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Blockchain Enabled Federated Learning-based Medical Cyber-Physical System

Blockchain Enabled Federated Learning-Based Medical Cyber-Ph...

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Electronics, Communication and signal Processing (ICECSP), international conference on

作者： Santanu Basak Ashish Kumar Kakali Chatterjee Department of CSE National Institute of Technology Patna Patna Bihar India

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

internet of Things (IoT) and Machine Learning (ML) techniques enlightened several areas and started a new era in industry. One challenging area is the Healthcare sector. The ML changed the direction and sped up the Healthcare sector and the Healthcare sector became Smart Healthcare (SH). The Medical Cyber-Physical System (MCPS) is effectively used in SH and it has several privacy and security issues. In such scenarios, Federated Learning (FL) has shown another direction to handle several challenges and opened an area for research. Additionally, blockchain (BC) technology has made the FL network secure. Inspired by several works in this paper Blockchain enabled FL- based MCPS is proposed that is capable of mitigating different adversarial attacks, Distributed Denial-of-Service, Sybil, etc. The performance of the FL is measured using accuracy, mean squared error (MSE), sensitivity, and specificity. After the final round achieved accuracy is 86%, MSE is 0.14, sensitivity is 89%, and sensitivity is 85%.

关键词： Privacy Sensitivity Accuracy Measurement uncertainty Medical services Cyber-physical systems Sensitivity and specificity Blockchains Security internet of Things

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A Data Leakage Traceability Scheme based on Differential Privacy and Fingerprint

A Data Leakage Traceability Scheme Based on Differential Pri...

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image Processing and Media Computing (ICIPMC), international conference on

作者： Mingyong Wang Shuli Zheng School of Computer and Information Science Hefei University of Technology Hefei China

ISBN: (数字)9798350386660

ISBN: (纸本)9798350386677

With the development of the internet and big data technology, massive amounts of data are transferred between different application systems and organizations, greatly increasing the difficulty of privacy protection and the risk of data leakage. Existing database fingerprinting schemes, while addressing the problem of traceability, are limited by the contradiction between the robustness of the fingerprints and the data distortion introduced by the embedding of fingerprints. They also struggle to protect sensitive data while maintaining data usability. To address such issues, this paper proposes a data leakage traceability scheme based on differential privacy and fingerprinting. This scheme processes the database using the Laplace mechanism for differential privacy, then treats the fingerprint information with the Arnold image transformation algorithm, and trains an approximate optimal key based on the fingerprint information using a genetic algorithm. This approach aims to enhance the robustness of the fingerprint scheme while reducing data distortion. Experimental results prove that this scheme has better robustness than existing fingerprint schemes after maintaining usability.

关键词： Differential privacy image transformation Costs Databases Fingerprint recognition Distortion Robustness

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Bridging the Gap in Train Navigation: GPS-based Navigation System as a Complement to Conventional signaling systems for Enhanced Train Driver Awareness

Bridging the Gap in Train Navigation: GPS-Based Navigation S...

引用

international conference on ICT For Smart Society (ICISS)

作者： Sinung Suakanto Dita Pramesti Riska Yanu Fa'rifah Tunggul Arief Nugroho Suhono Harso Supangkat Toto Warsito Information System Departement Telkom University Bandung Indonesia Information Technology Departement Institut Teknologi Harapan Bangsa Bandung Indonesia Electrical Engineering and Informatics Institute of Technology Bandung Bandung Indonesia Bussiness Development Aerocomm Bandung Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

In the railway transportation industry, precise and accurate navigation is a crucial factor for maintaining operational safety and efficiency. Conventional signal-based navigation systems often have limitations due to their dependence on complex infrastructure and susceptibility to human error. Therefore, the development of GPS-based train navigation tools is proposed as a complementary system to enhance situational awareness for train operators and improve travel safety. This system not only provides real-time information on train speed but also the precise location of trains to prevent potential collisions. Additionally, the position data from the GPS is updated to a server and displayed using data communication pathways via the internet of Things (IoT). Initial prototyping and implementation have shown promising results in visualizing the positions of trains relative to each other.

关键词： Navigation Transportation industry Data visualization Real-time systems Safety internet of Things Servers Reliability Testing Global Positioning System

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Visual Garment Collar Style Identification using Deep Neural Networks 14

Visual Garment Collar Style Identification using Deep Neural...

