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Signal Processing via Sampled-Data Control - A Challenge to Go Beyond Shannon

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There has been remarkable progress in sampled-data control theory in the last two decades. The main achievement here is that there exists a digital (discrete-time) control law that takes the intersample behavior into account and makes the overall analog (continuous-time) performance optimal, in the sense of H-infinity norm. This naturally suggests its application to digital signal processing where the same hybrid nature of analog and digital is always prevalent. A crucial observation here is that the perfect band-limiting hypothesis, widely accepted in signal processing, is often inadequate for many practical situations. In practice, the original analog signals (sounds, images, etc.) are neither fully band-limited nor even close to be band-limited in the current processing standards. 

The present talk describes how sampled-data control theory can be applied to reconstruct the lost high-frequency components beyond the so-called Nyquist frequency, and how this new method can surpass the existing signal processing paradigm. We will also review some concrete examples for sound processing, recovery of high frequency components for MP3 /AAC compressed audio signals, and super resolution for image (still/moving) processing. We review some commercial success in the production of custom LSI chips by SANYO semiconductor (later Onseminconductor) with cumulative production of more than 50 million chips.

This talk is part of the CUED Control Group Seminars series.

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