Grant Recipients

On-Going Projects
Project Urine-Powered Wireless Urinary Tract Infection Monitoring Sensor For Smart Diaper Platform
Principal
Investigators
Byunghoo Jung (Purdue University)
Babak Ziaie (Purdue University)
Student Weeseong Seo (Purdue University)
Year 2014.7-2018.6
Abstract
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Urinary tract infection (UTI) is the second most common infection in the body accounting for more than 7 million office visits and 100,000 hospitalizations per year. UTI can be a major source of morbidity and mortality in geriatric patients in particular those suffering from neurodegenerative diseases. Also UTI causes 51 per 100,000 children to be hospitalized annually and 174 per 100,000 infants to be hospitalized annually. Many of the disabled elderly, young children, and infants are not capable of understanding symptoms of a UTI, and many that are capable have difficulty communicating this to caregivers. Early identification and treatment of UTIs is vital to prevent major sequelae or death. The goal of the project is to develop a wearable self-powered wireless sensor for autonomously screen for UTI, and improve the quality of life of elderly, young children, infants, and those suffering from neurodegenerative diseases.
Report  2017 Annual Report
Publications
Project CMOS-Assisted Nano-Bio Array for Neurotechnology
Principal
Investigators
Donhee Ham (Harvard University)
Hongkun Park (Harvard University)
Student Jeffrey T. Abbott (Harvard University)
Year 2014.2-2018.1
Abstract
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The patch clamp technique can perform intracellular recording of neurons, but is not well suited for parallel recording; an array of microelectrodes can do parallel recording, but they are too large to access the interior of live neurons. In fact, no methods currently available are capable of both intracellular and parallel recording of mammalian neurons, while such dual ability can offer new possibilities in neurotechnology as well as fundamental neuroscience. Nano-bio interface may be one way to tackle this issue, as recently shown by co-PI, Park [Nature Nano. 7, 180 (2012)]: nanoelectrodes can penetrate into live neurons, acquiring intracellular access; an array of these nanoelectrodes––whose construction is possible via standard top-down fabrication––can then enable intracellular and parallel recording. Here we will develop a CMOS integrated circuit with an array of nanoelectrodes on top. The CMOS electronics (an array of analog amplifiers and digital control electronics) right below the nano-bio interface array will facilitate parallel operation of nanoelectrodes, and increase the recording sensitivity. The high impedance of nanoelectrodes and low-frequency sub-threshold neuronal signals pose a unique challenge for the semiconductor circuit design. Subsequently, we will use this unprecedented electrophysiological tool for pharmaceutical screening for neurological disorders and cellular-level neuroprosthesis.
Report 2016 Annual Report (pdf)
Publications
Project Self-Aware Computing for Cyber Physical Systems
Principal
Investigators
Mingoo Seok (Columbia University)
Peter Kinget (Columbia University)
Student SeongJong Kim (Columbia University)
Year 2013.1-2018.12 (Extended 2 years)
Abstract
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Novel cyber physical systems require digital systems that are very energy efficient. This program will develop a self-aware digital computation system that measures a range of static and dynamic variations, and adapts itself to them, so that performance, energy-efficiency, and reliability can be significantly increased. To enable this vision, we are investigating a range of approaches including the integration of a multitude of precision and flexible-supply-voltage analog sensors in the digital system but with a minimal area/testing overhead. We are further developing a framework to use the measured parameters, which can be imperfect, to effectively determine the optimal operating scenario. We strongly anticipate that such extensive instrumentation and control can advance the combination of performance, efficiency and reliability of digital computing systems beyond the current state of the art.
Report  2016 Final Report for 1st 4 Year Period (pdf)
Publications

Completed Projects
Project Ultra-low Power Sensors using Aluminum Nitride Micro-Electromechanical (MEMS) Resonators
Principal
Investigators
Xiaoguang Liu (University of California Davis)
David Horsley (University of California Davis)
Rajeevan Amirtharajah (University of California Davis)
Student Yuhao Liu (University of California Davis)
Year 2016.4-2017.3
Abstract
Report  Final Report
Publications
Project A Wireless Sensor Microsystem for Monitoring the Effects of Micro-Nutrients on Aging
Principal
Investigators
Gabor C. Temes (Oregon State University)
Patrick Y. Chiang (Oregon State University)
Tory M. Hagen (Oregon State University)
Gert Cauwenberghs (University of California San Diego)
Student Xin Meng and Eric Smith (Oregon State University)
Year 2011.9-2016.11
Abstract
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The goal of this project is to develop a wireless wearable biosensor for assessing the potential benefits of vitamins and micronutrient supplementation to increase healthspan, the time of one’s life spent free from disease and deficits of daily living. There are 40 million Americans over the age of 65, and this number will rapidly increase to 72 million in less than a generation. The biosensor developed under this research will enable the continuous monitoring of the most important biopotential signals in the elderly, and it also will make it possible to evaluate the effects of vitamins and micronutrients on ambulatory patients. It will integrate a capacitive non-contact sensor with a low-noise amplifier, an analog-to-digital data converter, a digital signal processor, and also a wireless transceiver into a self-contained low-profile device, which can be embedded within clothing for distributed sensing over the whole body. The sensor will be connected wirelessly to a host computer for data classification and interpretation.
Report  Final Report (pdf)
Publications
Project Design of a Low Power Smart Glove for Sensor-Brain-Interface to Reconstruct Somatosensory Feedback and Gestures (Supplemental Funding)
Principal
Investigators
Jan Van der Spiegel (University of Pennsylvania)
Tomothy Lucas (University of Pennsylvania)
Student Xilin LIU (University of Pennsylvania)
Year 2015.1-2015.12
Abstract
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The objective of this project is to develop a sensor-brain interface (SBI), which can restore the sense of touch and the sense of movement in a paralyzed hand. This proposal focuses on an ergonomic system for detecting force and vibration at the fingertips and movement at the finger joints. An ultra-low power, wireless, wearable sensor network solution is being developed. To improve functionality and circumvent the limitations of a discrete-component sensor node, an active sensor node will be powered by harvesting energy from the environment. Low-power sensors, including, but is not limited to, accelerometer, stretch sensor, and force sensor, will be used to develop a sensor network. A custom fully integrated detection circuit, consisting of 1) wireless power transmission, 2) multi-channel, low power analog front end, 3) wireless data transmission, and 4) an optional sensory encoder will be developed. The final outcome is a wireless, battery-free, wearable smart glove that can be utilized in the testing of in-vivo functionality of the SBI system.
