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Super-resolution of brain MRI images using overcomplete dictionaries and nonlocal similarity

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arXiv 2019年

作者： Li, Yinghua Song, Bin Guo, Jie Du, Xiaojiang Guizani, Mohsen State Key Laboratory of Integrated Services Networks Xidian University 710071 China Dept. of Computer and Information Sciences Temple University PhiladelphiaPA19122 United States Dept. of College of Engineering Qatar University Qatar Qatar

Recently, the Magnetic Resonance Imaging (MRI) images have limited and unsatisfactory resolutions due to various constraints such as physical, technological and economic considerations. Super-resolution techniques can obtain high-resolution MRI images. The traditional methods obtained the resolution enhancement of brain MRI by interpolations, affecting the accuracy of the following diagnose process. The requirement for brain image quality is fast increasing. In this paper, we propose an image super-resolution (SR) method based on overcomplete dictionaries and inherent similarity of an image to recover the high-resolution (HR) image from a single low-resolution (LR) image. We use the linear relationship among images in the measurement domain and frequency domain to classify image blocks into smooth, texture and edge feature blocks in the measurement domain. The dictionaries for different blocks are trained using different categories. Consequently, an LR image block of interest may be reconstructed using the most appropriate dictionary. Additionally, we explore the nonlocal similarity of the image to tentatively search for similar blocks in the whole image and present a joint reconstruction method based on compressive sensing (CS) and similarity constraints. The sparsity and selfsimilarity of the image blocks are taken as the constraints. The proposed method is summarized in the following steps. First, a dictionary classification method based on the measurement domain is presented. The image blocks are classified into smooth, texture and edge parts by analyzing their features in the measurement domain. Then, the corresponding dictionaries are trained using the classified image blocks. Equally important, in the reconstruction part, we use the CS reconstruction method to recover the HR brain MRI image, considering both nonlocal similarity and the sparsity of an image as the constraints. This method performs better both visually and quantitatively than some existing meth

关键词： Magnetic resonance imaging

On privacy protection of latent dirichlet allocation model training

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arXiv 2019年

作者： Zhao, Fangyuan Ren, Xuebin Yang, Shusen Yang, Xinyu School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi'an Jiaotong University China

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications. However, like many other machine learning algorithms, the process of training a LDA model may leak the sensitive information of the training datasets and bring significant privacy risks. To mitigate the privacy issues in LDA, we focus on studying privacy-preserving algorithms of LDA model training in this paper. In particular, we first develop a privacy monitoring algorithm to investigate the privacy guarantee obtained from the inherent randomness of the collapsed gibbs sampling (CGS) process in a typical LDA training algorithm on centralized curated datasets. Then, we further propose a locally private LDA training algorithm on crowdsourced data to provide local differential privacy for individual data contributors. The experimental results on real-world datasets demonstrate the effectiveness of our proposed algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistics

Performance Evaluation of IoT-Fag-Cloud Deployment for Healthcare servicies 4

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Performance Evaluation of IoT-Fag-Cloud Deployment for Healt...

4th International Conference on Cloud Computing Technologies and Applications, Cloudtech 2018

作者： El Kafhali, Said Salah, Khaled Ben Alla, Said Computer Networks Mobility and Modeling Laboratory Faculty of Sciences and Technologies Hassan 1st Univ Settat Morocco Electrical and Computer Engineering Department Khalifa University of Science and Technology United Arab Emirates LAVETE Laboratory National School of Applied Sciences Hassan 1st University Berrechid Morocco

ISBN: (纸本)9781728116372

Due to the proliferation of cloud computing and Internet of Things (IoT) systems in healthcare industry, remote real-time monitoring and tracking of patient conditions have become the norm. In a typical deployment, Medical IoT (MIoT) devices transmit patient data to a medical datacenters where the data get stored, analyzed, and made available to concerned parties or individuals. Healthcare providers, physicians and care takers can access remotely these medical data located in these medical datacenters which are typically deployed as a private cloud or public cloud. In such cloud deployment, the performance services to access the data in a timely manner is critical. One of the key performances metrics is the response time. It is important to deploy the medical datacenters in a cloud environment in a way to minimize the response time. Moreover, Fog/Edge computing layer is also used to perform localized processing and storage to further reduce response time. This paper studies, models, and shows how to reduce computing resources cost while guaranteeing health requests performance constraints-particularly response time of accessing medical data stored in a fog-cloud environment. To address this issue, this paper proposes a queuing model to predict the minimum required number of computing resources (both fog and cloud nodes) to meet the Service Level Agreement (SLA) for response time. We verify and cross validate our analytical model through discrete event simulation. Obtained results from our analytical models show that the proposed model is able to correctly and effectively predict the number of computing resources needed for health data services in order to achieve the required response time under different workload conditions. © 2018 IEEE.

