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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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A Novel Clustering Method Based on Density Peaks and Its Validation by Channel Measurement

A Novel Clustering Method Based on Density Peaks and Its Val...

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European Conference on Antennas and Propagation

作者： Hanqing Xue Lei Tian Yuxiang Zhang Chao Wang Jianhua Zhang Wei Li Hewen Wei Key Lab of Universal Wireless Communications Ministry of Education State Key Lab of Networking and Switching Technology Beijing University of Posts and Telecommunications P. O. Box 92 Beijing 100876 China Department of Electrical Engineering Northern Illinois University DeKalb IL 60115 National key laboratory of science and technology on blind signal processing Chengdu Sichuan 610041 China

ISBN: (纸本)9781785618154;9781785618161

In this paper, cluster centers are defined as local maxima in the density which is called density peaks. We use an algorithm based on density peaks to cluster multipath components (MPCs) whose parameters are estimated by the space-alternating generalized expectation-maximization (SAGE) algorithm. To validate its performance, we apply it to the data from a channel measurement in an indoor scenario at 28 GHz and compare it to the widely used KPowerMeans algorithm with the same distance metric of multiple component distance (MCD). Through the results, we find it can cluster MPCs more accurately validated by Calinski-Harabasz (CH) and Davies-Bouldin (DB) indices. More importantly, it overcomes the shortcoming of setting the number of clusters manually because we can determine the cluster centers automatically. Besides, the efficiency of DP clustering is far superior to KPowerMeans because what it needs is only a distance matrix and we only need to compute it once. At the end, statistical analysis of clusters are presented to provide insights in channel modeling of millimeter wave.

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Infinite factorial finite state machine for blind multiuser channel estimation

arXiv

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arXiv 2018年

作者： Ruiz, Francisco J.R. Valera, Isabel Svensson, Lennart Perez-Cruz, Fernando Department of Computer Science Columbia University in the City of New York United States Engineering Department University of Cambridge United Kingdom Department of Empirical Inference Max Planck Institute for Intelligent Systems in Tübingen Germany Department of Signal Processing and Communications University Carlos III Madrid Spain Chalmers University of Technology Gothenburg Sweden Swiss Data Science Center Zurich Switzerland

New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner. Thus, the number of users is an unknown and time-varying parameter that needs to be accurately estimated in order to properly recover the symbols transmitted by all users in the system. In this paper, we address the problem of joint channel parameter and data estimation in a multiuser communication channel in which the number of transmitters is not known. For that purpose, we develop the infinite factorial finite state machine model, a Bayesian nonparametric model based on the Markov Indian buffet that allows for an unbounded number of transmitters with arbitrary channel length. We propose an inference algorithm that makes use of slice sampling and particle Gibbs with ancestor sampling. Our approach is fully blind as it does not require a prior channel estimation step, prior knowledge of the number of transmitters, or any signaling information. Our experimental results, loosely based on the LTE random access channel, show that the proposed approach can effectively recover the data-generating process for a wide range of scenarios, with varying number of transmitters, number of receivers, constellation order, channel length, and signal-to-noise ratio. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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A survey on high-capacity OFDM-based passive optical networks

arXiv

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arXiv 2018年

作者： Ghanbarisabagh, Mohammad Vetharatnam, Gobi Giacoumidis, Elias Rouzegar, Hossein Mallouki, Nasreddine Department of Electrical Engineering Faculty of Electrical Engineering and Computer Sciences Islamic Azad University North Tehran Branch Hakimieh Tehran Iran Department of Electrical and Electronic Engineering Lee Kong Chian Faculty of Engineering and Science University Tunku Abdul Rahman Malaysia Dublin City University School of Electronic Engineering Radio and Optical Communications Lab Glasnevin 9 Dublin Ireland Department of Electrical and Computer Engineering Babol Noshirvani University of Technology Babol Iran Sys’comLab ENIT BP.37 Le Belvédère 1002Tunis Tunisia

The exponential growth of demand for high-speed internet and high bandwidth applications such as interactive entertainment in access networks mandates the requirement for higher optical signal speeds. On the other hand, since cost and energy efficiency should be concurrently preserved in access networks, passive optical networks (PONs) have emerged as a breakthrough solution. Currently, the 10 Gb/s (10GE-PON) standard which employs time division multiplexing is not sufficient to fulfill users’ requirements, and therefore, more advanced and spectrally efficient modulation formats are required. A potential solution is the implementation of hybrid wavelength division multiplexing (WDM) with orthogonal frequency-division multiplexing (OFDM), in which a specific wavelength and electronic subcarriers are assigned to each optical network unit according to users’ bandwidth requirements. This survey shows that future-proof PONs should be supported by an adaptively modulated WDM-OFDM architecture to maximize signal capacity and transmission-reach in PONs, while the employment of reflective semiconductor optical amplification and remodulation can potentially prevent the employment of an additional light source. Copyright © 2018, The Authors. All rights reserved.

