Thesis (M.S.)--Brigham Young University. Dept. of Electrical and Computer Engineering, 2006. / Includes bibliographical references (p. 63-71).

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 96-98).

Methods to improve performance of sensors with regard to sensor nonlinearity, sensor noise and sensor bandwidths are investigated and new algorithms are developed. The necessity of the proposed research has evolved from the ever-increasing need for greater precision and improved reliability in sensor measurements. After describing the current state of the art of sensor related issues like nonlinearity and bandwidth, research goals are set to create a new trend on the usage of sensors. We begin the investigation with a detailed distortion analysis of nonlinear sensors. A need for efficient distortion compensation procedures is further justified by showing how a slight deviation from the linearity assumption leads to a very severe distortion in time and in frequency domains. It is argued that with a suitable distortion compensation technique the danger of having an infinite bandwidth nonlinear sensory operation, which is dictated by nonlinear distortion, can be avoided. Several distortion compensation techniques are developed and their performance is validated by simulation and experimental results. Like any other model-based technique, modeling errors or model uncertainty affects performance of the proposed scheme, this leads to the innovation of robust signal reconstruction. A treatment for this problem is given and a novel technique, which uses a nominal model instead of an accurate model and produces the results that are robust to model uncertainty, is developed. The means to attain a high operating bandwidth are developed by utilizing several low bandwidth pass-band sensors. It is pointed out that instead of using a single sensor to measure a high bandwidth signal, there are many advantages of using an array of several pass-band sensors. Having shown that employment of sensor arrays is an economic incentive and practical, several multi-sensor fusion schemes are developed to facilitate their implementation. Another aspect of this dissertation is to develop means to deal with outliers in sensor measurements. As fault sensor data detection is an essential element of multi-sensor network implementation, which is used to improve system reliability and robustness, several sensor scheduling configurations are derived to identify and to remove outliers.

FPGA clock frequencies are slow enough that only a fraction of the interconnect’s bandwidth is used. By exploiting this bandwidth, the transfer of large amounts of data can be greatly accelerated. Alternatively, it may also be possible to save area on fixed-bandwidth links by using on-chip serial signaling. For datapath-intensive designs which operate on words instead of bits, this can reduce wiring congestion as well. This thesis proposes relatively simple circuit-level modifications to FPGA interconnect to enable high-bandwidth communication. High-level area estimates indicate a potential interconnect area savings of 10 to 60% when serial links are used. Two interconnect pipelining techniques, wave pipelining and surfing, are adapted to FPGAs and compared against each other and against regular FPGA interconnect in terms of throughput, reliability, area, power, and latency. Source-synchronous signaling is used to achieve high data rates with simple receiver design. Statistical models for high-frequency power supply noise are developed and used to estimate the probability of error of wave pipelined and surfing links as a function of link length and operating speed. Surfing is generally found to be more reliable and less sensitive to noise than wave pipelining. Simulation results in a 65nm process demonstrate a throughput of 3Gbps per wire across a 50-stage, 25mm link.

Aeroacoustic emissions were identified as a primary concern in the public acceptance of wind turbines. A review of literature involving sound localization was undertaken and led to the design of two microphone arrays to identify acoustic sources. A small-scale array composed of 27 sensors was produced with the intention of improving the quality of sound measurements over those made by a single microphone in a small, closed-loop wind tunnel. A large-scale array containing 30 microphones was also implemented to allow for measurements of aeroacoustic emissions from airfoils and rotating wind turbines. To minimize cost and pursue alternative sensor technologies, microelectromechanical microphones were selected for the array sensors and assembled into the arrays on printed circuit boards. Characterization of the microphones was completed using a combination of calibration techniques, primarily in a plane wave tube. Array response to known sources was quantified by analyzing source maps with respect to source location accuracy, beamwidth, and root mean square error. Multiple sources and rotating sources were tested to assess array performance. Following validation with known sources, wind tunnel testing of a 600 watt wind turbine was performed at freestream speeds of 2.5 m/s, 3.5 m/s, 4.5 m/s, and to 5.5 m/s. Significant aeroacoustic emissions were noted from the turbine in the 4.5 m/s and 5.5 m/s cases, with an increase of up to 12 dB over background levels. Source maps from the 5.5 m/s tests revealed that the primary location of aeroacoustic emissions was near the outer radii of the rotor, but not at the tip, and generally moved radially outward with increasing frequency. The azimuthal location of the greatest sound pressure levels was typically found to be between 120º and 130º measured counterclockwise from the upward vertical, coinciding with the predicted location of greatest emissions provided by an analytical model based on dipole directivity and convective amplification. Analysis of the acoustic spectra, turbine operating characteristics, and previous literature suggested that the sound emissions emanated from the trailing edge of the blades.

