Breadboard linear array scan imager using LSI solid-state technology

The performance of large scale integration photodiode arrays in a linear array scan (pushbroom) breadboard was evaluated for application to multispectral remote sensing of the earth's resources. The technical approach, implementation, and test results of the program are described. Several self scanned linear array visible photodetector focal plane arrays were fabricated and evaluated in an optical bench configuration. A 1728-detector array operating in four bands (0.5 - 1.1 micrometer) was evaluated for noise, spectral response, dynamic range, crosstalk, MTF, noise equivalent irradiance, linearity, and image quality. Other results include image artifact data, temporal characteristics, radiometric accuracy, calibration experience, chip alignment, and array fabrication experience. Special studies and experimentation were included in long array fabrication and real-time image processing for low-cost ground stations, including the use of computer image processing. High quality images were produced and all objectives of the program were attained

Innovative Concepts for the Electronic Interface of Massively Parallel MRI Phased Imaging Arrays

In Magnetic Resonance Imaging (MRI), the concept of parallel imaging shows significant enhancements in boosting the signal-to-noise ratio, reducing the imaging time, and enlarging the imaging field of view. However, this concept necessitates increased size, cost, and complexity of the MR system. This thesis introduces an innovative solution for the electronics of the MRI system that allows parallel imaging with massive number of channels while avoiding, at the same time, the associated drawback

Low Power Circuits for Smart Flexible ECG Sensors

Cardiovascular diseases (CVDs) are the world leading cause of death. In-home heart condition monitoring effectively reduced the CVD patient hospitalization rate. Flexible electrocardiogram (ECG) sensor provides an affordable, convenient and comfortable in-home monitoring solution. The three critical building blocks of the ECG sensor i.e., analog frontend (AFE), QRS detector, and cardiac arrhythmia classifier (CAC), are studied in this research. A fully differential difference amplifier (FDDA) based AFE that employs DC-coupled input stage increases the input impedance and improves CMRR. A parasitic capacitor reuse technique is proposed to improve the noise/area efficiency and CMRR. An on-body DC bias scheme is introduced to deal with the input DC offset. Implemented in 0.35m CMOS process with an area of 0.405mm2, the proposed AFE consumes 0.9W at 1.8V and shows excellent noise effective factor of 2.55, and CMRR of 76dB. Experiment shows the proposed AFE not only picks up clean ECG signal with electrodes placed as close as 2cm under both resting and walking conditions, but also obtains the distinct -wave after eye blink from EEG recording. A personalized QRS detection algorithm is proposed to achieve an average positive prediction rate of 99.39% and sensitivity rate of 99.21%. The user-specific template avoids the complicate models and parameters used in existing algorithms while covers most situations for practical applications. The detection is based on the comparison of the correlation coefficient of the user-specific template with the ECG segment under detection. The proposed one-target clustering reduced the required loops. A continuous-in-time discrete-in-amplitude (CTDA) artificial neural network (ANN) based CAC is proposed for the smart ECG sensor. The proposed CAC achieves over 98% classification accuracy for 4 types of beats defined by AAMI (Association for the Advancement of Medical Instrumentation). The CTDA scheme significantly reduces the input sample numbers and simplifies the sample representation to one bit. Thus, the number of arithmetic operations and the ANN structure are greatly simplified. The proposed CAC is verified by FPGA and implemented in 0.18m CMOS process. Simulation results show it can operate at clock frequencies from 10KHz to 50MHz. Average power for the patient with 75bpm heart rate is 13.34W

The 1992 4th NASA SERC Symposium on VLSI Design

Papers from the fourth annual NASA Symposium on VLSI Design, co-sponsored by the IEEE, are presented. Each year this symposium is organized by the NASA Space Engineering Research Center (SERC) at the University of Idaho and is held in conjunction with a quarterly meeting of the NASA Data System Technology Working Group (DSTWG). One task of the DSTWG is to develop new electronic technologies that will meet next generation electronic data system needs. The symposium provides insights into developments in VLSI and digital systems which can be used to increase data systems performance. The NASA SERC is proud to offer, at its fourth symposium on VLSI design, presentations by an outstanding set of individuals from national laboratories, the electronics industry, and universities. These speakers share insights into next generation advances that will serve as a basis for future VLSI design

