Statistical classification of road pavements using near field vehicle rolling noise measurements

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.

O declínio e a ascensão da comunicação word-of-mouth

As questões de liderança de opinião tornaram-se um tema cada vez mais interessante na nossa contemporaneidade. Quer se considere, ou não, que os ditos “novos media” provocaram uma revolução na forma como interagimos, o que é inquestionável é o seu papel na forma como somos influenciados e influenciamos, pelo menos, no que às sociedades ditas como desenvolvidas se refere. Partindo do interesse pelo estudo do word-of-mouth eletrónico, esta dissertação pretende contribuir para um melhor conhecimento do Twitter enquanto instrumento de influência. De um ponto de vista empírico este trabalho centrou-se na análise do Twitter de Arianna Huffington e dos seus seguidores no Twitter.

A relação entre satisfação de cliente e word-of-mouth

Dissertação apresentada à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Publicidade e Marketing.