引用

14th international conference on signal Processing systems, ICSPS 2022

作者： Li, Zhenxing Zhang, Diming Li, Yuanjiang Wang, Luohan College of Automation Jiangsu University of Science and Technology Zhenjiang China College of Oceanography Jiangsu University of Science and Technology Zhenjiang China

ISBN: (纸本)9798350336313

Modeling fashion style is key for fashion industry. While traditional manually analysis is slow and inefficient, we aim to automate it using deep neural networks. Particularly, in this paper, we propose a VGG16-based collar recognition network that is more suitable for commercial use to solve the problem of difficult collar recognition in the apparel field. Furthermore, to enable our research and further research using deep learning, we create a dataset called COLLAR 2000. To achieve the commercial network for recognition accuracy, network stability, network complexity and other requirements, we explored traditional machine learning methods as well as mature neural networks, respectively, by adjusting the parameters of PCA, SVM, LeNet, AlexNet, VGG16 and combining migration learning methods to make them more suitable for garment collar recognition. The experimental results show that the VGG16-based collar recognition network is the best for collar recognition (81.7% recognition accuracy, 83.0%precision, 81.7% recall, 0.82 F1-Score), the network stability and complexity are moderate, which is more suitable for commercial use. © 2022 IEEE.

关键词： image classification

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Uncertainty Quantification based on Gaussian Processes for image Segmentation Tasks

Uncertainty Quantification Based on Gaussian Processes for I...

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Mobile internet, Cloud Computing and Information Security (MICCIS), international conference on

作者： Bing Gao Rui Chen Tingting Yu Beijing Sunwise Information Technology Ltd Beijing Institute of Control Engineering Beijing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

Over the past several years, deep neural networks have permeated many fields of science research and have become an essential part of real-world applications. However, when the model encounters a test sample with uncertainty, compared with the distribution of the training set data, it is crucial for users to determine which of the model’s predicted outputs for those test data are trustworthy and which are not, rather than being forced to give an untrustworthy prediction output. To this end, it is necessary to evaluate the model uncertainty, which enables to improve utilization of the model prediction results, especially some safety-critical systems. For image segmentation tasks of safety-critical systems, this paper proposes an uncertainty quantification method based on Gaussian processes to evaluate the trustworthiness of the output predicted by neural network models for the given input, so as to facilitate the selection and optimization of segmentation models and provide the model interpretability. The simulation results indicate that for the pixel error rate of each image, this method can give the confidence interval of the predicted output and the stability of the predicted output, achieving the objective of quantifying and understanding the confidence level of deep learning models.

关键词： Training image segmentation Uncertainty Computational modeling Simulation Gaussian processes Predictive models

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Comparative Evaluation of Various Wavelet Techniques for image Compression

Comparative Evaluation of Various Wavelet Techniques for Ima...

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international conference on Applied Electromagnetics, signal Processing and Communication (AESPC)

作者： Hidayat Un Nisa Samrah Mehraj Shabir A. Parah dept. of Electronics and Instrumentation Technology University of Kashmir Srinagar India

ISBN: (数字)9798331528171

ISBN: (纸本)9798331528188

image compression is a critical component of digital systems, as it reduces the file size of images while preserving quality, thereby facilitating minimal storage requirements and seamless transmission. Wavelet transforms have emerged as powerful tools in image compression for providing both time and frequency information, making them particularly effective in capturing essential image features. Despite numerous advancements, there is still significant demand for image compression techniques that achieve higher compression ratios with minimal loss of quality. This paper presents a comprehensive analysis of various lossy image compression techniques, focusing on the strengths of wavelet-based methods. Techniques such as Discrete Wavelet Transform, Lifting Wavelet Transform, and Integer Wavelet Transform are examined in detail, with key metrics including Peak signal-to-Noise Ratio (PSNR), compression ratio, and time efficiency analyzed to provide a thorough understanding of each technique's effectiveness. The timing analysis is particularly crucial for assessing the practical applicability of these methods in real-world scenarios. This study aims to offer valuable insights for developing more advanced image compression methods that meet the growing demands of modern data management systems.

关键词： image quality Measurement Time-frequency analysis image coding PSNR Wavelet domain Wavelet analysis Real-time systems Discrete wavelet transforms Timing

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Case Study in Urban Community Farming: IoT-based Soil Moisture Monitoring System

Case Study in Urban Community Farming: IoT-Based Soil Moistu...

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international conference on Smart Sensors and Application (ICSSA)

作者： Intan Sorfina Zainal Abidin Muhamad Zuhairi Basari Noramalina Abdullah Nor Azizah Mohd Yusoff Aiffah Mohd Ali Zuraini Dahari School of Electrical & Electronic Engineering Universiti Sains Malaysia Pulau Pinang Malaysia School of Aerospace Engineering Universiti Sains Malaysia Pulau Pinang Malaysia

ISBN: (数字)9798350354294

ISBN: (纸本)9798350354300

internet of Things (IoT) technology has transformed every part of the lives of average people, making things easier. A network of independent devices is known as the internet of Things (IoT). The aim of this project is to propose an internet of Things (IoT)-based farming system that gives farmers access to real-time information on soil moisture level as well as surrounding temperature and humidity for efficient environmental monitoring. By using this method, farmers will be able to increase their total productivity and product quality and engage in smart farming practices. The smart farming system developed in this paper incorporated IoT technology and LoRaWAn communication protocol for long distance monitoring where Wi-Fi connection is unavailable. The system was also supplied by solar energy using solar panel integrated at the transmitter and receiver circuits. The RSSI and SNR levels were also measured at Line-of-Sight (LOS) test and during deployment at the urban community farm in Taman Sutera 2, Seberang Jaya, Pulau Pinang. The lowest Received signal Strength Indicator (RSSI) level recorded was −110 dBm at a LOS distance of 300 m between transmitter and receiver devices while the signal to Noise Ratio (SNR) recorded was at 5 dB. A 48-hours deployment testing at the farm was conducted and the results are presented in this paper.

