Poisson image deblurring with frame-based nonconvex regularization

GCN-based proximal unrolling matrix completion for piecewise smooth signal recovery

The Cox proportional hazards model is a popular semi-parametric model for survival analysis. In this paper, we aim at developing a federated algorithm for the Cox proportional hazards model over vertically partitioned data (i.e., data from the same patient are stored at different institutions). We propose a novel algorithm, namely VERTICOX, to obtain the global model parameters in a distributed fashion based on the Alternating Direction Method of Multipliers (ADMM) framework. The proposed model computes intermediary statistics and exchanges them to calculate the global model without collecting individual patient-level data. We demonstrate that our algorithm achieves equivalent accuracy for the estimation of model parameters and statistics to that of its centralized realization. The proposed algorithm converges linearly under the ADMM framework. Its computational complexity and communication costs are polynomially and linearly associated with the number of subjects, respectively. Experimental results show that VERTICOX can achieve accurate model parameter estimation to support federated survival analysis over vertically distributed data by saving bandwidth and avoiding exchange of information about individual patients. The source code for VERTICOX is available at: https://github.com/daiwenrui/VERTICOX.

The network identification plays a very prominent role for the network operator to accomplish the various objectives such as state-estimation, monitoring, control, planning, and real-time analytics. The network structure varies from time-to-time and its details are often not available with the network operator. To address this issue, an alternating direction method of multipliers (ADMM) based framework is presented herein to identify the network topology and line parameters using smart meter (SM) and micro phasor measurement unit (micro-PMU) measurements. The presented algorithm is divided into two sections, 1) approximate parameter evaluation through regression, to extract the partial topology information, and 2) complete network topology identification through the ADMM framework. This algorithm accomplishes the objectives of identifying the network configuration, branch parameters (e.g. conductance and susceptance), and change in branch parameters. Simulation results demonstrate the effectiveness of the presented algorithm on the benchmarked IEEE 13-bus and IEEE 123-bus feeders under various operating scenarios. Furthermore, the presented framework illustrates excellent network identification even with the presence of the stochastic nature of renewable power generation. The presented algorithm exhibits an excellent performance even with the consideration of noise in both measurements. In addition, the comparative performance is carried out on the benchmarked unbalanced IEEE 13-bus and balanced IEEE 33-bus feeders to highlight the efficacy of the presented framework over the state-of-art framework.

A low-complexity ADMM-based secure beamforming for IRS-assisted uplink cognitive radio networks with NOMA

Synchronous and asynchronous parallel computation for large-scale optimal control of connected vehicles

This article develops a fully decentralized approximate dynamic programming (FD-ADP) algorithm for stochastic energy management (SEM) of a networked microgrid (NMG) system. First, considering the ac power flow constraints, an alternating direction method of multipliers (ADMM)-based decentralized SEM framework is proposed for NMG coordination. Then, a transactive energy scheme is introduced to further decouple each microgrid (MG) optimization for privacy enhancement and computation reduction. Next, a FD-ADP algorithm is proposed to cope with the real-time uncertainties. The piecewise linear function (PLF) is employed for value function approximation, and a fully decentralized PLF slope update method based on ADMM framework is designed for decentralized property preservation, which trains the value function just through each MG local information and neighboring communication, thus the well-trained decentralized PLF slopes can help achieve the global optimal SEM strategy for NMG coordination under stochastic environments. Finally, case studies demonstrate the effectiveness of the proposed ADMM-based FD-ADP algorithm in terms of decentralized optimization, decentralized training, and global optimality.

SummaryExtreme learning machine (ELM) is a prominent example of neural network with its fast training speed, and good prediction performance. An online version of ELM called online sequential extreme learning machine (OS‐ELM) has also been proposed for the sequential training. Combined with the need for regularization to prevent over‐fitting in addition to the large number of neurons required in the hidden layer, OS‐ELM demands huge amount of computation power for the large‐scale data. In this article, a mixed norm () regularized online machine learning algorithm (MRO‐ELM) that is based on alternating direction method of multipliers (ADMM) is proposed. A linear combination of the mixed norm and the Frobenius norm regularization is applied using the ADMM framework and update formulas are derived. Graphics processing unit (GPU) accelerated version of MRO‐ELM (GPU‐MRO‐ELM) is also proposed to reduce the training time by processing appropriate parts in parallel using the implemented custom kernels. In addition, a novel automatic hyper‐parameter tuning method is incorporated to GPU‐MRO‐ELM using progressive validation with GPU acceleration. The experimental results show that the MRO‐ELM algorithm and its GPU version outperform OS‐ELM in terms of training speed, and testing accuracy. Also, compared to the cross validation, the proposed automatic hyper‐parameter tuning demonstrates dramatical reduction in the tuning time.

