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A Synergistic GPR Approach of Back Projection Algorithm and Full-Wave Inversion for Reconstruction in Unknown Multilayered Environments

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Xu, Zihan Li, Hongxu Jin, Xinfei Su, Fulin Xu, Guodong Lambot, Sebastien Catholic Univ Louvain Earth & Life Inst B-1348 Ottignies Louvain La Neuv Belgium Harbin Inst Technol Harbin 150001 Peoples R China

Ground-penetrating radar (GPR) has become indispensable for subsurface reconstruction, especially in complex, multilayered infrastructure environments. Efficient and accurate estimation of permittivity and layer thickness, crucial for understanding these scenes, presents significant challenges. This article explores the integration and interaction between the back projection (BP) algorithm and full-wave inversion (FWI) to optimize reconstruction outcomes. We propose a ray-based refraction method and a high-order moment (HOM)-oriented local BP estimation, each aimed at improving efficiency from the critical perspectives of "delay" and "summation" in the BP algorithm, respectively, with a focus on the target layer. Crucially, the BP estimation results provide a reliable initial model for FWI, reducing the risk of local minima and decreasing the iteration count for more precise parameter estimation. FWI also compensates for the BP algorithm's limitations in estimating parameters in nontarget layers. Furthermore, full-wave modeling (FWM) mitigates antenna effects prior to parameter estimation, thereby enhancing accuracy. During reconstruction, the BP algorithm primarily generates target imaging, while FWI provides detailed information about layer interfaces. This synergistic approach leverages the complementary strengths of both algorithms: the BP algorithm captures spatial information depicted by the targets observed in B-scans, while FWI incorporates detailed electromagnetic wave propagation along layer interfaces presented in A-scans via radar equations. Experiments systematically analyze the accuracy and effectiveness of the proposed approach using ideal simulation models, sandbox laboratory data, and road data from the Belgian Road Research Centre (BRRC) facilities. This comprehensive evaluation underscores the approach's substantial potential in advanced geophysical surveys and environmental research.

关键词： Permittivity Image reconstruction Estimation Imaging Antennas Media approximation algorithms Accuracy Roads Radar Back projection (BP) estimation full-wave modeling (FWM) and full-wave inversion (FWI) ground-penetrating radar (GPR) layered media

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CLUSTER BEFORE YOU HALLUCINATE: NODE-CAPACITATED NETWORK DESIGN AND ENERGY EFFICIENT ROUTING

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SIAM JOURNAL ON COMPUTING 2024年第3期53卷 588-623页

作者： Krishnaswamy, Ravishankar Nagarajan, Viswanath Pruhs, Kirk Stein, Clifford Microsoft Res Vigyan Bldg 9 Lavelle Rd Bangalore 560018 Karnataka India Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA Univ Pittsburgh Comp Sci Dept Pittsburgh PA 15260 USA Columbia Univ Dept Ind Engn & Operat Res New York NY 10027 USA

We consider the following node-capacitated network design problem. The input is an undirected graph, a set of demands, uniform node capacity, and arbitrary node costs. The goal is to find a minimum node-cost subgraph that supports all demands concurrently subject to the node capacities. We consider both single- and multicommodity demands and provide the first polylogarithmic approximation guarantees. For single-commodity demands (i.e., all request pairs have the same sink node), we obtain an O (log(2)n ) approximation to the cost with an O (log (3) n ) factor violation in node capacities. For multicommodity demands, we obtain an O (log (4 )n ) approximation to the cost with an O (log (10) n ) factor violation in node capacities. We use a variety of techniques, including single-sink confluent flows, low-load set cover, random sampling, and cut-sparsification. We also develop new techniques for clustering multicommodity demands into (nearly) node-disjoint clusters, which may be of independent interest. Moreover, this network design problem has applications to energy-efficient virtual circuit routing. In this setting, there is a network of routers that are speed scalable and that may be shut down when idle. We assume the standard model for power: the power consumed by a router with load (speed) s is sigma + s (alpha) , where sigma is the static power and the exponent alpha > 1. We obtain the first polylogarithmic approximation algorithms for this problem when speed-scaling occurs on nodes of a network.

