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Transistor level simulation of the CPU, while very accurate, brings also the performance challenge. MOS6502 CPU simulation algorithm is analysed with several optimisation techniques proposed. Application of these techniques improved the transistor level simulation speed by a factor of 3–4, bringing it to the levels on par with fastest RTL-level simulations so far.
In this paper, we introduce a new kind of automata systems, called state-synchronized automata systems of degree n. In general, they consists of n pushdown automata, referred to as their components. These systems can perform a computation step provided that the concatenation of the current states of all their components belongs to a prescribed control language. As its main result, the paper demonstrates...
We consider the Boussinesq PDE perturbed by a time-dependent forcing. Even though there is no smoothing effect for arbitrary smooth initial data, we are able to apply the method of self-consistent bounds to deduce the existence of smooth classical periodic solutions in the vicinity of 0. The proof is non-perturbative and relies on construction of periodic isolating segments in the Galerkin projections.
This paper concerns classification of high-dimensional yet small sample size biomedical data and feature selection aimed at reducing dimensionality of the microarray data. The research presents a comparison of pairwise combinations of six classification strategies, including decision trees, logistic model trees, Bayes network, Na¨ıve Bayes, k-nearest neighbours and sequential minimal optimization...
We propose a novel model of multilinear filtering based on a hierarchical structure of covariance matrices – each matrix being extracted from the input tensor in accordance to a specific set-theoretic model of data generalization, such as derivation of expectation values. The experimental analysis results presented in this paper confirm that the investigated approaches to tensor-based data representation...
Deep learning is a field of research attracting nowadays much attention, mainly because deep architectures help in obtaining outstanding results on many vision, speech and natural language processing – related tasks. To make deep learning effective, very often an unsupervised pretraining phase is applied. In this article, we present experimental study evaluating usefulness of such approach, testing...
We present a new subspace clustering method called SuMC (Subspace Memory Clustering), which allows to efficiently divide a dataset D RN into k 2 N pairwise disjoint clusters of possibly different dimensions. Since our approach is based on the memory compression, we do not need to explicitly specify dimensions of groups: in fact we only need to specify the mean number of scalars which is used to describe...
This paper presents a novel global thresholding algorithm for the binarization of documents and gray-scale images using Cross-Entropy Clustering. In the first step, a gray-level histogram is constructed, and the Gaussian densities are fitted. The thresholds are then determined as the cross-points of the Gaussian densities. This approach automatically detects the number of components (the upper limit...
Support Vector Machines (SVM) with RBF kernel is one of the most successful models in machine learning based compounds biological activity prediction. Unfortunately, existing datasets are highly skewed and hard to analyze. During our research we try to answer the question how deep is activity concept modeled by SVM. We perform analysis using a model which embeds compounds’ representations in a low-dimensional...
A subset S of vertices of a graph G = (V,E) is called a k-path vertex cover if every path on k vertices in G contains at least one vertex from S. Denote by Ψk (G) the minimum cardinality of a k-path vertex cover in G and form a sequence Ψ (G) = (Ψ1 (G), Ψ2 (G), . . . , Ψ|V| (G)), called the path sequence of G. In this paper we prove necessary and sufficient conditions for two integers to appear on...
Multithreshold Entropy Linear Classifier (MELC) is a recent classifier idea which employs information theoretic concept in order to create a multithreshold maximum margin model. In this paper we analyze its consistency over multithreshold linear models and show that its objective function upper bounds the amount of misclassified points in a similar manner like hinge loss does in support vector machines...
The selection of data representation and metric for a given data set is one of the most crucial problems in machine learning since it affects the results of classification and clustering methods. In this paper we investigate how to combine a various data representations and metrics into a single function which better reflects the relationships between data set elements than a single representation-metric...
In the paper a contour ensemble image segmentation concept is presented. It bases on the previously observed relationship between contours and classifiers. Because of the specificity of the active contour segmentation the method requires a special procedure to obtain ensemble members with desired properties. In this work it is achieved by early stopping of randomized optimization algorithm. The results...
This paper presents an optimal scheduling solution for a case of agents sharing a resource. The amount of resource can not satisfy all agents at once and in case of runout there is a penalty. Each agent randomly toggle its state between requiring and not requiring the resource. Using the knowledge of previous state and probability of change, the scheduling algorithm is able to calculate optimal number...
Deep understanding of microprocessor architecture, its internal structure and mechanics of its work is essential for engineers in the fields like computer science, integrated circuit design or embedded systems (including microcontrollers). Usually the CPU architecture is presented at the level of ISA, functional decomposition of the chip and data flows. In this paper we propose more tangible, interactive...
In this paper, a novel probabilistic tracking method is proposed. It combines two competing models: (i) a discriminative one for background classification; and (ii) a generative one as a track model. The model competition, along with a combinatorial data association, shows good signal and background noise separation. Furthermore, a stochastic and derivative-free method is used for parameter optimization...
This paper presents a formalised description of the models of influence propagation in social networks introduced in the classic paper of Kempe et al. The formal framework that we propose clarifies the structure of the most popular propagation models and helps rigorously re-establish the essential results concerning the problem of influence maximisation. We also introduce new models of propagation...
The paper concerns the construction scheme of Direct Digital Synthesis (DDS) generator based on widely developed Field Programmable Gate Arrays (FPGA) technology. Firstly, the division of all generators is presented regarding the fact whether they generate sinusoidal or non-sinusoidal signals and whether the they have positive or negative feedback. Next chapter concerns how to generate frequency directly...
This work presents the step by step tutorial for how to use cross entropy clustering for the iris segmentation. We present the detailed construction of a suitable Gaussian model which best fits for in the case of iris images, and this is the novelty of the proposal approach. The obtained results are promising, both pupil and iris are extracted properly and all the information necessary for human identification...
In this article we present the use of sparse representation of a signal and incoherent dictionary learning method for the purpose of network traffic analysis. In learning process we use 1D INK-SVD algorithm to detect proper dictionary structure. Anomaly detection is realized by parameter estimation of the analyzed signal and its comparative analysis to network traffic profiles. Efficiency of our method...
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