Nematic elastomers: Gamma-limits for large bodies and small particles

We compute the large-body and the small-particle Gamma-limit of a family of energies for nematic elastomers. We work under the assumption of small deformations (linearized kinematics) and consider both compressible and incompressible materials. In the large-body asymptotics, even if we describe the local orientation of the liquid crystal molecules according to the model of perfect order (Frank theory), we prove that we obtain a fully biaxial nematic texture (that of the de Gennes theory) as a by-product of the relaxation phenomenon connected to Gamma-convergence. In the case of small particles, we show that formation of new microstructure is not possible, and we describe the map of minimizers of the Gamma-limit as the phase diagram of the mechanical model

Textos e contextos dos problemas de mediação de altura em livros do renascimento.

Esta pesquisa retrata uma investigação e uma análise sobre textos e contextos dos problemas de medição de alturas, em livros do período do Renascimento. Tendo por base teórica ideias dos historiadores Marc Bloch e Fernand Braudel, recorre à conjuntura social, econômica e cultural vivida pelos autores dos livros analisados, a fim de contextualizá-las no processo de produção dos mesmos. O tempo delimitado foi de longa duração, o Renascimento, e os lugares, Itália e França, onde viveram os autores das obras analisadas. Trata-se de uma pesquisa qualitativa de abordagem histórica e documental. O tema central da pesquisa constitui-se numa abordagem interpretativa do panorama histórico dos problemas de medição de alturas de objetos, considerando os enunciados, as linguagens, as ilustrações, os processos matemáticos resolutivos e os instrumentos de medidas apresentados por cada autor. A análise ateve-se em três contextos distintos de resolução desses problemas. Considerando os instrumentos de medidas utilizados, investigou-se: o gnômon em Leon Battista Alberti (1404-1472), o quadrante geométrico em Oronce Finé (1494-1555) e o esquadro móvel em Ottavio Fabri (c. 1544-c.1612). A construção e o uso dos instrumentos para medição foram cruciais para o processo de solução de inúmeros problemas práticos de cada época; as ferramentas matemáticas usadas eram elementares, mas suficientes para resolução dos problemas. Todos os autores empregaram, basicamente, as mesmas propriedades geométricas no processo de solução dos problemas e, suas obras, refletem o contexto social e cultural em que viveram e no qual produziram seus trabalhos. Cada um deles teve algum tipo de relevância na sua sociedade e contribuíram para o desenvolvimento científico da época, escrevendo livros, a partir das necessidades e dos problemas vivenciados. Os resultados deste trabalho, para além da construção histórica conjunta em torno do tema, levantam questões para reflexão sobre a inter-relação existente entre a história da matemática e a educação matemática

Bamboo: A fast descriptor based on AsymMetric pairwise BOOsting

A robust hash, or content-based fingerprint, is a succinct representation of the perceptually most relevant parts of a multimedia object. A key requirement of fingerprinting is that elements with perceptually similar content should map to the same fingerprint, even if their bit-level representations are different. In this work we propose BAMBOO (Binary descriptor based on AsymMetric pairwise BOOsting), a binary local descriptor that exploits a combination of content-based fingerprinting techniques and computationally efficient filters (box filters, Haar-like features, etc.) applied to image patches. In particular, we define a possibly large set of filters and iteratively select the most discriminative ones resorting to an asymmetric pair-wise boosting technique. The output values of the filtering process are quantized to one bit, leading to a very compact binary descriptor. Results show that such descriptor leads to compelling results, significantly outperforming binary descriptors having comparable complexity (e.g., BRISK), and approaching the discriminative power of state-of-the-art descriptors which are significantly more complex (e.g., SIFT and BinBoost)

A visual sensor network for object recognition: Testbed realization

This work describes the implementation of an object recognition service on top of energy and resource-constrained hardware. A complete pipeline for object recognition based on the BRISK visual features is implemented on Intel Imote2 sensor devices. The reference implementation is used to assess the performance of the object recognition pipeline in terms of processing time and recognition accuracy

Compress-then-analyze vs. analyze-then-compress: Two paradigms for image analysis in visual sensor networks

We compare two paradigms for image analysis in vi- sual sensor networks (VSN). In the compress-then-analyze (CTA) paradigm, images acquired from camera nodes are compressed and sent to a central controller for further analysis. Conversely, in the analyze-then-compress (ATC) approach, camera nodes perform visual feature extraction and transmit a compressed version of these features to a central controller. We focus on state-of-the-art binary features which are particularly suitable for resource-constrained VSNs, and we show that the ”winning” paradigm depends primarily on the network conditions. Indeed, while the ATC approach might be the only possible way to perform analysis at low available bitrates, the CTA approach reaches the best results when the available bandwidth enables the transmission of high-quality images

Coding binary local features extracted from video sequences

Local features represent a powerful tool which is exploited in several applications such as visual search, object recognition and tracking, etc. In this context, binary descriptors provide an efficient alternative to real-valued descriptors, due to low computational complexity, limited memory footprint and fast matching algorithms. The descriptor consists of a binary vector, in which each bit is the result of a pairwise comparison between smoothed pixel intensities. In several cases, visual features need to be transmitted over a bandwidth-limited network. To this end, it is useful to compress the descriptor to reduce the required rate, while attaining a target accuracy for the task at hand. The past literature thoroughly addressed the problem of coding visual features extracted from still images and, only very recently, the problem of coding real-valued features (e.g., SIFT, SURF) extracted from video sequences. In this paper we propose a coding architecture specifically designed for binary local features extracted from video content. We exploit both spatial and temporal redundancy by means of intra-frame and inter-frame coding modes, showing that significant coding gains can be attained for a target level of accuracy of the visual analysis task