Dutch Experience in Irrigation Water Management Modelling

Modeling the soil-water-crop atmosphere system to improve agricultural water management in arid zones (SWATRE); Groundwater approach to drainage design in irrigated agriculture (SGMP); Computer Program for flume and weir design (FLUME 3.0); A hydrodynamic model in the design of operational controllers for water systems (MODIS/MATLAB); Decision support simulation model for water management at a regional and national scale (SIWARE); Management of water delivery systems (RIBASIM/OMIS); A water allocation, scheduling, and monitoring program (WASAM); and Irrigation agencies, farmers and computational decision support tools

Real time automatic scene classification

This work has been done as part of the EU VICAR (IST) project and the EU SCOFI project (IAP). The aim of the first project was to develop a real time video indexing classification annotation and retrieval system. For our systems, we have adapted the approach of Picard and Minka [3], who categorized elements of a scene automatically with so-called ’stuff’ categories (e.g., grass, sky, sand, stone). Campbell et al. [1] use similar concepts to describe certain parts of an image, which they named “labeled image regions”. However, they did not use these elements to classify the topic of the scene. Subsequently, we developed a generic approach for the recognition of visual scenes, where an alphabet of basic visual elements (or “typed patches”) is used to classify the topic of a scene. We define a new image element: a patch, which is a group of adjacent pixels within an image, described by a specific local pixel distribution, brightness, and color. In contrast with pixels, a patch as a whole can incorporate semantics. A patch is described by a HSI color histogram with 16 bins and by three texture features (i.e., the variance and two values based on the two eigen values of the covariance matrix of the Intensity values of a mask ran over the image. For more details on the features used we refer to Israel et al. [2]. We aimed at describing each image as a vector with a fixed size and with information about the position of patches that is not strict (strict position would limit generalization). Therefore, a fixed grid is placed over the image and each grid cell is segmented into patches, which are then categorized by a patch classifier. For each grid cell a frequency vector of its classified patches is calculated. These vectors are concate- nated. The resulting vector describes the complete image. Several grids were applied and several patch sizes with the grid cells were tested. Grid size of 3x2 combined with patches of size 16x16 provided the best system performance. For the two classification phases of our system, back-propagation networks were trained: (i) classification of the patches and (ii) classification of the image vector, as a whole. The system was tested on the classification of eight categories of scenes from the Corel database: interiors, city/street, forest, agriculture/countryside, desert, sea, portrait, and crowds. Each of these categories were relevant for the VICAR project. Based upon their relevance for these eight categories of scenes, we choose nine categories for the classification of the patches: building, crowd, grass, road, sand, skin, sky, tree, and water. This approach was found to be successful (for classification of the patches 87.5% correct, and classification of the scenes 73.8% correct). An advantage of our method is its low computational complexity. Moreover, the classified patches themselves are intermediate image representations and can be used for image classification, image segmentation as well as for image matching. A disadvantage is that the patches with which the classifiers were trained had to be manually classified. To solve this drawback, we currently develop algorithms for automatic extraction of relevant patch types. Within the IST project VICAR, a video indexing system was built for the Netherlands Institute for Sound and Vision1, consisting of four independent mod- ules: car recognition, face recognition, movement recognition (of people) and scene recognition. The latter module was based upon the afore mentioned approach. Within the IAP project SCOFI, a real time Internet pornography filter was built, based upon this approach. The system is currently running on several schools in Europe. Within the SCOFI filtering system, our image classification system (with a performance of 92% correct) works together with a text classi- fication system that includes a proxy server (FilterX, developed by Demokritos, Greece) to classify web-pages. Its total performance is 0% overblocking and 1% underblocking

Collision warning system based on probability density functions

In this paper, a collision warning method between the host vehicle and target object(s) is studied. A probabilistic collision warning method is proposed, which is, in particular, useful for objects, e.g. vulnerable road users, which trajectories can rapidly change heading and/or velocity with respect to time. A vehicle is equipped with the probabilistic collision warning system and its functionality is validated in experiments with a bicyclist as target object

Multi-Level Visual Alphabets

A central debate in visual perception theory is the argument for indirect versus direct perception; i.e., the use of intermediate, abstract, and hierarchical representations versus direct semantic interpretation of images through interaction with the outside world. We present a content-based representation that combines both approaches. The previously developed Visual Alphabet method is extended with a hierarchy of representations, each level feeding into the next one, but based on features that are not abstract but directly relevant to the task at hand. Explorative benchmark experiments are carried out on face images to investigate and explain the impact of the key parameters such as pattern size, number of prototypes, and distance measures used. Results show that adding an additional middle layer improves results, by encoding the spatial co-occurrence of lower-level pattern prototypes

Simplex solid states of SU(N) quantum antiferromagnets

I define a set of wavefunctions for SU(N) lattice antiferromagnets, analogous to the valence bond solid states of Affleck, Kennedy, Lieb, and Tasaki (AKLT), in which the singlets are extended over N-site simplices. As with the valence bond solids, the new simplex solid (SS) states are extinguished by certain local projection operators, allowing us to construct Hamiltonians with local interactions which render the SS states exact ground states. Using a coherent state representation, we show that the quantum correlations in each SS state are calculable as the finite temperature correlations of an associated classical model, with N-spin interactions, on the same lattice. In three and higher dimensions, the SS states can spontaneously break SU(N) and exhibit N-sublattice long-ranged order, as a function of a discrete parameter which fixes the local representation of SU(N). I analyze this transition using a classical mean field approach. For N>2 the ordered state is selected via an "order by disorder" mechanism. As in the AKLT case, the bulk representations fractionalize at an edge, and the ground state entropy is proportional to the volume of the boundary.Comment: 14 pages, 8 figures, minor typos correcte

Water-promoted ammonia oxidation by a platinum amine complex in zeolite HZSM-5 catalyst

In this study it was found that [Pt(NH3)(4)]HZSM-5 is an active catalyst for the oxidation of ammonia at low temperature that, in contrast with other catalysts, becomes more active in the presence of water. Furthermore, the selectivity to nitrogen was found to increase when water is presen