Report  Final Report (PDF file)
Publications
Project Single-Chip Closed-Loop Deep-Brain Stimulation for Treatment of Parkinson’s Disease
Principal
Investigators
Michael Flynn (University of Michigan)
Daryl Kipket (University of Michigan)
Student Hyo Gyuem (Ben) Rhew (University of Michigan)
Year 2008.9-2013.12
Abstract
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Parkinson’s disease is a progressive disorder of the central nervous system, affecting more than 1.5 million people in the United States alone. Deep Brain Stimulation (DBS) is one of the most effective treatments of Parkinson symptoms. In DBS, the Subthalamic Nucleus (STN) is excited with regular electrical pulses, in many patients dramatically reducing disease symptoms. Although generally effective, present state-of-the-art systems have two major drawbacks. Since the choice of stimulation parameter settings is guided solely by visually presented disease symptoms, it can take up to a year for a doctor to determine the optimum stimulation parameters for a given patient. Furthermore, the large size of the control electronics in present systems means that the neuro-stimulation electronics cannot be placed close to the implant site. The goal of this research program is to develop and demonstrate new techniques to enable a highly integrated CMOS implementation of closed-loop deep-brain stimulation for treatment and research of Parkinson’s disease.
Report   Final Report (pdf file)
Publications
Project Single-Chip Closed-Loop Neural Recording and Stimulation System with RF Communication Functionality (Supplemental Funding)
Principal
Investigators
Michael Flynn (University of Michigan)
Student
Year 2011.9-2013.8
Abstract
Report  Final Report (pdf file)
Publications
Project Nanoelectrochemical Sensors on CMOS (Supplemental Funding)
Principal
Investigators
Sameer Sonkusale (Tufts University)
Prof. David Walt (Tufts University)
Student
Year 2011.9-2012.8
Abstract
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The proposed supplemental funding research will facilitate realization of nanoelectrochemical systems through a marriage of microelectronics and electrochemistry for low-cost, high-throughput bench-top nanofabrication. The investigators propose a highly unconventional yet powerful candidate to realize such nanoelectrochemical device, that of top-down fabricated silicon CMOS (Complementary Metal Oxide Semiconductor) die as functional substrate for controlled assembly of already synthesized nanostructures. This new paradigm extends the role of CMOS technology beyond conventional areas of computation, signal processing and communication towards directed nanoassembly. The investigators plan to demonstrate an integrated chemical sensor array on CMOS based on carbon nanotubes, graphene and metal oxide nano wires.
Report
Publications
Project Wireless Optical Sensors for High Resolution Imaging of Biological Structures (Supplemental Funding)
Principal
Investigators
Valencia M. Joyner (Tufts University)
Student
Year 2010.9-2012.4
Abstract
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Near infrared (NIR) spectroscopy is emerging as a promising non-invasive imaging tool for fundamental studies of biological processes and structures, offering greater biochemical specificity, high temporal resolution, potential for concurrent intracellular and intravascular event measurement, and portability. Time-resolved NIR techniques allow explicit separation of optical absorption and scattering parameters related to biological structures, such as tissue, and (in theory) provide functional and metabolic information based on spectral and spatial imaging information. However, the visibility of superficial and deep structures remains fairly poor due to the lack of imaging sensor technology combining high-resolution spatial mapping, fast pixel response time, and broad spectral response. The goal of this CAREER program is to develop a new field of research on the development of highly integrated wireless imaging sensors, combining photonic devices, broadband analog/RF circuits, and free-space optical communication to improve the spatial resolution of time-resolved NIR images; and establish an interdisciplinary educational environment for engineers. The long-term goal is to further expand the field of biological imaging by developing true “mixed-mode” integrated systems combining microwave, acoustic, photonic, and nanoscale electronic circuits for concurrent measurement of multiple imaging modalities to increase the visibility of sub-millimeter structures.
Intellectual Merits: This CAREER program reaches beyond current state-of-the-art to develop imaging sensors incorporating arrayed pixels for phase-sensitive optical detection at high frequencies. Motivated by the need to detect low level optical signals at high-speed, dielectrically-isolated avalanche photodiode structures implemented in digital CMOS technology and integrated at pixel-level with RF analog signal processing circuits will be explored. New approaches to low-noise front-end amplifier design will be explored to enable detection of RF-modulated optical signals at incident power levels below 1nW using chopper stabilization techniques and resonant circuit topologies. Process-variant tolerant (PVT) circuit topologies will be employed to ensure amplitude and phase accuracy within 0.1%. A two-dimensional sensor readout architecture incorporating pixel/column-level data converters and high-speed serial data readout will be developed. As a result of detailed environmental noise (substrate, power/ground supply) modeling and test structure measurement, design guidelines for arrayed high-frequency imaging sensors will be provided to design engineers. Pixel-level/column-level ADC architectures will be explored for optimal performance in terms of pixel form factor, power, and resolution. For the first time, wireless access based on optical transmission will be explored to enable remote data transfer from a wearable NIR imaging device.
Broader Impacts: The proposed research and education program will have significant broader impacts on in vivo characterization of macroscopic optical properties of multiply scattering tissues and enable development of new theories relating to biophysical mechanisms and correlations between signals generated by complementary imaging modalities (e.g. MRI). The portability of NIR imaging instrumentation is a key merit of the technology, and therefore, this research develops a wearable imaging system with integrated wireless capabilities enabling signal acquisition during movement. Interactive workshops with scientists and students will be organized to guide sensor development. The education program is tightly coupled to the proposed research activities, including new undergraduate and graduate courses that vertically integrate topics from optical/electronic devices to circuits/systems and applications. A cross-campus undergraduate course on technical writing and communication will be developed in collaboration with Howard University to teach strategies in formulating and communicating technical ideas and engage students from under-represented groups in the CAREER program. The PI is committed to broadening opportunities to all engineers, including under-represented students. Workshops on graduate school admission, funding, and academic career opportunities will be organized during visits to minority serving institutions across the country. A complete wireless sensor module will be made available to researchers for experimental testing.
Report  Final Report (pdf file)
Publications
Project Digitally-Assisted Architectures for Next Generation RF Transceivers (Supplemental Funding)
Principal
Investigators
Joel L. Dawson (MIT)
Student
Year 2008.9-2010.8
Abstract
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The objective of this research is to discover circuit architectures that enable the next generation of wireless transceivers for communications and biomedical applications. The approach is to focus on digitally assisted architectures, which exploit digital signal processing to enable transceiver performance. The wireless and biomedical fields are the most important, highest-impact applications in integrated circuit design because of exploding commercial and military demand. The difficulty is that transistors in modern processes do not permit the use of analog architectures that have, for decades, served so well. For the first time in many years, therefore, there is an exciting opportunity to innovate at the architectural level in traditional RF and analog design.
