The Hough Transform,HT is a powerful method of parameter *** introduce the probabilistic adaptive Hough transform,PAH as an efficient way of implementing the Hough *** a small subset of edge points in image selected a...
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The Hough Transform,HT is a powerful method of parameter *** introduce the probabilistic adaptive Hough transform,PAH as an efficient way of implementing the Hough *** a small subset of edge points in image selected at random,is used as input for the Hough transform. The algorithm also used a small accumulation array and idea of a flexible iterative "coarse to fine" accumulation and search strategy to identify significant peaks in the *** method is substantially superior to the standard HT implementation in both storage and computation requirement. Experiment results are also presented.
The work described in this paper explores a new solution for tracking multiple and dynamic objects in complex environments. An XPF (Extended Particle Filter) is used to implement a multimodal distribution that will re...
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ISBN:
(纸本)0780389123
The work described in this paper explores a new solution for tracking multiple and dynamic objects in complex environments. An XPF (Extended Particle Filter) is used to implement a multimodal distribution that will represent the most probable estimation for each object position and velocity. A standard PF (Particle Filter) cannot be used with a variable number of obstacles;some other solutions have been tested in different previous works, but most of them require heavy computational resources at least for a high number of obstacles to be tracked. The solution described here includes a clustering procedure that increases the robustness of the probabilistic process in order to provide on-line adaptation to the variable number of clusters. The result is the XPFCP: Extended Particle Filter with Clustering Process. The presented algorithm has been tested using stereovision measurements;the results included in the paper show the efficiency of the proposed system.
This thesis is devoted to the analysis of a class ofiterative probabilistic algorithms in regular graphs, calledlocally greedy algorithms, which will provide bounds forgraph functions in regular graphs with large girt...
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This thesis is devoted to the analysis of a class ofiterative probabilistic algorithms in regular graphs, calledlocally greedy algorithms, which will provide bounds forgraph functions in regular graphs with large girth. This class isuseful because, by conveniently setting the parameters associatedwith it, we may derive algorithms for some well-known graphproblems, such as algorithms to find a large independent set, alarge induced forest, or even a small dominating set in an inputgraph G. The name ``locally greedy" comes from the fact that, inan algorithm of this class, the probability associated with therandom selection of a vertex v is determined by the currentstate of the vertices within some fixed distance of *** r > 2 and an r-regular graph G, we determine theexpected performance of a locally greedy algorithm in G,depending on the girth g of the input and on the degree r ofits vertices. When the girth of the graph is sufficiently large,this analysis leads to new lower bounds on the independence numberof G and on the maximum number of vertices in an induced forestin G, which, in both cases, improve the bounds previously *** also implies bounds on the same functions in graphs with largegirth and maximum degree r and in random regular graphs. As amatter of fact, the asymptotic lower bounds on the cardinality ofa maximum induced forest in a random regular graph improve earlierbounds, while, for independent sets, our bounds coincide withasymptotic lower bounds first obtained by Wormald. Our resultprovides an alternative proof of these bounds which avoids sharpconcentration *** main contribution of this work lies in the method presentedrather than in these particular new bounds. This method allows us,in some sense, to directly analyse prioritised algorithms inregular graphs, so that the class of locally greedy algorithms, orslight modifications thereof, may be applied to a wider range ofproblems in regular graphs with large girth
Práce se věnuje vytvoření knihovny pro vizualizaci algortimů. Knihovna ulehčuje tvorbu uživatelského rozhraní pro aplikace s algoritmami. Její funkce je demonstrovaná na nekolikaalgoritm...
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Práce se věnuje vytvoření knihovny pro vizualizaci algortimů. Knihovna ulehčuje tvorbu uživatelského rozhraní pro aplikace s algoritmami. Její funkce je demonstrovaná na nekolikaalgoritmech pro plánovaní cesty. Výsledné aplikace jsou prezentovány na webové stránce.
Tato práce se zabývá problémem plánování pohybu objektu ve 3D prostoru. Cílem je vytvořit program, který uživateli umožní vytvořit scénu, ve které bude hled&...
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Tato práce se zabývá problémem plánování pohybu objektu ve 3D prostoru. Cílem je vytvořit program, který uživateli umožní vytvořit scénu, ve které bude hledání cesty probíhat, automaticky pak cestu vyhledá a nakonec ji bude vizualizovat. Práce je zaměřena na pravděpodobnostní algoritmy, které jsou spolu s důležitými pojmy popsány v teoretické části. V praktické části je popsán návrh a implementace aplikace. Nakonec je provedeno několik experimentů pro porovnání výkonu jednotlivých algoritmů a demonstraci funkčnosti programu.
