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arXiv 2021年

作者： Nah, Seungjun Son, Sanghyun Lee, Suyoung Timofte, Radu Lee, Kyoung Mu Chen, Liangyu Zhang, Jie Lu, Xin Chu, Xiaojie Chen, Chengpeng Xiong, Zhiwei Xu, Ruikang Xiao, Zeyu Huang, Jie Zhang, Yueyi Xi, Si Wei, Jia Bai, Haoran Cheng, Songsheng Wei, Hao Sun, Long Tang, Jinhui Pan, Jinshan Lee, Donghyeon Lee, Chulhee Kim, Taesung Wang, Xiaobing Zhang, Dafeng Pan, Zhihong Lin, Tianwei Wu, Wenhao He, Dongliang Li, Baopu Li, Boyun Xi, Teng Zhang, Gang Liu, Jingtuo Han, Junyu Ding, Errui Tao, Guangpin Chu, Wenqing Cao, Yun Luo, Donghao Tai, Ying Lu, Tong Wang, Chengjie Li, Jilin Huang, Feiyue Chen, Hanting Chen, Shuaijun Guo, Tianyu Wang, Yunhe Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Shao, Ling Zuo, Yushen Ou, Yimin Chai, Yuanjun Shi, Lei Liu, Shuai Lei, Lei Feng, Chaoyu Zeng, Kai Yao, Yuying Liu, Xinran Zhang, Zhizhou Huang, Huacheng Zhang, Yunchen Jiang, Mingchao Zou, Wenbin Miao, Si Kim, Yangwoo Sun, Yuejin Deng, Senyou Ren, Wenqi Cao, Xiaochun Wang, Tao Suin, Maitreya Rajagopalan, A.N. Duong, Vinh Van Nguyen, Thuc Huu Yim, Jonghoon Jeon, Byeungwoo Li, Ru Xie, Junwei Han, Jong-Wook Choi, Jun-Ho Kim, Jun-Hyuk Lee, Jong-Seok Zhang, Jiaxin Peng, Fan Svitov, David Pakulich, Dmitry Kim, Jaeyeob Jeong, Jechang Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland University of Science and Technology of China China Megvii China Fudan University China Peking University China Netease Games AI Lab Nanjing University of Science and Technology China Guilin University of Electronic Technology China Samsung Electronics Co. Ltd Sunmoon University Asan Korea Republic of Samsung Research China Beijing China Baidu Research United States Department of Computer Vision Technology Baidu Inc China Fudan University China Megvii China Nanjing University China Tencent Noah's Ark Lab Huawei Technologies Co. Ltd Inception Institute of Artificial Intelligence Tsinghua University Beijing China North China University of Technology China Xiaomi South China University of Technology China Lab of Image Science and Technology Southeast University China China Design Group Co. Ltd China JOYY AI GROUP Fujian Normal University China Shanghai Advanced Research Institute Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China Huawei Noah's Ark Lab Indian Institute of Technology Madras India Department of ECE Sungkyunkwan University Korea Republic of Fuzhou University China Imperial Vision Co. Ltd School of Integrated Technology Yonsei University Korea Republic of Expasoft LLC Institute of Automation and Electrometry The SB RAS Image Communication & Signal Processing Laboratory Hanyang University Korea Republic of

Motion blur is a common photography artifact in dynamic environments that typically comes jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on image Deblurring. In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions. While both the tracks aim to recover a high-quality clean image from a blurry image, different artifacts are jointly involved. In track 1, the blurry images are in a low resolution while track 2 images are compressed in JPEG format. In each competition, there were 338 and 238 registered participants and in the final testing phase, 18 and 17 teams competed. The winning methods demonstrate the state-of-the-art performance on the image deblurring task with the jointly combined artifacts. © 2021, CC BY-NC-ND.

关键词： image enhancement

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PRUNE: Dynamic and decidable dataflow for signal processing on heterogeneous platforms

arXiv

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arXiv 2018年

作者： Boutellier, Jani Wu, Jiahao Huttunen, Heikki Bhattacharyya, Shuvra S. Laboratory of Pervasive Computing Tampere University of Technology Finland Department of Electrical and Computer Engineering University of Maryland United States Laboratory of Signal Processing Tampere University of Technology Finland Department of Electrical and Computer Engineering University of Maryland United States Laboratory of Pervasive Computing Tampere University of Technology Finland

The majority of contemporary mobile devices and personal computers are based on heterogeneous computing platforms that consist of a number of CPU cores and one or more Graphics processing Units (GPUs). Despite the high volume of these devices, there are few existing programming frameworks that target full and simultaneous utilization of all CPU and GPU devices of the platform. This article presents a dataflow-flavored Model of Computation (MoC) that has been developed for deploying signal processing applications to heterogeneous platforms. The presented MoC is dynamic and allows describing applications with data dependent run-time behavior. On top of the MoC, formal design rules are presented that enable application descriptions to be simultaneously dynamic and decidable. Decidability guarantees compile-time application analyzability for deadlock freedom and bounded memory. The presented MoC and the design rules are realized in a novel Open Source programming environment "PRUNE" and demonstrated with representative application examples from the domains of image processing, computer vision and wireless communications. Experimental results show that the proposed approach outperforms the state-of-the-art in analyzability, flexibility and performance. Copyright © 2018, The Authors. All rights reserved.