Grid-interactive efficient buildings can provide many benefits for the power grid. However, it is very challenging to coordinate their operations intelligently due to many considerations, e.g., privacy protection and scalability. To this end, this paper investigates an optimal coordination operation problem of multiple grid-interactive residential buildings under uncertainties to minimize the total peak demand violation of all residential buildings during the grid service periods while minimizing their respective operation cost. Taking into account multi-source uncertainties, temporal and spatial couplings, inexplicit thermal dynamics models, and the lack of information sharing among residential buildings incurred by privacy protection, it is difficult to design a centralized model-based optimization operation method. To overcome the challenge, we design an online distributed coordination operation algorithm based on the alternating direction method of multipliers (ADMM) and relative entropy regularized policy iteration (RERPI). The designed algorithm consists of two stages, i.e., independent offline training of each RERPI-based agent related to the building energy management system controller (BEMSC) in each residential building (stage-1) and online distributed execution for coordination between the aggregator controller and each BEMSC under the ADMM framework (stage-2). Simulation results verify the effectiveness and scalability of the designed algorithm.

Low-rank tensor approximation (LRTA) techniques have demonstrated great promise for seismic data denoising due to their ability to capture multi-dimensional dependencies. However, most existing methods fail to make full use of the correlations across different modes of tensors, leading to suboptimal denoising performance. In this paper, we propose a novel method, termed TR-UTV, which integrates tensor ring (TR) decomposition with unidirectional total variation (UTV) for highly effective seismic data denoising. The proposed TR-UTV approach exploits TR decomposition to capture the underlying low-rank (LR) structures of seismic data and integrates UTV to suppress footprint noise. Considering that real seismic data are typically non-stationary and contain complex noise patterns, a Laplacian scale mixture prior is employed to model sparse tensor coefficients, enabling joint estimation of both their values and variances. Additionally, an alternating direction method of multipliers (ADMM) algorithm is developed to effectively solve the proposed TR-UTV-based seismic data denoising problem. Extensive experiments on synthetic and field seismic data demonstrate that the proposed TR-UTV algorithm outperforms many state-of-the-art seismic data denoising methods in terms of both quantitative metrics and visual quality.

Dynamic magnetic resonance imaging (MRI) has been extensively utilized for enhancing medical living environment visualization, however, in clinical practice it often suffers from long data acquisition times. Dynamic imaging essentially reconstructs the visual image from raw (k,t)-space measurements, commonly referred to as big data. The purpose of this work is to accelerate big medical data acquisition in dynamic MRI by developing a non-convex minimization framework. In particular, to overcome the inherent speed limitation, both non-convex low-rank and sparsity constraints were combined to accelerate the dynamic imaging. However, the non-convex constraints make the dynamic reconstruction problem difficult to directly solve through the commonly-used numerical methods. To guarantee solution efficiency and stability, a numerical algorithm based on Alternating Direction Method of Multipliers (ADMM) is proposed to solve the resulting non-convex optimization problem. ADMM decomposes the original complex optimization problem into several simple sub-problems. Each sub-problem has a closed-form solution or could be efficiently solved using existing numerical methods. It has been proven that the quality of images reconstructed from fewer measurements can be significantly improved using non-convex minimization. Numerous experiments have been conducted on two in vivo cardiac datasets to compare the proposed method with several state-of-the-art imaging methods. Experimental results illustrated that the proposed method could guarantee the superior imaging performance in terms of quantitative and visual image quality assessments.

Color image restoration is a fundamental task in computer vision and image processing, with extensive real‐world applications. In practice, color images often suffer from degradations caused by sensor noise, optical blur, compression artifacts, and data loss during the acquisition, transmission, or storage. Unlike grayscale images, color images exhibit high correlations among their RGB channels. Directly extending grayscale restoration methods to color images often leads to issues such as color distortion and structural artifacts. To address these challenges, this paper proposes a novel quaternion‐based color image restoration framework. The method integrates low‐rank pseudo‐norm constraints with saturation‐value total variation (SVTV) regularization, effectively enhancing restoration quality in tasks including denoising, deblurring, and inpainting of degraded color images. The proposed algorithm is efficiently solved using the alternating direction method of multipliers (ADMM), and restoration performance is rigorously evaluated through quantitative metrics including peak signal‐to‐noise ratio (PSNR), structural similarity index measure (SSIM), and S‐CIELAB error. Extensive experimental results demonstrate the superior performance of our method compared to existing approaches.

