Soil properties play important roles in a lot of environmental issues like diffuse pollution, erosion hazards or precision agriculture. With the developments of soil process models and geographical information systems, the need for accurate knowledge about soil properties becomes more acute. However, while the sources of information become each year more numerous and diversified, they rarely provide us with data at the same time having the required level of spatial and attribute accuracy. An important challenge thus consists in combining those data sources at best so as to meet the high accuracy requirements. The Bayesian Maximum Entropy (BME) approach appears as a potential candidate for achieving this task: it is especially designed for managing simultaneously data of various nature and quality ("hard" and "soft" data, continuous or categorical). It relies on a two-steps procedure involving an objective way for obtaining a prior distribution in accordance with the general knowledge at hand (the ME part), and a Bayesian conditionalization step for updating this prior probability distribution function (pdf) with respect to the specific data collected on the study site. At each prediction location, an entire pdf is obtained, allowing subsequently the easy computation of elaborate statistics chosen for their adequacy with the objectives of the study. In this thesis, the theory of BME is explained in a simplified way using standard probabilistic notations. The recent developments towards categorical variables are incorporated and an attempt is made to formulate a unified framework for both categorical and continuous variables, thus emphasizing the generality and flexibility of the BME approach. The potential of the method for predicting continuous variables is then illustrated by a series of studies dealing with the soil texture fractions (sand, silt and clay). For the categorical variables, a case study focusing on the prediction of the status of the water table is presented. The use of multiple and sometimes contradictory data sources is also analyzed. Throughout the document, BME is compared to classic geostatistical techniques like simple, ordinary or indicator kriging. Thorough discussions point out the inconsistencies of those methods and explain how BME is solving the problems. Rather than being but another geostatistical technique, BME has to be considered as a knowledge processing approach. With BME, practitioners will find a valuable tool for analyzing their spatio-temporal data sets and for providing the stake-holders with accurate information about the environmental issues to which they are confronted. Read one of the articles extracted from Chapter V at : D'Or D., Bogaert P. and Christakos, G. (2001). Application of the BME Approach to Soil Texture Mapping. Stochastic Environmental Research and Risk Assessment 15(1): 87-100 ©Springer-2001. http://springerlink.metapress.com/app/home/contribution.asp?wasp=cbttlcpaeg1rqmdb4xv2&referrer=parent&backto=issue,6,6;journal,13,29;linkingpublicationresults,1,1

The aim of the thesis is to consider in which extent can be used a multiple sources financing of social services in the Czech republic. The aging of the population not only in the Czech Republic, but also throughout Europe signify increasing demand for social services and increase of their financial demands. Therefore, it is necessary to look for new ways of financig these services from sources outside the public budgets. In this respekt is also important the cooperation between the public sector, non-profit sector and private sector, which make the providing of social services more effective. The results of this work show that participation in the providing of social services is curently inadeguately used in the Czech Republic and the search for new sources for financing still has great potential for its development.

Source localization with a wireless sensor network remains an important area of research as the number of applications with this problem increases. This work considers the problem of source localization by a network of passive wireless sensors. The primary means by which localization is achieved is through direction-finding at each sensor, and in some cases, range estimation as well. Both single and multiple-target scenarios are considered in this research. In single-source environments, a solution that outperforms the classic least squared error estimation technique by combining direction and range estimates to perform localization is presented. In multiple-source environments, two solutions to the complex data association problem are addressed. The first proposed technique offers a less complex solution to the data association problem than a brute-force approach at the expense of some degradation in performance. For the second technique, the process of signal classification is considered as another approach to the data association problem. Environments in which each signal possesses unique features can be exploited to separate signals at each sensor by their characteristics, which mitigates the complexity of the data association problem and in many cases improves the accuracy of the localization. Two approaches to signal-selective localization are considered in this work. The first is based on the well-known cyclic MUSIC algorithm, and the second combines beamforming and modulation classification. Finally, the implementation of a direction-finding system is discussed. This system includes a uniform circular array as a radio frequency front end and the universal software radio peripheral as a data processor. / Master of Science

