We want to identify the factors of how to maintain a luxury fashion brand. To do this, we have to find what characterizes a real luxury brand. We want to go into depth and find the underlying and often invisible aspects within a luxury brand. The characteristics of a luxury brand are that the majority has their production in-house and provide high quality, made by an experienced and eccentric designer. The luxury brands have a distinct and clear image and identity. The brands are available in both stores and online shops and their offered products are in the high price sector. To maintain a luxury brand, the involvement and control of fabric, production and quality is essential. The designer must maintain the right appearance, image and identity in every step of the business. Moreover, a combination of different marketing channels is good to use, as it is a way to reach a large amount of people. If the luxury company decides to use social media, the engagement is vital. Regarding the distribution channels, a luxury brand should have an online shop and regular stores. Yet, it is important that the price reflect the products value. / Program: Magisterutbildning i Fashion Management

En studie av kopparvävens egenskaper i kombination med ljus.A study of the copper weave characteristics, in combination with light. / Program: Textildesignutbildningen

This thesis focuses on the task of dexterous manipulation of deformable objects, and in particular, clothing and garments. The task of manipulating deformable objects such as clothing can be broken down into a series of sub-tasks: (1) perceive and pick up garment, and then identify garment and recognize its pose; (2) using a manipulation strategy, regrasp the object to put it into a canonical state; (3) scan the surface of the object to find wrinkles, and use an iron to remove the wrinkles; (4) starting from the wrinkle-free state, fold the garment according to pre-planned sequence of manipulations with optimized trajectories; In this thesis, we will address all the phases of this process. A key contribution of the work is innovative use of simulation. We use offline simulation results to predict states of deformable objects (i.e. cloth, fabric, clothing) that are then recognized by a robotic vision/grasping system to correctly pick up and manipulate these objects. The recognition will use the simulation engine to deform the models in real time to find correct matches. The simulation will also be used to find the optimized trajectories for the manipulation of the garments, such as the garment folding.

Esta dissertação analisa os principais fatores que influenciaram a tipologia arquitetônica dos empreendimentos residenciais produzidos pela configuração produtiva incorporação imobiliária na cidade de São Paulo nos últimos 40 anos. Analisa também como a produção deste setor imobiliário vem alterando os tecidos urbanos pré-existentes da cidade com empreendimentos que alteram a sua tipologia arquitetônica original e o uso do espaço público no entorno das edificações, mantendo inalterado o traçado viário e demais espaços da cidade em outras palavras, as conseqüências do uso novo do traçado velho. / This dissertation analyzes the main factors that influenced the architectural typology of housing development projects built by real estate development corporations in the city of São Paulo in the last forty years. It also analyzes how the real estate sector\'s production has changed the city\'s pre-existent urban fabric with development projects that alter the architectural typology and the land use of the public space surrounding the new buildings, while at the same time keeping unchanged the road system and the remaining city spaces in other words, the consequences of the new use of the old grid.

El presente trabajo de investigación se desarrolló con la finalidad de ejercer la profesión de Administración de Negocios Globales, es por ello que se trabajó en las variables propuestas y su factibilidad en la ejecución en base a un estudio de mercado, un estudio organizacional sobre los aspectos legales y el cálculo de la inversión. El método de investigación utilizado fue el hipotético - deductivo y se aplicaron los diseños experimentales, longitudinales, prospectivos. Se consideró como muestra a 323 personas, todos de nacionalidad estadounidense. The present research work was developed in order to practice the profession of Global Business Administration, that is why we worked on the proposed variables and their feasibility in the execution based on a market study, an organizational study on the aspects legal and calculation of the investment. The research method used was the hypothetico - deductive and the experimental, longitudinal, prospective designs were applied. A sample of 323 people was considered, all of them of American nationality.