KAVUAKA: a low-power application-specific processor architecture for digital hearing aids

The power consumption of digital hearing aids is very restricted due to their small physical size and the available hardware resources for signal processing are limited. However, there is a demand for more processing performance to make future hearing aids more useful and smarter. Future hearing aids should be able to detect, localize, and recognize target speakers in complex acoustic environments to further improve the speech intelligibility of the individual hearing aid user. Computationally intensive algorithms are required for this task. To maintain acceptable battery life, the hearing aid processing architecture must be highly optimized for extremely low-power consumption and high processing performance.The integration of application-specific instruction-set processors (ASIPs) into hearing aids enables a wide range of architectural customizations to meet the stringent power consumption and performance requirements. In this thesis, the application-specific hearing aid processor KAVUAKA is presented, which is customized and optimized with state-of-the-art hearing aid algorithms such as speaker localization, noise reduction, beamforming algorithms, and speech recognition. Specialized and application-specific instructions are designed and added to the baseline instruction set architecture (ISA). Among the major contributions are a multiply-accumulate (MAC) unit for real- and complex-valued numbers, architectures for power reduction during register accesses, co-processors and a low-latency audio interface. With the proposed MAC architecture, the KAVUAKA processor requires 16 % less cycles for the computation of a 128-point fast Fourier transform (FFT) compared to related programmable digital signal processors. The power consumption during register file accesses is decreased by 6 %to 17 % with isolation and by-pass techniques. The hardware-induced audio latency is 34 %lower compared to related audio interfaces for frame size of 64 samples.The final hearing aid system-on-chip (SoC) with four KAVUAKA processor cores and ten co-processors is integrated as an application-specific integrated circuit (ASIC) using a 40 nm low-power technology. The die size is 3.6 mm2. Each of the processors and co-processors contains individual customizations and hardware features with a varying datapath width between 24-bit to 64-bit. The core area of the 64-bit processor configuration is 0.134 mm2. The processors are organized in two clusters that share memory, an audio interface, co-processors and serial interfaces. The average power consumption at a clock speed of 10 MHz is 2.4 mW for SoC and 0.6 mW for the 64-bit processor.Case studies with four reference hearing aid algorithms are used to present and evaluate the proposed hardware architectures and optimizations. The program code for each processor and co-processor is generated and optimized with evolutionary algorithms for operation merging,instruction scheduling and register allocation. The KAVUAKA processor architecture is com-pared to related processor architectures in terms of processing performance, average power consumption, and silicon area requirements

Energy analysis and optimisation techniques for automatically synthesised coprocessors

The primary outcome of this research project is the development of a methodology enabling fast automated early-stage power and energy analysis of configurable processors for system-on-chip platforms. Such capability is essential to the process of selecting energy efficient processors during design-space exploration, when potential savings are highest. This has been achieved by developing dynamic and static energy consumption models for the constituent blocks within the processors. Several optimisations have been identified, specifically targeting the most significant blocks in terms of energy consumption. Instruction encoding mechanism reduces both the energy and area requirements of the instruction cache; modifications to the multiplier unit reduce energy consumption during inactive cycles. Both techniques are demonstrated to offer substantial energy savings. The aforementioned techniques have undergone detailed evaluation and, based on the positive outcomes obtained, have been incorporated into Cascade, a system-on-chip coprocessor synthesis tool developed by Critical Blue, to provide automated analysis and optimisation of processor energy requirements. This thesis details the process of identifying and examining each method, along with the results obtained. Finally, a case study demonstrates the benefits of the developed functionality, from the perspective of someone using Cascade to automate the creation of an energy-efficient configurable processor for system-on-chip platforms

