We use the genetic programming (GP) paradigm for two tasks. The first task given a GP is the generation of rules for the target / clutter classification of a set of synthetic aperture radar (SAR) images, the second, the generation of rules for the identification of tanks in a second set of SAR images. To perform these tasks, previously defined feature sets are generated on the various images, a...

A sequential decision problem, based on the task of identifying the species of trees given acoustic echo data collected from them, is considered with well-known stochastic classifiers, including single and mixture Gaussian models. Echoes are processed with a preprocessing stage based on a model of mammalian cochlear filtering, using a new discrete low-pass filter characteristic. Stopping time p...

This paper presents a method for target recognition and classification in shallow water environment. It is based on timefrequency filtering matched to a free field reference target response. The decision strategy lies on the comparison of the reference and the filter output signal. The method is applied to an experimental data base containing target acoustic responses measured in a tank for typ...

Classification is one of the most popular and widely used supervised learning tasks, which categorizes objects into predefined classes based on known knowledge. Classification has been an important research topic in machine learning and data mining. Different classification methods have been proposed and applied to deal with various real-world problems. Unlike unsupervised learning such as clus...

This study proposes a method for classifying event-related fMRI responses in a specialized setting of many known but few unknown stimuli presented in a rapid event-related design. Compared to block design fMRI signals, classification of the response to a single or a few stimulus trial(s) is not a trivial problem due to contamination by preceding events as well as the low signal-to-noise ratio. ...

We consider feature selection and weighting for nearest neighbor classifiers. Atechnical challenge in this scenario is how to cope with discrete update of nearestneighbors when the feature space metric is changed during the learning process.This issue, called the target neighbor change, was not properly addressed in theexisting feature weighting and metric learning literature. I...

A process for replacing a voluminous image dictionary, which characterizes a certain target of interest in a constrained zone of effectiveness representing controlled states including scale and view angle, with a synthetic template has been developed. Synthetic template (ST) is a spatial map (grayscale image) obtained by combining the set of zone-specific training images that are ascribed to th...

A novel sonar array for mobile robots is presented with applications to localization and mapping of indoor environments. The ultrasonic sensor localizes and classifies multiple targets in two dimensions to ranges of up to 8 meters. By accounting for effects of temperature and humidity, the system is accurate to within 1 mm and 0.1 degrees in still air. Targets separated by 10 mm can be discrimi...

Based on our previous work we propose to track multiple ground targets with GMTI (GroundMoving Target Indicator) sensors as well as with imagery sensors. The scope of this paper is to fuse the attribute type information given by heterogeneous sensors with DSmT (Dezert Smarandache Theory) and to introduce the type results in the tracking process to improve its performances.