Title: Motivating factors influencing consumers’ Brand preferences for mobile phones: University of Gavle students Level: Final assignment for Master degree in Business Administration (MBA) Authors: Chinedu Ekemba and Emurla Emin Ali Supervisor: Professor Ehsanul Huda Chowdhury Examiner: Professor Maria Fregidou-Malama Date: 2017- June Aim: The aim of this study is to investigate the motivating factors that influence University Gavle students to prefer a particular Mobile phone brand. Method: A qualitative study is carried out based on primary data; the primary data was collected through semi-structured interview with twenty of University of Gavle Students by the use of face to face interview. Result & Conclusion: The study finds out that, the role of word of mouth as extrinsic factors serves as the highest motivating factor, while prestige serves as intrinsic factors of motivation. Thus, word of mouth and quality are the highest motivating factors that influence University of Gavle students to prefer a particular mobile phone brand. This finding of this research will help mobile phone marketers and managers to develop strategy on how to capture Swedish consumers to prefer their companies brand products. Suggestion for future research: Future research could be done by considering different or more widely target groups instead of students of University of Gavle. Different perspectives can be combined in future research thus, further research can be conducted by more broadly with a variety of age groups and in a wider area. Also, further research could include comparison between mobile phone brand types and may consider different type of products. This will give understanding of the different segments in mobile phone market, and to determine if these different segments can cause any variety and change of motivating factors. Additionally, further research could be conducted in the long time period, thus can be explored in detail benefiting from the longitudinal study and could be include observations to understand long-term variables on mobile phone market and effects of motivating factors. Hence, this could bring more effective conceptualization of influencing factors of smartphone buying consumers and helps to achieve a more in-depth research. More so, the same study can be conducted with a combination of different Universities in Sweden. And also, same research can be carried out using quantitative research method for future research. Contribution of the Study: This study contributes with knowledge on which motivating factors influence customers brand preferences, and how these factors affect their purchase decision.

