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Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guarantees

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arXiv 2022年

作者： Gan, Weijie Ying, Chunwei Eshraghi, Parna Wang, Tongyao Eldeniz, Cihat Hu, Yuyang Liu, Jiaming Chen, Yasheng An, Hongyu Kamilov, Ulugbek S. Department of Computer Science & Engineering Washington University in St. Louis St. Louis United States Department of Biomedical Engineering Washington University in St. Louis St. Louis United States Mallinckrodt Institute of Radiology Washington University in St. Louis St. Louis United States Department of Electrical & System Engineering Washington University in St. Louis St. Louis United States Department of Neurology Washington University in St. Louis St. Louis United States

Deep equilibrium models (DEQ) have emerged as a powerful alternative to deep unfolding (DU) for image reconstruction. DEQ models—implicit neural networks with effectively infinite number of layers—were shown to achieve state-of-the-art image reconstruction without the memory complexity associated with DU. While the performance of DEQ has been widely investigated, the existing work has primarily focused on the settings where groundtruth data is available for training. We present self-supervised deep equilibrium model (SelfDEQ) as the first self-supervised reconstruction framework for training model-based implicit networks from undersampled and noisy MRI measurements. Our theoretical results show that SelfDEQ can compensate for unbalanced sampling across multiple acquisitions and match the performance of fully supervised DEQ. Our numerical results on in-vivo MRI data show that SelfDEQ leads to state-of-the-art performance using only undersampled and noisy training data. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

Aerosol jet printed capacitive strain gauge for soft structural materials

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npj Flexible Electronics 2020年第1期4卷 279-287页

作者： Kiyo T.Fujimoto Jennifer K.Watkins Timothy Phero Doug Litteken Kevin Tsai Takoda Bingham Kshama Lakshmi Ranganatha Benjamin C.Johnson Zhangxian Deng Brian Jaques David Estrada Center for Advanced Energy Studies Boise State UniversityIdaho FallsIDUSA Micron School of Materials Science and Engineering Boise State UniversityBoiseIDUSA Measurement Sciences Department Idaho National LaboratoryIdaho FallsIDUSA Department of Advanced Fuel Manufacturing and Development Idaho National LaboratoryIdaho FallsIDUSA NASA Johnson Space Center HoustonTXUSA Department of Mechanical and Biomedical Engineering Boise State UniversityBoiseIDUSA Department of Electrical and Computer Engineering Boise State UniversityBoiseIDUSA

Soft structural textiles,or softgoods,are used within the space industry for inflatable habitats,parachutes and decelerator *** the safety and structural integrity of these systems occurs through structural health monitoring systems(SHM),which integrate non-invasive/non-destructive testing methods to detect,diagnose,and locate ***/load monitoring of these systems is limited while utilizing traditional strain gauges as these gauges are typically stiff,operate at low temperatures,and fail when subjected to high strain that is a result of high loading classifying them as unsuitable for SHM of soft structural *** this work,a capacitance based strain gauge(CSG)was fabricated via aerosol jet printing(AJP)using silver nanoparticle ink on a flexible polymer *** strain gauges were then compared to a commercially available high elongation resistance-based strain gauge(HE-RSG)for their ability to monitor strained Kevlar straps having a 26.7 kN(6 klbf)***,static and cyclic loads were used to characterize both types of strain monitoring *** CSGs demonstrated superior performance for high elongation strain measurements when compared to commonly used HE-RSGs,and were observed to operate with a gauge factor of 5.2 when the electrode arrangement was perpendicular to the direction of strain.

关键词： strain gauge structural

Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging task

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arXiv 2022年

作者： Giammarino, Vittorio Dunne, Matthew F. Moore, Kylie N. Hasselmo, Michael E. Stern, Chantal E. Paschalidis, Ioannis Ch. Division of Systems Engineering Boston University BostonMA02446 United States Dept. of Electrical and Computer Engineering Boston University BostonMA02446 United States Dept. of Biomedical Engineering Boston University BostonMA02215 United States Cognitive Neuroimaging Center Boston University BostonMA02215 United States Graduate Program for Neuroscience Boston University BostonMA02215 United States Center for Systems Neuroscience Boston University BostonMA02215 United States

We develop a simple framework to learn bio-inspired foraging policies using human data. We conduct an experiment where humans are virtually immersed in an open field foraging environment and are trained to collect the highest amount of rewards. A Markov Decision Process (MDP) framework is introduced to model the human decision dynamics. Then, Imitation Learning (IL) based on maximum likelihood estimation is used to train Neural Networks (NN) that map human decisions to observed states. The results show that passive imitation substantially underperforms humans. We further refine the human-inspired policies via Reinforcement Learning (RL) using the on-policy Proximal Policy Optimization (PPO) algorithm which shows better stability than other algorithms and can steadily improve the policies pretrained with IL. We show that the combination of IL and RL can match human results and that good performance strongly depends on combining the allocentric information with an egocentric representation of the environment. © 2022, CC BY.

