Due to growing transmission speed burst-forming errors tend to occur still more frequently not exclusively in data transmission. The presented paper concentrates on the search for alternative burst error correction solutions complementing the existing methods in use. Its objective is an elaboration of a detailed analysis of the issue of convolution codes for error burst correction which can be used in individual anti-error systems and thus an achievement of better results than those attained by mass application of the existing solutions. First the methods implemented to remove or suppress burst errors are briefly characterized. This part is followed by a detailed description of the individual systematic convolution codes by means of mathematical tools which extend the set of possible evaluative criteria of anti-error systems which can be applied while assessing proposals for individual solutions. The acquired code properties are compared with convolution codes as well as with other versions of proposals for message protection against an error burst. The processed convolution codes are subject to testing by means of Matlab mathematical programme simulation in order to validate the correctness of the derived mathematical tools. This is because simulation represents the principal method applied to verify and present an already proposed security process and enables the acquisition of a better overview of the issue at hand. The feasibility of the individual anti-error systems is then confirmed by way of creating a circuit behaviour description in the VHDL language. Its high portability presents a big advantage when drafting individual systems of the actual implementation.

This thesis inquires the existing methods on the field of image recognition with regards to gesture recognition. Some methods have been chosen for deeper study and these are to be discussed later on. The second part goes in for the concenpt of an algorithm that would be able of robust gesture recognition based on data acquired within the AMI and M4 projects. A new ways to achieve precise information on participants position are suggested along with dynamic data processing approaches toward recognition. As an alternative, recognition using Gaussian Mixture Models and periodicity analysis are brought in. The gesture class in focus are speech supporting gestures. The last part demonstrates the results and discusses future work.

Thesis deals with the detection and tracking of small moving objects from static images. This work shows a general overview of methods and approaches to detection and tracking of objects. There are also described some other approaches to the whole solution. It also included basic definitions, such a noise, convolution and mathematical morphology. The work described Bayesian filtering and Kalman filter. It described the theory of Wavelets, wavelets filters and transformations. The work deals with different ways of the blob`s detection. It is here the design and implementation of applications, which is based on the wavelets filters and Kalman filter. It`s implemented several methods of background subtraction, which are compared by testing. Testing and application are designed to detect vehicles, which are moving faraway (at least 200 m away).

