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.

Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric Mixing Models

Abstract: This paper is the second in a series evaluating the microwave dielectric behavior of soil-water mixtures as a function of water content and soil textural composition. Part II draws upon the data presented in Part 1 [13] to develop appropriate empirical and theoretical dielectric mixing models for the 1.4-to 18-GHz region. A semiempirical mixing model based upon the index of refraction is presented, requiring only easily ascertained soil physical parameters such as volumetric moisture and soil textural composition as inputs. In addition, a theoretical model accounting explicitly for the presence of a hydration layer of bound water adjacent to hydrophilic soil particle surfaces is presented. A four-component dielectric mixing model treats the soil-water system as a host medium of dry soil solids containing randomly distributed and randomly oriented disc-shaped inclusions of bound water, bulk water, and air. The bulk water component is considered to be dependent upon frequency, temperature, and salinity. The soil solution is differentiated by means of a soil physical model into 1) a bound component and 2) a bulk soil solution. The performance of each model is evaluated as a function of soil moisture, soil texture, and frequency, using the dielectric measurements of five soils ranging from sandy loam to silty clay (as presented in Part I [13]) at frequencies between 1.4 and 18 GHz. The semiempirical mixing model yields an excellent fit to the measured data at frequencies above 4 GHz. At 1.

Interpreting magnetic fields of the brain: minimum norm estimates

Abstract: The authors have applied estimation theory to the problem of determining primary current distributions from measured neuromagnetic fields. In this procedure, essentially nothing is assumed about the source currents, except that they are spatially restricted to a certain region. Simulation experiments show that the results can describe the structure of the current flow fairly well. By increasing the number of measurements, the estimate can be made more localised. The current distributions may be also used as an interpolation and an extrapolation for the measured field patterns.

Random projection in dimensionality reduction: applications to image and text data

Abstract: Random projections have recently emerged as a powerful method for dimensionality reduction. Theoretical results indicate that the method preserves distances quite nicely; however, empirical results are sparse. We present experimental results on using random projection as a dimensionality reduction tool in a number of cases, where the high dimensionality of the data would otherwise lead to burden-some computations. Our application areas are the processing of both noisy and noiseless images, and information retrieval in text documents. We show that projecting the data onto a random lower-dimensional subspace yields results comparable to conventional dimensionality reduction methods such as principal component analysis: the similarity of data vectors is preserved well under random projection. However, using random projections is computationally significantly less expensive than using, e.g., principal component analysis. We also show experimentally that using a sparse random matrix gives additional computational savings in random projection.