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2018 IEEE International Ultrasonics Symposium, IUS 2018

作者： Saniie, Jafar Wang, Boyang Huang, Xin Department of Electrical and Computer Engineering Illinois Institute of Technology Embedded Computing and Signal Processing Research Laboratory ChicagoIL United States

ISBN: (纸本)9781538634257

Ultrasonic signals can be utilized as a viable communication method to transmit information through gas, liquid, and solid channels or a mixed media consisting of a solid interfaced with liquid and/or gas. Underwater communication channels using sound waves cause many challenges, often unforeseen, due to absorption, scattering, refractions, reverberations, multipaths, doppler shifts, temperature, salinity, and acoustic scintillation. Ultrasonic communication through the air is less prone to the environmental challenges that are often encountered in the underwater communications. Ultrasonic communication in solid channels is also adversely affected by absorption, scattering, refractions, reverberations, beam skewing, dispersion, mode conversation, multipaths, and above all these challenges are compounded by the geometrical structure of solids and type of ultrasonic waves. With solid structures many different ultrasonic wave types can be generated including Longitudinal (Compressional), Shear (Transverse), Surface-Rayleigh, Plate-Lamb (Symmetrical or Extensional Mode and Asymmetric or Flexural Mode), Plate-Love, and Stoneley (Leaky Rayleigh). The type of waves is governed by the frequency and angle of the incident waves on a solid structure where the quality of the signal for communication is limited by the composition and geometrical shape of the solids. For this study we have developed a testbed platform using the ZYNQ SoC (System-on-Chip) FPGA by Xilinx which offers reconfigurability and high-performance computational capability, high speed signal converters, power amplifiers for excitation of transducers, low noise receiving amplifiers and high frequency ultrasonic transducers for transmitting and receiving information. Theoretically, ASK (amplitude shift keying) or any form of digital modulation can be realized with the system using the concept of the software-defined ultrasonic communications (SDUC). In practice, the received signal is very complex, primarily ca

关键词： Underwater acoustics

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Target localization with jammer removal using frequency diverse array

arXiv

引用

arXiv 2019年

作者： Liu, Qi Xu, Jingwei Ding, Zhi So, Hing Cheung Department of Electrical and Computer Engineering National University of Singapore Singapore National Laboratory of Radar Signal Processing Xidian University Xi'an Shaanxi710071 China Department of Electrical and Computer Engineering University of California at Davis DavisCA95616 United States Department of Electrical Engineering City University of Hong Kong Hong Kong

A foremost task in frequency diverse array multiple-input multiple-output (FDA-MIMO) radar is to efficiently obtain the target signal in the presence of interferences. In this paper, we employ a novel"low-rank + low-rank + sparse" decomposition model to extract the low-rank desired signal and suppress the jamming signals from both barrage and burst jammers. In the literature, the barrage jamming signals, which are intentionally interfered by enemy jammer radar, are usually assumed Gaussian distributed. However, such assumption is oversimplified to hold in practice as the interferences often exhibit non-Gaussian properties. Those non-Gaussian jamming signals, known as impulsive noise or burst jamming, are involuntarily deviated from friendly radar or other working radio equipment including amplifier saturation and sensor failures, thunderstorms and man-made noise. The estimation performance of the existing estimators, relied crucially on the Gaussian noise assumption, may degrade substantially since the probability density function (PDF) of burst jamming has heavier tails that exceed a few standard deviations than the Gaussian distribution. To capture a more general signal model with burst jamming in practice, both barrage jamming and burst jamming are included and a two-step"Go Decomposition" (GoDec) method via alternating minimization is devised for such mixed jamming signal model, where the a priori rank information is exploited to suppress two kinds of jammers and estimate the desired target. Simulation results verify the robust performance of the devised scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Jamming

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Solder joint image adaptive block compressive sensing with convex optimisation and Gini index

Solder joint image adaptive block compressive sensing with c...

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7th International Symposium on Test Automation and Instrumentation, ISTAI 2018

作者： Zhao, Huihuang Wang, Yaonan Zheng, Jinhua Qiao, Zhijun Zhang, Yun Department of Computer Science and Technology College of Computer Science and Technology Hengyang Normal University Hengyang Hunan China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang China Department of Electrical and Engineering College of Electrical and Information Engineering Hunan University Changsha China School of Mathematical and Statistical Sciences University of Texas Rio Grande ValleyTX United States Institute of Radio and TV Technology Communication University of Zhejiang Hangzhou310018 China

This study aims to improve the performance in solder joint image compression and reconstruction. A novel adaptive block compressive sensing with convex optimisation and Gini index (Ad_BCSGB_Gini) methodology for solder joint image compression and reconstruction is proposed. At first, the image is split into square blocks and each block is resized into a row which consists of a new image. Then, the new image is transformed into a sparse signal by an orthogonal basis matrix, and the image reconstruction is handled as a convex optimisation problem. Moreover, a gradient-based method which has fast computational speed is used to reconstruct image. There is a control factor which controls a norm l1 in the optimisation problem. To achieve the best performance, at last, the proposed method adaptively selects the best result by comparing Gini index of the reconstruction results based on different control factor values. Experimental results with different methods indicate that the Ad_BCSGB_Gini method is able to achieve the best performance in quantisation comparison than several classical algorithms, and Ad_BCSGB_Gini has a good robustness. © 2019 Institution of engineering and Technology. All rights reserved.

