Yes / Huntington’s disease (HD) is associated with impairments in dual-task performance. Despite that, only a few studies have investigated dual-tasking in HD. We examined dual-task performance in 15 participants in the early stages of HD and 15 healthy controls. Participants performed direct circle tracing (able to view arm) and indirect circle tracing (arm obscured) either on their own (single tasks) or paired with serial subtraction by twos or threes (dual tasks). Overall, our results suggested that HD participants were significantly slower and less accurate than controls. Both groups were slower and less accurate when performing indirect circle tracing compared with direct circle tracing. HD participants experienced greater dual-task interference in terms of accuracy when performing direct circle tracing compared with indirect circle tracing. Despite that, controls were more inclined to speed–accuracy trade-offs compared with HD participants. Importantly, unlike controls, HD participants were not disproportionately faster when performing direct circle tracing as a single task compared with the dual-task conditions. Our results suggest that simple tasks place greater attentional demands on HD participants compared with controls. These findings support that impaired automaticity may be responsible for some of the attentional deficits manifested in HD. / Supported by the School of Psychological Sciences, Monash University.

This case study used Zhongwei City in northwest China to quantify the urbanization and revegetation processes (1990-2011) through a unified sub-pixel measure of vegetation cover. Research strategies included: (1) Conduct sub-pixel vegetation mapping (1990, 1996, 2004, and 2011) with Random Forest (RF) algorithm by integrating high (OrbView-3) and medium spatial resolution (Landsat TM) data; (2) Examine simple Dark Object Subtraction (DOS) atmospheric correction method to support temporal generalization of sub-pixel mapping algorithm; (3) And characterize patterns of vegetation cover dynamics based on change detection analysis. We found the RF algorithm, combined with simple DOS, showed good generalization capability for sub-pixel vegetation mapping. Predicted sub-pixel vegetation proportions were consistent for "pseudo-invariant" pixels. Vegetation change analysis suggested persistent urban development within the city boundary, accompanied by a continuous expansion of revegetated area at the city fringe. Urban development occurred at both the suburban and urban core areas, and was mainly shaped by transportation networks. A transition in revegetation practices was documented: the large-scale governmental revegetation programs were replaced by the commercial afforestation conducted by industries. This study showed a slight increase in vegetation cover over the time period, balanced by losses to urban expansion, and a likely severe degradation of vegetation cover due to conversion of arable land to desert vegetation. The loss of arable land and the growth of artificial desert vegetation have yielded a dynamic equilibrium in terms of overall vegetation cover during 1990 to 2011, but in the long run vegetation quality is certainly reduced. / Master of Science

This thesis aims to give statistical methods applicating to video background subtraction. In the thesis, I will give out the problem introduction and analyze the problem with different statistical methods including histogram statistics, and Gaussian Mixture models methods. To study further, I will give out the time series analysis to make a more significant way: To build up the time series analysis way of video background subtraction with the Kalman filter and give out the predictions and evaluations.

