This study deals with semi-blind (SB) channel estimation of multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system using maximum likelihood (ML) technique. For the ML cost optimis...
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This study deals with semi-blind (SB) channel estimation of multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system using maximum likelihood (ML) technique. For the ML cost optimisation function, new expectation maximisation (EM) algorithms for the channel taps estimation are introduced. Different approximation/simplification approaches are proposed for the algorithm's computational cost reduction. The first approach consists of decomposing the MIMO-OFDM system into parallel multiple-input single-output OFDM systems. The EM algorithm is then applied to estimate the MIMO channel in a parallel way. The second approach takes advantage of the SB context to reduce the EM cost from exponential to linear complexity by reducing the size of the search space. Finally, the last proposed approach uses a parallel interference cancellation technique to decompose the MIMO-OFDM system into several single-input multiple-output OFDM systems. The latter are identified in a parallel scheme and with a reduced complexity. The performance of the proposed approaches are discussed, assessed through numerical experiments and compared with respect to the Cramer Rao Bound and to other EM-based solutions reported in the literature.
The maximum or minimum spanning tree problem is a classical combinatorial optimization problem. In this paper, we consider the partial inverse maximum spanning tree problem in which the weight function can only be dec...
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The maximum or minimum spanning tree problem is a classical combinatorial optimization problem. In this paper, we consider the partial inverse maximum spanning tree problem in which the weight function can only be decreased. Given a graph, an acyclic edge set, and an edge weight function, the goal of this problem is to decrease weights as little as possible such that there exists with respect to function containing the given edge set. If the given edge set has at least two edges, we show that this problem is APX-Hard. If the given edge set contains only one edge, we present a polynomial time algorithm.
As a superset of cell-aware faults, gate-exhaustive faults provide a target for the generation of a comprehensive test set. Considering two-cycle gate-exhaustive faults, the number of faults can be excessive, and many...
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As a superset of cell-aware faults, gate-exhaustive faults provide a target for the generation of a comprehensive test set. Considering two-cycle gate-exhaustive faults, the number of faults can be excessive, and many of the faults are undetectable. This article considers the efficient identification of undetectable two-cycle gate-exhaustive faults. To take advantage of the large numbers of necessary assignments the faults have, the procedure described in this article is based on the computation of input necessary assignments. This article also notes that gates with large percentages of undetectable two-cycle gate-exhaustive faults are more important to target for test generation. Experimental results are presented for such gates in benchmark circuits.
This paper analyzes a blockchain network forming a directed acyclic graph (DAG), called a DAG-type blockchain, from the view-point of graph algorithm theory. To use a DAG-type blockchain, NP-hard graph optimization pr...
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This paper analyzes a blockchain network forming a directed acyclic graph (DAG), called a DAG-type blockchain, from the view-point of graph algorithm theory. To use a DAG-type blockchain, NP-hard graph optimization problems on the DAG are required to be solved. Although various problems for undirected and directed graphs can be efficiently solved by using the notions of graph parameters, these currently known parameters are meaningless for DAGs, which implies that it is hopeless to design efficient algorithms based on the parameters for such problems. In this work, we propose a novel graph parameter for directed graphs called a DAG-pathwidth, which represents the closeness to a directed path. This is an extension of the pathwidth, a well-known graph parameter for undirected graphs. We analyze the features of the DAG-pathwidth and prove that computing the DAG-pathwidth of a DAG (directed graph in general) is NP-complete. Finally, we propose an efficient algorithm for a variant of the maximum k-independent set problem for the DAG-type blockchain when the DAG-pathwidth of the input graph is small.
We prove that unlessP=NP there exists no polynomial time (or even pseudo-polynomial time) algorithm that can test whether the optimal value of a nonlinear optimization problem where the objective and constraints are g...
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We prove that unlessP=NP there exists no polynomial time (or even pseudo-polynomial time) algorithm that can test whether the optimal value of a nonlinear optimization problem where the objective and constraints are given by low-degree polynomials is attained. If the degrees of these polynomials are fixed, our results along with previously-known "Frank-Wolfe type" theorems imply that exactly one of two cases can occur: either the optimal value is attained on every instance, or it is strongly NP-hard to distinguish attainment from non-attainment. We also show that testing for some well-known sufficient conditions for attainment of the optimal value, such as coercivity of the objective function and closedness and boundedness of the feasible set, is strongly NP-hard. As a byproduct, our proofs imply that testing the Archimedean property of a quadratic module is strongly NP-hard, a property that is of independent interest to the convergence of the Lasserre hierarchy. Finally, we give semidefinite programming (SDP)-based sufficient conditions for attainment of the optimal value, in particular a new characterization of coercive polynomials that lends itself to an SDP hierarchy.
Capon beamforming is associated with a high computational complexity, which limits its use as a real-time method in many applications. In this paper, we present an implementation of the Capon beamformer that exhibits ...
