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We describe a novel wireless receiver architecture that is a broadband generalization of narrowband heterodyning systems commonly used in radio. It can be constructed with cochlea-like traveling-wave structures. We show how this architecture exploits the efficiency of cochlear spectrum analysis to perform parallel, multi-scale analysis of wideband signals. We discuss analogies between spectrum analysis in our architecture and the process of successive-subranging analog-to-digital conversion. When combined with our prior work on an RF cochlea, such architectures may be useful in cognitive radios for creating “RF foveas” that select the narrowband components present within wideband, but spectrally sparse signals.

New electronics for non-invasive medical monitoring promise low-cost, maintenance-free, and lightweight devices. These devices are critical in long-term medical measurements and in home-based tele-monitoring services, which are extremely important for the reduction of health care costs. Here, we present several methods for reducing power consumption while retaining precision. In particular, we focus on the monitoring of the heart-because of its importance-and we describe a micropower electrocardiograph, an ultra-low-power pulse oximeter, an ultra-low-power phonocardiograph, an integrated-circuit switched-capacitor model of the heart, and a low-power RF-antenna-powered CMOS rectifier for energy harvesting. We also introduce an ultra-low-power platform for medical monitoring that enables the integration of monitoring circuitry in a wireless, low-cost, and battery-free device, and describe a method for audio localization of the device in case of a medical alarm.

A new tag for pervasive sensing applications consists of a custom integrated circuit, an antenna for radio frequency energy harvesting, and sensors for monitoring physiological parameters. This paper presents a wearable tag design that can monitor multiple signals. The tag generates an alarm when it suspects a patient emergency. To quickly cover a large portion of the population at risk, we kept the tag affordable (less than US$2 each when manufactured in volume), disposable, small, and easy to use. Such tags would be useful for hospitals, facilities for infants and the elderly, and ordinary homes to detect and alert caregivers to possible problems including SCA and SIDS.

We studied neuromorphic models of binocular disparity processing and mapped them onto a vision chip containing a massively parallel analog processor array. Our goal was to make efficient use of the available hardware while preserving the fundamental computations performed by the models. We also developed an optical fixture that used mirrors to simultaneously focus two images onto the vision chip. This fixture simulates two horizontally-separated virtual cameras, thereby allowing us to run our binocular disparity estimation algorithms using a single image sensor in real time.

We studied neuromorphic models of binocular disparity processing and mapped them onto a vision chip containing a massively parallel analog processor array. Our goal was to make efficient use of the available hardware while preserving the fundamental computations performed by the models. We also developed an optical fixture that used mirrors to simultaneously focus two images onto the vision chip. This fixture simulates two horizontally-separated virtual cameras, thereby allowing us to run our binocular disparity estimation algorithms using a single image sensor in real time. © 2010 IEEE.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009., This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections., Cataloged from student submitted PDF version of thesis., Includes bibliographical references (p. 677-710)., In this thesis, I present two examples of fast-and-highly-parallel analog computation inspired by architectures in biology. The first example, an RF cochlea, maps the partial differential equations that describe fluid-membrane-hair-cell wave propagation in the biological cochlea to an equivalent inductor-capacitor-transistor integrated circuit. It allows ultra-broadband spectrum analysis of RF signals to be performed in a rapid low-power fashion, thus enabling applications for universal or software radio. The second example exploits detailed similarities between the equations that describe chemical-reaction dynamics and the equations that describe subthreshold current flow in transistors to create fast-and-highly-parallel integrated-circuit models of protein-protein and gene-protein networks inside a cell. Due to a natural mapping between the Poisson statistics of molecular flows in a chemical reaction and Poisson statistics of electronic current flow in a transistor, stochastic effects are automatically incorporated into the circuit architecture, allowing highly computationally intensive stochastic simulations of large-scale biochemical reaction networks to be performed rapidly. I show that the exponentially tapered transmission-line architecture of the mammalian cochlea performs constant-fractional-bandwidth spectrum analysis with O(N) expenditure of both analysis time and hardware, where N is the number of analyzed frequency bins. This is the best known performance of any spectrum-analysis architecture, including the constant-resolution Fast Fourier Transform (FFT), which scales as O(N logN), or a constant-fractional-bandwidth filterbank, which scales as O (N2)., (cont.) The RF cochlea uses this bio-inspired architecture to perform real-time, on-chip spectrum analysis at radio frequencies. I demonstrate two cochlea chips, implemented in standard 0.13m CMOS technology, that decompose the RF spectrum from 600MHz to 8GHz into 50 log-spaced channels, consume < 300mW of power, and possess 70dB of dynamic range. The real-time spectrum analysis capabilities of my chips make them uniquely suitable for ultra-broadband universal or software radio receivers of the future. I show that the protein-protein and gene-protein chips that I have built are particularly suitable for simulation, parameter discovery and sensitivity analysis of interaction networks in cell biology, such as signaling, metabolic, and gene regulation pathways. Importantly, the chips carry out massively parallel computations, resulting in simulation times that are independent of model complexity, i.e., O(1). They also automatically model stochastic effects, which are of importance in many biological systems, but are numerically stiff and simulate slowly on digital computers. Currently, non-fundamental data-acquisition limitations show that my proof-of-concept chips simulate small-scale biochemical reaction networks at least 100 times faster than modern desktop machines. It should be possible to get 103 to 106 simulation speedups of genome-scale and organ-scale intracellular and extracellular biochemical reaction networks with improved versions of my chips. Such chips could be important both as analysis tools in systems biology and design tools in synthetic biology., by Soumyajit Mandal., Ph.D.

