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An iterative Least Squares Method for Proton CT Image Reconstruction

IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022年第3期6卷 304-312页

作者： DeJongh, Don F. DeJongh, Ethan A. ProtonVDA LLC Naperville IL 60563 USA

Clinically useful proton computed tomography images will rely on algorithms to find the 3-D proton stopping power distribution that optimally fits the measured proton data. We present a least squares iterative method with many features to put proton imaging into a more quantitative framework. These include the definition of a unique solution that optimally fits the protons, the definition of an iteration vector that takes into account proton measurement uncertainties, the definition of an optimal step size for each iteration individually, the ability to simultaneously optimize the step sizes of many iterations, the ability to divide the proton data into arbitrary numbers of blocks for parallel processing and use of graphical processing units, and the definition of stopping criteria to determine when to stop iterating. We find that it is possible, for any object being imaged, to provide assurance that the image is quantifiably close to an optimal solution, and the optimization of step sizes reduces the total number of iterations required for convergence. We demonstrate the use of these algorithms on real data.

关键词： iterative algorithm least squares parallel processing proton computed tomography proton imaging relaxation coefficient stopping criterion

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A Robust iterative algorithm for Parameter Estimation of the Generalized Gamma Distribution 17

A Robust Iterative Algorithm for Parameter Estimation of the...

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IEEE 17th International Conference on Computational Science and Engineering (CSE)

作者： Luo, Rui Zhao, Wei IBM Res Shanghai Peoples R China

ISBN: (纸本)9781479979813

This paper introduces a robust iterative algorithm to estimate the parameters of the generalized gamma (GG) distribution. The proposed algorithm loops on an interval I-p of the shape parameter p such that the log-likelihood function are simplified as a univariate objective function for each p is an element of I-p, which can be evaluated efficiently. An iterative procedure is then designed to maximize the objective function, which keeps squeezing the interval of search using translated asymptotes and interpolation. The results of maximization form a set of local maxima. Therefore, the estimates derived from the maximum of these local maxima. Comprehensive validation of the algorithm is performed as well, showing the robustness as well as efficiency as we expected. Due to the employment of the specific interpolation, the algorithm determines the roots with precision level of 10(-10) within four iterations, which is a rather well-pleasing performance. We also claim that the flexibility of the algorithm allows us to transplant the algorithm to a distributed platform for very large scale data processing. Further optimization is still open to be discussed.

关键词： parameter estimation the generalized gamma distribution maximum likelihood iterative algorithm robustness

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An iterative algorithm for generalized mixed equilibrium problem

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AFRIKA MATEMATIKA 2014年第4期25卷 857-867页

作者： Kazmi, K. R. Rizvi, S. H. Aligarh Muslim Univ Dept Math Aligarh 202002 Uttar Pradesh India

In this paper, we consider a generalized mixed equilibrium problem and its related an auxiliary problem in real Hilbert space. We prove a result for the existence and uniqueness of solutions of the auxiliary problem. This result is then used to define proximal mapping for generalized mixed equilibrium problem. Further, based on this result, we give an iterative algorithm which consists of a proximal mapping technique step followed by a suitable orthogonal projection onto a moving half-space for solving generalized mixed equilibrium problem. Furthermore we prove that the sequences generated by iterative algorithm converge weakly to a solution of generalized mixed equilibrium problem. Finally, we discuss some special cases of the main result.

关键词： Generalized mixed equilibrium problem Auxiliary problem iterative algorithm Fejer monotonicity Proximal mapping Projection mapping

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NEW iterative algorithm FOR THE COMPUTATION OF THE RANK-DEFICIENT CAPON FILTER IN SPECTRAL ESTIMATION 12

NEW ITERATIVE ALGORITHM FOR THE COMPUTATION OF THE RANK-DEFI...

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12th IEEE International Conference on Signal Processing (ICSP)

作者： Gao Zhijeng Peng Yu Peng Xiyuan Harbin Inst Technol Automat Test & Control Inst Harbin 150006 Peoples R China Shandong Univ Sch Mech Elect & Informat Weihai Peoples R China

ISBN: (纸本)9781479921867

In nonparametric complex spectral estimation, higher resolution is achieved when the covariance matrix is allowed to be rank-deficient. Several approaches are applied to remedy matrix singularity, such as diagonal loading, rank reduction and additional constraints. Conventional solution of the optimal Capon filter is derived from the gradient functions of the constraint minimization problem, and calculated via the input covariance matrix inversion. In this paper, the gradient functions are reformulated as a single one with the same solution, which is solved by an adaptive-step algorithm based on conjugate gradient method. The optimal Capon filter is iteratively calculated without any matrix inversion. The proposed algorithm is also directly applied to the computation of the optimal Capon filter with rank-deficient input covariance matrix. In simulations, performances of the proposed algorithm are compared with the existing methods. Higher convergence speed is achieved. The iteration number is selected to offset the frequency resolution and the signal mismatch problem.