Report
Publications
Project Multiplexed Incremental Data Converters for EEG Monitoring and Recording (Supplemental Funding)
Principal
Investigators
Gabor C. Temes (Oregon State University)
Luca Lucchese (Oregon State University)
Student
Year 2007.1-2007.12
Abstract
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The purpose of this research is to develop integrated analog-to-digital data converters (ADCs) which are suitable for the simultaneous acquisition of a large number (100 to 300) of electroencephalographic (EEG) signals. These signals are then processed by a computer. The EEG signals have narrow bandwidths, typically around 400 Hz, but their amplitudes are small and they need to be accurately acquired in the presence of high-level noise. This requires a large dynamic range, around 19 bits, for the devices implementing the data conversion. It is also preferable that the complete data acquisition structure be portable, and hence battery powered. This in turn requires low power dissipation by the data converters used.Sensor arrays detecting EEG signals have been successfully used in the noninvasive location of the sources of epilepsy in the brain [1],[2],[3]. Under this project we shall compare the available techniques (one ADC/channel, multiplexed delta-sigma ADCs, shared incremental ADCs) for this application. Using an ADC in each channel provides flexibility, but requires significant power dissipation, and may introduce mismatch effects. Conventional multiplexing of delta-sigma converters reduces power dissipation, but is subject to interchannel interference. Shared incremental data converters need more power than multiplexed delta-sigma ones, but they promise to be highly accurate under the specified conditions [4]. We shall compare these techniques by simulations, choose the most suitable method, and implement it by one or several integrated devices.To make sure that the devices developed under this research are truly useful for their intended applications, we shall interact with bioengineers active in the area of epilepsy diagnostics. Together, we shall establish a set of specifications for the multi-sensor array. Then, we shall develop the integrated ADC system which satisfies these specifications, and which is optimal for a portable sensor array.References
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[1] A.J. Fowle and C.D. Binnie, Uses and abuses of the EEG in epilepsy,” Epilepsia, 2000:41: S10-S18.
[2] G. Lantz et al., “Epileptic source location with high-density EEG: how many electrodes are needed?” Clinical Neurophysiology, 2003:114, pp.63-69.
[3] D.M. Tucker, “Spatial sampling of head electrical fields: the geodesic sensor net,” Electroencephalography and clinical neurophysiology, 1993:87, pp. 154-163.
[4] V. Quiquempoix et al., “A low-power 22-bit incremental ADC,” IEEE Journal of Solid-State Circuits, 2006:41, pp.1562-1571.
Report Final Report (pdf file)
Publications Please see the final report.
Project Neural Prosthesis Chip Fabrication (Supplemental Funding)
Principal
Investigators
Michael P. Flynn (University of Michigan)
Daryl Kipke (University of Michigan)
Student
Year 2007.1-2007.12
Abstract
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The WIMS center, funded by the U.S. National Science Foundation and by 20 companies, is researching wirelessly connected microsystems for environmental monitoring and biomedical applications. This multi-disciplinary work, focused on two test beds, involves 40 faculty and 100 doctoral researchers. One testbed is a family of neural prostheses aimed at treating disorders such as deafness, paralysis, epilepsy, and Parkinson’s disease. Michigan has pioneered this technology, which is now the focus for intense efforts worldwide. During the past year, the center realized the first implantable multi-channel microsystem for multipoint cortical recording.New research is underway for the treatment of Parkinson’s disease and epilepsy. An analog front-end chip comprised of amplifiers, filters, and an ADC for processing and digitizing neural signals is being designed. All these components are being optimized for processing neural signals. Circuitry for neural stimulation will also be implemented on the IC. A prototype is being developed for 0.18um CMOS. A collaboration with Professor Daryl Kipke’s Neural Engineering Llaboratory at Michigan’s Biomedical Engineering Department is guiding the specifications of the prototype.
Report Final Report (pdf file)
Publications Jongwoo Lee, Hyo-Gyuem Rhew, Daryl Kipke and Michael Flynn, “A 64 ChannelProgrammable Closed-loop Deep Brain Stimulatorwith 8 Channel Neural Amplifier and Logarithmic ADC” IEEE VLSI Symposium, June 2008.
Project Mixed-Signal VLSI Interface for High Aspect Ratio Optical/Electrochemical Hybrid Sensor Array (Supplemental Funding)
Principal
Investigators
Sameer Sonkusale (Tufts University)
David Walt (Tufts University)
Student
Year 2007.1-2007.12
Abstract
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The main goal of the project is to design an integrated device that can make simultaneous electrochemical and optical measurements using a novel platform that combines high density micro-electrode array with optical fiber array for multiplexed analysis. The coupling of electrochemistry and spectroscopy increases the amount of information that can be obtained from a given sensor, preventing false positives and expanding the capabilities of a single mode sensing system. One of the main requirements for such a platform is the ability to perform individual interrogation of optical or electrochemical (or both types of) sensors in an array format with high throughput and reliability. The Catalyst Foundation will support the design of such a mixed signal VLSI interface to facilitate individual electrical/optical interrogation and readout. The integrated platform being created in this project will allow applications in the clinical, environmental, and chemical and bioprocess control areas.
Report Final Report (pdf file)
Publications
Project Fully-Integrated CMOS Biochip Arrays for Multicolor Biomolecular Diagonistics (Supplemental Funding)
Principal
Investigators
Ken Shepard (Columbia University)
Rasti Levicky (Columbia University)
Ginger Chew (Columbia University)
Student
Year 2007.1-2007.12
Abstract
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The proposed effort will develop fundamentals and technology of multiplexed, CMOS-based sensors for biomolecular (DNA and protein) fluorescence-based measurement and detection. Guiding principles include leveraging of economies of scale to provide low-cost devices; optimizing device self-sufficiency and miniaturization to afford maximum portability; developing parallelized designs for detection of multiple target analytes; enabling sensor-level circuits for detection, calibration, and control; and optimizing dynamic range, sensitivity, and speed of detection. Applications include gene expression profiling to connect genetic information to cellular function and response, pathogen detection and identification, classification of diseases and biological cells, forensics, environmental testing/monitoring, and genotyping (e.g. to guide medical treatment). Extensive field studies in the detection of allergens that cause asthma in children will be done as part of this work.
Report Final Report (pdf file)
Publications Included in the final report.
Project Generalized Radix Design of Low-Power, High-Performance Piplined & Cyclic ADCs (Supplemental Funding)
Principal
Investigators
Gabor C. Tems (Oregon State University)
Un-Ku Moon (Oregon State University)
Student Vivek Sharma (Oregon State University)
Madhulata Bonu (Oregon State University)
Weilun Shen (Oregon State University)
Zhenyong Zhang (Oregon State University)
Year 2006.1-2006.12
Abstract
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This research focuses on improved design techniques for low-power and high-performance pipelined and cyclic analog-to-digital data converters. The proposed innovations include the use of a generalized radix, and of novel building blocks such as interstage amplifiers. The resulting data converters may find many useful applications in medical imaging, bioengineering and communications.
