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DPPAD-IE: Dynamic Polyhedra Permutating and Arnold Diffusing Medical Image Encryption Using 2D Cross Gaussian Hyperchaotic Map

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Quanjun Li, Qian Ling, Bingo Wing-Kuen Pun, Chi-Man Huang, Guoheng Yuan, Xiaochen Zhong, Guo Ayouni, Sarra Chen, Jianwu Guangdong University of Technology School of Advanced Manufacturing Jieyang522000 China Wuyi University School of Electronics and Information Engineering Jiangmen510006 China Guangdong University of Technology School of Information Engineering Guangzhou510006 China University of Macau Faculty of Science and Technology Macau China Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Macau Polytechnic University Faculty of Applied Sciences Macau China Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou510420 China Princess Nourah bint Abdulrahman University College of Computer and Information sciences Department of Information Systems Riyadh11671 Saudi Arabia Fujian Medical University Union Hospital Department of Radiation Oncology Fujian province Fuzhou China Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors Fujian Medical University Fujian province Fuzhou China Fujian province Fuzhou China

With the rapid improvement of medical consumer electronics (CE), the transmission of multiple images has become increasingly common in the medical field. During the transmission and storage of image data, sensitive information, including medical images, may be accessed by unauthorized parties, resulting in possible privacy violations. Consequently, several multi-image encryption (MIE) algorithms have been proposed to solve this real-world problem. Nevertheless, current MIE schemes exhibit several drawbacks, including insufficient security in permutation and diffusion algorithms, as well as the insensitivity of chaotic maps. This paper presents a new encryption algorithm called DPPAD-IE. The algorithm primarily consists of three components. First, we developed an innovative hyperchaotic map called the 2D Cross Gaussian Hyperchaotic Map (2D-CGHM), which has an expanded chaotic interval and is proficient in producing chaotic sequences with improved pseudorandomness. Second, we introduce a new Dynamic Polyhedra Permutation algorithm (DPP) that successfully alters pixel locations of the image, hence diminishing the connection between neighboring pixels. Third, we propose an Arnold Diffusion algorithm (AD), using chaotic sequences to further encrypt the permuted image. The DPPAD-IE is capable of encrypting images of any quantity and size concurrently. The experimental results confirm that this approach is both robust and effective, capable of defending against various security attacks. The code are available at:https://***/QuincyQAQ/DPPAD-IE. © 1975-2011 IEEE.

关键词： Medical imaging

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Low-Latency Immersive Display systems with FPGA for Remote Applications

Low-Latency Immersive Display Systems with FPGA for Remote A...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Den Tabata Taiga Kobori Yoshiki Yamaguchi Ryouhei Tsugami Toshihito Fujiwara Tatsuya Fukui Satoshi Narikawa College of Information Science University of Tsukuba Tsukuba Ibaraki Japan Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan Institute of Systems and Information Engineering University of Tsukuba Tsukuba Ibaraki Japan Research and Education Institute for Semiconductor and Informatics Kumamoto University Kumamoto Japan NTT Access Network Service Systems Laboratories NTT Corporation Musashino Tokyo Japan

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

This paper presents an FPGA-based hardware system designed to enhance immersive display and reduce latency in video streaming for remote-controlled and autonomous driving applications. The system improves operator immersion and response accuracy by utilizing a spherical display with real-time distortion correction. The system corrects spherical display distortions in real time by addressing challenges such as video distortion and data buffering through dedicated hardware solutions. It achieves a buffer reduction of approximately 40 %, enabling seamless video streaming even over limited network connections. Experimental evaluations confirm the system's capability to minimize latency while delivering high-quality, immersive visuals, positively impacting remote vehicle control precision.