Les exigences opérationnelles du métier de pilote sont susceptibles d'engendrer de la somnolence et des niveaux de charge mentale inadéquats (i.e., trop faible ou trop élevé) au cours des vols. Les dettes de sommeil et les perturbations circadiennes liées à divers facteurs (e.g., longues périodes de services, horaires de travail irrégulier, etc.) demandent aux pilotes de repousser sans cesse leurs limites biologiques. Par ailleurs, la charge de travail mental des pilotes présente de fortes variations au cours d'un vol : élevée au cours des phases critiques (i.e., décollage et atterrissage), elle devient très réduite pendant les phases de croisière. Lorsque la charge mentale devient trop élevée ou, à l'inverse, trop faible, les performances se dégradent et des erreurs de pilotage peuvent apparaître. La mise en oeuvre de méthodes de détection de l'état de somnolence et du niveau de charge mentale en temps quasi réel est un défi majeur pour le suivi et le contrôle de l'activité de pilotage. L'objectif de la thèse est de déterminer si la voix humaine peut permettre de détecter d'une part, l'état de somnolence et d'autre part, le niveau de charge mentale d'un individu. Dans une première étude, la voix de participants a été enregistrée lors d'une tâche de lecture avant et après une nuit de privation totale de sommeil (PTS). Les variations de l'état de somnolence consécutives à la PTS ont été évaluées au moyen de mesures auto-évaluatives et électrophysiologiques (ÉlectroEncéphaloGraphie [EEG] et Potentiels Évoqués [PEs]). Les résultats ont montré une variation significative après la PTS de plusieurs paramètres acoustiques liés : (a) à l'amplitude des impulsions glottiques (fréquence de modulation d'amplitude), (b) à la forme du signal acoustique (longueur euclidienne du signal et ses caractéristiques associées) et (c) au spectre du signal des voyelles (rapport harmonique sur bruit, fréquence du second formant, coefficient d'asymétrie, centre de gravité spectral, différences d'énergie, pente spectrale et coefficients cepstraux à échelle Mel). La plupart des caractéristiques spectrales ont montré une sensibilité différente à la privation de sommeil en fonction du type de voyelles. Des corrélations significatives ont été mises en évidence entre plusieurs paramètres acoustiques et plusieurs indicateurs objectifs (EEG et PEs) de l'état de somnolence. Dans une seconde étude, le signal vocal a été enregistré durant une tâche de rappel de listes de mots. La difficulté de la tâche était manipulée en faisant varier le nombre de mots dans chaque liste (i.e., entre un et sept, correspondant à sept conditions de charge mentale). Le diamètre pupillaire - qui est un indicateur objectif pertinent du niveau de charge mentale - a été mesuré simultanément avec l'enregistrement de la voix afin d'attester de la variation du niveau de charge mentale durant la tâche expérimentale. Les résultats ont montré que des paramètres acoustiques classiques (fréquence fondamentale et son écart type, shimmer, nombre de périodes et rapport harmonique sur bruit) et originaux (fréquence de modulation d'amplitude et variations à court-terme de la longueur euclidienne du signal) ont été particulièrement sensibles aux variations de la charge mentale. Les variations de ces paramètres acoustiques étaient corrélées à celles du diamètre pupillaire. L'ensemble des résultats suggère que les paramètres acoustiques de la voix humaine identifiés lors des expérimentations pourraient représenter des indicateurs pertinents pour la détection de l'état de somnolence et du niveau de charge mentale d'un individu. Les résultats ouvrent de nombreuses perspectives de recherche et d'applications dans le domaine de la sécurité des transports, notamment dans le secteur aéronautique. / Operational requirements of aircraft pilots may cause drowsiness and inadequate mental load levels (i.e., too low or too high) during flights. Sleep debts and circadian disruptions linked to various factors (e.g., long working periods, irregular work schedules, etc.) require pilots to challenge their biological limits. Moreover, pilots' mental workload exhibits strong fluctuations during flights: higher during critical phases (i.e., takeoff and landing), it becomes very low during cruising phases. When the mental load becomes too high or, conversely, too low, performance decreases and flight errors may manifest. Implementation of detection methods of drowsiness and mental load levels in near real time is a major challenge for monitoring and controlling flight activity. The aim of this thesis is therefore to determine if the human voice can serve to detect on one hand the drowsiness and on the other hand the mental load level of an individual. In a first study, the voice of participants was recorded during a reading task before and after a night of total sleep deprivation (TSD). Drowsiness variations linked to TSD were assessed using self-evaluative and electrophysiological measures (ElectroEncephaloGraphy [EEG] and Evoked Potentials [EPs]). Results showed significant variations after the TSD in many acoustic features related to: (a) the amplitude of the glottal pulses (amplitude modulation frequency), (b) the shape of the acoustic wave (Euclidean length of the signal and its associated features) and (3) the spectrum of the vowel signal (harmonic-to-noise ratio, second formant frequency, skewness, spectral center of gravity, energy differences, spectral tilt and Mel-frequency cepstral coefficients). Most spectral features showed different sensitivity to sleep deprivation depending on the vowel type. Significant correlations were found between several acoustic features and several objective indicators (EEG and PEs) of drowsiness. In a second study, voices were recorded during a task featuring word-list recall. The difficulty of the task was manipulated by varying the number of words in each list (i.e., between one and seven, corresponding to seven mental load conditions). Evoked pupillary response - known to be a useful proxy of mental load - was recorded simultaneously with speech to attest variations in mental load level during the experimental task. Results showed that classical features (fundamental frequency and its standard deviation, shimmer, number of periods and harmonic-to-noise ratio) and original features (amplitude modulation frequency and short-term variation in digital amplitude length) were particularly sensitive to variations in mental load. Variations in these acoustic features were correlated to those of the pupil size. Results suggest that the acoustic features of the human voice identified during these experiments could represent relevant indicators for the detection of drowsiness and mental load levels of an individual. Findings open up many research and applications perspectives in the field of transport safety, particularly in the aeronautical sector.

>Magister Scientiae - MSc / This research proposes an approach to recognizing facial expressions in the presence of rotations and partial occlusions of the face. The research is in the context of automatic machine translation of South African Sign Language (SASL) to English. The proposed method is able to accurately recognize frontal facial images at an average accuracy of 75%. It also achieves a high recognition accuracy of 70% for faces rotated to 60◦. It was also shown that the method is able to continue to recognize facial expressions even in the presence of full occlusions of the eyes, mouth and left/right sides of the face. The accuracy was as high as 70% for occlusion of some areas. An additional finding was that both the left and the right sides of the face are required for recognition. As an addition, the foundation was laid for a fully automatic facial expression recognition system that can accurately segment frontal or rotated faces in a video sequence.

