Estudio de verificación biométrica de voz

En este proyecto estudia la posibilidad de realizar una verificación de locutor por medio de la biometría de voz. En primer lugar se obtendrán las características principales de la voz, que serían los coeficientes MFCC, partiendo de una base de datos de diferentes locutores con 10 muestras por cada locutor. Con estos resultados se procederá a la creación de los clasificadores con los que luego testearemos y haremos la verificación. Como resultado final obtendremos un sistema capaz de identificar si el locutor es el que buscamos o no. Para la verificación se utilizan clasificadores Support Vector Machine (SVM), especializado en resolver problemas biclase. Los resultados demuestran que el sistema es capaz de verificar que un locutor es quien dice ser comparándolo con el resto de locutores disponibles en la base de datos. ABSTRACT. Verification based on voice features is an important task for a wide variety of applications concerning biometric verification systems. In this work, we propose a human verification though the use of their voice features focused on supervised training classification algorithms. To this aim we have developed a voice feature extraction system based on MFCC features. For classification purposed we have focused our work in using a Support Vector Machine classificator due to it’s optimization for biclass problems. We test our system in a dataset composed of various individuals of di↵erent gender to evaluate our system’s performance. Experimental results reveal that the proposed system is capable of verificating one individual against the rest of the dataset.

Estudo in silico das interações da protease NS3-NS2B de DENV-2 com o inibidor peptídico Bz-nKRR-H com finalidades terapêuticas

Dengue virus is an important patogen that causes Dengue desease in all world, and belongs to Flavivirus gender. The virus consists of enveloped RNA with a single strand positive sense, 11Kb genome. The RNA is translated into a polyprotein precursor, wich is cleaved into 3 structural proteins (C, prM e E) and 7 non-structural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B e NS5). The NS3 is a multifunctional protein, that besides to promote the polyprotein precursor cleavage, also have NTPase, helicase and RTPase activity. The NS3 needs a hydrophilic segment of 40 residues from the transmembrane NS2B protein (who acts like cofator) to realize this functions. Actually, there's no vacines available on the market, and the treatment are just symptomatic. The tetrapeptide inhibitor Bz-Nle-Lys-Arg-Arg-H (Ki de 5,8-7,0 M) was showed as a potent inhibitor μ for NS3prot in Dengue virus. That is a inteligent alternative to treat the dengue desease. The present work aimed analyse the interactions of the ligand bounded to the activity site to provid a clear and depth vision of that interaction. For this purpouse, it was conducted an in silico study, by using quantum mechanical calculations based on Density Functional Theory (DFT), with Generalized Gradient approximation (GGA) to describe the effects of exchange and correlation. The interaction energy of each amino acid belonging to the binding site to the ligand was calculated the using the method of molecular fragmentation with conjugated caps (MFCC). Besides energy, we calculated the distances, types of molecular interactions and atomic groups involved. The theoretical models used were satisfactory and show a more accurate description when the dielectric constant = 20 ε and 80 was used. The results demonstrate that the interaction energy of the system reached convergence at 13.5 A. Within a radius of 13,5A the most important residues were identified. Met49, Met84 and Asp81 perform interactions of hydrogen with the ligant. The Asp79 and Asp75 residues present high energy of attraction. Arg54, Arg85 and Lys 131 perform hydrogen interactions with the ligand, however, appear in BIRD graph having high repulsion energy with the inhibitor. The data also emphasizes the importance of residue Tyr161 and the involvement of the catalytic triad composed by Asp75, His51 and Ser135

Bioquímica quântica na diferenciação dos níveis de ativação de receptores AMPA por agonistas parciais Wilardina

In the central nervous system (CNS) of mammalian, fast synaptic transmission between nerve cells is performed primarily by α-amino-3-hydroxy-5-methyl-4- isoxazolepropionic acid (AMPA) receptors, an ionotropic glutamate receptor that is related with learning, memory and homeostasis of the nervous system. Impairments in their functions are correlated with development of many brain desorders, such as epilepsy, schizophrenia, autism, Parkinson and Alzheimer. The use of willardiine analogs has been shown a powerful tool to understanding of activation and desensitization mechanisms of this receptors, because the modification of a single ligand atom allows the observation of varying levels of efficacy. In this work, taking advantage of Fluorine Willardiine (1.35Å), Hydrogen Willardiine (1.65Å), Bromine Willardiine (1.8Å) and Iodine Willardiine (2.15Å) structures co-crystalized with GluA2 with codes 1MQI, 1MQJ, 1MQH and 1MQG, we attempted to energetically differentiate the four ligands efficacy. The complexes were submitted to energetic calculations based on density functional theory (DFT), under the optics of molecular fractionation with conjugate caps (MFCC) method. Obtained results show a relationship between the energetic values and willardiines efficacy order (FW> HW > BrW > IW), also show the importance of E705, R485, Y450, S654, T655, T480 e P478 as the amino acids that contribute most strongly with the interaction of four partial agonists. Furthermore, we outlined the M708 behaviour, attracted by FW and HW ligands, and repels by BrW and IW. With the datas reported on this work, it is possible for a better understanding of the AMPA receptor, which can serve as an aid in the development of new drugs for this system.

Detection and Classification of Whale Acoustic Signals

This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification.

In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information.

In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data.

Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear.

We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.