Report Final Report (pdf file)
Publications Please see the final report.
Project uWatt Computing -Application-Driven Circuits and Architectures for Wireless Sensor Devices (Supplemental Funding)
Principal
Investigators
David Brooks (Harvard University)
Gu-Yeon Wei (Harvard University)
Matt Welsh (Harvard University)
Student Mark Hempstead (Harvard)
Year 2006.1 – 2006.12
Abstract
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Recent years have seen a burgeoning interest in embedded wireless sensor networks with applications ranging from warehouse inventory tracking to medical and defense applications. With this growing interest in a seemingly limitless application space, development of computing devices for these networks will be an important growth area for the semiconductor industry. Embedded wireless sensor networks have several important attributes that require special attention to device design.These include the need for inexpensive, long-lasting, highly reliable devices coupled with minimal performance requirements. We seek to explore the potential for reliable, ultra-low-power computing and to leverage a multi-disciplinary approach encompassing an understanding of sensor network applications’ computational and communication requirements with novel low-power circuits and architectures.
Report Final Report (pdf file)
Publications Included in the final report.
Project Integrated Multi-Signal Adaptive Microphone
Principal
Investigators
Gert Cauwenberghs (University of California San Diego)
Levent Degertekin (Georgia Institute of Technology)
George Zweig (Signition Inc.)
Student Baris Bicen (Georgia Institute of Technology)
Abdullah Celik (University of California San Diego)
Year 2004.4 – 2008.12
Abstract
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The goal of the project is to develop the technology for signal processing to extract and separate acoustic sources and to integrate this technology with optical microphones that have low noise and high bandwidth.
The Gradient flow (GF) algorithm is used for source separation, and arrays of optical microphones with force feedback capability are used as array elements.
Both gradient and omnidirectional optical microphones are employed in this study.
Report Final Report (pdf file)
Publications Included in the final report.
Project Fly-Vision-Inspired Adoptive Optic-Flow Sensor with Learning
Principal
Investigators
Rahul Sarpeshkar (MIT)
Sebastian Seung (MIT)
Student Micah O’Halloran (MIT)
Year 2004.1 – 2008.6
Abstract
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The goal of the project is to develop the technology for signal processing to extract and separate acoustic sources and to integrate this technology with optical microphones that have low noise and high bandwidth.
The Gradient flow (GF) algorithm is used for source separation, and arrays of optical microphones with force feedback capability are used as array elements.
Both gradient and omnidirectional optical microphones are employed in this study.
Report Final Report (pdf file)
Publications Included in the final report.
Project Second Generation Retinal Implant Chip for the Blind
Principal
Investigators
John Wyatt (MIT)
Joseph Rizzo, M.D. (Massachusetts Eye and Ear Infirmary)
Student Luke Theogarajan (MIT)
Year 2003.1 – 2007.1
Abstract
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Neural prosthetic devices offer a means of restoring function that have been lost due to neural damage. The first part of this work investigates the design of a 15-channel low-power fully implantable stimulator chip. The chip is powered wirelessly and receives wireless commands. The chip features a CMOS only ASK detector, a single-differential converter based on a novel feedback loop, a low-power adaptive bandwidth DLL and 15 programmable current source that can be controlled via four commands. Though, it is feasible to build an implantable stimulator chip the amount of power required to stimulate more than 16 channels is prohibitively large.Clearly there is a need for a fundamentally different approach. The ultimate challenge is to design a self-sufficient neural interface. The ideal device will lend itself to seamless integration with the existing neural architecture. This necessitates that communication with the neural tissue should be performed via chemical rather than electrical messages. However, catastrophic destruction of neural tissue due to the release of large quantities of a neuroactive species precludes the storage of quantities large enough to suffice for the lifetime of the device. The ideal device then should actively sequester the chemical species from the body and release it upon receiving appropriate triggers in a power efficient manner.This work proposes the use of ionic gradients, specifically K+ ions, as an alternative chemical stimulation method. The required ions can readily be sequestered from the background extracellular fluid. The parameters of using such a stimulation technique are first established by performing in-vitro experiments on rabbit retinae. The results show that modest increases (~10mM) of K+ ions are sufficient to elicit a neural response.The first building block of making such a stimulation technique possible is the development of a potassium selective membrane. To achieve low-power the membranes must be ultrathin to allow operation in the diffusive transport regime. One method of achieving this is to use lyotropic self-assembly, unfortunately conventional lipid bilayers cannot be used since they are not robust enough. Furthermore the membrane cannot be made potassium selective by simply incorporating ion carriers since they would eventually leach away from the membrane.A single solution that solves all the above issues was then investigated in this work. A novel facile synwork of self-assembling receptor functionalized polymers was achieved. By combining the properties of hydrophobic and hydrophlic interactions of two polymers a triblock co-polymer was synthesized. The middle hydrophobic block is composed of biocompatible polysiloxanes and was further derivatized to posses ion recognition capabilities via pendant crown ether chains. The hydrophilic blocks were composed of biocompatible polyoxazoline. The membrane properties were studied by self-assembling them into vesicular structures. The ion responsive properties of these polymers were then examined. These polymers also show emergent behavior, such as spontaneous fusion and shape transformation to ionic stimuli, due to the synergy between form and function.The results from the work show that it is feasible to build a renewable chemically based neural proswork based on supramolecular architectures. However, there remains a lot of fundamental work that needs to be pursued in the future to bring the idea to complete fruition.
Report Final Report (pdf file)
Publications Included in the final report.
Project Micromachined Varactors and Their Applications to RF/Analog Integrared Circuits (Supplemental Funding)
Principal
Investigators
Ken Suyama (Columbia University)
Student Gregory Ionis
Year 2002 – 2003
Abstract
Report
Publications
Project Focal Plane Video Compression
Principal
Investigators
Sina Balkir (University of Nebraska-Lincoln)
Khalid Sayood (University of Nebraska-Lincoln)
Student Walter D. León (University of Nebraska-Lincoln)
Year 2001.9 – 2006.6
Abstract
Report Final Report (pdf file)
Publications The list of publications is included in the final report.
Project Research on Electronic Cytosensors
Principal
Investigators
Frank W.R. Chaplen (Oregon State University)
Vojtek J. Kolodziej (Oregon State University)
Un-Ku Moon (Oregon State University)
Gabor C. Temes (Oregon State University)
Student Thirumalai Rengachari (Oregon State University)
Vivek Kumar Sharma (Oregon State University)
Cheng-Yan Peng (Oregon State University)
Year 2001.1 – 2004.12
Abstract
Report 2003 Annual Report (PDF file)
Publications Included in the annual report.