关键词： Quality of service

SigNet: A novel deep learning framework for radio signal classification

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arXiv 2020年

作者： Chen, Zhuangzhi Cui, Hui Xiang, Jingyang Qiu, Kunfeng Huang, Liang Zheng, Shilian Chen, Shichuan Xuan, Qi Yang, Xiaoniu The Institute of Cyberspace Security The College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The College of Computer Science and Technology Zhejiang University of Technology Hangzhou China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China The Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518000 China The Utron Technology Co. Ltd. [as Hangzhou Qianjiang Distinguished Expert Hangzhou310056 China The Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China

Deep learning methods achieve great success in many areas due to their powerful feature extraction capabilities and end-to-end training mechanism, and recently they are also introduced for radio signal modulation classification. In this paper, we propose a novel deep learning framework called SigNet, where a signal-to-matrix (S2M) operator is adopted to convert the original signal into a square matrix first and is co-trained with a follow-up CNN architecture for classification. This model is further accelerated by integrating 1D convolution operators, leading to the upgraded model SigNet2.0. The simulations on two signal datasets show that both SigNet and SigNet2.0 outperform a number of well-known baselines. More interestingly, our proposed models behave extremely well in small-sample learning when only a small training dataset is provided. They can achieve a relatively high accuracy even when 1% training data are kept, while other baseline models may lose their effectiveness much more quickly as the datasets get smaller. Such result suggests that SigNet/SigNet2.0 could be extremely useful in the situations where labeled signal data are difficult to obtain. The visualization of the output features of our models demonstrates that our model can well divide different modulation types of signals in the feature hyper-space. Copyright © 2020, The Authors. All rights reserved.

关键词： Matrix algebra

Mobility Prediction Based Virtual Routing for Ad Hoc UAV Network

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Mobility Prediction Based Virtual Routing for Ad Hoc UAV Net...

IEEE Global Communications Conference

作者： Menglei Jiang Qixun Zhang Zhiyong Feng Zhu Han Wei Li Key Laboratory of Universal Wireless Communications Beijing University of Posts and Telecommunications Ministry of Education Beijing P.R. China Science and Technology on Communication Networks Laboratory Shijiazhuang P.R. China Electrical and Computer Engineering Department University of Houston Houston TX Department of Electrical Engineering Northern Illinois University Dekalb IL USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Due to the three dimensional mobility advantage of unmanned aerial vehicles (UAVs), a swarm of UAVs can be conveniently deployed and work cooperatively to enlarge the coverage and relay data for environment monitoring and emergency communication scenarios. However, the high mobility of UAV brings new challenges to guarantee the robust and efficient routing in a dynamic changing network topology. Therefore, a mobility prediction based virtual routing (MPVR) algorithm is proposed to enhance the performances of connectivity and routing lifetime among cooperative UAVs. And the probability density function of UAV movements is theoretically achieved using the Gaussian distribution model. Moreover, an optimal virtual routing model is designed to select the optimal relay node with the minimum distance between UAVs. Finally, our proposed MPVR algorithm is evaluated by simulation results, which can improve the average routing lifetime, the average link delay, and the packet delivery ratio compared to other conventional algorithms.

关键词： Routing Relays Unmanned aerial vehicles Heuristic algorithms Prediction algorithms Three-dimensional displays Solid modeling

Poster abstract: Mitigating erroneous wake-ups 15

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Poster abstract: Mitigating erroneous wake-ups

15th ACM Conference on Embedded Networked Sensor Systems, SenSys 2017

作者： Sutton, Felix Beutel, Jan Thiele, Lothar Computer Engineering and Networks Laboratory ETH Zurich Switzerland

ISBN: (纸本)9781450354592

We propose a novel method for mitigating erroneous wake-ups that are commonly associated with ultra-low power wake-up receivers. Recent research in low-power protocols has demonstrated significant improvements in energy-efficiency by employing ultra-low power wake-up receivers. However, due to the low-complexity receiver structures adopted, wake-up receivers are susceptible to external interference, which can cause the detection of non-existent wake-ups. The occurrence of these erroneous wake-ups wastes precious energy resources, thereby negating the potential energy savings in employing wake-up receivers. We address this challenging problem by extracting time-domain features from the output of the wake-up receiver, and construct a classifier to distinguish between correct and erroneous wake-ups. We describe the design of the proposed wake-up classifier and present preliminary results. © 2017 Association for Computing Machinery.

