This thesis presents the development of a Sensor Tower Platform (STP) comprised of an array of Passive Infra-Red (PIR) sensors along with a classification algorithm that enables the STP to distinguish between human intrusion, animal intrusion and clutter arising from wind-blown vegetative movement in an outdoor environment. The research was motivated by the aim of exploring the potential use of wireless sensor networks (WSNs) as an early-warning system to help mitigate human-wildlife conflicts occurring at the edge of a forest. While PIR sensors are in commonplace use in indoor settings, their use in an outdoor environment is hampered by the fact that they are prone to false alarms arising from wind-blown vegetation. Every PIR sensor is made up of one or more pairs of pyroelectric pixels arranged in a plane, and the orientation of interest in this thesis is one in which this plane is a vertical plane, i.e., a plane perpendicular to the ground plane. The intersection of the Field Of View (FOV) of the PIR sensor with a second vertical plane that lies within the FOV of the PIR sensor, is called the virtual pixel array (VPA). The structure of the VPA corresponding to the plane along which intruder motion takes place determines the form of the signal generated by the PIR sensor. The STP developed in this thesis employs an array of PIR sensors designed so as to result in a VPA that makes it easier to discriminate between human and animal intrusion while keeping to a small level false alarms arising from vegetative motion. The design was carried out in iterative fashion, with each successive iteration separated by a lengthy testing phase. There were a total of 5 design iterations spanning a total period of 14 months. Given the inherent challenges involved in gathering data corresponding to animal intrusion, the testing of the SP was carried out both using real-world data and through simulation. Simulation was carried out by developing a tool that employed animation software to simulate intruder and animal motion as well as some limited models of wind-blown vegetation. More specifically, the simulation tool employed 3-dimensional models of intruder and shrub motion that were developed using the popular animation software Blender. The simulated output signal of the PIR sensor was then generated by calculating the area of the 3-dimensional intruder when projected onto the VPA of the STP. An algorithm for efficiently calculating this to a good degree of approximation was implemented in Open Graphics Library (OpenGL). The simulation tool was useful both for evaluating various competing design alternatives as well as for developing an intuition for the kind of signals the SP would generate without the need for time-consuming and challenging animal-motion data collection. Real-world data corresponding to human motion was gathered on the campus of the Indian Institute of Science (IISc), while animal data was recorded at a dog-trainer facility in Kengeri as well as the Bannerghatta Biological Park, both located in the outskirts of Bengaluru. The array of PIR sensors was designed so as to result in a VPA that had good spatial resolution. The spatial resolution capabilities of the STP permitted distinguishing between human and animal motion with good accuracy based on low-complexity, signal-energy computations. Rejecting false alarms arising from vegetative movement proved to be more challenging. While the inherent spatial resolution of the STP was very helpful, an alternative approach turned out to have much higher accuracy, although it is computationally more intensive. Under this approach, the intruder signal, either human or animal, was modelled as a chirp waveform. When the intruder moves along a circular arc surrounding the STP, the resulting signal is periodic with constant frequency. However, when the intruder moves along a more likely straight-line path, the resultant signal has a strong chirp component. Clutter signals arising from vegetative motion does not exhibit this chirp behavior and an algorithm that exploited this difference turned in a classification accuracy in excess of 97%.

Ce travail se place dans le contexte du détecteur de vertex (VXD) composé de capteurs CMOS pour l'ILC, et dans celui du télescope en faisceau du projet européen AIDA. La thèse inclut les tests en faisceau des éléments du télescope AIDA : les super-plans SALAT et les échelles double faces PLUME. Elle questionne la valeur ajoutée en terme d'alignement, des couches double faces de capteurs CMOS pour le VXD de l'ILD. Une nouvelle méthode d'alignement autonome de chacune des 3 double couches du VXD grâce aux mini-vecteurs construits sur chaque zone de recouvrement inter-échelle est proposée et a été testée avec des particules de haute impulsion. Cependant, seules les particules du bruit de fond faisceau, de plus basses impulsions, permettent l'obtention d'une statistique suffisante pour cet alignement. Ce bruit de fond a alors été étudié et une estimation des taux d'occupation des capteurs du VXD a conduit à une ré-estimation des vitesses de lecture des capteurs de chaque couche du VXD. / This work was conducted in the context of a vertex detector (VXD) composed of CMOS sensors for ILD and in the context of the beam telescope of the european project AIDA. The provides the results of beam tests for the new telescope components : the SALAT super-planes and the PLUME double sided ladders. The thesis adresses the added value in terms of alignment, of double sided layers of CMOS sensors for the VXD of ILD. A new standalone alignment method of each of the three double sided layers of VXD with the mini-vectors built on each overlapping zone between the consecutive ladders is analysed. Such alignment was validated with high momentum particles. However, only the beam background particles, with lower momentum, can provide the minimum statistic for this kind of alignment. Thus, the beam background noise was studied and the occupancy rate of the VXD sensors was studied. This led to a reassessment of the readout speed for the sensors of each layer of the VXD.

Tato práce popisuje metodu analýzy translačního pohybu užitím křížové korelace. Ukazujeme, jakým způsobem se chová funkce křížové korelace obrazů s navzájem posunutými objekty, a jak nám to umožňuje nacházet jejich vektory posunu. Pro následnou implementaci je nalezena efektivní metoda pro hledání pouze požadovaného počtu lokálních maxim funkce.

This project informs about the basic image processing on grayscale and binary images. The project deals with analyzing the data obtained. Acquaints with terms such as morphology and image segmentation. Acquired knowledge is applied to create a program to complete the processing of microscopic images in LabView programming environment with subsequent analysis and plotting of results into image. For a better idea supporting data about operation in progress are continuous images.