关键词： Passive optical networks

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Secrecy performance analysis of SIMO underlay cognitive radio systems with outdated CSI

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IET communications 2017年第12期11卷 1961-1969页

作者： Lei, Hongjiang Zhang, Jianming Park, Ki-Hong Ansari, Imran Shafique Pan, Gaofeng Alouini, Mohamed-Slim Chongqing Key Lab of Mobile Communications Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Electrical and Computer Engineering (ECEN) Texas A and M University at Qatar (TAMUQ) Education City Doha Qatar Chongqing Key Laboratory of Non-linear Circuits and Intelligent Information Processing Southwest University Chongqing 400715 China

This study investigates the secrecy outage performance of a single-input multiple-output underlay cognitive radio network (CRN) with outdated channel state information (CSI). The confidential messages are transmitted from transmitter to the destination, while a multi-antenna eavesdropper exists. The maximal ratio combining and selection combining schemes are utilised at the receivers to improve the quality of the received signal-to-noise ratio. The exact and asymptotic closed-form expressions of secrecy outage probability are derived, and simulation results are provided to verify the authors' proposed analytical results. The results reveal that imperfect CSI of main channels deteriorates the secrecy outage performance while that of eavesdropping and interfering channels has contrary effect, and only a unity diversity order can be obtained in underlay CRNs with imperfect CSI. © The Institution of engineering and Technology.

关键词： single-input multiple-output underlay cognitive radio network telecommunication security interfering channels probability receivers diversity reception outdated CSI exact closed-form expressions secrecy performance analysis secrecy outage probability maximal ratio combining multiantenna eavesdropper SIMO underlay cognitive radio systems antenna arrays asymptotic closed-form expressions Antenna arrays transmitter outdated channel state information Radio links and equipment cognitive radio radio networks received signal-to-noise ratio quality selection combining schemes Other topics in statistics

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Smart non-uniform antenna arrays deployed above an imperfect ground lane at multiple frequencies

Smart non-uniform antenna arrays deployed above an imperfect...

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2017 IEEE-APS Topical Conference on Antennas and Propagation in Wireless communications, APWC 2017

作者： Salama, Dojana Abdallah, Mohammad N. Sarkar, T.K. Salazar-Palma, M. Department of Electrical Engineering and Computer Science Syracuse University SyracuseNY13244-1240 United States Department of Signal Theory and Communications Universidad Carlos III de Madrid Avenida de la Universidad 30 Leganés - Madrid28911 Spain

ISBN: (纸本)9781509044542

In this paper, it is illustrated how to carry out adaptive processing simultaneously at multiple frequencies using the same antenna array consisting of dissimilar antenna elements nonuniformly spaced and deployed above an imperfect ground plane. First, an electromagnetic transformation technique is described which can compensate for all non-ideal effects of an antenna array deployed in a real environment, such as mutual coupling between the elements, the presence of an imperfect ground, etc. Then, a direct data domain least squares method is used to extract the signal-of-interest (SOI) in the presence of jammer and clutter simultaneously at multiple frequencies, using a single snap shot of the data. The interferers/jammers can come from multiple directions, frequencies and can even be non-coherent. © 2017 IEEE.

关键词： Least squares approximations

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Introduction to the Issue on Photonics for Sensing

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IEEE Journal of Selected Topics in Quantum Electronics 2017年第2期23卷 5-7页

作者： Xiao, H. Arregui, F.J. Bao, X. Fan, X. Rao, Y. Wysocki, G. Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29631 United States Department of Electrical and Computer Engineering and Bioengineering Public University of Navarre Pampalona31006 Spain Department of Physics University of Ottawa OttawaONK1N 6N5 Canada Department of Biomedical Engineering University of Michigan Ann ArborMI48109 United States Key Lab of Optical Fiber Sensing and Communications University of Electronics Science and Technology of China Chengdu611731 China Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

The papers in this special section address the use of photonics for sensing applications. Light has long been used for sensing, metrology, imaging, and measurement in human history. The inventions of semiconductor sources and detectors, optical fibers and integrated optical waveguides in the last century have led to the emergence of photonics. The papers cover a wide variety of topics in the design, characterization, packaging and demonstration of new photonic devices, structures, functional materials, fabrication methods, and signal processing principles for sensing various physical, chemical and biological parameters. The sensing scales range from nanometers based on tip enhanced Raman scattering and quantum dots to kilometers based on distributed Raman or Brillouin scattering in optical fibers. The electromagnetic spectra cover microwave, terahertzrange, deep ultraviolet (UV), visible, and mid-infrared (IR).