We study energy-efficient power allocation among relays for lifetime maximization in a dual-hop relay network operated by amplify-and-forward relays with battery limitations. Power allocation algorithms are proposed for three different scenarios. First, we study the relay cooperation case where all the relays jointly support transmissions for a targeted data rate. By exploring the correlation of time-varying relay channels, we develop a prediction-based relay cooperation method for optimal power allocation strategy to improve the relay network lifetime over existing methods that do not predict the future channel state, or assume the current channel state remains static in the future. Next, we consider energy-efficient relay selection for the single source-destination case. Assuming finite transmission power levels, we propose a stochastic shortest path approach which gives the optimal relay selection decision to maximize the network lifetime. Due to the high computational complexity, a suboptimal prediction-based relay selection algorithm, directly coming from previous problem, is created. Finally, we extend our study to multiple source-destination case, where relay selection needs to be determined for each source-destination pair simultaneously. The network lifetime in the presence of multiple source-destination pairs is defined as the longest time when all source-destination pairs can maintain the target transmission rate. We design relay-to-destination mapping algorithms to prolong the network lifeii time. They all aim at maximizing the perceived network lifetime at the current time slot. The optimal max-min approach and suboptimal user-priority based approach are proposed with different levels of computational complexity. / UOIT

Current approaches integrating large scale data and information from a variety of sources to reveal molecular basis of cellular events do not adequately benefit from pathway information. Here, we portray a network structure based pathway enrichment system that fuses and exploits model and data: signalling pathways are taken as the biological models while microarray and ChIP-seq data are the sample input data sources among many other alternatives. Our model- and data-driven hybrid system allows to quantitatively assessing the biological activity of a cyclic pathway and simultaneous enrichment of the significant paths leading to the ultimate cellular response. Signal Transduction Score Flow (SiTSFlow) algorithm is the fundamental constituent of proposed network structure based pathway enrichment system. SiTSFlow algorithm converts each pathway into a cascaded graph and then gene scores are mapped onto the protein nodes. Gene scores are transferred to en route of the pathway to form a final activity score describing behaviour of a specific process in the pathway while enriching of the gene node scores. Because of cyclic pathways, the algorithm runs in an iterative manner and it terminates when the node scores converge. The converged final activity score provides a quantitative measure to assess the biological significance of a process under the given experimental conditions. The conversion of cyclic pathways into cascaded graphs is performed by using a linear time multiple source Breadth First Search Algorithm. Furthermore, proposed network structure based pathway enrichment system works in linear time in terms of nodes and edges of given pathways. In order to explore various biological responses of several processes in a global signalling network, the selected small pathways have been unified based on their common gene and process nodes. The merge algorithm for pathways also runs in linear time in terms of nodes and edges of given pathways. In the experiments, SiTSFlow algorithm proved the convergence behaviour of activity scores for several cyclic pathways and for a global signalling network. The biological results obtained by assessing of experimental data by described network structure based pathway enrichment system were in correlation with the expected cellular behaviour under the given experimental conditions.

