This paper aims to present some applications in Geospatial Technology area, from the use of UAV, Communication, IT and High Performance Computing tools. Important research topics in this area are the detection of changes in multiple images of a soil region to security, deforestation identification and changes in plantations, river courses, shorelines and glaciers. Students, teachers and researchers from four BRIC countries: Brazil, Russia, India and China are conducting joint research in low-cost systems involving aircraft powered by solar energy (Russian team), communication systems over long distances (Chinese team), change detection algorithms in the soil (Brazilian team) and cloud based distributed systems for identification of the type of change detected (Indian team). Some intermediate results already achieved individually by each team are also presented and discussed in this study.

Solar-Powered UAV Platform System: A Case Study for Ground Change Detection in BRIC Countries

This paper presents a method for the development of artificial neural networks (ANN) that consists in the use of a search space algorithm to adjust the components of an ANN’s initial structure, based on the performance obtained by different network configurations. Also, it is possible to represent an ANN’s structure as a genetic sequence, which enables directly loading a corresponding genetic sequence to instantly generate and run a previously trained ANN. This paper also shows some results obtained by different ANNs developed by this method, which demonstrate its features by analyzing its accuracy and trueness. As an example for application of this method, a case study is presented for a specific flight simulation, using data obtained from a helicopter’s flight dynamics simulator for ANN training. Helicopter flight dynamics is a relevant study, for it can be used, for example, to provide precise data to a flight simulator, which implies in an important issue for pilot training, and subsequently, this type of application may help reducing the probability of pilot’s faults in a real flight mission. Finally, some considerations are made about the work shown in this paper as the results, discussions and conclusions are presented.

Incremental Topology Generated for ANN: A Case Study on a Helicopter’s Flight Simulation

This paper presents a method for time series forecasting based on pattern recognition. As the system receives samples of time series, each of them representing one variable from the set of variables that describe the behavior of an application model, these samples are evaluated using a PCA algorithm, where each sample is represented by a feature vector. Different feature vectors (each of them representing a different sample of a particular case) are compared for pattern recognition. Once this sequence of steps is well performed, it’s possible to estimate time series for different states between those represented by the previously analyzed samples. As an example for application of this method, a case study is presented for some variables under specific flight conditions. The chosen application for this case study, helicopter flight dynamics is a relevant study, for it can be used, for example, to provide precise data to a flight simulator, which implies in an important issue for pilot training, and subsequently, this type of application may help reducing the probability of pilot's faults in real flight missions. To demonstrate the applicability of the method, this paper shows results obtained when the system generated forecasts for flight dynamics variables in a specific scenario of initial conditions and while the helicopter performed a maneuver of response to collective command. Finally, some considerations are made about the work shown in this paper as the results, discussions and conclusions are presented.

Pattern Recognition for Time Series Forecasting: A Case Study of a Helicopter’s Flight Dynamics

In this paper we seek to present the mathematical model relating to flight dynamics of a rotary-wing aircraft. The procedure starts by linearizing the translational and rotational dynamics and rotational kinematic equations of motion based on perturbation theory. Some procedures are simplified and are implemented here, due to the complex modeling process. The second step is to obtain the linear form, which is fundamental in order to describe the stability and response of the small movements of the helicopter around a specific attitude. Finally, the dynamic behavior of two helicopters (AS355-F2 Squirrel and BO105-S123) was simulated employing MATLAB Simulink v7.6 (2008) [1]. The input data, status and control derivatives from an existing helicopter were employed. It is noteworthy that the linearized model developed here is valid for applications where a deeper representation of the aircraft is not required.

Dynamic Simulation of the Flight Behavior of a Rotary-Wing Aircraft

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Dynamic Simulation of the Flight Behavior of a Rotary-Wing Aircraft

Incremental Topology Generated for ANN: A Case Study on a Helicopter’s Flight Simulation

Pattern Recognition for Time Series Forecasting: A Case Study of a Helicopter’s Flight Dynamics