This study sought to understand factors driving repurchase intentions for consumer electronics (CE) hardware and in particular mobile phones. The outcome of the study was expected to be of interest in academia and practice because it develops upon existing literature and identifies actionable variables that could be used to optimise market offerings. Based on a literature review it was hypothesised that the intent was driven by hedonic and utilitarian factors. These included conspicuousness and visibility; product bundling; reliability; technological features, usability of the product and the buyers’ age. The study tested these hypotheses using primary data. The method was employed to confirm the postulated drivers as well as to determine the direction of the effects. Data collection was conducted through a cross sectional internet survey enumerated in August 2010. The survey reached a broad sample of 144 responders. The analysis supported two of the six hypothesised drivers. The supported drivers were conspicuousness and usability. The recommendation was therefore to encourage the CE industry to focus on creating aesthetically appealing, fashionable devices that were intuitively easy to use requiring minimal assistance or product manuals. It also recommends that less emphasis be placed on durability, advanced features, on bundling additional extras and on targeting particular age groups. Copyright / Dissertation (MBA)--University of Pretoria, 2012. / Gordon Institute of Business Science (GIBS) / unrestricted

Le bégaiement est un trouble de la fluence de la parole qui se caractérise, entre autres, par une présence accrue d’accidents de parole venant entraver l’intelligibilité de l’énoncé. Ce travail de doctorat a pour objectif d’étudier les disfluences catégorisées comme pathologiques produites par des locuteurs qui bégaient et ce, en tâche de lecture et en situation de parole spontanée. Plus précisément, il s’agit, d’une part, de vérifier si des éléments morphologiques et phonétiques peuvent expliquer l’apparition d’un bégayage et, d’autre part, d’observer les événements articulatoires présents avant et pendant les disfluences. Pour mener à bien les études ayant trait aux éléments linguistiques posant le plus de difficultés aux personnes qui bégaient, 10 locuteurs francophones et 10 locuteurs slovacophones, tous atteints de ce trouble, ont été enregistrés en train de lire un texte et de parler spontanément dans leur langue maternelle. Quant aux travaux portant sur les événements moteurs se déroulant avant et durant les disfluences, ils ont été réalisés grâce à des données EMA acquises auprès de 4 locuteurs francophones (2 locuteurs qui bégaient et 2 sujets normo-fluents) en tâche de lecture. Nos résultats ont montré que les consonnes non-voisées et les occlusives faisaient partie des éléments les plus problématiques à prononcer pour les personnes bègues. L’étude morphologique a révélé que plus un mot contient de morphèmes et plus le risque de voir apparaître une disfluence est accru. Ce résultat doit notamment être mis en corrélation avec le nombre de syllabes présentes dans le mot. En ce qui concerne le second couple d’études, portant sur le niveau moteur de la parole bègue, nos données montrent, en particulier, des similitudes dans les événements articulatoires se déroulant au niveau supra-glottique entre les disfluences perçues acoustiquement comme des blocages et des prolongations. Enfin, une perturbation des gestes coarticulatoires a pu être relevée lors de la production de certaines disfluences. / Stuttering is a speech fluency disorder. It can be mainly characterized by an increased presence of disfluencies that affect the speech intelligibility. The aim of this thesis is to study stuttering-like disfluencies (SLDs) produced by persons who stutter (PWS) during reading tasks and during spontaneous speech. More specifically, we propose, as our first objective, to verify if any morphological or phonetic elements can explain the presence of these disfluencies. Our second objective is to observe articulatory events before and during SLDs. For the studies dealing with the linguistic and phonetic elements that can be problematic to PWS, 10 French-speaking and 10 Slovak-speaking PWS were recorded while reading a text and while having a conversation in their mother tongue. The studies on speech motor events taking place before and during SLDs were realized by means of an EMA. 4 French-speaking subjects participated in this part of the study (2 PWS and 2 control subjects). Our results show that non-voiced consonants and stops were part of the most problematic elements to produce for PWS. The morphological study reveals that the risk of a SLD appearance was higher when the word contained more morphemes. This result should be correlated to the number of syllables that constitute the word. As for the second couple of studies, they focus on the speech motor events in stuttered speech. Our data show that similar articulatory events can take place in the supraglottic cavity during disfluencies perceived as blocks or prolongations. Furthermore, a disruption of coarticulatory gestures was observed in certain disfluencies.

