Signal processing within the neurophysiological field is challenging and requires short processing time and reliable results. In this thesis, three main problems are considered. First, a modified line source model for simulation of muscle action potentials (APs) is presented. It is formulated in continuous-time as a convolution of a muscle-fiber dependent transmembrane current and an electrode dependent weighting (impedance) function. In the discretization of the model, the Nyquist criterion is addressed. By applying anti-aliasing filtering, it is possible to decrease the discretization frequency while retaining the accuracy. Finite length muscle fibers are incorporated in the model through a simple transformation of the weighting function. The presented model is suitable for modeling large motor units. Second, the possibility of discerning the individual AP components of the concentric needle electromyogram (EMG) is explored. Simulated motor unit APs (MUAPs) are prefiltered using Wiener filtering. The mean fiber concentration (MFC) and jitter are estimated from the prefiltered MUAPs. The results indicate that the assessment of the MFC may well benefit from the presented approach and that the jitter may be estimated from the concentric needle EMG with an accuracy comparable with traditional single fiber EMG. Third, automatic, rather than manual, detection and discrimination of recorded C-fiber APs is addressed. The algorithm, detects the Aps reliably using a matched filter. Then, the detected APs are discriminated using multiple hypothesis tracking combined with Kalman filtering which identifies the APs originating from the same C-fiber. To improve the performance, an amplitude estimate is incorporated into the tracking algorithm. Several years of use show that the performance of the algorithm is excellent with minimal need for audit.

Most applications in computer vision suffer from two major difficulties. The first is they are notoriously ridden with sub-optimal local minima. The second is that they typically require high computational cost to be solved robustly. The reason for these two drawbacks is that most problems in computer vision, even when well-defined, typically require finding a solution in a very large high-dimensional space. It is for these two reasons that multiscale methods are particularly well-suited to problems in computer vision. Multiscale methods, by way of looking at the coarse scale nature of a problem before considering the fine scale nature, often have the ability to avoid sub-optimal local minima and obtain a more globally optimal solution. In addition, multiscale methods typically enjoy reduced computational cost. This thesis applies novel multiscale active contour methods to several problems in computer vision, especially in simultaneous segmentation and reconstruction of tomography images. In addition, novel multiscale methods are applied to contour registration using minimal surfaces and to the computation of non-linear rotationally invariant optical flow. Finally, a methodology for fast robust image segmentation is presented that relies on a lower dimensional image basis derived from an image scale space. The specific advantages of using multiscale methods in each of these problems is highlighted in the various simulations throughout the thesis, particularly their ability to avoid sub-optimal local minima and their ability to solve the problems at a lower overall computational cost.

Die Fahrzeuglokalisierung ist im Bereich der Fahrerassistenzsysteme von entscheidender Bedeutung und Voraussetzung fur verschiedene Anwendungen der Robotik, wie z.B. Navigation oder Kollisionsvermeidung fur fahrerlose Transportsysteme (FTS). In dieser Arbeit wird ein Verfahren zur Lokalisierung mittels Landmarken vorgestellt, die eine Orientierung bezuglich einer Karte ermoglichen. Dabei werden der Erweiterte- Kalman-Filter und der Partikel-Filter fur diese Aufgabe untersucht und verglichen. Ein Schwerpunkt dieser Betrachtungen stellt dabei der Partikel-Filter dar. Die besondere Problematik der Initialisierung wird ausfuhrlich fur beide Filter dargestellt. Simulationen und Versuche zeigen, dass sich der Partikel-Filter fur eine robuste Lokalisierung der Fahrzeugposition verwenden lasst. Im Vergleich dazu kann der Erweiterte-Kalman-Filter nur im begrenzten Maße eingesetzt werden. / The localization of vehicles is of vital importance in the field of driver assistance systems and a requirement of different applications for robotics, i.e. navigation or collision avoidance for automatic guided vehicle systems. In this thesis an approach for localization by means of landmarks is introduced, which enables an orientation regarding a map. The extended Kalman filter and the particle filter are analyzed and compared. The main focus for this consideration is on the particle filter. The problematic for initialization is discussed in detail for both filters. Simulations and tests prove that the particle filter is suitable for robust localization of the vehicle position. Compared to this, the extended Kalman filter can only be used to a certain extend.

