Signal Processing, Inc. (SPI) is a new company, which started in April 2006. Although we are new, employees (full-time, part-time, and consultants) of SPI have been performing research and development in the past decade in the areas of image processing, speech processing, signal processing, fault diagnostics and health monitoring, prognostics, explosive detection, chemical and biological agent detection, and health monitoring and control applications. We all have advanced degrees and extensive experience in algorithm design and implementation, hardware implementation, and real-time system integration. We have close relationships with many leading universities such as University of Pennsylvania, Arizona State University, University of Missouri, Pennsylvania State University, University of Texas at Arlington, U. Texas at Dallas, Carnegie Mellon University, City University of Hong Kong, Tokyo Institute of Technology, Ohio State University, U. Colorado at Boulder, Rensselaer Polytechnic U., Washington U., Georgia Tech., U. New Orleans, North Carolina State U., U. Calgary, and Johns Hopkins U. The Chief Technology Officer of SPI, Dr. Chiman Kwan, had worked with customers in the past 10 years for Intelligent Automation, Inc., including almost all the government agencies, as well as many major companies such as Motorola, Ford Motor Company, Boeing, Lockheed Martin, Honeywell, and Stanford Telecom. 

Employees of SPI are active in the research community, especially in the area of diagnostics and prognostics, condition based maintenance, health monitoring, signal/image processing, and real-time controls. Dr. Chiman Kwan, was the invited speaker at the 2003 annual meeting on mechanics and materials division of Japan Society of Mechanical Engineering (Tokyo section), held at Tokyo Institute of Technology, Tokyo in September 2003. Dr. Kwan’s paper on fault diagnostics and prognostics was one of seven finalists for Kayamori Best Paper Award in the 2003 Int. Conference on Robotics and Automation for a paper entitled “Integrated High Speed Intelligent Utility Tie Unit for Disbursed/Renewable Generation Facilities”. Moreover, Dr. Kwan received 2003 Jeff Collins Best Paper Award (Automation and Robotics Research Institute, The U. of Texas at Arlington) for a paper entitled “Design and Implementation of Industrial Neural Network Controller Using Backstepping” which was published in IEEE Transactions on Industrial Electronics. He also received the 2004 Industrial and Commercial Power Systems (I&CPS) Ralph H. Lee Department Prize Paper Award for the paper, entitled “Integrated High Speed Intelligent Utility Tie Unit for Disbursed/Renewable Generation Facilities.”