Technological advances in DNA sequencing along with the emergence of new informatics approaches have created new possibilities in many biological fields. In this bachelor thesis, I will focus on the informatics approa...
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Technological advances in DNA sequencing along with the emergence of new informatics approaches have created new possibilities in many biological fields. In this bachelor thesis, I will focus on the informatics approaches used in speciation genomics, that is research field focused on the problematics of the origin of new species. I will introduce some statistical methods used by these approaches for parameter estimation. The four particular methods I will write about are Maximum likelihood estimation, Bayesian model, Markov chain Monte Carlo and Iterative approach. I will describe several methods used for the detection of interspecific hybrids and recent as well as historical interspecific gene flow. These methods include NewHybrids, the hybrid index, genomic and spatial clines and coalescent-based methods. The thesis demonstrates the usefulness of the connection of applied mathematics and genomics for addressing general biological issues, and speciation particularly.
Factor analysis (FA) provides linear factors that describe the relationships between individual variables of a data set. We extend this classical formulation into linear factors that describe the relationships between...
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Factor analysis (FA) provides linear factors that describe the relationships between individual variables of a data set. We extend this classical formulation into linear factors that describe the relationships between groups of variables, where each group represents either a set of related variables or a data set. The model also naturally extends canonical correlation analysis to more than two sets, in a way that is more flexible than previous extensions. Our solution is formulated as a variational inference of a latent variable model with structural sparsity, and it consists of two hierarchical levels: 1) the higher level models the relationships between the groups and 2) the lower models the observed variables given the higher level. We show that the resulting solution solves the group factor analysis (GFA) problem accurately, outperforming alternative FA-based solutions as well as more straightforward implementations of GFA. The method is demonstrated on two life science data sets, one on brain activation and the other on systems biology, illustrating its applicability to the analysis of different types of high-dimensional data sources.
A consensus protocol enables a system of n asynchronous processes, some of which are faulty, to reach agreement. There are two kinds of faulty processes: fail-stop processes that can only die and malicious processes t...
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A consensus protocol enables a system of n asynchronous processes, some of which are faulty, to reach agreement. There are two kinds of faulty processes: fail-stop processes that can only die and malicious processes that can also send false messages. The class of asynchronous systems with fair schedulers is defined, and consensus protocols that terminate with probability 1 for these systems are investigated. With fail-stop processes, it is shown that ⌈(n + 1)/2⌉ correct processes are necessary and sufficient to reach agreement. In the malicious case, it is shown that ⌈(2n + 1)/3⌉ correct processes are necessary and sufficient to reach agreement. This is contrasted with an earlier result, stating that there is no consensus protocol for the fail-stop case that always terminates within a bounded number of steps, even if only one process can fail. The possibility of reliable broadcast (Byzantine Agreement) in asynchronous systems is also investigated. Asynchronous Byzantine Agreement is defined, and it is shown that ⌈(2n + 1)/3⌉ correct processes are necessary and sufficient to achieve it.
In this paper we present and study a new algorithm for the Maximum Satisfiability (Max Sat) problem. The algorithm is based on the Method of Conditional Expectations (MOCE, also known as Johnson's Algorithm) and a...
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In this paper we present and study a new algorithm for the Maximum Satisfiability (Max Sat) problem. The algorithm is based on the Method of Conditional Expectations (MOCE, also known as Johnson's Algorithm) and applies a greedy variable ordering to MOCE. Thus, we name it Greedy Order MOCE (GO-MOCE). We also suggest a combination of GO-MOCE with CCLS, a state-of-the-art solver. We refer to this combined solver as GO-MOCE-CCLS. We conduct a comprehensive comparative evaluation of GO-MOCE versus MOCE on random instances and on public competition benchmark *** show that GO-MOCE reduces the number of unsatisfied clauses by tens of percents, while keeping the runtime almost the same. The worst case time complexity of GO-MOCE is linear. We also show that GO-MOCE-CCLS improves on CCLS consistently by up to about 80%. We study the asymptotic performance of GO-MOCE. To this end, we introduce three measures for evaluating the asymptotic performance of algorithms for Max Sat. We point out to further possible improvements of GO-MOCE, based on an empirical study of the main quantities managed by GO-MOCE during its execution. (c) 2022 Elsevier B.V. All rights reserved.
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