Objetivou-se com este trabalho avaliar a utilização da metodologia no infravermelho com Transformada de Fourier (ITF) na determinação do pH e ponto de congelamento (PC) no leite bovino. No primeiro estudo foram avaliados os efeitos das diferentes condições de conservação em amostras de leite para posterior análise do pH e do PC. Para tanto, foram coletadas, do tanque de 57 propriedades, amostras de três litros de leite. Cada amostra foi subdividida e transferida para 45 frascos de 40mL e distribuídas de acordo com a temperatura de armazenamento (-30C° - Congelado; 7°C - Resfriado; 25°C - Ambiente), idade da amostra (0, 3, 6 e 9dias) e níveis de adição de água (0, 2, 4 e 6%). Foram adicionadas pastilhas de bronopol (Microtabs®) aos 44 frascos, sendo que um foi mantido sem adição do conservante para tratamento controle. Os resultados obtidos, considerando as diferentes condições de conservação das amostras, e ainda, o efeito da adição do bronopol, foram avaliados por comparação de médias. As metodologias (referência e alternativa) foram correlacionadas em função da idade da amostra para o PC por análise de regressão linear. A sensibilidade e a especificidade foram calculadas para avaliação do desempenho do equipamento MilkoScanTM FT+ na detecção de água. A adição do bronopol não alterou as médias do pH, porém reduziu significativamente o PC. Para eliminação desse efeito, foram calculados fatores de correção para os resultados do PC nas duas metodologias empregadas nesse estudo. O aumento da temperatura de armazenamento reduziu significativamente as médias do pH e do PC em amostras com seis e nove dias de coleta. Não houve efeito significativo nas médias do pH e do PC com o aumento na idade da amostra a -30° e 7°C. As correlações entre as metodologias em função da idade da amostra para o PC foram altas e significativas. A sensibilidade e especificidade do equipamento MilkoScanTM FT+ na detecção de água, em relação ao crioscópio eletrônico, foram de 90,9% e de 86,8%, respectivamente. No segundo estudo, objetivou-se caracterizar a atual situação do ponto de congelamento (PC) em rebanhos brasileiros, e avaliar o efeito da lactose e da contagem bacteriana total (CBT) sobre os resultados do PC. Foram analisados 137.443 dados por meio de estatística descritiva e de análise da variância, para caracterizar a atual situação do PC e para avaliação dos efeitos da lactose e da CBT sobre o PC. A média e o respectivo desviopadrão do PC foi de -0,522 (0,011)°C, e, constatou-se que o percentual de amostras em conformidade com a Instrução Normativa nº 51 foi de 92,48%, enquanto que para amostras em não conformidade, com indicativo de adição de água ou de soluto, foi de 7,10% e 0,41% respectivamente. A lactose e a CBT influenciaram significativamente nos níveis do PC. / The objective of this study was to evaluate the use of the methodology Fourier Transform Infrared (FTIR) for determining the pH and freezing point (FP) in bovine milk. The first study evaluated the effects of different storage conditions on samples of milk for subsequent analysis of pH and the PC. For both, were collected from the tank of 57 properties, samples of three liters of milk. Each sample was divided and transferred to 45 vials of 40mL and distributed according to storage temperature (-30C°- Frozen, 7°C - Cold, 25°C - Environment), age of the sample (0, 3, 6 and 9 days) and levels of added water (0, 2, 4 and 6%). Tablets to bronopol (Microtabs®) were added to 44 bottles, of which one was maintained without addition of preservative for control treatment. The results, considering the different conditions of storage of samples, and the effect of adding bronopol were evaluated by comparison of means. The methods (reference and alternative) were correlated with age of the sample to the FP for linear regression analysis. The sensitivity and specificity were calculated to evaluate the performance of MilkoScanTM FT+ equipment for detecting water. The addition of bronopol did not alter the average pH, but significantly reduced the FP. To combat this effect, were calculated correction factors for the results of the PC in the two methodologies employed in this study. Increasing the temperature of storage significantly reduced the average pH and the FP samples with six and nine days of collection. There was no significant effect on mean pH and the PC with the increasing age of the sample at -30°C and 7°C. The correlations between the methods depending on the age of the sample for the FP were high and significant. The sensitivity and specificity of the equipment MilkoScanTM FT+ in the detection of water on the thermistor cryoscope were 90.9% and 86.8% respectively. While in the second study aimed to characterize the current state of the freezing point (FP) in Brazilian herds and Evaluated the effect of lactose and the total bacterial count (TBC) on the results of the FP. 137,443 data were analyzed using descriptive statistics and analysis of variance, to characterize the current state of the FP and to evaluated the effects of lactose and TBC on the FP. The mean and standard deviation of the FP was -0.522 (0.011)°C, and it was found that the percentage of samples in accordance with Normative Instruction nº 51 was 92.48%, while for samples not in accordance with an indication of added water and solute was 7.10% and 0.41% respectively. The lactose and TBC significantly influenced the levels of PC.