VLSI Design of Heart Model

Heart disease is a leading cause of death in the United States and abroad. Research interests arise in understanding the nature of the dynamics of the heart and seeking methods to control and suppress arrhythmias. Simulation of the heart electrical activity is a useful approach to study the heart because it yields some quantities of interest that cannot practically be obtained in any other way. However, the complexity of the human heart leads to complicated mathematical models, and consequently, modeling arrhythmias of a whole heart with computers is extremely data intensive and computational challenging. In this dissertation, we introduce an analog VLSI design that simulates cardiac electrical activities. The selected cardiac model is based on the Beeler-Reuter equations and the continuous core-conductor model. The Beeler-Reuter equations formulate the membrane ionic kinetics of ventricular cells, and the core-conductor model describes the electrical signal conduction on cardiac tissues. We discuss the design flows of mapping equations into circuits and present a set of circuit blocks of basic mathematical function units. The transistor circuits for realizing the ionic model of a single cell is introduced, and capacitors are used to calculate time directives. A method of shifting the initial conditions of differential equations to zero is discussed for saving the circuit which sets up the initial voltages of the capacitors. We also introduce a method of implementing reaction-diffusion systems using non-linear RC networks, and present the circuit which simulates the reaction-diffusion process, i.e. the electrical propagation, of the heart. Error analysis is carried out for the circuit-realized Beeler-Reuter model by comparing the simulated functions with the equation calculated values. The PSpice simulation results show that the circuit created action potential is satisfactory. The important reentry phenomena, the primary mechanism underlying fibrillation, is presented, and an anatomical reentry in the 1-dimensional model and a functional reentry (spiral wave) in the 2-dimensional model are successfully simulated in circuits. The presented methods of implementing equations with analog VLSI circuit contribute to the fundamentals for a novel technique of obtaining numerical solutions and potential fast application-specified analog computational devices if the circuits are fabricated on chips. Unlike computing with digital computers, which is mainly a serial process and needs to discretize the space and the time domain for finding numerical solutions of the discretization points one by one, computation with analog VLSI relies on the physics of the electrical devices and takes advantage of the integration properties of capacitors and, hence, computing in analog circuit hardware is a parallel process and can be real-time, that is, the calculation time is the time simulated by equations

Architectures and circuits for low-voltage energy conversion and applications in renewable energy and power management

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 337-343).In this thesis we seek to develop smaller, less expensive, and more efficient power electronics. We also investigate emerging applications where the proper implementation of these new types of power converters can have a significant impact on the overall system performance. We have developed a new two-stage dc-dc converter architecture suitable for low-voltage CMOS power delivery. The architecture, which combines the benefits of switched-capacitor and inductor-based converters, achieves both large voltage step-down and high switching frequency, while maintaining good efficiency. We explore the benefits of a new soft-charging technique that drastically reduces the major loss mechanism in switched-capacitor converters, and we show experimental results from a 5-to-1 V, 0.8 W integrated dc-dc converter developed in 180 nm CMOS technology. The use of power electronics to increase system performance in a portable thermophotovoltaic power generator is also investigated in this thesis. We show that mechanical non-idealities in a MEMS fabricated energy conversion device can be mitigated with the help of low-voltage distributed maximum power point tracking (MPPT) dc-dc converters. As part of this work, we explore low power control and sensing architectures, and present experimental results of a 300 mW integrated MPPT developed in 0.35 um CMOS with all power, sensing and control circuitry on chip. The final piece of this thesis investigates the implementation of distributed power electronics in solar photovoltaic applications. We explore the benefits of small, intelligent power converters integrated directly into the solar panel junction box to enhance overall energy capture in real-world scenarios. To this end, we developed a low-cost, high efficiency (>98%) power converter that enables intelligent control and energy conversion at the sub-panel level. Experimental field measurements show that the solution can provide up to a 35% increase in panel output power during partial shading conditions compared to current state-of-the-art solutions.by Robert C. N. Pilawa-Podgurski.Ph.D

Low-Power and Programmable Analog Circuitry for Wireless Sensors

Embedding networks of secure, wirelessly-connected sensors and actuators will help us to conscientiously manage our local and extended environments. One major challenge for this vision is to create networks of wireless sensor devices that provide maximal knowledge of their environment while using only the energy that is available within that environment. In this work, it is argued that the energy constraints in wireless sensor design are best addressed by incorporating analog signal processors. The low power-consumption of an analog signal processor allows persistent monitoring of multiple sensors while the device\u27s analog-to-digital converter, microcontroller, and transceiver are all in sleep mode. This dissertation describes the development of analog signal processing integrated circuits for wireless sensor networks. Specific technology problems that are addressed include reconfigurable processing architectures for low-power sensing applications, as well as the development of reprogrammable biasing for analog circuits