关键词： Special issues and sections Photonics Sensing Remote sensing Image processing Metrology Electromagnetics

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BALSON: BAYESIAN LEAST SQUARES OPTIMIZATION WITH NONNEGATIVE L1-NORM CONSTRAINT

BALSON: BAYESIAN LEAST SQUARES OPTIMIZATION WITH NONNEGATIVE...

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IEEE Workshop on Machine Learning for signal processing

作者： Jiyang Xie Zhanyu Ma Guoqiang Zhang Jing-Hao Xue Jen-Tzung Chien Zhiqing Lin Jun Guo Pattern Recognition and Intelligent Systems Lab. Beijing University of Posts and Telecommunications China School of Computing and Communications University of Technology Sydney Australia Department of Statistical Science University College London United Kingdom Department of Electrical and Computer Engineering National Chiao Tung University Taiwan

A Bayesian approach termed the BAyesian Least Squares Optimization with Nonnegative L 1 -norm constraint (BALSON) is proposed. The error distribution of data fitting is described by Gaussian likelihood. The parameter distribution is assumed to be a Dirichlet distribution. With the Bayes rule, searching for the optimal parameters is equivalent to finding the mode of the posterior distribution. In order to explicitly characterize the nonnegative L 1 -norm constraint of the parameters, we further approximate the true posterior distribution by a Dirichlet distribution. We estimate the moments of the approximated Dirichlet posterior distribution by sampling methods. Four sampling methods have been introduced and implemented. With the estimated posterior distributions, the original parameters can be effectively reconstructed in polynomial fitting problems, and the BALSON framework is found to perform better than conventional methods.

关键词： Bayes methods Optimization Monte Carlo methods Sampling methods Linear programming Laplace equations Data models

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Benchmarking super-resolution algorithms on real data

arXiv

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arXiv 2017年

作者： Köhler, Thomas Bätz, Michel Naderi, Farzad Kaup, André Maier, Andreas K. Riess, Christian Pattern Recognition Lab Dept. of Computer Science Multimedia Communications and Signal Processing Dept. of Electrical Electronic and Communication Engineering Erlangen-Nürnberg Erlangen Germany

Over the past decades, various super-resolution (SR) techniques have been developed to enhance the spatial resolution of digital images. Despite the great number of methodical contributions, there is still a lack of comparative validations of SR under practical conditions, as capturing real ground truth data is a challenging task. Therefore, current studies are either evaluated 1) on simulated data or 2) on real data without a pixel-wise ground truth. To facilitate comprehensive studies, this paper introduces the publicly available Super-Resolution Erlangen (SupER) database that includes real low-resolution images along with high-resolution ground truth data. Our database comprises image sequences with more than 20k images captured from 14 scenes under various types of motions and photometric conditions. The datasets cover four spatial resolution levels using camera hardware binning. With this database, we benchmark 15 single-image and multi-frame SR algorithms. Our experiments quantitatively analyze SR accuracy and robustness under realistic conditions including independent object and camera motion or photometric variations. Copyright © 2017, The Authors. All rights reserved.

关键词： Database systems

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Noise and speech estimation as auxiliary tasks for robust speech recognition 1

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5th International Conference on Statistical Language and Speech processing, SLSP 2017

作者： Pironkov, Gueorgui Dupont, Stéphane Wood, Sean U. N. Dutoit, Thierry Circuit Theory and Signal Processing Lab University of Mons Boulevard Dolez 31 Mons7000 Belgium NECOTIS Department of Electrical and Computer Engineering University of Sherbrooke 2500 Boulevard de l’Université SherbrookeQCJ1K 2R1 Canada

ISBN: (数字)9783319684567

ISBN: (纸本)9783319684550

Dealing with noise deteriorating the speech is still a major problem for automatic speech recognition. An interesting approach to tackle this problem consists of using multi-task learning. In this case, an efficient auxiliary task is clean-speech generation. This auxiliary task is trained in addition to the main speech recognition task and its goal is to help improve the results of the main task. In this paper, we investigate this idea further by generating features extracted directly from the audio file containing only the noise, instead of the clean-speech. After demonstrating that an improvement can be obtained through this multi-task learning auxiliary task, we also show that using both noise and clean-speech estimation auxiliary tasks leads to a 4% relative word error rate improvement in comparison to the classic single-task learning on the CHiME4 dataset. © Springer International Publishing AG 2017.

关键词： Speech recognition

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