Helsinki University of Technology

About: Helsinki University of Technology is a based out in . It is known for research contribution in the topics: Thin film & Vortex. The organization has 8962 authors who have published 20136 publications receiving 723787 citations. The organization is also known as: TKK & Teknillinen korkeakoulu.

Understanding mobile contexts

Abstract: Mobile urban environments present a challenge for context-aware computers because they differ from fixed indoor contexts such as offices, meeting rooms, and lecture halls in many important ways. Internal factors such as tasks and goals are different—external factors such as social resources are dynamic and unpredictable. An empirical, user-centred approach is needed to understand mobile contexts. In this paper, we present insights from an ethnomethodologically inspired study of 25 adult urbanites in Helsinki. The results describe typical phenomena in mobility: how situational and planned acts intermesh in navigation, how people construct personal and group spaces, and how temporal tensions develop and dissolve. Furthermore, we provide examples of social solutions to navigation problems, examine mobile multitasking, and consider design implications for mobile and context-aware human–computer interaction.

Efficient Variant of Algorithm FastICA for Independent Component Analysis Attaining the CramÉr-Rao Lower Bound

Abstract: FastICA is one of the most popular algorithms for independent component analysis (ICA), demixing a set of statistically independent sources that have been mixed linearly. A key question is how accurate the method is for finite data samples. We propose an improved version of the FastICA algorithm which is asymptotically efficient, i.e., its accuracy given by the residual error variance attains the Cramer-Rao lower bound (CRB). The error is thus as small as possible. This result is rigorously proven under the assumption that the probability distribution of the independent signal components belongs to the class of generalized Gaussian (GG) distributions with parameter alpha, denoted GG(alpha) for alpha>2. We name the algorithm efficient FastICA (EFICA). Computational complexity of a Matlab implementation of the algorithm is shown to be only slightly (about three times) higher than that of the standard symmetric FastICA. Simulations corroborate these claims and show superior performance of the algorithm compared with algorithm JADE of Cardoso and Souloumiac and nonparametric ICA of Boscolo on separating sources with distribution GG(alpha) with arbitrary alpha, as well as on sources with bimodal distribution, and a good performance in separating linearly mixed speech signals