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.

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.

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

Pós-graduação em Biofísica Molecular - IBILCE

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.

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.

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

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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

Pós-graduação em Ciência da Computação - IBILCE

Territorialização das agroindústrias canavieiras no Pontal do Paranapanema e os desdobramentos para o trabalho

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Ectoparasitos de tilápias do Nilo (Oreochromis niloticus) cultivados em sistemas de tanques-rede e da ictiofauna associada à piscicultura na represa de Chavantes, município de Ipaussu, São Paulo

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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)

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.

Rapid test for the evaluation of the activity of the prodrug hydroxymethylnitrofurazone in the processing of Trypanosoma cruzi messenger RNAs

No fully effective treatment has been developed since the discovery of Chagas' disease by Carlos Chagas in 1909. Since drug-resistant Trypanosoma cruzi strains are occurring and the current therapy is effectiveness in the acute phase but with various adverse side effects, more studies are needed to characterize the susceptibility of T. cruzi to new drugs. Many natural and/or synthetic substances showing trypanocidal activity have been used, even though they are not likely to be turned into clinically approved drugs. Originally, drug screening was performed using natural products, with only limited knowledge of the molecular mechanism involved in the development of diseases. Trans-splicing, which is unusual RNA processing reaction and occurs in nematodes and trypanosomes, implies the processing of polycistronic transcription units into individual mRNAs; a short transcript spliced leader (SL RNA) is trans-spliced to the acceptor pre-mRNA, giving origin to the mature mRNA. In the present study, permeable cells of T. cruzi epimastigote forms (Y, BOL and NCS strains) were treated to evaluate the interference of two drugs (hydroxymethylnitrofurazone - NFOH-121 and nitrofurazone) in the trans-splicing reaction using silver-stained PAGE analysis. Both drugs induced a significant reduction in RNA processing at concentrations from 5 to 12.5 µM. These data agreed with the biological findings, since the number of parasites decreased, especially with NFOH-121. This proposed methodology allows a rapid and cost-effective screening strategy for detecting drug interference in the trans-splicing mechanism of T. cruzi.

Recent advances in brain physiology and cognitive processing

The discovery of participation of astrocytes as active elements in glutamatergic tripartite synapses (composed by functional units of two neurons and one astrocyte) has led to the construction of models of cognitive functioning in the human brain, focusing on associative learning, sensory integration, conscious processing and memory formation/retrieval. We have modelled human cognitive functions by means of an ensemble of functional units (tripartite synapses) connected by gap junctions that link distributed astrocytes, allowing the formation of intra- and intercellular calcium waves that putatively mediate large-scale cognitive information processing. The model contains a diagram of molecular mechanisms present in tripartite synapses and contributes to explain the physiological bases of cognitive functions. It can be potentially expanded to explain emotional functions and psychiatric phenomena. © MSM 2011.

Trypanosoma cruzi: Evaluation of (-)-cubebin derivatives activity in the messenger RNAs processing

No fully effective treatment has been developed since the discovery of Chagas' disease. Since drug-resistant Trypanosoma cruzi strains are occurring and the current therapy is effective in the acute phase but with various adverse side effects, more studies are needed to characterize the susceptibility of T. cruzi to new drugs. Pre-mRNA maturation in trypanosomatids occurs through a process called trans-splicing, which is unusual RNA processing reaction, and it implies the processing of polycistronic transcription units into individual mRNAs; a short transcript spliced leader (SL RNA) is trans-spliced to the acceptor pre-mRNA, giving origin to the mature mRNA. Cubebin derivatives seem to provide treatments with less collateral effects than benznidazole and showed similar or better trypanocidal activities than benznidazole. Therefore, the cubebin derivatives ((-)-6,6′-dinitrohinokinin (DNH) and (-)-hinokinin (HQ)) interference in the mRNA processing was evaluated using T. cruzi permeable cells (Y and BOL (Bolivia) strains) following by RNase protection reaction. These substances seem to intervene in any step of the RNA transcription, promoting alterations in the RNA synthesis, even though the RNA processing mechanism still occurs. Furthermore, HQ presented better activity against the parasites than DNH, meaning that BOL strain seems to be more resistant than Y. © 2011 Springer-Verlag.