanced data analysis techniques to provide valuable insights into oceanographic phenomena. © 2024 IEEE.

关键词： Buoys

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A survey of artificial intelligence models for wireless capsule endoscopy videos for superior automatic diagnosis: problems and solutions

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Multimedia Tools and Applications 2025年 1-35页

作者： El-Gammal, Eman M. El-Shafai, Walid Taha, Taha E. El-Fishawy, Adel S. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Center of Advanced Software and Biomedical Clinical Engineering Consultations Faculty of Engineering Cairo University Giza Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia

Wireless Capsule Endoscopy (WCE) emerged as an innovative and patient-centric approach for non-invasive and painless examination of the gastrointestinal (GI) tract. It serves as a pivotal tool in helping medical practitioners in the early detection of anomalies within the intricate domain of the human GI tract. The automated identification of aberrations assumes paramount significance, contributing not only to temporal efficiency but also to timely diagnosis. Within the realm of academic literature, a plethora of Artificial Intelligence (AI) methodologies have been proposed to effectuate the automatic classification, segmentation, and synthesis of anomalies inherent in WCE images. This scholarly endeavor undertakes a comprehensive and meticulous survey, elucidating the spectrum of anomaly classification, summarization, and detection techniques harnessed for computer-Aided Diagnosis (CAD) in the context of WCE images. The survey begins by delineating the methodologies underpinning WCE image classification and video processing. Subsequently, an exhaustive evaluation of techniques for analyzing WCE images is furnished, accompanied by an in-depth exploration of pertinent surveys, in addition to a judicious assessment of their merits and demerits. Furthermore, this study undertakes a rigorous evaluation of prevailing datasets utilized to benchmark the efficacy of various WCE techniques. In summary, this survey serves as a vanguard, delineating promising avenues for future investigations in the realm of WCE image analysis models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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Active Thermal Control for Power Semiconductor using Peltier-Device

Active Thermal Control for Power Semiconductor using Peltier...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Yamaguchi, Masamichi Watanabe, Hiroki Itoh, Jun-Ichi Lee, Kyo-Beum Nagaoka University of Technology Department of Science of Technology Innovation Nagaoka Japan Nagaoka University of Technology Department of Electrical Electronics and Information Engineering Nagaoka Japan Ajou University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350376067

The paper proposes an Active Thermal Control (ATC) approach employing a Peltier device as a heat exchanger for temperature regulation. The control strategy relies on Model Predictive Control (MPC) utilizing an equivalent thermal model. It elucidates the current control methodology for the Peltier device based on this model and predicted temperature. Moreover, the predicted temperature and additional thermal sensor estimate the loss amount at the heat source without any electrical sensor, such as a current or a voltage sensor. The simulation result affirms the effectiveness of the proposed control and loss estimation method. Finally, the experimental result demonstrates the validity of the proposed control, evidenced by a 2.6% maximum error in temperature control. © 2024 IEEE.

关键词： Predictive control systems

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Denoising of MRI and Brain Tumor Classification using Local Binary Pattern and SVM Classifier 3

Denoising of MRI and Brain Tumor Classification using Local ...

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3rd International Conference on electronics and Renewable Systems, ICEARS 2025

作者： Kavin Kumar, K. Kumar, Neelam Sanjeev Senthilkumar, C. Nanmaran, R. Sangar, G. Chandrasekaran, Gokul Kongu Engineering College Department of Electronics and Communication Engineering Erode India Vadapalani Chennai India Simats University Simats Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai602 105 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani Chennai India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331509675

All data collection systems, including magnetic resonance imaging, are susceptible to noise. Modern high-field scanners can achieve high signal-to-noise ratios;however, noise becomes a significant issue that requires careful management when signals become extremely faint due to specific acquisition processes. The proposed scheme consists of steps which include denoising, feature extraction and classification. For denoising, the Non-Local Means filtering approach is used, and the performance of the denoising method is analyzed using PSNR values. The major objective here is to study texture classification methods to distinguish between various types of brain cancers, such as gliomas, meningiomas, and pituitary tumors. We then evaluate its performance on an additional dataset. The obtained photos serve both educational and evaluative purposes. The accuracy of two methods is computed and compared. The feature extraction performance is compared between two separate classifiers using standard deviation, variance, and mean as performance measures. Among the methods tested, SVM achieved the best accuracy rate of 96.67%. © 2025 IEEE.