关键词： Convex optimization

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Automatic flower and visitor detection system 26

Automatic flower and visitor detection system

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26th European Signal processing Conference, EUSIPCO 2018

作者： Tran, Dat Thanh Høye, Toke Thomas Gabbouj, Moncef Iosifidis, Alexandros Laboratory of Signal Processing Tampere University of Technology Finland Department of Bioscience Aarhus University Aarhus Denmark Department of Engineering Electrical and Computer Engineering Aarhus University Denmark

ISBN: (纸本)9789082797015

The visit patterns of insects to specific flowers at specific times during the diurnal cycle and across the season play important roles in pollination biology. Thus, the ability to automatically detect flowers and visitors occurring in video sequences greatly reduces the manual human efforts needed to collect such data. Data-dependent approaches, such as supervised machine learning algorithms, have become the core component in several automation systems. In this paper, we describe a flower and visitor detection system using deep Convolutional Neural Networks (CNN). Experiments conducted in image sequences collected during field work in Greenland during June-July 2017 indicate that the system is robust to different shading and illumination conditions, inherent in the images collected in the outdoor environments. © EURASIP 2018.

关键词： Deep neural networks

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Physically-Constrained Transfer Learning through Shared Abundance Space for Hyperspectral Image Classification

arXiv

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arXiv 2020年

作者： Qu, Ying Baghbaderani, Razieh Kaviani Li, Wei Gao, Lianru Zhang, Yuxiang Qi, Hairong The Advanced Imaging and Collaborative Information Processing Group Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Hyperspectral image (HSI) classification is one of the most active research topics and has achieved promising results boosted by the recent development of deep learning. However, most state-of-the-art approaches tend to perform poorly when the training and testing images are on different domains, e.g., source domain and target domain, respectively, due to the spectral variability caused by different acquisition conditions. Transfer learning-based methods address this problem by pre-training in the source domain and fine-tuning on the target domain. Nonetheless, a considerable amount of data on the target domain has to be labeled and non-negligible computational resources are required to retrain the whole network. In this paper, we propose a new transfer learning scheme to bridge the gap between the source and target domains by projecting the HSI data from the source and target domains into a shared abundance space based on their own physical characteristics. In this way, the domain discrepancy would be largely reduced such that the model trained on the source domain could be applied on the target domain without extra efforts for data labeling or network retraining. The proposed method is referred to as physically-constrained transfer learning through shared abundance space (PCTL-SAS). Extensive experimental results demonstrate the superiority of the proposed method as compared to the state-of-the-art. The success of this endeavor would largely facilitate the deployment of HSI classification for real-world sensing scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Distributed resource scheduling for large-scale MEC systems: A multi-agent ensemble deep reinforcement learning with imitation acceleration

arXiv

引用

arXiv 2020年

作者： Jiang, Feibo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system. To address this problem, we propose a distributed intelligent resource scheduling (DIRS) framework, which includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. More specifically, we first introduce a novel multi-agent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Secondly, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Lvy flight search. Finally, an imitation acceleration scheme is presented to pre-train all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. Extensive simulations are conducted to demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Deep CNN for Extraction of Sidelobes from SAR Imagery in Spectrally Restricted Environment

Deep CNN for Extraction of Sidelobes from SAR Imagery in Spe...

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Asilomar Conference on Signals, Systems & computers

作者： Lam H. Nguyen Trac D. Tran RF Signal Processing and Modeling Branch CCDC Army Research Laboratory Adelphi MD USA Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA

ISBN: (数字)9781728143002

ISBN: (纸本)9781728143019

This paper explores the critical problem of spectral estimation and recovery via deep convolutional neural networks (CNNs). This problem often arises in ultra-wideband (UWB) synthetic aperture radar (SAR) applications where the wide available bandwidth dictates the final SAR image resolution and quality. Our simple robust solution here also complements well the recently developed stepped-frequency radars built by the U.S. Army Research laboratory, allowing the system to bypass prohibited frequency bands specified by local frequency management agencies and congested frequency bands that are deemed to contain too much interference. Extensive experimental results confirm the validity as well as the promising prospect of the proposed approach - our CNN-based generative network can still robustly recover SAR images when up to 90% of the operating spectrum is missing.

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Measuring success for a future vision: Defining impact in science gateways/virtual research environments

Measuring success for a future vision: Defining impact in sc...

