Geolocation of Multiple Targets from Airborne Video Without Terrain Data

The task of geolocating targets from airborne video is required for many applications in surveillance, law enforcement, reconnaissance, etc. The usual approaches to target geolocation involve terrain data, single target tracking, gimbal control of camera heads, altimeters, etc. The main goal of this research is to eliminate those requirements and still develop an accurate, efficient, and robust vision-based method for geolocation that can be carried out for multiple targets simultaneously. In that sense, our main contributions to the state-of-the-art in geolocation are fourfold: 1) to eliminate the requirement for gimbal control of the cameras or any particular path planning control for the UAV; 2) to perform instaneous geolocation of multiple targets; 3) to eliminate the requirements for geo-referenced terrain database (elevation maps) or for an altimeter that provides the UAV’s and target’s altitudes; and 4) to use one single camera while still maintaining good overall accuracy. In order to achieve that, the only requirements for our proposed method are: that the intrinsic parameters of the camera be known; that the on board camera be equipped with global positioning system (GPS) and inertial measurement unit (IMU); and that the height of the vehicle can be calculated using feature points extracted from the ground surrounding the image of the targets. To satisfy the first two requirements, we developed and tested a robust calibration procedure that can estimate not only the intrinsic parameters of the camera, but also the IMU-camera parameters (also know in the robotic circles as the hand-eye calibration). The last requirement was addressed using a pseudo-stereo vision technique that maximizes the distance between stereo pairs (baseline) while keeping large the number of common feature points extracted by the algorithm. The result is a method that can reach approximately 25 m of accuracy for an UAV flying at 155 m away from the target. Such performance is demonstrated by computer simulation, in-scale data using a model city, and real airborne video with ground truth.     

Model-based testing approaches selection for software projects

Selecting software technologies for software projects represents a challenge to software engineers. It is known that software projects differ from each other by presenting different characteristics that can complicate the selection of such technologies. This is not different when considering model-based testing. There are many approaches with different characteristics described in the technical literature that can be used in software projects. However, there is no indication as to how they can fit a software project. Therefore, a strategy to select model-based testing approaches for software projects called Porantim is fully described in this paper. Porantim is based on a body of knowledge describing model-based testing approaches and their characterization attributes (identified by secondary and primary experimental studies), and a process to guide by adequacy and impact criteria regarding the use of this sort of software technology that can be used by software engineers to select model-based testing approaches for software projects.     

Algorithms for the Homogeneous Set Sandwich Problem

A homogeneous set is a non-trivial module of a graph, i.e. a non-empty, non-unitary, proper subset of a graph's vertices such that all its elements present exactly the same outer neighborhood. Given two graphs the Homogeneous Set Sandwich Problem (HSSP) asks whether there exists a sandwich graph which has a homogeneous set. In 2001 Tang et al. published an all-fast algorithm which was recently proven wrong, so that the HSSP's known upper bound would have been reset thereafter at the former determined by Cerioli et al. in 1998. We present, notwithstanding, new deterministic algorithms which have it established at We give as well two even faster randomized algorithms, whose simplicity might lend them didactic usefulness. We believe that, besides providing efficient easy-to-implement procedures to solve it, the study of these new approaches allows a fairly thorough understanding of the problem.     

Extraction and characterization of highly purified collagen from bovine pericardium for potential bioengineering applications

Bovine pericardium is widely used as a raw material in bioengineering as a source of collagen, a fundamental structural molecule. The physical, chemical, and biocompatibility characteristics of these natural fibers enable their broad use in several areas of the health sciences. For these applications, it is important to obtain collagen of the highest possible purity. The lack of a method to produce these pure biocompatible materials using simple and economically feasible techniques presents a major challenge to their production on an industrial scale. This study aimed to extract, purify, and characterize the type I collagen protein originating from bovine pericardium, considered to be an abundant tissue resource. The pericardium tissue was collected from male animals at slaughter age. Pieces of bovine pericardium were enzymatically digested, followed by a novel protocol developed for protein purification using ion-exchange chromatography. The material was extensively characterized by electrophoresis, scanning electron microscopy, energy dispersive X-ray spectroscopy, and infrared spectroscopy. The results showed a purified material with morphological properties and chemical functionalities compatible with type I collagen and similar to a highly purified commercial collagen. Thus, an innovative and relatively simple processing method was developed to extract and purify type I collagen from bovine tissue with potential applications as a biomaterial for regenerative tissue engineering.     

An Analysis of Public Clouds Elasticity in the Execution of Scientific Applications: a Survey

Elasticity can be seen as the ability of a system to increase or decrease the computing resources allocated in a dynamic and on demand way. It is an important feature provided by cloud computing, that has been widely used in web applications and is also gaining attention in the scientific community. Considering the possibilities of using elasticity in this context, a question arises: “Are the available public cloud solutions suitable to provide elasticity to scientific applications?” To answer the question, in a first moment we present a survey on the use of cloud computing in scientific scenarios, providing an overview of the subject. Next, we describe the elasticity mechanisms offered by major public cloud providers and analyzes the limitations of the solutions in providing elasticity for scientific applications. As the main contribution of the article, we also present an analysis over some initiatives that are being developed to overcome the current challenges. In our opinion, current computational clouds are developing rapidly but have not yet reached the necessary maturity level to meet all scientific applications elasticity requirements. We expect that in the coming years the efforts being taken by numerous researchers in this area identify and address these challenges and lead to better and more mature technologies that will improve cloud computing practices.     

A new multi-objective optimization method for master production scheduling problems using simulated annealing

Tactic planning or master production scheduling focuses on time and spatial decomposition of the aggregate planning targets and forecasts, as well as, forecast and provision of needed resources. This process becomes extremely hard and time consuming with the increase of number of products, resources and periods considered. In face of such obstacles, this work shows a study of an Artificial Intelligence technique called Simulated Annealing applied to the optimization of production planning problem, more specifically, Master Production Scheduling. This work reviews some of the fundamental theory of simulated annealing, the methodology for master production scheduling calculation, the applicability of simulating annealing to planning problems, most important results and suggestions for further studies.