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arXiv 2023年

作者： Yuan, Yi Wang, Cheng-Xiang Cheng, Xiang Ai, Bo Laurenson, David I. The Joint Research Institute for Signal and Image Processing School of Engineering & Physical Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom The Research Institute for Modern Communications School of Electronics Engineering & Computer Science Peking University Beijing100871 China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Joint Research Institute for Signal and Image Processing Institute for Digital Communications University of Edinburgh EdinburghEH9 3JL United Kingdom

This paper proposes a novel three-dimensional (3D) theoretical regular-shaped geometry-based stochastic model (RS-GBSM) and the corresponding sum-of-sinusoids (SoS) simulation model for non-isotropic multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) Ricean fading channels. The proposed RS-GBSM, combining line-of-sight (LoS) components, a two-sphere model, and an elliptic-cylinder model, has the ability to study the impact of the vehicular traffic density (VTD) on channel statistics, and jointly considers the azimuth and elevation angles by using the von Mises Fisher distribution. Moreover, a novel parameter computation method is proposed for jointly calculating the azimuth and elevation angles in the SoS channel simulator. Based on the proposed 3D theoretical RS-GBSM and its SoS simulation model, statistical properties are derived and thoroughly investigated. The impact of the elevation angle in the 3D model on key statistical properties is investigated by comparing with those of the corresponding two-dimensional (2D) model. It is demonstrated that the 3D model is more accurate to characterize real V2V channels, in particular for pico cell scenarios. Finally, close agreement is achieved between the theoretical model, SoS simulation model, and simulation results, demonstrating the utility of the proposed models. © 2023, CC BY.

关键词： Stochastic systems

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Dynamic analysis and image encryption using a novel 4D map for secure transmission using WDM 4*20 Gbps techniques

Franklin Open

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Franklin Open 2025年 10卷

作者： Fotsing, J. Kengne, R. Tiedeu, A. Tonye, E. Department of Physics Faculty of Science University of Buea P.O.Box 63 Cameroon Instrumentation Signal and Image Laboratory National Advanced School of Engineering of Yaounde University of Yaounde I P.O.Box 8390 Cameroon Laboratoire d'Ingénierie Mathématique et Systèmes d'Information National Advanced School of Engineering University of Yaoundé I P.O. Box 8390 Yaoundé Cameroon Laboratory of Condensed Matter Electronics and Signal Processing Department of Physics University of Dschang P. O. Box 67 Dschang Cameroon

This study presents a detailed examination of the design, dynamic analysis, and applications of a novel four-dimensional hyperchaotic system, which has not been explored in previous literature. We propose a new 4D autonomous hyperjerk system, characterized by its distinctive nonlinearity, specifically the Van der Pol nonlinearity. To validate our theoretical findings, we implemented the system both in PSpice software and through a real analog circuit built in the laboratory. This setup successfully reproduced the chaotic attractor phase portrait observed in numerical simulations, confirming the accuracy of the theoretical analysis. Notably, this system is the first to incorporate an adaptive backstepping controller in a PSpice environment, validating both theoretical and numerical synchronization results. Furthermore, we applied this system to image encryption, introducing a novel encryption method based on the proposed 4D hyperjerk system. Based on the NIST SP800–22 standard, the hyperjerk key sequence generated by the algorithm passed all 15 test metrics, proving its excellent randomness. The encrypted images and sequences were subjected to various tests, including statistical and differential attacks, key sensitivity, correlation, and key space analysis. These tests demonstrated the effectiveness and reliability of the proposed system. Notably, when the key was altered by even a tiny value (10⁻¹⁵ difference from the correct key), the data could not be decoded, ensuring secure transmission. The cryptosystem also showed robustness against known attacks, such as chosen plaintext attacks (CPA) and chosen ciphertext attacks (CCA), proving its resilience in real-world scenarios. The optical communication system utilizing 4 × 20 Gbps WDM technology was also evaluated. Simulations showed that the system provided reliable, secure transmission over long distances. Performance metrics, including Quality Factor (Q-factor), Bit Error Rate (BER), and eye diagram ana

关键词： 4D hyperjerk system image encryption Security Vand der Pol nonlinearity WDM 4*20 Gbps

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One to multiple mapping dual learning: Learning multiple sources from one mixed signal

arXiv

引用

arXiv 2021年

作者： Liu, Ting Wang, Wenwu Zhang, Xiaofei Yan, Jingwen Guo, Yina The Department of Electronics and Communication Engineering Taiyuan University of Science and Technology Taiyuan030024 China The Centre for Vision Speech and Signal Processing University of Surrey Guildford SurreyGU2 7XH United Kingdom The Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Technology

