Amit Gore

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—Rapid and accurate detection of pathogens using conductometric biosensors requires potentiostats that can measure small variations in conductance. In this paper, we present an architecture and implementation of a multichannel poten-tiostat array based on a novel semi-synchronous sigma–delta (61) analog-to-digital conversion algorithm. The algorithm(More)
—For many recognition systems, the feature extraction unit forms the most computationally intensive and power consuming component. In this paper, we present a design of an analog-to-information converter that directly produces a pulse-encoded representation of linear predictive coded (LPC) features corresponding to an input analog signal. At the core of(More)
—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(More)
—Many source separation algorithms fail to deliver robust performance when applied to signals recorded using high-density sensor arrays where the distance between sensor elements is much less than the wavelength of the signals. This can be attributed to limited dynamic range (determined by analog-to-digital conversion) of the sensor which is insufficient to(More)
A key challenge in designing analog-to-digital converters for cortically implanted prosthesis is to sense and process high-dimensional neural signals recorded by the micro-electrode arrays. In this paper, we describe a novel architecture for analog-to-digital (A/D) conversion that combines Σ∆ conversion with spatial de-correlation within a single module.(More)
—In this paper, we present a framework for constructing Σ∆ learning algorithms and hardware that can identify and track low-dimensional manifolds embedded in a high-dimensional analog signal space. At the core of the proposed approach is a min-max stochastic optimization of a regularized cost function that combines machine learning with Σ∆ modulation. As a(More)
—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 lo-calization cues become difficult due to precision artifacts. In this paper we propose a framework which(More)