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Flooding disaster resilience information framework for smart and connected communities

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Journal of Reliable Intelligent Environments 2019年第1期5卷 3-15页

作者： Kumar, Sathish A. P. Bao, Shaowu Singh, Vivek Hallstrom, Jason Department of Computing Sciences Coastal Carolina University ConwaySC29528 United States Department of Coastal and Marine System Sciences Coastal Carolina University ConwaySC29528 United States School of Communication Rutgers University New BrunswickNJ08901 United States College of Engineering and Computer Science Florida Atlantic University Boca RatonFL33431 United States

This paper presents the research challenges of designing a combined physical sensor- and social sensor-based information framework to collect heterogeneous flooding disaster data, and then to fuse those data and generate actionable understandings. Our overall objective is to improve the response preparedness of critical infrastructures, contributing to the goal of smart and connected communities. We propose methods to model physical and social sensors, and open demographic data integration with regional knowledge, and to leverage these fused data for understanding impending events and conditions deleterious to lives and properties. In addition, the proposed system will predict the disaster events and provide knowledge-based recommendations to inform emergency management personnel to enable the resilience of the smart and connected communities. Preliminary experiments for the framework are promising. Further work is needed to validate the framework in collaboration with the local emergency managers. © 2019, Springer Nature Switzerland AG.

关键词： Disasters

Distributed machine learning for wireless communication networks: Techniques, architectures, and applications

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arXiv 2020年

作者： Hu, Shuyan Chen, Xiaojing Ni, Wei Hossain, Ekram Wang, Xin State Key Laboratory of ASIC and System School of Information Science and Technology Fudan University Shanghai200433 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China SydneyNSW2122 Australia Department of Electrical and Computer Engineering University of Manitoba WinnipegMBR3T 5V6 Canada State Key Laboratory of ASIC and System Shanghai Institute for Advanced Communication and Data Science Department of Communication Science and Engineering Fudan University Shanghai200433 China

Distributed machine learning (DML) techniques, such as federated learning, partitioned learning, and distributed reinforcement learning, have been increasingly applied to wireless communications. This is due to improved capabilities of terminal devices, explosively growing data volume, congestion in the radio interfaces, and increasing concern of data privacy. The unique features of wireless systems, such as large scale, geographically dispersed deployment, user mobility, and massive amount of data, give rise to new challenges in the design of DML techniques. There is a clear gap in the existing literature in that the DML techniques are yet to be systematically reviewed for their applicability to wireless systems. This survey bridges the gap by providing a contemporary and comprehensive survey of DML techniques with a focus on wireless networks. Specifically, we review the latest applications of DML in power control, spectrum management, user association, and edge cloud computing. The optimality, scalability, convergence rate, computation cost, and communication overhead of DML are analyzed. We also discuss the potential adversarial attacks faced by DML applications, and describe state-of-the-art countermeasures to preserve privacy and security. Last but not least, we point out a number of key issues yet to be addressed, and collate potentially interesting and challenging topics for future research. Copyright © 2020, The Authors. All rights reserved.

关键词： Data privacy

Automatic reorientation by deep learning to generate short-axis SPECT myocardial perfusion images

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arXiv 2022年

作者： Zhu, Fubao Wang, Guojie Zhao, Chen Malhotra, Saurabh Zhao, Min He, Zhuo Shi, Jianzhou Jiang, Zhixin Zhou, Weihua School of Computer and Communication Engineering Zhengzhou University of Light Industry Henan Zhengzhou450000 China Department of Applied Computing Michigan Technological University HoughtonMI49931 United States Division of Cardiology Cook County Health and Hospitals System ChicagoIL60612 United States Division of Cardiology Rush Medical College ChicagoIL60612 United States Department of Nuclear Medicine Xiangya Hospital Central South University Changsha410008 China Department of Cardiology The First Affiliated Hospital of Nanjing Medical University Nanjing210000 China Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI49931 United States

