Prediction of both big and small decisions is something most companies have to make on a daily basis. The importance of having a highly accurate technique for different decision-making is not something that is new. However, even though the importance of prediction is a fact to most people, current techniques for estimation are still often highly inaccurate. The consequences of an inaccurate prediction can be huge in the differences between the misclassifications. Not just in the industry but for many different areas. Machine learning have in the recent couple of years improved significantly and are now considered a reliable method to use for prediction. The main goal of this research is to predict casting defects with the help of a machine-learning algorithm based on process parameters. In order to achieve the main goal, some sub-objectives have been identified to successfully reach those goals. A problem when dealing with machine learning is an unbalanced dataset. When training a network, it is essential that the dataset is balanced. In this research we have successfully balanced the dataset. Undersampling was the method used in our research to establish our balanced dataset. The research compares and evaluates a couple of different undersample methods in order to see which undersampling is best suited for this project. Three different machine models, “random forest”, “artificial neural network”, and “k-nearest neighbor”, are also compared to each other to see what model performs best. The conlcusion reached was that the best method for both undersampling and machine learning model varied due to many different reasons. So, in order to find the best model with the best method for a specific job, all the models and methods need to be tested. However, the undersampling method that provided best performances most times in our research was the NearMiss version 2 model. Artificial Neural Network was the machine learning model that had most success in our research. It performed best in two out of three evaluations and comparisons.

This Master’s thesis deals with digital decimation filter design for undersampling and filtering of sigma-delta ADC signal. Filter cascade is designed in Matlab according to given requirements and is then described in VHDL language aiming for minimum area. Implemented filter functionality is compared to Matlab-generated reference filters in created verification environment. Finally the design is synthesized in specified technology and verified on gate level.

<p dir="ltr">Gestational Diabetes Mellitus (GDM) is defined as glucose intolerance with first onset during pregnancy in women without previous history of diabetes. The global prevalence of GDM oscillates between 2% and 17%, varying across countries and ethnicities. In the United States (U.S.), every year up to 13% of pregnancies are affected by this disease. Several risk factors for GDM are well established, such as race, age and BMI, while additional factors have been proposed that could affect the risk of developing the disease; some of them are modifiable, such as diet, while others are not, such as environmental factors.</p><p dir="ltr">Taking effective preventive actions against GDM require the early identification of women at highest risk. A crucial task to this end is the establishment of factors that increase the probabilities of developing the disease. These factors are both individual characteristics and choices and likely include environmental conditions.</p><p dir="ltr">The first part of the dissertation focuses on examining the relationship between food insecurity and GDM by using the National Health and Nutrition Examination Survey (NHANES), which has a representative sample of the U.S. population. The aim of this analysis is to determine a national estimate of the impact of food environment on the likelihood of developing GDM stratified by race and ethnicity. A survey weighted logistic regression model is used to assess these relationships which are described using odds ratios.</p><p dir="ltr">The goal of the second part of this research is to determine whether a woman’s risk of developing GDM is affected by her environment, also referred to in this work as level 2 variables. For that purpose, Medicaid claims information from Indiana was analyzed using a multilevel logistic regression model with sample balancing to improve the class imbalance ratio.</p><p dir="ltr">Finally, for the third part of this dissertation, a simulation study was performed to examine the impact of balancing on the prediction quality and inference of model parameters when using multilevel logistic regression models. Data structure and generating model for the data were informed by the findings from the second project using the Medicaid data. This is particularly relevant for medical data that contains measurements at the individual level combined with other data sources measured at the regional level and both prediction and model interpretation are of interest.</p>

The goals for most of today’s receiver system are sampling at high-speed, with high resolution and with as few errors as possible. This master thesis describes the design of a high-speed sampling system with"state-of-the-art"components available on the market. The system is designed with a parallel Analog-to-digital converter (ADC) architecture, also called time interleaving. It aims to increase the sampling speed of the system. The system described in this report uses four 12-bits ADCs in parallel. Each ADC can sample at 125 MHz and the total sampling speed will then theoretically become 500 Ms/s. The system has been implemented and manufactured on a printed circuit board (PCB). Up to four boards can be connected in parallel to get 2 Gs/s theoretically. In an approach to increase the systems performance even further, a timing error estimation algorithm will be used on the sampled data. This algorithm estimates the timing errors that occur when sampling with non-uniform time interval between samples. After the estimations, the sampling clocks can be adjusted to correct the errors. This thesis is concerning some ADC theory, system design and PCB implementation. It also describes how to test and measure the system’s performance. No measurement results are presented in this thesis because measurements will be done after this project. The last part of the thesis discusses future improvementsto achieve even higher performance.

