一个关于Hamilton环(路)的多项式时间算法

该研究为Hamilton环路（道路）问题设计出了一个多项式时间算法,论证了它的正确性。根据该算法编制了程序,进行了大量的实例计算。文章公布了主要研究方法、过程、实验数据,以及粗略的算法步骤。详细的算法步骤和证明将在随后的论文中发表。由于Hamilton环路（道路）为著名的NP完全问题,而作者认为自己已彻底解决了NP复杂问题。     

A Direct Locality Preserving Projections (DLPP) Algorithm for Image Recognition

This paper proposes a novel locality preserving projections (LPP) algorithm for image recognition, namely, the direct locality preserving projections (DLPP), which directly optimizes locality preserving criterion on high-dimensional raw images data via simultaneous diagonalization, without any dimensionality reduction preprocessing. Our algorithm is a direct and complete implementation of LPP. Experimental results on the PolyU palmprint database and ORL face database show the effectiveness of the proposed algorithm.     

Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm

This paper presents the inception and subsequent revisions of an immune-inspired supervised learning algorithm, Artificial Immune Recognition System (AIRS). It presents the immunological components that inspired the algorithm and describes the initial algorithm in detail. The discussion then moves to revisions of the basic algorithm that remove certain unnecessary complications of the original version. Experimental results for both versions of the algorithm are discussed and these results indicate that the revisions to the algorithm do not sacrifice accuracy while increasing the data reduction capabilities of AIRS.     

一种新的粒子滤波SLAM算法

粒子滤波SLAM算法的复杂度与特征个数呈线性关系,对于大规模SLAM有明显的计算优势,但是这些算法不能长时间满足一致性要求.将边缘粒子滤波技术(marginal particle filtering,MPF)运用到SLAM技术中,并利用Unscented Kalman滤波(UKF)来计算提议分布,得到了一种新的粒子滤波SLAM算法.新算法避免了从不断增长的高维状态空间采样,非常有效地提高了算法中的有效粒子数,大大降低了粒子的权值方差,保证了粒子的多样性,同时也满足一致性要求.该算法克服了一般粒子滤波SLAM算法的缺点,性能优势十分明显.     

Smoothness-Increasing Accuracy-Conserving (SIAC) Filtering for Discontinuous Galerkin Solutions: Improved Errors Versus Higher-Order Accuracy

Smoothness-increasing accuracy-conserving (SIAC) filtering has demonstrated its effectiveness in raising the convergence rate of discontinuous Galerkin solutions from order $k+\frac{1}{2}$ to order 2k+1 for specific types of translation invariant meshes (Cockburn et al. in Math. Comput. 72:577?C606, 2003; Curtis et al. in SIAM J. Sci. Comput. 30(1):272?C289, 2007; Mirzaee et al. in SIAM J. Numer. Anal. 49:1899?C1920, 2011). Additionally, it improves the weak continuity in the discontinuous Galerkin method to k?1 continuity. Typically this improvement has a positive impact on the error quantity in the sense that it also reduces the absolute errors. However, not enough emphasis has been placed on the difference between superconvergent accuracy and improved errors. This distinction is particularly important when it comes to understanding the interplay introduced through meshing, between geometry and filtering. The underlying mesh over which the DG solution is built is important because the tool used in SIAC filtering??convolution??is scaled by the geometric mesh size. This heavily contributes to the effectiveness of the post-processor. In this paper, we present a study of this mesh scaling and how it factors into the theoretical errors. To accomplish the large volume of post-processing necessary for this study, commodity streaming multiprocessors were used; we demonstrate for structured meshes up to a 50× speed up in the computational time over traditional CPU implementations of the SIAC filter.     

Gases Brownian Motion Optimization: an Algorithm for Optimization (GBMO)

In recent years, different optimization methods have been developed for optimization problem. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new algorithm for optimization inspired by the gases brownian motion and turbulent rotational motion is introduced, which is called Gases Brownian Motion Optimization (GBMO). The proposed algorithm is created using the features of gas molecules. The proposed algorithm is an efficient approach to search and find an optimum solution in search space. The efficiency of the proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various functions.     

