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Generative Adversarial Network-based Satellite image Enhancement 1st

Generative Adversarial Network-Based Satellite Image Enhance...

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1st international conference on Microelectronics, Communication systems, Machine Learning, and internet of Things, MCMI 2020

作者： Pandey, Rudra Narayan Shubhankar, Shreyas Acharya, Bibhudendra Mishra, Sudhansu Kumar Department of Electrical and Electronics Engineering Birla Institute of Technology Ranchi Mesra India Department of Electronics and Communication Engineering National Institute of Technology Chattisgarh Raipur India

ISBN: (纸本)9789811919053

In recent times, satellite images are gaining popularity in the monitoring of agricultural fields, forest ranges, surveillance, and public commute convenience. In many places, it is difficult to implant cameras;thus in that scenario, satellite images establish to be very helpful. However, the raw image taken by the satellite needs to be preprocessed to remove cloud cover, improve contrast, perform color correction, and improve inter-image contrast. In this paper, a method is devised to remove cloud cover and improve inter-image contrast. Since deep learning is gaining immense popularity, a deep learning approach utilizing generative adversarial networks (GANs) has been used for the enhancement of satellite images. To improve inter-image contrast, histogram matching technique has been used. The technique proposed in this paper has been investigated by considering the satellite images collected from SKYMAP Global. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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Link Quality Analysis of Wireless Sensor Networks based on CART Decision Tree 2

Link Quality Analysis of Wireless Sensor Networks Based on C...

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2nd international conference on image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Zha, Minghu Zhu, Li Zhu, Yunyun Hu, Tao Hubei Minzu University College of Intelligent Systems Science and Engineering Enshi China Hubei Engineering Research Center for Intelligent Technology of Selenium Food Nutrition and Health Enshi China Key Laboratory of Performing Arts Equipment System Technology and Culture and Tourism of the Ministry of Culture Beijing China

ISBN: (纸本)9798350331417

Aiming at the problem of link quality reliability in wireless sensor networks, a method for evaluating link quality based on the CART decision tree algorithm is proposed. Firstly, the link quality datasets is divided into three levels according to the link quality evaluation metric, and all kinds of data are balanced by random oversampling. Then, the received signal strength indication is used to generate the synthetic feature as the feature inputs. Finally, the evaluation model is constructed based on the CART decision tree algorithm. Experiments show that the number of decision tree layers affects the accuracy of the overall model. By analyzing the influence of the number of tree layers on the accuracy of link quality evaluation, it is determined that when the number of tree layers is 6, the accuracy of link quality evaluation reaches the highest 94.8 %, and the accuracy, precision, recall and F1 value are used to verify the reliability of the conclusion. © 2023 IEEE.

关键词： CART decision tree link quality evaluations wireless sensor network

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Research on Substation Video Surveillance Data Encryption based on Improved Tent Chaos Map 4

Research on Substation Video Surveillance Data Encryption Ba...

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4th international conference on Smart Power and internet Energy systems (SPIES)

作者： Gao, Qian Zheng, Weiliang Tao, Xue Teng, Jun Yingkou Power Supply Co State Grid Liaoning Elect Power Co Ltd Equipment Div Yingkou Peoples R China

ISBN: (纸本)9781665489577

In recent years, the video surveillance system data of various facilities, places and key parts in the power industry has been widely used, and a large number of hidden dangers of security vulnerabilities and data leakage have occurred. The use of video surveillance systems to obtain important asset information in key places is worrying. Therefore, this paper proposes a research based on the improved Tent chaotic map in the encryption of video surveillance data in substations. This paper constructs an improved Tent map, and compares the t classical tent chaotic map, the classic Logtisic chaotic map, and the improved Tent chaotic map. In terms of performance in bifurcation graph, correlation dimension value, etc., experimental research shows that this improved chaotic map has improved mapping range, randomness, uniformity, etc. compared with the classical Tent map. based on this chaotic map, the permutation group strategy is used to obtain the ciphertext image, which realizes the encryption of the video surveillance data of the substation.

关键词： tent mapping video encryption chaotic system

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Biometric-based Access Control systems with Robust Facial Recognition in IoT Environments

Biometric-Based Access Control Systems with Robust Facial Re...

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IEEE international conference on Intelligent Techniques in Control, Optimization and signal Processing (INCOS)

作者： B.V. Satish Babu J. Manoranjini Ravindra Changala Mohd Aarif S. Suma Christal Mary I Infant Raj Department of IT Prasad V Potluri Siddhartha Institute of Technology Vijayawada India Department of AI & ML Chaitanya Bharathi Institute of Technology Hyderabad India Department of Information Technology Guru Nanak Institutions Technical Campus Hyderabad India Associate Editor-Publishing Global Research Network Noida India Department of Information Technology Panimalar Engineering College Poonamalle Chennai India Department of Computer Science and Engineering K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