Single photon emission computed tomography (SPECT) myocardial perfusion images (MPI) can be displayed both in traditional short-axis (SA) cardiac planes and polar maps for interpretation and quantification. It is essential to reorient the reconstructed transaxial SPECT MPI into standard SA slices. This study is aimed to develop a deep-learning-based approach for automatic reorientation of MPI. Methods: A total of 254 patients were enrolled, including 228 stress SPECT MPIs and 248 rest SPECT MPIs. Five-fold cross-validation with 180 stress and 201 rest MPIs was used for training and internal validation;the remaining images were used for testing. The rigid transformation parameters (translation and rotation) from manual reorientation were annotated by an experienced operator and used as the ground truth. A convolutional neural network (CNN) was designed to predict the transformation parameters. Then, the derived transform was applied to the grid generator and sampler in spatial transformer network (STN) to generate the reoriented image. A loss function containing mean absolute errors for translation and mean square errors for rotation was employed. A three-stage optimization strategy was adopted for model optimization: 1) optimize the translation parameters while fixing the rotation parameters;2) optimize rotation parameters while fixing the translation parameters;3) optimize both translation and rotation parameters together. Results: In the test set, the correlation coefficients of the translation distances and rotation angles between the model prediction and the ground truth were 0.99 in X axis, 0.99 in Y axis, 0.99 in Z axis, 0.99 along X axis, 0.99 along Y axis and 0.99 along Z axis, respectively. For the 46 stress MPIs in the test set, the Pearson correlation coefficients were 0.95 in scar burden and 0.95 in summed stress score;for the 46 rest MPIs in the test set, the Pearson correlation coefficients were 0.95 in scar burden and 0.95 in summed rest score. Conclu

关键词： Deep learning

Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem

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Journal of Healthcare engineering 2021年 2021卷

作者： Ahmad, Ijaz Ullah, Inam Khan, Wali Ullah Ur Rehman, Ateeq Adrees, Mohmmed S. Saleem, Muhammad Qaiser Cheikhrouhou, Omar Hamam, Habib Shafiq, Muhammad School of Pattern Recognition and Intelligent System Shenzhen Institute of Advance Technology Chinese Academy of Science Shenzhen China Changzhou Campus Changzhou213022 China School of Information Science and Engineering Shandong University Qingdao266071 China Department of Electrical Engineering Government College University Lahore54000 Pakistan College of Computer Science and Information Technology Al Baha University Al Baha Saudi Arabia College of CIT Taif University P.O. Box 11099 Taif21944 Saudi Arabia Faculty of Engineering Université de Moncton MonctonNBE1A3E9 Canada Sfax Tunisia Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Object detection plays a vital role in the fields of computer vision, machine learning, and artificial intelligence applications (such as FUSE-AI (E-healthcare MRI scan), face detection, people counting, and vehicle detection) to identify good and defective food products. In the field of artificial intelligence, target detection has been at its peak, but when it comes to detecting multiple targets in a single image or video file, there are indeed challenges. This article focuses on the improved K-nearest neighbor (MK-NN) algorithm for electronic medical care to realize intelligent medical services and applications. We introduced modifications to improve the efficiency of MK-NN, and a comparative analysis was performed to determine the best fuse target detection algorithm based on robustness, accuracy, and computational time. The comparative analysis is performed using four algorithms, namely, MK-NN, traditional K-NN, convolutional neural network, and backpropagation. Experimental results show that the improved K-NN algorithm is the best model in terms of robustness, accuracy, and computational time. © 2021 Ijaz Ahmad et al.