Project Micropower Analog VLSI Continuous Speech Recognition
Principal
Investigators
Gert Cauwenberghs (Johns Hopkins University)
Herve Bourlard (Dalle Molle Institute of Perceptual Artificial Intelligence, Switzerland)
Jayadeva (Indian Institute of Technology, Delhi)
Student Shantanu Chakrabartty (Johns Hopkins University)
Year 1998.6 – 2004.8
Abstract
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Driven by the proliferation of portable devices like cellular phones, personal digital assistants (PDAs) and smart wrist watches there has been an ever increasing demand for efficient and robust user interfaces. An intelligent speech interface offers an attractive alternative to other means of communication and provides hands free communication with these portable devices. Miniature handheld and wristworn devices require extreme low power solutions to support the use of very small batteries. Micropower analog VLSI provides a viable technology to implement a speech recognition user interface efficiently enough so that it can run off a wristwatch battery. From a computational perspective, parallel analog techniques are feasible because most of the computation involved in recognition is of a probabilistic nature that does not require high precision.
In the first part of the project we designed and developed efficient speech processing and recognition algorithms for small vocabulary systems, in light of efficient implementation in analog hardware. A flexible and scalable design approach allowed to reduce the complexity of the hardware by trading implementation accuracy for reduced silicon area and power dissipation. Theoretical research in this area has resulted in forward decoding kernel machines (FDKM), a maximum-a-posteriori (MAP) based sequence decoding scheme that combines traditional hidden markov models (HMM) with support vector machines (SVMs). The SVMs process acoustic features and produce HMM transition probabilities and a HMM forward decoding block integrates these probabilities to discriminate between phonetic utterances. The performance of FDKM depends on the discriminatory ability of the SVM generating margin classifier. Further investigation in this area has led to the development of the Gini-support vector machine (SVM), a sparse large margin classifier that generates normalized output probability scores. Both Gini-SVM and FDKM have demonstrated state-of-art performance on various signal processing tasks in speech and image recognition.
In the second part of the project the GiniSVM and FDKM algorithms were mapped onto parallel architecture, and implemented in low-power current-mode CMOS analog VLSI. Non-volatile floating-gate MOS storage provides full analog programmability and trainability throughout all stages of the architecture. A calibration scheme, coupled with a chip-in-loop retraining procedure, cancels imprecision due to fabrication-induced mismatch in the analog circuit implementation. A GiniSVM/FDKM processor was prototyped and fabricated in 0.5um CMOS technology. In experiments on a speaker verification task, the chip yielded real-time recognition accuracy at par with floating-point software, but consumed sub-microwatt power.Further materials resulting from this project: http://bach.ece.jhu.edu/catalyst/fdkm
Report Final Report and NIPS 2004 slides (PDF file)
Publications
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“Sub-Microwatt Analog VLSI Support Vector Machine for Pattern Classification and Sequence Estimation ,” S. Chakrabartty and G. Cauwenberghs, Adv. Neural Information Processing Systems (NIPS’2004), Cambridge: MIT Press, 17, 2005.”Spike Sorting with Support Vector Machines,” R.J. Vogelstein, K. Murari, P.H. Thakur, G. Cauwenberghs, S. Chakrabartty and C. Diehl, Proc. 26th Ann. Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBS’2004), San Francisco, Sept. 1-4, 2004 (Region 2 Finalist, EMBS-Whitaker Student Paper Competition).”Analog Auditory Perception Model for Robust Speech Recognition,” Y. Deng, S. Chakrabartty and G. Cauwenberghs, Proc. IEEE Int. Joint Conf. Neural Networks (IJCNN’2004), Budapest Hungary, July 25-29, 2004.”Robust Speech Feature Extraction by Growth Transformation in Reproducing Kernel Hilbert Space,” S. Chakrabartty, Y. Deng and G. Cauwenberghs, Proc. IEEE Int. Conf. Acoustics Speech and Signal Processing (ICASSP’2004), Montreal Canada, May 17-21, 2004.”Margin Propagation and Forward Decoding in Analog VLSI,” S. Chakrabartty and G. Cauwenberghs, Proc. IEEE Int. Symp. Circuits and Systems (ISCAS’2004), Vancouver Canada, May 23-26, 2004.”Three-Decade Programmable Fully Differential Linear OTA,” Y. Deng, S. Chakrabartty and G. Cauwenberghs, Proc. IEEE Int. Symp. Circuits and Systems (ISCAS’2004), Vancouver Canada, May 23-26, 2004.”Silicon Support Vector Machine with On-Line Learning,” R. Genov, S. Chakrabartty and G. Cauwenberghs, Int. J. Pattern Recognition and Artificial Intelligence, vol. 17 (3), pp. 385-404, 2003.”Sparse Probability Regression by Label Partitioning,” S. Chakrabartty, G. Cauwenberghs and Jayadeva, Proc. 16th Conf. Computational Learning Theory (COLT’03), Washington DC, Aug. 24-27, 2003.”Power Dissipation Limits and Large Margin in Wireless Sensors,” S. Chakrabartty and G. Cauwenberghs, Proc. IEEE Int. Symp. Circuits and Systems (ISCAS’2003), Bangkok Thailand, May 25-28, 2003.”Robust Cephalometric Landmark Identification Using Support Vector Machines,” S. Chakrabartty, M Yagi, T. Shibata and G. Cauwenberghs, Proc. IEEE Int. Conf. Acoustics Speech and Signal Processing (ICASSP’2003), Hong Kong, Apr. 6-10, 2003.”Expectation Maximization of Forward Decoding Kernel Machines,” S. Chakrabartty and G. Cauwenberghs, Proc. 9th Int. Workshop Artificial Intelligence and Statistics (AISTATS’2003), Key West FL, Jan. 3-6, 2003.”Forward-Decoding Kernel-Based Phone Sequence Recognition,” S. Chakrabartty and G. Cauwenberghs, Adv. Neural Information Processing Systems (NIPS’2002), Cambridge: MIT Press, vol. 15, 2003.”Forward Decoding Kernel Machines: A Hybrid HMM/SVM Approach to Sequence Recognition,” S. Chakrabartty and G. Cauwenberghs, Proc. SVM’2002, Lecture Notes in Computer Science, vol. 2388, pp. 278-292, 2002.”Sequence Estimation and Channel Equalization Using Forward Decoding Kernel Machines,” S. Chakrabartty and G. Cauwenberghs, Proc. IEEE Int. Conf. Acoustics Speech and Signal Processing (ICASSP’2002), Orlando FL, May 13-17, 2002.”Hybrid Support Vector Machine, Hidden Markov Model Approach for Continuous Speech Recognition,” S. Chakrabartty and G. Cauwenberghs, Proc. 43rd IEEE Midwest Symp. Circuits and Systems (MWSCAS’2000), Lansing MI, August 8-11, 2000.
