Institution
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.
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the Enzymatic Hydrolysis of Cellulose: is the current theory of the Mechanisms of Hydrolyseys valid? Critical Reviews in Biotechnology: Vol. 5, No. 1, pp. 67-87.
Abstract: (1987). Enzymatic Hydrolysis of Cellulose: is the Current Theory of the Mechanisms of Hydrolysis Valid? Critical Reviews in Biotechnology: Vol. 5, No. 1, pp. 67-87.
146 citations
24 Apr 2006
TL;DR: The main contributions of this thesis are to show how the recently developed discrete-time unscented Kalman filter, particle filter, and the corresponding smoothers can be applied in the continuous-discrete setting.
Abstract: This thesis is concerned with recursive Bayesian estimation of non-linear dynamical systems, which can be modeled as discretely observed stochastic differential equations. The recursive real-time estimation algorithms for these continuous-discrete filtering problems are traditionally called optimal filters and the algorithms for recursively computing the estimates based on batches of observations are called optimal smoothers. In this thesis, new practical algorithms for approximate and asymptotically optimal continuous-discrete filtering and smoothing are presented. The mathematical approach of this thesis is probabilistic and the estimation algorithms are formulated in terms of Bayesian inference. This means that the unknown parameters, the unknown functions and the physical noise processes are treated as random processes in the same joint probability space. The Bayesian approach provides a consistent way of computing the optimal filtering and smoothing estimates, which are optimal given the model assumptions and a consistent way of analyzing their uncertainties. The formal equations of the optimal Bayesian continuous-discrete filtering and smoothing solutions are well known, but the exact analytical solutions are available only for linear Gaussian models and for a few other restricted special cases. The main contributions of this thesis are to show how the recently developed discrete-time unscented Kalman filter, particle filter, and the corresponding smoothers can be applied in the continuous-discrete setting. The equations for the continuous-time unscented Kalman-Bucy filter are also derived. The estimation performance of the new filters and smoothers is tested using simulated data. Continuous-discrete filtering based solutions are also presented to the problems of tracking an unknown number of targets, estimating the spread of an infectious disease and to prediction of an unknown time series.
146 citations
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TL;DR: Passive RC polyphase filters (PPFs) are analyzed in detail in this paper and the rules for optimal pole frequency planning to maximize the image-reject ratio provided by a PPF are given.
Abstract: Passive RC polyphase filters (PPFs) are analyzed in detail in this paper. First, a method to calculate the output signals of an n-stage PPF is presented. As a result, all relevant properties of PPFs, such as amplitude and phase imbalance and loss, are calculated. The rules for optimal pole frequency planning to maximize the image-reject ratio provided by a PPF are given. The loss of PPF is divided into two factors, namely the intrinsic loss caused by the PPF itself and the loss caused by termination impedances. Termination impedances known a priori can be used to derive such component values, which minimize the overall loss. The effect of parasitic capacitance and component value deviation are analyzed and discussed. The method of feeding the input signal to the first PPF stage affects the mechanisms of the whole PPF. As a result, two slightly different PPF topologies can be distinguished, and they are separately analyzed and compared throughout this paper. A design example is given to demonstrate the developed design procedure.
146 citations
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TL;DR: In this paper, linear and star-shaped polylactides were prepared by using alcohols with different numbers of hydroxyl groups as co-initiators, and the results showed that the polymerisation rate increases with the number of hydrastic groups in the coinitiator.
146 citations
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TL;DR: In this article, a model of tar decomposition over a dolomite catalyst in gasification conditions was modeled using benzene as a tar model compound and the reactions of the gas main components were included in the models studied.
Abstract: Tar decomposition over a dolomite catalyst in gasification conditions was modeled using benzene as a tar model compound. The reactions of the gas main components were included in the models studied. Kinetic studies were carried out at 750−925 °C and under ambient pressure in a plug flow reactor using a mixture of simulated gasification gas. Operation conditions without external or internal mass-transfer limitations were applied. Mechanistic models of the Langmuir−Hinshelwood type describing benzene decomposition were developed and tested. Experimental results could be best described by a kinetic rate equation based on the assumption that single-site adsorption of benzene was the rate-determining step and that adsorption of hydrogen inhibited benzene decomposition.
145 citations
Authors
Showing all 8962 results
Name | H-index | Papers | Citations |
---|---|---|---|
Ashok Kumar | 151 | 5654 | 164086 |
Hannu Kurki-Suonio | 138 | 433 | 99607 |
Nicolas Gisin | 125 | 827 | 64298 |
Anne Lähteenmäki | 116 | 485 | 81977 |
Riitta Hari | 111 | 491 | 43873 |
Andreas Richter | 110 | 769 | 48262 |
Mika Sillanpää | 96 | 1019 | 44260 |
Markku Leskelä | 94 | 876 | 36881 |
Ullrich Scherf | 92 | 735 | 36972 |
Mikko Ritala | 91 | 584 | 29934 |
Axel H. E. Müller | 89 | 564 | 30283 |
Karl Henrik Johansson | 88 | 1089 | 33751 |
T. Poutanen | 86 | 120 | 33158 |
Elina Lindfors | 86 | 420 | 23846 |
Günter Breithardt | 85 | 554 | 33165 |