关键词： Object detection

Noise bias compensation for reconstructed HDR images in two layer coding

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IEEJ Transactions on Electronics, Information and Systems

IEEJ Transactions on Electronics, Information and systems 2019年第12期139卷 1442-1451页

作者： Minewaki, Sayaka Hamzah, Fairoza Amira Binti Yoshida, Taichi Iwahashi, Masahiro Kiya, Hitoshi Department of Computer Science and Engineering National Institute of Technology Yuge College 1000 Kamijima-cho Ochi-gun Ehime794-2593 Japan Kyoto College of Graduate Studies for Informatics 7 Tanakamonzen-cho Sakyo-ku Kyoto606-8225 Japan Department of Computer and Network Engineering University of Electro-Communications 1-5-1 Chofugaoka Chofu Tokyo182-8585 Japan Department of Electrical Electronics and Information Engineering Nagaoka University of Technology 1603-1 Kamitomioka Nagaoka Niigata940-2188 Japan Department of Information and Communication Systems Faculty of System Design Tokyo Metropolitan University 6-6 Asahigaoka Hino Tokyo191-0065 Japan

We propose a two-layer coding method for HDR images with noise bias compensation (NBC). Although the mean of image noise is assumed to be zero originally, it becomes nonzero (that is, noise bias is present) after tone mapping, because of the nonlinearity of tone mapping. In the conventional two-layer coding, the reconstructed LDR image is decoded using a lossy encoder of a base layer that includes noise;the noise is included in an HDR image that is inverse tone mapped from a reconstructed LDR image with noise bias. We aim to improve the quality of reconstructed HDR images by compensating for noise bias. NBC classifies pixels in a noisy image into several subsets according to the pixel value after tone mapping, and compensates for the pixel value in each subset with a compensation value determined in advance. The compensation values are determined by using all pixel values in the LDR image that is input to the base layer, in addition to those in the HDR image that is inversely tone mapped from the reconstructed LDR image. We perform NBC following inverse tone mapping for the reconstructed LDR image. Experimental results show the effectiveness of the proposed method. © 2019 The Institute of Electrical Engineers of Japan.

关键词： Image denoising

Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution

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arXiv 2022年

作者： Li, Wenjie Li, Juncheng Gao, Guangwei Deng, Weihong Zhou, Jiantao Yang, Jian Qi, Guo-Jun The Intelligent Visual Information Perception Laboratory Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210046 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China The School of Communication and Information Engineering Shanghai University Shanghai200444 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210094 China The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China The Research Center for Industries of the Future The School of Engineering Westlake University Hangzhou310024 China OPPO Research SeattleWA98101 United States

Recently, Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks due to the ability of global feature extraction. However, the capabilities of Transformers that need to incorporate contextual information to extract features dynamically are neglected. To address this issue, we propose a lightweight Cross-receptive Focused Inference Network (CFIN) that consists of a cascade of CT Blocks mixed with CNN and Transformer. Specifically, in the CT block, we first propose a CNN-based Cross-Scale Information Aggregation Module (CIAM) to enable the model to better focus on potentially helpful information to improve the efficiency of the Transformer phase. Then, we design a novel Cross-receptive Field Guided Transformer (CFGT) to enable the selection of contextual information required for reconstruction by using a modulated convolutional kernel that understands the current semantic information and exploits the information interaction within different self-attention. Extensive experiments have shown that our proposed CFIN can effectively reconstruct images using contextual information, and it can strike a good balance between computational cost and model performance as an efficient model. Source codes will be available at https://***/IVIPLab/CFIN. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

Random Sampling on an High-Dimensional Sphere for Solving SVP

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Random Sampling on an High-Dimensional Sphere for Solving SV...

Computing Conference, 2019

作者： Qin, Ling Zhang, Xue Wang, Xiaoyun Zheng, Zhongxiang Qi, Wenfeng National Digital Switching System Engineering & Technological Research Center Zhengzhou China Institute for Advanced Study Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Science and Technology on Communication Security Laboratory Chengdu China

ISBN: (纸本)9783030228675

In this paper, we present a random sampling algorithm to solve the shortest vector problem (SVP). The idea of our algorithm comes from the Gaussian hypothesis, which is one of the most important and commonly used hypotheses in solving SVP. The main idea of our method is to generate the points which approximately satisfy Gaussian hypothesis of a given lattice. To find the shortest vector, we set some rules to make the result of this sampling algorithm get shorter progressively. We evaluate our algorithm by statistical and geometrical tools and give a basic analysis of the efficiency of algorithm. According to our analysis, parameters can be optimized to achieve high efficiency. Practical experiments have been conducted to validate our result where we solve SVP instances with dimension from 80 to 100. © 2019, Springer Nature Switzerland AG.