Project Retinal Implant Chip for the Blind
Principal
Investigators
John Wyatt (MIT)
Joseph Rizzo, M.D. (Massachusetts Eye and Ear Infirmary)
Student Shawn K. Kelly (MIT)
Year 1997.1 – 2002.12
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The Retinal Implant Project, a collaboration between the Massachusetts Institute of Technology and the Massachusetts Eye and Ear Infirmary, involves the design of a microelectronic prosthesis to help restore some functional vision to patients with retinal diseases such as retinitis pigmentosa and macular degeneration. These patients lose vision as a result of degeneration of the photoreceptor cell layer in the retina. The implant chip will sit against the retina and receive power and visual signal information from a wireless driver outside the eye. It will electrically stimulate the healthy ganglion cells on the front surface of the retina, which send visual information to the brain. The concept is similar in principal to the cochlear implant for the deaf.
One focus of the project has been retinal stimulation trials on humans. In these trials, a retinal surgeon temporarily inserts a flexible microfabricated electrode array into the eye and against the retina. The other end of this device remains outside the eye, connected to stimulation circuitry. The patient describes what he or she sees when current is passed through the electrodes. After a few hours of stimulation, the electrode array is removed from the eye. These trials have generated phosphene perception, and some very crude form percepts. MIT graduate student Shawn Kelly designed, built and tested the battery-powered stimulator, put it through hospital approval proceedures, and operates it during surgery as part of the experimental team. This work was funded in part by Catalyst.
Mr. Kelly’s doctoral project is to create an efficient power system for an implantable chip. In his RF-driven design, a primary coil outside the eye generates a magnetic field, which is received by a secondary coil implanted in the eye. The resulting AC voltage passes through an active rectifier, which is more efficient than a standard diode. The electrodes are stimulated in an efficient manner, and capacitively stored charge is recovered from stimulated electrodes to stimulate other electrodes. This system will allow efficient power delivery to the electrodes, reducing excess heat generation in the eye, and allowing more electrodes to be used. All the power-saving features in Mr. Kelly’s design would be beneficial in other implantable stimulation systems, such as cardiac pacemakers and cochlear implants. We are grateful to Catalyst for funding this doctoral project in its entirety.
Publications
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1. Rizzo, J.F., J.L. Wyatt, J. Loewenstein, S. Kelly and D. Shire,
“Accuracy and Reproducibility of Percepts Elicited by Electrical Stimulation of the Retinas of Blind and Normal Subjects,” ARVO Lecture Abstract, Investigative Ophthalmology and Visual Science, vol. 42, no. 4, May 2001, p. s942.2. Caulfield, R.E., J.L. Wyatt Jr., and J.F. Rizzo, “Calculated Power Limits Affecting Retinal Prosthesis Design,”
ARVO Poster Session Abstract, Investigative Ophthalmology and Visual Science, vol. 42, no. 4, May 2001, p. s814.3. Shire, D.B., J.L. Wyatt and J.F. Rizzo, “Progress Toward an Inflatable Neural Prosthesis,” ARVO Poster Session Abstract, Investigative Ophthalmology and Visual Science, vol. 42, no. 4, May 2001, p. s812.4. Rizzo, J.F., J. Wyatt, M. Humayun, E. DeJuan, W. Liu, A. Chow, R. Eckmiller, E. Zrenner, T. Yagi, G. Abrams, “Retinal Prosthesis: An Encouraging First Decade with Major Challenges Ahead,” Editorial, Ophthalmology, vol. 108, no. 1, January 2001.5. Grumet, A.E., J.L. Wyatt, Jr., J.F. Rizzo, “Multi-electrode stimulation and recording in the isolated retina,”
Journal of Neuroscience Methods, 101, pp. 31-42, 2000.6. Loewenstein, J., J.F. Rizzo, J. Wyatt and S. Kelly, “Acute Intraocular Electrical Stimulation of the Human Retina,” XIV Int’l. Congress of Eye Research, Oct, 2000, Santa Fe, NM.7. Shahin, M.E., J.F. Rizzo, J. Wyatt, J. Loewenstein, “Evaluation of External Electrical Stimulation of the Eye as a Screening Test for Acute Intraocular Retinal Stimulation Studies,” ARVO Poster Session Abstract, Investigative Ophthalmology and Visual Science, vol. 41, no. 4, April-May 2000, p. s860.8. Rizzo, J.F., J. Wyatt, J. Loewenstein, S. Kelly, “Acute Intraocular Retinal Stimulation in Normal and Blind Humans,” ARVO Lecture Abstract, Investigative Ophthalmology and Visual Science, vol. 41, no. 4, April-May 2000, p. s102.9. Grumet, A.E., J.F. Rizzo, J. Wyatt,
“In-Vitro Electrical Stimulation of Human Retinal Ganglion Cell Axons,”
ARVO Poster Session Abstract, Investigative Ophthalmology and Visual Science, vol. 41, no. 4, April-May 2000, p. s10.10. Rizzo, J.F., J. Loewenstein, S. Kelly, D. Shire, T. Herndon and J.L. Wyatt, “Electrical Stimulation of Human Retina with a Micro-Fabricated Electrode Array,” The Association for Research in Vision and Ophthalmology Annual Meeting (ARVO), Ft. Lauderdale, FL, p. S783, May 1999.11. Moss, J.D., M.M. Socha, J.L. Wyatt and J.F. Rizzo, “Hermetic Encapsulation Testing for a Retinal Prosthesis,” The Association for Research in Vision and Ophthalmology Annual Meeting (ARVO), Ft. Lauderdale, FL, p. S732, May 1999.12. Socha, M.M., J.D. Moss, M. Shahin, T. Herndon, J.L. Wyatt and J.F. Rizzo,
“Mechanical Design and Surgical Implantation of Second Generation Retinal Prosthesis,” The Association for Research in Vision and Ophthalmology Annual Meeting (ARVO), Ft. Lauderdale, FL, p. S735, May 1999.13. Grumet, A.E., J.F. Rizzo and J.L. Wyatt, “Ten Micron Diameter Electrodes Directly Stimulate Rabbit Retinal Ganglion Cell Axons,” The Association for Research in Vision and Ophthalmology Annual Meeting (ARVO), Ft. Lauderdale, FL, p. 734, May 1999.14. Rizzo, J.F., J. Loewenstein and J. Wyatt “Development of an Epiretinal Electronic Visual Prosthesis: The Harvard-Medical Massachusetts Institute of Technology Research Program,” Retinal Degenerative Disease and Experimental Theory, pp. 463-47, Kluwer Academic/Plenum Publishers, 1999.15. Rizzo, J.F. and J.L. Wyatt,
“Retinal Prosthesis,” Age-Related Macular Degeneration, J. Berger, S.L. Fine, M.G. Maguire, eds., Mosby Publishers, 1999, pp. 413–432.16. Grumet, A.E., J.L. Wyatt, and J.F. Rizzo, “Multi-Electrode Recording and Stimulation of the Salamander Retina In Vitro,” The Association for Research in Vision and Ophthalmology Annual Meeting (ARVO), Ft. Lauderdale, FL, May 1998.17. Rizzo, J.F. and J. Wyatt, “Prospects for a Visual Prosthesis,”
The Neuroscientist, vol. 3, no. 4, pp. 251–262, 1997.18. Wyatt, J. and J. Rizzo, “Ocular implants for the blind,” IEEE Spectrum, pp. 47–53, May 1996.
