Helsinki University of Technology

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

PicSOM-self-organizing image retrieval with MPEG-7 content descriptors

Abstract: Development of content-based image retrieval (CBIR) techniques has suffered from the lack of standardized ways for describing visual image content. Luckily, the MPEG-7 international standard is now emerging as both a general framework for content description and a collection of specific agreed-upon content descriptors. We have developed a neural, self-organizing technique for CBIR. Our system is named PicSOM and it is based on pictorial examples and relevance feedback (RF). The name stems from "picture" and the self-organizing map (SOM). The PicSOM system is implemented by using tree structured SOMs. In this paper, we apply the visual content descriptors provided by MPEG-7 in the PicSOM system and compare our own image indexing technique with a reference system based on vector quantization (VQ). The results of our experiments show that the MPEG-7-defined content descriptors can be used as such in the PicSOM system even though Euclidean distance calculation, inherently used in the PicSOM system, is not optimal for all of them. Also, the results indicate that the PicSOM technique is a bit slower than the reference system in starting to find relevant images. However, when the strong RF mechanism of PicSOM begins to function, its retrieval precision exceeds that of the reference system.

Project Portfolio Control and Portfolio Management Performance in Different Contexts

Abstract: This article investigates the nature and relationship of project portfolio control techniques and portfolio management performance, and how this relationship is moderated by situational idiosyncrasies of internal and external dynamics, industries, governance types, and geographic location. A worldwide questionnaire with 242 responses was used, of which 136 high-performing responses were filtered out for quantitative analysis of best practices. Three portfolio control factors were identified: portfolio selection, portfolio reporting, and decision-making style. Two measures for portfolio management performance were identified: achievement of desired portfolio results and achievement of project and program purpose. The results indicate that different portfolio control mechanisms are associated with different performance measures. A contingency model was developed, including moderating effects by contextual variables.

Surface-plasmon-resonance sensor based on three-hole microstructured optical fiber.

Abstract: We propose a novel surface-plasmon-resonance sensor design based on coating the holes of a three-hole microstructured optical fiber with a low-index dielectric layer on top of which a gold layer is deposited. The use of all three fiber holes and their relatively large size should facilitate the fabrication of the inclusions and the infiltration of the analyte. Our numerical results indicate that the optical loss of the Gaussian guided mode can be made very small by tuning the thickness of the dielectric layer and that the refractive-index resolution for aqueous analytes is 1×10-4.

The distribution of the product of independent Rayleigh random variables

Abstract: We derive the exact probability density functions (pdf) and distribution functions (cdf) of a product of n independent Rayleigh distributed random variables. The case n=1 is the classical Rayleigh distribution, while n/spl ges/2 is the n-Rayleigh distribution that has recently attracted interest in wireless propagation research. The distribution functions are derived by using an inverse Mellin transform technique from statistics, and are given in terms of a special function of mathematical physics, the Meijer G-function. Series forms of the distribution function are also provided for n=3, 4, 5. We also derive a computationally simple moment-based estimator for the parameter occurring in the distribution, and evaluate its variance.