关键词： Visualization Display systems Keyboards Streaming media Distortion Real-time systems Hardware Time factors Low latency communication Autonomous vehicles

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Intelligent Automatic Number Plate Recognition System

TechRxiv

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TechRxiv 2023年

作者： Shivam, S. Cherukuri, Aswani Kumar Kamalov, Firuz Khalil, Ashraf Nazir, Amril Jonnalagadda, Annapurna Thong, Chee Ling School of Information Technology and Engineering Vellore Institute of Technology Vellore632014 India Department of Electrical Engineering Canadian University Dubai United Arab Emirates Department of Information Systems and Technology Management Zayed University Abu Dhabi United Arab Emirates School of Computer Science & Engineering Vellore Institute of Technology Vellore India Institute of Computer Science and Digital Innovation UCSI University Malaysia

Automatic Number Plate Recognition (ANPR) systems were created in order to address the challenges created due to rise in the volume, velocity and density of automobile vehicles. Most modern ANPR systems heavily depend on image processing techniques like contouring and gray-scaling to segment the license plate and use optical character recognition to extract the number from the manipulated image of the number plate. The major advantage of such systems is that they require less computational power and are quite cost-effective. But these systems process the complete images rather than just the region of interest. The more modern ANPR systems rely on object detection algorithms to overcome this challenge and process only the area of interest from the image. But these systems are not suitable for real-time applications due to high computational overhead and high processing time. To overcome the limitations of the existing ANPR systems, we propose an intelligent ANPR is a system for detecting and extracting number plate details from images or sequences of images of vehicles. The proposed deep learning model is based on an improved version of the You Only Look Once (YOLOv4) algorithm. The performance evaluation demonstrates that our model is able to recognize the number plates with an accuracy of above 95% under varied conditions such as high-speed moving vehicles, varying lighting, and vehicle dense area on Indian roads. The system is robust enough to detect the number plates of fast moving vehicles (speed≤ 80km/hr) as well as vehicles in highly traffic dense areas. © 2023, CC BY.

关键词： Optical character recognition

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A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data

A Trustable LSTM-Autoencoder Network for Cyberbullying Detec...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Akter, Mst Shapna Shahriar, Hossain Cuzzocrea, Alfredo Wu, Fan Rodriguez-Cardenas, Juanjose University of West Florida Department of Intelligent Systems and Robotics United States University of West Florida Center of Cybersecurity United States University of Calabria IDEA Lab Rende Italy Tuskegee University Dept. of Computer Science Tuskegee United States Kennesaw State University Dept. of Information Technology Georgia United States

ISBN: (纸本)9798350324457

Social media cyberbullying has a detrimental effect on human life. As online social networking grows daily, the amount of hate speech also increases. Such terrible content can cause depression and actions related to suicide. This paper proposes a trustable LSTM-Autoencoder Network for cyberbullying detection on social media using synthetic data. We have demonstrated a cutting-edge method to address data availability difficulties by producing machine-translated data. However, several languages such as Hindi and Bangla still lack adequate investigations due to a lack of datasets. We carried out experimental identification of aggressive comments on Hindi, Bangla, and English datasets using the proposed model and traditional models, including Long Short-Term Memory (LSTM), Bidirectional Long ShortTerm Memory (BiLSTM), LSTM-Autoencoder, Word2vec, Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-trained Transformer 2 (GPT-2) models. We employed evaluation metrics such as f1-score, accuracy, precision, and recall to assess the models' performance. Our proposed model outperformed all the models on all datasets, achieving the highest accuracy of 95%. Our model achieves state-of-the-art results among all the previous works on the dataset we used in this paper. © 2023 IEEE.

关键词： Cyber-bullying Deep Learning Natural Language Processing Neural Networks

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Estimating Finger Joint Angles with Wearable System Based on Machine Learning Model Utilizing 3D Computer Graphics

Estimating Finger Joint Angles with Wearable System Based on...

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IEEE International Conference on systems, Man and Cybernetics

作者： T. Obinata D. Yoshikawa A. Uehara H. Kawamoto Graduate School of Science and Technology University of Tsukuba Ibaraki Japan Japan Society for the Promotion of Science Center for Cybernics Research Institute of Systems and Information Engineering University of Tsukuba Tsukuba Ibaraki Japan

