Past Seminars >
JSS-2009
| Date | Speaker | Title | Abstract |
|---|---|---|---|
| November 13, 2009 | Petros Boufounos | Recent Advances in Signal Acquisition, Sensing, and Quantization | The increasing availability of computing power, thanks to the advances of Moore's law, has put significant pressure on sensing technology to follow suit. Although sensor hardware cannot always keep pace, recent theoretical developments such as Compressive Sensing and Computational Imaging have demonstrated how smart sensor design can exploit cheap computation to improve sensing and signal acquisition technology. The hallmarks of these theoretical developments are randomization, non-linear reconstruction, and emphasis on signal models. In this talk we emphasize how a model of the acquisition system can be taken into account in the design of the reconstruction algorithms. Specifically, we examine how the randomization of the measurements interacts with measurement quantization in analog-to-digital conversion. We first consider finite-range quantizers and demonstrate that, counter to common intuition, we can often decrease the error due to quantization by increasing the saturation rate of the quantizer. Then we consider the extreme case of 1-bit quantization and we demonstrate that we can significantly improve performance by explicitly incorporating the appropriate quantization model in the reconstruction. Finally, we consider signals measured though non-linear distortions, and we demonstrate that we can still reconstruct the signal from the measurements, even if the distortion itself is not known. |
| October 2, 2009 | Paris Smaragdis | Making Machines Listen | Enabling machines to perceive the world using various modalities is one of the holy grails of artificial intelligence. In this talk I will present some research on creating machines that do as such by listening. I will discuss some of the unique difficulties in this field and present a thread of research which spans a range of computational disciplines relating to signal processing, machine learning and cryptography. This research will be introduced in the context of classic audio problems such as time/frequency analysis, music transcription, source separation, recognition in mixtures and more. I’ll show how this work generalizes and finds applications to other domains, what its practical implications are, and what it takes to move it from the whiteboard to the real-world. |
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