For the last several years, speaker diarization has been attracting substantial research attention as one of the spoken language technologies applied for the improvement, or enrichment, of recording transcriptions. Recordings of meetings, compared to other domains, exhibit an increased complexity due to the spontaneity of speech, reverberation effects, and also due to the presence of overlapping speech. Overlapping speech refers to situations when two or more speakers are speaking simultaneously. In meeting data, a substantial portion of errors of the conventional speaker diarization systems can be ascribed to speaker overlaps, since usually only one speaker label is assigned per segment. Furthermore, simultaneous speech included in training data can eventually lead to corrupt single-speaker models and thus to a worse segmentation. This thesis concerns the detection of overlapping speech segments and its further application for the improvement of speaker diarization performance. We propose the use of three spatial cross-correlationbased parameters for overlap detection on distant microphone channel data. Spatial features from different microphone pairs are fused by means of principal component analysis, linear discriminant analysis, or by a multi-layer perceptron. In addition, we also investigate the possibility of employing longterm prosodic information. The most suitable subset from a set of candidate prosodic features is determined in two steps. Firstly, a ranking according to mRMR criterion is obtained, and then, a standard hill-climbing wrapper approach is applied in order to determine the optimal number of features. The novel spatial as well as prosodic parameters are used in combination with spectral-based features suggested previously in the literature. In experiments conducted on AMI meeting data, we show that the newly proposed features do contribute to the detection of overlapping speech, especially on data originating from a single recording site. In speaker diarization, for segments including detected speaker overlap, a second speaker label is picked, and such segments are also discarded from the model training. The proposed overlap labeling technique is integrated in Viterbi decoding, a part of the diarization algorithm. During the system development it was discovered that it is favorable to do an independent optimization of overlap exclusion and labeling with respect to the overlap detection system. We report improvements over the baseline diarization system on both single- and multi-site AMI data. Preliminary experiments with NIST RT data show DER improvement on the RT ¿09 meeting recordings as well. The addition of beamforming and TDOA feature stream into the baseline diarization system, which was aimed at improving the clustering process, results in a bit higher effectiveness of the overlap labeling algorithm. A more detailed analysis on the overlap exclusion behavior reveals big improvement contrasts between individual meeting recordings as well as between various settings of the overlap detection operation point. However, a high performance variability across different recordings is also typical of the baseline diarization system, without any overlap handling.

This thesis attempts to study the architectural and the social changes brought by the multi-story apartment buildings which emerged in the nineteenth century istanbul, in especially Galata-Pera region. A brief introduction to the modernization attempts of the Ottoman Empire, and also to the traditional dwellings and daily life of the Ottoman households before the nineteenth century constitute the first sections of the study. The architectural and the urban developments such as the new building regulations, architectural styles and building types as well as the social and cultural changes that brought new cultural habits and life styles in the modernization period, are also studied in this context. A group of apartments with different plans, size and locations are chosen as a sample so as to point out and discuss the layout of the constituent spaces like the halls, foyers, corridors, substantial rooms and wet spaces. Respectively the changing meaning of the &lsquo / house&rsquo / and daily life are also pointed out. In relation to these, facade organizations, plans, functional and spatial features and the privacy of spaces in the sample apartment buildings and their flats, and the daily life and the privacy of the apartment residents are studied and discussed in comparison to the traditional Ottoman house and the contemporary Parisian apartments to present a comparative perspective. Consequently, &lsquo / similarities&rsquo / , &lsquo / differences&rsquo / , and &lsquo / innovations&rsquo / concerning the nineteenth century istanbul apartments are discussed and listed at the end of the thesis. Several tables which are designed to contribute to the arguments presented in the study are also added to the thesis.