Robotic rehabilitation for paralyzed hands utilizes exoskeletons and soft gloves equipped with active mechanisms to provide support for hand motion. From a safety and control perspective, it is imperative to measure finger joint angles during motion support provided by a soft robotic wearable system. However, embedding sensors into these devices can be inconvenient as it may lead to bulkiness or structural difficulties. This study aims to develop a machine learning model for estimating finger joint angles from images utilizing data created with computer graphics (CG), and to validate the feasibility of this method through basic experiments. The three-dimensional CG (3DCG) hand model includes bones corresponding to the major joints of the fingers, and a wearable system of the index finger imported from a computer-aided design software was attached to the 3DCG hand model. After rendering the integrated motion between the hand model and the wearable system for finger flexion and extension, images in conjunction with the finger joint angles were recorded as the training data. The finger joint angle estimator was based on a pre-trained vision transformer model and was learned using the created 3DCG training dataset. The basic experiments showed that the developed machine learning model enabled the estimation of finger joint angles with reproducibility for four types of hand postures with the wearable system. Furthermore, the differences between the joint angles measured in the real world and those estimated by the developed model were smaller than those for an existing hand pose estimation model. The developed machine learning model, which utilizes 3DCG, has the potential to estimate finger joint angles with the wearable system using image videos.

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Functional Verification of Extended Kalman Filter on FPGA for Railway Wheelset Parameters Estimation

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IEEE Access 2025年 13卷 96646-96659页

作者： Mal, Khakoo Memon, Tayab Din Kalwar, Imtiaz Hussain Junejo, Allah Rakhio Memon, Majid Hussain Memon, Tarique Rafique Memon, Sufyan Ali Chowdhry, Bhawani Shankar Sukkur IBA University Sukkur65200 Pakistan Mehran University of Engineering & Technology NCRA-Condition Monitoring Systems Lab Jamshoro76062 Pakistan Torrens University Center for Artificial Intelligence Research and Optimization Faculty of Design and Creative Technology Melbourne Australia Iqra University Faculty of Engineering Sciences and Technology Karachi75500 Pakistan Iqra University Department of Artificial Intelligence Karachi75500 Pakistan Department of Electronic Engineering Quaid-e-Awam University of Engineering Science and Technology Nawabshah67450 Pakistan Department of Defense Systems Engineering Sejong University Seoul Korea Republic of

The forces generated at the wheel-rail contact patch are important and crucial to the railway vehicle's operation because they control the dynamic behavior of the entire rolling stock. The transfer of the tractive force applied by the locomotive's traction and braking system requires a certain amount of adhesion force at the wheel-rail contact area. The wheel may skid while decelerating and slip when accelerating due to the maximal adhesion force at the wheel-rail interface may be less than the applied tractive force. A major difficulty is accurately estimating adhesion conditions and implementing them in real time because the contact forces between the wheel and rail are complicated and are highly non-linear. The fast-processing speed and hardware flexibility of field programmable gate arrays (FPGAs) make them ideal for the implementation of nonlinear condition monitoring systems. The goal of our work is to estimate various railway wheelset characteristics relating to wheel-rail contact forces under variable track-contact situations by designing and implementing the extended Kalman filter (EKF) model utilizing MATlab and FPGA. The onboard estimation of wheel-rail interaction parameters is verified using the Xilinx® System-On-Chip (SoC) Zynq and Xilinx Spartan-3 FPGA devices, built-in with the National Instruments (NI) myRIO® development board and sbRIO® single-board controller. Two FPGA platforms are used to test the dataset generated by the MATlab railway nonlinear wheelset model under different track circumstances, which include the vehicle's acceleration and deceleration modes. The 95% of the simulation results achieved. The proposed model's hardware synthesizes on FPGA are validated, and the area-performance is analyzed. Both FPGA devices take 52 cycles per sample and hence throughput becomes 769 kilo samples per second. Comparing the Xilinx System-On-Chip Zynq FPGA device to the Spartan-3, it is found that the latter offers a worse area-performance tradeoff fo

关键词： Extended Kalman filters

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Anti-phase dual wavelength phase demodulation for extrinsic fabry-perot interferometric sensors 26

Anti-phase dual wavelength phase demodulation for extrinsic ...

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26th Optoelectronics and Communications Conference, OECC 2021

作者： Zhang, Wanjin Lu, Ping Qu, Zhiyuan Liu, Deming National Engineering Laboratory for Next Generation Internet Access Systems School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China

We report an anti-phase dual wavelength phase demodulation to overcome drawback of traditional dual wavelength method such as unable to interrogate signals with small amplitude. Our method shows good linearity and exc... 详细信息

ISBN: (纸本)9781557528209

关键词： Demodulation

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An effective security assessment approach for Internet banking services via deep analysis of multimedia data

An effective security assessment approach for Internet banki...

