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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 potentiostat array based on a novel semi-synchronous sigma–delta ( ) analog-to-digital conversion algorithm. The algorithm combines(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 localization cues become difficult due to precision artifacts. In this paper we propose a framework which overcomes(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 pulseencoded representation of linear predictive coded (LPC) features corresponding to an input analog signal. At the core of(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 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 modulation. As a result,(More)
On chip signal compression is one of the key technologies driving development of energy efficient biotelemetry devices. In this paper, we describe a novel architecture for analog-to-digital (A/D) conversion that combines sigma delta conversion with the spatial data compression in a single module. The architecture called multiple-input multiple-output (MIMO)(More)
We describe the fabrication and characterization of components of a biosensor system that can be used for simultaneous screening of multiple pathogens in a sample. The two sub-systems are: (1) a mixed antibody immunosensor based on molecular biowires and (2) a multi-channel potentiostat for measuring conductance across the immunosensor. The immunosensor(More)
Continuous monitoring of respiratory patterns and physical activity levels can be useful for remote health management of patients with conditions such as heart disease and chronic obstructive pulmonary disease. In a clinical setting, spirometers serve as the gold standard for monitoring respiratory patterns such as breathing rate and changes in lung volume.(More)
Rapid detection of pathogens using field deployable biosensors requires integrated sensing and data processing. Detection of low concentration of biological agents is possible using accurate and real-time signal characterization devices. This paper presents a multi-channel conductometric array that can detect and measure current up to femtoampere range. The(More)