关键词： Reinforcement learning

Set-based state estimation and fault diagnosis of linear discrete-time descriptor systems using constrained zonotopes

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arXiv 2020年

作者： Rego, Brenner S. Raimondo, Davide M. Raffo, Guilherme V. Graduate Program in Electrical Engineering Federal University of Minas Gerais Belo Horizonte Brazil Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Department of Electronics Engineering Federal University of Minas Gerais Belo Horizonte Brazil

This paper presents new methods for set-valued state estimation and active fault diagnosis of linear descriptor systems. The algorithms are based on constrained zonotopes, a generalization of zonotopes capable of describing strongly asymmetric convex sets, while retaining the computational advantages of zonotopes. Additionally, unlike other set representations like intervals, zonotopes, ellipsoids, paralletopes, among others, linear static constraints on the state variables, typical of descriptor systems, can be directly incorporated in the mathematical description of constrained zonotopes. Therefore, the proposed methods lead to more accurate results in state estimation in comparison to existing methods based on the previous sets without requiring rank assumptions on the structure of the descriptor system and with a fair trade-off between accuracy and efficiency. These advantages are highlighted in two numerical examples. Copyright © 2020, The Authors. All rights reserved.

关键词： Failure analysis

Optimal localization with sequential pseudorange measurements for moving users in a time division broadcast positioning system

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arXiv 2021年

作者： Zhao, Sihao Zhang, Xiao-Ping Cui, Xiaowei Lu, Mingquan Department of Electrical Computer and Biomedical Engineering Ryerson University TorontoONM5B 2K3 Canada Department of Electronic Engineering Tsinghua University Beijing100084 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

In a time division broadcast positioning system (TDBPS), a user device (UD) determines its position by obtaining sequential time-of-arrival (TOA) or pseudorange measurements from signals broadcast by multiple synchronized base stations (BSs). The existing localization method using sequential pseudorange measurements and a linear clock drift model for the TDPBS, namely LSPM-D, does not compensate the position displacement caused by the UD movement and will result in position error. In this paper, depending on the knowledge of the UD velocity, we develop a set of optimal localization methods for different cases. First, for known UD velocity, we develop the optimal localization method, namely LSPM-KVD, to compensate the movement-caused position error. We show that the LSPM-D is a special case of the LSPM-KVD when the UD is stationary with zero velocity. Second, for the case with unknown UD velocity, we develop a maximum likelihood (ML) method to jointly estimate the UD position and velocity, namely LSPM-UVD. Third, in the case that we have prior distribution information of the UD velocity, we present a maximum a posteriori (MAP) estimator for localization, namely LSPM-PVD. We derive the Cramér-Rao lower bound (CRLB) for all three estimators and analyze their localization error performance. We show that the position error of the LSPM-KVD increases as the assumed known velocity deviates from the true value. As expected, the LSPM-KVD has the smallest position error while the LSPM-PVD and the LSPM-UVD are more robust when the prior knowledge of the UD velocity is limited. Numerical results verify the theoretical analysis on the optimality and the positioning accuracy of the proposed methods. © 2021, CC BY-NC-ND.

关键词： Time of arrival

Semidefinite programming two-way TOA Localization for User Devices with Motion and Clock Drift

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arXiv 2021年

作者： Zhao, Sihao Zhang, Xiao-Ping Cui, Xiaowei Lu, Mingquan The Department of Electrical Computer and Biomedical Engineering Ryerson University TorontoONM5B 2K3 Canada The Department of Electronic Engineering Tsinghua University Beijing100084 China The Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

In two-way time-of-arrival (TOA) systems, a user device (UD) obtains its position by round-trip communications to a number of anchor nodes (ANs) at known locations. The objective function of the maximum likelihood (ML) method for two-way TOA localization is nonconvex. Thus, the widely-adopted Gauss-Newton iterative method to solve the ML estimator usually suffers from the local minima problem. In this paper, we convert the original estimator into a convex problem by relaxation, and develop a new semidefinite programming (SDP) based localization method for moving UDs, namely SDP-M. Numerical result demonstrates that compared with the iterative method, which often fall into local minima, the SDP-M always converge to the global optimal solution and significantly reduces the localization error by more than 40%. It also has stable localization accuracy regardless of the UD movement, and outperforms the conventional method for stationary UDs, which has larger error with growing UD velocity. © 2021, CC BY-NC-ND.

关键词： Maximum likelihood

Simres-TV: Noise and residual similarity for parameter estimation in total variation 4

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Simres-TV: Noise and residual similarity for parameter estim...

