Plusieurs travaux ont montré que les aires cortico-motrices, localisées dans le cortex frontal et responsables des mouvements volontaires, pouvaient être impliquées dans le processus de compréhension de mots d'action. De ce point de vue, il a été rapporté que la performance d'un acte moteur simple (e.g.: attraper un objet) pouvait être améliorée par la prononciation, la lecture ou l'écoute de mots évoquant une action. Nous avons approché la relation entre parole et action à travers l'étude de l'effet de verbes d'action ainsi que d'autres stimuli cognitifs, l'imagerie kinesthésique (IK) et la soustraction mentale (SM), sur la performance d'un acte moteur complexe, le Squat Vertical Jump (SVJ, ou saut vertical accroupi). Nous avons mesuré la hauteur du SVJ chez des hommes (7 expériences, n = 114) et des femmes (2 expériences, n = 41) à l'aide de deux systèmes de mesure, l'Optojump® et le Myotest®. Les résultats ont montré que la prononciation silencieuse et à haute voix du verbe d'action spécifique au SVJ (saute, conjugué à la première personne de l'impératif), ainsi que l'IK et la SM, améliorent significativement la performance du saut, chez les hommes (jusqu'à + 2,7 cm) et, de manière moins prononcée, chez les femmes (jusqu'à + 1 cm dans 2 expériences). Le reste des résultats obtenus avec les hommes ont indiqué que la prononciation du verbe d'action non spécifique au saut (pince) augmente également la hauteur en SVJ, alors que la prononciation ou l'écoute d'autres verbes sans lien avec le saut (lèche, bouge) n'ont pas eu d'effet significatif sur le SVJ. C'est également le cas du verbe d'état 'rêve et d'un verbe incompréhensible par les sujets (tiào : saute en Chinois) ou encore des verbes qui contredisent et/ou qui s'opposent au déroulement de l'action de sauter (tombe et stoppe). La hauteur du saut a été par ailleurs significativement impactée lorsque les sujets ont prononcé des verbes à fort attributs émotifs (gagne et son antonyme perds) / Several studies have shown that cortical motor areas, located in the frontal cortex and responsible for voluntary movement, might be involved in the process of understanding action words. From this point of view, it has been reported that the performance of a simple motor act (e.g.: catching an object) might be improved by the pronunciation, reading or listening to words referring to the action. We approached the relationship between speech and action through the study of the effect of action verbs and other cognitive stimuli, kinesthetic imagery (KI) and mental subtraction (MS), upon the performance of a complex motor act, the Squat vertical jump (SVJ). We measured the height of SVJ in young naive men (7 experiments, n = 114) and women (2 experiments, n = 41) using an Optojump® and a Myotest® apparatuses. The results showed that the silent and loud pronunciation of specific action verb to SVJ (jump), the KI and the MS improved significantly the performance of the movement, in men (up to 2.7 cm) but less in women (up to + 1 cm in the 2 experiments). The results of other experiments obtained with men indicated that pronunciation of the action verb nonspecific to the jump (pinch) increased also the SVJ performance, while the pronunciation or listening to other verbs unrelated to the jump (Jick, move) had no significant effect on the SVJ. A meaningless verb for the French subjects (tiao = jump in Chinese) showed, in turn, no effect as did dream, faJJ and stop. The verb win improved significantly the SVJ height as much as its antonym Jose, thus suggesting a possible influence of affects in the subjects' performance. It appears that the effects of the specific action verb jump did seem effective but not totally exclusive for the enhancement of the SVJ performance, since non-linguistic stimuli (IK) or unrelated to action (MS) may have had a positive effect on the improvement in motor performance. Moreover verbs referring to emotion, unrelated to action, increased the height of SVJ similarly to the specific action verb jump. The results led us to consider the hypothesis that improving the performance of a complex gesture is dependent, a minima, upon the individual's intention, attention, emotions and also, and perhaps most importantly, concepts (we call concepts, the mental representations) as they may be induced by the cerebral processing of words

The thesis deals with the processing and detection of specific objects in the image on the Android mobile platform. The main objective of this work was to design and then implement a mobile application for Android operating system, which allows control of pipetting processes based on images from a mobile device camera. The OpenCV library is used in the application for image processing. The resulting application should serve primarily in laboratories as a tool for complete analysis of the pipetting process. The work is divided into two main chapters, which further consist of sections and smaller subsections. The first chapter is devoted to the theoretical analysis of this work. Here is also describes used technology, Android operating system, OpenCV library and important parts of image processing. The second chapter deals with the proposal and subsequent practical solution of this work. There is a proposal and the following procedure for solving this work, important techniques, methods of processing and analysis of the camera image. The conclusion of the thesis is an evaluation of the results of the solution of this work.