The integration of biometric-based access control systems with robust facial recognition technology within internet of Things (IoT) environments holds paramount significance in addressing contemporary security challenges. The novelty of the research endeavours to contribute to this critical area by investigating and implementing a novel approach, employing Convolutional Neural Networks (CNN) for facial classification within the biometric framework. The significance of this study lies in its potential to enhance security measures in IoT environments, ensuring reliable and efficient access control. Previous attempts at integrating facial recognition into IoT have encountered notable challenges, including suboptimal accuracy, vulnerability to adversarial attacks, and scalability issues. This research builds upon these prior issues by introducing CNN for facial classification, leveraging its capabilities in image processing and pattern recognition to achieve enhanced accuracy and resilience against potential security threats. The novelty of the study is underscored by the application of CNN within the biometric context of IoT, offering a cutting-edge solution tailored to the intricacies of interconnected devices. Experiments conducted on benchmark datasets showcase the promising capabilities of the proposed system, with face recognition accuracies reaching an impressive 98%. These results underscore the potential of this research to significantly advance the state-of-the-art in biometric-based access control, particularly within the context of IoT environments. The utilization of CNN introduces a pioneering element, contributing to the evolution of secure and efficient access control solutions in the era of IoT.

关键词： Access control Face recognition Scalability image processing signal processing Convolutional neural networks internet of Things

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Deraining of image Using UNet-based Conditional Generative Adversarial Network 4th

Deraining of Image Using UNet-Based Conditional Generative A...

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4th international conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Bose, Samprit Chavan, Deep R. Kolekar, Maheshkumar H. Indian Institute of Technology Patna Bihar India PDPM-IIITDM Jabalpur India

ISBN: (纸本)9789819914784

Adverse weather conditions like rain, fog and storm cause degradation in the quality of an image. Computer vision operations such as detection, classification and various monitoring of objects activities are adversely affected due to image degradation. Hence, image enhancement is an important pre-processing step. We propose a method for the elimination of rain streaks from rain-affected images by making use of conditional generative adversarial networks. We have used structural similarity index measure and peak signal-to-noise ratio as evaluation metrics evaluate the model. We have tested our model on one synthetic and four real-world datasets and compared the performance with other state-of-the-art methods. We have obtained a generated image that has a close resemblance with the ground truth images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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Bin Reservation based on IVPO Reversible Data Hiding Using Smoothness Mode Selection Strategy 17th

Bin Reservation Based on IVPO Reversible Data Hiding Using S...

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17th international conference on Intelligent Information Hiding and Multimedia signal Processing (IIH-MSP) / 14th international conference on Frontiers of Information technology, Applications and Tools (FITAT)

作者： Lee, Chin-Feng Shen, Jau-Ji Lin, Zong-Ye Chaoyang Univ Technol Dept Informat Management Taichung 41349 Taiwan Natl Chung Hsing Univ Dept Management Informat Syst Taichung 40277 Taiwan

ISBN: (纸本)9789811910531;9789811910524

Pixel value ordering (PVO) is a popular reversible data hiding (RDH) technology. The embedding strategy of the PVO method is to divide the image into blocks of the same size, sort the pixel values in each blocks, and embed the secret data to the maximum and minimum pixel values when embeddable conditions are satisfied. The embedding capacity of the PVO method will change due to the complexity of the block. If the pixels in the block is too different from each other, the embedding capacity of the PVO method will be limited. Therefore, in view of this limitation of the PVO method, this research proposes the smoothness mode selection (SMS) strategy. The SMS strategy determines the image shape according to the complexity of the pixels within image blocks, and then uses the bin reservation based on improved PVO RDH method to embed the secret data in the picture. The embedding strategy of this method enables each block of four pixels to carry at most four bits, so it has a significant higher embedding capacity. The experimental results show that the embedding capacity of our proposed method is higher than other PVO-based RDH schemes, and it also maintains a certain good image quality.

关键词： Block complexity Improved pixel value ordering Pixel value ordering Reversible data hiding

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Measuring the Actual Office Workspace Utilization in a Desk Sharing Environment based on IoT Sensors 18th

Measuring the Actual Office Workspace Utilization in a Desk ...

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18th international conference on Design Science Research in Information systems and technology (DESRIST)

作者： von Straussenburg, Arnold F. Arz Blazevic, Mevludin Riehle, Dennis M. Univ Koblenz Univ Str 1 D-56070 Koblenz Germany

ISBN: (纸本)9783031328077;9783031328084

Businesses that follow the trend of flexible work arrangements with home offices and hybrid work models are increasingly utilizing desk sharing to allocate available desks in the office efficiently. In this work, we investigate the utilization of flexible work arrangements, specifically the implementation of desk sharing in hybrid work models. To optimize the occupancy rate of shared desks and simultaneously meet demand and save costs, it is crucial to have real-time knowledge of their availability which can be obtained by recording occupancy over time. We utilize the Design Science Research (DSR) methodology to develop a prototype solution for real-time monitoring of shared desks occupancy in hybrid work models. The proposed prototype is a low-cost solution utilizing internet of Things (IoT) technology consisting of a motion sensor and accelerometer that collect occupancy data and transmit it via Long Range Wide Area Network (LoRaWAN). signal processing utilizing cloud computing is employed to enhance the quality and accuracy of the data, which is subsequently made available through a web dashboard. The prototype's effectiveness is evaluated through a small-scale pilot deployment in a real-world environment over a two-week period, with reference values obtained during periods of non-use serving as a benchmark for the evaluation.