Project Architecture of Miniature, Programmable Archival Tags and Pelagic Floats for Physical/Biological Oceanographic Applications
Principal
Investigators
Godi Fischer (University of Rhode Island)
Tom Rossby (University of Rhode Island)
Conrad Recksiek (University of Rhode Island)
Student Michael Obara
Year 1997.6 – 2002.12
Abstract
Report
Publications
Project Analog Circuits for Real-Time Spatiotemporal Feature Extraction of Acoustical Signals
Principal
Investigators
Jan Van der Spiegel (University of Pennsylvania)
Paul Mueller (University of Pennsylvania)
Student Ahmed M. Abdelatty Ali, and Athanasios Mouchtaris (University of Pennsylvania)
Year 1996.1 – 2003.12
Abstract
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Current state-of-the-art automatic speech recognition (ASR) systems perform reasonably well on clean speech. Their performance, however, deteriorates considerably in presence of noise or any mismatch between the testing and the training environments. On the other hand, the human system is capable of a much more robust performance even in the presence of significant noise. One of the reasons behind this deficiency in ASR systems is the limited knowledge about the human auditory system, speech characteristics and perception.
In the first part of our work, we investigated the problems of auditory-based processing and feature-based recognition, so as to improve the robustness of speech recognition systems. A new auditory-based front-end speech processing system was developed, called the Average Localized Synchrony Detector (ALSD). We also investigated the acoustic-phonetic characteristics of the obstruents in the framework of a front-end feature-based speaker-independent phoneme-based continuous speech recognition. We studied several acoustic features for their information content and their possible role in the recognition. The features that proved to be vital and rich in their information were extracted and new rule-based algorithms were developed for manipulating these information-rich features for ASR.
In the second part of our work, our focus was to extend the aforementioned research by the addition of a front-end, which will offer the advantage of enhancing the capabilities of that system in cases of performance degradation. We focus on two particular problems of deterioration of ASR, (a) due to inter-speaker variability, and (b) due to noisy background conditions with very low Signal-to-Noise (SNR) ratios. For addressing case (a), we proposed applying voice conversion methods that have been popular within the area of Text-To-Speech (TTS) synthesis. For this project, we were interested in conversion techniques that offer the possibility of feature transformation, which can be useful when the initially extracted features are far from those that the system has been designed for. We proposed a new conversion algorithm that addressed previous shortcomings of existing algorithms in this area, with great success. For case (b) we followed the novel approach that the problem of speech conversion has similarities with that of speech enhancement, when the source speech becomes the noisy speech, and the target speech becomes the clean speech. Our results have shown that our feature conversion techniques can efficiently estimate the clean speech features from the noisy speech features, resulting in a significant improvement in performance especially in very noisy conditions.
Finally, another direction where we concentrated on, was towards implementing the initially proposed rule-based system. We performed a great amount of work towards the design and successful implementation of a real-time low-cost front-end for speech recognition that realizes the rule-based algorithm that was proposed in the initial stages of this research.
Report Final Report Summary (PDF File)
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1. A. M. Abdelatty Ali, Jan Van der Spiegel and Paul Mueller, “An Acoustic-Phonetic Feature-based System for the Automatic Recognition of Fricative Consonants”, in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-98), vol. II, pp. 961-964, 19982. A. M. Abdelatty Ali, Jan Van der Spiegel and Paul Mueller, “Acoustic-phonetic features for the automatic recognition of stop consonants”, Journal of the Acoustical Society of America, pp. 2777-2778, 103 (5), 19983. A. M. Abdelatty Ali, Jan Van der Spiegel and Paul Mueller, “Acoustic-Phonetic Features for the Automatic Recognition of Stop Consonants”, in Proc. 16th International Congress on Acoustics (ICA) and 135th Meeting of the Acoustical Society of America (ASA), pp. 275-276, 19984. A. M. Abdelatty Ali, Jan Van der Spiegel and Paul Mueller, “Automatic Detection and Classification of Stop Consonants using an Acoustic-Phonetic Feature-Based System”, XIVth International Congress of Phonetic Sciences (ICPhS’99), pp. 1709-1712, 19995. A. M. Abdelatty Ali, Jan Van der Spiegel and Paul Mueller, “A GUI System for Speech Synthesis through Graphical Manipulation of Spectrograms”, in Proc. IEEE International Symposium on Circuits and Systems (ISCAS-99), pp. III-106 – III-109, 19996. A. M. Abdelatty Ali, Jan Van der Spiegel and Paul Mueller, “An Acoustic-Phonetic Feature-Based System for Automatic Phoneme Recognition in Continuous Speech”, in Proc. IEEE International Symposium on Circuits and Systems (ISCAS-99), III-118 – III-121, 1999.7. A. M. Abdelatty Ali, Jan Van der Spiegel and Paul Mueller, “Auditory-Based Acoustic-Phonetic Feature Extraction for the Segmentation and Recognition of Continuous Speech”, The 33rd Annual Conference on Information Sciences and systems, CISS’99, March 17-19, 1999, The Johns Hopkins University, Baltimore, Maryland.8. A. M. Abdelatty Ali, “gAuditory-based acoustic-phonetic signal processing for robust continuous speech processing”, Ph.D. Thesis, University of Pennsylvania, December, 1999.9. A. M. Abdelatty Ali, Jan Van der Spiegel and Paul Mueller, “Auditory-based speech processing based on the average localized synchrony detection”, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-2000), Vol. 3. pp. 1623-1626, 2000.10. A. M. Abdelatty Ali, Jan Van der Spiegel and Paul Mueller, “Speech processing using the average localized synchrony detection “, Journal of the Acoustical Society of America, pp. 2908, 107, 2000.11. A. M. Abdelatty Ali, Jan Van der Spiegel and Paul Mueller, “Robust Classification of Stop Consonants using Auditory-based Speech Processing”, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-2001), vol.1, pp. 81-84, 2001.12. A. M. Abdelatty Ali, Jan Van der Spiegel and Paul Mueller , “Auditory-based signal processing for robust speech recognition”, 35th Annual Conference on Information Sciences and Systems (CISS) – Neuromorphic Engineering and MEMS Sensory Systems, March 2001, Baltimore.