The aim of the master thesis is to derive and implement image porcessing methods for people detection and tracking in images or videos. The overall solution was chosen as a combination of modern approaches and methods which were recently presented. The proposed algorithm is able to create trajectory of the person moving in indoor building spaces even under influence of full or partial occlusion for a short period of time. The scene of interest is surveyed by a static camera having direct view on targets. Selected methods are implemented in C# programming language based on OpenCV library. Graphical user interface was created to show the final output of algorithm.

This diploma thesis deals with methods of noise suppression for speech recognition systems. In theoretical part are discussed basic terms of this topic and also methods for noise suppression. These are spectral subtraction, Wiener filtering, RASTA, mapping of spectrogram or algorithms based on noise estimation. In second part types of noise are analyzed, there is proposal and implementation of spectral subtraction method of noise suppression for speech recognition system. Also extensive testing of spectral subtractive algorithms in comparison with Wiener filter is conducted. Assessment of this testing is done with defined metrics, successfulness of recognition, recognition system score and signal to noise ratio.

The thesis deals with single channel and multiple channel algorithms for speech enhancement. The goal of this work is to perform the deep analysis of both single channel and multiple channel algorithms in sense of their behaviour in noisy environment of combat vehicles and platforms. Based on this analysis a new composite speech enhancement algorithm will be designed. This new approach is expected to increase quality of the processed speech in military communications systems. These systems are characterised by their operation under very noisy conditions where background noise is very high and signal-to-noise ratio extremely low. These noisy conditions are typical for the range of military and combat platforms and vehicles.

This thesis deals with segmentation of tennis player´s body parts for analysis its motion. Testing code was written in C++ with use of OpenCV library. Some image processing techniques such as thresholding, background subtraction or finding the largest contours were implemented. There was used a linear triangulation technique for calculating the 3D coordinates of segmented points.

Traffic monitoring using computer vision is becoming the desired system in practice. It allows nondestructive installation and also is very useful in many applications. This thesis focuses on automatic detection of vehicles approaching to a crossroads. This work also includes description of selected methods for detecting moving vehicles and the way of tracking them. On the basis of these methods is designed application that is implemented and tested in different lighting and weather conditions and various direction of approaching vehicles.

Cette thèse présente plusieurs contributions au software developé pour le traitement d’images dans le cadre du LSST. Notre objectif est d'utiliser le code et les algorithmes LSST existants, afin de créer un pipeline dédié à la détection des supernovae de type Ia. Pour la détection des supernovae nous utilisons une technique appelée soustraction optimale d'images qui implique la construction de coadditions. Nous étudions aussi le comportement des différents objets dans le temps et construisons des courbes de lumière qui représentent leur cycle de vie en fonction de l'intensité lumineuse de chaque détection sur plusieurs nuits. Enfin, pour analyser un nombre excessif de candidats, nous utilisons des algorithmes d'apprentissage machine.Notre première contribution concerne le développement des taches de coaddition automatisée adaptées pour construire des images de référence et de science avec un haut rapport signal-sur-bruit. La contribution suivante est lié à l’addition de mesures et l’étude de résidus des images d’analyse de différence, y-compris la sélection des seuils adaptés et l'étiquetage basée sur les valeurs quantitativess des résidus pour identifier les mauvaises détections, les artéfacts et les flux réellement significatifs. Notre suivante contribution est un algorithme pour sélectionner et générer les courbes de lumière candidates. Finalement, on applique une classification machine learning pour trouver des type Ia supernovae en utilisant la méthode random forest. Ces résultats ont permis l’identification des supernovae de type Ia simulées et réelles parmis les candidats avec une haute précision. / This thesis will present several contributions to the software developed for the LSST telescope with the purpose of contributing to the detection of type Ia supernovae. Our objective is to use the existing LSST code and algorithms, in order to create a type Ia supernovae detection dedicated pipeline.Since detecting supernovae requires a special type of processing, we use a technique known as the Optimal Image Subtraction which implies the construction of coadditions. Afterwards, we study the behavior of the different objects through time and build light curves that represent their life cycle in terms of the light intensity of each detection on several nights. Lastly, in order to analyze an excessive number of candidates, we employ machine learning algorithms to identify what curves are more probable to be type Ia supernovae. Our first contribution concerns the development of adapted and automatized coaddition tasks for building high signal-to-noise reference and science images. The next contribution is related to the addition of measurements and study of the residuals on difference image analysis, including the selection with adapted thresholding and the assignation of labels. We also propose, as contributions, an algorithm to select and generate the different candidate light curves through the selection of objects with recurrent detections through time and in the different bandpasses. Finally, we apply the machine learning classification approach to find type Ia supernovae by means of using a random forest classifier and based strictly on geometrical features that are present in the light curves.

