A Canonical Genetic Algorithm for Blind Inversion of Linear Channels

It is well known the relationship between source separation and blind deconvolution: If a filtered version of an unknown i.i.d. signal is observed, temporal independence between samples can be used to retrieve the original signal, in the same manner as spatial independence is used for source separation. In this paper we propose the use of a Genetic Algorithm (GA) to blindly invert linear channels. The use of GA is justified in the case of small number of samples, where other gradient-like methods fails because of poor estimation of statistics.

Improving a leaves automatic recognition process using PCA

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used, and Principal Component Analysis (PCA) is applied in order to study which is the best number of components for the classification task, implemented by means of a Support Vector Machine (SVM) System. Obtained results are satisfactory, and compared with [4] our system improves the recognition success, diminishing the variance at the same time.

Automatic Recognition of Leaves by Shape Detection Pre-Processing with Ica

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used to characterize the leaves. Independent Component Analysis (ICA) is then applied in order to study which is the best number of components to be considered for the classification task, implemented by means of an Artificial Neural Network (ANN). Obtained results with ICA as a pre-processing tool are satisfactory, and compared with some references our system improves the recognition success up to 80.8% depending on the number of considered independent components.

Empirical mode decomposition-based face recognition system

In this work we explore the multivariate empirical mode decomposition combined with a Neural Network classifier as technique for face recognition tasks. Images are simultaneously decomposed by means of EMD and then the distance between the modes of the image and the modes of the representative image of each class is calculated using three different distance measures. Then, a neural network is trained using 10- fold cross validation in order to derive a classifier. Preliminary results (over 98 % of classification rate) are satisfactory and will justify a deep investigation on how to apply mEMD for face recognition.

Automatització d'un sistema de neteja Cleaning In Place

El projecte es realitza en una empresa embotelladora d'aigua, on el procés d'embotellat estàautomatitzat. En canvi les tasques de sanitització de les línies es realitzen de maneramanual.Actualment, l'empresa es troba en fase de llançament de nous productes (refrescos) i pertant serà necessària una nova línia de producció. Es vol que la tasca de neteja dels equipsinstal•lats per a la realització dels nous productes sigui totalment automatitzada.El sistema de neteja automatitzat escollit és del tipus Cleaning In Place (CIP), que consisteixa netejar sense haver de desmuntar cap part del procés, garantint la correcta sanitització detotes les parts que estan en contacte amb el producte durant el procés de producció. Esdissenya el mòdul mesclador on es faran els xarops que formen part de les noves begudes.El disseny s'ha d'adaptar a les necessitats productives i a les exigències pròpies d'unasanitització.Es realitza la selecció dels actuadors, pre-actuadors i sensors necessaris per al'automatització del procés de neteja i preparació de xarops. Aquests elements esgovernaran a través d'un autòmat, on es carregaran els programes de neteja i preparació dexarops amb llenguatge de programació grafcet. Finalment, també es crea l'SCADA devisualització i control dels processos a través d'un panell tàctil

Competing linguae france: the place of english and spanish for chinese students

This research project focuses on the role of English and Spanish as linguae francae. More specifically, the research attempts to answer the following questions: (i) What is the place of English and Spanish as linguae francae in the world, in general, and in China, in particular? (ii) What kinds of foreign language teaching/learning attitudes and practices are characteristic of the Chinese educational system? (iii) What are the motivations, expectations and experience of Chinese students in study abroad programmes, in general, and in the programme of the University of Lleida, in particular? The study constitutes an attempt to answer each of these questions in two ways: a review of the literature and a pilot study with 26 Chinese students at UdL. The research reveals that even though English is a very dominant foreign language in China, Spanish is a language on the rise and mainly for economic reasons. The results of the study also point at the impact of the dominance of the grammar-translation method in the perspective of Chinese students about language learning. Finally, the study shows the relevance of taking part in a SA programme for Chinese students as well as their experience of them.

A hybrid algorithm combining metaheuristic with Monte Carlo simulation for solving the Stochastic Flow Shop problem

In this paper, a hybrid simulation-based algorithm is proposed for the StochasticFlow Shop Problem. The main idea of the methodology is to transform the stochastic problem into a deterministic problem and then apply simulation to the latter. In order to achieve this goal, we rely on Monte Carlo Simulation and an adapted version of a deterministic heuristic. This approach aims to provide flexibility and simplicity due to the fact that it is not constrained by any previous assumption and relies in well-tested heuristics.

A hybrid algorithm combining metaheuristic with Monte Carlo simulation for solving the Stochastic Flow Shop problem

In this paper, a hybrid simulation-based algorithm is proposed for the StochasticFlow Shop Problem. The main idea of the methodology is to transform the stochastic problem into a deterministic problem and then apply simulation to the latter. In order to achieve this goal, we rely on Monte Carlo Simulation and an adapted version of a deterministic heuristic. This approach aims to provide flexibility and simplicity due to the fact that it is not constrained by any previous assumption and relies in well-tested heuristics.