关键词： iterative algorithm rank-deficient matrix robust Capon filter conjugate gradient method

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Heuristic Dynamic Programming iterative algorithm Design Based on BP Neural Network

Heuristic Dynamic Programming Iterative Algorithm Design Bas...

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International Forum on Materials Analysis and Testing Technology (IFMATT 2013)

作者： Zhao, Yu Yang, Jiye Jiangsu Food & Pharmaceut Sci Coll Dept Math Huaian 223000 Peoples R China

ISBN: (纸本)9783037859537

The time-delay delay phenomenon is a kind of widespread physical and biological phenomenon. The existence of time-delay not only give the stability of system analysis and controller design brings great difficulties but also usually make the systems unstable and even cause the system performance deteriorated. We use the adaptive dynamic iterative algorithm to solve this equation. By using the neural network to achieve the iterative algorithm, get the optimal control law of the systems with time delay. The simulation results show that the adaptive dynamic programming method to solve the optimal control of the nonlinear system is effective.

关键词： Heuristic Dynamic Programming Neural Network iterative algorithm

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Improvement of Lattice Parameter Accuracy in Single Crystal XRD Based on a Laser-Induced X-Ray Source

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高压物理学报 2025年第4期39卷 9-15页

作者： LIU Jin WANG Qiannan LI Jiangtao National Key Laboratory of Shock Wave and Detonation Physics Institute of Fluid PhysicsChina Academy of Engineering PhysicsMianyang 621999SichuanChina Pico Electron Microscopy Center Innovation Institute for Ocean Materials Characterization TechnologyCenter for Advanced Studies in Precision InstrumentHainan UniversityHaikou 570228HainanChina State Key Laboratory for Environment-Friendly Energy Materials Southwest University of Science and TechnologyMianyang 621010SichuanChina

The lattice parameter,measured with sufficient accuracy,can be utilized to evaluate the quality of single crystals and to determine the equation of state for *** propose an iterative method for obtaining more precise lattice parameters using the interaction points for the pseudo-Kossel pattern obtained from laser-induced X-ray diffraction(XRD).This method has been validated by the analysis of an XRD experiment conducted on iron single ***,the method was used to calculate the compression ratio and rotated angle of an LiF sample under high pressure *** technique provides a robust tool for in-situ characterization of structural changes in single crystals under extreme *** has significant implications for studying the equation of state and phase transitions.

关键词： lattice parameter measurement accuracy single crystal X-ray diffraction iterative algorithm high pressure ratio of compression

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An iterative solution technique for capacitated two-stage time minimization transportation problem

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4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH 2022年第4期20卷 637-684页

作者： Xie, Fanrong Li, Zuoan Sichuan Univ Sci & Engn Sch Math & Stat 180 Xueyuan St Huixing Rd Zigong 643000 Sichuan Peoples R China Chinese Acad Sci Inst Computat Math & Sci Engn Comp Beijing 100080 Peoples R China Nanchang Univ Dept Math Nanchang 330031 Peoples R China

Capacitated two-stage time minimization transportation problem is an important optimization problem arising in industries. In literature, there is only one approach to solving this problem, but it has a deficiency of resulting in memory overflow in implementation on computer for large scale instances. In this paper, this problem is reduced to a series of finding the feasible flow in a network with lower and upper arc capacities, and two iterative algorithms are proposed as more robust solution method for this problem as compared to the existing approach. It is proved that both iterative algorithms find the optimal solution to this problem in a polynomial time. Due to fully utilizing the network structure characteristics inherent to this problem, both iterative algorithms have the advantage of easy implementation on computer and high computational efficiency, and successfully overcome the deficiency of existing approach. Computational experiments validate that as compared to the existing approach, both iterative algorithms are efficient and more robust method to solve this problem, where one iterative algorithm significantly outperforms the other in terms of computational time, especially for large scale instances.

