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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： D.S. Lalush B.M.W. Tsui Department of Biomedical Engineering and Department of Radiology University of North Carolina Chapel Hill USA

The authors study the statistical convergence properties of two fast iterative reconstruction algorithms, the rescaled block-iterative (RBI) and ordered subset (OS) EM algorithms, in the context of cardiac SPECT with 3D detector response modeling. The Monte Carlo method was used to generate nearly noise-free projection data modeling the effects of attenuation, detector response, and scatter from the MCAT phantom. One thousand noise realizations were generated with an average count level approximating a typical Tl-201 cardiac study. Each noise realization was reconstructed using the RBI and OS algorithms for cases with and without detector response modeling. For each iteration up to twenty, the authors generated mean and variance images, as well as covariance images for six specific locations. Both OS and RBI converged in the mean to results that were not significantly different from the noise-free ML-EM result using the same projection model. When detector response was not modeled in the reconstruction, RBI exhibited considerably lower noise variance than OS for the same resolution. When 3D detector response was modeled, the two algorithms provided the same noise variance to resolution recovery tradeoff, while OS required about half the number of iterations of RBI to reach the same point. The authors conclude that OS is faster than RBI, but may be sensitive to errors in the projection model. Both OS-EM and RBI-EM are effective alternatives to the ML-EM algorithm, but noise level and speed of convergence depend on the projection model used.

关键词： Algorithm design and analysis Reconstruction algorithms Detectors Convergence Noise generators Noise level Image reconstruction iterative algorithms Context modeling Attenuation

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iterative reweighted algorithms for matrix rank minimization

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2012年第1期13卷

作者： Karthik Mohan Maryam Fazel Department of Electrical Engineering University of Washington Seattle WA

The problem of minimizing the rank of a matrix subject to affine constraints has applications in several areas including machine learning, and is known to be NP-hard. A tractable relaxation for this problem is nuclear norm (or trace norm) minimization, which is guaranteed to find the minimum rank matrix under suitable assumptions. In this paper, we propose a family of iterative Reweighted Least Squares algorithms IRLS-p (with 0 ≤ p ≤ 1), as a computationally efficient way to improve over the performance of nuclear norm minimization. The algorithms can be viewed as (locally) minimizing certain smooth approximations to the rank function. When p = 1, we give theoretical guarantees similar to those for nuclear norm minimization, that is, recovery of low-rank matrices under certain assumptions on the operator defining the constraints. For p < 1, IRLS-p shows better empirical performance in terms of recovering low-rank matrices than nuclear norm minimization. We provide an efficient implementation for IRLS-p, and also present a related family of algorithms, sIRLS-p. These algorithms exhibit competitive run times and improved recovery when compared to existing algorithms for random instances of the matrix completion problem, as well as on the MovieLens movie recommendation data set.

关键词： iterative algorithms matrix completion matrix rankminimization null-space property

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Exploring Potential Barrier Estimation Mechanism Based on Quantum Dynamics Framework

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Chinese Journal of Electronics 2025年第1期34卷 350-364页

作者： Quan Tang Peng Wang Chengdu Institution of Computer Application Chinese Academy of Science University of Chinese Academy of Sciences School of Computer Science and Engineering Southwest Minzu University

Due to the probability characteristics of quantum mechanism, the combination of quantum mechanism and intelligent algorithm has received wide attention. Quantum dynamics theory uses the Schr?dinger equation as a quantum dynamics equation. Through three approximation of the objective function, quantum dynamics framework(QDF) is obtained which describes basic iterative operations of optimization algorithms. Based on QDF, this paper proposes a potential barrier estimation(PBE) method which originates from quantum mechanism. With the proposed method, the particle can accept inferior solutions during the sampling process according to a probability which is subject to the quantum tunneling effect, to improve the global search capacity of optimization *** effectiveness of the proposed method in the ability of escaping local minima was thoroughly investigated through double well function(DWF), and experiments on two benchmark functions sets show that this method significantly improves the optimization performance of high dimensional complex functions. The PBE method is quantized and easily transplanted to other algorithms to achieve high performance in the future.