关键词： network design approximation algorithms routing

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Introducing a New Edge Centrality Measure: The Connectivity Rank Index

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第5期54卷 2757-2764页

作者： Zhou, Jin Zhang, Yanqi Lu, Jun-an Chen, Guanrong Wuhan Univ Sch Math & Stat Wuhan 430072 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

A new edge centrality measure, connectivity rank index (CRI), is proposed based on the effect of an edge on the network algebraic connectivity. Compared with the existing indices, the CRI can determine the importance of a present edge as well as an absent edge. For large-scale networks, the algorithm based on original CRI definition has high-time complexity. Therefore, an approximation algorithm is designed using the eigenvector elements corresponding to the second smallest Laplacian eigenvalue. This algorithm can identify the most influential edges and the least influential ones easily, which reduces the time complexity from the exhaustive searching scheme with O(N-5) to O(N-3) in a network of size N . Some examples are shown to verify the effectiveness of the algorithm and the theoretical results.

关键词： Laplace equations Eigenvalues and eigenfunctions Manganese approximation algorithms Indexes Time complexity Upper bound Algebra connectivity complex network edge centrality eigenvalue eigenvector

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A Generalized Adjusted Min-Sum Decoder for 5G LDPC Codes: Algorithm and Implementation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第6期71卷 2911-2924页

作者： Ren, Yuqing Harb, Hassan Shen, Yifei Balatsoukas-Stimming, Alexios Burg, Andreas Ecole Polytech Fed Lausanne EPFL Telecommun Circuits Lab TCL CH-1015 Lausanne Switzerland Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands

5G New Radio (NR) has stringent demands on both performance and complexity for the design of low-density parity-check (LDPC) decoding algorithms and corresponding VLSI implementations. Furthermore, decoders must fully support the wide range of all 5G NR blocklengths and code rates, which is a significant challenge. In this paper, we present a high-performance and low-complexity LDPC decoder, tailor-made to fulfill the 5G requirements. First, to close the gap between belief propagation (BP) decoding and its approximations in hardware, we propose an extension of adjusted min-sum decoding, called generalized adjusted min-sum (GA-MS) decoding. This decoding algorithm flexibly truncates the incoming messages at the check node level and carefully approximates the non-linear functions of BP decoding to balance the error-rate and hardware complexity. Numerical results demonstrate that the proposed fixed-point GA-MS has only a minor gap of 0.1 dB compared to floating-point BP under various scenarios of 5G standard specifications. Secondly, we present a fully reconfigurable 5G NR LDPC decoder implementation based on GA-MS decoding. Given that memory occupies a substantial portion of the decoder area, we adopt multiple data compression and approximation techniques to reduce 42.2% of the memory overhead. The corresponding 28nm FD-SOI ASIC decoder has a core area of 1.823 mm(2) and operates at 895 MHz. It is compatible with all 5G NR LDPC codes and achieves a peak throughput of 24.42 Gbps and a maximum area efficiency of 13.40 Gbps/mm(2) at 4 decoding iterations.

关键词： Decoding 5G mobile communication Iterative decoding Throughput Hardware Standards approximation algorithms LDPC codes generalized adjusted min-sum (GA-MS) decoding belief propagation (BP) hardware implementation 5G NR wireless communications

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Coordinating scheduling and rejection decisions in a two-machine flow shop scheduling problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第3期316卷 887-898页

作者： Shabtay, Dvir Gerstl, Enrique Ben Gurion Univ Negev Dept Ind Engn & Management Beer Sheva Israel Hebrew Univ Jerusalem Sch Business Adm Jerusalem Israel

We study a two-machine flow shop scheduling problem where any operation can be rejected at a certain cost. A solution for such a problem requires two sets of decisions. The first involves the partition of the set of operations into two subsets: the set of operations that are accepted for scheduling in the shop, and the set of rejected operations. The second decision involves scheduling the set of accepted operations in the shop. The objective is to find a solution that minimizes the sum of the makespan and the total rejection cost. We prove that the problem is NP -hard even if all processing operations have identical processing times and identical rejection costs on either one of the two machines. We show, however, that the problem is fixed parameterized tractable with respect to a parameter that combine the number of different processing times on both machines with the number of different rejection costs on one out of the two machines. We also provide a pseudo-polynomial time algorithm for the problem, which we then convert into a fully polynomial time approximation scheme. This is achieved by dividing the problem into a set of subproblems and deriving a fully polynomial time approximation scheme for each one of them, separately. Finally, we present an integer linear programming formulation of the problem and two simple 2 -approximation algorithms.