关键词： Transportation problem Time minimization Maximum flow iterative algorithm Network with lower and upper arc capacities

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An algorithm for Adaptive Assessment in Time Series Seasonal Oscillations and Its Testing Based on the Example of Variations in the CO2 Concentration in the Atmosphere

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IZVESTIYA ATMOSPHERIC AND OCEANIC PHYSICS 2022年第7期58卷 681-707页

作者： Deshcherevskii, A. V. Sidorin, A. Ya. Russian Acad Sci Schmidt Inst Phys Earth Moscow 123242 Russia

An adaptive model is proposed to describe time-varying seasonal effects. The seasonal average function is constructed using an iterative algorithm that provides a neat decomposition of the signal into a generalized trend, seasonal and residual components. By a trend, we mean long-term evolutionary changes in the average signal level, both unidirectional and chaotic, in the form of a slow random drift. This algorithm allows one to obtain unbiased estimates for each of the signal components, even in the presence of a significant number of missing observations. The series length is not required to be a multiple of an integer number of years. In contrast to the usual "Climate Normals" (CN) model, the considered adaptive model of seasonal effects assumes a continuous slow change in the properties of the seasonal component over time. The degree of allowable variability in seasonal effects from year to year is entered as a tunable parameter of the model. In particular, this allows one to show the dynamics of the growth of the amplitude of seasonal fluctuations in time in the form of a continuous (smooth) function without necessarily linking these changes to predetermined calendar epochs. The algorithm was tested on the atmospheric CO2 concentration monitoring series at Barrow, Mauna Loa, Tutuila, and South Pole stations located at different latitudes. The form of the seasonal variation was estimated, and the average amplitude of the seasonal variation and the rate of its change at each station were calculated. Noticeable differences in the dynamics of the studied parameters between stations are demonstrated. Mean amplitude of seasonal variation in CO2 concentration at Barrow, Mauna Loa, Tutuila, and South Pole stations in the epoch 2010-2019 was estimated as 18.15, 7.08, 1.30, and 1.26 ppm, respectively, and the average rate of increase in the amplitude of the seasonal variation in the increase in CO2 concentration in the interval 1976-2019 is 0.085, 0.0100, 0.0165, and 0.

关键词： time series time series analysis time series decomposition iterative algorithm trend periodic components seasonal periodicity algorithm testing carbon dioxide CO2 concentration in the atmosphere

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Secure IRS-Aided Cognitive UAV Communications 23

Secure IRS-Aided Cognitive UAV Communications

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Li, An Guo, Tao Wu, Qingqing Wang, Zhiwei Yan, Liyan Hong, Sheng Nanchang University Department of Electronic Information Engineering Nanchang330031 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China

ISBN: (纸本)9798350325959

In this article, we study a novel intelligent reflecting surface (IRS)-aided cognitive unmanned aerial vehicle (Co-UAV) secure communication network, in which an IRS is exploited to improve the information transmit security. Our goal is to optimize the IRS's phase shifts, the UAV's transmit power and trajectory jointly. Aiming at the non-convex and intractable nature of this optimization objective, we present an effective iterative algorithm by combining alternating optimization (AO) technique and successive convex approximation (SCA) method so as to acquire a local suboptimal solution. Simulation results confirm the proposed algorithm is effective in significantly improving the secondary secrecy performance assisted by IRS subject to the interference temperature limit on the primary user. © 2023 IEEE.

关键词： Cognitive unmanned aerial vehicle (Co-UAV) communication intelligent reflecting surface iterative algorithm physical layer security power control

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A TV-minimization image-reconstruction algorithm without system matrix

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JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2021年第5期29卷 851-865页

作者： Qiao, Zhiwei Lu, Yang Shanxi Univ Sch Comp & Informat Technol 92 Wucheng Rd Taiyuan 030006 Shanxi Peoples R China

PURPOSE: Total Variation (TV) minimization algorithm is a classical compressed sensing (CS) based iterative image reconstruction algorithm that can accurately reconstruct images from sparse-view projections in computed tomography (CT). However, the system matrix used in the algorithm is often too large to be stored in computer memory. The purpose of this study is to investigate a new TV algorithm based on image rotation and without system matrix to avoid the memory requirement of system matrix. METHODS: Without loss of generality, a rotation-based adaptive steepest descent-projection onto convex sets (R-ASD-POCS) algorithm is proposed and tested to solve the TV model in parallel beam CT. Specifically, simulation experiments are performed via the Shepp-Logan, FORBILD and real CT image phantoms are used to verify the inverse-crime capability of the algorithm and evaluate the sparse reconstruction capability and the noise suppression performance of the algorithm. RESULTS: Experimental results show that the algorithm can achieve inverse-crime, accurate sparse reconstruction and thus accurately reconstruct images from noisy projections. Compared with the classical ASD-POCS algorithm, the new algorithm may yield the similar image reconstruction accuracy without use of the huge system matrix, which saves the computational memory space significantly. Additionally, the results also show that R-ASD-POCS algorithm is faster than ASD-POCS. CONCLUSIONS: The proposed new algorithm can effectively solve the problem of using huge memory in large scale and iterative image reconstruction. Integrating with ASD-POCS frame, this no-system-matrix based scheme may be readily extended and applied to any iterative image reconstructions.

关键词： Rotation-based reconstruction system matrix TV minimization iterative algorithm computed tomography

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