关键词： Potential energy Electric potential Shape Heuristic algorithms Estimation Tunneling Linear programming Approximation algorithms iterative algorithms Optimization

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iterative Channel Estimation for Multi-User OTFS Uplink Systems With Superimposed Full Pilots

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第3期74卷 4485-4497页

作者： Zhou, Maowu Chen, Fangjiong Xia, Ming Zhang, Xing Yu, Hua South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Peoples R China Guangdong Prov Key Lab Short Range Wireless Detect Guangzhou 510640 Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

Existing pilots based channel estimation schemes for orthogonal time frequency space (OTFS) systems usually require sufficient guard intervals to avoid pilot-data interference, which inevitably reduces the spectral efficiency (SE). This SE loss can be remedied by using superimposed pilots (SP). In this paper, we propose an effective superimposed full pilots (SFP)-aided iterative channel estimation (SFPCE) scheme for multi-user OTFS uplink systems. First, we derive a multi-user minimum root mean square error (MMSE) channel estimation algorithm and a message passing (MP) data detection algorithm based on the proposed multi-user SFP structure. Then, a de-interference iterative detection (DIID) algorithm is proposed to improve the accuracy of channel estimation and symbol detection. The DIID algorithm only requires a few iterations to achieve optimal performance. Besides, a hybrid superimposed pilot (HSP) structure is introduced to estimate the delay/Doppler taps without sacrificing the SE. Simulation results demonstrate that the accuracy of channel estimation and symbol detection can be significantly improved after several iterations. Moreover, the proposed SFPCE scheme is superior to the superimposed single pilot-aided channel estimation (SSIPCE) and the superimposed sparse pilot-aided channel estimation (SSPPCE) schemes in the bit error rate (BER) performance.

关键词： Time-frequency analysis Accuracy Simulation Bit error rate Channel estimation Symbols Interference iterative algorithms Resource management Uplink Orthogonal time frequency space (OTFS) superimposed full pilots (SFP) channel estimation

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Directional Correspondence Based Cross-Source Point Cloud Registration for USV-AAV Cooperation in Lentic Environments

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第2期10卷 1601-1608页

作者： Yoon, Byoungkwon Hong, Seokhyun Lee, Dongjun Seoul Natl Univ Dept Mech Engn IAMD Seoul 08826 South Korea Seoul Natl Univ IOER Seoul 08826 South Korea

We propose a novel cross-source point cloud registration (CSPR) method for USV-AAV cooperation in lentic environments. In the wild outdoors, which is the typical working domain of the USV-AAV team, CSPR faces significant challenges due to platform-domain problems (complex unstructured surroundings and viewing angle difference) in addition to sensor-domain problems (varying density, noise pattern, and scale). These characteristics make large discrepancies in local geometry, causing existing CSPR methods that rely on point-to-point correspondence based on local geometry around key points (e.g. surface normal, shape function, angle) to struggle. To address this challenge, we propose the novel concept of a directional correspondence-based iterative cross-source point cloud registration algorithm. Instead of using point-to-point correspondence under large discrepancies in local geometry, we build correspondence about directions to enable robust registration in the wild outdoors. Also, since the proposed directional correspondence uses bearing angle and normalized coordinate, we can separate scale estimation with transformation, effectively resolving the problem of different scales between two point clouds. Our algorithm outperforms the state-of-the-art methods, achieving an average error of 1.60 degrees for rotation and 1.83% for translation. Additionally, we demonstrated a USV-AAV team operation with enhanced visual information achieved with the proposed method.

