Choice of the acquisition parameters for frequency estimation of a sine wave by interpolated DFT method

The accuracy of the frequency estimation of a sine wave corrupted by quantization noise using the Interpolated Discrete Fourier Transform (IpDFT) method is affected by systematic and random errors. The systematic errors are independent of quantization noise and are due to the interferences from the image parts of the sine wave spectrum. They depend of the acquisition parameter – integer number of recorded sine wave cycles. The random errors are due to the quantization noise and depend on the acquisition parameter – number of acquired samples. In this paper for the situations in which the IpDFT method uses the maximum side lobe decay windows a condition for the integer part of the number of recorded sine wave cycles is derived to ensure that the systematic errors are very small compared with the quantization errors. Also, a condition for the number of samples is derived to ensure that the absolute error of the normalized frequency due to the random errors is smaller than a desired value with a high confidence level. Carried out simulations confirm the validity of each derived condition.     

FAO’s AVHRR-based Agricultural Stress Index System (ASIS) for global drought monitoring

Agricultural production is highly dependent on climate variability in many parts of the world. In particular, drought may severely reduce crop yields, potentially affecting food availability at local, regional, and global scales. The Food and Agriculture Organization of the United Nations (FAO) operates the Global Early Warning System (GIEWS), which monitors global food supply and demand. One of the key challenges is to obtain synoptic information on a recurrent and timely basis about drought-affected agricultural zones. This is needed to quickly identify areas requiring immediate attention. The Agricultural Stress Index System (ASIS), based on imagery from the Advanced Very High Resolution Radiometer (AVHRR) sensors on board the National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational Satellite (METOP) satellites, was specifically developed to meet this need. The system is based on a methodology developed by Rojas, Vrieling, and Rembold over the African continent. This approach has been modified and adapted to the global scale to produce an agricultural stress index (ASI) representing, per administrative unit, the percentage of cropland (or pasture) areas affected by drought over the growing season. The vegetation health index (VHI), based on normalized difference vegetation index (NDVI) and temperature anomalies, is used as a drought indicator. A fused time series of AVHRR data from METOP and NOAA was used to produce a consistent time series of VHI at 1 km resolution. Global phenology maps, indicating the number of growing seasons and their start and end dates, were derived from a multi-annual image set of SPOT-Vegetation (1999–2011). The VHI time series and phenology maps were then combined to produce the ASI for the years 1984 to the present. This allowed evaluation of the suitability of the ASIS to identify drought using historical reports and ancillary data. As a result of this analysis, ASIS was positively evaluated to support the FAO early warning system.     

A framework for virtual organization requirements

Virtual organizations (VOs) are formed by an alliance of organizations linked by a partnership for dealing with emerging challenges. Information and communication technologies play a fundamental role facilitating cooperation, communication and collaboration among the VO members. The formal identification and representation of Requirements Engineering (RE) for one organization have been researched to a large extent along with several elicitation techniques. However, these techniques are not adequate for covering the major challenges of RE for VO. We explore the work done in the management and RE fields to propose a model-based framework for eliciting VOs’ requirements. The goal is to analyze the VO from two points of view: border (intra-organizational, inter-organizational and extra-organizational) and abstract (intentional, organizational and operational). This article includes the framework validation with a case study and a transformation process to develop partial Business Process Diagram from the intentional models.     

A participative end-user method for multi-perspective business process elicitation and improvement

A business process can be characterized by multiple perspectives (intentional, organizational, operational, functional, interactional, informational, etc). Business process modeling must allow different stakeholders to analyze and represent process models according to these different perspectives. This representation is traditionally built using classical data acquisition methods together with a process representation language such as BPMN or UML. These techniques and specialized languages can easily become hard, complex and time consuming. In this paper, we propose ISEA, a participative end-user modeling approach that allows the stakeholders in a business process to collaborate together in a simple way to communicate and improve the business process elicitation in an accurate and understandable manner. Our approach covers the organizational perspective of business processes, exploits the information compiled during the elicitation of the organizational perspective and touches lightly an interactional perspective allowing users to create customized interface sketches to test the user interface navigability and the coherence within the processes. Thus, ISEA can be seen as a participative end-user modeling approach for business process elicitation and improvement.     

