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The partitioned preemptive EDF scheduling of implicit-deadline sporadic task systems on an identical multiprocessor platform is considered. Lookup tables, at any selected degree of accuracy, are pre-computed for the multiprocessor platform. By using these lookup tables, task partitioning can be performed in time polynomial in the representation of the task system being partitioned. Although the partitioning...
A new set-membership filtering method, adaptive set-membership normalized least mean squares (ASM-NLMS), is presented that introduces a forgetting factor into the conventional set-membership normalized least mean squares (SM-NLMS) method. The proposed ASM-NLMS method is more effective in dealing with non-stationary systems compared to the SM-NLMS method. The performance of the proposed ASM-NLMS method...
Given data drawn from a mixture of multivariate Gaussians, a basic problem is to accurately estimate the mixture parameters. We give an algorithm for this problem that has running time and data requirements polynomial in the dimension and the inverse of the desired accuracy, with provably minimal assumptions on the Gaussians. As a simple consequence of our learning algorithm, we we give the first...
We present a new algorithm for learning a convex set in n-dimensional space given labeled examples drawn from any Gaussian distribution. The complexity of the algorithm is bounded by a fixed polynomial in n times a function of k and ϵ where k is the dimension of the normal subspace (the span of normal vectors to supporting hyperplanes of the convex set) and the output is a hypothesis that correctly...
In this paper, we consider the feature correspondence task as a graph matching problem. Our approach tends to maximize a similarity objective function, which consists of not only the feature vectors but also their corresponding constrained global spatial structures, by a new polynomial-time approximate optimization algorithm. This algorithm allows every node in a smaller graph to potentially be linked...
We present a new method for solving large scale nonnegative least squares problems. Firstly, nonnegative least squares problem was transformed into monotone linear complementarity problem. Then we apply potential-reduction interior point algorithm to monotone linear complementarity problem which is based on the Newton direction and centering direction. We show that this algorithm have the polynomial...
Statistical query (SQ) learning model of Kearns is a natural restriction of the PAC learning model in which a learning algorithm is allowed to obtain estimates of statistical properties of the examples but cannot see the examples themselves [18]. We describe a new and simple characterization of the query complexity of learning in the SQ learning model. Unlike the previously known bounds on SQ learning...
We study a feasible interior-point method for solving a class of nonnegative least squares problems. Firstly, nonnegative least squares problem was transformed into linear complementarily problem. Then we present a feasible interior point algorithm for monotone linear complementarity problem. We show that the algorithm have the polynomial complexity if a feasible starting point is available. At last,...
To decide whether a given graph is the visibility graph of some simple polygon, is not known to be NP, nor is it known to be NP-hard. It is only known to be PSPACE. The problem of characterizing visibility graphs of an arbitrary simple polygons and the related problem of efficiently recognizing such graphs have remained important open problem in computational geometry. In this paper, an algorithm...
Nominal power estimation is quick but gives minimal information. Statistical power analysis can provide information on yield, chip robustness, etc., but current methods are unnecessarily slow and complex. This is primarily because existing leakage-power models, which model leakage power as lognormal distribution and calculate chip leakage power based on Wilkinson's approach, are not directly additive...
Image registration is an important task in the field of computer vision and pattern recognition. And the applied values is also reflected in the study of remote sensing, medical imaging and the object indentifying of multi-sensor fusion. In this paper, a new subpixel registration methods which based on wavelet analysis was proposed by improving polynomial subdivision algorithm and pixel level registration...
Template matching is one of the fundamental techniques for signal and image processing. It has many applications such as detection, recognition, registration, retrieval, etc. One of the drawbacks of the template matching is the high computational complexity. In this paper, we focus on the two-dimensional image template matching with fixed aspect ratio and propose a method for speeding up the calculation...
A theorem of Green, Tao, and Ziegler can be stated (roughly) as follows: ifR is a pseudorandom set, and D is a dense subset of R, then D may be modeled by a set M that is dense in the entire domain such that D and M are indistinguishable. (The precise statement refers to"measures" or distributions rather than sets.) The proof of this theorem is very general, and it applies to notions of...
We study the learnability of sets in Ropfn under the Gaussian distribution, taking Gaussian surface area as the "complexity measure" of the sets being learned. Let CS denote the class of all (measurable) sets with surface area at most S. We first show that the class CS is learnable to any constant accuracy in time nO(S2), even in the arbitrary noise ("agnostic'') model. Complementing...
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