published_or_final_version / Education / Master / Master of Education

La prothèse cochléaire est un appareillage destiné à la réhabilitation des surdités profondes et totales dont un appareillage conventionnel est inefficace. Elle assure la stimulation directe des neurones cochléaires à travers un faisceau d’électrodes. Différents travaux de recherches ont été établis afin d'évaluer l'intelligibilité de la parole chez les sujets bilatéralement implantés en environnements silencieux et bruité. Les résultats ont montré une bonne intelligibilité de la parole en milieu silencieux. Toutefois, les capacités de perception de la parole chez les patients implantés se dégradent en environnement bruité. Nous avons de ce fait proposé trois approches de traitement du signal en vue d'une amélioration de l'intelligibilité de la parole dans le cas de l'implant cochléaire bilatéral: la stimulation bilatérale décalée, l'algorithme de la soustraction spectrale bi-voie et l'algorithme de la soustraction interspectrale. Des améliorations de l'intelligibilité de la parole entre 4% et 10% ont été notées dans le cas de la stimulation bilatérale décalée par rapport à la stimulation bilatérale symétrique. L'approche basée sur l'algorithme de la soustraction spectrale bi-voie présentait des améliorations variables entre 10% et 17%. De meilleures performances ont été obtenues lorsque l'approche basée sur l'algorithme de la soustraction interspectrale est considérée où les améliorations étaient entre 15% et 27% / Cochlear prostheses are intended for persons suffering from deep or total deafness where conventional prostheses proved ineffective. In quiet listening conditions, most bilateral cochlear implant (BCI) users can now achieve even more than 80% word recognition scores regardless the used device. However, under more challenging listening conditions, BCI recipients perform poorly, compared to normal-hearing listeners. In this work, we proposed three speech processing approaches for speech intelligibility improvement. The first is based on shifted bilateral cochlear implant stimulation; the second is based on dual-channel spectral subtraction algorithm and finally the cross power spectral subtraction algorithm was considered. Experimental results showed a speech intelligibility improvement between 4% and 10% when the shifted bilateral cochlear implant stimulation is considered. Performance amelioration was observed when the dual-channel spectral subtraction based speech enhancement algorithm was considered and the improvement was between 10% and 17%. The better performance was obtained when noisy speech signals were processed using cross power spectral subtraction algorithm and the improvement was between 15% and 27%

The purpose of this study is to examine teachers, in the elementary grades 2 and 3, view on pupils ' understanding of regrouping in subtraction algorithms and the use of concrete materials to facilitate pupils' understanding of regrouping in subtraction algorithms. The study emanates from teachers who teach, or have taught, in regrouping in subtraction algorithms in the elementary grades of 2 and 3. With the use of a semi-structured interview, we were able to gain an understanding of teachers' opinions and experience about pupils' understanding of regrouping in subtraction algorithms and their use of concrete materials to facilitate pupils' understanding.  The teachers in our study agree on the fact that pupils can have difficulties with regrouping in subtraction algorithms. The study shows that the teachers´ opinions and experience indicate that concrete materials can facilitate pupils' understanding of mathematics, but they also show different opinions and experiences for whom the opportunities exist. Furthermore, the results of the study show that the teachers who participated in the study can also see limitations in using concrete materials in their mathematics teaching, such as the amount of material that is available, that the concrete material invites to play and more. Most of the teachers teach with concrete material for regrouping in subtraction algorithms to facilitate the pupils understanding and to provide clarity for the pupils, as seen from the results of the study. Furthermore, the results show that there are teachers who do not teach with concrete materials for regrouping in subtraction algorithms.

På vilket sätt kan vi hjälpa alla elever att bli förtrogna med matematikens uttrycksformer? Ett sätt är att bygga en stadig aritmetisk grund för eleverna där de befäster talens innehåll. Det är vad den här uppsatsen handlar om. Uppsatsen beskriver vad som skiljer användandet av del-helhetsrelationer från andra sätt att lösa öppna utsagor på. Uppsatsen beskriver även vilka kritiska aspekter om öppna utsagor som kan förekomma hos elever i årskurs 1 och 2. Uppsat-sen är skriven ur en fenomenografisk ansats med variationsteoretiska inslag eftersom de två teorierna är nära besläktade. Studien genomfördes genom filmade intervjuer med 11 elever som valdes ut genom en munt-lig och en skriftlig diagnos samt ett skriftligt arbetsblad. Resultatet visar att elever som använ-der automatiserade del-helhetsrelationer har en fördel när de löser öppna utsagor jämfört med elever som använder andra lösningsmetoder. Skillnaderna syns tydligt när det gäller lösandet av öppna subtraktionsutsagor där helheten saknas. En väg till den abstrakta förståelsen för tals del-helhetsrelationer går via fingertalen. Min slutsats är att eleverna redan tidigt i skolan måste få undervisning om fingertalen samt talens del-helhetsrelationer för att undvika att de utvecklar matematiksvårigheter. / How can we help all students to become confident with the concepts of mathematics? One way is to build a firm arithmetic foundation for students where they consolidate the content of the numbers. That is what this thesis is about. The thesis describes what differentiates the use of part-whole relationships from other ways to solve missing number bonds. The thesis also describes the critical aspects of missing number bonds that may be found in students in grades 1 and 2. The thesis is written from a phenomenographic approach with elements of variation theory, since the two theories are closely related. The study was conducted by videotaped interviews with 11 students selected through an oral test, a written test and a written worksheet. The result shows that the students who use auto-mated part-whole relationships when solving missing number bonds have an advantage com-pared to students who use other solving methods. The differences are clearly visible when it comes to solving missing number bonds in subtraction where the whole is missing. One path to the abstract understanding of the part-whole relationships goes through the finger num-bers. My conclusion is that the students must be taught the finger numbers and the part-whole relationships early in the education, to prevent them from getting into mathematical difficulties.