Mining frequent conjunctive queries in relational databases through dependency discovery

We present an approach for mining frequent conjunctive in arbitrary relational databases. Our pattern class is the simple, but appealing subclass of simple conjunctive queries. Our algorithm, called Conqueror $^+$ , is capable of detecting previously unknown functional and inclusion dependencies that hold on the database relations as well as on joins of relations. These newly detected dependencies are then used to prune redundant queries. We propose an efficient database-oriented implementation of our algorithm using SQL and provide several promising experimental results.     

Polytope-based computation of polynomial ranges

Polynomial ranges are commonly used for numerically solving polynomial systems with interval Newton solvers. Often ranges are computed using the convex hull property of the tensorial Bernstein basis, which is exponential size in the number n of variables. In this paper, we consider methods to compute tight bounds for polynomials in n variables by solving two linear programming problems over a polytope. We formulate a polytope defined as the convex hull of the coefficients with respect to the tensorial Bernstein basis, and we formulate several polytopes based on the Bernstein polynomials of the domain. These Bernstein polytopes can be defined by a polynomial number of halfspaces. We give the number of vertices, the number of hyperfaces, and the volume of each polytope for n=1,2,3,4, and we compare the computed range widths for random n-variate polynomials for n?10. The Bernstein polytope of polynomial size gives only marginally worse range bounds compared to the range bounds obtained with the tensorial Bernstein basis of exponential size.     

Experimental tracking of silica dispersion into silicone polymer

This work deals with the experimental tracking of a dispersion of very fine silica particles in PDMS using a new, easily implemented methodology. The dispersion experiments are carried out in an 8-l capacity mixer equipped with a torquemeter. Using a Couette analogy, the torque-rotational speed data can be converted into process viscosity-effective shear rate data. Simultaneously, the Particle Size Distribution (PSD) evolution is followed through Laser Light Scattering measurements on diluted samples. The PSD evolution allowed us to interpret the dispersion process in terms of different mechanisms, such as breaking-up and erosion of aggregates, as well as, in some cases, re-agglomeration (or flocculation) of particles.     

Photocatalytic degradation of a phenylurea, chlortoluron, in water using an industrial titanium dioxide coated media

The degradation of an herbicide, chlortoluron, by UV/TiO₂ photocatalysis in water using industrial titanium dioxide coated non-woven paper was studied. The influence of parameters such as adsorption capacity, initial concentration and TiO₂ implementation (coated or in suspension) was investigated. The results emphasize the importance of operational conditions and reactor geometry on the kinetic degradation rate. The analysis of the first organic by-products suggests that a hydroxyl radical attack occurs on the phenyl ring and methyl groups of the chlortoluron before the opening of the aromatic ring. The fate of hetero-atoms has been investigated. The fates of the two nitrogen atoms in the molecule are different, not only did it depend on their initial oxidation degree, as both had the same oxidation degree, but also on the initial oxidation state of neighboring carbons. The chlorine atoms were completely released as chloride ions. The study of the influence of the oxygen concentration showed the importance of the oxygen mass transfer when designing an industrial photocatalytic reactor. An original calculation process was carried out to evaluate the adsorption constant of oxygen over the whole photocatalysis period and not only in the initial conditions as previously provided. This work points out the necessity of extending the understanding of the efficiency of chlortoluron removal or of the removal of various complex pesticide mixtures found in agricultural wastewater, using alternative, more industrially realistic, reactors.     

Experience with a Hybrid Processor: K-Means Clustering

We discuss hardware/software co-processing on a hybrid processor for a compute- and data-intensive multispectral imaging algorithm, k-means clustering. The experiments are performed on two models of the Altera Excalibur board, the first using the soft IP core 32-bit NIOS 1.1 RISC processor, and the second with the hard IP core ARM processor. In our experiments, we compare performance of the sequential k-means algorithm with three different accelerated versions. We consider granularity and synchronization issues when mapping an algorithm to a hybrid processor. Our results show that speedup of 11.8X is achieved by migrating computation to the Excalibur ARM hardware/software as compared to software only on a Gigahertz Pentium III. Speedup on the Excalibur NIOS is limited by the communication cost of transferring data from external memory through the processor to the customized circuits. This limitation is overcome on the Excalibur ARM, in which dual-port memories, accessible to both the processor and configurable logic, have the biggest performance impact of all the techniques studied.