关键词： Point cloud compression Geometry Feature extraction Shape Sensor phenomena and characterization Robot kinematics Noise iterative algorithms Azimuth Translation Aerial Systems: applications field robots mapping point cloud registration

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An Inversion-Free iterative Algorithm With a Scalar Tuning Parameter for Coupled Riccati Matrix Equations Arising in LQ Optimal Control of Markov Jump Systems

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第3期70卷 1913-1920页

作者： Jiang, Kaiwen Li, Zhi Zhang, Ying Harbin Inst Technol Shenzhen 518055 Peoples R China

In this article, iterative algorithms are investigated to solve the Riccati algebraic matrix equations arising in the context of linear quadratic (LQ) optimal control of discrete-time Markov jump systems. By using the matrix inversion lemma, an equivalent form for the considered coupled Riccati matrix equation is constructed, and then two intermediate variables are introduced to further construct a new equivalent form in order to avoid the operation of matrix inversion. With the aid of the obtained equivalent form, an inversion-free iterative algorithm (IFIA) is developed to obtain the positive definite solutions of the discrete coupled Riccati matrix equation via the principle of fixed points. To improve the convergence performance, the latest updated information is exerted in the presented algorithm. Furthermore, the convergence analysis is conducted by mathematical induction for the obtained IFIA. At the end, a numerical example is given to demonstrate the effectiveness of the proposed algorithm by making comparisons with existing results.

关键词： iterative methods Convergence Matrices Optimal control iterative algorithms Riccati equations Mathematical models Upper bound Symmetric matrices Linear systems discrete coupled Riccati matrix equations (DCRMEs) inversion-free iteration LQ optimal control Markov jump systems

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Total Least Squares Algorithm for Errors-in-Variables Systems: iterative Algorithm or Two-Step Algorithm

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 10753-10763页

作者： Chen, Jing Na, Jing Li, Junhong Zhu, Quanmin Jiangnan Univ Sch Sci Wuxi 214122 Peoples R China Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650550 Peoples R China Nantong Univ Sch Elect Engn Nantong 226019 Peoples R China Univ West England Sch Engn Bristol BS16 1QY England

The total least squares (TLS) algorithm is a superior identification tool for low-order errors-in-variables (EIV) systems, where the estimate can be obtained by solving an eigenvector of the minimum eigenvalue of an augmented matrix. However, the TLS algorithm demonstrates inefficiency when applied to high-order EIV systems. This study introduces two innovative TLS algorithms: an iterative TLS algorithm, offering superior performance for low-order EIV models, and a two-step TLS algorithm, designed to effectively handle high-order EIV models. In comparison to the conventional TLS algorithm, these proposed methodologies present noteworthy advantages, including: 1) reduced computational costs, 2) the utilization of an iterative technique to calculate the inverse, and 3) the diversification of EIV identification methods. Simulation bench test examples are selected to show the efficacy of the proposed algorithms and transparent procedure for applications.

关键词： Eigenvalues and eigenfunctions Mathematical models Noise Computational modeling Convergence iterative algorithms Large-scale systems Cost function Automation Accuracy Errors-in-variables system total least squares iterative algorithm two-step algorithm eigenvalue calculation

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A Co-Evolution Algorithm With Dueling Reinforcement Learning Mechanism for the Energy-Aware Distributed Heterogeneous Flexible Flow-Shop Scheduling Problem

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第3期55卷 1794-1809页

作者： Zhao, Fuqing Yin, Fumin Wang, Ling Yu, Yang Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Jiangsu Univ Technol Coll Elect & Informat Engn Changzhou 213001 Peoples R China

The production process of steelmaking continuous casting (SCC) is a typical heterogeneous distributed manufacturing system. The scheduling problem in heterogeneous distributed manufacturing systems is a complex combinatorial optimization problem. In this article, the energy-aware distributed heterogeneous flexible flow shop scheduling problem (EADHFFSP) with variable speed constraints is studied with objectives, including total tardiness (TTD) and total energy consumption (TEC). A mixed-integer linear programming (MILP) model is constructed for the EADHFFSP. A co-evolution algorithm with dueling reinforcement learning mechanism (DRLCEA) is presented to address EADHFFSP. In DRLCEA, a knowledge-based hybrid initialization operation is proposed to generate the initial population of the problem. A global search based on adversarial generative learning is designed to search the solution space. The dueling double deep Q -network (DDQN) is applied to select the operator for the local search. A speed adjustment strategy and an energy-saving strategy based on knowledge are proposed to reduce TTD and TEC of the EADHFFSP with regard to the properties of EADHFFSP. The results of experiments show that the performance of DRLCEA is superior to certain state-of-the-art comparison algorithms in solving EADHFFSP.

