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This study examines the relationship between pupils' choice of orientation on high school and their socio-economic backgrounds. It also examines the student’s intentions of further studies and their socio-economic backgrounds. The factors of the social background, or social status, in this examination is the parents educational level and their occupation. The result is based on a survey answered by 81 students from four different study orientations on high school, two with a theoretical orientation and two with a practical orientation. The answers from the survey was compiled and analysed and the conclusion of the study is that students with parents that have a higher socioeconomic status are overrepresented in the theoretical orientations, whom also is more interested in higher education, while students that studies on a practical orientation have parents with a lower socioeconomic status. This study as well as others shows that students are affected by their socioeconomic background, both in terms of study results and in aspirations to further studies. The earlier research also shows that one important factor of student’s successes in school is based on the families reading habits. The knowledge based on studies like this  can be used by school staff and leaders to better understand, help and guide students with low socioeconomic status to better results and higher degrees of education.

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This thesis presents the measurement of the cosmic microwave background (CMB) power spectrum for the Atacama Cosmology Telescope (ACT) experiment and its polarized upgrade, ACTPol. I present the tools that I have developed for constructing unbiased and nearly optimal statistical estimators. I discuss how to separate the cosmological and the astrophysical signal and how to characterize instrumental systematics. The goal of this work is to obtain accurate power spectra measurement that can be used for cosmological parameter estimation. I first present the analysis of the complete ACT data set. The high resolution of the telescope allows us to recover power spectra to ℓ = 10000. I report the measurement of the power spectra at 148 GHz and 218 GHz, as well as the cross-frequency spectrum between the two channels. The power spectrum measurement is consistent with the ΛCDM model and a basic foreground model. I then present the cross correlation of maps from the Atacama Cosmology Telescope with maps from the Planck satellite in two overlapping regions covering 592 square degrees. I find excel- lent agreement between the two datasets at both frequencies, quantified using the variance of the residuals between the ACT power spectra and the ACT×Planck cross-spectra. The next generation of CMB experiments are focused on measuring its polarization. I present efficient algorithms for CMB lensing simulation and power spectrum estimation for flat-sky CMB polarization maps. Finally, I discuss the first temperature and polarization power spectra measurement from the ACTPol experiment. They are the first attempt to measure the polarization of the CMB at high resolution.

Cheung Kai Chung Mars = 宇宙學中的題目 / 張啓聰. / Thesis submitted in: September 2005. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 93-95). / Text in English; abstracts in English and Chinese. / Cheung Kai Chung Mars = Yu zhou xue zhong de ti mu / Zhang Qicong. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- "Hubble's Law, the cosmic scale factor and redshift" --- p.1 / Chapter 1.2 --- The Big Bang Model and Cosmic Microwave Background --- p.3 / Chapter 1.3 --- An overview of the universe --- p.4 / Chapter 1.4 --- Current Observation Results and Motivation --- p.6 / Chapter 1.5 --- Review of CMB calculation --- p.7 / Chapter 1.5.1 --- Friedmann Cosmologies --- p.8 / Chapter 1.5.2 --- The Perturbed Robertson-Walker Metric --- p.10 / Chapter 1.5.3 --- Boltzmann Equations --- p.13 / Chapter 1.5.4 --- Perturbative Einstein Equations --- p.16 / Chapter 2 --- Ionization History of The Universe --- p.18 / Chapter 2.1 --- Saha equation --- p.18 / Chapter 2.2 --- Peebles recombination --- p.19 / Chapter 2.3 --- RECFAST --- p.20 / Chapter 3 --- CMB Anisotropics --- p.22 / Chapter 3.1 --- The CMBA spectra --- p.23 / Chapter 3.1.1 --- Tight Coupling Limit --- p.23 / Chapter 3.1.2 --- Free Streaming --- p.25 / Chapter 3.1.3 --- The Anisotropy Spectrum --- p.27 / Chapter 3.1.4 --- CMBFAST --- p.28 / Chapter 4 --- Variation of Fundamental Constant and CMBA spectra --- p.30 / Chapter 4.1 --- The problem of units --- p.31 / Chapter 4.2 --- Modification of CMBFAST and conversion of units --- p.32 / Chapter 4.3 --- The constraints of varying constants using CMBA spectra --- p.35 / Chapter 4.4 --- Physics involved and variation of the spectra --- p.36 / Chapter 4.4.1 --- Effect of Recombination --- p.36 / Chapter 4.4.2 --- Variation of hP --- p.38 / Chapter 4.4.3 --- Variations of e and me --- p.41 / Chapter 4.4.4 --- Variations of g and c --- p.48 / Chapter 4.4.5 --- Constraints on the constants --- p.57 / Chapter 5 --- MCMC and CMBA Spectra --- p.59 / Chapter 5.1 --- Method --- p.60 / Chapter 5.1.1 --- Algorithm --- p.60 / Chapter 5.1.2 --- Check of Convergency --- p.61 / Chapter 5.2 --- Likelihood function --- p.62 / Chapter 5.3 --- Results --- p.66 / Chapter 5.3.1 --- MCMC investigation of varying α in the temperature spectrum --- p.67 / Chapter 5.3.2 --- MCMC investigation of varying α in the polarization spectrum --- p.69 / Chapter 5.3.3 --- MCMC investigation of varying α and cosmological parameters --- p.71 / Chapter 5.3.4 --- Summary --- p.73 / Chapter 6 --- Extra Dimensions and Cosmology --- p.74 / Chapter 6.1 --- A review of KK cosmology --- p.74 / Chapter 6.2 --- Non-flat extra dimension universe --- p.80 / Chapter 6.2.1 --- Close Extra Dimensions and Flat Usual Dimensions --- p.83 / Chapter 6.2.2 --- Open Extra Dimensions and Flat Usual Dimensions --- p.87 / Chapter 6.2.3 --- Summary: Possibility of Extra Dimension(s) --- p.91 / Bibliography --- p.93

