Improving Tuberculosis Diagnostics using Deep Learning and Mobile Health Technologies among Resource-poor Communities in Peru

As part of the mini-symposium entitled “Research on Digital Health for Designing Scalable Pervasive Healthcare Monitoring, Rehabilitation, and Home-based Healthcare Systems,” Dr. Alcantara discusses a project to improve the tuberculosis diagnosis in resource poor communities in Peru

Real-time action recognition using a multilayer descriptor with variable size

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Video analysis technology has become less expensive and more powerful in terms of storage resources and resolution capacity, promoting progress in a wide range of applications. Video-based human action detection has been used for several tasks in surveillance environments, such as forensic investigation, patient monitoring, medical training, accident prevention, and traffic monitoring, among others. We present a method for action identification based on adaptive training of a multilayer descriptor applied to a single classifier. Cumulative motion shapes (CMSs) are extracted according to the number of frames present in the video. Each CMS is employed as a self-sufficient layer in the training stage but belongs to the same descriptor. A robust classification is achieved through individual responses of classifiers for each layer, and the dominant result is used as a final outcome. Experiments are conducted on five public datasets (Weizmann, KTH, MuHAVi, IXMAS, and URADL) to demonstrate the effectiveness of the method in terms of accuracy in real time. (C) 2016 SPIE and IS&TVideo analysis technology has become less expensive and more powerful in terms of storage resources and resolution capacity, promoting progress in a wide range of applications. Video-based human action detection has been used for several tasks in surveill2501FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)SEM INFORMAÇÃOSEM INFORMAÇÃ

Algoritmo Paralelo para Morfismo de Imagem em Arquitetura Multinúcleo

Este artigo aborda a paralelização do algoritmo clássico demorfismo de imagens baseado em malha deformável. Devido às suas características, este algoritmo demanda intenso processamento computacional. Por outro lado, tem havido uma popularização de computadores multinúcleo oferecendo uma relação proveitosa entre custo e poder computacional. O objetivo deste trabalho é demonstrar o potencial de uma proposta de paralelismo, para um algoritmo clássico de morfismo, utilizando uma arquitetura multinúcleo popular e a linguagem Python. Foram realizados experimentos e discutidos seus resultados