<p>The goals for most of today’s receiver system are sampling at high-speed, with high resolution and with as few errors as possible. This master thesis describes the design of a high-speed sampling system with"state-of-the-art"components available on the market. The system is designed with a parallel Analog-to-digital converter (ADC) architecture, also called time interleaving. It aims to increase the sampling speed of the system. The system described in this report uses four 12-bits ADCs in parallel. Each ADC can sample at 125 MHz and the total sampling speed will then theoretically become 500 Ms/s. The system has been implemented and manufactured on a printed circuit board (PCB). Up to four boards can be connected in parallel to get 2 Gs/s theoretically. </p><p>In an approach to increase the systems performance even further, a timing error estimation algorithm will be used on the sampled data. This algorithm estimates the timing errors that occur when sampling with non-uniform time interval between samples. After the estimations, the sampling clocks can be adjusted to correct the errors. </p><p>This thesis is concerning some ADC theory, system design and PCB implementation. It also describes how to test and measure the system’s performance. No measurement results are presented in this thesis because measurements will be done after this project. The last part of the thesis discusses future improvementsto achieve even higher performance.</p>

Cost reduction has been and will continue to be a primary driving force in the evolution of hardware design and associated technologies. The objective of this research is to design low-cost signal acquisition systems for characterizing wideband and high-speed signals. As the bandwidth and the speed of such signals increase, the cost of testing also increases significantly; therefore, innovative hardware and algorithm co-design are needed to relieve this problem. In Chapter 2, a low-cost multi-rate system is proposed for characterizing the spectra of wideband signals. The design is low-cost in the sense of the actual component cost, the system complexity, and the effort required for calibration. The associated algorithms are designed such that the hardware can be implemented with low-complexity yet be robust enough to deal with various hardware variations. A hardware prototype is built not only to verify the proposed hardware scheme and algorithms but to serve as a concrete example that shows that characterizing signals with sub-Nyqusit sampling rate is feasible. Chapter 3 introduces a low-cost time-domain waveform reconstruction technique, which requires no mutual synchronization mechanisms. This brings down cost significantly and enables the implementation of systems capable of capturing tens of Gigahertz (GHz) signals for significantly lower cost than high-end oscilloscopes found in the market today. For the first time, band-interleaving and incoherent undersampling techniques are combined to form a low-cost solution for waveform reconstruction. This is enabled by co-designing the hardware and the back-end signal processing algorithms to compensate for the lack of coherent Nyquist rate sampling hardware. A hardware prototype was built to support this work. Chapter 4 describes a novel test methodology that significantly reduces the required time for crosstalk jitter characterization in parallel channels. This is done by using bit patterns with coprime periods as channel stimuli and using signal processing algorithms to separate multiple crosstalk coupling effects. This proposed test methodology can be applied seamlessly in conjunction with the current test methodology without re-designing the test setup. More importantly, the conclusion derived from the mathematical analysis shows that only such test stimuli give unbiased characterization results, which are critical in all high-precision test setups. Hardware measurement results and analysis are provided to support this methodology. This thesis starts with an overview of the background and a literature review. Three major previously mentioned works are addressed in three separate chapters. Each chapter documents the hardware designs, signal processing algorithms, and associated mathematical analyses. For the purpose of verification, the hardware measurement setups and results are discussed at the end of these three chapters. The last chapter presents conclusions and future directions for work from this thesis.