Project Novel Techniques for Detection and Suppression of Mechanical Vibrations in Turbine-Generators
Principal
Investigators
Gabor C. Temes (Oregon State University)
Wojtek J. Kolodziej (Oregon State University)
Kenzo Watanabe (Shizuoka University)
Student Bo Wang (Oregon State University)
Year 1996 – 2000
Abstract
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A detection technique was developed to identify the optimal sensor locations and to provide vibration signature patterns. The primary criterion was to maximize the measurement sensitivity with respect to vibrations corresponding to natural shaft frequencies, while controlling the influence of the electromagnetic noise. The best location for 16-sensors mounted on a coupled, two segments drive shaft was found numerically. Two models of the actual electromechanical machines were studied: a 300hp drive and a generator at John Day power plant (part of the North Western American Power System).A new architecture for a sensor interface circuit using a delta-sigma modulator was developed, specifically for the monitoring of sub-synchronous resonances (SSRs). A surface MEMS accelerometer sensor detects any unexpected vibration indicative of SSR.The interface circuit is implemented on a separate chip, to allow the use of commercial sensors, and the multiplexing of many sensor chips placed at strategically chosen points on the turbine-generator shaft. The major challenge is measuring extremely small capacitance variations, of the order of attofarads, occuring in the sensors in the presence of large parasitic capacitances as large as 20 picofarads.A novel high-order delta-sigma structure was used to improve the SNR. The newly proposed structure consists of a simple, trimm-free digital compensator and a novel multi-level force feedback branch using mismatch shaping. A new time-averaged force-feedback scheme is used to linearize the multi-level force feedback. This provides proper operation in the presence of process variations in the MEMS, e.g., imperfections in the mask alignment, as well as clock jitter and inaccuracies of the force-feedback period.Also, a new correlated-double sampling (CDS) integrator was developed, which reduces signal errors due to the op-amp’s finite gain and offset voltage and to charge injection and clock feedthrough noise from the switches. The cross-coupled CDS integrator structure reduces errors caused by the large parasitic capacitors, and enhances the small sensor signal superimposed on the large common-mode signal. An efficient dithering circuit was also developed.
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1. Tetsuya Kajita, Un-Ku Moon and Gabor C. Temes, “A noise-shaping accelerometer interface circuit for two-chip implementation,” Proc. of IEEE International Symposium of Circuits and Systems, vol. IV, pp. 337-340, May 2000.2. Tetsuya Kajita, Un-Ku Moon and Gabor C. Temes, “A noise-shaping accelerometer interface circuit for two-chip implementation,” IEEE IMTC (Instrumentation and Measurement Technology Conference), May 2001.3. Tetsuya Kajita, Un-Ku Moon and Gabor C. Temes, “A noise-shaping accelerometer interface circuit for two-chip implementation,” VLSI Design, to appear in 2001.4. Tetsuya Kajita, Gabor Temes and Un-Ku Moon, “Correlated double sampling integrator insensitive to parasitic capacitance,” Electronics Letters, to appear.5. Kolodziej, W.J., and Khapalov A.Y.,
“Controllability and placement of FACTS devices,” Proc. of Stockholm Power Tech International Symposium on Electric Power Engineering, Stockholm, Sweden, June 1997.
Project Switching-Noise Analysis for Mixed Analog-Digital ICs
Principal
Investigators
Andrew Yang (University of Washington)
David Allstot (Oregon State University/ Carnegie Mellon University)
Student Kevin J. Kerns
Year 1994 – 1998
Abstract
Report
Publications

Other Projects
Project Support for GeoHazards International
Principal
Investigators
Brian Tucker, President
Student
Year 1993 – 2010
Abstract
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Geohazards International (GHI) was established in 1993 as a nonprofit organization to reduce death and suffering caused by earthquakes in the world’s most vulnerable communities
GHI is a global network of people alarmed by the world’s growing earthquake risk, aware of methods that could reduce human suffering, and determined to help. GHI’s Board of Trustees is comprised of earthquake specialists with strong ties to the academic, business, and government sectors of the United States, Japan and Europe. GHI’s Board of Advisors is a group of international experts in the earthquake risk of developing countries; they provide technical guidance to GHI, and, on occasion, participate in GHI projects. GHI’s staff, which performs day-to-day operations, is located in Palo Alto, California, within easy access to many of the world’s most experienced earthquake risk managers in business, government and academic sectors. Among them, the members of the Board of Trustees, the Board of Advisors, and the staff have literally centuries of experience in seismology, earthquake engineering, and risk management.GHI is unique in its goals, values, and effectiveness. No other organization has its particular mission, free of competing political, business, religious, or research priorities. GHI believes in international assistance as well as in local responsibility. Administrative costs are low because its staff is small in number, well educated, and highly motivated, having witnessed the consequences of both earthquakes and earthquake preparation.GHI reduces death and injury by helping vulnerable communities recognize their risk and the methods to manage it. In particular, GHI makes a community safer by raising awareness of its risk, building local institutions to manage that risk, and strengthening schools to protect and train the community’s future generations.In 2000-2001, GHI primary accomplishments were: Completing, with the UN Centre for Regional Development, a two-year research-development pilot project called the Global Earthquake Safety Initiative (GESI). GESI tested a method developed by GHI that evaluates the earthquake risk of a community and assesses the most effective means to manage that risk. In GESI, this method was applied to 21 cities around the world; participants evaluated the potential of GESI to motivate local earthquake risk mitigation actions Collaborating with the National Society of Earthquake Technology, Nepal (NSET) in the School Earthquake Safety Project. In this project, the schools of several small villages near Kathmandu were strengthened to withstand earthquakes and the local masons were trained in earthquake-resistant construction techniques. Contributing to a project managed by the Sustainable Environment and Ecological Development Society (SEEDS) of India, in which a village in Gujarat, India, which was destroyed in the January 2001 earthquake, was reconstructed and the villagers were trained, in the process, in seismic resistant construction methods. Commencing a project to apply the GHI/GESI method to three cities in Gujarat with high earthquake risk.
Publications