关键词： internet of Things Flexible Office Desk Sharing Workplace Utilization

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Structural Tensor Nonlinear Complex Diffusion-based Method for Sonar image Denoising 14

Structural Tensor Nonlinear Complex Diffusion-Based Method f...

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14th international conference on signal Processing systems, ICSPS 2022

作者： Tan, Minjia Yang, Yifei School of Automation Nanjing University of Science & Technology Nanjing China Ocean College Jiangsu University of Science & Technology Zhenjiang China

ISBN: (纸本)9798350336313

This paper presents a sonar image denoising method based on structural tensor and nonlinear complex diffusion (STNCD). The complex diffusion in STNCD utilizes the imaginary value generated by the diffusion process as the directional structure indicator, then controls the diffusion coefficient by introducing the eigenvalue of the structural tensor and the spatial variation structure factor related to the eigenvector. As a result, the complex value of STNCD ensures that the phase information of the image is preserved during the noise suppression process. Furthermore, we make use of the difference between the principal eigenvectors of the structural tensor to describe the local coherence of the features, and then solve the problem that the phase angle cannot be accurately estimated when constructing the structural tensor. Experimental results show that STNCD not only suppresses sonar image noise but also enhances the signal of image edge, which has a wide range of applications. © 2022 IEEE.

关键词： Eigenvalues and eigenfunctions

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Compressed Sensing of Skin Conductance Level for IoT-based wearable sensors

Compressed Sensing of Skin Conductance Level for IoT-based w...

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IEEE international Instrumentation and Measurement technology conference (I2MTC)

作者： Iadarola, Grazia Poli, Angelica Spinsante, Susanna Polytech Univ Marche Dept Informat Engn Ancona Italy

ISBN: (纸本)9781665483605

In the context of physiological monitoring applications, wearable sensor platforms usually have to sustain long-term acquisitions, necessary to assess correctly the individual health status, but also to compensate for uncertainty and noise effects, due to the uncontrolled conditions in which data are collected. Therefore, smart measurement systems based on the internet of Things paradigm require approaches able to optimize the on-board resources, either in terms of computational effort and power consumption. With a view to ensuring such an optimization, this paper presents a Compressed Sensing (CS) method for the reconstruction of the Skin Conductance Level, a signal able to encode individual traits of observed subjects, useful also in bio-metric applications. The proposed CS method employs a deterministic measurement matrix and it exploits the sparsity in the domain defined by the Discrete Cosine Transform. The Percentage of Root-mean-squared Difference, chosen as figure of merit, shows very low values for a Compression Ratio as high as 12, thus proving the excellent performance of the proposed method.

关键词： Compressed Sensing data compression remote health monitoring internet of Things wearable devices Skin Conductance Electrodermal Activity

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An IoT Machine Learning Model-based Real-Time Diagnostic and Monitoring System 4th

An IoT Machine Learning Model-Based Real-Time Diagnostic and...

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4th international conference on Machine Intelligence and signal Processing, MISP 2022

作者： Awotunde, Joseph Bamidele Misra, Sanjay Ajagbe, Sunday Adeola Ayo, Femi Emmanuel Gurjar, Ramchandra Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Computer Science and Communication Ostfold University College Halden Norway Department of Computer Engineering Ladoke Akintola University of Technology Ogbomosho Nigeria Department of Mathematical Sciences Olabisi Onabanjo University Ago-Iwoye Ogun State Nigeria Shri Govindram Seksaria Institute of Technology and Science Indore India

ISBN: (纸本)9789819900848

internet of things (IoT) and machine learning (ML) have recently changed the paradigm in the area of applications in health-care systems with significant results. The ML-based models enabled with IoT-based systems have greatly expedited the medical diagnosis, real-time monitoring, and treatment in the health-care system, thus reducing the burden of health-care processes and the cost of taking care of patients by caregivers and physicians. Therefore, in this paper, a framework is proposed based on an ML model-enabled IoT-based system for real-time diagnosis of various diseases using data collected from wearable devices and sensors attached within the body of patients. The captured data are stored using an integrated cloud database for further preprocessing and processes using ML-based models. ML model was used to select the most relevant features from the cloud database and thus use the preprocessed data for the diagnosis of various diseases. The findings from the study show that the XGBoost performs reliably better when compared with other existing ML-based models with an accuracy of 95.7%, recall of 95.9%, precision of 96.0%, F1-score of 94.8%, and Receiver Operating Characteristic Curve (ROC) of 94.3%. The results proposed a framework to show that the system can be widely used to diagnose various diseases and can be used in remote areas for better monitoring of patients. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： internet of things

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