Wepropose a variant of alternating direction method of multiplier (ADMM) to solve constrained trajectory optimization problems. Our ADMM framework breaks a joint optimization into small sub-problems, leading to a low iteration cost and decentralized parameter updates. Starting from a collision-free initial trajectory, our method inherits the theoretical properties of primal interior point method (P-IPM), i.e., guaranteed collision avoidance and homotopy preservation throughout optimization, while being orders of magnitude faster. We have analyzed the convergence and evaluated our method for time-optimal multi-UAV trajectory optimizations and simultaneous goal-reaching of multiple robot arms, where we take into consider kinematics-, dynamics-limits, and homotopy-preserving collision constraints. Our method highlights an order of magnitude’s speedup, while generating trajectories of comparable qualities as state-of-the-art P-IPM solver.

An efficient non-convex total variation approach for image deblurring and denoising

A contact hole pattern as well as a line-and-space pattern is a basic element for the fabrication of semiconductor devices. The critical dimension uniformity and edge roughness of contact hole patterns are critical issues for extreme ultraviolet (EUV) lithography. In this study, the quality of the latent images of contact hole patterns was investigated in terms of the chemical gradient using a simulation on the basis of the reaction mechanisms of chemically amplified EUV resists. The latent image formation of contact hole patterns was compared with that of line-and-space patterns. The latent image quality of contact hole patterns was significantly lower than that of line-and-space patterns. To obtain the same latent image quality as line-and-space patterns, the exposure dose should be increased 1.4±0.1 times. Similarly to the line-and-space patterns, the increase of acid generator concentration and effective reaction radius is effective for the enhancement of latent image quality. For the enhancement of latent image quality of contact hole patterns, the enhancement of the effective reaction radius is more effective in a low exposure dose region than a high exposure dose region.

In recent several years, motivated by the need to reduce radiation doses in CT exams, all of the major CT manufacturers have commercialized different iterative image reconstruction techniques and these innovative techniques were used in clinical routines with increasing popularity. However, due to the intrinsic nonlinearity of these new techniques, the well accepted quantitative image quality assessment metrics such as spatial resolution and contrast to noise ratio are not sufficient to provide the needed quantitative metrics for assessing image quality and for guiding the CT scan protocol optimization. This symposium aims at providing a thorough update to AAPM community on what we have understood in the past for linear CT imaging system, what are the new challenges and opportunities offered by the nonlinear iterative image reconstruction, and what would be the future directions in quantitative image quality assessment that the AAPM community can work together to address the challenges and to adapt the nonlinear image reconstruction methods to routine clinical practice to improve patient care. Three invited speakers will lead the discussions in this symposium.Dr. Ke Li from the University of Wisconsin‐Madison will be presenting the new challenges introduced in model‐based iterative reconstruction (MBIR) method with a focus on how the nonlinear nature of the MBIR reconstruction poses challenges in quantitative image quality metrics such as spatial resolution, noise power spectrum, and the limitations of CNR in protocol optimization which eventually leads to the need of task‐based detectability index as the metric.Dr. Shuai Leng from Mayo Clinic will then present how the CNR metric is insufficient for nonlinear reconstruction algorithms, and how task based model observers can be used to more accurately quantify image quality, and to optimize imaging system and scanning protocols for nonlinear iterative image reconstruction algorithms based on specific imaging task. Practical considerations regarding the application of task based image quality metrics will also be discussed.Dr. Ehsan Samei from Duke University will start the symposium by summarizing a to‐be‐released AAPM Task Group report (TG 233: Performance Evaluation of Computed Tomography Systems). He will cover what has been understood and accepted for linear CT imaging system, what are the new challenges in CT image quality assessment introduced by the nonlinear iterative reconstruction (IR) techniques, and summarize both taskneutral and task‐based metrologies developed in TG 233 to partially address these challenges, and foresee additional challenges in the future. Learning Objectives: Understand the challenges in quantitative image quality assessment introduced by nonlinear iterative reconstruction techniques Understand the challenges and methods in noise performance assessment for nonlinear iterative reconstruction techniques Understand the challenges and methods in in spatial resolution assessment for nonlinear iterative reconstruction techniques Understand the challenges and methods in in task‐based observer performance assessment for nonlinear iterative reconstruction techniquesFunding support received from NIH and DOD; Funding support received from GE Healthcare; Funding support received from Siemens AX; Patent royalties received from GE Healthcare; S. Leng, R01 EB071095; U01 EB017185; E. Samei, Research grant, Siemens; Research grant, GE; K. Li, Funding from NIH, DOD and AAPM