Gonzalo Arce, Charles Black Evans Professor
Prof. Arce's research interests lie on signal processing and its broad applications in imaging, communications, audio, and computer networks. In these applications, the information-carrying signals are, in general, non-deterministic and consequently statistical methods are at the core of the research. Dithering and noise describe physical random phenomena having a broad and significant impact on acquired signals. Dr. Arce's research has focused on exploiting the statistical models of dithering and noise to build optimal signal processing systems. Blue- and Green –noise processes, for instance, have proved to be fundamental in the development of optimal digital ink-jet and laser printing systems – a multibillion dollar industry. Similar concepts are also applicable in digital video and audio signal processing. Dithering itself constitutes information and thus it can be exploited in security and information hiding tasks. An emerging area of interest is the design of covert dither patterns that synthesize visual cryptographic shares for authentication applications. When the signal's random phenomena is best characterized by non-Gaussian statistics, efficient processing methods are in general non-linear. Prof. Arce's group has pioneered the development of nonlinear signal processing tools mirroring that of Wiener and Widrow, extensively used in linear signal processing.