University of Memphis Research Foundation

Southern College of Optometry

Khan M Iftekharuddin

Lan Wang

Ross Deming

Robert Kozma

Orges Furxhi

John Keith Anderson

Pinakin Gunvant Davey

Sergi Consul

Young June Kim

A system for detecting object movement including a sensory network having at least one sensory device using modulated radar for detecting an object in proximity to the sensory network. The sensory network including wireless transmission means and a base station having computer processing means located remote from the sensory network and including wireless transmission means to communicate with the sensory network. The base station having computer readable program code means for causing the computer processing means to analyze data received from the sensory network to determine motion characteristics of the object in proximity to the sensory network.

Surveillance and tracking system and method

A system for detecting object movement including a sensory network having at least one sensory device using modulated radar for detecting an object in proximity to the sensory network. The sensory network including a wireless transmission system and a base station having a computer processing system located remote from the sensory network and including a wireless transmission system to communicate with the sensory network. The base station having a computer readable program code for causing the computer processing system to analyze data received from the sensory network to determine motion characteristics of the object in proximity to the sensory network.

A method of image processing using a cellular simultaneous recurrent network (CSRN) is provided. In particular, the method includes a processor that sets initial parameters of the CSRN and generates a target image based on the initial parameters. Then, a training process is executed to learn an image processing task. The training process performs an image processing task on one of a plurality of sub-images of an input image and then the sub-image is stored after the training process. The training process is then repeated until the image processing task has been performed on each of the plurality of sub-images of the input image. Once the training process is complete, an image transformation is performed on the input image and an output image is displayed based on the transformed input image.

Image processing using cellular simultaneous recurrent network

One aspect of the invention provides a method for training a classification algorithm to detect a retinal pathology. The method includes: for a plurality of pseudo two-dimensional data sets of one-dimensional data points, each pseudo two-dimensional data point representing RNFL thickness values for a subject and corresponding index values for the data points: performing fractal analysis on the data set to calculate a plurality of fractal dimensions and calculating a plurality of slopes between each fractal dimension; combining the plurality of slopes for subjects labeled as pathologic into a pathologic data set; combining the plurality of slopes for subjects labeled as healthy into a healthy data set; and applying a linear discriminant function the pathologic data set and the healthy data set; thereby training a classification algorithm to detect the retinal pathology.

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Virginia Beach, VA, United States of America

Khan M Iftekharuddin