No description available.

This thesis focuses on bare hand gesture recognition by proposing a new architecture to solve the problem of real-time vision-based hand detection, tracking, and gesture recognition for interaction with an application via hand gestures. The first stage of our system allows detecting and tracking a bare hand in a cluttered background using face subtraction, skin detection and contour comparison. The second stage allows recognizing hand gestures using bag-of-features and multi-class Support Vector Machine (SVM) algorithms. Finally, a grammar has been developed to generate gesture commands for application control. Our hand gesture recognition system consists of two steps: offline training and online testing. In the training stage, after extracting the keypoints for every training image using the Scale Invariance Feature Transform (SIFT), a vector quantization technique will map keypoints from every training image into a unified dimensional histogram vector (bag-of-words) after K-means clustering. This histogram is treated as an input vector for a multi-class SVM to build the classifier. In the testing stage, for every frame captured from a webcam, the hand is detected using my algorithm. Then, the keypoints are extracted for every small image that contains the detected hand posture and fed into the cluster model to map them into a bag-of-words vector, which is fed into the multi-class SVM classifier to recognize the hand gesture. Another hand gesture recognition system was proposed using Principle Components Analysis (PCA). The most eigenvectors and weights of training images are determined. In the testing stage, the hand posture is detected for every frame using my algorithm. Then, the small image that contains the detected hand is projected onto the most eigenvectors of training images to form its test weights. Finally, the minimum Euclidean distance is determined among the test weights and the training weights of each training image to recognize the hand gesture. Two application of gesture-based interaction with a 3D gaming virtual environment were implemented. The exertion videogame makes use of a stationary bicycle as one of the main inputs for game playing. The user can control and direct left-right movement and shooting actions in the game by a set of hand gesture commands, while in the second game, the user can control and direct a helicopter over the city by a set of hand gesture commands.

Travel is now the largest online business-to-consumer product in the United States. Online hotel bookings are the second largest segment of online travel. Leisure travelers online spending will increase dramatically from 2002 to 2007. However, a majority of hospitality companies do not currently take advantage of the Internet as the cheapest and most efficient distribution medium. The purpose of this study examined leisure travelers' demographic and psychographic characteristics, online booking and travel frequency that influence travelers' desired hotel Website features and functions. The results found out that demographics (gender, occupation, and ethnicity), and psychographics (travel benefit sought), number of leisure travel trips per year, and number of online hotel bookings per year have impact on hotel Website attribute preferences.

In den letzten vier Jahrzehnten hat sich auf dem Gebiet der vergleichenden Phraseologieforschung eine kontinuierliche Entwicklung vollzogen, die sich auf den Forschungsbedarf der Übersetzungswissenschaft, der Lexikographie und der Fremdsprachendidaktik gründet. Vor allem seit den 1990er Jahren entstanden viele auf theoretischer und auch praktischer Ebene fundierte germanistische Studien. Zu nennen sind zum Beispiel Barbara Wotjaks „Verbale Phraseolexeme in System und Text“ (1992), Csaba Földes’ „Deutsche Phraseologie kontrastiv. Intra- und interlinguale Zugänge“ (1996) oder der von Harmut Lenk und Stephan Stein herausgegebene Band „Phraseologismen in Textsorten“ (2011). Zum Tschechischen liegen wichtige Untersuchungen vor wie die von Franz Schindler, „Das Sprichwort im heutigen Tschechischen. Empirische Untersuchung und semantische Beschreibung“ (1993) oder Helgunde Henschels „Die Phraseologie der tschechischen Sprache“ (1993).

Automated recognition of selected terrain features from their cartographic representation. This diploma thesis is dedicated to automatic classification of selected terrain shapes and their cartographic representation. The main aim of this thesis is to design methodological approach for automatic recognition of terrain shapes (hills and valleys) with the use of Machine Learning (Deep Learning). The first part of suggested method divides rough terrain segmentation into two categories, which will be then classified with convolutional neural network. The second part of the thesis is dedicated to the very classification of pre-segmented terrain shapes using Machine Learning. Both parts of the processing are using photos SRTM30 as an input data. The whole proposed method was developed in Python programming language with the usage of Arcpy, TensorFlow and Keras libraries. Keywords: Digital cartography, GIS, terrain shapes, Machine Learning, Deep Learning, recognition, classification, segmentation