关键词： KNN LBP MDC NLM SVM

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Dynamic Modeling and Quantum-Enhanced Forecasting of Multi-Seasonal Energy Prices in Simulated Microgrid Environments

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IEEE Access 2025年 13卷 90362-90388页

作者： Dash, Ritesh Sinha, Anupa Jyotheeswara Reddy, K. Dhanamjayulu, C. Kamwa, Innocent Kalinga University Research Scholar Computer Science Engineering Raipur India Kalinga University Computer Science Engineering Raipur India REVA University School of Electrical and Electronics Engineering Bangalore India Vellore Institute of Technology School of Electrical Engineering Vellore632014 India Laval University Department of Electrical Engineering and Computer Engineering Quebec G1V 0A6 Canada

The study address the challenge of forecasting per unit energy prices in a microgrid environment consisting of solar and hydro power resources under multi-seasonal *** deep learning techniques such as LSTM,GRU and ESN often struggle with non-linear dependencies and volatility in energy market. To overcome these we propose a hybrid framework incorporating Adiabatic Quantum Computing (AQC) for electricity price forecasting. The proposed AQC model encodes-32 system and market related variables into quantum states and applies adiabatic evolution to derive optimized price prediction. Simulation results using real microgrid data set-up based on HIL shows that AQC reduces forecasting error by 17.03% compared to LSTM, 14.29% to GRU and 13.88% to ESN over 24-hrs and 48-hrs horizons. The enhanced accuracy and robustness of the quantum assisted model demonstrates its potential for next generation energy market forecasting and decisions making *** entire framework is tested using a synthetic microgrid dataset designed to emulate real-world seasonal and operational dynamics. While this enables controlled validation of the models, the generalizability of the results to real world deployment requires further empirical evaluations on physical microgrid data set. © 2013 IEEE.

关键词： Qubits

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Pilot Super-Resolution Network (PSRN)-Based Mango Fruit Classification

Pilot Super-Resolution Network (PSRN)-Based Mango Fruit Clas...

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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： Yeswanth, P.V. Kushal, Sammeta Kumar, V. Tharun Ackshay, N.R. Gangudi, Ravindra Kumar, Molapally Tharun Deivalakshmi, S. Thanya, Y. Lokesh Kumar, K.M. Department of Electronics and Communication Engineering National Institute of Technology Tamil Nadu Tiruchirappalli India Department of Computer and Communication Engineering Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India

ISBN: (纸本)9783031513374

Mango as a fruit has many varieties, and identification of the same with the naked eye can be tiresome. Pilot Super-Resolution Network (PSRN) model is proposed to produce super-resolution images from that of the low-resolution mango fruit images and identify the varieties in this paper. The novel PSRN model is executed on the Mango Variety and Grading dataset for super-resolution factors 2, 4, and 6. The metrics recorded include PSNR values of 30.37, 32.936, and 35.428, SSIM values of 0.8275, 0.9146, and 0.9279, and classification accuracy values of 99.45, 98.86, and 97.64 on factors 2, 4, and 6, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image classification

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Optimization of Memory Usage in High-Speed Cameras using FPGA 3

Optimization of Memory Usage in High-Speed Cameras using FPG...

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3rd International Conference on electronics and Renewable Systems, ICEARS 2025

作者： Kavin Kumar, K. Kumar, Neelam Sanjeev Senthilkumar, C. Velmurugan, V. Sangar, G. Chandrasekaran, Gokul Kongu Engineering College Department of Electronics and Communication Engineering Erode638060 India Vadapalani Chennai India Simats University Simats Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai602 105 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani Chennai India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331509675

High-speed cameras generate large amounts of data, making memory optimization difficult for real-time processing. This project minimizes data size by converting RGBA (Red, Green, Blue, Alpha) images to RGB, eliminating the Alpha channel to reduce memory usage. The captured images are provided as input to Verilog code in hexadecimal format, with the conversion done by MATLAB. Bilinear Interpolation is applied to reduce the potential quantization errors during the conversion, using the values of four surrounding pixels to smooth the image and maintain quality. After processing, the image is reconstructed using MATLAB, ensuring the integrity of the output. By utilizing the parallel processing capabilities of FPGAs, this method ensures real-time performance while optimizing memory usage. It is ideal for high-speed imaging applications where both efficiency and image quality are crucial. © 2025 IEEE.