关键词： Potential energy

3D multi-drone-cell trajectory design for efficient IoT data collection

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arXiv 2019年

作者： Shi, Weisen Li, Junling Cheng, Nan Lyu, Feng Dai, Yanpeng Zhou, Haibo Shen, Xuemin Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada State Key Laboratory of Integrated Service Networks Xidian University Xian710071 China School of Electronic Science and Engineering Nanjing University Nanjing210093 China

Drone cell (DC) is an emerging technique to offer flexible and cost-effective wireless connections to collect Internet-of-things (IoT) data in uncovered areas of terrestrial networks. The flying trajectory of DC significantly impacts the data collection performance. However, designing the trajectory is a challenging issue due to the complicated 3D mobility of DC, unique DC-to-ground (D2G) channel features, limited DC-to-BS (D2B) backhaul link quality, etc. In this paper, we propose a 3D DC trajectory design for the DC-assisted IoT data collection where multiple DCs periodically fly over IoT devices and relay the IoT data to the base stations (BSs). The trajectory design is formulated as a mixed integer non-linear programming (MINLP) problem to minimize the average user-to-DC (U2D) pathloss, considering the state-of-the-art practical D2G channel model. We decouple the MINLP problem into multiple quasi-convex or integer linear programming (ILP) sub-problems, which optimizes the user association, user scheduling, horizontal trajectories and DC flying altitudes of DCs, respectively. Then, a 3D multi-DC trajectory design algorithm is developed to solve the MINLP problem, in which the sub-problems are optimized iteratively through the block coordinate descent (BCD) method. Compared with the static DC deployment, the proposed trajectory design can lower the average U2D pathloss by 10-15 dB, and reduce the standard deviation of U2D pathloss by 56%, which indicates the improvements in both link quality and user fairness. Copyright © 2019, The Authors. All rights reserved.

关键词： Trajectories

A Game Theoretical Pricing Scheme for Vehicles in Vehicular Edge Computing

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A Game Theoretical Pricing Scheme for Vehicles in Vehicular ...

International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Chaogang Tang Chunsheng Zhu Huaming Wu Xianglin Wei Qing Li Joel J. P. C. Rodrigues School of Computer Science and Technology China University of Mining and Technology Xuzhou China Mine Digitization Engineering Research Center of the Ministry of Education China University of Mining and Technology Xuzhou China SUSTech Institute of Future Networks Southern University of Science and Technology Shenzhen China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China Center for Applied Mathematics Tianjin University Tianjin China The 63rd Research Institute National University of Defense Technology Changsha China The Hong Kong Polytechnic University Hong Kong Federal University of Piauí Teresina - Pi Brazil Instituto de Telecomunicações Portugal

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

Vehicular edge computing (VEC) brings the computing resources to the edge of the networks and thus provisions better computing services to the vehicles in terms of response latency. Meanwhile, the edge server can earn their revenues by leasing the computing resources. However, a higher price does not always bring forth more benefits for the edge server in VEC, since it may discourage vehicles from renting more computing resources from VEC. To the best of our knowledge, few of previous works have focused on the real-time pricing problem for VEC. We investigate in this paper the pricing problem from the viewpoints of both vehicles and the edge server, so as to optimize the utility values and revenues of vehicles and the edge server, respectively. We resort to the Stackelberg game for modeling the interactions between vehicles and edge server, and a distributed algorithm for this pricing problem is proposed in the paper. Experimental results have displayed the efficiency and effectiveness of the proposed algorithm.

关键词： Computational modeling Pricing Games Sensors Servers Distributed algorithms Edge computing

Energy-efficient hybrid precoding design for integrated multicast-unicast millimeter wave communications with SWIPT

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arXiv 2020年

作者： Hao, Wanming Sun, Gangcan Zhou, Fuhui Mi, De Shi, Jia Xiao, Pei Leung, Victor C.M. School of Information Engineering Zhengzhou University Zhengzhou450001 China 5G Innovation Center Institute of Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom 5G Innovation Center Institute of Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210000 China State Key Laboratory of Integrated Services Networks Xi'dian University Xian710071 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T1Z4 Canada

In this paper, we investigate the energy-efficient hybrid precoding design for integrated multicast-unicast millimeter wave (mmWave) system, where the simultaneous wireless information and power transform is considered at receivers. We adopt two sparse radio frequency chain antenna structures at the base station (BS), i.e., fully-connected and subarray structures, and design the codebook-based analog precoding according to the different structures. Then, we formulate a joint digital multicast, unicast precoding and power splitting ratio optimization problem to maximize the energy efficiency of the system, while the maximum transmit power at the BS and minimum harvested energy at receivers are considered. Due to its difficulty to directly solve the formulated problem, we equivalently transform the fractional objective function into a subtractive form one and propose a two-loop iterative algorithm to solve it. For the outer loop, the classic Bi-section iterative algorithm is applied. For the inner loop, we transform the formulated problem into a convex one by successive convex approximation techniques and propose an iterative algorithm to solve it. Meanwhile, to reduce the complexity of the inner loop, we develop a zero forcing (ZF) technique-based low complexity iterative algorithm. Specifically, the ZF technique is applied to cancel the inter-unicast interference and the first order Taylor approximation is used for the convexification of the non-convex constraints in the original problem. Finally, simulation results are provided to compare the performance of the proposed algorithms under different schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency