An  important  task   during  the  construction  process  is  to  validate  the dimensions  and  properties  of  a  given  structure.  The  dimensions  like  for instance the thickness of a construction element should  be measured after finishing  building  it.  The  aim  is  to  compare  the  measured  value  with  the design  value   to  avoid   that   elements  do   not  correspond  to  the  input requirements.  Moreover,  the  measurements  are  helpful  to  analyse  the statistical    distribution    of    the    investigated    geometrical    property    by computing e.g. a histogram, which visualises the dispersion and  enable the calculation of the probability of failure for a specific structure or element.In  this  work,  a  shotcrete  layer  has  been  analysed  in  order  to  provide information  about  the  homogeneity  of  the  shotcrete  thickness  in  a  pre-determined  tunnel  section.  The  calculation  method  is  based  on  two  laser scans,  before  and  after  applying  the  shotcrete.  Due  to  the  construction process, the shotcrete layer will not be totally equal, which might be a safety problem. Especially, when  the shotcrete layer is thinner than  required and hence, the actual variation of the shotcrete must be considered and verified.To determine the statistical distribution, correlograms and histograms have been  computed  for  a  wall  area  in  a  tunnel  in  Southern  Sweden.  The correlogram shows the distance where the values have a correlation to each other  and  usually  this  distance  is  called  scale  of  fluctuation.  For  the  wall section, this scale of fluctuation has been calculated for the length (0.8m) as well  as  the  height  (0.8m).  Compared  to  the  original  sample  distance,  e.g. distance of the rock bolts, the variance for the calculation of the probability of failure might be reduced.

International Telemetering Conference Proceedings / October 17-20, 1988 / Riviera Hotel, Las Vegas, Nevada / This paper discusses one approach to determine a characteristic of West - Java's air and ground segment as a block-box to accomodate radio waves propagation, especially in L-band ranges, by evaluating both the topographical data and radio reception pattern as measured from ground based telemetry receiving-end system. All the measured signals are random and assumed to be stationair and ergodic. In order to characterize the channel for polarization diversity reception, some statistical analysis are applied to the signal strength measured of both - RHCP and LHCP components of 1531 MHz propagated waves as transmitted fr om NC212-200 PK-NZJ-aircraft. Some computer calculated correlograms of measured data are shown herewith, it is focused for a certain radio corridor at radial 265E relative to the ground based receiving antenna. More over some curves of predicted multipath gain factor are also presented to gain more theoretical back ground. When this paper is written, a further field experiments on the matter concerned is beeing conducted.

Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-08-16T18:29:06Z No. of bitstreams: 1 JoberthSilva.pdf: 6383640 bytes, checksum: f18918eb45c49cb426b560e4daddf994 (MD5) / Made available in DSpace on 2017-08-16T18:29:06Z (GMT). No. of bitstreams: 1 JoberthSilva.pdf: 6383640 bytes, checksum: f18918eb45c49cb426b560e4daddf994 (MD5) Previous issue date: 2013-02-20 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Breast cancer is worldwide the most common form of cancer affecting woman, sometimes in their lives, at the proportion of either one to nine or one to thirteen women who reach the age of ninety in the west world (LAURENCE, 2006). Breast cancer is caused by frequent reproduction of cells in various parts of the human body. At certain times, and for reasons yet unknown, some cells begin to reproduce at a higher speed, causing the onset of cellular masses called neoplasias, or tumors, which are new tissue formation, but from pathological origin. This work has proposed a method of automatic detection of masses in digital mammograms, using the Quality Threshold (QT), and the Supporting Vector Machine (MVS). The images processing steps were as follows: firstly, the pre-processing phase took place which consisted of removing the background image, smoothing it with a low pass filter, to increase the degree of contrast, and then, in sequence, accomplishing an enhancement of the Wavelet Transform (WT) by changing their coefficients with a linear function. After the pre-processing phase, came the segmentation with the use of the QT which divided the image in to clusters with pre-defined diameters. Then, the post-processing occurred with the selection of the best candidates to mass formed by the MVS analysis of the shape descriptors. For the extraction phase of texture features the Haralick descriptors and the function correlogram were used. As for the classification stage, the MVS was used again for training, validation of the MVS model and final test. The achieved results were: sensitivity of 92. 31%, specificity of 82.2%, accuracy of 83,53%, a false positive rate per image of 1.12 and an area under a FROC curve of 0.8033. / O câncer de mama é, mundialmente, a forma mais comum de câncer em mulheres afetando, em algum momento suas vidas, aproximadamente uma em cada nove a uma em cada treze mulheres que atingem os noventa anos no mundo ocidental (LAURANCE, 2006). O câncer de mama é ocasionado pela reprodução frequente de células de diversas partes do corpo humano. Em certos momentos e por motivos ainda desconhecidos algumas células começam a se reproduzir com uma velocidade maior, ocasionando o surgimento de massas celulares denominadas de neoplasias ou tumores que são tecidos de formação nova, mas de origem patológica. Neste trabalho foi proposto um método de detecção automática de massas em mamografias digitais usando o Quality Threshold (QT), e a Máquina de Vetores de Suporte (MVS). As etapas de processamento das imagens foram as seguintes: primeiramente veio a fase de pré-processamento que consiste em retirar o fundo da imagem, suavizá-la com um filtro passa-baixa, aumentar a escala de contraste, e na sequencia realizar um realce com a Transformada de Wavelet (WT) através da alteração dos seus coeficientes com uma função linear. Após a fase de pré-processamento vem a seguimentação utilizando o QT que segmenta a imagem em clusters com diâmetros pré-definidos. Em seguida, vem o pós-processamento com a seleção dos melhores candidatos à massa feita através da análise dos descritores de forma pela MVS. Para fase de extração de características de textura foram utiliza os descritores de Haralick e a função correlograma. Já na fase de classificação a MVS novamente foi utilizada para o treinamento, validação do modelo MVS e teste final. Os resultados alcançados foram: sensibilidade de 92,31%, especificidade de 82,2%, Acurácia de 83,53%, uma taxa de falsos positivos por imagem de 1,12 e uma área sob a curva FROC de 0,8033.

This thesis consists of five articles and a summary. Thethesis deals with methods for measuring properties of thevoice. The methods are all computer-based, but utilisedifferent approaches for measuring different aspects of thevoice. Paper I introduces the Visual Sort and Rate (VSR) method forperceptual rating of voice quality. The method is based on theVisual Analogue Scale (VAS), but simultaneously shows allstimuli as icons along the VAS on the computer screen. As thelistener places similar-sounding stimuli close to each otherduring the rating process, comparing stimuli becomeseasier. Paper II introduces the correlogram. Fundamental frequencyF0 sometimes cannot be strictly defined, particularly forperturbed voice signals. The method displays multipleconsecutive correlation functions in a grey scale image. Thus,the correlogram avoids selecting a single F0 value. Rather itpresents an unbiased image of periodicity, allowing theinvestigator to select among several candidates, ifappropriate. PaperIII introduces a method for detection of phonation tobe utilised in voice accumulators. The method uses twomicrophones attached near the subject’s ears. Phase andamplitude relations of the microphone signals are used to forma phonation detector. The output of the method can be used tomeasure phonation time, speaking time and fundamental frequencyof the subject, as well as sound pressure level of both thesubject’s voicing and the ambient sounds. Paper IV introduces a method for Fourier analysis ofhigh-speed laryngoscopic imaging. The data from the consecutiveimages are re-arranged to form time-series that reflect thetime-variation of light intensity in each pixel. Each of thesetime series is then analysed by means of Fouriertransformation, such that a spectrum for each pixel isobtained. Several ways of displaying these spectra aredemonstrated. Paper V examines a test set-up for simultaneous recording ofairflow, intra-oral pressure, electro-glottography, audio andhigh-speed imaging. Data are analysed with particular focus onsynchronisation between glottal area and inverse filteredairflow. Several methodological aspects are also examined, suchas the difficulties in synchronising high-speed imaging datawith the other signals. / QC 20100609

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