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Contrast Limited Adaptive Histogram Equalization based Multi-Objective Improved Cat Swarm Optimization for image Contrast Enhancement

Contrast Limited Adaptive Histogram Equalization based Multi...

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2023 international conference on Integrated Intelligence and Communication systems, ICIICS 2023

作者： Borra, Subba Reddy Tejaswini, N.P. Malathy, V. Magesh Kumar, B. Habelalmateen, Mohammed I. Malla Reddy Engineering College for Women Hyderabad India Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bengaluru India SR University Department of Ece School of Engineering Warangal India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India The Islamic University Najaf Iraq

ISBN: (纸本)9798350315455

The contrast enhancement technique is utilized to enhance the image quality which produce good quality images for various image processing tasks. The Histogram Equalization (HE) is one of the widely utilized technique for enhance the contrast and estimates the intensity frequency in an image. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is one of the local HE technique. Therefore, the CLAHE can handle the issues of classical HE algorithm. The CLAHE utilizes two various parameters such as number of tiles and clip limit, moreover the effectiveness depends on these two parameters. In this paper, Multi-Objective Improved Cat Swarm Optimization (MOICSO) is proposed to determine the optimum parameters for CLAHE to improve the image contrast. The first fitness function is utilized for estimating entropy and second is utilized for calculating Fast Noise Variance Estimation (FNVE) which produce local preservations and avoid noise amplification in an output image. The parameters like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak signal-to-noise Ratio (PSNR) and Computing Time (CT) are utilized to evaluate the proposed methodology. The obtained result shows that the MOICSO achieves less MAE of 6.28 which is which is better than other existing methods. © 2023 IEEE.

关键词： Cat Swarm Optimization Contrast Limited Adaptive Histogram Equalization Entropy Fast Noise Variance Estimation Fitness Function

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HIERARCHICAL TRAINING FOR DISTRIBUTED DEEP LEARNING based ON MULTIMEDIA DATA OVER BAND-LIMITED NETWORKS 29

HIERARCHICAL TRAINING FOR DISTRIBUTED DEEP LEARNING BASED ON...

引用

IEEE international conference on image Processing (ICIP)

作者： Qi, Siyu Chamain, Lahiru D. Ding, Zhi Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Distributed deep learning (DL) plays a critical role in many wireless internet of Things (IoT) applications including remote camera deployment. This work addresses three practical challenges in cyber-deployment of distributed DL over band-limited channels. Specifically, many IoT systems consist of sensor nodes for raw data collection and encoding, and servers for learning and inference tasks. Adaptation of DL over band-limited network data links has only been scantly addressed. A second challenge is the need for pre-deployed encoders being compatible with flexible decoders that can be upgraded or retrained. The third challenge is the robustness against erroneous training labels. Addressing these three challenges, we develop a hierarchical learning strategy to improve image classification accuracy over band-limited links between sensor nodes and servers. Experimental results show that our hierarchically-trained models can improve link spectrum efficiency without performance loss, reduce storage and computational complexity, and achieve robustness against training label corruption.

关键词： Hierarchical training image compression and classification auto-encoders information theory

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Enhanced Data Hiding based on First, Middle, and Last Order Pixel in Spatial Domain images 10

Enhanced Data Hiding Based on First, Middle, and Last Order ...

引用

10th international conference on signal Processing and Communication, ICSC 2025

作者： Hidayat, Rahmad Chanda D'Layla, Adifa Widyadhani De La Croix, Ntivuguruzwa Jean Ahmad, Tohari Institut Teknologi Sepuluh Nopember Surabaya Department of Informatics Indonesia Stain Sultan Abdurrahman Kepulauan Riau Indonesia Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia University of Rwanda Kigali Rwanda Intitut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia

ISBN: (纸本)9798331511890

The rapid advancement of internet technology has revolutionized global information sharing, enabling faster and more efficient digital communication. However, the increased reliance on online platforms for exchanging sensitive information raises significant concerns about data security, as unauthorized access and misuse of transmitted data pose severe risks, including identity theft and financial fraud. Steganography has emerged as a promising technique for securing data by embedding it imperceptibly into digital media. This study introduces a novel steganographic method that utilizes the difference between pixels in the cover image to embed secret messages while maintaining high image quality. The proposed scheme divides image pixels into blocks of 9. It calculates the difference between pixels with each block's first, middle, or last pixel to ensure minimal distortion and superior visual quality post-embedding. Experimental results validate the effectiveness of the method, achieving consistently higher PSNR (Peak signal-to-Noise Ratio) values compared to a baseline approach, with a PSNR of 64.9205 dB for a 10 KB payload and similarly superior results for payloads of 20 KB and 40 KB. © 2025 IEEE.

关键词： Steganography

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Real Time Cloud-based Weather Monitoring System using NodeMCU 9

Real Time Cloud-Based Weather Monitoring System using NodeMC...

