During the past two decades, techniques and devices were developed to transmit and receive signals with a phased array instead of a single coil in the MRI (Magnetic Resonance Imaging) system. The two techniques to sim...
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During the past two decades, techniques and devices were developed to transmit and receive signals with a phased array instead of a single coil in the MRI (Magnetic Resonance Imaging) system. The two techniques to simultaneously transmit and receive RF signals using phased arrays are called parallel excitation (pTx) and parallel imaging (PI), respectively. These two techniques lead to shorter transmit pulses for higher imaging quality and faster data acquisition correspondingly. This dissertation focuses on improving the efficiency of the pTx pulse design and the PI reconstruction in MRI. Both PI and pTx benefit from the increased number of elements of the array. However, efficiency concerns may arise which include: (1) In PI, the computation cost of the reconstructions and the achievable acceleration factors and (2) in pTx, the pulse design speed and memory cost. The work presented in this dissertation addresses these issues. First, a correlation based channel reduction algorithm is developed to reduce the computation cost of PI reconstruction. In conventional k-domain methods, the individual channel data is reconstructed via linear interpolation of the neighbourhood data from all channels. In this proposed algorithm, we choose only a subset of the channels based on the spatial correlation. The results have shown that the computation cost can be significantly reduced with similar or higher reconstruction accuracy. Then, a new parallel imaging method named parallel imaging using localized receive arrays with Sinc interpolation(PILARS) is proposed to improve the actual acceleration factor and to reduce the computation cost. It employs the local support of individual coils and pre-determines the magnitude of the reconstruction coefficients. Thus, it requires much less auto-calibration signals (ACS) data and achieves higher acceleration factors. The results show that this method can increase the acceleration factor and the reconstruction speed while achieving the same
Subsethood and similarity measures are important concepts in fuzzy set (FS) theory. There are many different definitions of them, for both type-1 (T1) FSs and interval type-2 (IT2) FSs. In this paper, Rickard et al.&#...
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ISBN:
(纸本)9781424469208
Subsethood and similarity measures are important concepts in fuzzy set (FS) theory. There are many different definitions of them, for both type-1 (T1) FSs and interval type-2 (IT2) FSs. In this paper, Rickard et al.'s definition of IT2 FS subsethood measure, extended from Kosko's T1 FS subsethood measure using the Representation Theorem, and Nguyen and Kreinovich's IT2 FS similarity measure, extended from the Jaccard similarity measure for T1 FSs, are introduced. efficient algorithms for computing them are also proposed. Simulations demonstrate that our proposed algorithms outperform existing algorithms in the literature.
In this thesis we study efficient algorithms for solving very large linear algebra problems. We first consider the Kaczmarz method for solving linear systems, and develop a variant that is robust to a small number of ...
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In this thesis we study efficient algorithms for solving very large linear algebra problems. We first consider the Kaczmarz method for solving linear systems, and develop a variant that is robust to a small number of large corruptions, while still requiring only a small working memory. We provide both theoretical guarantees for certain data distributions as well as empirical results showing that our approach works well in practice. We then turn our attention to problems of quickly learning spectral information about a matrix. The first such problem is PSD-testing where we give optimal query complexity bounds (with respect to types of types of queries) for distinguishing between a matrix being positive semi-definite versus having a large negative eigenvalue. Building on part of this work, we then develop optimal sketches for learning the entire spectrum of a matrix to within additive error. Finally we return our attention to solving linear systems and give new algorithms that achieve optimal communication complexity for solving least-squares regression problems.
作者：
Uehara, RUno, YJAIST
Dept Informat Proc Sch Informat Sci Ishikawa Japan Osaka Prefecture Univ
Coll Integrated Arts & Sci Dept Math & Informat Sci Sakai Osaka 591 Japan
The longest path problem is to find a longest path in a given graph. While the graph classes in which the Hamiltonian path problem can be solved efficiently are widely investigated, very few graph classes are known wh...
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ISBN:
(纸本)3540241310
The longest path problem is to find a longest path in a given graph. While the graph classes in which the Hamiltonian path problem can be solved efficiently are widely investigated, very few graph classes are known where the longest path problem can be solved efficiently. For a tree, a simple linear time algorithm for the longest path problem is known. We first generalize the algorithm, and it then solves the longest path problem efficiently for weighted trees, block graphs, ptolemaic graphs, and cacti. We next propose three new graph classes that have natural interval representations, and show that the longest path problem can be solved efficiently on those classes. As a corollary, it is also shown that the problem can be solved efficiently on threshold graphs.
Multiple autonomous agents working cooperatively have contributed to the development of robust large-scale systems. While substantial work has been done in manufacturing and domestic environments, a key consideration ...