运用类复制变异和JPF技术生成类间测试用例

采用类复制变异方法,运用模型检测器Java PathFinder(JPF)来保证软件执行过程中产生的错误在输出结果中可见,同时将类间测试用例生成问题转化成模型检测中寻找反例的问题,自动生成满足变异覆盖准则的类测试用例,提出一种适用于类间调用的测试用例自动生成方法,并在程序模型检测器JPF上实现.实验结果表明,本文提出的方法能生成高效的Java类间测试输入数据,变异覆盖率高,可发现隐藏错误,并能显著减少测试生成的代价.     

Improved Approximation Algorithm for k-level Uncapacitated Facility Location Problem (with Penalties)

We study the k-level uncapacitated facility location problem (k-level UFL) in which clients need to be connected with paths crossing open facilities of k types (levels). In this paper we first propose an approximation algorithm that for any constant k, in polynomial time, delivers solutions of cost at most α _k times OPT, where α _k is an increasing function of k, with \(\lim _{k\to \infty } \alpha _{k} = 3\). Our algorithm rounds a fractional solution to an extended LP formulation of the problem. The rounding builds upon the technique of iteratively rounding fractional solutions on trees (Garg, Konjevod, and Ravi SODA’98) originally used for the group Steiner tree problem. We improve the approximation ratio for k-level UFL for all k ≥ 3, in particular we obtain the ratio equal 2.02, 2.14, and 2.24 for k = 3,4, and 5.     

Orientation Invariant Skeleton Feature (OISF): a new feature for Human Activity Recognition

Multimedia Tools and Applications - Human Activity Recognition is the process of identifying the activity of a person by analyzing continuous frames of a video. In many application areas, human...     

Cooperative and Geometric Learning Algorithm (CGLA) for path planning of UAVs with limited information

In this paper, we propose a new learning algorithm, named as the Cooperative and Geometric Learning Algorithm (CGLA), to solve problems of maneuverability, collision avoidance and information sharing in path planning for Unmanned Aerial Vehicles (UAVs). The contributions of CGLA are three folds: (1) CGLA is designed for path planning based on cooperation of multiple UAVs. Technically, CGLA exploits a new defined individual cost matrix, which leads to an efficient path planning algorithm for multiple UAVs. (2) The convergence of the proposed algorithm for calculating the cost matrix is proven theoretically, and the optimal path in terms of path length and risk measure from a starting point to a target point can be calculated in polynomial time. (3) In CGLA, the proposed individual weight matrix can be efficiently calculated and adaptively updated based on the geometric distance and risk information shared among UAVs. Finally, risk evaluation is introduced first time in this paper for UAV navigation and extensive computer simulation results validate the effectiveness and feasibility of CGLA for safe navigation of multiple UAVs.     

An Improved (and Practical) Parameterized Algorithm for the Individual Haplotyping Problem MFR with Mate-Pairs

The Individual Haplotyping MFR problem is a computational problem that, given a set of DNA sequence fragment data of an individual, induces the corresponding haplotypes by dropping the minimum number of fragments. Bafna, Istrail, Lancia, and Rizzi proposed an algorithm of time O(2^2k m ² n+2^3k m ³) for the problem, where m is the number of fragments, n is the number of SNP sites, and k is the maximum number of holes in a fragment. When there are mate-pairs in the input data, the parameter k can be as large as 100, which would make the Bafna-Istrail-Lancia-Rizzi algorithm impracticable. The current paper introduces a new algorithm PM-MFR of running time , where k ₁ is the maximum number of SNP sites that a fragment covers (k ₁ is smaller than n), and k ₂ is the maximum number of fragments that cover a SNP site (k ₂ is usually about 10). Since the time complexity of the algorithm PM-MFR is not directly related to the parameter k, the algorithm solves the Individual Haplotyping MFR problem with mate-pairs more efficiently and is more practical in real biological applications. This research was supported in part by the National Natural Science Foundation of China under Grant Nos. 60433020 and 60773111, the Program for New Century Excellent Talents in University No. NCET-05-0683, the Program for Changjiang Scholars and Innovative Research Team in University No. IRT0661, and the Scientific Research Fund of Hunan Provincial Education Department under Grant No. 06C526.     