Cavitating flow through five perforated plates with different number of holes with preserved constant flow cross-section area in sum were experimentally examined. Dynamic characteristics such as dependence of pressure amplitudes and dominant frequencies on cavitation number in all regimes of cavitating flow: incipient cavitation, partial cavitation, fully developed cavitation and supercavitation are obtained. For determination of dominant frequencies several pressure transducers in two regimes of measurement were used. Results were validated with frequency spectra obtained from picture analysis based on high-speed camera records.

The diploma thesis deals with vibration measurement. The measurement is made by laserinterferometer ML10 GOLD in non – contact way. Different methods and two softwares are used for the measurement. The target of this diploma thesis is to describe these methods, to make the measurement, to analyze the measurement and to compare the results.

In large antenna systems, traditional channel estimation is costly and infeasible in some situations. Compressive sensing was proposed to estimate the channel with fewer measurements. Most of the previous work uses a predefined discrete Fourier transform matrix or overcomplete Fourier transform matrix to approximate the channel. Then, a learned dictionary trained by K-singular value decomposition (K-SVD) was proposed and was proved superiority using orthogonal matching pursuit (OMP) to reconstruct the sparse channel. However, with the development of compressive sensing, there are plenty of dictionary learning algorithms and sparse recovery algorithms. It is important to identify the effect and the performance of different algorithms when transforming the high dimensional channel vectors to low dimensional representations. In this thesis, we use a spatial channel model to generate channel vectors. Dictionaries are trained by K-SVD and method of optimal directions (MOD). Several sparse recovery algorithms are used to find the sparse approximation of the channel like OMP and gradient descent with sparsification (GraDeS). We present simulation results and discuss the performance of the various algorithms in terms of accuracy, sparsity, and complexity. We find that predefined dictionaries works with most of the algorithms in sparse recovery but learned dictionaries only work with pursuit algorithms, and only show superiority when the algorithm coincides with the algorithm in the sparse coding stage. / I stora antennsystem är traditionell kanaluppskattning kostsam och omöjlig i vissa situationer. Kompressionsavkänning föreslogs för att uppskatta kanalen med färre mätningar. Det mesta av det tidigare arbetet använder en fördefinierad diskret Fourier transformmatris eller överkompletterad Fourier -transformmatris för att approximera kanalen. Därefter föreslogs en inlärd ordbok som utbildats av K-SVD och bevisades överlägsen med hjälp av OMP för att rekonstruera den glesa kanalen. Men med utvecklingen av komprimerad avkänning finns det gott om algoritmer för inlärning av ordlistor och glesa återställningsalgoritmer. Det är viktigt att identifiera effekten och prestandan hos olika algoritmer när de högdimensionella kanalvektorerna omvandlas till lågdimensionella representationer. I denna avhandling använder vi en rumslig kanalmodell för att generera kanalvektorer. Ordböcker tränas av K-SVD och MOD. Flera glesa återställningsalgoritmer används för att hitta den glesa approximationen av kanalen som OMP och GraDeS. Vi presenterar simuleringsresultat och diskuterar prestanda för de olika algoritmerna när det gäller noggrannhet, sparsamhet och komplexitet. Vi finner att fördefinierade ordböcker fungerar med de flesta algoritmerna i gles återhämtning, men inlärda ordböcker fungerar bara med jaktalgoritmer och visar bara överlägsenhet när algoritmen sammanfaller med algoritmen i det glesa kodningsstadiet.