关键词： computer aided design

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Deep learning methods for lung cancer segmentation in whole-slide histopathology images - The ACDC@lungHP challenge 2019

arXiv

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arXiv 2020年

作者： Li, Zhang Zhang, Jiehua Tan, Tao Teng, Xichao Sun, Xiaoliang Li, Yang Liu, Lihong Xiao, Yang Lee, Byungjae Li, Yilong Zhang, Qianni Sun, Shujiao Zheng, Yushan Yan, Junyu Li, Ni Hong, Yiyu Ko, Junsu Jung, Hyun Liu, Yanling Chen, Yu-Cheng Wang, Ching-Wei Yurovskiy, Vladimir Maevskikh, Pavel Khanagha, Vahid Jiang, Yi Feng, Xiangjun Liu, Zhihong Li, Daiqiang Schüffler, Peter J. Yu, Qifeng Chen, Hui Tang, Yuling Litjens, Geert College of Aerospace Science and Engineering National University of Defense Technology Changsha410073 China Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands and ScreenPoint Medical Eindhoven Nijmegen5600 MB Netherlands Pingan Technology Shenzhen China Lunit Inc. Seoul Korea Republic of School of Electrical Engineering and Computer Science Queen Mary University of London London United Kingdom Image Processing Center School of Astronautics Beihang University Beijing102206 China Beijing Advanced Innovation Center for Biomedical Engineering Beijing100191 China Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Image Processing Center School of Astronautics Beihang University Beijing102206 China AstLab School of Automatationm Beihang University Beijing China Department of R&D Center Arontier Co. Ltd. Seoul Korea Republic of Frederick National Laboratory United States Center of Computer Vision and Medical Imaging Graduate Institute of Biomedical Engineering Graduate institute of Applied Science and Technology National Taiwan University of Science and Technology AI Explore Taipei Taiwan Research Department Sky-chain Global Yekaterinburg Russia Audio Solutions Team Motorola Solutions Inc. PlantationFL United States Second Xiangya Hospital Central South University Changsha China Hunan Biotechnology Ltd. Changsha China Hunan Cancer Hospital Central South University Changsha China Memorial Sloan Kettering Cancer Center United States First Hospital of Changsha City Changsha China Radboud University Medical Center Nijmegen Netherlands

Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using an annotated dataset of 150 training images and 50 test images from 200 patients. This paper reviews this challenge and summarizes the top 10 submitted methods for lung cancer segmentation. All methods were evaluated using the false positive rate, false negative rate, and DICE coefficient (DC). The DC ranged from 0.7354±0.1149 to 0.8372±0.0858. The DC of the best method was close to the inter-observer agreement (0.8398±0.0890). All methods were based on deep learning and categorized into two groups: multi-model method and single model method. In general, multi-model methods were significantly better (p Copyright © 2020, The Authors. All rights reserved.

关键词： Biological organs

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Neural Signatures of Motor Skill in the Resting Brain

Neural Signatures of Motor Skill in the Resting Brain

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ozan Özdenizci Timm Meyer Felix Wichmann Jan Peters Bernhard Schölkopf Müjdat Çetin Moritz Grosse-Wentrup Department of Electrical and Computer Engineering Northeastern University Boston MA United States Max Planck Institute for Intelligent Systems Tübingen Germany Faculty of Science University of Tübingen and Bernstein Center for Computational Neuroscience Tübingen Tübingen Germany FG Intelligente Autonome Systeme Technische Universität Darmstadt Darmstadt Germany Signal Processing and Information Systems Laboratory Faculty of Engineering and Natural Sciences Sabanci University Istanbul Turkey

Stroke-induced disturbances of large-scale cortical networks are known to be associated with the extent of motor deficits. We argue that identifying brain networks representative of motor behavior in the resting brain would provide significant insights for current neurorehabilitation approaches. Particularly, we aim to investigate the global configuration of brain rhythms and their relation to motor skill, instead of learning performance as broadly studied. We empirically approach this problem by conducting a three-dimensional physical space visuomotor learning experiment during electroencephalographic (EEG) data recordings with thirty-seven healthy participants. We demonstrate that across-subjects variations in average movement smoothness as the quantified measure of subjects' motor skills can be predicted from the global configuration of resting-state EEG alpha-rhythms (8-14 Hz) recorded prior to the experiment. Importantly, this neural signature of motor skill was found to be orthogonal to (independent of) task-as well as to learning-related changes in alpha-rhythms, which we interpret as an organizing principle of the brain. We argue that disturbances of such configurations in the brain may contribute to motor deficits in stroke, and that reconfiguring stroke patients' brain rhythms by neurofeedback may enhance post-stroke neurorehabilitation.

