Abstract: The present invention describes the use of autonomous devices, which can be arranged in networks, such as neural networks, to better identify, track, and acquire sources of signals present in an environment. The environment may be a physical environment, such as a battlefield, or a more abstract environment, such as a communication network. The devices may be mobile, in the form of vehicles with sensors, or may be information agents, and may also interact with one another, thus allowing for a great deal of flexibility in carrying out a task. In some cases, the devices may be in the form of autonomous vehicles which can collaboratively sense, identify, or classify a number of sources or targets concurrently. The autonomous devices may function as mobile agents or attractors in a network, such as a neural network. The devices may also be aggregated to form a network of networks and provide scalability to a system in which the autonomous devices are operating.

Abstract: The present invention describes the use of autonomous devices, which can be arranged in networks, such as neural networks, to better identify, track, and acquire sources of signals present in an environment. The environment may be a physical environment, such as a battlefield, or a more abstract environment, such as a communication network. The devices may be mobile, in the form of vehicles with sensors, or may be information agents, and may also interact with one another, thus allowing for a great deal of flexibility in carrying out a task. In some cases, the devices may be in the form of autonomous vehicles which can collaboratively sense, identify, or classify a number of sources or targets concurrently. The autonomous devices may function as mobile agents or attractors in a network, such as a neural network. The devices may also be aggregated to form a network of networks and provide scalability to a system in which the autonomous devices are operating.

Abstract: A regularization system and method for image restoration in homogeneous or inhomogeneous environments. The system and method includes features similar to a neural network with intermediate levels of structure including a pixel having processing capabilities; clusters consisting of a plurality of interconnected pixels and also having processing capabilities; and an image space comprised of a plurality of interconnected pixels and clusters and also having processing capabilities. The system and method also include means for assigning a regularization parameter to each pixel depending on the local variance of intensity of pixels; decomposing the image space into clusters of pixels, each cluster having the same regularization parameter; imposing a blurring function on each pixel; rapidly forming a regularized image by simultaneous local and global encoding of a regularization matrix onto each pixel directed through a process of gradient energy decent; and a means of assessing the output image.

Abstract: A network of networks system and method includes a neuron having processing capabilities; a first level module including a network of a plurality of interconnected neurons, the first level module also having processing capabilities; and a second level module including a network of interconnected networks or interconnected neurons, the second level module also having processing capabilities; wherein the first and second level modules are interconnected through neuron to neuron connections such that simultaneous processing can be carried out at by the neuron and by the first and second level modules. The system and method also includes means for forming a boundary between a first module having a first memory state and a second module having a second memory state such that the module comprising the boundary attains a third memory state distinct from the first and second memory states.