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.

Topographic Independent Component Analysis

Abstract: In ordinary independent component analysis, the components are assumed to be completely independent, and they do not necessarily have any meaningful order relationships. In practice, however, the estimated "independent" components are often not at all independent. We propose that this residual dependence structure could be used to define a topographic order for the components. In particular, a distance between two components could be defined using their higher-order correlations, and this distance could be used to create a topographic representation. Thus, we obtain a linear decomposition into approximately independent components, where the dependence of two components is approximated by the proximity of the components in the topographic representation.

Resonator-based analysis of the combination of mobile handset antenna and chassis

Abstract: The performance of the mobile phone handset antenna-chassis combination is analyzed based on an approximate decomposition of the waves on the structure into two resonant wavemodes: the antenna-element wavemode and the chassis wavemode. A double resonator equivalent circuit model is presented and used to estimate the impedance bandwidth and the respective distributions of radiation losses with typical parameter values at 900 and 1800 MHz. It is noticed that at 900 MHz, the radiation losses of the antenna element wavemode represent typically less than 10% of the total power. Thus, the antenna element works mainly as a matching element, which couples to the low-Q resonant wavemode of the chassis. At 1800 MHz, the contribution of the antenna element wavemode is larger. By enhancing the coupling and by tuning the chassis resonance, it is possible to obtain an impedance bandwidth of over 50% (6 dB return loss) at both at 900 and 1800 MHz. The results given by the equivalent circuit study are fully supported by those of three-dimensional phone-model simulations, including calculation of the SAR and efficiency values. In prototyping, the 6 dB bandwidth of 5.5% was obtained at 980 MHz with a nonradiating coupling element with a volume of 1.6 cm/sup 3/ on a 120 mm long chassis.

Glottal wave analysis with Pitch Synchronous Iterative Adaptive Inverse Filtering

Abstract: A new glottal wave analysis method, Pitch Synchronous Iterative Adaptive Inverse Filtering (PSIAIF) is presented. The algorithm is based on a previously developed method, Iterative Adaptive Inverse Filtering (IAIF). In the IAIF-method the glottal contribution to the speech spectrum is first estimated with an iterative structure. The vocal tract transfer function is modeled after eliminating the average glottal contribution. The glottal excitation is obtained by cancelling the effects of the vocal tract and lip radiation by inverse filtering. In the new PSIAIF-method the glottal pulseform is computed by applying the IAIF-algorithm twice to the same signal. The first IAIF-analysis gives as a result a glottal excitation that spans over several pitch periods. This pulseform is used in order to determine positions and lengths of frames for the pitch synchronous analysis. The final result is obtained by analysing the original speech signal with the IAIF-algorithm one fundamental period at a time. The PSIAIF-algorithm was applied in glottal wave analysis using both synthetic and natural vowels. The results show that the method is able to give a fairly accurate estimate for the glottal flow excluding the analysis of vowels with a low first formant that are produced with a pressed phonation type.

Statistical pattern recognition with neural networks: benchmarking studies

Abstract: Three basic types of neural-like networks (backpropagation network, Boltzmann machine, and learning vector quantization), were applied to two representative artificial statistical pattern recognition tasks, each with varying dimensionality. The performance of each network's approach to solving the tasks was evaluated and compared, both to the performance of the other two networks and to the theoretical limit. The learning vector quantization was further benchmarked against the parametric Bayes classifier and the k-nearest-neighbor classifier using natural speech data. A novel learning vector quantization classifier called LVQ2 is introduced. >