This report shows that a reliable motion detection is needed to make an accurate prediction of future activity. Several experiments are carried out to obtain information about the object ́s behaviour and the best settings for the motion detection. A moving object is captured using two cameras, for two image sequences, and motion detection is applied to the stereoscopic data. Background subtraction algorithm followed by image segmentation algorithm, morphology algorithm, and blob analy- sis are performed on the images to find the coordinates for the centroid of the moving object. Two models are created to make a statistical inter- pretation of the data: one model for the height over the width and one statistical model for the distance between the cameras and the moving object over the width. The mean and standard deviation values are calculated to make a reliable interpretation of the captured images and the moving object. The Kalman filter is used for the prediction of future activity. The filters of the statistical models are trained with the first coordinates of the detected balls, and the next coordinates are predicted.

Background subtraction is the algorithmic process that segments out the region of interest often known as foreground from the background. Extensive literature and numerous algorithms exist in this domain, but most research have focused on videos captured by static cameras. The proliferation of portable platforms equipped with cameras has resulted in a large amount of video data being generated from moving cameras. This motivates the need for foundational algorithms for foreground/background segmentation in videos from moving cameras. In this dissertation, I propose three new types of background subtraction algorithms for moving cameras based on appearance, motion, and a combination of them. Comprehensive evaluation of the proposed approaches on publicly available test sequences show superiority of our system over state-of-the-art algorithms. The first method is an appearance-based global modeling of foreground and background. Features are extracted by sliding a fixed size window over the entire image without any spatial constraint to accommodate arbitrary camera movements. Supervised learning method is then used to build foreground and background models. This method is suitable for limited scene scenarios such as Pan-Tilt-Zoom surveillance cameras. The second method relies on motion. It comprises of an innovative background motion approximation mechanism followed by spatial regulation through a Mega-Pixel denoising process. This work does not need to maintain any costly appearance models and is therefore appropriate for resource constraint ego-vision systems. The proposed segmentation combined with skin cues is validated by a novel application on authenticating hand-gestured signature captured by wearable cameras. The third method combines both motion and appearance. Foreground probabilities are jointly estimated by motion and appearance. After the mega-pixel denoising process, the probability estimates and gradient image are combined by Graph-Cut to produce the segmentation mask. This method is universal as it can handle all types of moving cameras.

Shadows in indoor scenarios are usually characterized with multiple light sources that produce complex shadow patterns of a single object. Without removing shadow, the foreground object tends to be erroneously segmented. The inconsistent hue and intensity of shadows make automatic removal a challenging task. In this thesis, a dynamic thresholding and transfer learning-based method for removing shadows is proposed. The method suppresses light shadows with a dynamically computed threshold and removes dark shadows using an online learning strategy that is built upon a base classifier trained with manually annotated examples and refined with the automatically identified examples in the new videos. Experimental results demonstrate that despite variation of lighting conditions in videos our proposed method is able to adapt to the videos and remove shadows effectively. The sensitivity of shadow detection changes slightly with different confidence levels used in example selection for classifier retraining and high confidence level usually yields better performance with less retraining iterations.