Técnicas de paralelização em GPGPU aplicadas em algoritmo para remoção de ruído multiplicativo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Using GPU to exploit parallelism on cryptography

In this article we explore the NVIDIA graphical processing units (GPU) computational power in cryptography using CUDA (Compute Unified Device Architecture) technology. CUDA makes the general purpose computing easy using the parallel processing presents in GPUs. To do this, the NVIDIA GPUs architectures and CUDA are presented, besides cryptography concepts. Furthermore, we do the comparison between the versions executed in CPU with the parallel version of the cryptography algorithms Advanced Encryption Standard (AES) and Message-digest Algorithm 5 (MD5) wrote in CUDA. © 2011 AISTI.

Implementação do algoritmo de treinamento do classificador Floresta de Caminhos Ótimos em GPU

Pós-graduação em Ciência da Computação - IBILCE

Image Re-Ranking Acceleration on GPUs

Huge image collections are becoming available lately. In this scenario, the use of Content-Based Image Retrieval (CBIR) systems has emerged as a promising approach to support image searches. The objective of CBIR systems is to retrieve the most similar images in a collection, given a query image, by taking into account image visual properties such as texture, color, and shape. In these systems, the effectiveness of the retrieval process depends heavily on the accuracy of ranking approaches. Recently, re-ranking approaches have been proposed to improve the effectiveness of CBIR systems by taking into account the relationships among images. The re-ranking approaches consider the relationships among all images in a given dataset. These approaches typically demands a huge amount of computational power, which hampers its use in practical situations. On the other hand, these methods can be massively parallelized. In this paper, we propose to speedup the computation of the RL-Sim algorithm, a recently proposed image re-ranking approach, by using the computational power of Graphics Processing Units (GPU). GPUs are emerging as relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. We address the image re-ranking performance challenges by proposing a parallel solution designed to fit the computational model of GPUs. We conducted an experimental evaluation considering different implementations and devices. Experimental results demonstrate that significant performance gains can be obtained. Our approach achieves speedups of 7x from serial implementation considering the overall algorithm and up to 36x on its core steps.

Using GPUs for beamforming acceleration on SAFT imaging

SAFT techniques are based on the sequential activation, in emission and reception, of the array elements and the post-processing of all the received signals to compose the image. Thus, the image generation can be divided into two stages: (1) the excitation and acquisition stage, where the signals received by each element or group of elements are stored; and (2) the beamforming stage, where the signals are combined together to obtain the image pixels. The use of Graphics Processing Units (GPUs), which are programmable devices with a high level of parallelism, can accelerate the computations of the beamforming process, that usually includes different functions such as dynamic focusing, band-pass filtering, spatial filtering or envelope detection. This work shows that using GPU technology can accelerate, in more than one order of magnitude with respect to CPU implementations, the beamforming and post-processing algorithms in SAFT imaging. ©2009 IEEE.

A path- and label-cost propagation approach to speedup the training of the optimum-path forest classifier

In general, pattern recognition techniques require a high computational burden for learning the discriminating functions that are responsible to separate samples from distinct classes. As such, there are several studies that make effort to employ machine learning algorithms in the context of big data classification problems. The research on this area ranges from Graphics Processing Units-based implementations to mathematical optimizations, being the main drawback of the former approaches to be dependent on the graphic video card. Here, we propose an architecture-independent optimization approach for the optimum-path forest (OPF) classifier, that is designed using a theoretical formulation that relates the minimum spanning tree with the minimum spanning forest generated by the OPF over the training dataset. The experiments have shown that the approach proposed can be faster than the traditional one in five public datasets, being also as accurate as the original OPF. (C) 2014 Elsevier B. V. All rights reserved.

Predição de sítios de ligação para a cisplatina e transplatina baseada em ligações de hidrogênio

Pós-graduação em Biofísica Molecular - IBILCE

Implementação do algoritmo de treinamento do classificador floresta de caminhos ótimos em GPU

Técnicas de reconhecimento de padrões tem como principal objetivo classificar um conjunto de amostras, sendo o processo de aprendizado a fase de maior consumo de tempo. O problema pode piorar em ferramentas de classificação interativas, o que pode ser inaceitável para grandes bases de dados. Um exemplo de classificador é o baseado em Floresta de Caminhos Ótimos [8] - OPF. Dado que muitos trabalhos tem sido orientados à implementação de algoritmos de reconhecimento de padrões em ambiente General Purpose Graphics Processing Unit - GPGPU, o presente estudo objetivou a implementação da etapa de treinamento do classificador Floresta de Caminhos Ótimos em CUDA, visando aumentar a sua eficiência. A otimização do classificador em CUDA demonstrou uma fase de treinamento mais rápida que a versão original.

Kaposi's sarcoma-associated herpesvirus latency-associated nuclear antigen 1 mimics Epstein-Barr virus EBNA1 immune evasion through central repeat domain effects on protein processing

Kaposi's sarcoma-associated herpesvirus (KSHV/human herpesvirus 8 [HHV8]) and Epstein-Barr virus (EBV/HHV4) are distantly related gammaherpesviruses causing tumors in humans. KSHV latency-associated nuclear antigen 1 (LANA1) is functionally similar to the EBV nuclear antigen-1 (EBNA1) protein expressed during viral latency, although they have no amino acid similarities. EBNA1 escapes cytotoxic lymphocyte (CTL) antigen processing by inhibiting its own proteosomal degradation and retarding its own synthesis to reduce defective ribosomal product processing. We show here that the LANA1 QED-rich central repeat (CR) region, particularly the CR2CR3 subdomain, also retards LANA1 synthesis and markedly enhances LANA1 stability in vitro and in vivo. LANA1 isoforms have half-lives greater than 24 h, and fusion of the LANA1 CR2CR3 domain to a destabilized heterologous protein markedly decreases protein turnover. Unlike EBNA1, the LANA1 CR2CR3 subdomain retards translation regardless of whether it is fused to the 5′ or 3′ end of a heterologous gene construct. Manipulation of sequence order, orientation, and composition of the CR2 and CR3 subdomains suggests that specific peptide sequences rather than RNA structures are responsible for synthesis retardation. Although mechanistic differences exist between LANA1 and EBNA1, the primary structures of both proteins have evolved to minimize provoking CTL immune responses. Simple strategies to eliminate these viral inhibitory regions may markedly improve vaccine effectiveness by maximizing CTL responses. Copyright © 2007, American Society for Microbiology. All Rights Reserved.

