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作者： Liu, Jingxian Xiong, Ke Ng, Derrick Wing Kwan Fan, Pingyi Zhong, Zhangdui Letaief, Khaled Ben School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China School of Electrical Engineering and Telecommunications UNSW Sydney SydneyNSW2052 Australia Department of Electronic Engineering Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China Hong Kong Peng Cheng Laboratory Shenzhen518055 China

This paper investigates the wireless-powered hierarchical fog-cloud computing networks, where multiple energy-constrained users harvest energy from a hybrid access point (HAP) firstly and then use their harvested energy to offload their computation tasks to fog/cloud servers via the HAP or compute their tasks locally. To pursue multi-user fairness, an optimization problem is formulated to maximize the minimal energy balance among all users by jointly optimizing time assignments, computation central processing unit (CPU) frequencies, and the computing mode selection. Since the problem is mixed-integer combinatorial non-convex, which is intractable, a generalized Benders decomposition (GBD)-based method is proposed, which guarantees the globally optimal solution. To release the high computational complexity of the proposed GBD-based method, a penalized successive convex approximation (P-SCA)-based algorithm is designed as an alternative to obtain a suboptimal solution with low computational complexity. Numerical results show that among different optimizable factors in the system, computing mode selection is the dominant one on affecting the system performance. Moreover, for each user, local computing is a better choice, if it is with relatively poor channel gain and small local computing delay. Otherwise, fog/cloud computing may be a better choice. Additionally, for the users with relatively high channel gains, if their local computing delays are less than those selecting fog computing, cloud computing should be a better choice. © 2002-2012 IEEE.

关键词： Energy transfer

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eRAKI: Fast robust artificial neural networks for k-space interpolation (RAKI) with coil combination and joint reconstruction

arXiv

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arXiv 2021年

作者： Yu, Heng Dong, Zijing Arefeen, Yamin Liao, Congyu Setsompop, Kawin Bilgic, Berkin Department of Automation Tsinghua University Beijing China Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Athinoula A. Martinos Center for Biomedical Imaging CharlestownMA United States Radiological Sciences Laboratory Stanford University StanfordCA United States Department of Electrical Engineering Stanford University StanfordCA United States Harvard Medical School BostonMA United States Harvard-MIT Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States

RAKI can perform database-free MRI reconstruction by training models using only auto-calibration signal (ACS) from each speciflc scan. As it trains a separate model for each individual coil, learning and inference with RAKI can be computationally prohibitive, particularly for large 3D datasets. In this abstract, we accelerate RAKI by more than 200 times by directly learning a coil-combined target and further improve the reconstruction performance using joint reconstruction across multiple echoes together with an elliptical-CAIPI sampling approach. We further deploy these improvements in quantitative imaging and rapidly obtain T2 and T2* parameter maps from a fast EPTI scan. © 2021, CC0.

关键词： Neural networks

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Real-time reversible data hiding in encrypted images based on hybrid embedding mechanism

Real-time reversible data hiding in encrypted images based o...

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作者： Zhang, Wei Kong, Ping Yao, Heng Hu, Yu-Chen Cao, Fang School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Shanghai Key Laboratory for Molecular Imaging Shanghai University of Medicine and Health Sciences Shanghai201318 China Department of Computer Science and Information Management Providence University Taichung43301 Taiwan College of Information Engineering Shanghai Maritime University No. 1550 Haigang Ave Shanghai201306 China

In this paper, we propose a novel real-time scheme of separable reversible data hiding in encrypted images, which consists of image encryption, data embedding, data extraction and image recovery. In image encryption phase, the content owner divides the original image into a number of non-overlapping blocks and encrypts blocks by stream cipher and permutation. During the data embedding phase, the data hider classifies encrypted blocks into smooth region and complex region according to the threshold and replaces the MSB layer of a part of pixels in blocks of smooth region with the secret data. Then, the LSB layers of other pixels are collected and compressed to generate a room for embedding the secret data again. When the receiver receives the marked image, he can divide the marked image into blocks and decrypt them by the encryption key to obtain a similar image with good quality. If the receiver only has the data hiding key, he can classify the blocks into smooth region and complex region according to the threshold and extract the embedded data by the data hiding key. If the receiver has both encryption key and data hiding key, he can extract the embedded data from the marked image and recover the original image perfectly. The proposed scheme can achieve satisfactory quality of decrypted image and high embedding rate. Experimental results demonstrate the effectiveness and computational efficiency of our scheme. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Pixels