This thesis deals with design and implementation of an application for medical data imaging in real-time. The first part of project is focused on methods for obtaining data in medical practice and visualization of large volume data on computer using familiar rendering approaches. Similar applications are used outside of medicine in other fields, such as chemistry to display molecular structures or microorganisms. Another part of project will focus on benefits of visualization of volumetric data using programmable hardware and new methods of parallelization of algorithms on graphics card using CUDA technology, and OpenCL. The resulting application will display the volume of medical data based on selected method accelerated by programmable shaders, and time-consuming operations will be paralleled on graphics card.

For the X-ray astronomy project Advanced Telescope for High ENergy Astrophysics (Athena) wafer-scale DEpleted P-channel Field Effect Transistor (DEPFET) detectors are proposed as Focal Plane Array (FPA) for the Wide Field Imager (WFI). Prototype structures with different pixel layouts, each consisting of 64 x 64 pixels, were fabricated to study four different DEPFET designs. This thesis reports on the results of the electrical and spectroscopic characterization of the different DEPFET designs. With the electrical qualification measurements the transistor properties of the DEPFET structures are investigated in order to determine whether the design intentions are reflected in the transistor characteristics. In addition, yield and homogeneity of the prototypes can be studied on die, wafer and batch level for further improvement of the production technology with regard to wafer-scale devices. These electrical characterization measurements prove to be a reliable tool to preselect the best detector dies for further integration into full detector systems. The spectroscopic measurements test the dynamic behavior of the designs as well as their spectroscopic performance. In addition, it is revealed how the transistor behavior translates into the detector performance. This thesis, as the first systematic study of different DEPFET designs on die and detector level, shows the limitations of the current DEPFET assessment methods. Thus, it suggests a new concise characterization procedure for DEPFET detectors as well as guidelines for expanded testing in order to increase the general knowledge of the DEPFET. With this study of four different DEPFET variants not only designs suitable for Athena mission have been found but also improvement impulses for the starting wafer-scale device production are provided.

The aim of this study was to propose a non-destructive method for measuring Pinus sylvestris seedlings, to determine whether water stress would be evident in laboratory spectra of pines, to compare whether the response of pines would differ by ecotype, and to investigate whether two ecotypes of Pinus sylvestris could be distinguished using laboratory and image spectroscopy. For these purposes, hyperspectral images of seed orchards from August 2020 were processed and a three-month laboratory experiment was conducted, in which stress from water deficit was induced in two-year-old pine seedlings from the upland and hilly ecotypes. Spectral data were analysed using mixed statistical models, analysis of variance, principal component analysis, training of supervised pixel classifiers, vegetation indices, and linear regression. Based on the analyses, it was found that water stress can be detected in severely stressed Pinus sylvestris seedlings. The most sensitive spectral bands to water content were observed in the region between 1000-2500 nm. The initial response to water stress did not differ by ecotype, but a faster recovery was observed at the upland ecotype after the period of draught. The two Pinus sylvestris ecotypes were distinguished with high accuracy from both laboratory and image spectral...

This diploma thesis is focused on the analysis of results accuracy of astronomical positioning. It describes observation methods and the latest surveying systems used in geodetic astronomy. Further in the thesis there is the description of surveying system MAAS-1. Subject of the elaboration is the data obtained by measuring with this system. Testing is focused on digital camera. There is also an analysis of the results of geographical coordinates, depending on the accuracy of determining the angled pixel size and also on the accuracy of the input coordinates.

This thesis represents master's thesis focused on acceleration of Genetic algorithms using GPU. First chapter deeply analyses Genetic algorithms and corresponding topics like population, chromosome, crossover, mutation and selection. Next part of the thesis shows GPU abilities for unified computing using both DirectX/OpenGL with Cg and specialized GPGPU libraries like CUDA. The fourth chapter focuses on design of GPU implementation using CUDA, coarse-grained and fine-grained GAs are discussed, and completed by sorting and random number generation task accelerated by GPU. Next chapter covers implementation details -- migration, crossover and selection schemes mapped on CUDA software model. All GA elements and quality of GPU results are described in the last chapter.

[pt] A partir de melhorias incrementais em uma conhecida rede neural convolucional (U-Net), diferentes técnicas são avaliadas quanto às suas performances na tarefa de segmentação semântica em imagens sísmicas. Mais especificamente, procura-se a identificação e delineamento de estruturas salinas no subsolo, o que é de grande relevância na indústria de óleo e gás para a exploração de petróleo em camadas pré-sal, por exemplo. Além disso, os desafios apresentados no tratamento destas imagens sísmicas se assemelham em muito aos encontrados em tarefas de áreas médicas como identificação de tumores e segmentação de tecidos, o que torna o estudo da tarefa em questão ainda mais valioso. Este trabalho pretende sugerir uma metodologia adequada de abordagem à tarefa e produzir redes neurais capazes de segmentar imagens sísmicas com bons resultados dentro das métricas utilizadas. Para alcançar estes objetivos, diferentes estruturas de redes, transferência de aprendizado e técnicas de aumentação de dados são testadas em dois datasets com diferentes níveis de complexidade. / [en] Through incremental improvements in a well-known convolutional neural network (U-Net), different techniques are evaluated regarding their performance on the task of semantic segmentation of seismic images. More specifically, the objective is the better identification and outline of subsurface salt structures, which is a task of great relevance for the oil and gas industry in the exploration of pre-salt layers, for example. Besides that application, the challenges imposed by the treatment of seismic images also resemble those found in medical fields like tumor detection and tissue segmentation, which makes the study of this task even more valuable. This work seeks to suggest a suitable methodology for the task and to yield neural networks that are capable of performing semantic segmentation of seismic images with good results regarding specific metrics. For that purpose, different network structures, transfer learning and data augmentation techniques are applied in two datasets with different levels of complexity.