Regularization theory and neural networks architectures
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Convex Optimization
A low-power correlation-derivative CMOS VLSI circuit for bearing estimation
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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Localization of acoustic sources using miniature microphone arrays poses a significant challenge due to fundamental limitations imposed by the physics of sound propagation. With sub-wavelength distances between the microphones, resolving acute localization cues become difficult due to precision artifacts. In this paper we propose a framework which overcomes this limitation by integrating signal-measurement (analog-to-digital conversion) with statistical learning (bearing estimation). At the core of the proposed approach is a min-max stochastic optimization of a regularized cost function that embeds manifold learning within ΣΔ modulation. As a result, the algorithm directly produces a quantized sequence of the bearing estimates whose precision can be improved asymptotically similar to a conventional ΣΔ modulators. In this paper we present a hardware implementation of a miniture acoustic source localizer which comprises of: (a) a common-mode canceling microphone array and (b) a ΣΔ integrated circuit which produces bearing parameters. The parameters are then combined in an estimation procedure that can achieve a linear range from 0°-90°. Measured results from a prototype fabricated in a 0.5 µm CMOS process demonstrate that the proposed localizer can reliably estimate the bearing of an acoustic source with a resolution less than 2° while consuming less than 75 µW of power.