Fabrics are unique materials which consist of various properties affecting their performance and end-uses. A computerized fabric property evaluation and analysis method plays a crucial role not only in textile industry but also in scientific research. An accurate analysis and measurement of fabric property provides a powerful tool for gauging product quality, assuring regulatory compliance and assessing the performance of textile materials. This thesis investigated the solutions for applying computerized methods to evaluate and intelligently interpret the texture and material properties of fabric in an inexpensive and efficient way. Firstly, a method which allows automatic recognition of basic weave pattern and precisely measuring the yarn count is proposed. The yarn crossed-areas are segmented by a spatial domain integral projection approach. Combining fuzzy c-means (FCM) and principal component analysis (PCA) on grey level co-occurrence matrix (GLCM) feature vectors extracted from the segments enables to classify detected segments into two clusters. Based on the analysis on texture orientation features, the yarn crossed-area states are automatically determined. An autocorrelation method is used to find weave repeats and correct detection errors. The method was validated by using computer simulated woven samples and real woven fabric images. The test samples have various yarn counts, appearance, and weave types. All weave patterns of tested fabric samples are successfully recognized and computed yarn counts are consistent to the manual counts. Secondly, we present a methodology for using the high resolution 3D surface data of fabric samples to measure surface roughness in a nondestructive and accurate way. A parameter FDFFT, which is the fractal dimension estimation from 2DFFT of 3D surface scan, is proposed as the indicator of surface roughness. The robustness of FDFFT, which consists of the rotation-invariance and scale-invariance, is validated on a number of computer simulated fractal Brownian images. Secondly, in order to evaluate the usefulness of FDFFT, a novel method of calculating standard roughness parameters from 3D surface scan is introduced. According to the test results, FDFFT has been demonstrated as a fast and reliable parameter for measuring the fabric roughness from 3D surface data. We attempt a neural network model using back propagation algorithm and FDFFT for predicting the standard roughness parameters. The proposed neural network model shows good performance experimentally. Finally, an intelligent approach for the interpretation of fabric objective measurements is proposed using supported vector machine (SVM) techniques. The human expert assessments of fabric samples are used during the training phase in order to adjust the general system into an applicable model. Since the target output of the system is clear, the uncertainty which lies in current subjective fabric evaluation does not affect the performance of proposed model. The support vector machine is one of the best solutions for handling high dimensional data classification. The complexity problem of the fabric property has been optimally dealt with. The generalization ability shown in SVM allows the user to separately implement and design the components. Sufficient cross-validations are performed and demonstrate the performance test of the system.

Recent archaeological surveys on Peter Pond Lake have provided an opportunity to better elucidate the Late Woodland period of occupation in the upper Churchill River basin of northwestern Saskatchewan. Previous studies identified two pottery-bearing cultures in the region. This included the Kisis complex of the well-documented Selkirk composite, which is characterized by Winnipeg Fabric-impressed ware and dates from approximately A.D. 1300 through to the historic period. The second was a lesser-known archaeological entity represented by limited pottery recoveries from just two sites in the region. Early interpretations proposed this latter Narrows pottery represented an incursion of the plains adapted Old Womens phase into the region, with a suggested age of approximately A.D. 1000 to 1300. <p> Through a comprehensive analysis of pottery assemblages recently recovered from over twenty sites in the Peter Pond Lake region, this study was able to further validate the original description of the Kisis complex, but more significantly, propose a new pottery ware and complex for the enigmatic Narrows pottery assemblages. As a result, this pottery is now formally classified as Narrows Fabric-impressed ware and is considered characteristic of the Buffalo Lake complex. This complex is centered on Peter Pond Lake and appears to have an extensive presence in the region. Recently obtained radiocarbon dates indicate it is slightly younger than originally believed, and spanned from approximately A.D 1200 to 1500. Unlike previous interpretations, which suggest this pottery represents a plains influence in the region, the Buffalo Lake complex is considered a boreal forest manifestation, with origins that lie in the woodlands to the southeast. The pottery shares particular affinities with contemporaneous Sandy Lake ware that has a distribution extending from east-central Saskatchewan through to northwestern Ontario and Minnesota, with additional influences evident from Winnipeg Fabric-impressed ware found throughout northern Saskatchewan, Manitoba and Ontario.

Recent archaeological surveys on Peter Pond Lake have provided an opportunity to better elucidate the Late Woodland period of occupation in the upper Churchill River basin of northwestern Saskatchewan. Previous studies identified two pottery-bearing cultures in the region. This included the Kisis complex of the well-documented Selkirk composite, which is characterized by Winnipeg Fabric-impressed ware and dates from approximately A.D. 1300 through to the historic period. The second was a lesser-known archaeological entity represented by limited pottery recoveries from just two sites in the region. Early interpretations proposed this latter "Narrows" pottery represented an incursion of the plains adapted Old Women's phase into the region, with a suggested age of approximately A.D. 1000 to 1300. <p> Through a comprehensive analysis of pottery assemblages recently recovered from over twenty sites in the Peter Pond Lake region, this study was able to further validate the original description of the Kisis complex, but more significantly, propose a new pottery ware and complex for the enigmatic Narrows pottery assemblages. As a result, this pottery is now formally classified as Narrows Fabric-impressed ware and is considered characteristic of the Buffalo Lake complex. This complex is centered on Peter Pond Lake and appears to have an extensive presence in the region. Recently obtained radiocarbon dates indicate it is slightly younger than originally believed, and spanned from approximately A.D 1200 to 1500. Unlike previous interpretations, which suggest this pottery represents a plains influence in the region, the Buffalo Lake complex is considered a boreal forest manifestation, with origins that lie in the woodlands to the southeast. The pottery shares particular affinities with contemporaneous Sandy Lake ware that has a distribution extending from east-central Saskatchewan through to northwestern Ontario and Minnesota, with additional influences evident from Winnipeg Fabric-impressed ware found throughout northern Saskatchewan, Manitoba and Ontario.