Multimodal function optimization generally focuses on algorithms to find either a local optimum or the global optimum while avoiding local optima. However, there is another class of optimization problems which have the objective of finding multiple optima with either equal or unequal function values. The knowledge of multiple local and global optima has several advantages such as obtaining an insight into the function landscape and selecting an alternative solution when dynamic nature of constraints in the search space makes a previous optimum solution infeasible to implement. Applications include identification of multiple signal sources like sound, heat, light and leaks in pressurized systems, hazardous plumes/aerosols resulting from nuclear/ chemical spills, fire-origins in forest fires and hazardous chemical discharge in water bodies, oil spills, deep-sea hydrothermal vent plumes, etc. Signals such as sound, light, and other electromagnetic radiations propagate in the form of a wave. Therefore, the nominal source profile that spreads in the environment can be represented as a multimodal function and hence, the problem of localizing their respective origins can be modeled as optimization of multimodal functions. Multimodality in a search and optimization problem gives rise to several attractors and thereby presents a challenge to any optimization algorithm in terms of finding global optimum solutions. However, the problem is compounded when multiple (global and local) optima are sought. This thesis develops a novel glowworm swarm optimization (GSO) algorithm for simultaneous capture of multiple optima of multimodal functions. The algorithm shares some features with the ant-colony optimization (ACO) and particle swarm optimization (PSO) algorithms, but with several significant differences. The agents in the GSO algorithm are thought of as glowworms that carry a luminescence quantity called luciferin along with them. The glowworms encode the function-profile values at their current locations into a luciferin value and broadcast the same to other agents in their neighborhood. The glowworm depends on a variable local decision domain, which is bounded above by a circular sensor range, to identify its neighbors and compute its movements. Each glowworm selects a neighbor that has a luciferin value more than its own, using a probabilistic mechanism, and moves toward it. That is, they are attracted to neighbors that glow brighter. These movements that are based only on local information enable the swarm of glowworms to partition into disjoint subgroups, exhibit simultaneous taxis-behavior towards, and rendezvous at multiple optima (not necessarily equal) of a given multimodal function. Natural glowworms primarily use the bioluminescent light to signal other individuals of the same species for reproduction and to attract prey. The general idea in the GSO algorithm is similar in these aspects in the sense that glowworm agents are assumed to be attracted to move toward other glowworm agents that have brighter luminescence (higher luciferin value). We present the development of the GSO algorithm in terms of its working principle, various algorithmic phases, and evolution of the algorithm from the first version of the algorithm to its present form. Two major phases ¡ splitting of the agent swarm into disjoint subgroups and local convergence of agents in each subgroup to peak locations ¡ are identified at the group level of the algorithm and theoretical performance results related to the latter phase are obtained for a simplified GSO model. Performance of the GSO algorithm against a large class of benchmark multimodal functions is demonstrated through simulation experiments. We categorize the various constants of the algorithm into algorithmic constants and parameters. We show in simulations that fixed values of the algorithmic constants work well for a large class of problems and only two parameters have some influence on algorithmic performance. We also study the performance of the algorithm in the presence of noise. Simulations show that the algorithm exhibits good performance in the presence of fairly high noise levels. We observe graceful degradation only with significant increase in levels of measurement noise. A comparison with the gradient based algorithm reveals the superiority of the GSO algorithm in coping with uncertainty. We conduct embodied robot simulations, by using a multi-robot-simulator called Player/Stage that provides realistic sensor and actuator models, in order to assess the GSO algorithm's suitability for multiple source localization tasks. Next, we extend this work to collective robotics experiments. For this purpose, we use a set of four wheeled robots that are endowed with the capabilities required to implement the various behavioral primitives of the GSO algorithm. We present an experiment where two robots use the GSO algorithm to localize a light source. We discuss an application of GSO to ubiquitous computing based environments. In particular, we propose a hazard-sensing environment using a heterogeneous swarm that consists of stationary agents and mobile agents. The agents deployed in the environment implement a modification of the GSO algorithm. In a graph of mini mum number of mobile agents required for 100% source-capture as a function of the number of stationary agents, we show that deployment of the stationary agents in a grid configuration leads to multiple phase-transitions in the heterogeneous swarm behavior. Finally, we use the GSO algorithm to address the problem of pursuit of multiple mobile signal sources. For the case where the positions of the pursuers and the moving source are collinear, we present a theoretical result that provides an upper bound on the relative speed of the mobile source below which the agents succeed in pursuing the source. We use several simulation scenarios to demonstrate the ecacy of the algorithm in pursuing mobile signal sources. In the case where the positions of the pursuers and the moving source are non-collinear, we use numerical experiments to determine an upper bound on the relative speed of the mobile source below which the pursuers succeed in pursuing the source.

Thesis focus on funding possibilities of social enterprises in Czech Republic, different ways of funding from various sources and the ability of enterprises to reach these resources. Work has theoretical and empirical character. The theoretical part comes with overview of the history of social enterprises in Europe and in the Czech Republic, their development and legislative framework. Part of the work is devoted to theories of civic sector organizations, on the basis of which I am trying to point out the factors that might have an impact on financing social enterprises. Social enterprises tend to have other sources of funding than those that are available for commercial business. In connection with these sources, the common term to describe it, is multi-source or hybrid financing. Empirical part is preceded by an analysis of hybrid financial instruments that are available in the Czech Republic and which are designed specifically for social enterprises. Empirical part is composed of analysis of financial reports from selected social enterprises and demonstrates the true structure of financial resources for the studied period. Powered by TCPDF (www.tcpdf.org)