Development of a R package to facilitate the learning of clustering techniques

This project explores the development of a tool, in the form of a R package, to ease the process of learning clustering techniques, how they work and what their pros and cons are. This tool should provide implementations for several different clustering techniques with explanations in order to allow the student to get familiar with the characteristics of each algorithm by testing them against several different datasets while deepening their understanding of them through the explanations. Additionally, these explanations should adapt to the input data, making the tool not only adept for self-regulated learning but for teaching too.Grado en Ingeniería Informátic

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Multipath channel identification by using global optimization in ambiguity function domain

Cataloged from PDF version of article.A new transform domain array signal processing technique is proposed for identification of multipath communication channels. The received array element outputs are transformed to delay-Doppler domain by using the cross-ambiguity function (CAF) for efficient exploitation of the delay-Doppler diversity of the multipath components. Clusters of multipath components can be identified by using a simple amplitude thresholding in the delay-Doppler domain. Particle swarm optimization (PSO) can be used to identify parameters of the multipath components in each cluster. The performance of the proposed PSO-CAF technique is compared with the space alternating generalized expectation maximization (SAGE) technique and with a recently proposed PSO based technique at various SNR levels. Simulation results clearly quantify the superior performance of the PSO-CAF technique over the alternative techniques at all practically significant SNR levels. (C) 2011 Elsevier B.V. All rights reserved

Bioinformatics Applications Based On Machine Learning

The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems

Towards a more representative parametrisation of hydrologic models via synthesizing the strengths of Particle Swarm Optimisation and Robust Parameter Estimation

The development of methods for estimating the parameters of hydrologic models considering uncertainties has been of high interest in hydrologic research over the last years. In particular methods which understand the estimation of hydrologic model parameters as a geometric search of a set of robust performing parameter vectors by application of the concept of data depth found growing research interest. Bárdossy and Singh (2008) presented a first Robust Parameter Estimation Method (ROPE) and applied it for the calibration of a conceptual rainfall-runoff model with daily time step. The basic idea of this algorithm is to identify a set of model parameter vectors with high model performance called good parameters and subsequently generate a set of parameter vectors with high data depth with respect to the first set. Both steps are repeated iteratively until a stopping criterion is met. The results estimated in this case study show the high potential of the principle of data depth to be used for the estimation of hydrologic model parameters. In this paper we present some further developments that address the most important shortcomings of the original ROPE approach. We developed a stratified depth based sampling approach that improves the sampling from non-elliptic and multi-modal distributions. It provides a higher efficiency for the sampling of deep points in parameter spaces with higher dimensionality. Another modification addresses the problem of a too strong shrinking of the estimated set of robust parameter vectors that might lead to overfitting for model calibration with a small amount of calibration data. This contradicts the principle of robustness. Therefore, we suggest to split the available calibration data into two sets and use one set to control the overfitting. All modifications were implemented into a further developed ROPE approach that is called Advanced Robust Parameter Estimation (AROPE). However, in this approach the estimation of the good parameters is still based on an ineffective Monte Carlo approach. Therefore we developed another approach called ROPE with Particle Swarm Optimisation (ROPE-PSO) that substitutes the Monte Carlo approach with a more effective and efficient approach based on Particle Swarm Optimisation. Two case studies demonstrate the improvements of the developed algorithms when compared with the first ROPE approach and two other classical optimisation approaches calibrating a process oriented hydrologic model with hourly time step. The focus of both case studies is on modelling flood events in a small catchment characterised by extreme process dynamics. The calibration problem was repeated with higher dimensionality considering the uncertainty in the soil hydraulic parameters and another conceptual parameter of the soil module. We discuss the estimated results and propose further possibilities in order to apply ROPE as a well-founded parameter estimation and uncertainty analysis tool

