Attenuation correction in positron emission tomography

We describe a mathematical method for computing the attenuation distribution from Positron emission tomography (PET) data. The method renders possible the correction of PET data for attenuation without a transmission scan. It can be applied if the attenuation distribution is sufficiently simple (abdomen and bead). We give the results for computer-generated data and for data on patients.     

An automatic regularization parameter selection algorithm in the total variation model for image deblurring

Image restoration is an inverse problem that has been widely studied in recent years. The total variation based model by Rudin-Osher-Fatemi (1992) is one of the most effective and well known due to its ability to preserve sharp features in restoration. This paper addresses an important and yet outstanding issue for this model in selection of an optimal regularization parameter, for the case of image deblurring. We propose to compute the optimal regularization parameter along with the restored image in the same variational setting, by considering a Karush Kuhn Tucker (KKT) system. Through establishing analytically the monotonicity result, we can compute this parameter by an iterative algorithm for the KKT system. Such an approach corresponds to solving an equation using discrepancy principle, rather than using discrepancy principle only as a stopping criterion. Numerical experiments show that the algorithm is efficient and effective for image deblurring problems and yet is competitive.     

Numerical experiments with the inhomogeneity indicator in positron emission tomography

By means of numerical methods, the question is studied of applicability of the inhomogeneity indicator in positron emission tomography. The signal registered by the tomograph is described in terms of an imitation model using the Monte Carlo method. The possibility is demonstrated of the effective use of the inhomogeneity indicator for solving the problem under consideration. Some numerical results are presented in graphical form for reconstructing the boundaries of unknown activity sources.     

Squared polynomial extrapolation methods with cycling: an application to the positron emission tomography problem

Roland and Varadhan (Appl. Numer. Math., 55:215–226, 2005) presented a new idea called “squaring” to improve the convergence of Lemaréchal’s scheme for solving nonlinear fixed-point problems. Varadhan and Roland (Squared extrapolation methods: A new class of simple and efficient numerical schemes for accelerating the convergence of the EM algorithm, Department of Biostatistics Working Paper. Johns Hopkins University, , 2004) noted that Lemaréchal’s scheme can be viewed as a member of the class of polynomial extrapolation methods with cycling that uses two fixed-point iterations per cycle. Here we combine these two ideas, cycled extrapolation and squaring, and construct a new class of methods, called squared polynomial methods (SQUAREM), for accelerating the convergence of fixed-point iterations. Our main goal is to evaluate whether the squaring device is effective in improving the rate of convergence of cycled extrapolation methods that use more than two fixed-point iterations per cycle. We study the behavior of the new schemes on an image reconstruction problem for positron emission tomography (PET) using simulated data. Our numerical experiments show the effectiveness of first- and higher-order squared polynomial extrapolation methods in accelerating image reconstruction, and also their relative superiority compared to the classical, “unsquared” vector polynomial methods.     

Numerically stable and efficient algorithm for vector channel parameter and frequency offset estimation

In this paper, we present a novel and numerically efficient algorithm for vector channel and calibration vector estimation, which works when frequency offset error caused by either unstable oscillator or Doppler effect is present in Spread Spectrum antenna system. We propose an estimation algorithm based on Gauss–Seidal algorithm rather than using eigen-decomposition or SVD in computing eigenvalues and eigenvectors at each iteration. The algorithm is based on the two-step procedures, one for estimating both channel and frequency offset and the other for estimating the unknown array gain and phase. Consequently, estimates of the DOAs, the multi-path impulse response of the reference signal source, and the carrier frequency offset as well as the calibration of antenna array are provided. The analytic performance improvement in multiplications number is presented. The performance of the proposed algorithm is investigated by means of computer simulations. Throughout the analytic and computer simulation, we show that the proposed algorithm reduces the number of multiplications by order of one.     

Subset selection for parameter estimation in an HIV model

This paper discusses methodologies for subset selection for nonlinear least squares parameter estimation. In particular, we will present approaches for partitioning the parameter space into well-conditioned and ill-conditioned subsets. The algorithms are applied to a simplified mathematical model of the physiologic response of the human immunodeficiency virus (HIV) in humans. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Framework for efficient feature selection in genetic algorithm based data mining

We present the design of more effective and efficient genetic algorithm based data mining techniques that use the concepts of feature selection. Explicit feature selection is traditionally done as a wrapper approach where every candidate feature subset is evaluated by executing the data mining algorithm on that subset. In this article we present a GA for doing both the tasks of mining and feature selection simultaneously by evolving a binary code along side the chromosome structure used for evolving the rules. We then present a wrapper approach to feature selection based on Hausdorff distance measure. Results from applying the above techniques to a real world data mining problem show that combining both the feature selection methods provides the best performance in terms of prediction accuracy and computational efficiency.     

fastWKendall: an efficient algorithm for weighted Kendall correlation

The Kendall correlation is a non-parametric method that measures the strength of dependence between two sequences. Like Pearson correlation and Spearman correlation, Kendall correlation is widely applied in sequence similarity measurements and cluster analysis. We propose an efficient algorithm, fastWKendall, to compute the approximate weighted Kendall correlation in \(O(n\log n)\) time and O(n) space complexity. This is an improvement to the state-of-the-art \(O(n^2)\) time requirement. The proposed method can be incorporated to perform conventional sequential similarity measurement and cluster analysis much more rapidly. This is important for analysis of huge-volume datasets, such as genome databases, streaming stock market data, and publicly available huge datasets on the Internet. The code which is implemented in R is available for public access.     

