Recent measurements show that the spectrum is under-utilized by licensed users in wireless communication. Cognitive radio (CR) has been proposed as a suitable solution to manage the inefficient usage of the spectrum and increase coverage area of wireless networks. The concept is based on allowing a group of secondary users (SUs) to share the unused radio spectrum originally owned by the primary user (PUs). The operation of CR should not cause harmful interference to the PUs. In the other hand, relayed transmission increases the coverage and achievable capacity of communication systems and in particular in CR systems. In fact there are many types of cooperative communications, however the two main ones are decode-and-forward (DAF) and amplify-and-forward (AAF). Adaptive relaying scheme is a relaying technique by which the benefits of the amplifying or decode and forward techniques can be achieved by switching the forwarding technique according to the quality of the signal. In this dissertation, we investigate the power allocation for an adaptive relaying protocol (ARP) scheme in cognitive system by maximizing the end-to-end rate and searching the best carriers pairing distribution. The optimization problem is under the interference and power budget constraints. The simulation results confirm the efficiency of the proposed adaptive relaying protocol in comparison to other relaying techniques, and the consequence of the choice of the pairing strategy.

As autonomous systems develop an ever expanding range of capabilities, monolithic systems (systems with multiple capabilities on a single platform) become increasingly expensive to build and vulnerable to failure. A promising alternative to these monolithic systems is a distributed team with different capabilities that can provide equivalent or greater overall functionality through cooperation. Such systems benefit from decreased individual system cost, robustness to partial system failure, and the possibility of operating over larger geographical areas. However, these benefits come at the cost of increased planning, control, perception, and computational complexity, as well as novel algorithm development. This thesis contributes to the start-of-the-art in distributed systems by drawing on techniques from the fields of formal methods to address problems in team task and motion planning, and from computer vision to address problems in multi-robot perception (specifically multi-image feature matching). These problems arise in persistent surveillance, robotic agriculture, post-disaster search and rescue, and autonomous driving applications. Overall, this work enables resilient hierarchical planning for robot teams and solves the distributed multi-image feature matching problem, both of which were previously intractable to solve in many cases. We begin by exploring distributed multi-image feature matching for distributed perception and object tracking for a robot team or camera network. We then look at homogeneous multi-agent planning from rich infinite-time specifications that includes a secondary objective of optimizing local sensor information entropy. Next, we address heterogeneous multi-agent task planning from rich, timed specifications based on agent capabilities, and then detail mechanisms for online replanning due to agent loss. Finally, we address safe, reactive, and timed motion planning subject to temporal logic constraints. Accompanying each topic are a number of simulations and experiments that demonstrate their utility on real hardware. Overall, this thesis focuses on four primary contributions: 1) distributed multi-image feature matching, 2) motion planning for a homogeneous robotic team subject to distributed sensing and temporal logic constraints, 3) task planning for a heterogeneous robotic team with reactivity to changing agent availability, and 4) safe motion planning for an individual system that is reactive to disturbances and satisfies timed temporal logic constraints. / 2022-09-30T00:00:00Z

This paper addresses the minimum weight edge cover problem (MEC), which is stated as follows: Given a graph <i>G= (V,E)</i>, find a set of edges <i>S:S⊆E </i>and ∑_e∈S^w(e)</∑<sub>e∈Q^w(e)∀Q: Q is an edge cover. Where an edge cover <i>P</i> is a set of edges such that ∀v∈V <i>v</i> is incident to at least one edge in <i>P</i>. An efficient implementation of a 4/3-approximation for MEC is provided. Empirical results obtained experimentally from practical data sets are reported and compared against various other approximation algorithms for MEC.<br>