Computer Vision and Pattern Recognition Techniques for 2-D and 3-D MR Cerebral Cortical Segmentation (Part I): A State-of-the-Art Review

Extensive growth in functional brain imaging, perfusion-weighted imaging, diffusion-weighted imaging, brain mapping and brain scanning techniques has led tremendously to the importance of the cerebral cortical segmentation, both in 2-D and 3-D, from volumetric brain magnetic resonance imaging data sets. Besides that, recent growth in deformable brain segmentation techniques in 2-D and 3-D has brought the engineering community, such as the areas of computer vision, image processing, pattern recognition and graphics, closer to the medical community, such as to neuro-surgeons, psychiatrists, oncologists, neuro-radiologists and internists. This paper is an attempt to review the state-of-the-art 2-D and 3-D cerebral cortical segmentation techniques from brain magnetic resonance imaging based on three main classes: region-based, boundary/surface-based and fusion of boundary/surface-based with region-based techniques. In the first class, region-based techniques, we demonstrated more than 18 different techniques for segmenting the cerebral cortex from brain slices acquired in orthogonal directions. In the second class, boundary/surface-based, we showed more than ten different techniques to segment the cerebral cortex from magnetic resonance brain volumes. Particular emphasis will be placed by presenting four state-of-the-art systems in the third class, based on the fusion of boundary/surface-based with region-based techniques outlined in Part II of the paper, also called regional-geometric deformation models, which take the paradigm of partial differential equations in the level set framework. We also discuss the pros and cons of various techniques, besides giving the mathematical foundations for each sub-class in the cortical taxonomy. Received: 25 August 2000, Received in revised form: 28 March 2001, Accepted: 28 March 2001     

Study of the Radio Frequency (RF) performance of a Wafer-Level Package (WLP) with Through Silicon Vias (TSVs) for the integration of RF-MEMS and micromachined waveguides in the context of 5G and Internet of Things (IoT) applications. Part 2: parameterised 3D model and optimisation

Microsystem Technologies - This study discusses the exploitation of a full-3D methodology for the electromagnetic simulation of a Wafer-Level Packaging solution featuring Through Silicon Vias...     

A Case-Based Micro Interactive Genetic Algorithm (CBMIGA) for interactive learning and search: Methodology and application to groundwater monitoring design

Interactive optimization algorithms use real–time interaction to include decision maker preferences based on the subjective quality of evolving solutions. In water resources management problems where numerous qualitative criteria exist, use of such interactive optimization methods can facilitate in the search for comprehensive and meaningful solutions for the decision maker. The decision makers using such a system are, however, likely to go through their own learning process as they view new solutions and gain knowledge about the design space. This leads to temporal changes (nonstationarity) in their preferences that can impair the performance of interactive optimization algorithms. This paper proposes a new interactive optimization algorithm – Case-Based Micro Interactive Genetic Algorithm – that uses a case-based memory and case-based reasoning to manage the effects of nonstationarity in decision maker’s preferences within the search process without impairing the performance of the search algorithm. This paper focuses on exploring the advantages of such an approach within the domain of groundwater monitoring design, though it is applicable to many other problems. The methodology is tested under non-stationary preference conditions using simulated and real human decision makers, and it is also compared with a non-interactive genetic algorithm and a previous version of the interactive genetic algorithm.     

Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research

Artificial Intelligence Review - Since early 2020, the whole world has been facing the deadly and highly contagious disease named coronavirus disease (COVID-19) and the World Health Organization...     

A scheme for LR(k) parsing with error recovery part II: error recovery

The paper is the second in a series of three papers devoted to a detailed study of LR(k) parsing with error recovery and correction. Error recovery in LR(k) parsing of a context-free grammar is formalized by extending an LR(k) parser of the grammar such that it accepts all strings over the terminal vocabulary. The parse produced by this extension for a terminal string is a right parse if the string is in the language. In the case of a string not in the language the parse produced by the extension contains so-called error productions which represent the error recovery actions performed by the extension. The treatment is based on the formalization of LR(k) parsing presented in the first paper in the series and it covers practically all error recovery methods designed for LR(k) parsing.