Electrostatic Halftoning

We introduce a new global approach for image dithering, stippling, screening and sampling. It is inspired by the physical principles of electrostatics. Repelling forces between equally charged particles create a homogeneous distribution in flat areas, while attracting forces from the image brightness values ensure a high approximation quality. Our model is transparent and uses only two intuitive parameters: One steers the granularity of our halftoning approach, and the other its regularity. We evaluate two versions of our algorithm: A discrete version for dithering that ties points to grid positions, as well as a continuous one which does not have this restriction, and can thus be used for stippling or sampling density functions. Our methods create very few visual artefacts, reveal favourable blue‐noise behaviour in the frequency domain, and have a lower approximation error under Gaussian convolution than state‐of‐the‐art methods.     

Phaseolin diversity as a possible strategy to improve the nutritional value of common beans (Phaseolus vulgaris)

This article proposes a new way to improve the protein quality of the common bean (Phaseolus vulgaris). It is based on the natural variability found in the different types of phaseolin, its main storage protein (40–50% of the total protein). Despite the fact that it is deficient in methionine content, phaseolin still represents the main source of that amino acid in the seed. More than 40 genetic variants, differing in subunit number (2–6) and molecular weight (40–54 kDa) have been analyzed. The similarity of the amino acid composition among phaseolins, suggests that a nutritional improvement cannot be expected from that side. Conversely, important variation in phaseolin susceptibility to proteolysis (ranging from 57% to 96% after cooking) has been observed, increasing the theoretical availability of methionine by up to 37%. Therefore, breeding programs based on highly-digestible phaseolin types could lead to the production of beans with higher protein quality.     

Forecast of mycotoxins levels in soft wheat, durum wheat and maize before harvesting with Qualim��tre?

Some mycotoxins are produced by several Fusarium species during cultivation and are found in wheat and maize grain. Since 2000, Syngenta has organised a large field survey. Agronomic and climatic data and grain samples have been collected for mycotoxin analysis in France and Belgium. The importance of the agroclimatic factors and their interactions on the mycotoxin levels in grain has been estimated. The climate around flowering stage is the major factor for deoxynivalenol (DON) in wheat. The main agronomic criteria are residue management and the variety sensitivity to this mycotoxin. For DON, zearalenone and fumonisins in maize, the climate from flowering stage until harvest is the major factor. Then, according to each mycotoxin, the main agronomic criteria are the harvest condition (date and grain moisture), the corn borer infestation and the variety sensitivity to these mycotoxins. Over the years, the database has been used to define models to predict the mycotoxin risk before harvesting. Grain purchasers enter the required agronomic data via the Syngenta Internet site and define their grain purchasing areas. They also define the flowering period for wheat and corn borer infestation for maize. After calculation which integrates climatic data, the purchasers receive reports with forecasts of mycotoxin levels. Prediction is based on different agro-climatic statistical models specifically configured according to the different regions of production in France and Belgium. This approach is called Qualimètre^? and was the first service in France and Belgium to forecast the grain mycotoxin level for wheat in 2004 and maize in 2006.     

Markov random fields for catadioptric image processing

Images obtained with catadioptric sensors contain significant deformations which prevent the direct use of classical image treatments. Thus, Markov random fields (MRF) whose usefulness is now obvious for projective image processing, cannot be used directly on catadioptric images because of the inadequacy of the neighborhood. In this paper, we propose to define a new neighborhood for MRF by using the equivalence theorem developed for central catadioptric sensors. We show the importance of this adaptation for segmentation, image restoration and motion detection.     

Block cipher based on reversible cellular automata

We propose a new encryption algorithm relying on reversible cellular automata (CA). The behavior complexity of CA and their parallel nature makes them interesting candidates for cryptography. The proposed algorithm belongs to the class of symmetric key systems. Marcin Seredynski: He is a Ph.D. student at University of Luxembourg and Polish Academy of Sciences. He received his M.S. in 2004 from Faculty of Electronics and Information Technology in Warsaw University of Technology. His research interests include cryptography, cellular automata, nature inspired algorithms and network security. Currently he is working on intrusion detection algorithms for ad-hoc networks. Pascal Bouvry, Ph.D.: He earned his undergraduate degree in Economical & Social Sciences and his Master degree in Computer Science with distinction (’91) from the University of Namur, Belgium. He went on to obtain his Ph.D. degree (’94) in Computer Science with great distinction at the University of Grenoble (INPG), France. His research at the IMAG laboratory focussed on Mapping and scheduling task graphs onto Distributed Memory Parallel Computers. Next, he performed post-doctoral researches on coordination languages and multi-agent evolutionary computing at CWI in Amsterdam. He gained industrial experience as manager of the technology consultant team for FICS in the banking sector (Brussels, Belgium). Next, he worked as CEO and CTO of SDC (Ho Chi Minh city, Vietnam) in the telecom, semi-conductor and space industry. After that, He moved to Montreal Canada as VP Production of Lat45 and Development Director for MetaSolv Software in the telecom industry. He is currently serving as Professor in the group of Computer Science and Communications (CSC) of the Faculty of Sciences, Technology and Communications of Luxembourg University and he is heading the Intelligent & Adaptive Systems lab. His current research interests include: ad-hoc networks & grid-computing, evolutionary algorithms and multi-agent systems.     

Possibilistic uncertainty handling for answer set programming

In this work, we introduce a new framework able to deal with a reasoning that is at the same time non monotonic and uncertain. In order to take into account a certainty level associated to each piece of knowledge, we use possibility theory to extend the non monotonic semantics of stable models for logic programs with default negation. By means of a possibility distribution we define a clear semantics of such programs by introducing what is a possibilistic stable model. We also propose a syntactic process based on a fix-point operator to compute these particular models representing the deductions of the program and their certainty. Then, we show how this introduction of a certainty level on each rule of a program can be used in order to restore its consistency in case of the program has no model at all. Furthermore, we explain how we can compute possibilistic stable models by using available softwares for Answer Set Programming and we describe the main lines of the system that we have developed to achieve this goal.