关键词： Production facilities Job shop scheduling Metaheuristics Production Search problems Energy consumption Steel Reinforcement learning iterative algorithms Processor scheduling Co-evolution distributed heterogeneous flexible flow shop scheduling dueling double deep Q network (DDQN) energy-saving strategy

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Deep Learning for Near-Field XL-MIMO Transceiver Design: Principles and Techniques

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IEEE COMMUNICATIONS MAGAZINE 2025年第1期63卷 52-58页

作者： Yu, Wentao Ma, Yifan He, Hengtao Song, Shenghui Zhang, Jun Letaief, Khaled B. Hong Kong Univ Sci & Technol Hong Kong Peoples R China

Massive multiple-input multiple-output (MIMO) has been a critical enabling technology in 5th generation (5G) wireless networks. With the advent of 6G, a natural evolution is to employ even more antennas, potentially an order of magnitude more, to meet the ever-increasing demand for spectral efficiency. This is beyond a mere quantitative scale-up. The enlarged array aperture brings a paradigm shift towards near-field communications, departing from traditional far-field approaches. However, designing advanced transceiver algorithms for near-field systems is extremely challenging because of the enormous system scale, the complicated channel characteristics, and the uncertainties in the propagation environments. Hence, it is important to develop scalable, low-complexity, and robust algorithms that can efficiently characterize and leverage the properties of the near-field channel. In this article, we discuss the principles and advocate two general frameworks to design deep learning-based near-field transceivers covering both iterative and non-iterative algorithms. Case studies on channel estimation and beam focusing are presented to provide a hands-on tutorial. Finally, we discuss open issues and shed light on future directions.

关键词： Deep learning Uncertainty Spectral efficiency Wireless networks Focusing Channel estimation Tutorials Transceivers iterative algorithms MIMO communication

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A modified two-step optimal iterative method for solving nonlinear models in one and higher dimensions

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MATHEMATICS AND COMPUTERS IN SIMULATION 2025年 229卷 448-467页

作者： Chang, Chih-Wen Qureshi, Sania Argyros, Ioannis K. Chicharro, Francisco I. Soomro, Amanullah Natl United Univ Dept Mech Engn Miaoli Taiwan Lebanese Amer Univ Dept Comp Sci & Math POB 13-5053 Beirut Lebanon Near East Univ Dept Math TR-99138 Mersin Turkiye Cameron Univ Dept Comp & Math Sci Lawton OK 73505 USA Univ Politecn Valencia Inst Univ Matemat Multidisciplinar Valencia 46022 Spain Mehran Univ Engn & Technol Dept Basic Sci & Related Studies Jamshoro 76062 Pakistan

iterative methods are essential tools in computational science, particularly for addressing nonlinear models. This study introduces a novel two-step optimal iterative root-finding method designed to solve nonlinear equations and systems of nonlinear equations. The proposed method exhibits the optimal convergence, adhering to the Kung-Traub conjecture, and necessitates only three function evaluations per iteration to achieve a fourth-order optimal iterative process. The development of this method involves the amalgamation of two well-established third-order iterative techniques. Comprehensive local and semilocal convergence analyses are conducted, accompanied by a stability investigation of the proposed approach. This method marks a substantial enhancement over existing optimal iterative methods, as evidenced by its performance in various nonlinear models. Extensive testing demonstrates that the proposed method consistently yields accurate and efficient results, surpassing existing algorithms in both speed and accuracy. Numerical simulations, including real-world models such as boundary value problems and integral equations, indicate that the proposed optimal method outperforms several contemporary optimal iterative techniques.

关键词： Root-finding methods Nonlinear models iterative algorithms Efficiency index Computational order of convergence Computational cost

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