关键词： Electroencephalography End effectors Task analysis Integrated circuits Stroke (medical condition) Planning Visualization

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Non-rigid registration of Fluorescein Angiography and Optical Coherence Tomography via scanning laser ophthalmoscope imaging 39

Non-rigid registration of Fluorescein Angiography and Optica...

引用

39th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2017

作者： Ghasemi Kamasi, Zeinab Mokhtari, Marzieh Rabbani, Hossein Department of Computer Science and Electrical Engineering WVU 26505 United States Department of Biomedical Engineering Medical Image and Signal Processing Research Center Isfahan University of Medical Sciences Isfahan Iran

ISBN: (纸本)9781509028092

Fluorescein Angiography (FA) imaging is the gold standard technique for neurovascular imaging regarding assessing neurovascular diseases such as Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME). On the other hand, as FA imaging is invasive and does not provide any depth information, Optical Coherence Tomography (OCT) imaging technique is a good complementary for it in diagnosis process. To correlate the information of both FA and OCT images, an image alignment/registration process is needed. In absence of an automatic registration software, the clinician should do intuitive comparison to integrate these data which is a subjective and time consuming process. In this paper, we demonstrate a non-rigid registration method called multi-step correlation-based registration algorithm to automatically register FA and OCT images together. Our algorithm consists of two steps including rigid/global and non-rigid/local registration. We evaluate our algorithm's performance by labeling Micro-Aneurysm (MA) spots -hallmarks of DR- on FA images and determining MA regions on OCT B-scans after registration. Our Results show that our algorithm performs accurately regarding registration of FA images and OCT B-scans. © 2017 IEEE.

关键词： Software algorithms Diabetes Retina image segmentation Standards Angiography

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An Efficient Successive Cancellation Polar Decoder Based on New Folding Approaches 12

An Efficient Successive Cancellation Polar Decoder Based on ...

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2017 IEEE 12th International Conference on ASIC

作者： Xiao Liang Yechao She Harish Vangala Xiaohu You Chuan Zhang Emanuele Viterbo Lab of Efficient Architectures for Digital-communication and Signal-processing(LEADS) National Mobile Communications Research Laboratory Southeast University Department of Electronic and Computer Engineering HKUST Department of Electrical and Computer Systems Engineering Monash University

ISBN: (纸本)9781509066261;9781509066254

In this paper,an efficient successive cancellation(SC) polar decoder based on new folding approaches is *** main approach of this paper is called k-level decomposition with 2 *** k and p,the derived architecture can have a very low processing complexity with proper combinations of decomposition method and folding *** to state-of-the-art designs,hardware utilization ratio(HUR) of processing elements can be drastically improved with small latency ***,the memory complexity remains ***,decomposition and folding operations can also be applied to a family of hybrid polar *** validate efficiency of these approaches,two folded SC decoders with N = 64 and 1024 respectively are implemented with Altera Stratix V *** two demos require only 68.3% and 39.1%ALMs,compared to the non-decomposed SC decoder.

关键词： Polar code SC decoder k-level decomposition folding technique VLSI implementation

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Machine learning for forecasting mid price movement using limit order book data

arXiv

引用

arXiv 2018年

作者： Nousi, Paraskevi Tsantekidis, Avraam Passalis, Nikolaos Ntakaris, Adamantios Kanniainen, Juho Tefas, Anastasios Gabbouj, Moncef Iosifidis, Alexandros Department of Informatics Aristotle University of Thessaloniki Greece Laboratory of Signal Processing Tampere University of Technology Tampere Finland Laboratory of Industrial and Information Management Tampere University of Technology Tampere Finland Department of Engineering Electrical and Computer Engineering Aarhus University Denmark

Forecasting the movements of stock prices is one the most challenging problems in financial markets analysis. In this paper, we use Machine Learning (ML) algorithms for the prediction of future price movements using limit order book data. Two different sets of features are combined and evaluated: handcrafted features based on the raw order book data and features extracted by ML algorithms, resulting in feature vectors with highly variant dimensionalities. Three classifiers are evaluated using combinations of these sets of features on two different evaluation setups and three prediction scenarios. Even though the large scale and high frequency nature of the limit order book poses several challenges, the scope of the conducted experiments and the significance of the experimental results indicate that Machine Learning highly befits this task carving the path towards future research in this field. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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RECOVERY OF UWB RADAR signalS IN SPECTRALLY RESTRICTED ENVIRONMENTS

RECOVERY OF UWB RADAR SIGNALS IN SPECTRALLY RESTRICTED ENVIR...