An efficient rough feature selection algorithm with a multi-granulation view

Feature selection is a challenging problem in many areas such as pattern recognition, machine learning and data mining. Rough set theory, as a valid soft computing tool to analyze various types of data, has been widely applied to select helpful features (also called attribute reduction). In rough set theory, many feature selection algorithms have been developed in the literatures, however, they are very time-consuming when data sets are in a large scale. To overcome this limitation, we propose in this paper an efficient rough feature selection algorithm for large-scale data sets, which is stimulated from multi-granulation. A sub-table of a data set can be considered as a small granularity. Given a large-scale data set, the algorithm first selects different small granularities and then estimate on each small granularity the reduct of the original data set. Fusing all of the estimates on small granularities together, the algorithm can get an approximate reduct. Because of that the total time spent on computing reducts for sub-tables is much less than that for the original large-scale one, the algorithm yields in a much less amount of time a feature subset (the approximate reduct). According to several decision performance measures, experimental results show that the proposed algorithm is feasible and efficient for large-scale data sets.     

Implementing an efficient minimum capacity cut algorithm

In this paper, we present an efficient implementation of theO(mn + n ² logn) time algorithm originally proposed by Nagamochi and Ibaraki (1992) for computing the minimum capacity cut of an undirected network. To enhance computation, various ideas are added so that it can contract as many edges as possible in each iteration. To evaluate the performance of the resulting implementation, we conducted extensive computational experiments, and compared the results with that of Padberg and Rinaldi's algorithm (1990), which is currently known as one of the practically fastest programs for this problem. The results indicate that our program is considerably faster than Padberg and Rinaldi's program, and its running time is not significantly affected by the types of the networks being solved.Corresponding author.     

Apply a novel evolutionary algorithm to the solution of parameter selection problems

In this study, a modified line-up competition algorithm (LCA) is used to solve parameter selection problems. The so-called parameter selection problems contain parameter identification problems and optimal control problems. Once the later problems are transformed by control parametrization, the parameters embedded in both problems are selected by the proposed method under the framework of integration approach. Two parameter identification problems and one optimal control problem are given to demonstrate the use of LCA. The results show that in addition to being insensitive to the initial conditions, LCA is very efficient in solving highly nonlinear parameter selection problems.     

非线性不适定算子方程的双参数正则化方法

对非线性不适定算子方程,引入一种双参数正则化方法求解,讨论了这种正则化方法解的存在性、稳定性和收敛性.     

The selection of the optimal parameter in the modulus-based matrix splitting algorithm for linear complementarity problems

The modulus-based matrix splitting (MMS) algorithm is effective to solve linear complementarity problems (Bai in Numer Linear Algebra Appl 17: 917–933, 2010). This algorithm is parameter dependent, and previous studies mainly focus on giving the convergence interval of the iteration parameter. Yet the specific selection approach of the optimal parameter has not been systematically studied due to the nonlinearity of the algorithm. In this work, we first propose a novel and simple strategy for obtaining the optimal parameter of the MMS algorithm by merely solving two quadratic equations in each iteration. Further, we figure out the interval of optimal parameter which is iteration independent and give a practical choice of optimal parameter to avoid iteration-based computations. Compared with the experimental optimal parameter, the numerical results from three problems, including the Signorini problem of the Laplacian, show the feasibility, effectiveness and efficiency of the proposed strategy.

     

Asymptotically efficient parameter estimation for ordinary differential equations

Parameter estimation for ordinary differential equations arises in many fields of science and engineering. To be the best of our knowledge, traditional methods are often either computationally intensive or inaccurate for statistical inference. Ramsay et al. (2007) proposed a generalized profiling procedure. It is easily implementable and has been demonstrated to have encouraging numerical performance. However, little is known about statistical properties of this procedure. In this paper, we provide a theoretical justification of the generalized profiling procedure. Under some regularity conditions, the procedure is shown to be consistent for a broad range of tuning parameters. When the tuning parameters are sufficiently large, the procedure can be further shown to be asymptotically normal and efficient.     

Numerical realization of an algorithm for reconstructing an inhomogeneous medium in an X-ray tomography problem

Under study is the X-ray tomography problem that is an inverse problem for the transport differential equation. We take into account the absorption of particles by the medium and their single scattering. The statement of the problem corresponds to multiple probing. The medium is unknown; while the densities of the outcoming flux averaged over energy are given. The object in question is the discontinuity surfaces of the coefficients of the equation. This corresponds to searching for the boundaries between various substances contained in the medium that we probe. The solution is constructive, and a numerical realization of the obtained algorithm is presented.     

An efficient algorithm for voting sequences

The chaos theorems show that given almost any alternatives x and y, there exists voting sequence from x to y. However, proofs of such results have been purely existential; that is, there is no algorithm by which such a voting path can be constructed. In this paper, we present such an algorithm for one standard example. Furthermore, it is shown that the algorithm has the property that the voting sequence involves the fewest possible number of steps.     

An efficient algorithm for solving inequalities

An efficient algorithm for solving a finite system of inequalities in a finite number of iterations is described and analyzed.This work was supported by the UK Science and Engineering Research Council