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Conical functions appear in a large number of applications in physics and engineering. In this paper we describe an extension of our module Conical (Gil et al., 2012) for the computation of conical functions. Specifically, the module includes now a routine for computing the function R − 1 2 + i τ m ( x ) , a real-valued numerically satisfactory companion of the function P − 1 2 + i τ m ( x ) for x > 1 . In this way, a natural basis for solving Dirichlet problems bounded by conical domains is provided. The module also improves the performance of our previous algorithm for the conical function P − 1 2 + i τ m ( x ) and it includes now the computation of the first order derivative of the function. This is also considered for the function R − 1 2 + i τ m ( x ) in the extended algorithm. Program summary Program Title: Module Conical Program Files doi: http://dx.doi.org/10.17632/rpw5d8gdkg.1 Licensing provisions: CC by 4.0 Programming language: Fortran 90 External routines/libraries: The module Conical uses a Fortran 90 version of the routine dkia (developed by the authors) for computing the modified Bessel functions K i a ( x ) and its derivative. This routine is available at http://toms.calgo.org . Nature of problem: These functions are the natural function basis for solving, for example, the Laplace’s problem in spherical coordinates for two intersecting cones or for regions bounded by two intersecting spheres, or by one or two confocal hyperboloids of revolution when using toroidal coordinates. The conical function P − 1 2 + i τ m ( x ) is also used in the Mehler–Fock integral transform for problems in potential and heat theory, Solution method: The algorithm uses different methods of computation depending on the function under consideration ( P − 1 2 + i τ m ( x ) or R − 1 2 + i τ m ( x ) ) and the values of x , τ and m : numerical quadrature, asymptotic expansions in terms of elementary functions, asymptotic expansions in terms of Bessel functions, asymptotic expansions for tau large and backward/forward recursion of three-term recurrence relations. Restrictions: In order to avoid underflow/overflow problems in standard IEEE double precision arithmetic, the admissible parameter ranges for computing the conical function P − 1 2 + i τ m ( x ) in the routine conicp are: − 1 < x < 1 , 0 < τ < = 100 , 0 ≤ m ≤ 40 1 < x ≤ 100 , 0 < τ ≤ 100 , 0 ≤ m ≤ 100 . When using the routines conicr and conicpr , the admissible parameter ranges for computing the functions P − 1 2 + i τ m ( x ) and R − 1 2 + i τ m ( x ) are 1 < x ≤ 100 , 0 < τ ≤ 100 , 0 ≤ m ≤ 100 . [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel imaging and Doppler parameter estimation algorithm for ground maneuvering targets. Since the cross-track acceleration will induce the quadratic chirp rate (third-order phase) in the phase history, it may cause the maneuvering target severely smeared in the Doppler domain. To obtain a well-focused target imaging result, the quadratic chirp rate must be estimated accurately. Though cubic phase function (CPF) is efficient in estimating the parameters of a single maneuvering target, it may suffer from the identifiability problem when dealing with multiple maneuvering targets. To address these issues, an axis mapping (AM) based coherently integrated cubic phase function (CICPF) algorithm is proposed. This algorithm consists of two stages. Firstly, the linear chirp rate migration (i.e. quadratic chirp rate) of target in the time and chirp-rate domain is corrected by AM. After that, a dechirping technique is utilized to coherently integrate the auto-terms, and suppress the cross-terms and spurious peaks. Compared with several existing quadratic chirp rate estimation approaches, AM based CICPF (AMCICPF) algorithm can acquire lower signal-to-noise ratio threshold and estimate the centroid frequency, chirp rate and quadratic chirp rate of maneuvering target simultaneously. By compensating the chirp rate and quadratic chirp rate, a finely focused maneuvering target imaging can be obtained. Both simulated and real data processing results show that the AMCICPF algorithm serves as a good candidate for maneuvering target Doppler parameter estimation and imaging. [ABSTRACT FROM AUTHOR]

Abstract: Entity resolution is the problem of identifying the tuples that represent the same real world entity. In this paper, we propose a complete solution to the problem of entity resolution over probabilistic data (ERPD), which arises in many applications that have to deal with probabilistic data. To deal with the ERPD problem, we distinguish between two classes of similarity functions, i.e. context-free and context-sensitive. We propose a PTIME algorithm for context-free similarity functions, and an approximation algorithm for context-sensitive similarity functions. We validated our algorithms through experiments over both synthetic and real datasets. Our extensive performance evaluation shows the effectiveness of our algorithms. [Copyright &y& Elsevier]

Abstract: Capacitated Arc Routing Problem (CARP) has drawn much attention during the last few years. In addition to the goal of minimizing the total cost of all the routes, many real-world applications of CARP also need to balance these routes. The Multi-objective CARP (MO-CARP) commonly exists in practical applications. In order to solve MO-CARP efficiently and accurately, this paper presents a Multi-population Cooperative Coevolutionary Algorithm (MPCCA) for MO-CARP. Firstly, MPCCA applies the divide-and-conquer method to decompose the whole population into multiple subpopulations according to their different direction vectors. These subpopulations evolve separately in each generation and the adjacent subpopulations can share their individuals in the form of cooperative subpopulations. Secondly, multiple subpopulations are used to search different objective subregions simultaneously, so individuals in each subpopulation have a different fitness function, which can be modeled as a Single Objective CARP (SO-CARP). The advanced MAENS approach for single-objective CARP can be used to search each objective subregion. Thirdly, the internal elitism archive is used to construct evolutionary pool for each subregion, which greatly speeds up the convergence. Lastly, the fast nondominated ranking and crowding distance of NSGA-II are used for selecting the offspring and keeping the diversity. MPCCA is tested on 91 CARP benchmarks. The experimental results show that MPCCA obtains better generalization performance over the compared algorithms. [Copyright &y& Elsevier]