Single channel blind source separation (SCBSS) refers to separate multiple sources from a mixed signal collected by a single sensor. The existing methods for SCBSS mainly focus on separating two sources and have weak generalization performance. To address these problems, an algorithm is proposed in this paper to separate multiple sources from a mixture by designing a parallel dual generative adversarial Network (PDualGAN) that can build the relationship between a mixture and the corresponding multiple sources to realize one-to-multiple cross-domain mapping. This algorithm can be applied to any mixed model such as linear instantaneous mixed model and convolutional mixed model. Besides, one-to-multiple datasets are created which including the mixtures and corresponding sources for this study. The experiment was carried out on four different datasets and tested with signals mixed in different proportions. Experimental results show that the proposed algorithm can achieve high performance in peak signal-to-noise ratio (PSNR) and correlation, which outperforms state-of-the-art algorithms. © 2021, CC BY.

关键词： Mapping

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AI for Extreme Event Modeling and Understanding: Methodologies and Challenges

arXiv

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arXiv 2024年

作者： Camps-Valls, Gustau Fernández-Torres, Miguel-Ángel Cohrs, Kai-Hendrik Höhl, Adrian Castelletti, Andrea Pacal, Aytac Robin, Claire Martinuzzi, Francesco Papoutsis, Ioannis Prapas, Ioannis Pérez-Aracil, Jorge Weigel, Katja Gonzalez-Calabuig, Maria Reichstein, Markus Rabel, Martin Giuliani, Matteo Mahecha, Miguel Popescu, Oana-Iuliana Pellicer-Valero, Oscar J. Ouala, Said Salcedo-Sanz, Sancho Sippel, Sebastian Kondylatos, Spyros Happé, Tamara Williams, Tristan Image Processing Laboratory Universitat de València Spain Technical University of Munich Munich Germany Department of Electronics Information and Bioengineering Politecnico di Milano Italy Bremen Germany Max Planck Institute of Biogeochemistry Jena Germany Leipzig University Leipzig Germany National Technical University of Athens Greece National Observatory of Athens Athens Greece Department of Signal Processing and Communications Universidad de Alcalá Madrid Spain Jena Germany IMT Atlantique Brest France Institute for Environmental Studies VU Amsterdam Netherlands Leipzig Germany Institute for Earth System Science & Remote Sensing Leipzig University Leipzig Germany Leipzig University Leipzig Germany Institut für Physik der Atmosphäre Oberpfaffenhofen Germany

In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. However, the latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous and limited annotated data. This paper reviews how AI is being used to analyze extreme events (like floods, droughts, wildfires and heatwaves), highlighting the importance of creating accurate, transparent, and reliable AI models. We discuss the hurdles of dealing with limited data, integrating information in real-time, deploying models, and making them understandable, all crucial for gaining the trust of stakeholders and meeting regulatory needs. We provide an overview of how AI can help identify and explain extreme events more effectively, improving disaster response and communication. We emphasize the need for collaboration across different fields to create AI solutions that are practical, understandable, and trustworthy for analyzing and predicting extreme events. Such collaborative efforts aim to enhance disaster readiness and disaster risk reduction. © 2024, CC BY.

关键词： Floods

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Correction to: A novel cost-effective sparsity-aware algorithm with Kalman-based gain for the identification of long acoustic impulse responses

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signal, image and Video processing 2020年第2期15卷 439-439页

作者： Ayoub Tedjani Ahmed Benallal Signal Processing and Image Laboratory (LATSI) – Department of Electronics Faculty of Technology University of Blida 1 Blida Algeria

Unfortunately, the above article has been published with some typos in it.

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Automated Exudates Detection in Retinal Fundus image Using Morphological Operator and Entropy Maximization Thresholding

引用

Journal of Biomedical Science and Engineering 2019年第3期12卷 212-224页

作者： G. H. Kom B. C. Wouantsa Tindo J. R. Mboupda Pone A. B. Tiedeu Research Unit of Automation and Applied Informatics (URAIA) Department of Electrical Engineering IUT-FV Bandjoun University of Dschang Bandjoun Cameroon Research Group on Medical Technologies Applied to the Tropics (GRETMAT) Laboratory of Electronics and Signal Processing (LETS) ENSP University of Yaoundé I Yaoundé Cameroon Research Unit for Condensed Matter Electronics and Signal Processing (LAMACETS) Department of Physics University of Dschang Dschang Cameroon Signal Image and Systems Laboratory Ebolowa Cameroon