Wireless coverage using unmanned aerial vehicles

The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civilian application domains including real-time monitoring, search and rescue, and wireless coverage. UAVs can be used to provide wireless coverage during emergency cases where each UAV serves as an aerial wireless base station when the cellular network goes down. They can also be used to supplement the ground base station in order to provide better coverage and higher data rates for the users. During such situations, the UAVs need to return periodically to a charging station for recharging, due to their limited battery capacity. Given the recharging requirements, the problem of minimizing the number of UAVs required for a continuous coverage of a given area is first studied in this dissertation. Due to the intractability of the problem, partitioning the coverage graph into cycles that start at the charging station is proposed and the minimum number of UAVs to cover such a cycle is characterized based on the charging time, the traveling time and the number of subareas to be covered by a cycle. Based on this analysis, an efficient algorithm is proposed to solve the problem. In the second part of this dissertation, the problem of optimal placement of a single UAV is studied, where the objective is to minimize the total transmit power required to provide wireless coverage for indoor users. Three cases of practical interest are considered and efficient solutions to the formulated problem under these cases are presented. Due to the limited transmit power of a UAV, the problem of minimizing the number of UAVs required to provide wireless coverage to indoor users is studied and an efficient algorithm is proposed to solve the problem. In the third part of this dissertation, the problem of maximizing the indoor wireless coverage using UAVs equipped with directional antennas is studied. The case that the UAVs are using one channel is considered, thus in order to maximize the total indoor wireless coverage, the overlapping in their coverage volumes is avoided. Two methods are presented to place the UAVs; providing wireless coverage from one building side and from two building sides. The results show that the upside-down arrangements of UAVs can improve the total coverage by 100% compared to providing wireless coverage from one building side. In the fourth part of this dissertation, the placement problem of UAVs is studied, where the objective is to determine the locations of a set of UAVs that maximize the lifetime of wireless devices. Due to the intractability of the problem, the number of UAVs is restricted to be one. Under this special case, the problem is formulated as a convex optimization problem under a restriction on the coverage angle of the ground users and a gradient projection based algorithm is proposed to find the optimal location of the UAV. Based on this, an efficient algorithm is proposed for the general case of multiple UAVs. The problem of minimizing the number of UAVs required to serve the ground users such that the time duration of uplink transmission of each wireless device is greater than or equal to a threshold value is also studied. Two efficient methods are proposed to determine the minimum number of UAVs required to serve the wireless devices

Gemeinsame Kommunikation und Positionierung basierend auf Interleave-Division Multiplexing

