Proper characterization of human Group Activity (GA) interactions can help to detect and prevent certain pertinent threats efficiently. In this paper, we present a model-based scheme for robust group activity characterization. The proposed approach takes advantage of synergy of multi-sensors data to track and identify key individual and group activity events based on fusion of imagery and acoustic sensors data. Each activity event is attributed by a set of tagged features. By matching and correlating attributes of events, the model attempts to associate sensory observations to a priori known ontology. The proposed model benefits from a fusion process that achieves perceptual grouping of activities by spatiotemporal correlation and association of fragmented perceptions extracted from attributed events. In this paper, we present the results of our experimental work and demonstrate the effective and robustness of the decision fusion technique in terms of properly classifying group activities and generating semantic messages describing dynamics of human group activities that, in turn, improves situational awareness.

In this paper, we have proposed a time efficient, energy saving and robust broadcast/multicast protocol for reconfigurable cluster-based sensor network. In our broadcast protocol, a broadcast can be executed in O(hd2+ D2) rounds and each node needs to be awake in O(D2) rounds, where D and d are the degrees of G and the sub-network induced by the network backbone, respectively, and h is the height of the backbone. When k channels are available, the broadcast can be executed in O((hd2+ D2)/k) rounds and each. We show that our broadcast protocol can be readily modified to the one for multicast. The cluster-based architecture used in this paper for a sensor network is an improved version. The proposed network architecture is self-constructible and self-reconfigurable by using two topological management operations: node-move-in and node-move-out. Details of the protocol along with experimental results are discussed. Simulation results show that the protocol performance is much better than that in the theoretical analysis.

This paper discusses technical challenges and navigational skill requirements of mobile robots for traversable path planning in natural environments similar to Mars surface terrains. Different methods for detecting salient terrain features based on imaging texture analysis techniques are described. In particular, three competing soft computing techniques are presented for terrain traversability assessment: a rule-based terrain classifier, a neural network-based terrain classifier, and a fuzzy-logic terrain classifier. Each terrain classifier divides a region of natural terrain into finite sub-terrain regions and classifies terrain condition exclusively within each sub-terrain region based on terrain visual clues. Image processing techniques are applied for aggregative fusion of sub-terrain assessment results. Results of a comparative performance evaluation of all three terrain classifiers are presented. The last two terrain classifiers are shown to have remarkable capability for traversability assessment, which facilitates navigation in unstructured natural terrain environments.

A planar three-degree-of-freedom robot is designed and fabricated with a built-in forward an inverse kinematic algorithm. The robot may be programmed to generate planar enveloping curves; in the generator (tool) is an infinite line. Several examples are presented to illustrate the forward and inverse design of this particular planar robot for generation of algebraic curves.>

The problem encountered with coordination of two industrial robots are discussed. Some possibilities of incorporation of "Artificial Intelligence (AI)" with two arm coordination of industrial robots are considered. Also, employment of "natural language" interaction with the user is briefly discussed. Two cartesian 3-axis robot arms are considered in a common complex environment. The basic pick-and-place and assembly tasks are analyzed when performed by two robots. A simple example of a two-arm robotic work environment is illustrated by implementation of a simple game.