Abstract
We propose two methods to characterize the Non-Common Path Aberrations (NCPA) on the TMT/NFIRAOS system; these techniques are known as Phase Diversity and Focal Plane Sharpening. We demonstrate the feasibility of these techniques on an experimental bench. We also explore the operational effects of Phase Diversity and how it might be best applied to a NFIRAOS-like system. In particular we explore the technique of single image Phase Diversity along with the effects of i) estimating either Zernike modes or Disk Harmonics, ii) using multiple diverse images, and III) using diversities other than focus. These operational considerations are explored in a simulation of the NFIRAOS system and we aim to find the best estimation of the NFIRAOS NCPA in the presence of different levels of noise. We find a realistic estimation of NFIRAOS NCPA would be with multi-image Phase Diversity-with focus-diverse images sampled at asymmetric positions on either side of the focal plane (with no estimation of the object).

Abstract
Agribusiness is an activity that generates huge amounts of temporal data. There are research centers that collect, store and create indexes of agricultural activities, providing multidimensional time series composed by years of data. In this paper, we are interested in studying the behavior of these time series, especially in what regards the evolution of agricultural price indexes over the years. We explore data mining techniques tailored to analyze temporal data, aiming to generate spatio-temporal trajectories of grains price indexes for six years of data. We propose the use of Tucker decomposition to both analyze the temporal patterns of these price indexes and map trajectories that represent their behavior over time in a concise and representative low-dimensional subspace. The case study presents an application of this methodology to real databases of price indexes of corn and soybeans in Brazil and the United States.

Abstract
Spatial analyses and competition assessments can be used by firms to identify target geographic markets for entry. By integrating these two kinds of analysis, this paper presents an innovative method that identifies target geographic markets for health care providers. In these target markets, supply is potentially insufficient to satisfy demand and competition problems that make entry unsuccessful are not expected to occur. Considering the Portuguese hospital health care market, an application of the method in a case study illustrates how the method works in practice.

Abstract
Developing robust and universal methods for unsupervised segmentation of moving objects in video sequences has proved to be a hard and challenging task. State-of-the-art methods show good performance in a wide range of situations, but systematically fail when facing more challenging scenarios. Lately, a number of image processing modules inspired in biological models of the human visual system have been explored in different areas of application. This paper proposes a bio-inspired boosting method to address the problem of unsupervised segmentation of moving objects in video that shows the ability to overcome some of the limitations of widely used state-of-the-art methods. An exhaustive set of experiments was conducted and a detailed analysis of the results, using different metrics, revealed that this boosting is more significant when challenging scenarios are faced and state-of-the-art methods tend to fail.

Abstract
In bioacoustic recognition approaches, a "flat" classifier is usually trained to recognize several species of anuran, where the number of classes is equal to the number of species. Consequently, the complexity of the classification function increases proportionally to the amount of species. To avoid this issue we propose a "hierarchical" approach that decomposes the problem into three taxonomic levels: the family, the genus, and the species level. To accomplish this, we transform the original single-label problem into a multi-dimensional problem (multi-label and multi-class) considering the Linnaeus taxonomy. Then, we develop a top-down method using a set of classifiers organized as a hierarchical tree. Thus, it is possible to predict the same set of species as a flat classifier, and additionally obtain new information about the samples and their taxonomic relationship. This helps us to understand the problem better and achieve additional conclusions by the inspection of the confusion matrices at the three levels of classification. In addition, we carry out our experiments using a Cross-Validation performed by individuals. This form of CV avoids mixing syllables that belong to the same specimens in the testing and training sets, preventing an overestimate of the accuracy and generalizing the predictive capabilities of the system. We tested our system in a dataset with sixty individual frogs, from ten different species, eight genus, and four families, achieving a final Micro-and Average-accuracy equal to 86% and 62% respectively.

Abstract
In this article we discuss the use of LBL acoustic networks for operations with multiple AUVs. Differently from standard LBL configurations, we propose to use the One-Way-Travel-Time of acoustic signals to compute the ranges between all the devices. Moreover, we derive the suitable algorithms for both the navigation of multiple vehicles, but also their external tracking. Experimental results are provided that support the evidence that our approach is successful in operations for multiple vehicles.