Device for characterization of thermal effusivity of liquids using photothermal beam deflection

We propose and study a novel optoelectronic device for thermal characterization of materials. It is based on monitoring the photothermal deflection of a laser beam within a slab of a thermo-optic material in thermal contact with the sample under study. An optical angle sensor is used to measure the laser deflection providing a simple and experimental arrangement. We demonstrate its principle and a simple procedure to measure thermal effusivity of liquids. The proposed device could be implemented into a compact sensor head for remote measurements using electrical and fiber optic links.     

Guidelines for the incremental identification of aspects in requirements specifications

The desired principle of separation of concerns in software development can be jeopardized by the so-called crosscutting concerns, which tend to be scattered over (and tangled with) the functionality of the modular units of a system. The correct identification of such concerns (and their encapsulation into separate artifacts) is thereby considered a way to improve software understanding and evolution. Pursuing a proper management of concerns from the requirements engineering stage can greatly benefit the entire software life-cycle. In this paper, we propose conceptual guidelines on how to perform the identification of crosscutting concerns in the process of building requirements specifications. We argue that the identification must be carried out in an incremental way, to encapsulate apart the crosscutting concerns even if they have not emerged completely yet.

Segmentation of cervical cell nuclei in high-resolution microscopic images: A new algorithm and a web-based software framework

In order to automate cervical cancer screening tests, one of the most important and longstanding challenges is the segmentation of cell nuclei in the stained specimens. Though nuclei of isolated cells in high-quality acquisitions often are easy to segment, the problem lies in the segmentation of large numbers of nuclei with various characteristics under differing acquisition conditions in high-resolution scans of the complete microscope slides. We implemented a system that enables processing of full resolution images, and proposes a new algorithm for segmenting the nuclei under adequate control of the expert user. The system can work automatically or interactively guided, to allow for segmentation within the whole range of slide and image characteristics. It facilitates data storage and interaction of technical and medical experts, especially with its web-based architecture. The proposed algorithm localizes cell nuclei using a voting scheme and prior knowledge, before it determines the exact shape of the nuclei by means of an elastic segmentation algorithm. After noise removal with a mean-shift and a median filtering takes place, edges are extracted with a Canny edge detection algorithm. Motivated by the observation that cell nuclei are surrounded by cytoplasm and their shape is roughly elliptical, edges adjacent to the background are removed. A randomized Hough transform for ellipses finds candidate nuclei, which are then processed by a level set algorithm. The algorithm is tested and compared to other algorithms on a database containing 207 images acquired from two different microscope slides, with promising results.     

Intelligent system for time series classification using support vector machines applied to supply-chain

To be able of anticipate demand is a key factor for commercial success in the supply-chain sector. The benefits can be grouped around two main concepts: firstly the optimization of operations through the development of optimal strategies for procurement and secondly the stock reduction that reduces storage costs, handling, etc. There is currently a variety of methods for making predictions, these methods vary from pure statistical methods such as exponential smoothing Holt-Winters or ARIMA models, to those based on artificial intelligence techniques like neural networks or fuzzy systems. However, despite being able to build accurate models, in managing the supply chain based on forecasts there is a problem known as “Forrester effect” irrespective of the model chosen. To monitor the impact of this effect, given the volume of information handled in large corporations, is a very expensive task (often manual) for such corporations because it requires investigating issues such as the adequacy of the model, allocation of known models to the sales time series, discovery of new patterns of behavior, etc. This article proposes an intelligent system based on support vector machines to solve problems concerning the allocation and discovery of new models. With this focus in mind, the system objective is to build groups of time series that share the same forecasting model. For the identification of new models, the system will assign “virtual models” for those groups that do not have a predefined pattern. Using the proposed method, it has been possible to group a sample of more than 14,000 time series (real data taken from a store) in around 70 categories, of which only 12 of them already grouped over 98% of the total.     

A consensus model for group decision making problems with linguistic interval fuzzy preference relations

Sometimes, we find decision situations in which it is difficult to express some preferences by means of concrete preference degrees. In this paper, we present a consensus model for group decision making problems in which the experts use linguistic interval fuzzy preference relations to represent their preferences. This model is based on two consensus criteria, a consensus measure and a proximity measure, and on the concept of coincidence among preferences. We compute both consensus criteria in the three representation levels of a preference relation and design an automatic feedback mechanism to guide experts in the consensus reaching process.     

Two-level classifier ensembles for credit risk assessment

Many techniques have been proposed for credit risk assessment, from statistical models to artificial intelligence methods. During the last few years, different approaches to classifier ensembles have successfully been applied to credit scoring problems, demonstrating to be generally more accurate than single prediction models. The present paper goes one step beyond by introducing composite ensembles that jointly use different strategies for diversity induction. Accordingly, the combination of data resampling algorithms (bagging and AdaBoost) and attribute subset selection methods (random subspace and rotation forest) for the construction of composite ensembles is explored with the aim of improving the prediction performance. The experimental results and statistical tests show that this new two-level classifier ensemble constitutes an appropriate solution for credit scoring problems, performing better than the traditional single ensembles and very significantly better than individual classifiers.     

Dynamic Simulation of Multi-Body Systems on Internet Using CORBA,Java and XML

In this paper a prototype of a computer program for multi-bodysimulation based on the use of CORBA, Java and XML is presented. Thisprototype makes use of a recursive dynamic formalism which outperformsmany implementations based on global formulations. The prototypepresented has been implemented using distributed Object OrientedProgramming techniques. The definition of the multi-body system is donethrough a data file defined using XML (eXtendible Markup Language). Thisprototype implements a `simulation service' on a network of computers,following a client-server scheme. This `simulation service' can operatein two different ways: offering a remote simulation on the server, orproviding a Java compiled code for local simulation on the client. Thepaper describes the practical implementation of such a service. The useof recursive functions and OOP permits the implementation of ageneral-purpose simulation code, which is compact, clear, clean,re-usable and easy to maintain. Once this Java code is compiled, it isstored in less than 125 Kbytes (including the numerical integrators).The Java programming language is used in order to assure compatibilitybetween different platforms. Finally, practical examples which make useof this approach are shown.     

3D Parallel Simulations of Fluctuation Effects in pHEMTs

The use of 3D simulations is essential in order to study the effects of fluctuations when devices are scaled to deep submicron dimensions. A 3D drift-diffusion device simulator has been developed to effectively simulate pseudomorphic high electron mobility transistors (pHEMTs) on a distributed memory multiprocessor computer. The drift-diffusion equations are discretized using a finite element method on an unstructured tetrahedral mesh. The obtained set of equations is solved in parallel on an arbitrary number of processors using the message-passing interface library. We have applied our simulator to a 120 nm pHEMT based on the Al_0.3Ga_0.7As/In_0.2Ga_0.8As interface and carried out a calibration to real experimental data.