Blindness which is considered as degrading disabling disease is the final stage that occurs when a certain threshold of visual acuity is overlapped. It happens with vision deficiencies that are pathologic states due to many ocular diseases. Among them, diabetic retinopathy is nowadays a chronic disease that attacks most of diabetic patients. Early detection through automatic screening programs reduces considerably expansion of the disease. Exudates are one of the earliest signs. This paper presents an automated method for exudates detection in digital retinal fundus image. The first step consists of image enhancement. It focuses on histogram expansion and median filter. The difference between filtered image and his inverse reduces noise and removes background while preserving features and patterns related to the exudates. The second step refers to blood vessel removal by using morphological operators. In the last step, we compute the result image with an algorithm based on Entropy Maximization Thresholding to obtain two segmented regions (optical disk and exudates) which were highlighted in the second step. Finally, according to size criteria, we eliminate the other regions obtain the regions of interest related to exudates. Evaluations were done with retinal fundus image DIARETDB1 database. DIARETDB1 gathers high-quality medical images which have been verified by experts. It consists of around 89 colour fundus images of which 84 contain at least mild non-proliferative signs of the diabetic retinopathy. This tool provides a unified framework for benchmarking the methods, but also points out clear deficiencies in the current practice in the method development. Comparing to other recent methods available in literature, we found that the proposed algorithm accomplished better result in terms of sensibility (94.27%) and specificity (97.63%).

关键词： Diabetic Retinopathy Retinal Fundus image Exudates Entropy Maximization Thresholding

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Set-Membership Fast-NLMS Algorithm for Acoustic Echo Cancellation

Set-Membership Fast-NLMS Algorithm for Acoustic Echo Cancell...

引用

International Conference on signal, image, Vision and their Applications (SIVA )

作者： Islam HASSANI Madjid AREZKI Ahmed BENALLAL dept. of electronics Signal Processing and Image Laboratory(LATSI) University of Blida Blida Algeria

ISBN: (纸本)9781538671214;9781538671207

This communication deals with the problem of Acoustic Echo Cancellation (AEC) by reduced complexity adaptive filtering algorithms. In this context, we propose to use the Set-Membership filtering concept with the Fast Normalized Least Mean Square Algorithm (SM-FNLMS). The proposed SM-FNLMS algorithm shows a superior convergence speed performances compared with the classical Set-Membership NLMS (SM-NLMS) and NLMS algorithms.

关键词： signal processing algorithms Filtering Adaptive filters Echo cancellers Prediction algorithms Convergence

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Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan images

arXiv

引用

arXiv 2020年

作者： Rajinikanth, V. Dey, Nilanjan Raj, Alex Noel Joseph Hassanien, Aboul Ella Santosh, K.C. Raja, N. Sri Madhava Department of Electronics and Instrumentation Engineering St. Joseph’s College of Engineering Chennai600119 India Department of Information Technology Techno India College of Technology KolkataWest Bengal700156 India Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronic Engineering College of Engineering Shantou University Shantou Guangdong China Faculty of Computers and Artificial Intelligence Cairo University Egypt Department of Computer Science University of South Dakota 414 E Clark St VermillionSD57069 United States

Pneumonia is one of the foremost lung diseases and untreated pneumonia will lead to serious threats for all age groups. The proposed work aims to extract and evaluate the Coronavirus disease (COVID-19) caused pneumonia infection in lung using CT scans. We propose an image-assisted system to extract COVID-19 infected sections from lung CT scans (coronal view). It includes following steps: (i) Threshold filter to extract the lung region by eliminating possible artifacts;(ii) image enhancement using Harmony-Search-Optimization and Otsu thresholding;(iii) image segmentation to extract infected region(s);and (iv) Region-of-interest (ROI) extraction (features) from binary image to compute level of severity. The features that are extracted from ROI are then employed to identify the pixel ratio between the lung and infection sections to identify infection level of severity. The primary objective of the tool is to assist the pulmonologist not only to detect but also to help plan treatment process. As a consequence, for mass screening processing, it will help prevent diagnostic burden. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Benchmarking quality-dependent and cost-sensitive score-level multimodal biometric fusion algorithms

arXiv

引用

arXiv 2021年

作者： Poh, Norman Bourlai, Thirimachos Kittler, Josef Allano, Lorene Alonso-Fernandez, Fernando Ambekar, Onkar Baker, John Dorizzi, Bernadette Fatukasi, Omolara Fierrez, Julian Ganster, Harald Ortega-Garcia, Javier Maurer, Donald Salah, Albert Ali Scheidat, Tobias Vielhauer, Claus Centre for Vision Speech and Signal Processing School of Electronics and Physical Sciences University of Surrey Guildford SurreyGU2 7XH United Kingdom Biometrics Center Lane Department of Computer Science and Electrical Engineering College of Engineering and Mineral Resources West Virginia University MorgantownWV26506-6109 United States Institut Telecom Telecom and Management SudParis Evry91011 France Biometric Recognition Group – ATVS Escuela Politecnica Superior Universidad Autonoma de Madrid Madrid28049 Spain Amsterdam1098 XG Netherlands Applied Physics Laboratory Johns Hopkins University LaurelMD20723 United States Electronics and Physics Department Institut Telecom Telecom and Management SudParis Evry91011 France Institute of Digital Image Processing Joanneum Research Graz8010 Austria ISLA-ISIS University of Amsterdam Amsterdam1098 XG Netherlands Department of Informatics and Media Brandenburg University of Applied Sciences Brandenburg an der HavelD-14770 Germany Research Group Multimedia and Security Department of Technical and Business Information Systems Faculty of Computer Science Otto-von-Guericke-University of Magdeburg MagdeburgD-39106 Germany