To navigate successfully in a previously unexplored environment, a mobile robot must be able to estimate the spatial relationships of the objects of interest accurately. A Simultaneous Localization and Mapping (SLAM) sys- tem employs its sensors to build incrementally a map of its surroundings and to localize itself in the map simultaneously. The aim of this research project is to develop a SLAM system suitable for self propelled household lawnmowers. The proposed bearing-only SLAM system requires only an omnidirec- tional camera and some inexpensive landmarks. The main advantage of an omnidirectional camera is the panoramic view of all the landmarks in the scene. Placing landmarks in a lawn field to define the working domain is much easier and more flexible than installing the perimeter wire required by existing autonomous lawnmowers. The common approach of existing bearing-only SLAM methods relies on a motion model for predicting the robot’s pose and a sensor model for updating the pose. In the motion model, the error on the estimates of object positions is cumulated due mainly to the wheel slippage. Quantifying accu- rately the uncertainty of object positions is a fundamental requirement. In bearing-only SLAM, the Probability Density Function (PDF) of landmark position should be uniform along the observed bearing. Existing methods that approximate the PDF with a Gaussian estimation do not satisfy this uniformity requirement. This thesis introduces both geometric and proba- bilistic methods to address the above problems. The main novel contribu- tions of this thesis are: 1. A bearing-only SLAM method not requiring odometry. The proposed method relies solely on the sensor model (landmark bearings only) without relying on the motion model (odometry). The uncertainty of the estimated landmark positions depends on the vision error only, instead of the combination of both odometry and vision errors. 2. The transformation of the spatial uncertainty of objects. This thesis introduces a novel method for translating the spatial un- certainty of objects estimated from a moving frame attached to the robot into the global frame attached to the static landmarks in the environment. 3. The characterization of an improved PDF for representing landmark position in bearing-only SLAM. The proposed PDF is expressed in polar coordinates, and the marginal probability on range is constrained to be uniform. Compared to the PDF estimated from a mixture of Gaussians, the PDF developed here has far fewer parameters and can be easily adopted in a probabilistic framework, such as a particle filtering system. The main advantages of our proposed bearing-only SLAM system are its lower production cost and flexibility of use. The proposed system can be adopted in other domestic robots as well, such as vacuum cleaners or robotic toys when terrain is essentially 2D.

Made available in DSpace on 2014-12-17T15:48:11Z (GMT). No. of bitstreams: 1 HeloinaAA_DISSERT.pdf: 1661373 bytes, checksum: df9fe39185a27ded472f2f72284acdf6 (MD5) Previous issue date: 2014-02-24 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Data clustering is applied to various fields such as data mining, image processing and pattern recognition technique. Clustering algorithms splits a data set into clusters such that elements within the same cluster have a high degree of similarity, while elements belonging to different clusters have a high degree of dissimilarity. The Fuzzy C-Means Algorithm (FCM) is a fuzzy clustering algorithm most used and discussed in the literature. The performance of the FCM is strongly affected by the selection of the initial centers of the clusters. Therefore, the choice of a good set of initial cluster centers is very important for the performance of the algorithm. However, in FCM, the choice of initial centers is made randomly, making it difficult to find a good set. This paper proposes three new methods to obtain initial cluster centers, deterministically, the FCM algorithm, and can also be used in variants of the FCM. In this work these initialization methods were applied in variant ckMeans.With the proposed methods, we intend to obtain a set of initial centers which are close to the real cluster centers. With these new approaches startup if you want to reduce the number of iterations to converge these algorithms and processing time without affecting the quality of the cluster or even improve the quality in some cases. Accordingly, cluster validation indices were used to measure the quality of the clusters obtained by the modified FCM and ckMeans algorithms with the proposed initialization methods when applied to various data sets / Agrupamento de dados ? uma t?cnica aplicada a diversas ?reas como minera??o de dados, processamento de imagens e reconhecimento de padr?es. Algoritmos de agrupamento particionam um conjunto de dados em grupos, de tal forma, que elementos dentro de um mesmo grupo tenham alto grau de similaridade, enquanto elementos pertencentes a diferentes grupos tenham alto grau de dissimilaridade. O algoritmo Fuzzy C-Means (FCM) ? um dos algoritmos de agrupamento fuzzy de dados mais utilizados e discutidos na literatura. O desempenho do FCM ? fortemente afetado pela sele??o dos centros iniciais dos grupos. Portanto, a escolha de um bom conjunto de centros iniciais ? muito importante para o desempenho do algoritmo. No entanto, no FCM, a escolha dos centros iniciais ? feita de forma aleat?ria, tornando dif?cil encontrar um bom conjunto. Este trabalho prop?e tr?s novos m?todos para obter os centros iniciais dos grupos, de forma determin?stica, no algoritmo FCM, e que podem tamb?m ser usados em variantes do FCM. Neste trabalho esses m?todos de inicializa??o foram aplicados na variante ckMeans. Com os m?todos propostos, pretende-se obter um conjunto de centros iniciais que esteja pr?ximo dos centros reais dos grupos. Com estas novas abordagens de inicializa??o deseja-se reduzir o n?mero de itera??es para estes algoritmos convergirem e o tempo de processamento, sem afetar a qualidade do agrupamento ou at? melhorar a qualidade em alguns casos. Neste sentido, foram utilizados ?ndices de valida??o de agrupamento para medir a qualidade dos agrupamentos obtidos pelos algoritmos FCM e ckMeans, modificados com os m?todos de inicializa??o propostos, quando aplicados a diversas bases de dados