We exploit the detailed similarities between electronics and chemistry to develop efficient, scalable bipolar or subthreshold log-domain circuits that are dynamically equivalent to networks of chemical reactions. Our circuits can be used for transient and steady-state simulations, parameter estimations and sensitivity analyses of large-scale biochemical networks. They allow the topology, rate constants, inputs, outputs and initial conditions of the reaction network to be programmed. When reactants are present in low concentrations, random fluctuations in reaction rates become significant; we can also model such stochastic effects. We present experimental results from a proof-of-concept chip implemented in 0.18 mum CMOS technology., Massachusetts Institute of Technology. Poitras Pre-Doctoral Fellowship

We have developed a wearable, battery-free tag that monitors heart sounds. The tag powers up by harvesting ambient RF energy, and contains a low-power integrated circuit, an antenna and up to four microphones. The chip, which consumes only 1.0 uW of power, generates digital events when the outputs of any of the microphones exceeds a programmable threshold voltage, combines such events together by using a programmable logic array, and transmits them to a base station by using backscatter modulation. The chip can also be programmed to trade-off microphone sensitivity for power consumption. In this paper, we demonstrate that the tag, when attached to the chest, can reliably measure heart rate at distances up to 7 m from an FCC-compliant RF power source. We also suggest how delays between signals measured by microphones at the wrist and neck can be used to provide information about relative blood-pressure variations.

Fast wideband spectrum analysis is expensive in power and hardware resources. We show that the spectrum-analysis architecture used by the biological cochlea is extremely efficient: analysis time, power and hardware usage all scale linearly with N, the number of output frequency bins, versus N log(N) for the Fast Fourier Transform. We also demonstrate two on-chip radio frequency (RF) spectrum analyzers inspired by the cochlea. They use exponentially-tapered transmission lines or filter cascades to model cochlear operation: Inductors map to fluid mass, capacitors to membrane stiffness and active elements (transistors) to active outer hair cell feedback mechanisms. Our RF cochlea chips, implemented in a 0.13 mum CMOS process, are 3 mm times 1.5 mm in size, have 50 exponentially-spaced output channels, have 70 dB of dynamic range, consume <300 mW of power and analyze the radio spectrum from 600 MHz to 8 GHz. Our work, which delivers insight into the efficiency of analog computation in the ear, may be useful in the front ends of ultra-wideband radio systems for fast, power-efficient spectral decomposition and analysis. Our novel rational cochlear transfer functions with zeros also enable improved audio silicon cochlea designs with sharper rolloff slopes and lower group delay than prior all-pole versions., Massachusetts Institute of Technology. Poitras Pre-Doctoral Fellowship

We use analogies between chemistry and subthreshold electronics to develop fast, scalable, digitally programmable hardware models of stochastic genetic networks. We also present experimental results from a prototype 0.18 à ¿m CMOS chip that was designed for rapid simulations of gene activation, transcription and translation.

We analyze and present an input gain-varying scheme for maximizing dynamic range in a well-known G[subscript m]-C bandpass filter by both minimizing noise for small input signals, and by achieving balanced swing levels at all filter nodes for large input signals. A micropower bandpass filter suitable for use in cochlear implants and other power-constrained biomedical applications was implemented and tested in subthreshold CMOS. At a center frequency of 1.4 kHz and quality factor of 4, the filter has 70 dB of dynamic range (57 dB maximum SNR, 2.5% total harmonic distortion (THD)) and consumes 2.55 muW of power.

This paper presents work on ultra-low-power circuits for brain-machine interfaces with applications for paralysis prosthetics, prosthetics for the blind, and experimental neuroscience systems. The circuits include a micropower neural amplifier with adaptive power biasing for use in multi-electrode arrays; an analog linear decoding and learning architecture for data compression; radio-frequency (RF) impedance modulation for low-power data telemetry; a wireless link for efficient power transfer; mixed-signal system integration for efficiency, robustness, and programmability; and circuits for wireless stimulation of neurons. Experimental results from chips that have recorded from and stimulated neurons in the zebra-finch brain and from RF power-link systems are presented. Circuit simulations that have successfully processed prerecorded data from a monkey brain and from an RF data telemetry system are also presented.