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Workplace design practitioners and organizational managers are increasingly noticing all the various aspects in which the workplace affects organizations and their employees. The studies on the relationships between the workplace, organizations and their employees are more focused on psychology or facility management than the socio-spatial perspective. Workplace design, configuration and spatial features impact how well and how much a company can benefit from its human capital. Although the concept of the relation of workplace to an organization is not new, it is relatively unexamined. This thesis introduces a new set of spatial variables to workplace studies, following the concept of personal control. The discussed spatial variables effectively describe the features of workplace floor plan and the characteristics of a workstation. Furthermore, this dissertation develops a method that creates the link between workplace spatial setting and a sense of belonging, organizational outcomes – organizational commitment, work motivation, job satisfaction and work performance. Based on the detailed statistical analyses of a field survey that included 336 participants from 16 organizations, a model of spatial features influence sense of belonging and organizational outcomes was identified within this study. The research findings provide evidence for creating a workplace with a sense of belonging and better organizational outcomes through spatial design. This dissertation is comprised of six chapters. Chapter 1, an introduction, provides a general study background, discusses the problems to be solved in the study, and proposes an approach to deal with the target problems. Chapter 2 firstly reviews the current workplace studies related to spatial features. Secondly, it discusses the influence of workstation design on the human muscle system. Thirdly, it discusses the most relevant psychological issues at a workplace as stated by previous researches. Finally, the chapter reveals how a workplace affects the work of an organization. Chapter 3 specifies how workplace influences an employee’s sense of belonging and environmental control, and introduces the conceptual model. It also introduces the independent and dependent variables, generates research hypotheses. Chapter 4 describes the field survey design, procedures and the participants. It also covers the initial data analysis of the field survey: how the survey instrument, the questionnaire, was developed, commenting on all the aspects it includes – spatial experiences, work motivation, commitment, sense of belonging, job satisfaction and work performance. Chapter 5 is data analysis. This chapter discusses the research findings on workplace design features in relation to employees’ sense of belonging, satisfaction with ambient physical environment, and organizational outcomes – commitment, work motivation, job satisfaction and individual work performance. The final chapter summarizes the findings, comments on design implications of the research results, and draws conclusions. The dissertation ends in admitting the limitations of this research and discussing practical implications for future investigation.

Intelligent vehicles require perception systems with high performances. Usually, perception system consists of multiple sensors, such as cameras, 2D/3D lidars or radars. The works presented in this Ph.D thesis concern several topics on cameras and lidar based perception for understanding dynamic scenes in urban environments. The works are composed of four parts.In the first part, a stereo vision based visual odometry is proposed by comparing several different approaches of image feature detection and feature points association. After a comprehensive comparison, a suitable feature detector and a feature points association approach is selected to achieve better performance of stereo visual odometry. In the second part, independent moving objects are detected and segmented by the results of visual odometry and U-disparity image. Then, spatial features are extracted by a kernel-PCA method and classifiers are trained based on these spatial features to recognize different types of common moving objects e.g. pedestrians, vehicles and cyclists. In the third part, an extrinsic calibration method between a 2D lidar and a stereoscopic system is proposed. This method solves the problem of extrinsic calibration by placing a common calibration chessboard in front of the stereoscopic system and 2D lidar, and by considering the geometric relationship between the cameras of the stereoscopic system. This calibration method integrates also sensor noise models and Mahalanobis distance optimization for more robustness. At last, dynamic occupancy grid mapping is proposed by 3D reconstruction of the environment, obtained from stereovision and Lidar data separately and then conjointly. An improved occupancy grid map is obtained by estimating the pitch angle between ground plane and the stereoscopic system. The moving object detection and recognition results (from the first and second parts) are incorporated into the occupancy grid map to augment the semantic meanings. All the proposed and developed methods are tested and evaluated with simulation and real data acquired by the experimental platform "intelligent vehicle SetCar" of IRTES-SET laboratory.