In the thesis the design of the Data Storage System (DSS) is proposed with the respect to the V-Model methodology. The design is based on users requirements, from which the system requirements are created and the technical specification of the DSS is developed. In the technical specifications the functionality of the DMM and HMI DSS subsystems are described and sub-system requirements are assigned to them, then they are subdivided and assigned to individual DMM (Data memory module) and HMI hardware items. Moreover, requirements are analyzed on hardware items, specific electronic components, are selected and implemented into the block design of the DMM hardware. Based on the block design of hardware, the hardware of the DMM subsystem is designed, selectively simulated and implemented along with the printed circuit board. On the implemented hardware of the DMM subsystems measurements are performed in order to verify the basic functionality of the hardware and the calculated, assimilated and measured values are compared as well. At the end of the thesis there is a short description of the implementation of the software design and its use for basic initialization of the selected processor, together with the verification of its basic function - measuring the frequency of the internal clock sources and the clock domains. The work is completed by sending a message of defined parameters to the selected communication line and sapling it by an oscilloscope, so that the basic function of the DMM subsystem is verified.

This thesis focuses on the Internet of Things and some of the most important problems it faces today. Among these are the overdependence on the Cloud and lack of autonomy, poor security and privacy, complicated initialization and power consumption. The work aims to implement a complex IoT solution that solves the discussed problems. The project is part of a collaboration with NXP Semicondutors and will be used to showcase the company's technologies.

The subject of the diploma thesis "Management of Construction Projects" is a description of the management process and its' application to a model case. The first part deals with the theoretical structure of project management, construction management and construction project delivery methods. The second part applies the theoretical knowledge to a concrete construction project. Primarily, the benefit analysis is performed for the chosen project delivery methods. Subsequently, the personal proposal for the management process is developed, which incorporates organizational, time, cost, quality and risk components of the project. Finally, the model construction project. is summarized and assessed.

Nachrichtentechnische Systeme werden stets durch unvermeidbare zufällige Störungen beeinflußt. Neben anderen Komponenten sind davon besonders Oszillatoren betroffen. Die durch die Störungen verursachten zufälligen Schwankungen in der Oszillatorausgabe können als Amplituden- und Phasenabweichungen modelliert werden. Dabei zeigt sich, daß vor allem zufällige Phasenfluktuationen von Bedeutung sind. Zufällige Phasenfluktuationen können unter Verwendung stochastischer Prozesse zweiter Ordnung mit kurzem oder langem Gedächtnis modelliert werden. Inhalt der Dissertation ist die Herleitung eines Verfahrens zur Simulation zufälliger Phasenfluktuationen von Oszillatoren mit kurzem Gedächtnis unter Berücksichtigung von Datenblattangaben.

We consider a mechanistic non-linear machine learning approach to learning signals in financial time series data. A modularised and decoupled algorithm framework is established and is proven on daily sampled closing time-series data for JSE equity markets. The input patterns are based on input data vectors of data windows preprocessed into a sequence of daily, weekly and monthly or quarterly sampled feature measurement changes (log feature fluctuations). The data processing is split into a batch processed step where features are learnt using a Stacked AutoEncoder (SAE) via unsupervised learning, and then both batch and online supervised learning are carried out on Feedforward Neural Networks (FNNs) using these features. The FNN output is a point prediction of measured time-series feature fluctuations (log differenced data) in the future (ex-post). Weight initializations for these networks are implemented with restricted Boltzmann machine pretraining, and variance based initializations. The validity of the FNN backtest results are shown under a rigorous assessment of backtest overfitting using both Combinatorially Symmetrical Cross Validation and Probabilistic and Deflated Sharpe Ratios. Results are further used to develop a view on the phenomenology of financial markets and the value of complex historical data under unstable dynamics.