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9th international conference on signal Processing and Communication, ICSC 2023

作者： Kumar, Saurav Rawat, Vandana Dabral, Shubham Maurya, Sudhanshu Negi, Ayush Alaskar, Kamal Department of Computer Application Graphic Era Deemed to be University Dehradun India Nagpur Campus Pune India Kolhapur India

ISBN: (纸本)9798350383201

Cloud-based real-time weather monitoring systems are emerging internet of Things (IoT) technologies that enable the collection, storage, and remote access of weather parameters. The system uses IoT devices and a cloud platform to collect and store constant weather data for small areas, including: B. Temperature, humidity, rain, pressure. The collected information is transmitted in real-time on the internet to a cloud servers on ***, making the data easily accessible and visualized through the website interface. The purpose of this system is to provide an advanced weather monitoring solution leveraging IoT technology and provide easy access to a wide range of real-time weather data. The system integrates IoT devices such as sensors and uses the NodeMCU platform to send the collected data to cloud servers. It uses cloud computing to store and process data efficiently and allows users to access weather information remotely. The data can be visualized and analysed through a user-friendly website interface, facilitating monitoring and analysis of weather conditions in specific regions. Integrating IoT and cloud computing into a real-time weather monitoring system offers several advantages. First, it enables the collection of 80% accurate and up-to-date weather data, facilitating better decision making in many sectors such as agriculture, transportation and disaster management. Second,cloud-based approach ensures scalability and flexibility when dealing with large amounts of data. Additionally, the system's architecture facilitates expansion and integration with other IoT devices and services, enhancing its capabilities for future applications. © 2023 IEEE.

关键词： Rain

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Research on Terminal Data Security technology based on NLP Label Tree

Research on Terminal Data Security Technology based on NLP L...

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2022 international conference on image, signal Processing, and Pattern Recognition, ISPP 2022

作者： Lu, Teng Cui, Zhaowei Cheng, Jie Hu, Wei Li, Jing Wang, Guoqing Liu, Siyao Yang, Dali State Grid Information & Telecommunication Branch Beijing China State Grid Corporation of China Beijing China State Grid Ningxia Electric Power Company China Zhongfu Information Co. Ltd Beijing China

ISBN: (纸本)9781510654846

With the in-depth development of informatization, the internet has become an important position for data security protection, an important network terminal of key infrastructure, and an important target for the infiltration and deep latent of hostile forces. In view of the risk of disclosure of important national and enterprise information caused by illegal transmission of important files by internal personnel, this paper studies the key technologies of content security based on NLP, and proposes a text classification method based on label tree, which effectively improves the accurate management of terminal data. © 2022 SPIE.

关键词： Classification (of information)

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Deep image Classification Model based on Dual-Branch 10th

Deep Image Classification Model Based on Dual-Branch

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10th international conference on Communications, signal Processing, and systems

作者： Chen, Haoyu Lv, Qi Zhou, Wei Zheng, Jiang Wang, Jian Nanjing Univ Sch Elect Sci & Engn 163 Xianlin Rd Nanjing 210023 Jiangsu Peoples R China Unit 31106 PLA Nanjing 210016 Jiangsu Peoples R China

ISBN: (纸本)9789811903908;9789811903892

The performance of deep learning in the field of computer vision is better than traditional machine learning technology, and the image classification problem is one of the most prominent research topics. The methods of computer vision are used in industry production. We proposed an image classification model which segments the image at different scales on the basis of Deep-ViT to obtain image information of different scales. When this model is applied to the classification of tube head shapes, the accuracy rate can reach more than 90%, and towards the classification of tube head materials, the accuracy is about 98%.

关键词： image classification Deep-learning Deep-ViT

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Developing an IoT-based Surface Electromyography signal Detection Device for Sarcopenia 6th

Developing an IoT-Based Surface Electromyography Signal Dete...

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6th international conference on Biomedical and Health Informatics-ICBHI

作者： Wang, Po-Xiang Jiang, Ching-Fen I Shou Univ Program Smart Healthcare & Bioinformat Kaohsiung Taiwan

ISBN: (纸本)9783031863226;9783031863233

Sarcopenia is characterized by a persistent reduction in skeletal muscle mass and function, which, if undetected and untreated, can lead to serious consequences such as increased fall risk and long-term immobility. Although sarcopenia has gained attention in recent years, there are currently no effective pharmacological treatments. Muscle loss can only be mitigated in the early stages through resistance training and nutritional interventions. However, precise detection methods used in hospitals are often costly and inaccessible to the general population. Traditional screening methods, like the Sarcopenia Self-Screening Questionnaire (SARC-F), proposed by the Asian Working Group for Sarcopenia, rely on subjective patient reports and lack objectivity. This study aims to address this gap by developing a rapid and convenient sarcopenia screening device that can be used at home or in community settings. Leveraging internet of Things (IoT) technology, we developed a surface electromyography (sEMG) system to measure the muscle activity of the gastrocnemius, tibialis anterior, and quadriceps during walking and standing. The muscle signals were wirelessly transmitted using the MQTT protocol for backend analysis, enabling real-time gait and muscle strength monitoring. Twenty-eight healthy elderly individuals from the community participated in this study. To validate the effectiveness of the device, traditional sarcopenia-related physical measurements were obtained using standard methods. We first classified participants into four sarcopenia risk levels using the above data. In the meantime, we used deep image gait analysis software (GaitBEST) to classify it into four risk levels. Preliminary results show that these two classification levels had a slight discrepancy. However, the sEMG signals obtained using the developed device reflect the risk level determined by gait pattern, indicating the necessity of further analysis to explore the relation between gait and skeletal muscl

