Mr. Paul S. Cadaret Profile

Paul S. Cadaret

at Unicon Inc

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Proceedings Article | 16 April 2008 Paper

Massive-scale video anti-piracy monitoring

Paul Cadaret

Proceedings Volume 6943, 69431C (2008) https://doi.org/10.1117/12.777929

KEYWORDS: Video, Video processing, Neural networks, Pattern recognition, Inspection, Neurons, Image processing, Chemical elements, Feature extraction, Commercial off the shelf technology

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The author's recent participation in the Small Business Innovative Research (SBIR) program has resulted in the development of a patent pending technology that enables the construction of very large and fast artificial neural networks. Through the use of UNICON's CogniMax^TM pattern recognition technology we believe that systems can be constructed that exploit the power of "exhaustive learning" for the benefit of certain types of challenging pattern recognition problems. The Viacom lawsuit against YouTube^TM in early 2007 brought to light the magnitude of the video piracy problem and caused us to examine the associated technical challenges to determine whether our technology might enable an effective solution. This paper presents a theoretical study that describes how a massive-scale anti-piracy video pattern recognition system might be constructed using a large/fast Radial Basis Function (RBF) artificial Neural Network (NN) to enable a solution. Several daunting technical challenges exist. First, the amount of copyrighted video content that has been generated over time and now must be protected is enormous. Second, the activity level that is generally present on a large file-sharing site such as YouTube presents any pattern recognition system with a torrent of video content to inspect. Third, the concept of "fair-use" implies that an anti-piracy policy is not simply based on identifying a few copyrighted video frames. To determine system feasibility, this paper derives a set of example requirements for such a system, lays out a hypothetical anti-piracy data processing architecture, and evaluates the performance of the example system configuration.

Proceedings Article | 16 April 2008 Paper

Computational acceleration using neural networks

Paul Cadaret

Proceedings Volume 6943, 69431F (2008) https://doi.org/10.1117/12.777924

KEYWORDS: Neural networks, Control systems, Computing systems, Neurons, Pattern recognition, Chemical elements, Control systems design, Brain mapping, Artificial neural networks, Nonlinear control

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The author's recent participation in the Small Business Innovative Research (SBIR) program has resulted in the development of a patent pending technology that enables the construction of very large and fast artificial neural networks. Through the use of UNICON's CogniMax™ pattern recognition technology we believe that systems can be constructed that exploit the power of "exhaustive learning" for the benefit of certain types of complex and slow computational problems. This paper presents a theoretical study that describes one potentially beneficial application of exhaustive learning. It describes how a very large and fast Radial Basis Function (RBF) artificial Neural Network (NN) can be used to implement a useful computational system. Viewed another way, it presents an unusual method of transforming a complex, always-precise, and slow computational problem into a fuzzy pattern recognition problem where other methods are available to effectively improve computational performance. The method described recognizes that the need for computational precision in a problem domain sometimes varies throughout the domain's Feature Space (FS) and high precision may only be needed in limited areas. These observations can then be exploited to the benefit of overall computational performance. Addressing computational reliability, we describe how existing always-precise computational methods can be used to reliably train the NN to perform the computational interpolation function. The author recognizes that the method described is not applicable to every situation, but over the last 8 months we have been surprised at how often this method can be applied to enable interesting and effective solutions.

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