University of Pennsylvania

Rca Inc.

The United States of America As Represented by the Administrator of the

Nabil H Farhat

Hua-Kuang Liu

Chwan-Hwa Wu

Jacob Barhen

A system and methods offering a dynamical model of cortical behavior is provided. In an illustrative implementation, the present invention offers a corticonic network comprising at least one parametrically coupled logistic map network (PCLMN)(). The PCLMN offers a non-linear iterative map of cortical modules (or netlets) that when executed exhibit substantial cortical behaviors. The PCLMN accepts dynamic and/or static spatio-temporal input () and determines a fixed point attractor in state-space for that input. The PCLM () operates such that if the same or similar dynamic and/or static spatio-temporal input is offered over several iterations, the PCLMN converges to the same fixed point attractor is provided rendering adaptive learning. Further, the present invention contemplates the memorization or association of inputs using the corticonic network in a configuration where the PCLMN cooperates with another cortical module model (e.g. another PCLMN, associative memory module, etc.)().

Dynamical brain model for use in data processing applications

A unipolar terminal-attractor based neural associative memory (TABAM) system with adaptive threshold for perfect convergence is presented. By adaptively setting the threshold values for the dynamic iteration for the unipolar binary neuron states with terminal-attractors for the purpose of reducing the spurious states in a Hopfield neural network for associative memory and using the inner-product approach, perfect convergence and correct retrieval is achieved. Simulation is completed with a small number of stored states (M) and a small number of neurons (N) but a large M/N ratio. An experiment with optical exclusive-OR logic operation using LCTV SLMs shows the feasibility of optoelectronic implementation of the models. A complete inner-product TABAM is implemented using a PC for calculation of adaptive threshold values to achieve a unipolar TABAM (UIT) in the case where there is no crosstalk, and a crosstalk model (CRIT) in the case where crosstalk corrupts the desired state.

Unipolar terminal-attractor based neural associative memory with

Data analysis systems are provided, especially target imaging and identification systems, which utilize a CAM that associatively stores a plurality of known data sets such as target data sets in a synaptic interconnectivity matrix modeled upon the model of learning of neural networks. In accordance with preferred embodiments the systems are able to identify unknown objects when only a partial data set from the object is available. The system is robust and fast, utilizing parallel processing due to the massive interconnectivity of neural elements so that the image produced exhibits the properties of super-resolution. Since the system is modeled after a neural network, it is fault tolerant and highly reliable.

Super resolution

Electronically steered antenna system is provided by placing a panel of dielectric material with specific thickness and dielectric constant spaced forward of an array of piezoelectric transducers. These piezoelectric transducers present a refelective surface. High frequency wavefronts are transmitted toward the dielectric panel and pass on to the reflective surface. The phase of the reflected waves depends upon the spacing of the reflective surfaces from the panel. A change in this spacing is provided by electrical control signals being applied to the piezoelectric transducers.

Growing community of inventors

Philadelphia, PA, United States of America

Nabil H Farhat