Interest in joint communication and positioning is steadily increasing because the combination of both techniques offers a wide range of advantages. On the one hand, synergy effects between communication and positioning like enhanced resource allocation can be exploited. On the other hand, new applications are enabled. Examples comprise a wide area of interest and include the automated localisation of emergency calls, tracking and guiding fire fighters or policemen on a mission, monitoring people with special needs in a hospital or a nursing home, asset tracking, location-based services and so forth. However, it is a challenging task to combine communication and positioning because their prerequisites are quite different. On the one hand, high data rates with little training overhead and low bit error rate are desirable for communication. On the other hand, localisation aims at precise position estimates. Much training is typically spent for that purpose. Given a single transmit signal supporting communication as well as positioning, it is very difficult to fulfil all requirements at the same time. Hence, a flexible configuration is desirable for a joint communication and positioning system with a unified signal structure in order to adjust the tradeoff between both parts to the instantaneous needs. In this thesis, a new system concept for joint communication and positioning with a unified signal structure is proposed and investigated. The system concept is based on interleave-division multiplexing (IDM) in combination with pilot layer aided channel estimation (PLACE) and multilateration via the time of arrival (TOA). On the one hand, IDM seems to be a suitable candidate for a joint communication and positioning system because of its flexible but simple transmitter structure. On the other hand, multilateration via the TOA enables precise localisation. The connection between the communication and the positioning part is accomplished via an enhanced PLACE unit. Through the incorporation of a channel parameter estimator, not only the channel coefficients of the equivalent discrete-time channel model, that are needed for data detection, but also parameters of the physical channel, that are required for positioning, can be estimated. A priori information about pulse shaping and receive filtering is exploited for that purpose. The main aim of this thesis is to show the feasibility of the proposed joint communication and positioning system. Hence, a fundamental system setup is analysed systematically. Since many applications of joint communication and positioning are located in urban or indoor environments, a very high positioning accuracy in the centimetre region is desirable. Unfortunately, positioning is most challenging in these environments due to severe multipath propagation. In order to achieve the required accuracies, the positioning part of the proposed system concept can be complemented by other localisation sources like GPS/Galileo and/or motion sensors via sensor fusion. However, the stand-alone performance of the proposed joint communication and positioning system is evaluated by means of Monte Carlo simulations in this thesis. The achieved results are compared to performance limits in terms of Cramer-Rao lower bounds. In order to improve the overall system performance and to enable sensor fusion, soft information with respect to the parameter as well as the position estimates is taken into account. The accuracy of the soft information is analysed with the help of curvature measures. Altogether, promising results are obtained.Das Interesse an gemeinsamer Kommunikation und Positionierung nimmt aufgrund vieler Vorteile stetig zu: Durch die Kombination beider Techniken können Synergieeffekte wie beispielsweise eine verbesserte Ressourcenverteilung ausgenutzt werden. Des Weiteren werden neue Anwendungen in den unterschiedlichsten Bereichen ermöglicht: Notrufe können automatisch lokalisiert werden, Feuerwehrmänner und Polizisten im Einsatz können durch eine Verfolgung ihrer Position und gegebenenfalls eine Überwachung ihrer Vitalwerte besser angeleitet und koordiniert werden, Patienten mit speziellen Bedürfnissen in Krankenhäusern können durch ein effizientes Monitoring besser versorgt werden, Ein- und Auslagerungsprozesse in Warenhäusern können erleichtert werden, positionsbezogene Dienste können realisiert werden und vieles anderes mehr. Aufgrund der verschiedenen Anforderungen von Kommunikations- und Positionierungsdiensten ist es schwierig, diese beiden Bereiche zu vereinen. Einerseits sollen große Datenraten mit geringem Trainingsaufwand als auch geringen Bitfehlerraten erreicht werden. Andererseits ist eine hohe Positionierungsgenauigkeit erwünscht, die einen großen Trainingsaufwand erfordert. In einem Systementwurf mit einer einheitlichen Signalstruktur ist es schwer, alle Anforderungen gleichzeitig zu erfüllen. Daher ist ein flexibler Systementwurf von Vorteil, um den Abtausch zwischen Kommunikation und Positionierung an die aktuellen Bedürfnisse anpassen zu können. Im Rahmen dieser Arbeit wird ein neues gemeinsames Kommunikations- und Positionierungssystem mit einer einheitlichen Signalstruktur vorgeschlagen und untersucht. Der Systementwurf basiert auf Interleave-Division Multiplexing (IDM) in Kombination mit einer Pilotlayer basierten Kanalschätzung und Multilateration mit Hilfe der Signalankunftszeit, im Folgenden Time of Arrival (TOA) genannt. Einerseits ist IDM aufgrund seiner flexiblen, jedoch einfachen Senderstruktur gut für ein gemeinsames Kommunikations- und Positionierungssystem geeignet. Andererseits ermöglicht eine Multilateration mit Hilfe der TOA hohe Positionierungsgenauigkeiten. Die Verbindung zwischen beiden Komponenten wird durch eine erweiterte Pilotlayer basierte Kanalschätzung erreicht: Durch die Verwendung eines Kanalparameterschätzers können sowohl die Kanalkoeffizienten des äquivalenten zeitdiskreten Ersatzkanalmodells, die für die Datendetektion benötigt werden, als auch Parameter des physikalischen Kanals, die für die Lokalisierung erforderlich sind, geschätzt werden. A priori Information bezüglich des Pulsformungs- und Empfangsfilters werden hierfür ausgenutzt. Das Hauptziel dieser Arbeit ist es, die Realisierbarkeit des vorgeschlagenen gemeinsamen Kommunikations- und Positionierungssystems zu zeigen. Daher wird ein grundlegender Systementwurf systematisch analysiert. Da viele Anwendungen von gemeinsamer Kommunikation und Positionierung innerhalb von Städten oder Gebäuden angesiedelt sind, ist eine sehr hohe Positionierungsgenauigkeit im Zentimeter-Bereich wünschenswert. Unglücklicherweise ist es in diesen Gebieten aufgrund von starker Mehrwegeausbreitung besonders schwer, die Position eines Objektes zu bestimmen. Allerdings kann die Positionierungskomponente durch andere Lokalisierungsquellen wie beispielsweise GPS/Galileo und/oder Bewegungssensoren mittels Sensorfusion ergänzt werden, um die erforderlichen Genauigkeiten zu erreichen. In Rahmen dieser Arbeit wird jedoch nur die eigenständige Leistungsfähigkeit des vorgeschlagenen Systementwurfs mit Hilfe von Monte Carlo Simulationen untersucht. Die Simulationsergebnisse werden mit Leistungsgrenzen in Form von Cramer-Rao Untergrenzen verglichen. Dabei wird Zuverlässigkeitsinformation bezüglich der geschätzten Parameter und der geschätzten Position berücksichtigt, um die gesamte Systemleistung zu verbessern und Sensorfusion zu ermöglichen. Die Genauigkeit der Zuverlässigkeitsinformation wird mit Hilfe von Krümmungsmaßen analysiert. Insgesamt werden vielversprechende Ergebnisse erzielt