关键词： Sarcopenia Deep image Analysis IoT Surface Electromyography (sEMG)

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Deep Learning-based SNR Estimation with Covariance Input 23

Deep Learning-Based SNR Estimation with Covariance Input

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23rd IEEE international conference on Communication technology, ICCT 2023

作者： Chen, Shurun Zheng, Shilian Yang, Zhuang Chen, Tao Zhao, Zhijin Yang, Xiaoniu Hangzhou Dianzi University Hangzhou China National Key Laboratory of Electromagnetic Space Security Jiaxing China Zhejiang University of Technology Hangzhou China

ISBN: (纸本)9798350325959

signal-to-noise ratio (SNR) is an important indicator for evaluating the quality of the received signals in wireless communication systems and its estimation plays an important role in demodulation and decoding processing. In this paper, we propose an SNR estimation method combining deep learning and covariance matrix (CM). In the method, CM is used as the input of a convolutional nueral network (CNN) to reduce the compuational complexity. We compare our proposed method with other SNR estimate methods, including classical estimation methods and existing deep learning-based approaches. Results show that our proposed method performs the best in low to medium SNR range in terms of estimation accuracy. In high SNR range, it acheives comparable performance with existing deep learning-based estimation methods. More importantly, our proposed method has far lower computational complexity than existing deep learning-based SNR estimation methods. © 2023 IEEE.

关键词： convolutional neural network covariance matrix deep learning SNR

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A Novel Pixel Block-based Data Hiding Method for Improved Payload Capacity Using 2-Dimensional Block Segmentation 16

A Novel Pixel Block-Based Data Hiding Method for Improved Pa...

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16th IEEE international conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Tahara, Enrico Almer Dlayla, Adifa Widyadhani Chandra De La Croix, Ntivuguruzwa Jean Ahmad, Tohari Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia College of Science and Technology University of Rwanda Kigali Rwanda

ISBN: (纸本)9798331505264

Steganography is the practice of hiding information by embedding it as secret data within various types of digital media to strengthen security. Numerous algorithms have been proposed for image steganography with a common purpose of minimizing the perceptibility of the secret data existence to the human vision system. The most used in images steganography is the Least Significant Bit (LSB) technique;however, it is vulnerable to steganalysis and lacks robust verification processes. To address this, in this article, a new steganographic approach is proposed to optimize the image's pixels use for embedding the secret data. The proposed method, considering adjacent pixels, introduces a new data-hiding technique that enhances payload capacity by using two-dimensional block segmentation to embed information within pixel blocks. Experimental results show that the method outperforms previous studies reviewed in this work, achieving a Peak signal-to-Noise Ratio (PSNR) between 49.7054 and 73.0848 decibels (dB) and Structural Similarity Index Measure (SSIM) between 0.9977 to 1.0000 for the selected difference range between -4 and 4, indicating high-quality stego images. © 2024 IEEE.

关键词： data hiding digital images processing information security steganalysis steganography

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Transformer Fault Diagnosis Method based on Gramian Angular Field and Graph Convolution Network 8

Transformer Fault Diagnosis Method Based on Gramian Angular ...

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8th international conference on Energy System, Electricity, and Power, ESEP 2023

作者： Zhang, Chenyu Yang, Zhen Fang, Zhenggang Li, Yigang Li, Xiang Zhang, Yulong China Electric Power Research Institute Beijing100192 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

Power transformers, being pivotal in electrical power systems, necessitate vigilant monitoring for operational sustainability and reliability. The traditional approach to such monitoring, Dissolved Gas Analysis (DGA), has been grounded in gas volume fraction ratio analysis-a technique that, while effective, is constrained by static thresholds and simplistic assumptions that often do not encapsulate the intricacies of fault progression. In light of these limitations, the exploration of Artificial Intelligence (AI) methodologies presents a compelling alternative, offering a dynamic and data-driven avenue for fault detection and diagnosis. This paper proposes a transformer fault diagnosis method based on the Gramian Angular Field (GAF) transformation and Graph Convolutional Networks (GCN). Firstly, the one-dimensional DGA fault samples are converted into three-dimensional DGA feature image samples using the Gramian Angular Field transformation method. Then, a dataset expansion is performed on the image samples using a GAN-based data augmentation method to meet the data volume input requirements of the deep learning model. Subsequently, a graph convolutional network based on GCN layers and a Self-Attention Graph Pooling (SAGPool) mechanism is constructed. This network aggregates neighbor nodes according to the node features and topological structure of the three-dimensional DGA graph signal to adaptively mine fault features and diagnose fault types. The experimental results show that the method proposed in this paper can serve as an effective transformer fault diagnosis method. It is more accurate compared to the traditional gas ratio method for diagnosis, and its advantages are more significant compared to other traditional fault diagnosis deep learning methods. © 2024 SPIE.

关键词： Failure analysis

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