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Multiple autonomous agents working cooperatively have contributed to the development of robust large-scale systems. While substantial work has been done in manufacturing and domestic environments, a key consideration for small hardware agents engaged in collaborative factory automation and welfare support systems is limited area and power on-board. When the agents attempt to meet for performing a task, it is natural for them to encounter obstacles and it is desirable for each agent to optimize its resources during its navigation. In this paper, we develop efficient geometric algorithms to find a point, termed as the gathering point (and denoted by P-G), for the agents that minimizes the maximum of path lengths. In particular, we present an O(n log(2) n) time algorithm for calculation of P-G for an environment with two agents and n static polygonal obstacles. We then use the notion of a weighted minimax point to derive an efficient algorithm (with complexity of O(k(2) + kn log(2) n)) for computing PG for an environment with k agents and n obstacles. An enhancement to a dynamic environment is then presented. We also present details of an efficient hardware realization of the algorithms. Each agent, equipped with only an ATmega328P microcontroller and no external memory, executes the algorithms. Experiments with multiple agents navigating amidst static as well as dynamic obstacles are reported.
The subclass of directed series-parallel graphs plays an important role in computer science. Whether a given graph is series-parallel is a well studied problem in algorithmic graph theory, for which fast sequential an...
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The subclass of directed series-parallel graphs plays an important role in computer science. Whether a given graph is series-parallel is a well studied problem in algorithmic graph theory, for which fast sequential and parallel algorithms have been developed in a sequence of papers. Also methods are known to solve the reachability and the decomposition problem for series-parallel graphs time efficiently. However, no dedicated results have been obtained for the space complexity of these problems when restricted to series-parallel graphs - the topic of this paper. Deterministic algorithms are presented for the recognition, reachability, decomposition and the path counting problem for series-parallel graphs that use only logarithmic space. Since for arbitrary directed graphs reachability and path counting are believed not to be solvable in Logspace, the main contribution of this work are novel deterministic path finding routines that work correctly in series-parallel graphs, and a characterization of series-parallel graphs by forbidden subgraphs that can be tested space-efficiently. The space bounds are best possible, i.e. the decision problem is shown to be L-complete with respect to AC(0)-reductions. They have also implications for the parallel time complexity of these problems when restricted to series-parallel graphs. Finally, we sketch how these results can be generalized to extension of the series-parallel graph family: to graphs with multiple sources or multiple sinks and to the class of minimal vertex series-parallel graphs. (c) 2004 Elsevier Inc. All rights reserved.
作者：
Ji, RuyiPeking Univ
Sch Comp Sci Minist Educ Key Lab High Confidence Software Technol Beijing Peoples R China
The automatic synthesis of algorithms can effectively reduce the difficulty of algorithm design. However, multiple challenges exist for synthesizing algorithms. Among them, scalability of the synthesizer is the most p...
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ISBN:
(纸本)9798400703843
The automatic synthesis of algorithms can effectively reduce the difficulty of algorithm design. However, multiple challenges exist for synthesizing algorithms. Among them, scalability of the synthesizer is the most prominent one because of the significant complexity of efficient algorithms. To address this scalability challenge, we propose several approaches from two aspects, improving the efficiency of existing program synthesizers and reducing the difficulty of algorithm synthesis by properly using algorithmic knowledge, respectively.
In order to provide sufficient coverage and capacity for indoor users, in-building distributed antenna system (IB-DAS) has been considered. A passive IB-DAS deploys distributed antennas inside the building and connect...
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ISBN:
(纸本)9781467363068
In order to provide sufficient coverage and capacity for indoor users, in-building distributed antenna system (IB-DAS) has been considered. A passive IB-DAS deploys distributed antennas inside the building and connects the antennas to a base station (BS) through coaxial cables and power equipment. Power is distributed from the BS to each of the antennas. We consider an optimal design of passive IB-DAS with the target to minimize the cable usage, together with the consideration of power distribution. We decompose the problem into two sub-problems, namely the topological design and the equipment selection. Mixed integer linear models are developed for solving the sub-problems. Local search is designed to combine the sub-problems and balance their objectives. Application over realistic IB-DAS deployment and comparison with previous studies demonstrate the effectiveness of the algorithm.
In the weighted set-cover problem, we are given a finite set S and a collection. of its subsets. A non-negative weight w(j) is associated with each U-j is an element of(sic) The problem is to find a subcollection of (...
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In the weighted set-cover problem, we are given a finite set S and a collection. of its subsets. A non-negative weight w(j) is associated with each U-j is an element of(sic) The problem is to find a subcollection of (sic) with minimum weight so that the union of its sets is equal to S. This problem is a classic NP-hard combinatorial problem. In this paper, we investigate two special cases of the problem that can be solved in strongly polynomial time. The first is the case where any two sets either have empty intersection or overlap completely. It is shown how this case may be transformed to a minimum cut problem. The second case is where we can sort the elements of S into an order in which the elements of each set of (sic) appear consecutively. It is proved that this case reduces to a shortest path problem. In either case, examples are given to illustrate the methods.
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