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MAGIC: Manifold and graph integrative convolutional network for Low-dose CT reconstruction

arXiv

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arXiv 2020年

作者： Xia, Wenjun Lu, Zexin Huang, Yongqiang Shi, Zuoqiang Liu, Yan Chen, Hu Chen, Yang Zhou, Jiliu Zhang, Yi College of Computer Science Sichuan University Chengdu610065 China Department of Mathematical Sciences Tsinghua University Beijing100084 China School of Electrical Engineering Information Sichuan University Chengdu610065 China Laboratory of Image Science and Technology Southeast University Nanjing210096 China

Low-dose computed tomography (LDCT) scans, which can effectively alleviate the radiation problem, will degrade the imaging quality. In this paper, we propose a novel LDCT reconstruction network that unrolls the iterative scheme and performs in both image and manifold spaces. Because patch manifolds of medical images have low-dimensional structures, we can build graphs from the manifolds. Then, we simultaneously leverage the spatial convolution to extract the local pixel-level features from the images and incorporate the graph convolution to analyze the nonlocal topological features in manifold space. The experiments show that our proposed method outperforms both the quantitative and qualitative aspects of state-of-the-art methods. In addition, aided by a projection loss component, our proposed method also demonstrates superior performance for semi-supervised learning. The network can remove most noise while maintaining the details of only 10% (40 slices) of the training data labeled. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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Secrecy capacity bounds for visible light communications with signal-dependent noise

arXiv

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arXiv 2021年

作者： Wang, Jin-Yuan Fu, Xian-Tao Wang, Jun-Bo Lin, Min Cheng, Julian Alouini, Mohamed-Slim Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing210003 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China College of Telecommunications & Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China National Mobile Communications Research Laboratory Southeast University Nanjing211111 China School of Engineering The University of British Columbia KelownaBCV1V 1V7 Canada Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In physical-layer security, one of the most fundamental issues is the secrecy capacity. The objective of this paper is to determine the secrecy capacity for an indoor visible light communication system consisting of a transmitter, a legitimate receiver and an eavesdropping receiver. In such a system, both signal-independent and signal-dependent Gaussian noises are considered. Under non-negativity and average optical intensity constraints, lower and upper secrecy capacity bounds are first derived by the variational method, the dual expression of the secrecy capacity, and the concept of "the optimal input distribution that escapes to infinity". Numerical results show that the secrecy capacity upper and lower bounds are tight. By an asymptotic analysis at large optical intensity, there is a small performance gap between the asymptotic upper and lower bounds. Then, by adding a peak optical intensity constraint, we further analyze the exact and asymptotic secrecy capacity bounds. Finally, the tightness of the derived bounds is verified by numerical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Asymptotic analysis

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Adding seemingly uninformative labels helps in low data regimes

arXiv

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arXiv 2020年

作者： Matsoukas, Christos Hernandez, Albert Bou I. Liu, Yue Dembrower, Karin Miranda, Gisele Konuk, Emir Haslum, Johan Fredin Zouzos, Athanasios Lindholm, Peter Strand, Fredrik Smith, Kevin School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Science for Life Laboratory Stockholm Sweden AstraZeneca Gothenburg Sweden Karolinska Institutet Stockholm Sweden Capio Sankt Gran Hospital Stockholm Sweden Karolinska University Hospital Stockholm Sweden

Evidence suggests that networks trained on large datasets generalize well not solely because of the numerous training examples, but also class diversity which encourages learning of enriched features. This raises the question of whether this remains true when data is scarce – is there an advantage to learning with additional labels in low-data regimes? In this work, we consider a task that requires difficult-to-obtain expert annotations: tumor segmentation in mammography images. We show that, in low-data settings, performance can be improved by complementing the expert annotations with seemingly uninformative labels from non-expert annotators, turning the task into a multi-class problem. We reveal that these gains increase when less expert data is available, and uncover several interesting properties through further studies. We demonstrate our findings on CSAW-S, a new dataset that we introduce here, and confirm them on two public datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Large dataset