关键词： Scheduling Flow shop approximation algorithms Rejection Integer linear programming

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Finite-Time Error Bounds of Biased Stochastic approximation With Application to TD-Learning

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 950-962页

作者： Wang, Gang Beijing Inst Technol State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401120 Peoples R China

Motivated by the recent success of reinforcement learning algorithms, this paper studies a class of biased stochastic approximation (SA) procedures under a mild "ergodicity-like" assumption on the random noise sequence. Building on a multistep Lyapunov function that looks ahead to several future updates to accommodate the stochastic perturbations (thus gaining control over the bias), we prove a general result on the convergence of the SA iterates, and use it to derive non-asymptotic bounds on the mean-square error in the case of constant stepsizes. This novel viewpoint renders finite-time analysis of biased SA algorithms under a family of stochastic perturbations possible. For direct comparison with prior work, we demonstrate these bounds by applying them to TD-learning with linear function approximation, under the Markov chain observation model. The resultant finite-time error bound for TD-learning is the first of its kind, in the sense that it holds i) for the unmodified versions (i.e., without any modification to the updates) using even nonlinear approximators;as well as for Markov chains ii) under sublinear mixing conditions and iii) starting from any initial distribution, at least one of which has to be violated for existing results to be applicable.

关键词： approximation algorithms Markov processes Signal processing algorithms Convergence Mean square error methods Function approximation Reinforcement learning Stochastic averaging finite-time analysis Lyapunov analysis Markovian noise

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Successive Convex approximation Based Off-Policy Optimization for Constrained Reinforcement Learning

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 1609-1624页

作者： Tian, Chang Liu, An Huang, Guan Luo, Wu Peking Univ Sch Elect State Key Lab Adv Opt Commun Syst & Networks Beijing 100871 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China

Constrained reinforcement learning (CRL), also termed as safe reinforcement learning, is a promising technique enabling the deployment of RL agent in real-world systems. In this paper, we propose a successive convex approximation based off-policy optimization (SCAOPO) algorithm to solve the general CRL problem, which is formulated as a constrained Markov decision process (CMDP) in context of the average cost. The SCAOPO is based on solving a sequence of convex objective/feasibility optimization problems obtained by replacing the objective and constraint functions in the original problem with convex surrogate functions. The proposed SCAOPO enables reuse of experiences from previous updates, thereby significantly reducing implementation cost when deployed in real-world engineering systems that need to online learn the environment. In spite of the time-varying state distribution and the stochastic bias incurred by off-policy learning, the SCAOPO with a feasible initial point can still provably converge to a Karush-Kuhn-Tucker (KKT) point of the original problem almost surely.

关键词： Convergence Costs Signal processing algorithms Optimization approximation algorithms Reinforcement learning Markov processes Constrained Safe reinforcement learning off-policy theoretical convergence

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A Novel Complexity Algorithm for Quantification of Consciousness Levels

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IEEE ACCESS 2024年 12卷 118665-118672页

作者： Shen, Chengyuan Mitsukura, Yasue Cao, Jianting Saitama Inst Technol Fukaya 3690203 Japan Keio Univ Grad Sch Sci & Technol Yokohama Kanagawa 2238522 Japan RIKEN Ctr Adv Intelligence Project AIP Saitama Inst Technol Fukaya 3690203 Japan

The level of human consciousness is a critical indicator in medicine, essential for the accurate diagnosis of specific conditions such as brain death. This study aims to provide an objective and visual consciousness indicator for medical applications by proposing a new algorithm to quantify consciousness. Traditionally, consciousness levels have been qualitatively assessed using scales like the Glasgow Coma Scale (GCS). In this study, we introduce a novel algorithm, termed D-MaxApEn, designed to quantify consciousness. This algorithm was applied to EEG data from four different states: wakefulness, anesthesia, coma, and brain death. Through calculation and analysis, we objectively quantified these four levels of consciousness and validated the effectiveness of this algorithm in a real-time brain death diagnosis system. The results demonstrate the significant potential of this algorithm for the future diagnosis of the level of consciousness. Its use could revolutionize early intervention, improve patient outcomes, and reduce the burden on individuals and healthcare systems.