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作者： Calyam, Prasad Wilkins-Diehr, Nancy Miller, Mark Brookes, Emre H. Arora, Ritu Chourasia, Amit Jennewein, Douglas M. Nandigam, Viswanath Drew LaMar, M. Cleveland, Sean B. Newman, Greg Wang, Shaowen Zaslavsky, Ilya Cianfrocco, Michael A. Ellett, Kevin Tarboton, David Jeffery, Keith G. Zhao, Zhiming González-Aranda, Juan Perri, Mark J. Tucker, Greg Candela, Leonardo Kiss, Tamas Gesing, Sandra Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO United States San Diego Supercomputer Center University of California San DiegoCA United States Department of Biochemistry University of Texas Health Science Center HoustonTX United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Information Technology Services University of South Dakota VermillionSD United States Biology Department College of William and Mary WilliamsburgVA United States Information Technology Services–Cyberinfrastructure University of Hawaii HonoluluHI United States Natural Resource Ecology Laboratory Colorado State University Fort CollinsCO United States Department of Geography and Geographic Information Science University of Illinois UrbanaIL United States Department of Biological Chemistry and Life Sciences Institute University of Michigan Ann ArborMI United States Pervasive Technology Institute Indiana University BloomingtonIN United States Civil and Environmental Engineering Utah State University LoganUT United States Keith G. Jeffery Consultants Faringdon United Kingdom Multiscale Networked Systems Group University of Amsterdam Amsterdam Netherlands Chief Technology Office LifeWatch ERIC Seville Spain Chemistry Department Sonoma State University Rohnert ParkCA United States Department of Geological Sciences CIRES University of Colorado Boulder BoulderCO United States National Research Council of Italy Pisa Italy Research Center for Parallel Computing University of Westminster London United Kingdom Center for Research Computing University of Notre Dame Notre DameIN United States

Scholars worldwide leverage science gateways/virtual research environments (VREs) for a wide variety of research and education endeavors spanning diverse scientific fields. Evaluating the value of a given science gateway/VRE to its constituent community is critical in obtaining the financial and human resources necessary to sustain operations and increase adoption in the user community. In this article, we feature a variety of exemplar science gateways/VREs and detail how they define impact in terms of, for example, their purpose, operation principles, and size of user base. Further, the exemplars recognize that their science gateways/VREs will continuously evolve with technological advancements and standards in cloud computing platforms, web service architectures, data management tools and cybersecurity. Correspondingly, we present a number of technology advances that could be incorporated in next-generation science gateways/VREs to enhance their scope and scale of their operations for greater success/impact. The exemplars are selected from owners of science gateways in the Science Gateways Community Institute (SGCI) clientele in the United States, and from the owners of VREs in the International Virtual Research Environment Interest Group (VRE-IG) of the Research Data Alliance. Thus, community-driven best practices and technology advances are compiled from diverse expert groups with an international perspective to envisage futuristic science gateway/VRE innovations. © 2020 John Wiley & Sons, Ltd.

关键词： Web services

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Center-extraction-based three dimensional nuclei instance segmentation of fluorescence microscopy images

arXiv

引用

arXiv 2019年

作者： Ho, David Joon Han, Shuo Fu, Chichen Salama, Paul Dunn, Kenneth W. Delp, Edward J. Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West LafayetteIN United States Department of Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIN United States Division of Nephrology School of Medicine Indiana University IndianapolisIN United States

Fluorescence microscopy is an essential tool for the analysis of 3D subcellular structures in tissue. An important step in the characterization of tissue involves nuclei segmentation. In this paper, a two-stage method for segmentation of nuclei using convolutional neural networks (CNNs) is described. In particular, since creating labeled volumes manually for training purposes is not practical due to the size and complexity of the 3D data sets, the paper describes a method for generating synthetic microscopy volumes based on a spatially constrained cycle-consistent adversarial network. The proposed method is tested on multiple real microscopy data sets and outperforms other commonly used segmentation techniques. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Jointly learning structured analysis discriminative dictionary and analysis multiclass classifier

arXiv

引用

arXiv 2019年

作者： Zhang, Zhao Jiang, Weiming Qin, Jie Zhang, Li Li, Fanzhang Zhang, Min Yan, Shuicheng School of Computer Science and Technology Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China Computer Vision Laboratory ETH Zürich Zürich8092 Switzerland Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

In this paper, we propose an analysis mechanism based structured Analysis Discriminative Dictionary Learning (ADDL) framework. ADDL seamlessly integrates the analysis discriminative dictionary learning, analysis representation and analysis classifier training into a unified model. The applied analysis mechanism can make sure that the learnt dictionaries, representations and linear classifiers over different classes are independent and discriminating as much as possible. The dictionary is obtained by minimizing a reconstruction error and an analytical incoherence promoting term that encourages the sub-dictionaries associated with different classes to be independent. To obtain the representation coefficients, ADDL imposes a sparse l2,1-norm constraint on the coding coefficients instead of using l0 or l1-norm, since the l0 or l1-norm constraint applied in most existing DL criteria makes the training phase time consuming. The codes-extraction projection that bridges data with the sparse codes by extracting special features from the given samples is calculated via minimizing a sparse codes approximation term. Then we compute a linear classifier based on the approximated sparse codes by an analysis mechanism to simultaneously consider the classification and representation powers. Thus, the classification approach of our model is very efficient, because it can avoid the extra time-consuming sparse reconstruction process with trained dictionary for each new test data as most existing DL algorithms. Simulations on real image databases demonstrate that our ADDL model can obtain superior performance over other state-of-the-arts. Copyright © 2019, The Authors. All rights reserved.

关键词： Codes (symbols)

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