Automatically verifying the identity of a person by means of biometrics (e.g., face and fingerprint) is an important application in our day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject in the literature, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw biometric images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both the template and the query data. The response to the call of the evaluation campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this campaign is the first attempt to benchmark quality-based multimodal fusion algorithms. In the presence of changing

关键词： Benchmarking

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NTIRE 2021 Challenge on image Deblurring

arXiv

引用

arXiv 2021年

作者： Nah, Seungjun Son, Sanghyun Lee, Suyoung Timofte, Radu Lee, Kyoung Mu Chen, Liangyu Zhang, Jie Lu, Xin Chu, Xiaojie Chen, Chengpeng Xiong, Zhiwei Xu, Ruikang Xiao, Zeyu Huang, Jie Zhang, Yueyi Xi, Si Wei, Jia Bai, Haoran Cheng, Songsheng Wei, Hao Sun, Long Tang, Jinhui Pan, Jinshan Lee, Donghyeon Lee, Chulhee Kim, Taesung Wang, Xiaobing Zhang, Dafeng Pan, Zhihong Lin, Tianwei Wu, Wenhao He, Dongliang Li, Baopu Li, Boyun Xi, Teng Zhang, Gang Liu, Jingtuo Han, Junyu Ding, Errui Tao, Guangpin Chu, Wenqing Cao, Yun Luo, Donghao Tai, Ying Lu, Tong Wang, Chengjie Li, Jilin Huang, Feiyue Chen, Hanting Chen, Shuaijun Guo, Tianyu Wang, Yunhe Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Shao, Ling Zuo, Yushen Ou, Yimin Chai, Yuanjun Shi, Lei Liu, Shuai Lei, Lei Feng, Chaoyu Zeng, Kai Yao, Yuying Liu, Xinran Zhang, Zhizhou Huang, Huacheng Zhang, Yunchen Jiang, Mingchao Zou, Wenbin Miao, Si Kim, Yangwoo Sun, Yuejin Deng, Senyou Ren, Wenqi Cao, Xiaochun Wang, Tao Suin, Maitreya Rajagopalan, A.N. Duong, Vinh Van Nguyen, Thuc Huu Yim, Jonghoon Jeon, Byeungwoo Li, Ru Xie, Junwei Han, Jong-Wook Choi, Jun-Ho Kim, Jun-Hyuk Lee, Jong-Seok Zhang, Jiaxin Peng, Fan Svitov, David Pakulich, Dmitry Kim, Jaeyeob Jeong, Jechang Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland University of Science and Technology of China China Megvii China Fudan University China Peking University China Netease Games AI Lab Nanjing University of Science and Technology China Guilin University of Electronic Technology China Samsung Electronics Co. Ltd Sunmoon University Asan Korea Republic of Samsung Research China Beijing China Baidu Research United States Department of Computer Vision Technology Baidu Inc China Fudan University China Megvii China Nanjing University China Tencent Noah's Ark Lab Huawei Technologies Co. Ltd Inception Institute of Artificial Intelligence Tsinghua University Beijing China North China University of Technology China Xiaomi South China University of Technology China Lab of Image Science and Technology Southeast University China China Design Group Co. Ltd China JOYY AI GROUP Fujian Normal University China Shanghai Advanced Research Institute Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China Huawei Noah's Ark Lab Indian Institute of Technology Madras India Department of ECE Sungkyunkwan University Korea Republic of Fuzhou University China Imperial Vision Co. Ltd School of Integrated Technology Yonsei University Korea Republic of Expasoft LLC Institute of Automation and Electrometry The SB RAS Image Communication & Signal Processing Laboratory Hanyang University Korea Republic of

Motion blur is a common photography artifact in dynamic environments that typically comes jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on image Deblurring. In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions. While both the tracks aim to recover a high-quality clean image from a blurry image, different artifacts are jointly involved. In track 1, the blurry images are in a low resolution while track 2 images are compressed in JPEG format. In each competition, there were 338 and 238 registered participants and in the final testing phase, 18 and 17 teams competed. The winning methods demonstrate the state-of-the-art performance on the image deblurring task with the jointly combined artifacts. © 2021, CC BY-NC-ND.

关键词： image enhancement

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