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The threaded fastener is by far the most common method for securing components together and plays a significant role in determining the quality of a product. Atlas Copco offers industrial tools for tightening these fasteners, which are today suffering from errors in the applied torque. These errors have been found to behave in periodic patterns which indicate that the errors can be predicted and therefore compensated for. However, this is only possible by knowing the rotational position of the tool. Atlas Copco is interested in the possibility of acquiring this rotational position without installing sensors inside the tools. To address this challenge, the thesis explores the feasibility of estimating the rotational position by analysing the behaviour of the errors and finding periodicities in the data. The objective is to determine whether these periodicities can be used to accurately estimate the rotation of the torque errors of unknown data relative to errors of data where the rotational position is known. The tool analysed in this thesis exhibits a periodic pattern in the torque error with a period of 11 revolutions.  Two methods for estimating the rotational position were evaluated: a simple nearest neighbour method that uses mean squared error (MSE) as distance measure, and a more complex circular fully convolutional network (CFCN). The project involved data collection from a custom-built setup. However, the setup was not fully completed, and the models were therefore evaluated on a limited dataset. The results showed that the CFCN method was not able to identify the rotational position of the signal. The insufficient size of the data is discussed to be the cause for this. The nearest neighbour method, however, was able to estimate the rotational position correctly with 100% accuracy across 1000 iterations, even when looking at a fragment of a signal as small as 40%. Unfortunately, this method is computationally demanding and exhibits slow performance when applied to large datasets. Consequently, adjustments are required to enhance its practical applicability. In summary, the findings suggest that the nearest neighbour method is a promising approach for estimating the rotational position and could potentially contribute to improving the accuracy of tools. / Skruvförband är den vanligaste typen av förband för att sammanfoga komponenter och är avgörande för en produkts kvalitet. Atlas Copco tillverkar industriverktyg avsedda för sådana skruvförband, som dessvärre lider av små avvikelser i åtdragningsmomentet. Avvikelserna uppvisar ett konsekvent periodiskt mönster, vilket indikerar att de är förutsägbara och därför möjliga att kompenseras för. Det är dock endast möjligt genom att veta verktygets vinkelposition. Atlas Copco vill veta om det är möjligt att erhålla vinkelpositionen utan att installera sensorer i verktygen. Denna uppsats undersöker möjligheten att uppskatta vinkelpositionen genom att analysera beteendet hos avvikelserna i åtdragningsmomentet och identifiera periodiciteter i datan, samt undersöka om dessa periodiciteter kan utnyttjas för att uppskatta rotationen hos avvikelserna hos okänd data i förhållande till tidigare data. Det verktyget som används i detta projekt uppvisar en tydlig periodicitet med en period på 11 varv. Två metoder för att uppskatta vinkelpositionen utvärderades: en simpel nearest neighbour-metod som använder mean squared error (MSE) som mått för avstånd, och ett mer komplext circular fully convolutional network (CFCN). Projektet innefattade datainsamling från en egendesignad testrigg som tyvärr aldrig blev färdigställd, vilket medförde att utvärderingen av modellerna utfördes på ett begränsat dataset.  Resultatet indikerade att CFCN-metoden kräver en större datamängd för att kunna uppskatta rotationen hos den okända datan. Nearest neighbour-metoden lyckades uppskatta rotationen med 100% noggrannhet över 1000 iterationer, även när endast ett segment så litet som 40% av signalen utvärderades. Tyvärr lider denna metod av hög beräkningsbelastning och kräver förbättringar för att vara praktiskt tillämpbar. Sammantaget visade resultaten att nearest neighbour-metoden har potential att vara ett lovande tillvägagångssätt för att uppskatta vinkelpositionen och kan på så sätt bidra till förbättring av verktygens noggrannhet.

An inconsistency exists in bubble force models used in the standard Euler-Euler simulations. The bubble force models are typically developed by assuming that the forces act on the bubbles' centers of mass. However, in the standard Euler-Euler model, each bubble force is a function of the local gas volume fraction because the phase-averaging method is used. This inconsistency can lead to gas over-concentration in the center or near the wall of a channel when the bubble diameter is larger than the computational cell size. Besides, a mesh-independent solution may not exist in such cases. In addition, the bubble deformation is not fully considered in the standard Euler-Euler model. In this thesis, a particle-center-averaging method is used to represent the bubble forces as forces that act on the bubbles' centers of mass. A particle-center-averaged Euler-Euler approach for bubbly flow simulations is developed by combining the particle-center-averaged Euler-Euler framework with a Gaussian convolution method. The convolution method is used to convert the phase-averaged and the particle-center-averaged quantities. The remediation of the inconsistency in the standard Euler-Euler model by the particle-center-averaging method is demonstrated using a simplified two-dimensional test case. Bubbly flows in different vertical pipes are used to validate the particle-center-averaged Euler-Euler approach. The bubbly flow simulation results for the particle-center-averaged Euler-Euler model and the standard Euler-Euler model are compared with experimental data. For monodisperse simulations, the particle-center-averaging method alleviates the over-predictions of the gas volume fraction peaks for wall-peaking cases and for finely dispersed flow case. Whereas, no improvement is found in the simulated gas volume fraction for center-peaking cases because the over-prediction caused by the inconsistency has been smoothed by the turbulent dispersion. Moreover, the axial gas and liquid velocities simulated with both Euler-Euler models are similar, which proves that the closure models for bubble forces and turbulence are correctly applied in the particle-center-averaged Euler-Euler model. For fixed polydisperse simulations, the particle-center-averaging method can also alleviate the over-prediction of the gas volume fraction peak in the center or near the wall of a pipe. The axial gas velocities simulated with both Euler-Euler models are about the same. Comparisons are also made for the simulation results of bubbly flows in a cylindrical bubble column and the experimental data. The gas volume fractions and the axial gas velocities simulated with both Euler-Euler models almost coincide with each other, which indicates that the sink and source terms for the continuity equations and the degassing boundary are set correctly in the particle-center-averaged Euler-Euler model. An oblate ellipsoidal bubble shape is considered in the particle-center-averaged Euler-Euler simulations by an anisotropic diffusion. The influence of bubble shape on the simulation results of bubbly pipe flows is investigated. The results show that considering the oblate ellipsoidal bubble shape in simulations can further alleviate the over-predictions of the gas volume fraction peaks for wall peaking cases, but it has little influence on the gas volume fractions of center-peaking cases and the axial gas velocities.