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作者： Khattak, Sana Jan, Sadeeq Ahmad, Iftikhar Wadud, Zahid Khan, Fazal Qudus National Center for Cyber Security-UETP Department of Computer Science and IT University of Engineering and Technology Peshawar Pakistan Department of Computer Science and IT University of Engineering and Technology Peshawar Pakistan Department of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan Department of Information Technology FCIT King Abdulaziz University Jeddah Saudi Arabia

With the emergence of cyber technology, the biggest evolution has been observed in the use of Internet for financial purposes, in particular for the Internet banking sector. However, with the increase in the number of Internet banking users, many security issues have been discovered. In the recent past, there have been many successful cyber-attacks on the Internet banking services (IBS) throughout the world. There exists a huge amount of various data about the security of the banking systems, however, proper analysis of such data using various learning techniques is needed for security assessment. In this research work, we aim to assess the security of IBS by developing a framework based on deep analysis of big data (available in various formats) and the existing security requirements of the country. We propose a framework consisting of 93 data categories to assess the security of the IBS. We evaluate our proposed approach on a case study consisting of the banks providing IBS in Pakistan. A total of 21 Pakistani banks providing Internet banking services are analyzed thoroughly using our proposed framework. The result uncovered many deficiencies in the Internet banking services of the analyzed banks. All these deficiencies are conveyed to the respective banks for verification and helping them to take corrective measures. In addition, a comprehensive set of security recommendations is developed for the banks, their customers and the regularity authority for improving Internet banking security. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Network security

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SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning

arXiv

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arXiv 2025年

作者： Hennebelle, Alain Dieng, Qifan Ismail, Leila Buyya, Rajkumar Abu Dhabi United Arab Emirates Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates Emirates Center for Mobility Research United Arab Emirates National Water and Energy Center UAE University Abu Dhabi Al-Ain United Arab Emirates

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model *** Codes 68T01, 68T09, 68M14, 68W10, 68W15 © 2025, CC BY.

关键词： Electronic health record

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A multi-objective cuckoo search algorithm using generalized Lèvy flight and dissimilar egg identification for multispectral image thresholding

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Applied Soft Computing 2025年 175卷

作者： Pal, Ramen Roy, Pritam Mallick, Srijon Mukhopadhyay, Somnath Sarkar, Sunita Hinchey, Mike Department of Computer Science and Engineering Assam University Silchar Assam India Cognizant Technology Solutions India Pvt. Ltd. India Samsung Semiconductor India Research India Lero the SFI Research Centre for Software Department of Computer Science & Information Systems University of Limerick Limerick Ireland

Cuckoo Search (CS) stands as a highly efficient meta-heuristic optimization algorithm. Existing literature showcases the ability of CS in multi-objective scenarios, delineated by the three fundamental rules. However, the first rule of the algorithm incurs the cost of a generation to update a single nest, while the third rule necessitates hit-and-trial methods for parameter adjustment. To address these concerns, a multi-objective cuckoo search algorithm is proposed in this paper. The algorithm builds upon a generalized concept of Lèvy Flight for generating new solutions. Problem-specific, constraint-based strategies for identifying the best nest and dissimilar eggs are also introduced. The algorithm is further applied to solve multispectral remote sensing image thresholding problem. Prior studies have underscored the efficiency of entropy and clustering-based thresholding methods over other techniques. Nevertheless, most entropy-based approaches entail converting color images to grayscale before segmentation, potentially sacrificing crucial spectral information and consequently degrading segmentation algorithm's performance. To avoid these limitations, this research introduces an entropy-based thresholding method to segment a color image without converting it to grayscale. The experiments are carried out using very high resolution (VHR) and coarse resolution (CR) multispectral (MS) images from the satellite sensors Pl'eidas-1B and Sentinel-2b, respectively. The proposed methods undergo validation against four state-of-the-art techniques on benchmark functions and six clustering indexes, respectively. © 2025 Elsevier B.V.

关键词： Heuristic algorithms

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