Simplified architecture of a neurofuzzy controller applied on industrial systems for fluid flow control

This work presents a simplified architecture of a neurofuzzy controller for general purpose applications that tries to minimize the processing used in the several stages of hazy modeling of systems. The basic procedures of fuzzification and defuzzification are simplified to the maximum while the inference procedures are computed in a private way. The simplified architecture allows a fast and easy configuration of the neurofuzzy controller and the structuring rules that define the control actions is automatic. Th controller's Limits and performance are standardized and the control actions are previously calculated. For application, the industrial systems of fluid flow control will be considered.

Design of a fuzzy controller with simplified architecture

This work presents the design of a fuzzy controller with simplified architecture. This architecture tries to minimize the time processing used in? the several stages of hazy modeling of systems and processes. The basic procedures of fuzzification and defuzzification are simplified to the maximum while the inference procedures are computed in private way. Therefore, the simplified architecture allows a fast and easy configuration of the fuzzy controller.All rules that define the control actions are determined by inference procedures and the defuzzification is made automatically using a simplified algorithm. The fuzzy controller operation is standardized and the control actions are previously calculated For general-purpose application? ann results, the industrial systems of fluid pow cona ol will be considered.

New approach of induction machine drive using an adaptive digital PID controller with gain planning based on artificial neural networks

The present work introduces a new strategy of induction machines speed adjustment using an adaptive PID (Proportional Integral Derivative) digital controller with gain planning based on the artificial neural networks. This digital controller uses an auxiliary variable to determine the ideal induction machine operating conditions and to establish the closed loop gain of the system. The auxiliary variable value can be estimated from the information stored in a general-purpose artificial neural network based on CMAC (Cerebellar Model Articulation Controller).

NaOCl effect on biofilm produced by Staphylococcus aureus isolated from the milking environment and mastitis infected cows

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Analysis of the chronic intake of and withdrawal from diazepam on emotional reactivity and sensory information processing in rats

It has been demonstrated that, on abrupt withdrawal, patients with chronic exposure can experience a number of symptoms indicative of a dependent state. In clinical patients, the earliest to arise and most persistent signal of withdrawal from chronic benzodiazepine (Bzp) treatment is anxiety. In laboratory animals, anxiety-like effects following abrupt interruption of chronic Bzp treatment can also be reproduced. In fact, signs that oscillate from irritability to extreme fear behaviours and seizures have been described already. As anxiety remains one of the most important symptoms of Bzp withdrawal, in this study we evaluated the anxiety levels of rats withdrawn from diazepam. Also studied were the effects on the motor performance and preattentive sensory gating process of rats under diazepam chronic treatment and upon 48-h withdrawal on three animal models of anxiety, the elevated plus-maze (EPM), ultrasonic vocalizations (USV) and startle + prepulse inhibition tests. Data obtained showed an anxiolytic- and anxiogenic-like profile of the chronic intake of and withdrawal from diazepam regimen in the EPM test, 22-KHz USV and startle reflex. Diazepam chronic effects or its withdrawal were ineffective in promoting any alteration in the prepulse inhibition (PPI). However, an increase of PPI was achieved in both sucrose and diazepam pretreated rats on 48-h withdrawal, suggesting a procedural rather than a specific effect of withdrawal on sensory gating processes. It is also possible that the prepulse can function as a conditioned stimulus to informing the delivery of an aversive event, as the auditory startling-eliciting stimulus. All these findings are indicative of a sensitization of the neural substrates of aversion in diazepam withdrawn animals without concomitant changes on the processing of sensory information

Queda de pressão estática por meio de uma coluna de noz macadâmia

As unidades de beneficiamento de macadâmia adotam silos secadores distintos, para cada etapa de secagem, a fim de garantir a manutenção da qualidade do produto pela redução da umidade a níveis desejáveis. Diante da necessidade de quantificar a resistência apresentada pelas nozes, submetidas a diferentes fluxos de ar durante a secagem, bem como avaliar a possibilidade de utilização de modelos empíricos, que estimem o gradiente de pressão a partir da vazão de ar, conduziram-se vários testes em laboratório para obtenção de dados experimentais e ajuste de modelos. Frutos de macadâmia (M. integrifolia), com umidade de 0,11 b.s., após limpeza e classificação, foram colocados no interior de um protótipo constituído por uma coluna de chapa galvanizada (com tomadas para medição da pressão estática), plenum e ventilador, sendo submetidos a diferentes fluxos de ar. Os testes consistiram de três medidas por profundidade, para cada um dos três lotes de nozes, perfazendo um total de nove medidas de pressão estática por profundidade na coluna. Os resultados obtidos permitiram concluir que os fluxos de ar testados apresentaram efeito significativo sobre a queda de pressão estática na coluna de macadâmia, a qual aumentou linearmente com a profundidade. Os dados experimentais ajustaram-se muito bem aos modelos de Shedd e Hunter, sugerindo sua boa aplicabilidade para a macadâmia.