Este trabalho tem por objetivo propor um sistema computacional automático capaz de identificar, a partir de imagens de vídeos, o comportamento do tráfego veicular rodoviário. Todos os códigos gerados foram escritos em Python, com o uso da biblioteca OpenCV. O primeiro passo do sistema proposto foi remover o background do frame do vídeo. Para isso, foram testados três métodos disponíveis no OpenCV, com métricas baseadas em uma Matriz de Contingência. O MOG2 foi escolhido como melhor método, processando 64 FPS, com mais de 95% de taxa de exatidão. O segundo passo do sistema envolveu detectar, rastrear e agrupar features dos veículos em movimento. Para isso, foi usado o algoritmo de Shi-Tomasi, junto com funções de fluxo ótico para o rastreamento. No agrupamento, usou-se a distância entre os pixels e as velocidades relativas de cada feature. No passo final, foram extraídos tanto as informações microscópicas quanto as informações macroscópicas em arquivos de relatório. Os arquivos têm padrões definidos, salvos em CSV. Também foi gerado, em tempo de execução, um diagrama espaço-tempo. Desse diagrama, é possível extrair informações importantes para as operações de sistemas de transportes. A contagem e a velocidade dos veículos foram usadas para validar as informações extraídas, comparadas a métodos tradicionais de coletas. Na contagem, o erro médio em todos os vídeos foi de 12,8%. Na velocidade, o erro ficou em torno de 9,9%. / The objective of this research is to propose an automatic computational system capable to identify, based on video images, traffic behavior on highways. All written code was made using Python, with the OpenCV library. The first step of the proposed system is to subtract the background from the frame. We tested three different background subtraction methods, using a contingency table to extract performance metrics. We decided that MOG2 was the best method for this research, processing frames at 64 FPS and scoring more than 95% on accuracy rate. The second step of the system was to detect, track and group all moving vehicle features. We used Shi-Tomasi detection method with optical flow to track features. We grouped features with a mixture of distance between pixels and relative velocity. For the last step, the algorithm exported microscopic and macroscopic information on CSV files. The system also produced a space-time diagram at runtime, in which it was possible to extract important information to transportation system operators. To validate the information extracted, we compared vehicle counting and velocities with traditional extraction methods. The algorithm had a mean error rate of 12.8% on counting vehicles, while achieving 9.9% error rate in velocity.

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