关键词： MATLAB

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Internet of Things Enabled Smart E-Nose System for Pollutants Hazard Detection and Real-Time Monitoring in Indoor Mosquito Repellents

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SN computer Science 2024年第5期5卷 438页

作者： Kumar, Kanak Tripathi, Suman Lata Mahmud, Mufti Department of Electronics Engineering Indian Institute of Technology BHU Varanasi India Lovely Professional University Punjab India Department of Computer Science and Engineering Nottingham Trent University Nottingham United Kingdom

Mosquito-repellent is one of the most utilized products for dispelling mosquitoes that produce a significant quantity of gaseous, and particulate pollutants and are hazardous to human health. An indoor chamber experiment is conducted to identify the primary pollutants from mosquito repellents. This paper proposes an internet-of-things (IoT) based E-nose for volatile-organic compounds (VOCs)/gases/odors detection and real-time monitoring released from different mosquito repellents used in indoor environments. E-noses generate unique signature patterns for various VOCs/gases/odors. A gas sensor node interface is developed using eight cross-selective tin-oxide (MOX) based gas sensor arrays with a low-powered microcontroller to capture real-time signature patterns of VOCs/gases/odors released by different repellents when they are put in use. This microcontroller sends the gas sensor responses to the Amazon web services (AWS) cloud platform, from where data is ported to a remote data processing station (RDPS) for further analysis for its use in real-time. In this experiment, seven different types of mosquito repellents, viz mosquito coil, fast card, herbal cones, herbal spray, mosquito spray, hit spray and all-out liquid have been used. A dataset consisting of 1200 samples was captured. These sensor array responses are pre-processed using the two-stage analysis space transformation method, i.e., standardised linear discriminant analysis (SLDA) is used in the first stage. Subsequently, the second processing stage used Adaboost, random forest and recursive discriminant analysis (RDA) classifiers. The proposed e-nose was tested using 40 unknown VOCs/gases/odors samples that were not used during the training and validation. Experimentally, the RDA classifier trained in the SLDA transformed dataset could classify all 40 test samples with 100% accuracy. The lowest mean squared error achieved was 2.40 × 10^–7. © The Author(s), under exclusive licence to Springer Natur

关键词： Amazon web services E-nose Indoor pollutants Internet of things Mosquito repellents MSE RDA RF SLDA VOCs

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Internet of Things and Artificial Intelligence Enabled Smart Wheel Chair 6

Internet of Things and Artificial Intelligence Enabled Smart...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Narayanan, K. Lakshmi Niranjana, R. Vinothini, S. Krishnan, R. Santhana Rajkumar, G. Vinoth Rachel, L. Department of Electronics and Communication Engineering Francis Xavier Engineering College Tirunelveli India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Applications PSNA College of Engineering and Technology Dindigul India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering JP College of Engineering Thenkasi India Department of Electronics and Communication Engineering Latha Mathavan Engineering College Madurai India

ISBN: (纸本)9798350398496

A smart wheelchair with IoT based interactive health monitoring system is proposed. IoT based live monitoring of elderly people health parameters like temperature, pressure, fall detection and emergency notification button to intimate the emergency situation to the care taker so that the care taker can give instructions from a IoT app to an Alexa device connected with a camera and monitor in the wheelchair system and it also helps elderly people is sitting and getting up from a chair using hydraulics. This smart system can support the elderly and disabled people with high accuracy and efficiency. The proposed Smart Wheelchair consists of various sensors to monitor the body parameters from the wheelchair connected to the central Raspberry Pi 4. In addition to this an emergency notification system is included to intimate the caretaker so that the care taker can give instructions from their Android mobile to an Alexa device, which in turn connected to a display with camera in the wheelchair system. For easy seating and getting off from the chair a mechanical hydraulics system is designed to lift the seat while sitting andto get up from the seat. © 2023 IEEE.

关键词： Hydraulics

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