Advances on Time Series Analysis using Elastic Measures of Similarity

A sequence is a collection of data instances arranged in a structured manner. When this arrangement is held in the time domain, sequences are instead referred to as time series. As such, each observation in a time series represents an observation drawn from an underlying process, produced at a specific time instant. However, other type of data indexing structures, such as space- or threshold-based arrangements are possible. Data points that compose a time series are often correlated with each other. To account for this correlation in data mining tasks, time series are usually studied as a whole data object rather than as a collection of independent observations. In this context, techniques for time series analysis aim at analyzing this type of data structures by applying specific approaches developed to leverage intrinsic properties of the time series for a wide range of problems, such as classification, clustering and other tasks alike. The development of monitoring and storage devices has made time se- ries analysis proliferate in numerous application fields, including medicine, economics, manufacturing and telecommunications, among others. Over the years, the community has gathered efforts towards the development of new data-based techniques for time series analysis suited to address the problems and needs of such application fields. In the related literature, such techniques can be divided in three main groups: feature-, model- and distance-based methods. The first group (feature-based) transforms time series into a collection of features, which are then used by conventional learning algorithms to provide solutions to the task under consideration. In contrast, methods belonging to the second group (model-based) assume that each time series is drawn from a generative model, which is then har- nessed to elicit knowledge from data. Finally, distance-based techniques operate directly on raw time series. To this end, these methods resort to specially defined measures of distance or similarity for comparing time series, without requiring any further processing. Among them, elastic sim- ilarity measures (e.g., dynamic time warping and edit distance) compute the closeness between two sequences by finding the best alignment between them, disregarding differences in time, and thus focusing exclusively on shape differences. This Thesis presents several contributions to the field of distance-based techniques for time series analysis, namely: i) a novel multi-dimensional elastic similarity learning method for time series classification; ii) an adap- tation of elastic measures to streaming time series scenarios; and iii) the use of distance-based time series analysis to make machine learning meth- ods for image classification robust against adversarial attacks. Throughout the Thesis, each contribution is framed within its related state of the art, explained in detail and empirically evaluated. The obtained results lead to new insights on the application of distance-based time series methods for the considered scenarios, and motivates research directions that highlight the vibrant momentum of this research area