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The probability density function (PDF) optimized quantization has been shown to be more efficient than the conventional quantization methods. In practical application, the data with bounded support can be modelled better with bounded support distribution (e.g. beta distribution, Dirichlet distribution) and a better quantization performance could be achieved by a more reasonable modelling. In this...
The problem of converting floating point algorithms to implementation friendly fixed point formats is often solved as an optimization problem where the precision is traded to gain in the implementation cost. The complexity of the problem is known to grow exponentially with more optimizable variables. This paper proposes a divide and conquer technique to solve the growing size of the problem. The approach...
Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace clusters. Their results are typically highly redundant, i.e. many clusters are detected multiple times in...
We propose a modified discrete HMM that handles multimodalities. We assume that the feature space is partitioned into subspaces generated by different sources of information. To combine these heteregoneous modalities we propose a multi-stream discrete HMM that assigns a relevance weight to each subspace. The relevance weights are set local and depend on the symbols and the states. In particular, we...
This paper proposes an algorithm based on a transfer graph model to compute a multimodal shortest path (MSP) between a given source node s and a destination node d. The first step of this algorithm consists of computing a database saving a large part of necessary data to provide a response to a user itinerary request. Whe show in this paper that using ant colony optimization metaheuristic (ACO) to...
Information theory predicts the need for information maximization as sensory information must be compressed into a limited range of responses that spiking neurons can generate. We propose computational theory and learning rules based on information theory that lead to information maximization using intrinsic plasticity in a stochastically spiking neuron model. Computer simulations are used to verify...
This paper presents an approach to the shortest path problem in time-dependent multimodal networks. The approach derives, from the initial graph, a more simplified and non-time-dependent structure, called abstract graph, by using ant colony optimization. Then, a time-dependent Dijkstra's algorithm is used to compute the shortest path on the new structure. This approach improves two previous solutions...
Traditional input shaping filters are linear mappings between the reference input and the system input. These filters are often unnecessarily conservative with respect to input and output bounds if multiple references with different amplitudes are applied. This conservatism is due to the off-line computation of the prefilter. This paper presents an on-line input prefilter design approach to overcome...
The FIND algorithm is a fast algorithm designed to calculate entries of the inverse of a sparse matrix. Such calculation is critical in many applications, e.g., quantum transport in nano-devices. For a 2D device discretized as N times N mesh, the best known algorithms have a running time of O(N4), whereas FIND only requires O(N3), although with a larger constant factor. By exploiting the extra sparsity...
As the scale and complexity of parallel systems continue to grow, failures become more and more an inevitable fact for solving large-scale applications. In this research, we present an analytical study to estimate execution time in the presence of failures of directed acyclic graph (DAG) based scientific applications and provide a guideline for performance optimization. The study is four fold. We...
In the present paper the automated optimization of the exposure dose for UV moving-mask lithography is presented. Moving-mask lithography is a promising new technique for micro patterning thick positive photo resists. The design task is to design the mask and the mask motion such that the requested micro pattern is achieved as good as possible. As an intermediate step towards a computational engineering...
We consider constrained minimization of a sum of convex functions over a convex and compact set, when each component function is known only to a specific agent in a time-varying peer to peer network. We study an iterative optimization algorithm in which each agent obtains a weighted average of its own iterate with the iterates of its neighbors, updates the average using the subgradient of its local...
A Bayes net has qualitative and quantitative aspects: The qualitative aspect is its graphical structure that corresponds to correlations among the variables in the Bayes net. The quantitative aspects are the net parameters. This paper develops a hybrid criterion for learning Bayes net structures that is based on both aspects. We combine model selection criteria measuring data fit with correlation...
We propose an approach for inferring bounds on the finite-horizon return of a control policy from an off-policy sample of trajectories collecting state transitions, rewards, and control actions. In this paper, the dynamics, control policy, and reward function are supposed to be deterministic and Lipschitz continuous. Under these assumptions, a polynomial algorithm, in terms of the sample size and...
Big progress has been made in the area of using DNA computing to solve NP problems and NP-hard problems. However, research on arithmetic almost made no progress. Just for this reason, a three-segment coding scheme of real number was proposed; and a DNA addition model based on this coding scheme was proposed, too. This model solved the carry and borrow problem in computing with DNA strands. The addends...
For precise control of tension and thickness in a tandem cold mill in acceleration and deceleration conditions, we employ a nonlinear model including the rolling speed as a time-varying parameter. It is demonstrated in simulation results that nonlinear receding horizon control by real-time optimization achieves satisfactory performance for the nonlinear model.
We present a novel method for transforming graph theoretical problems into optimization problems and computing the minimum spanning tree (MST) of weighted and undirected graphs. The computing using Cellular Neural Networks (CNN) is considered for ultra-fast solutions when dealing with graphs of complex topology. As proof of concepts of the proposed method, simulations are performed on graphs of magnitude...
Focusing on inventory management in cluster supply chains (CSC), the method of constraints control is explored to adapt to the actual systems. The model is established to combine real-time optimization with constraints of inventory and order. The approach of solving the optimal issues is developed to overcome model uncertainties and external disturbance through application of robust unvariable sets...
Classical finite-difference time-domain (FDTD) method has been widely used in computational electromagnetics, but for electrically large domains and for late-time analysis, FDTD method begins to show its limitations due to the accumulation of phase errors. To solve this problem, several methods have been proposed such as high-order schemes and four-stage Runge-Kutta integrator. Recently, the symplectic...
Service level agreements (SLAs) is currently one of the major research topics in grid computing. Among many system components for the supporting of SLA-aware Grid- based workflow, the SLA mapping module receives important positions. Optimizing execution time is an important task of the mapping module as it helps in finding out a feasible solution when the Grid is busy or by eliminating the negative...
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