Magnetic resonance imaging is a diagnostic method to form images of the organs in the body. Long acquisition times are the main disadvantage, however it is possible to accelerate the data acquisition with the method of compressed sensing by sensing fewer samples and formulating an optimization method for image reconstruction. The aim of this thesis is to describe and compare the common optimization methods and to create a software capable of solving them. Another objective is to observe how much the data acquisition can be accelarated without the loss of image quality when dealing with real data. The most promising method in the experiment was total generalized variation (TGV) regularization which was able to reconstruct an image with a proper quality using only a quarter of the data.

Electrical bioimpedance spectroscopy (EBIS) has been used to assess the status or composition of various types of tissue, and examples of EBIS include body composition analysis (BCA) and tissue characterisation for skin cancer detection. EBIS is a non-invasive method that has the potential to provide a large amount of information for diagnosis or monitoring purposes, such as the monitoring of pulmonary oedema, i.e., fluid accumulation in the lungs. However, in many cases, systems based on EBIS have not become generally accepted in clinical practice. Possible reasons behind the low acceptance of EBIS could involve inaccurate models; artefacts, such as those from movements; measurement errors; and estimation errors. Previous thoracic EBIS measurements aimed at pulmonary oedema have shown some uncertainties in their results, making it difficult to produce trustworthy monitoring methods. The current research hypothesis was that these uncertainties mostly originate from estimation errors. In particular, time-varying behaviours of the thorax, e.g., respiratory and cardiac activity, can cause estimation errors, which make it tricky to detect the slowly varying behaviour of this system, i.e., pulmonary oedema. The aim of this thesis is to investigate potential sources of estimation error in transthoracic impedance spectroscopy (TIS) for pulmonary oedema detection and to propose methods to prevent or compensate for these errors.   This work is mainly focused on two aspects of impedance spectrum estimation: first, the problems associated with the delay between estimations of spectrum samples in the frequency-sweep technique and second, the influence of undersampling (a result of impedance estimation times) when estimating an EBIS spectrum. The delay between frequency sweeps can produce huge errors when analysing EBIS spectra, but its effect decreases with averaging or low-pass filtering, which is a common and simple method for monitoring the time-invariant behaviour of a system. The results show the importance of the undersampling effect as the main estimation error that can cause uncertainty in TIS measurements.  The best time for dealing with this error is during the design process, when the system can be designed to avoid this error or with the possibility to compensate for the error during analysis. A case study of monitoring pulmonary oedema is used to assess the effect of these two estimation errors. However, the results can be generalised to any case for identifying the slowly varying behaviour of physiological systems that also display higher frequency variations.  Finally, some suggestions for designing an EBIS measurement system and analysis methods to avoid or compensate for these estimation errors are discussed. / <p>QC 20140604</p>

The common objective of this project is implementation of software defined radio (SDR) into FPGA. The text contains review and comparison of several hardware concepts intended for SDRs implementation then the methods for digital implementation of various components of radios as the filters, mixers and others are mentioned. Part of the text introduces used hardware platform and describes software support for designing, simulations and implementation into hardware. Significant part of project describes complex of external hardware components as filter, amplifier and control panel designed and built within the project realization. But the main part of project demonstrates design of the software solution of radio receiver. There is specified architecture of radio for FM broadcast receiving, next the more complex systems with carrier recovery algorithm are presented. These systems are able to work with AM, BPSK and QPSK modulations. It is possible to implement all these receivers into hardware and verify their operation. The practical laboratory theme has been outlined within the project run.

This thesis deals with a theoretical analysis of the basic parameters of receivers, input circuit architecture and signal digitization. According to the specified assignment it is outlined block scheme of front end for software receiver with specified components and the total bilance is calculated. Individual parts of the system are designed and realized. This is a set of four input filters for bandwidths: short waves up to 30 MHz, 87,5-108 MHz, 144-148 MHz and 174-230 MHz. The main point of design is a circuit containing a low-noise amplifiers, switches, and two amplifiers with adjustable amplification. Mainly are used integrated circuits from Analog Devices corporation. To control the various switches and adjustable amplifiers was designed a separate panel, which is connected to the main circuit via a cable. In the last phase was the whole system and its components subjected to measurements. Thanks to a number of mounted SMA connectors it is possible to measure different parts of the system and we are able to modify it partially.