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.

Efficient mutual data authentication using manually authenticated strings

Abstract: Solutions for an easy and secure setup of a wireless connection between two devices are urgently needed for WLAN, Wireless USB, Bluetooth and similar standards for short range wireless communication. All such key exchange protocols employ data authentication as an unavoidable subtask. As a solution, we propose an asymptotically optimal protocol family for data authentication that uses short manually authenticated out-of-band messages. Compared to previous articles by Vaudenay and Pasini the results of this paper are more general and based on weaker security assumptions. In addition to providing security proofs for our protocols, we focus also on implementation details and propose practically secure and efficient sub-primitives for applications.

Analysis and design of full-order flux observers for sensorless induction motors

Abstract: This paper deals with the flux estimation for sensorless induction motor drives. The linearized model of the speed-adaptive full-order flux observer is applied to help choosing the observer gain and the speed-adaptation gains. It is shown that the linearized model reveals potential instability problems that are difficult to find by other means. An observer gain and a method to vary the speed-adaptation gains in the field-weakening region are proposed. Experimental results show stable operation in a very wide speed range.

Self-Organization of Very Large Document Collections: State of the Art

Abstract: The Self-Organizing Map (SOM) forms a nonlinear projection from a high-dimensional data manifold onto a low-dimensional grid. A representative model of some subset of data is associated with each grid point. The SOM algorithm computes an optimal collection of models that approximates the data in the sense of some error criterion and also takes into account the similarity relations of the models. The models then become ordered on the grid according to their similarity. When the SOM is used for the exploration of statistical data, the data vectors can be approximated by models of the same dimensionality. When mapping documents, one can represent them statistically by their word frequency histograms or some reduced representations of the histograms that can be regarded as data vectors. We have made SOMs of collections of over one million documents. Each document is mapped onto some grid point, with a link from this point to the document database. The documents are ordered on the grid according to their contents and neighboring documents can be browsed readily. Keywords or key texts can be used to search for the most relevant documents first. New effective coding and computing schemes of the mapping are described.