Reconeixement de veu mitjançant xarxes neuronals

La interacció home-màquina per mitjà de la veu cobreix moltes àrees d’investigació. Es destaquen entre altres, el reconeixement de la parla, la síntesis i identificació de discurs, la verificació i identificació de locutor i l’activació per veu (ordres) de sistemes robòtics. Reconèixer la parla és natural i simple per a les persones, però és un treball complex per a les màquines, pel qual existeixen diverses metodologies i tècniques, entre elles les Xarxes Neuronals. L’objectiu d’aquest treball és desenvolupar una eina en Matlab per al reconeixement i identificació de paraules pronunciades per un locutor, entre un conjunt de paraules possibles, i amb una bona fiabilitat dins d’uns marges preestablerts. El sistema és independent del locutor que pronuncia la paraula, és a dir, aquest locutor no haurà intervingut en el procés d’entrenament del sistema. S’ha dissenyat una interfície que permet l’adquisició del senyal de veu i el seu processament mitjançant xarxes neuronals i altres tècniques. Adaptant una part de control al sistema, es podria utilitzar per donar ordres a un robot com l’Alfa6Uvic o qualsevol altre dispositiu.

User cost of capital with delayed investment grants

The usual assumption when considering investment grants is that grant payments are automatic when investments are undertaken. However, evidence from case studies shows that there can exist some time lag until funds are received by granted firms. In this paper the effects of delays in grant payments on the optimal investment policy of the firm are analyzed. It is shown how these delays lead not only to a higher financing cost but to an effective reduction in the investment grant rate, and in some cases, how benefits from investment grants could be canceled due to interactions with tax effects.

Application of fuzzy-rule-based postprocessing to correlation methods in pattern recognition

One of the most important problems in optical pattern recognition by correlation is the appearance of sidelobes in the correlation plane, which causes false alarms. We present a method that eliminate sidelobes of up to a given height if certain conditions are satisfied. The method can be applied to any generalized synthetic discriminant function filter and is capable of rejecting lateral peaks that are even higher than the central correlation. Satisfactory results were obtained in both computer simulations and optical implementation.

User cost of capital with delayed investment grants

The usual assumption when considering investment grants is that grant payments are automatic when investments are undertaken. However, evidence from case studies shows that there can exist some time lag until funds are received by granted firms. In this paper the effects of delays in grant payments on the optimal investment policy of the firm are analyzed. It is shown how these delays lead not only to a higher financing cost but to an effective reduction in the investment grant rate, and in some cases, how benefits from investment grants could be canceled due to interactions with tax effects.

Use of palm-oil by-products in chicken and rabbit feeds: effect on the fatty acid and tocol composition of meat, liver and plasma

This study was undertaken in the framework of a larger European project dealing with the characterization of fat co- and by-products from the food chain, available for feed uses. In this study, we compare the effects, on the fatty acid (FA) and tocol composition of chicken and rabbit tissues, of the addition to feeds of a palm fatty acid distillate, very low in trans fatty acids (TFA), and two levels of the corresponding hydrogenated by-product, containing intermediate and high levels of TFA. Thus, the experimental design included three treatments, formulated for each species, containing the three levels of TFA defined above. Obviously, due to the use of hydrogenated fats, the levels of saturated fatty acids (SFA) show clear differences between the three dietary treatments. The results show that diets high in TFA (76 g/kg fat) compared with those low in TFA (4.4 g/kg fat) led to a lower content of tocopherols and tocotrienols in tissues, although these differences were not always statistically significant, and show a different pattern for rabbit and chicken. The TFA content in meat, liver and plasma increased from low-to-high TFA feeds in both chicken and rabbit. However, the transfer ratios from feed were not proportional to the TFA levels in feeds, reflecting certain differences according to the animal species. Moreover, feeds containing fats higher in TFA induced significant changes in tissue SFA, monounsaturated fatty acids and polyunsaturated fatty acids composition, but different patterns can be described for chicken and rabbit and for each type of tissue.

User experience strategy

L'objectiu d'aquest projecte era desenvolupar una metodologia de user experience strategy per aconseguir que el client d'una pàgina web tingui una mateixa experiència tant si fa servir una plataforma com una altra, aplicant diferents tècniques de disseny centrades en l'usuari.

Maximum likelihood Linear Programming Data Fusion for Speaker Recognition

Biometric system performance can be improved by means of data fusion. Several kinds of information can be fused in order to obtain a more accurate classification (identification or verification) of an input sample. In this paper we present a method for computing the weights in a weighted sum fusion for score combinations, by means of a likelihood model. The maximum likelihood estimation is set as a linear programming problem. The scores are derived from a GMM classifier working on a different feature extractor. Our experimental results assesed the robustness of the system in front a changes on time (different sessions) and robustness in front a change of microphone. The improvements obtained were significantly better (error bars of two standard deviations) than a uniform weighted sum or a uniform weighted product or the best single classifier. The proposed method scales computationaly with the number of scores to be fussioned as the simplex method for linear programming.

Application of the Mutual Information Minimization to speaker recognition / identification improvement

In this paper we propose the inversion of nonlinear distortions in order to improve the recognition rates of a speaker recognizer system. We study the effect of saturations on the test signals, trying to take into account real situations where the training material has been recorded in a controlled situation but the testing signals present some mismatch with the input signal level (saturations). The experimental results for speaker recognition shows that a combination of several strategies can improve the recognition rates with saturated test sentences from 80% to 89.39%, while the results with clean speech (without saturation) is 87.76% for one microphone, and for speaker identification can reduce the minimum detection cost function with saturated test sentences from 6.42% to 4.15%, while the results with clean speech (without saturation) is 5.74% for one microphone and 7.02% for the other one.

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.

Remote collaborative multi-user informal learning experiences: design and evaluation

This paper presents a customizable system used to develop a collaborative multi-user problem solving game. It addresses the increasing demand for appealing informal learning experiences in museum-like settings. The system facilitates remote collaboration by allowing groups of learners tocommunicate through a videoconferencing system and by allowing them to simultaneously interact through a shared multi-touch interactive surface. A user study with 20 user groups indicates that the game facilitates collaboration between local and remote groups of learners. The videoconference and multitouch surface acted as communication channels, attracted students’ interest, facilitated engagement, and promoted inter- and intra-group collaboration—favoring intra-group collaboration. Our findings suggest that augmentingvideoconferencing systems with a shared multitouch space offers newpossibilities and scenarios for remote collaborative environments and collaborative learning.

A Secure Mobile-based Authentication System

Financial information is extremely sensitive. Hence, electronic banking must provide a robust system to authenticate its customers and let them access their data remotely. On the other hand, such system must be usable, affordable, and portable.We propose a challengeresponse based one-time password (OTP) scheme that uses symmetriccryptography in combination with a hardware security module. The proposed protocol safeguards passwords from keyloggers and phishing attacks.Besides, this solution provides convenient mobility for users who want to bank online anytime and anywhere, not just from their owntrusted computers.