In this thesis, we study the following three types of periodic string patterns and some of their variants. Firstly, we consider maximal d-repetitions. These are substrings that are at least 2+d times as long as their minimum period. Secondly, we consider 3-cadences. These are arithmetic subsequence of three equal characters. Lastly, we consider maximal pairs. These are pairs of identical substrings. Maximal d-repetitions and maximal pairs of uncompressed strings are already well-researched. However, no non-trivial upper bound for distinct occurrences of these patterns that take the compressed size of the underlying strings into account were known prior to this research. We provide upper bounds for several variants of these two patterns that depend on the compressed size of the string, the logarithm of the string's length, the highest allowed power and d. These results also lead to upper bounds and new insights for the compacted directed acyclic word graph and the run-length encoded Burrows-Wheeler transform. We prove that cadences with three elements can be efficiently counted in uncompressed strings and can even be efficiently detected on grammar-compressed binary strings. We also show that even slightly more difficult variants of this problem are already NP-hard on compressed strings. Along the way, we extend the underlying geometry of the convolution from rectangles to arbitrary polygons. We also prove that this non-rectangular convolution can still be efficiently computed.:1 Introduction 2 Preliminaries 3 Non-Rectangular Convolution 4 Alphabet Reduction 39 5 Maximal (Sub-)Repetitions 6 Cadences 7 Maximal Pairs A Propositions

High-efficiency point cloud 3D object detection is important for autonomous driving. 3D object detection based on point cloud data is naturally more complex and difficult than the 2D task based on images. Researchers keep working on improving 3D object detection performance in autonomous driving scenarios recently. In this report, we present our optimized point cloud 3D object detection model based on CenterPoint method. CenterPoint detects centers of objects using a keypoint detector on top of a voxel-based backbone, then regresses to other attributes. On the basis of this, our modified model is featured with an improved Region Proposal Network (RPN) with extended receptive field, an added sub-head that produces an IoU-aware confidence score, as well as box ensemble inference strategies with more accurate predictions. These model enhancements, together with class-balanced data pre-processing, lead to a competitive accuracy of 72.02 mAP on ONCE Validation Split, and 79.09 mAP on ONCE Test Split. Our model gains the fifth place of ICCV 2021 Workshop SSLAD Track 3D Object Detection Challenge. / Högeffektiv punktmoln 3D-objektdetektering är viktig för autonom körning. 3D-objektdetektering baserad på punktmolnsdata är naturligtvis mer komplex och svårare än 2D-uppgiften baserad på bilder. Forskare fortsätter att arbeta med att förbättra 3D-objektdetekteringsprestandan i scenarier för autonom körning nyligen. I den här rapporten presenterar vi vår optimerade 3D-objektdetekteringsmodell baserad på CenterPoint. CenterPoint upptäcker objektcentrum med hjälp av en nyckelpunktsdetektor ovanpå en voxelbaserad ryggrad och går sedan tillbaka till andra attribut. På grundval av detta presenteras vår modifierade modell med ett förbättrat regionförslagsnätverk med utökat receptivt fält, en extra underrubrik som producerar en IoU-medveten konfidenspoäng och ensemblestrategier med mer exakta förutsägelser. Dessa modellförbättringar, tillsammans med klassbalanserad dataförbehandling, leder till en konkurrenskraftig noggrannhet på 72,02 mAP på ONCE Validation Split och 79,09 mAP på ONCE Test Split. Vår modell vinner femteplatsen i ICCV 2021 Workshop SSLAD Track 3D Object Detection Challenge.