The own-group bias in face recognition (OGB) is the greater facility to distinguish and recognize people from one's own group at the expense of people from other-groups. The OGB has been studied for many years, however, very little research focuses on finding a way to decrease or eliminate it, through training. Reporting five studies involving memory or matching tasks, the aim of the present thesis was to develop and to explore to what extent training can decrease or remove the OGB. French White participants, and South African White, Black and Coloured participants took part in different studies, using Black and White faces as stimuli. In each study, White participants from both countries presented the expected OGB prior to any intervention. However, the presence of the OGB in South African Black participants was detected only in one (matching task) study, instead recording a higher discrimination performance by Black participants for White faces in the other studies. As expected, South African Coloured participants did not display increased discrimination performance for any of the other stimuli groups, both being out-group stimuli. Results from the training studies revealed either (a) no effect of a distributed training in feature focus over 5 weeks; (b) an increase of the OGB after a focus on critical facial features; (c) a decrease of the OGB in a task-specific training using pictures whose quality had been manipulated, and; (d) an important implication of the presence/absence of the target in a field detection study. With some promising results, the present work contributes to our understanding of how training could be used to improve face-recognition, and especially other-group face recognition.

Reservoir history matching is becoming increasingly important with the growing demand for higher quality formation characterization and forecasting and the increased complexity and expenses for modern hydrocarbon exploration projects. History matching has long been dominated by adjusting reservoir parameters based solely on well data whose spatial sparse sampling has been a challenge for characterizing the flow properties in areas away from the wells. Geophysical data are widely collected nowadays for reservoir monitoring purposes, but has not yet been fully integrated into history matching and forecasting fluid flow. In this thesis, I present a pioneering approach towards incorporating different time-lapse geophysical data together for enhancing reservoir history matching and uncertainty quantification. The thesis provides several approaches to efficiently integrate multiple geophysical data, analyze the sensitivity of the history matches to observation noise, and examine the framework’s performance in several settings, such as the Norne field in Norway. The results demonstrate the significant improvements in reservoir forecasting and characterization and the synergy effects encountered between the different geophysical data. In particular, the joint use of electromagnetic and seismic data improves the accuracy of forecasting fluid properties, and the usage of electromagnetic data has led to considerably better estimates of hydrocarbon fluid components. For volatile oil and gas reservoirs the joint integration of gravimetric and InSAR data has shown to be beneficial in detecting the influx of water and thereby improving the recovery rate. Summarizing, this thesis makes an important contribution towards integrated reservoir management and multiphysics integration for reservoir history matching.

A common task in many database applications is the migration of legacy data from multiple sources into a new one. This requires identifying semantically related elements of the source and target systems and the creation of mapping expressions to transform instances of those elements from the source format to the target format. Currently, data migration is typically done manually, a tedious and timeconsuming process, which is difficult to scale to a high number of data sources. In this paper, we describe QuickMig, a new semi-automatic approach to determining semantic correspondences between schema elements for data migration applications. QuickMig advances the state of the art with a set of new techniques exploiting sample instances, domain ontologies, and reuse of existing mappings to detect not only element correspondences but also their mapping expressions. QuickMig further includes new mechanisms to effectively incorporate domain knowledge of users into the matching process. The results from a comprehensive evaluation using real-world schemas and data indicate the high quality and practicability of the overall approach.

In this dissertation, we provide combinatorial methods to obtain the probabilistic mul-tivariate generating function that counts the occurrences of patterns in a text generated by a Markovian source. The generating function can then be expanded into the Taylor series in which the power of a term gives the size of a text and the coeÿcient provides the proba-bilities of all possible pattern occurrences with the text size. The analysis is on the basis of the inclusion-exclusion principle to pattern counting (Goulden and Jackson, 1979 and 1983) and its application that Bassino et al. (2012) used for obtaining the generating function in the context of the Bernoulli text source. We followed the notations and concepts created by Bassino et al. in the discussion of distinguished patterns and non-reduced pattern sets, with modifications to the Markovian dependence. Our result is derived in the form of a linear matrix equation in which the number of linear equations depends on the size of the alphabet. In addition, we compute the moments of pattern occurrences and discuss the impact of a Markovian text to the moments comparing to the Bernoulli case. The methodology that we use involves the inclusion-exclusion principle, stochastic recurrences, and combinatorics on words including probabilistic multivariate generating functions and moment generating functions.<br>