4th International Workshop on Photogrammetric and computer Vision Techniques for Video Surveillance, Biometrics and Biomedicine, PSBB 2021

作者： Prasath, V.B. Surya Hien, Nguyen Ngoc Thanh, Dang N.H. Dvoenko, Sergey Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center CincinnatiOH45229 United States Department of Pediatrics University of Cincinnati College of Medicine CincinnatiOH45257 United States Department of Biomedical Informatics College of Medicine University of Cincinnati OH45267 United States Department of Electrical Engineering and Computer Science University of Cincinnati OH45221 United States Dong Thap University Cao Lanh City Viet Nam Department of Information Technology School of Business Information Technology University of Economics Ho Chi Minh City Viet Nam Institute of Applied Mathematics and Computer Science Tula State University Russia

Image restoration with regularization models is very popular in the image processing literature. Total variation (TV) is one of the important edge preserving regularization models used, however, to obtain optimal restoration results the regularization parameter needs to be set appropriately. We propose here a new parameter estimation approach for total variation based image restoration. By utilizing known noise levels we compute the regularization parameter by reducing the similarity between residual and noise variances. We use the split Bregman algorithm for the total variation along with this automatic parameter estimation step to obtain a very fast restoration scheme. Experimental results indicate the proposed parameter estimation obtained better denoised images and videos in terms of PSNR and SSIM measures and the computational overload is less compared with other approaches. © Authors 2021. CC BY 4.0 License.

关键词： Image reconstruction

Deep large-scale multi-task learning network for gene expression inference 24th

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Deep large-scale multi-task learning network for gene expres...

24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020

作者： Dizaji, Kamran Ghasedi Chen, Wei Huang, Heng Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh United States Department of Biomedical Informatics School of Medicine University of Pittsburgh Pittsburgh United States Department of Pediatrics UPMC Children’s Hospital of Pittsburgh University of Pittsburgh Pittsburgh United States

ISBN: (纸本)9783030452568

Gene expressions profiling empowers many biological studies in various fields by comprehensive characterization of cellular status under different experimental conditions. Despite the recent advances in high-throughput technologies, profiling the whole-genome set is still challenging and expensive. Based on the fact that there is high correlation among the expression patterns of different genes, the above issue can be addressed by a cost-effective approach that collects only a small subset of genes, called landmark genes, as the representative of the entire genome set and estimates the remaining ones, called target genes, via the computational model. Several shallow and deep regression models have been presented in the literature for inferring the expressions of target genes. However, the shallow models suffer from underfitting due to their insufficient capacity in capturing the complex nature of gene expression data, and the existing deep models are prone to overfitting due to the lack of using the interrelations of target genes in the learning framework. To address these challenges, we formulate the gene expression inference as a multi-task learning problem and propose a novel deep multi-task learning algorithm with automatically learning the biological interrelations among target genes and utilizing such information to enhance the prediction. In particular, we employ a multi-layer sub-network with low dimensional latent variables for learning the interrelations among target genes (i.e. distinct predictive tasks), and impose a seamless and easy to implement regularization on deep models. Unlike the conventional complicated multi-task learning methods, which can only deal with tens or hundreds of tasks, our proposed algorithm can effectively learn the interrelations from the large-scale ($$\sim $$10,000) tasks on the gene expression inference problem, and does not suffer from cost-prohibitive operations. Experimental results indicate the superiority of our method c

关键词： Learning algorithms

A new toa localization and synchronization system with virtually synchronized periodic asymmetric ranging network

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arXiv 2021年

作者： Zhao, Sihao Zhang, Xiao-Ping Cui, Xiaowei Lu, Mingquan Department of Electrical Computer and Biomedical Engineering Ryerson University TorontoONM5B 2K3 Canada Department of Electronic Engineering Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China

In this article, we design a new time-of-arrival (TOA) system for simultaneous user device (UD) localization and synchronization with a periodic asymmetric ranging network, namely PARN. The PARN includes one primary anchor node (PAN) transmitting and receiving signals, and many secondary ANs (SAN) only receiving signals. All the UDs can transmit and receive signals. The PAN periodically transmits sync signal and the UD transmits response signal after reception of the sync signal. Using TOA measurements from the periodic sync signal at SANs, we develop a Kalman filtering method to virtually synchronize ANs with high accuracy estimation of clock parameters. Employing the virtual synchronization, and TOA measurements from the response signal and sync signal, we then develop a maximum likelihood (ML) approach, namely ML-LAS, to simultaneously localize and synchronize a moving UD. We analyze the UD localization and synchronization error, and derive the Cramér-Rao lower bound (CRLB). Different from existing asymmetric ranging network-based TOA systems, the new PARN i) uses the periodic sync signals at the SAN to exploit the temporal correlated clock information for high accuracy virtual synchronization, and ii) compensates the UD movement and clock drift using various TOA measurements to achieve consistent and simultaneous localization and synchronization performance. Numerical results verify the theoretical analysis that the new system has high accuracy in AN clock offset estimation and simultaneous localization and synchronization for a moving UD. We implement a prototype hardware system and demonstrate the feasibility and superiority of the PARN in real-world applications by experiments. © 2021, CC BY-NC-ND.

关键词： Kalman filters