[pt] Teste de injetividade é uma técnica convencional em engenharia de reservatórios, utilizada para a recuperação de óleo em reservatórios e avaliação de formações. Geralmente utiliza-se água como fluido injetado, que resulta em um deslocamento do óleo presente devido ao aumento da pressão nos poros. Durante o teste, a resposta de pressão medida fornece diversas informações sobre os parâmetros do reservatório, tal como dados de permeabilidade. Desta forma, pesquisadores têm se dedicado em encontrar equações matemáticas que modelam a resposta de pressão desses testes com objetivo de gerenciamento e manutenção preditiva do reservatório. Neste trabalho, apresentamos uma nova solução analítica para a análise de testes de injetividade, que combina a técnica de convolução pressão-pressão com um modelo radial composto de duas zonas. Essa solução permite avaliar o teste de injetividade mesmo na ausência de dados precisos de vazão, uma vez que a convolução pressão-pressão utiliza exclusivamente os dados de pressão adquiridos em diferentes posições do reservatório. O modelo considerado consiste em dois poços, um injetor, localizado na zona interna do reservatório, e um observador, na zona externa. A validação da solução proposta foi realizada por meio da comparação dos resultados analíticos com aqueles obtidos em um simulador comercial baseado em diferenças finitas. / [en] The injectivity test is a conventional technique in reservoir engineering used for oil recovery and formation evaluation. Typically, water is injected to displace the existing oil by increasing the pressure in the pores. In this test, the pressure response measurement provides valuable information about the reservoir parameters, including permeability data. Therefore, researchers aim to develop mathematical equations that could accurately model pressure response during these tests for reservoir management and maintenance prediction purposes. This work introduces a new analytical solution for injectivity test analysis. The solution combines the pressure-pressure convolution technique with a two-zone radial model. It allows the evaluation of the injectivity test without precise flow rate data, as the pressure-pressure convolution exclusively uses the pressure data acquired at different positions in the reservoir. The reservoir model comprises an injector well in the inner zone of the reservoir and an observation well in the outer zone for measuring pressure response. The proposed solution was validated by comparing the analytical results with those obtained from a finite differences-based commercial simulator.

Histogram data are widely used for compressing high-frequency time-series signals due to their ability to capture distributional informa-tion. However, this compression comes at the cost of increased di-mensionality and loss of contextual details from the original features.This study addresses the challenge of effectively capturing changesin distributions over time and their contribution to failure prediction.Specifically, we focus on the task of predicting Time to Event (TTE) forturbocharger failures.In this thesis, we propose a novel approach to improve failure pre-diction by fine-tuning lower-dimensional representations of bi-variatehistograms. The goal is to optimize these representations in a waythat enhances their ability to predict component failure. Moreover, wecompare the performance of our learned representations with hand-crafted histogram features to assess the efficacy of both approaches.We evaluate the different representations using the Weibull Time ToEvent - Recurrent Neural Network (WTTE-RNN) framework, which isa popular choice for TTE prediction tasks. By conducting extensive ex-periments, we demonstrate that the fine-tuning approach yields supe-rior results compared to general lower-dimensional learned features.Notably, our approach achieves performance levels close to state-of-the-art results.This research contributes to the understanding of effective failureprediction from time series histogram data. The findings highlightthe significance of fine-tuning lower-dimensional representations forimproving predictive capabilities in real-world applications. The in-sights gained from this study can potentially impact various indus-tries, where failure prediction is crucial for proactive maintenanceand reliability enhancement.