I den här studien undersöks om och vilken typ av lärarstöd avseende Mathematical Knowledge of Teaching (MKT) lärarhandledningar till Favorit matematik 3A och Rik matematik 3A. Vi analyserar vilket stöd som lärarhandledningarna ger till lärare i områdena addition och subtraktion för årskurs 3. Analysen utgår från MKT:s analysmodell med stöd av fyra utvalda kategorier: Common Content Knowledge, Subject Matter Knowledge, Knowledge of Content and Students, Knowledge of Content and Teaching och Knowledge of Content and Curriculum. En komparativ analys genomfördes med MKT som teoretiskt ramverk. Resultatet visar på att lärarhandledningarna innehåller stöd till läraren i addition och subtraktion och delar av MKT. Lärarhandledningarna ger stöd till lärarna vid planering av undervisning, begreppsanvändning i undervisning, olika undervisningsmetoder, nivåökning av kapitel och hur läraren ges kunskap om materialet. Lärarhandledningarnas struktur förmedlar stödet på ett varierande sätt vilket också synliggör likheter och skillnader om de delar av MKT som lärarhandledningarna inte innehåller. / This study examines whether and what type of teacher support regarding Mathematical knowledge of teaching (MKT) contains teacher guides for Favorite Mathematics 3A and Rich Mathematics 3A. We analyze the support of teacher guides provide to teachers in the areas of addition and subtraction for year 3. The analysis is based on MKT's analysis model with the support of four selected categories; Common Content Knowledge, Subject Matter Knowledge, Knowledge of Content and Students, Knowledge of Content and Teaching and Knowledge of Content and Curriculum. A comparative analysis was performed with MKT as the theoretical framework. The results prove that the teacher guides contain support for the teacher in addition and subtraction and parts of MKT. The teacher guides provide support to the teachers when planning teaching, use of concepts in teaching, different teaching methods, level increase of chapters and how the teacher is given knowledge of the material. The structure of the teacher guides the support in a varying way, which also highlights similarities and differences about the parts of MKT that the teacher guides do not contain.

Context : Air being the free source is used in different ways commercially. In earlier days windmills generate power, water, and electricity. The excessive establishment of windmills for commercial purposes affected avifauna. Most of the birds lost their lives due to collisions with windmills. Turbines used to generate power near airports are also one of the causes for the extinction of birdlife. According to a survey in 2011 in Canada a total of 23,300 bird deaths were caused by wind turbines and also it is estimated that the number of deaths would increase to 2,33,000 in the following 10-15 years. Objectives : The main objective of this thesis is to find a suitable software solution to detect the birds on a series of grayscale images in real-time and in minimum full HD resolution with at least a 15 FPS rate. User-Driven Design Methodology is used for developing, tools are Python and Open-CV. Methods : In this research, a system is designed to detect the bird in an HD Video. Possible methods that can be considered are convolutional neural networks (CNN), vision based detection with background subtraction, contour detection and confusion matrix classification. These methods detect birds in raw images and with help of a classifier make it possible to see the bird in desired pixels with full resolution. We will investigate a bird classification method consisting of two steps, background subtraction, and then object classification. Background subtraction is a fundamental method to extract moving objects from a fixed background. For classification, we will use the YOLO v3 model version for object classification. Results : The project is expected to result in a system design and prototype for the bird identification on a grayscale video stream in at least full HD resolution in a minimum of 15 FPS. The bird should be distinguished from other moving objects like wind turbine blades, trees, or clouds. The proposed solution should identify up to 5 birds simultaneously. Conclusion : After completing each step and arriving at the classification, the methods we have tried, such as Haar Cascades and mobile-net SSD, were not providing us with the desired results. So we opted to use YOLO v3, which had the best accuracy in classifying different objects. By using the YOLO v3 classifier, we have detected the bird with 95% accuracy, blades with 90% accuracy, clouds with 80% accuracy, trees with 70% accuracy. Moreover, we conclude that there is a need for further empirical validation of the models in full-scale industry trials.