Spherical means are a widespread model in modern imaging modalities like photoacoustic tomography. We develop Fourier based algorithms for an efficient computation of mean values. Furthermore we consider iterative reconstruction schemes, where we employ different regularization techniques.

Synthetic X-ray or digitally reconstructed radiographs (DRRs) are simulated X-ray images projected from computed tomography (CT) data that are commonly used for CT and real X-Ray image registration. High-quality synthetic X-ray images can facilitate various applications such as guiding images for virtual reality (VR) simulation and training data for deep learning methods such as creating CT data from X-Ray images. It is challenging to generate high-quality synthetic X-ray images from CT slices, especially in various view angles, due to gaps between CT slices, high computational cost, and the complexity of algorithms. Most synthetic X-ray generation methods use fast ray-tracing in a situation where the image quality demand is low. We aim to improve image quality while maintaining good accuracy and use two steps; 1) to generate synthetic X-ray images from CT data and 2) to increase the resolution of the synthetic X-ray images. Our synthetic X-ray image generation method adopts a matrix-based projection method and dynamic multi-segment lookup tables, which shows better image quality and efficiency compared to conventional synthetic X-ray image generation methods. Our method is tested in a real-time VR training system for image-guided intervention procedures. Then we proposed two novel approaches to raise the quality of synthetic X-ray images through deep learning methods. We use a reference-based super-resolution (RefSR) method as a base model to upsampling low-resolution images into higher resolution. Even though RefSR can produce fine details by utilizing the reference image, it inevitably generates some artifacts and noise. We propose texture transformer super-resolution with frequency domain (TTSR-FD) which introduces frequency domain loss as a constraint to improve the quality of the RefSR results with fine details and without apparent artifacts. To the best of our knowledge, this is the first work that utilizes frequency domain as a part of loss functions in the field of super-resolution (SR). We observe improved performance in evaluating TTSR-FD when tested on our synthetic X-ray and real X-ray image datasets. A typical SR network is trained with paired high-resolution (HR) and low-resolution (LR) images, where LR images are created by downsampling HR images using a specific kernel. The same downsampling kernel is also used to create test LR images from HR images. As a result, most SR methods only perform well when the testing image is acquired using the same downsampling kernel used during the training process. We also propose TTSR-DMK, which uses multiple downsampling kernels during training to generalize the model and adopt a dual model that trains together with the main model. The dual model can form a closed-loop with the main model to learn the inverse mapping, which further improves the model’s performance. Our method works well for testing images produced by multiple kernels used during training. It can also help improve the model performance when testing images are acquired with kernels not used during training. To the best of our knowledge, we are the first to use the closed-loop method in RefSR. We have achieved: (i) synthetic X-ray image generation from CT data, which is based on a matrix-based projection and lookup tables ; (ii) TTSR-FD: synthetic X-ray image super-resolution using a novel frequency domain loss ; (iii) TTSR-DMK: an adaptation network to overcome the performance drop for testing data which do not match to downsampling kernels used in training. Our TTSR-FD results show improvements (PSNR from 37.953 to 39. 009) compared to the state-of-the-art methods TTSR. Our experiment with real X-Ray images using TTSR-FD can remove visible artifacts in the qualitative study even though PSNR is similar. Our proposed adaptation network, TTSR-DMK, improved model performance for multiple kernels even with unknown kernel situations.

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