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An infinite class of Neumaier graphs and non-existence results

arXiv

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arXiv 2021年

作者： Abiad, Aida Castryck, Wouter de Boeck, Maarten Koolen, Jack H. Zeijlemaker, Sjanne Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Mathematics: Analysis Logic and Discrete Mathematics Ghent University Flanders Belgium Department of Mathematics and Data Science Vrije Universiteit Brussel Belgium Imec-COSIC Department of Electrical Engineering KU Leuven Belgium Department of Mathematics: Algebra and Geometry Ghent University Flanders Belgium School of Mathematical Sciences University of Science and Technology of China Hefei China CAS Wu Wen-Tsun Key Laboratory of Mathematics University of Science and Technology of China Hefei China

A Neumaier graph is a non-complete edge-regular graph containing a regular clique. A Neumaier graph that is not strongly regular is called a strictly Neumaier graph. In this work we present a new construction of strictly Neumaier graphs, and using Jacobi sums, we show that our construction produces infinitely many instances. Moreover, we prove some necessary conditions for the existence of (strictly) Neumaier graphs that allow us to show that several parameter sets are not admissible. Copyright © 2021, The Authors. All rights reserved.

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Erratum — A 3D semantic segmentation network for accurate neuronal soma segmentation

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Journal of Innovative Optical Health sciences 2025年第1期18卷

作者： Li Ma Qi Zhong Yezi Wang Xiaoquan Yang Qian Du School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan 430074 P. R. China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics MoE Key Laboratory for Biomedical Photonics Huazhong University of Science and Technology Wuhan 430074 P. R. China HUST-Suzhou Institute for Brainsmatics JITRI Suzhou 215123 P. R. China Department of Electrical and Computer Engineering Mississippi State University Mississippi State MS 39762 USA

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Learning collision-free space detection from stereo images: Homography matrix brings better data augmentation

arXiv

引用

arXiv 2020年

作者： Fan, Rui Wang, Hengli Cai, Peide Wu, Jin Bocus, Mohammud Junaid Qiao, Lei Liu, Ming Department of Computer Science and Engineering Department of Ophthalmology University of California San Diego San DiegoCA92093 United States Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Electrical and Electronic Engineering University of Bristol BS8 1UB United Kingdom State Key Laboratory of Ocean Engineering School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai200240 China

Collision-free space detection is a critical component of autonomous vehicle perception. The state-of-the-art algorithms are typically based on supervised deep learning. Their performance is dependent on the quality and amount of labeled training data. It remains an open challenge to train deep convolutional neural networks (DCNNs) using only a small quantity of training samples. Therefore, in this paper, we mainly explore an effective training data augmentation approach that can be employed to improve the overall DCNN performance, when additional images captured from different views are available. Due to the fact that the pixels in collision-free space (generally regarded as a planar surface) between two images, captured from different views, can be associated using a homography matrix, the target image can be transformed into the reference view. This provides a simple but effective way to generate training data from additional multi-view images. Extensive experimental results, conducted with six state-of-the-art semantic segmentation DCNNs on three datasets, validate the effectiveness of the proposed method for enhancing collision-free space detection performance. When validated on the KITTI road benchmark, our approach provides the best results, compared with other state-of-the-art stereo vision-based collision-free space detection approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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A Privacy-Preserving TPA-aided Remote Data Integrity Auditing Scheme in Clouds 5th

A Privacy-Preserving TPA-aided Remote Data Integrity Auditin...

引用

5th International Conference of Pioneer computer Scientists, Engineers and Educators, ICPCSEE 2019

作者： Zhao, Meng Ding, Yong Wang, Yujue Wang, Huiyong Wang, Bingyao Liu, Lingang School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9789811501173

The remote data integrity auditing technology can guarantee the integrity of outsourced data in clouds. Users can periodically run an integrity auditing protocol by interacting with cloud server, to verify the latest status of outsourced data. Integrity auditing requires user to take massive time-consuming computations, which would not be affordable by weak devices. In this paper, we propose a privacy-preserving TPA-aided remote data integrity auditing scheme based on Li et al.’s data integrity auditing scheme without bilinear pairings, where a third party auditor (TPA) is employed to perform integrity auditing on outsourced data for users. The privacy of outsourced data can be guaranteed against TPA in the sense that TPA could not infer its contents from the returned proofs in the integrity auditing phase. Our construction is as efficient as Li et al.’s scheme, that is, each procedure takes the same time-consuming operations in both schemes, and our solution does not increase the sizes of processed data, challenge and proof. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Cloud storage

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