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DWT-SVD Based Image Steganography Using Threshold Value Encryption Method

computers, Materials & Continua 2022年第8期72卷 3299-3312页

作者： Jyoti Khandelwal Vijay Kumar Sharma Dilbag Singh Atef Zaguia School of Computing and Information Technology(SCIT) Manipal University JaipurJaipur302004India School of Electrical Engineering and Computer Science Gwangju Institute of Science and TechnologyGwangju61005Korea Department of Computer Science College of Computers and Information Technology Taif University11099Saudi Arabia

Digital image steganography technique based on hiding the secret data behind of cover image in such a way that it is not detected by the human visual *** paper presents an image scrambling method that is very useful for grayscale secret *** this method,the secret image decomposes in three parts based on the pixel’s threshold *** division of the color image into three parts is very easy based on the color channel but in the grayscale image,it is difficult to *** proposed image scrambling method is implemented in image steganography using discrete wavelet transform(DWT),singular value decomposition(SVD),and sorting *** is no visual difference between the stego image and the cover *** extracted secret image is also similar to the original secret *** proposed algorithm outcome is compared with the existed image steganography *** comparative results show the strength of the proposed technique.

关键词： Image steganography threshold value sorting discrete wave transformation singular value decomposition color band division permutation

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Dynamic modelling and optimal operation of the photovoltaic/thermal systems under various weather conditions

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2024年第6期22卷 82-86页

作者： Sredenšek, K. Simonič, E. Seme, S. Faculty of Energy Technology University of Maribor Hočevarjev trg 1 Krško8270 Slovenia Faculty of Electrical Engineering and Computer Science University of Maribor Koroška cesta 46 Maribor2000 Slovenia

This paper aims to introduce a dynamic model for a photovoltaic/thermal system, highlighting an approach and synthesis of existing dynamic models to depict the full photovoltaic/thermal system. The model was validated using data from an experimental setup at the Institute of Energy Technology, Faculty of Energy Technology, University of Maribor, under different weather conditions. The model's accuracy was assessed through normalized Root Mean Square Error, normalized Mean Bias Error, Correlation Coefficient, and the coefficient of determination error metrics, focusing on the temperature and electrical output power of the photovoltaic/thermal modules, as well as the temperature of the thermal energy storage tank. Furthermore, the paper also focuses on the optimal operation of the photovoltaic/thermal system under different mass flow rates of the working medium. © 2024, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： dynamic modelling electrical and thermal energy experimental system the photovoltaic/thermal system

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A variable structure model of neurons’ application in computer vision

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Multimedia Tools and Applications 2025年 1-11页

作者： Sridi, Alaeddine Attia, Mouna Bouallegue, Kais Electronics and Micro-Electronics Laboratory Faculty of Sciences of Monastir University of Monastir Monastir Tunisia The Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse Tunisia Laboratory of Computer Science Parallelism and Intelligent Communicating Systems Faculty of Sciences of Tunisia University of Tunis ELManar Tunis Tunisia Department of Electrical Engineering Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia

The neuron doctrine defines the neuron as the basic unit of the nervous system, which drives the dynamic behavior of our organs. This has led to neurons becoming the focus of modern neuroscience research and to the rise of neurocomputing, in which we try to digitally simulate human behavior. The goal being to better understand the human nervous system and gain more control over neuron-related diseases, helping doctors establish faster and more reliable medical diagnoses. In this context, we aim to simulate eye diseases and vision problems by convolving input images using a new class of neural networks based on a variable structure model of neurons presented in [1]. In this work, we focus on recreating the common convolution filters as a first step. This is the first time in neurocomputing literature that dendritic neurons have been used for dynamic convolution kernels generation and eye diseases simulation. We start with a presentation of our upgrades to the model’s flexibility and accuracy. Then, we propose a novel kernel generator algorithm to convolve the input images. Finally, we show its promising results in creating the outline, pixelization, and night convolution kernels, as well as control some metrics, including brightness and data retention. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurons

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Motif-Based Occupancy Prediction for Energy Efficiency in HVAC 3

Motif-Based Occupancy Prediction for Energy Efficiency in HV...

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3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Al Mohammad, Ahlam Hirmiz, Hadeel Perera, Geanna Maleki, Mina University of Detroit Mercy Dept. of Electrical & Computer Engineering & Computer Science DetroitMI United States

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Heat, ventilation, and air conditioning (HVAC) systems account for the majority of energy consumption in buildings. People's behavior and occupancy patterns differ greatly from day to day and even seasonally, which is a major obstacle when it comes to reducing energy consumption in HVAC systems through building management systems. The main goal of occupancy prediction is to come up with a learning algorithm that can learn and then predict occupants' behaviors to control the HVAC system efficiently. In this paper, a motif-based machine learning approach using clustering and classification is proposed to predict occupancy based on Wi-Fi connected devices and other sensors. In the first phase of this study, K-means clustering is utilized to group similar occupancy profiles. Each cluster is given a cluster ID, which is then used as a label for each sample. The labeled samples are then strung together in sequence, creating a motif. The next phase is classification, which predicts the occupancy of the next time shift of fifteen minutes based on motifs. The classification techniques utilized are K-Nearest Neighbors (KNN), Decision Trees (DT), Support Vector Machines (SVM), and Naive Bayes (NB). The simulations' results on the dataset collected from an academic building indicate that motif-based prediction algorithms based on K-means clustering are better than motifs created by threshold clustering with an accuracy of 94%. © 2022 IEEE.

关键词： Support vector machines

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Constrained Dynamical Neural ODE for Time Series Modelling: A Case Study on Continuous Emotion Prediction 48

Constrained Dynamical Neural ODE for Time Series Modelling: ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Dang, Ting Dimitriadis, Antoni Wu, Jingyao Sethu, Vidhyasaharan Ambikairajah, Eliathamby University of Cambridge Department of Computer Science and Technology United Kingdom Unsw School of Electrical Engineering and Telecommunications Sydney Australia

ISBN: (纸本)9781728163277

weA number of machine learning applications involve time series prediction, and in some cases additional information about dynamical constraints on the target time series may be available. For instance, it might be known that the desired quantity cannot change faster than some rate or that the rate is dependent on some known factors. However, incorporating these constraints into deep learning models, such as recurrent neural networks, is not straightforward. In this paper, we propose constrained dynamical neural ordinary differential equation (CD-NODE) models, which treat the desired time series as a dynamic process that can be described by an ODE. CD-NODEs model the rate of change of the time series as a function of both itself and the current input features, parameterised as a neural network. We explore the effect of constraining the dynamics of the model by placing explicit restrictions on the rate of change. The proposed model is evaluated on speech-based continuous emotion prediction, where such dynamical constraints are expected, using the publicly available RECOLA dataset. Results suggest that the model achieves performances comparable with the state-of-the-art despite using significantly fewer parameters. Additional analyses reveal that imposing these constraints on the model leads to faster convergence and better performance, especially with smaller training data sets. © 2023 IEEE.

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Impact of the Increasing Integration of Photovoltaic Systems and Charging Stations for Electric Vehicles with Vehicle-To-Grid Concept on Voltage Stability of the Distribution Grid 20

Impact of the Increasing Integration of Photovoltaic Systems...

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20th International Conference on Smart Technologies, EUROCON 2023

作者： Kljajic, Ruzica Misljenovic, Nemanja Maric, Predrag Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9781665463973

The increasing integration of renewable energy sources (RES) and variable loads can stress the operation of the distribution grid during certain times of the day. In another way, electric vehicles, which are assumed to take an increasing share in the overall transportation structure, are considered as variable loads with the ability to store energy and thus support stability of the distribution grid. This paper focuses on a model of a prosumer-rich distribution grid with increasing integration of photovoltaic (PV) systems, electric vehicle charging stations (EVCS) as well as vehicle-to-grid (V2G) in the foreseeable future. The impact of integration on voltage stability was analyzed for the different operating scenarios considering voltage profiles and characteristic P-V curves. © 2023 IEEE.

关键词： Vehicle-to-grid

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Vulnerability Analysis of Deep Learning Model for OCTA Image Classification 5th

Vulnerability Analysis of Deep Learning Model for OCTA Ima...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Trivedi, Munesh Chandra Bharadwaj, Siddhant Department of Computer Science and Engineering Pandit Sunderlal Sharma Central Institute of Vocational Education Bhopal India Department of Electrical and Electronics Engineering Manipal Institute of Technology Manipal India

ISBN: (纸本)9789819761029

The prevalence of Deep Learning in healthcare has revolutionized medical diagnostics, yet the vulnerability of these models to adversarial attacks threatens their security. This study investigates the susceptibility of a sophisticated deep learning model, trained to classify OCTA images as healthy or diabetic, to adversarial perturbations. Despite achieving high accuracy on unperturbed data, the model remains vulnerable to adversarial noise. The study employs techniques like Project Gradient Descent (PGD) and Fast Gradient Sign Method (FGSM) to generate adversarial examples and tests their efficacy against the model. Results show that even minor perturbations can lead to misclassification, emphasizing the need for adversarial robustness in healthcare models. As healthcare decisions are critical, incorporating adversarial training is must to mitigate the impact of adversarial vulnerabilities in deep learning-based medical diagnoses. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Securing Secondary Distribution with IoT-Based Smart Meters: Detection and Prevention of Power Theft 4

Securing Secondary Distribution with IoT-Based Smart Meters:...

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4th International Conference on Electronic and electrical engineering and Intelligent System, ICE3IS 2024

作者： Khan, Sher Dali Alamsyah, Nurwahyu Ul Haq, Qazi Mazhar Ahmad, Waqas Mufti, Naveed Haq, Muhamad Amirul Universiti Teknikal Malaysia Melaka Faculty of Electrical and Electronic Engineering Technology Melaka Malaysia Universitas Muhammadiyah Yogyakarta Information Technology Department Yogyakarta Indonesia Yuan Ze University Department of Computer Science and Engineering and IBPI Taoyuan Taiwan University of Engineering and Technology Department of Electrical Engineering Mardan Pakistan University of Engineering and Technology Department of Telecommunication Engineering Mardan Pakistan Universitas Muhammadiyah Surabaya Computer Science Department Surabaya Indonesia

ISBN: (纸本)9798350378368

electrical energy distribution faces the significant issue of losses, with power theft being a main factor contributing to these losses. This difficulty is not limited to Pakistan but is also prevalent in several other nations worldwide. This study introduces a technique that utilizes smart meters to identify the approximate whereabouts of power theft. In addition to the detection of power theft, this study also presents a mechanism for the eradication of unauthorized users. The methodology used involves comparing the overall energy with the energy that is lawfully utilized. Wireless communication systems, such as Global System for Mobile Communications (GSM) and Network Repository Function (NRF), are used for communication between the meters. © 2024 IEEE.

关键词： NRF based Smart Meter Power Theft Elimination Power Theft Prevention Smart Meter

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A Traffic Sign Detection Technique Using Road Scene Images and GPS/GIS Information

A Traffic Sign Detection Technique Using Road Scene Images a...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yang, Sung-Chi Lin, Huei-Yung Fan, Yu-Hsiang Wei, Shih-Han National Chung Cheng University Department of Electrical Engineering Chiayi621 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106 Taiwan

ISBN: (纸本)9781665410205

With the advancement of computational intelligence, autonomous driving has become the future development trend of the automotive industry. Since the safety is commonly considered as the first priority of self-driving and driver assistance systems, the understanding of transportation infrastructure is an essential problem. In this paper, a technique for traffic sign detection and recognition is proposed. Different from the general image-based methods, we also incorporate the vehicle position and geographic information to improve the accuracy. Based on the approximate location of a traffic sign obtained from GPS and GIS, it can be used to increase the confidence level of network detection results. In the experiments, the training datasets are derived from Google Street View images and collected with an in-vehicle camera. The performance evaluation compared to the image-only methods has demonstrated the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Automotive industry

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AI-based Secure Intrusion Detection Framework for Digital Twin-enabled Critical Infrastructure 14

AI-based Secure Intrusion Detection Framework for Digital Tw...

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14th International Conference on Information and Knowledge Technology, IKT 2023

作者： Patel, Tanisha Jadav, Nilesh Kumar Rathod, Tejal Tanwar, Sudeep Garg, Deepak Shahinzadeh, Hossein Institute of Technology Nirma University Department of Computer Science and Engineering Gujarat Ahmedabad India SR University School of Computer Science and Artificial Intelligence Warangal506371 India Amirkabir University of Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350349412

This paper discusses the importance of securing modern society's critical infrastructure in the face of physical and digital threats. It emphasizes the vulnerabilities introduced by programmable logic controllers (PLCs) and supervisory control and data acquisition (SCADA) systems that highlight the potential of digital twins to enhance security. The paper presents a research contribution aimed at improving the integrity of data exchange among PLCs equipped with digital twins in critical infrastructure systems. It leverages artificial intelligence (AI) algorithms to detect and mitigate different security attacks, such as denial of service (DoS), injection attacks, and data tampering attacks. The proposed framework efficiently classifies the malicious and non-malicious data of digital twin-based critical infrastructure. Further, the trained AI model is deployed on a critical infrastructure's intrusion detection system that continuously monitors network traffic and system logs in real time, identifying unusual patterns or behaviors that may indicate an intrusion. Further, the performance of the proposed framework is evaluated using accuracy, log loss, and validation curves. © 2023 IEEE.

关键词： Critical infrastructures

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