关键词： Electroencephalography Complexity theory White noise Heuristic algorithms Electrodes approximation algorithms Anesthesia Consciousness assessment Glasgow coma scale D-MaxApEn electroencephalography

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Multi-Prototypes Convex Merging Based K-Means Clustering Algorithm

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第11期36卷 6653-6666页

作者： Li, Dong Zhou, Shuisheng Zeng, Tieyong Chan, Raymond H. Xidian Univ Sch Math & Stat Xian 710071 Peoples R China Chinese Univ Hong Kong Dept Math Shatin Hong Kong Peoples R China City Univ Hong Kong Dept Math Kowloon Tong Hong Kong Peoples R China Hong Kong Ctr Cerebrocardiovasc Hlth Engn Pak Shek Kok Hong Kong Peoples R China

K-Means algorithm is a popular clustering method. However, it has two limitations: 1) it gets stuck easily in spurious local minima, and 2) the number of clusters k has to be given a priori. To solve these two issues, a multi-prototypes convex merging based K-Means clustering algorithm (MCKM) is presented. First, based on the structure of the spurious local minima of the K-Means problem, a multi-prototypes sampling (MPS) is designed to select the appropriate number of multi-prototypes for data with arbitrary shapes. Then, a merging technique, called convex merging (CM), merges the multi-prototypes to get a better local minima without k being given a priori. Specifically, CM can obtain the optimal merging and estimate the correct k. By integrating these two techniques with K-Means algorithm, the proposed MCKM is an efficient and explainable clustering algorithm for escaping the undesirable local minima of K-Means problem without given k first. Two theoretical proofs are given to guarantee that the cost of MCKM (MPS+CM) can achieve a constant factor approximation to the optimal cost of the K-Means problem. Experimental results performed on synthetic and real-world data sets have verified the effectiveness of the proposed algorithm.

关键词： Clustering algorithms Prototypes Merging Partitioning algorithms approximation algorithms Costs Shape Convex merging K-means multi-prototypes multi-prototypes sampling

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Bike rebalancing: How to find a balanced matching in the k center problem?

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第3期316卷 845-855页

作者： Gan, Jinxiang Zhang, Guochuan Zhang, Yuhao Hong Kong Univ Sci & Technol Hong Kong Peoples R China Zhejiang Univ Hangzhou Zhejiang Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

In the bike-sharing system, inspired by the problem proposed by O'Mahony and Shmoys (AAAI 2015), we present a new model called the balanced k center problem with matching constraints. Given a network containing the same number of two types of points, supply points and demand points, we aim to allocate k (supply) centers for supply points, and k (demand) centers for demand points. The k supply centers should be perfectly matched with the k demand centers, within an allowed distance. The goal is to minimize the maximum distance from each supply (or demand) point to its closest supply (or demand) centers. The model is motivated by the real -world application in rebalancing bikes, where the bikes are collected in supply centers, and delivered to the matched demand centers, and then distributed from there to serve their demand vertices. Therefore, to successfully complete this rebalancing procedure, two matched groups must have the same number of points. Otherwise, the bikes collected at the supply center may not be enough to serve the demand stations assigned to the demand center. To fully capture the problem, we bring an identical restriction L , which restricts the exact number of vertices assigned to a single center. As a result, all matched centers must serve the same number of bikes. Hence, they are balanced. Our main result is a 9 approximation algorithm. The algorithm is built on the technique called transfer preserving network that successfully combines the approach for the capacitated k center problem with the maximum flow network.

关键词： Linear programming approximation algorithms Capacitated k-center problem Matching

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