This thesis identifies the causes and the process of the change of the historic city center of Samsun since the beginning of the 20th century by the role of the development and conservation activities. Because of rapid urban growth and the lack of conservation principles in city plans, most of the city centers have been subjected to growth-conservation contradiction. As a result of this, the historic city centers lose their unique characteristics and cultural heritage while they change rapidly. The city of Samsun became an important trade and transport center since the middle of the 19th century and significantly improved in economic, physical and cultural aspects. As the tobacco production has developed, tobacco factory has been founded and the port of Samsun has become the most important port of the Black Sea by the middle of the 19th century, the Non-Muslim traders began to settle in Samsun. Hence, Samsun became an important center of the Black Sea region with its commercial activities and multicultural social structure. However, after the middle of the 20th century, as a result of the rapid urban growth, Samsun started to lose its unique urban tissue and cultural heritage increasingly. Today the urban fabric of the city does not reflect its historical importance. The remaining cultural heritage of the city belonging to early 20th century is not legible within today&acute / s urban tissue. By identifying the change in urban tissue, the thesis aims to present the current situation of the cultural properties within today&acute / s urban fabric and prepare a basis for conservation.

Fabrics are unique materials which consist of various properties affecting their performance and end-uses. A computerized fabric property evaluation and analysis method plays a crucial role not only in textile industry but also in scientific research. An accurate analysis and measurement of fabric property provides a powerful tool for gauging product quality, assuring regulatory compliance and assessing the performance of textile materials. This thesis investigated the solutions for applying computerized methods to evaluate and intelligently interpret the texture and material properties of fabric in an inexpensive and efficient way. Firstly, a method which allows automatic recognition of basic weave pattern and precisely measuring the yarn count is proposed. The yarn crossed-areas are segmented by a spatial domain integral projection approach. Combining fuzzy c-means (FCM) and principal component analysis (PCA) on grey level co-occurrence matrix (GLCM) feature vectors extracted from the segments enables to classify detected segments into two clusters. Based on the analysis on texture orientation features, the yarn crossed-area states are automatically determined. An autocorrelation method is used to find weave repeats and correct detection errors. The method was validated by using computer simulated woven samples and real woven fabric images. The test samples have various yarn counts, appearance, and weave types. All weave patterns of tested fabric samples are successfully recognized and computed yarn counts are consistent to the manual counts. Secondly, we present a methodology for using the high resolution 3D surface data of fabric samples to measure surface roughness in a nondestructive and accurate way. A parameter FDFFT, which is the fractal dimension estimation from 2DFFT of 3D surface scan, is proposed as the indicator of surface roughness. The robustness of FDFFT, which consists of the rotation-invariance and scale-invariance, is validated on a number of computer simulated fractal Brownian images. Secondly, in order to evaluate the usefulness of FDFFT, a novel method of calculating standard roughness parameters from 3D surface scan is introduced. According to the test results, FDFFT has been demonstrated as a fast and reliable parameter for measuring the fabric roughness from 3D surface data. We attempt a neural network model using back propagation algorithm and FDFFT for predicting the standard roughness parameters. The proposed neural network model shows good performance experimentally. Finally, an intelligent approach for the interpretation of fabric objective measurements is proposed using supported vector machine (SVM) techniques. The human expert assessments of fabric samples are used during the training phase in order to adjust the general system into an applicable model. Since the target output of the system is clear, the uncertainty which lies in current subjective fabric evaluation does not affect the performance of proposed model. The support vector machine is one of the best solutions for handling high dimensional data classification. The complexity problem of the fabric property has been optimally dealt with. The generalization ability shown in SVM allows the user to separately implement and design the components. Sufficient cross-validations are performed and demonstrate the performance test of the system.