关键词： Computational geometry

State of the Art of Audio- and Video-Based Solutions for AAL

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arXiv 2022年

作者： Aleksic, Slavisa Atanasov, Michael Agius, Jean Calleja Camilleri, Kenneth Čartolovni, Anto Climent-Pérez, Pau Colantonio, Sara Cristina, Stefania Despotovic, Vladimir Ekenel, Hazım Kemal Erakin, Ekrem Florez-Revuelta, Francisco Germanese, Danila Grech, Nicole Sigurðardóttir, Steinunn Gróa Emirzeoğlu, Murat Iliev, Ivo Jovanovic, Mladjan Kampel, Martin Kearns, William Klimczuk, Andrzej Lambrinos, Lambros Lumetzberger, Jennifer Mucha, Wiktor Noiret, Sophie Pajalic, Zada Pérez, Rodrigo Rodriguez Petrova, Galidiya Petrovica, Sintija Pocta, Peter Poli, Angelica Pudane, Mara Spinsante, Susanna Salah, Albert Ali Santofimia, Maria Jose Islind, Anna Sigríður Stoicu-Tivadar, Lacramioara Tellioğlu, Hilda Zgank, Andrej Leipzig University of Applied Sciences Germany TU Wien Austria Department of Anatomy University of Malta Malta Centre for Biomedical Cybernetics Faculty of Engineering Department of Systems and Control Engineering Malta Catholic University of Croatia Croatia Universidad de Alicante Spain Institute of Information Science and Technologies The National Research Council of Italy Italy Department of Systems and Control Engineering University of Malta Malta University of Luxembourg Luxembourg Dept. of Computer Engineering Istanbul Technical University Turkey Reykjavik University School of Computer Science Iceland Sağlık Bilimleri Fakültesi Fizyoterapi ve Rehabilitasyon Karadeniz Technical University Research Information System Turkey TU Sofia Bulgaria Singidunum University Belgrade Serbia University of South Florida United States SGH Warsaw School of Economics Poland Department of Communication and Internet Studies Cyprus University of Technologies Cyprus Technical University of Vienna Austria VID Specialized University Oslo Norway Getafe University Hospital Spain Riga Technical University Latvia University of Zilina Slovakia Università Politecnica Marche Italy Utrecht University Netherlands Universidad de Castilla-La Mancha Spain Reykjavik University Iceland University Politehnica Timisoara Romania University of Maribor Slovenia

It is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may

关键词： computer vision

Deterministic and Stochastic Simulators for Non-Isotropic V2V-MIMO Wideband Channels

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China communications 2018年第7期15卷 18-29页

作者： Yiran Li Xiang Cheng Nan Zhang State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Wireless Algorithm Department Product Research and Development System ZTE Corporation

In this paper, we consider a novel two-dimensional(2D) geometry-based stochastic model(GBSM) for multiple-input multiple-output(MIMO) vehicle-to-vehicle(V2V) wideband fading channels. The proposed model employs the combination of a two-ring model and a multiple confocal ellipses model, where the signal is sum of the line-of-sight(Lo S) component, single-bounced(SB) rays, and double-bounced(DB) rays. Based on the reference model, we derive some expressions of channel statistical properties, including space-time correlation function(STCF), Doppler spectral power density(DPSD), envelope level crossing rate(LCR) and average fade duration(AFD). In addition, corresponding deterministic and stochastic simulation models are developed based on the reference model. Moreover, we compare the statistical properties of the reference model and the two simulation models in different scenarios and investigate the impact of different vehicular traffic densities(VTDs) on the channel statistical properties of the proposed model. Finally, the great agreement between simulation models and the reference model demonstrates not only the utility of simulation models, but also the correctness of theoretical derivations and simulations.

关键词： vehicle-to-vehicle wideband channels simulation model statistical properties