引用

IEEE International Conference on image processing

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

This paper presents a novel technique to recover the missing spectral information due to RF spectral restriction in ultra-wideband (UWB) synthetic aperture radar (SAR) imaging. We address a critical problem in UWB radar imaging: radar transmission is prohibited in reserved frequency bands specified by local frequency management agencies. We model the problem in a compressed sensing setup where a time-sparse received radar signal at each aperture is collected in the incoherent frequency domain using a stepped-frequency implementation. Using electromagnetic (EM) finite-difference, time-domain (FDTD) data of various targets and clutter objects computed at all viewing aspect angles, we show that the proposed technique can successfully recover the relevant information on targets of interest even from a large percentage of missing frequency bands.

关键词： Synthetic aperture radar Ultra wideband radar Radar imaging Time-frequency analysis Time-domain analysis

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Robust autocalibrated structured low-rank EPI ghost correction

arXiv

引用

arXiv 2019年

作者： Lobos, Rodrigo A. Scott Hoge, W. Javed, Ahsan Liao, Congyu Setsompop, Kawin Nayak, Krishna S. Haldar, Justin P. Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Signal and Image Processing Institute University of Southern California Los AngelesCA United States Department of Biomedical Engineering University of Southern California Los AngelesCA United States Department of Radiology Brigham and Women's Hospital BostonMA United States Department of Radiology Harvard Medical School BostonMA United States Athinoula A. Martinos Center for Biomedical Imaging CharlestownMA United States #317 3710 McClintock Avenue Los AngelesCA90036

Purpose: We propose and evaluate a new structured low-rank method for EPI ghost correction called Robust Autocalibrated LORAKS (RAC-LORAKS). The method can be used to suppress EPI ghosts arising from the differences between different readout gradient polarities and/or the differences between different shots. It does not require conventional EPI navigator signals, and is robust to imperfect autocalibration data. Methods: Autocalibrated LORAKS is a previous structured low-rank method for EPI ghost correction that uses GRAPPA-type autocalibration data to enable high-quality ghost correction. This method works well when the autocalibration data is pristine, but performance degrades substantially when the autocalibration information is imperfect. RAC-LORAKS generalizes Autocalibrated LORAKS in two ways. First, it does not completely trust the information from autocalibration data, and instead considers the autocalibration and EPI data simultaneously when estimating low-rank matrix structure. And second, it uses complementary information from the autocalibration data to improve EPI reconstruction in a multi-contrast joint reconstruction framework. RAC-LORAKS is evaluated using simulations and in vivo data, and compared to state-of-the-art methods. Results: RAC-LORAKS is demonstrated to have good ghost elimination performance compared to state-of-the-art methods in several complicated acquisition scenarios (including gradient-echo brain imaging, diffusion-encoded brain imaging, and cardiac imaging). Conclusion: RAC-LORAKS provides effective suppression of EPI ghosts and is robust to imperfect autocalibration data. Copyright © 2019, The Authors. All rights reserved.

关键词： Matrix algebra

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Using deep learning for price prediction by exploiting stationary limit order book features

arXiv

引用

arXiv 2018年

作者： Tsantekidis, Avraam Passalis, Nikolaos Tefas, Anastasios Kanniainen, Juho Gabbouj, Moncef Iosifidis, Alexandros Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Laboratory of Industrial and Information Management Tampere University of Technology Tampere Finland Laboratory of Signal Processing Tampere University of Technology Tampere Finland Department of Engineering Electrical and Computer Engineering Aarhus University Denmark

The recent surge in Deep Learning (DL) research of the past decade has successfully provided solutions to many difficult problems. The field of quantitative analysis has been slowly adapting the new methods to its problems, but due to problems such as the non-stationary nature of financial data, significant challenges must be overcome before DL is fully utilized. In this work a new method to construct stationary features, that allows DL models to be applied effectively, is proposed. These features are thoroughly tested on the task of predicting mid price movements of the Limit Order Book. Several DL models are evaluated, such as recurrent Long Short Term Memory (LSTM) networks and Convolutional Neural Networks (CNN). Finally a novel model that combines the ability of CNNs to extract useful features and the ability of LSTMs' to analyze time series, is proposed and evaluated. The combined model is able to outperform the individual LSTM and CNN models in the prediction horizons that are tested. Copyright © 2018, The Authors. All rights reserved.

关键词： Long short-term memory

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