Les systèmes de perception, qui sont à la base du concept du véhicule intelligent, doivent répondre à des critères de performance à plusieurs niveaux afin d’assurer des fonctions d’aide à la conduite et/ou de conduite autonome. Aujourd’hui, la majorité des systèmes de perception pour véhicules intelligents sont basés sur la combinaison de données issues de plusieurs capteurs (caméras, lidars, radars, etc.). Les travaux de cette thèse concernent le développement d’un système de perception à base d’un capteur de vision stéréoscopique et d’un capteur lidar pour l’analyse de scènes dynamiques en environnement urbain. Les travaux présentés sont divisés en quatre parties.La première partie présente une méthode d’odométrie visuelle basée sur la stéréovision, avec une comparaison de différents détecteurs de primitives et différentes méthodes d’association de ces primitives. Un couple de détecteur et de méthode d’association de primitives a été sélectionné sur la base d’évaluation de performances à base de plusieurs critères. Dans la deuxième partie, les objets en mouvement sont détectés et segmentés en utilisant les résultats d’odométrie visuelle et l’image U-disparité. Ensuite, des primitives spatiales sont extraites avec une méthode basée sur la technique KPCA et des classifieurs sont enfin entrainés pour reconnaitre les objets en mouvement (piétons, cyclistes, véhicules). La troisième partie est consacrée au calibrage extrinsèque d’un capteur stéréoscopique et d’un Lidar. La méthode de calibrage proposée, qui utilise une mire plane, est basée sur l’exploitation d’une relation géométrique entre les caméras du capteur stéréoscopique. Pour une meilleure robustesse, cette méthode intègre un modèle de bruit capteur et un processus d’optimisation basé sur la distance de Mahalanobis. La dernière partie de cette thèse présente une méthode de construction d’une grille d’occupation dynamique en utilisant la reconstruction 3D de l’environnement, obtenue des données de stéréovision et Lidar de manière séparée puis conjointement. Pour une meilleure précision, l’angle entre le plan de la chaussée et le capteur stéréoscopique est estimé. Les résultats de détection et de reconnaissance (issus des première et deuxième parties) sont incorporés dans la grille d’occupation pour lui associer des connaissances sémantiques. Toutes les méthodes présentées dans cette thèse sont testées et évaluées avec la simulation et avec de données réelles acquises avec la plateforme expérimentale véhicule intelligent SetCar” du laboratoire IRTES-SET. / Intelligent vehicles require perception systems with high performances. Usually, perception system consists of multiple sensors, such as cameras, 2D/3D lidars or radars. The works presented in this Ph.D thesis concern several topics on cameras and lidar based perception for understanding dynamic scenes in urban environments. The works are composed of four parts.In the first part, a stereo vision based visual odometry is proposed by comparing several different approaches of image feature detection and feature points association. After a comprehensive comparison, a suitable feature detector and a feature points association approach is selected to achieve better performance of stereo visual odometry. In the second part, independent moving objects are detected and segmented by the results of visual odometry and U-disparity image. Then, spatial features are extracted by a kernel-PCA method and classifiers are trained based on these spatial features to recognize different types of common moving objects e.g. pedestrians, vehicles and cyclists. In the third part, an extrinsic calibration method between a 2D lidar and a stereoscopic system is proposed. This method solves the problem of extrinsic calibration by placing a common calibration chessboard in front of the stereoscopic system and 2D lidar, and by considering the geometric relationship between the cameras of the stereoscopic system. This calibration method integrates also sensor noise models and Mahalanobis distance optimization for more robustness. At last, dynamic occupancy grid mapping is proposed by 3D reconstruction of the environment, obtained from stereovision and Lidar data separately and then conjointly. An improved occupancy grid map is obtained by estimating the pitch angle between ground plane and the stereoscopic system. The moving object detection and recognition results (from the first and second parts) are incorporated into the occupancy grid map to augment the semantic meanings. All the proposed and developed methods are tested and evaluated with simulation and real data acquired by the experimental platform “intelligent vehicle SetCar” of IRTES-SET laboratory.

Workplace design practitioners and organizational managers are increasingly noticing all the various aspects in which the workplace affects organizations and their employees. The studies on the relationships between the workplace, organizations and their employees are more focused on psychology or facility management than the socio-spatial perspective. Workplace design, configuration and spatial features impact how well and how much a company can benefit from its human capital. Although the concept of the relation of workplace to an organization is not new, it is relatively unexamined. This thesis introduces a new set of spatial variables to workplace studies, following the concept of personal control. The discussed spatial variables effectively describe the features of workplace floor plan and the characteristics of a workstation. Furthermore, this dissertation develops a method that creates the link between workplace spatial setting and a sense of belonging, organizational outcomes – organizational commitment, work motivation, job satisfaction and work performance. Based on the detailed statistical analyses of a field survey that included 336 participants from 16 organizations, a model of spatial features influence sense of belonging and organizational outcomes was identified within this study. The research findings provide evidence for creating a workplace with a sense of belonging and better organizational outcomes through spatial design. This dissertation is comprised of six chapters. Chapter 1, an introduction, provides a general study background, discusses the problems to be solved in the study, and proposes an approach to deal with the target problems. Chapter 2 firstly reviews the current workplace studies related to spatial features. Secondly, it discusses the influence of workstation design on the human muscle system. Thirdly, it discusses the most relevant psychological issues at a workplace as stated by previous researches. Finally, the chapter reveals how a workplace affects the work of an organization. Chapter 3 specifies how workplace influences an employee’s sense of belonging and environmental control, and introduces the conceptual model. It also introduces the independent and dependent variables, generates research hypotheses. Chapter 4 describes the field survey design, procedures and the participants. It also covers the initial data analysis of the field survey: how the survey instrument, the questionnaire, was developed, commenting on all the aspects it includes – spatial experiences, work motivation, commitment, sense of belonging, job satisfaction and work performance. Chapter 5 is data analysis. This chapter discusses the research findings on workplace design features in relation to employees’ sense of belonging, satisfaction with ambient physical environment, and organizational outcomes – commitment, work motivation, job satisfaction and individual work performance. The final chapter summarizes the findings, comments on design implications of the research results, and draws conclusions. The dissertation ends in admitting the limitations of this research and discussing practical implications for future investigation.