关键词： Smart agriculture Transmitters System performance Soil moisture Receivers Real-time systems internet of Things Received signal strength indicator Farming signal to noise ratio

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Automatic Tumour Segmentation Network based on PLHN Architecture and Dynamic Contrast-Enhanced Magnetic Resonance Imaging 3

Automatic Tumour Segmentation Network Based on PLHN Architec...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Xie, Yaoxuan Li, Jiajian Rong, Chenyang Cheng, Yan College of Software Taiyuan University of Technology Taiyuan China

ISBN: (纸本)9798331508456

Tumor diagnosis often requires hemodynamic information to be judged. DCE-MRI technology can record and compare information well and has become an important tool in the diagnosis process. However, at this stage, the automatic segmentation technology for tumors is still not perfect, which is mainly due to the diversity of tumor morphology and signal intensity, and the insufficient performance of existing deep learning models in dealing with complex structures and image noise. In order to solve these problems, this paper proposes an automatic tumor segmentation method based on pyramid hierarchical network (PLHN) architecture and DCE-MRI. This method enhances the network's ability to recognize tumor boundaries and details through multi-scale feature extraction and hierarchical processing, and shows stronger robustness when dealing with dynamic changes in DCE-MRI. According to our experimental results on the data set, the method proposed in this paper can effectively improve the accuracy of tumor segmentation, which has strong research and practical significance. © 2025 IEEE.

关键词： image segmentation

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image Cryptography Scheme based on Improvement Chaos Logistic Function, Chaos Arnold Cat Map and Gingerbread Man Process

Image Cryptography Scheme Based on Improvement Chaos Logisti...

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Artificial Intelligence and Mechatronics systems (AIMS), international conference on

作者： Mohammed Mahdi Hashim Abdul Razzaq Jabr Almajidi Raed Khalid Ibrahim Bashar I. Jasem Mohammed Ayad Saad Abdullah A. Nahi Ministry of Higher Education and Scientific Research Baghdad Iraq AL-Esraa University Baghdad Iraq Department of Medical instruments engineering Al-Farahidi University Baghdad Iraq Department of Computer Technology Engineering Al-Hadba University College Mosul Iraq Department of Medical Instrumentations Technique Engineering Jannat Iraq College Ramadi Iraq Department of Information Technologies Management Technical College Southern Technical University Basrah Iraq

Recently, information security has emerged as a crucial aspect of information technology and communication considering the internet's proliferation. The development of image cryptography emerged as a method to enhance the confidentiality of communication. In order to achieve these aims, a framework comprising three primary phases has been developed for the purpose of image cryptography. During the first phase, a fresh key generator was developed, incorporating a substantial degree of randomness by utilizing several chaotic maps and the suggested Improvement Chaos Logistic function (ICLF). In the second phase, the proposed method involves the introduction of a confusion process. This phase entails the sorting of the key generator in ascending order, followed by the permutation of the image pixels depending on the sorting key. Finally, the diffusion phase is described, which involves the utilization of the Gingerbread Man Method (GGM), Arnold Cat Map (ACM) transform, and XOR operation. based on the findings, it has been confirmed that the suggested image cryptography method has a better result in the field of cryptography when compared with existing methods.

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Underdetermined Source signal Reconstruction based on Multi-modal Fusion

Underdetermined Source Signal Reconstruction Based on Multi-...

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international conference on Communication technology (ICCT)

作者： Hongyi Pan Lichun Li Hailong Zhang Weinian Li Siyao Tian School of Information Systems Engineering Information Engineering University Zhengzhou China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

In complex electromagnetic environments, non-cooperative wide-band receivers encounter the challenge of separating and identifying unknown source signals from time-frequency aliased signals. When the number of received channels is smaller than the number of source signals, it leads to the under-determined signal separation problem, making the task particularly *** circumvent this limitation, this paper proposes an under-determined source signal reconstruction method based on multi-modal fusion (URBM).The proposed method effectively utilizes the complementary characteristics of different modal signals such as time-frequency maps and wave-frequency maps, thereby improving the accuracy and robustness of source signal reconstruction.

关键词： Time-frequency analysis Source separation Accuracy Simulation Receivers signal reconstruction Robustness Electromagnetics image reconstruction signal to noise ratio

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