Recent years have brought many new developments in the processing of speech and acoustic signals. Yet, despite this, the process of acquiring signals has gone largely unchanged. Adding spatial diversity to the repertoire of signal acquisition has long been known to offer advantages for processing signals further. The processing capabilities of mobile devices had not previously been able to handle the required computation to handle these previous streams of information. But current processing capabilities are such that the extra workload introduced by the addition of mutiple sensors on a mobile device are not over-burdensome. How these extra data streams can best be handled is still an open question. The present work deals with the examination of one type of spatial processing technique, known as beamforming. A microphone array test platform is constructed and verified through a number of beamforming agorithms. Issues related to speech acquisition through microphones arrays are discussed. The algorithms used for verification are presented in detail and compared to one another.

This dissertation describes the fabrication and operation of porous silicon gas sensors. The first chapter describes the motivation behind gas sensor research and provides the reader with background knowledge of gas sensors including the terminology and a review of various gas sensors. The following two chapters describe both how the porous silicon gas sensors are created and how they have been tested in the laboratory. Chapter 4 describes the steps required to create arrays of gas sensors to provide for a selective device through the application of patented selective coatings. Chapter 5 proposes a physical model that leads to a numerical solution for predicting the operation of the gas sensor. The next chapter builds from this model to analyze and optimize the experimental methods that are used to test both this and other gas sensors. The final chapter of this dissertation describes the prototype gas sensor system that has most recently been created, the company that was formed to further the development of that system, and the future applications of the porous silicon gas sensor.

Abstract Because of the rapid development of mobile communications, the bandwidth efficiency and the communication quality have become interesting issues for researchers. Multipath fading and multiple-access interference (MAI) are the main barriers of performance improvement. Fortunately, spread spectrum techniques have an inherent ability of anti-interference and anti-multipath. Thus, the commercialized CDMA systems are developed on the basis of spread spectrum techniques. Recently, smart antennas using adaptive array techniques have been applied in CDMA communications. There are two different approaches to realize smart antenna systems: one is named as ¡¥switching-beam arrays¡¦; the other is called ¡¥tracking-beam arrays¡¦. Switching-beam arrays use a number of fixed beams at an antenna site. The mobiles select the beam that offers the best signal enhancement and interference reduction. On the other hand, tracking-beam arrays can adjust their pattern to track desired signals, reduce interference, and collect correlated multipath power. In this thesis, several smart antenna systems based on tracking-beam arrays in DS-CDMA systems are developed. The proposed smart antennas comprise two main structures, i.e., the direction-of-arrival (DOA) tracking and the dynamic beamforming. The improved recursively updated method and the Kalman filter algorithm are employed to track the mobiles¡¦ DOAs. By referring to the estimated DOA, the minimum- variance-distortionless-response (MVDR) dynamic beamformer produces a main beam to target on a certain mobile and simultaneously, interferences from other mobiles are suppressed. Therefore, the MAI in DS-CDMA systems can be suppressed properly at all times. Besides, the inherent processing gain in DS-CDMA systems is utilized to break through the limit that the number of mobiles must be smaller than that of sensors. The proposed smart antennas work properly even the number of mobiles exceeds that of sensor. Simulation results show that the advantages of the proposed technique over the conventional tracking-beam arrays.

Methods to improve performance of sensors with regard to sensor nonlinearity, sensor noise and sensor bandwidths are investigated and new algorithms are developed. The necessity of the proposed research has evolved from the ever-increasing need for greater precision and improved reliability in sensor measurements. After describing the current state of the art of sensor related issues like nonlinearity and bandwidth, research goals are set to create a new trend on the usage of sensors. We begin the investigation with a detailed distortion analysis of nonlinear sensors. A need for efficient distortion compensation procedures is further justified by showing how a slight deviation from the linearity assumption leads to a very severe distortion in time and in frequency domains. It is argued that with a suitable distortion compensation technique the danger of having an infinite bandwidth nonlinear sensory operation, which is dictated by nonlinear distortion, can be avoided. Several distortion compensation techniques are developed and their performance is validated by simulation and experimental results. Like any other model-based technique, modeling errors or model uncertainty affects performance of the proposed scheme, this leads to the innovation of robust signal reconstruction. A treatment for this problem is given and a novel technique, which uses a nominal model instead of an accurate model and produces the results that are robust to model uncertainty, is developed. The means to attain a high operating bandwidth are developed by utilizing several low bandwidth pass-band sensors. It is pointed out that instead of using a single sensor to measure a high bandwidth signal, there are many advantages of using an array of several pass-band sensors. Having shown that employment of sensor arrays is an economic incentive and practical, several multi-sensor fusion schemes are developed to facilitate their implementation. Another aspect of this dissertation is to develop means to deal with outliers in sensor measurements. As fault sensor data detection is an essential element of multi-sensor network implementation, which is used to improve system reliability and robustness, several sensor scheduling configurations are derived to identify and to remove outliers.