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Capon beamforming is associated with a high computational complexity, which limits its use as a real-time method in many applications. In this paper, we present an implementation of the Capon beamformer that exhibits real-time performance when applied in a typical cardiac ultrasound imaging setting. To achieve this performance, we make use of the parallel processing power found in modern graphics processing units (GPUs), combined with beamspace processing to reduce the computational complexity as the number of array elements increases. For a three-dimensional beamspace, we show that processing rates supporting real-time cardiac ultrasound imaging are possible, meaning that images can be processed faster than the image acquisition rate for a wide range of parameters. Image quality is investigated in an in vivo cardiac data set. These results show that Capon beamforming is feasible for cardiac ultrasound imaging, providing images with improved lateral resolution both in element-space and beamspace.
An adaptive fuzzy cerebellar model articulation controller-based (CMAC) nonlinear control with the advantage of architecture learning is proposed. To cope with the tradeoff between the complexity of CMAC architecture ...
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An adaptive fuzzy cerebellar model articulation controller-based (CMAC) nonlinear control with the advantage of architecture learning is proposed. To cope with the tradeoff between the complexity of CMAC architecture and the quality of system convergence, a dynamic architecture learning scheme is introduced, where the associative memory reinforcement and the associative memory reorganization are involved. In the memory reinforcement process, new associative memories will be generated when the memory cells in the current architecture are found insufficient. On the other hand, the inefficient memories will be detected and reorganized in the memory reorganization process. With the proposed approach, the task of fuzzy CMAC architecture determination by preliminary knowledge or trials can be freed when a well-organized and well-parameterized CMAC is represented to achieve desired approximation performance. Thus, with the proposed CMAC, a dynamic control approach is presented. In this paper, according to the adaptive control theory, the fuzzy CMAC (FCMAC) is utilized as the main controller to mimic the ideal computation controller and a supervisory controller is designed to compensate the approximation error. In the FCMAC, all the controller parameters are online tuned based on the Lyapunov stability theorem such that the stability of closed-loop system can be guaranteed. Simulation results and comparisons are presented for verification. (C) 2011 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
In this paper, we study the inapproximability of the following NP-complete number theoretic optimization problems introduced by Rossner and Seifert [C. Rossner, J.P. Seifert, The complexity of approximate optima for g...
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In this paper, we study the inapproximability of the following NP-complete number theoretic optimization problems introduced by Rossner and Seifert [C. Rossner, J.P. Seifert, The complexity of approximate optima for greatest common divisor computations, in: Proceedings of the 2nd International Algorithmic Number Theory Symposium, ANTS-II, 1996, pp. 307-322]: Given n numbers a(1),..., a(n) epsilon Z, find an l(infinity)-minimum GCD multiplier for a(1),..., a(n), i.e., a vector x epsilon Z(n) with minimum max 1 <= i <= n |x(i)| satisfying Sigma(n)(i=1) x(i)a(i) = gcd(a1,...,a(n)). We show that assuming P not equal NP, it is NP-hard to approximate the Minimum GCD Multiplier in l(infinity) norm (GCDMc(infinity)) within a factor n(c/log log n) for some constant c > 0 where n is the dimension of the given vector. This improves on the best previous result. The best result so far gave 2((logn)1-epsilon) factor hardness by Rossner and Seifert [C. Rossner, J.P. Seifert, The complexity of approximate optima for greatest common divisor computations, in: Proceedings of the 2nd international Algorithmic Number Theory Symposium, ANTS-II, 1996, pp. 307-322], where epsilon > 0 is an arbitrarily small constant. (C) 2007 Elsevier B.V. All rights reserved.
We introduce the notion of rewriting systems for sets, and consider the complexity of two reachability problems for these systems, showing that these problems are PSPACE-complete. Using such problems as bridge between...
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We introduce the notion of rewriting systems for sets, and consider the complexity of two reachability problems for these systems, showing that these problems are PSPACE-complete. Using such problems as bridge between Turing Machine computations and Binary Systolic Tree Automata (BSTA) we are able to prove that a number of important decision problems concerning these automata are PSPACE-complete. Finally, as a consequence, we show that the emptiness problem and the finiteness problem for EOL systems are PSPACE-complete, solving a long standing open problem.
The first stage in hierarchical approaches to floorplan design determines certain topological relations between the positions of indivisible cells on a VLSI chip. Various optimizations are then performed on this initi...
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The first stage in hierarchical approaches to floorplan design determines certain topological relations between the positions of indivisible cells on a VLSI chip. Various optimizations are then performed on this initial layout to minimize certain cost measures such as the chip area. We consider optimization problems in fixing the orientations of the cells and simultaneously fixing the directions of the cuts that are specified by a given slicing tree;the goal is to minimize the area of the chip. We prove that these problems are NP-hard in the ordinary sense, and we describe a pseudo-polynomial time algorithm for them. We also present fully polynomial time approximation schemes for these problems. (C) 2002 Elsevier Science B.V. All rights reserved.
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