The thesis explores the application of approximate string matching in scientific publication record linkage process. An introduction to record matching along with five commonly used metrics for string distance (Levenshtein, Jaro, Jaro-Winkler, Cosine distances and Jaccard coefficient) are provided. These metrics are applied on publication metadata from V3S current research information system of the Czech Technical University in Prague. Based on the findings, optimal thresholds in the F​1​, F​2​ and F​3​-measures are determined for each metric.

I developed a high power blue laser for use in scientific and technical applications (eg. precision spectroscopy, semiconductor inspection, flow cytometry, etc). It is linearly polarized, single longitudinal and single transverse mode, and a convenient fiber coupled continuous wave (cw) laser source. My technique employs external cavity frequency doubling and provides better power and beam quality than commercially available blue diode lasers. I use a fiber Bragg grating (FBG) stabilized infrared (IR) semiconductor laser source with a polarization maintaining (PM) fiber coupled output. Using a custom made optical and mechanical design this output is coupled with a mode matching efficiency of 96% into the doubling cavity. With this carefully designed and optimized cavity, measurements were carried out at various fundamental input powers. A net efficie ncy of 81 % with an output power of 680 mW at 486 nm was obtained using 840 mW of IR input. Also I report an 87.5 % net efficiency in coupling of blue light from servo locked cavity into a single mode PM fiber. Thus I have demonstrated a total fiber to fiber efficiency of 71% can be achieved in our approach using periodically poled potassium titanyl phosphate (PPKTP). To obtain these results, all losses in the system were carefully studied and minimized.

This report explores of the feasibility of using pattern matching for implementing a robust Natural Language Interface (NLI) over a limited Application Programming Interface (API). Because APIs are used to such a great extent today and often in mobile applications, it becomes more important to find simple ways of making them accessible to end users. A very intuitive way to access information via an API is using natural language. Therefore, this study first explores the possibility of building a corpus of the most common phrases used for a particular API. It is then explored how those phrases adhere to patterns, and how these patterns can be used to extract meaning from a phrase. Finally it evaluates an implementation of an NLI using pattern matching system based on the patterns. The result of the building of the corpus shows that although the amount of unique phrases used with our API seems to increase quite steadily, the amount of patterns those phrases follow converges to a constant quickly. This implies that it is possible to use these patterns to create an NLI that is robust enough to query an API effectively. The evaluation of the pattern matching system indicates that this technique can be used to successfully extract information from a phrase if its pattern is known by the system. / Den här rapporten utforskar hur genomförbart det är att använda mönstermatchning för att implementera ett robust användargränssnitt för styrning med naturligt språk (Natural Language Interface, NLI) över en begränsad Application Programming Interface (API). Eftersom APIer används i stor utsträckning idag, ofta i mobila applikationer, har det blivit allt mer viktigt att hitta sätt att göra dem ännu mer tillgängliga för slutanvändare. Ett mycket intuitivt sätt att komma åt information är med hjälp av naturligt språk via en API. I den här rapporten redogörs först för möjligheten att bygga ett korpus för en viss API and att skapa mönster för mönstermatchning på det korpuset. Därefter utvärderas en implementation av ett NLI som bygger på mönstermatchning med hjälp av korpuset. Resultatet av korpusuppbyggnaden visar att trots att antalet unika fraser som används för vårt API ökar ganska stadigt, så konvergerar antalat mönster på de fraserna relativt snabbt mot en konstant. Detta antyder att det är mycket möjligt att använda desssa mönster för att skapa en NLI som är robust nog för en API. Utvärderingen av implementationen av mönstermatchingssystemet antyder att tekniken kan användas för att framgångsrikt extrahera information från fraser om mönstret frasen följer finns i systemet.