Psychophysical Inference from Centroid Estimation

Performance statistics in centroid tasks are not the same as those used in classic decision tasks. In psychophysical experiments using decision tasks, signal detection theory and drift-diffusion models provide the frameworks for statistical inference from error rates and reaction times. However, neither of these frameworks are appropriate for psychophysical inference with centroid task data. In this dissertation, we explore a modeling framework for double-pass experiments with centroid tasks, and show its potential to (1) detect performance differences, and infer experimental effects without additional process model assumptions, (2) falsify properties of a latent process using nested model assumptions, (3) investigate neurocomputational models of the process, and (4) investigate properties of spatial attention at a deeper level than is possible using decision-based paradigms

A comprehensive study of evolution of photospheric magnetic field and flows associated with solar eruptions

The rapid, irreversible change of the photospheric magnetic field has been recognized as an important element of the solar flare process. Recent theoretical work has shown that such a change would imply Lorentz force perturbations acting on both the outer solar atmosphere and the solar surface. This research uses vector magnetograms obtained with the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory to study a number of flares, which range from GOES-class C4 to X5 and occur in four active regions. In all the events, a permanent and rapid change of photospheric magnetic field closely associated with the flare occurrence is found. The change is predominantly in the form of an enhancement of the horizontal magnetic field, which is located around the magnetic polarity inversion line between flare ribbons. The area integral of the field change and the derived Lorentz force change both show a strong correlation with flare magnitude. For seven events associated with coronal mass ejections (CMEs), the CME mass is estimated using the observed CME velocity and the impulse provided by the upward Lorentz force. Furthermore, the flow field vorticity of selected sunspots away from flare kernels in the AR 11158 is calculated using the Differential Affine Velocity Estimator. It is found that some spots exhibit a sharp acceleration of rotation co-temporal with the rapid rising of the soft X-ray flux, and that such rotational disturbance may be driven by the Lorentz-force change in the horizontal direction

Design and numerical simulation of the real-time particle charge and size analyser

The electrostatic charge and size distribution of aerosol particles play a very important role in many industrial applications. Due to the complexity and the probabilistic nature of the different charging mechanisms often acting simultaneously, it is difficult to theoretically predict the charge distribution of aerosol particles or even estimate the relative effect of the different mechanisms. Therefore, it is necessary to measure the size and also the bipolar charge distribution on aerosol particles. The main aim of this research project was to design, implement and simulate a signal processing system for novel, fully functional measurement instrument capable of simultaneously measuring in real time the bipolar charge and size distribution of medical aerosols. The Particle Size and Charge Analyser (PSCA), investigated in this thesis, uses Phase Doppler Anemometry (PDA) technique. The PDA system was used to track the motion of charged particles in the presence of an electric field. By solving the equation of particle motion in a viscous medium combined with the simultaneous measurement of its size and velocity, the magnitude as well as the polarity of the particle charge can be obtained. Different signal processing systems in different excitation fields have been designed and implemented. These systems include: velocity estimation system using spectral analysis in DC excitation field, velocity estimation system based on Phase Locked Loop (PLL) technique working in DC as well as sine-wave excitation fields, velocity estimation system based on Quadrature Demodulation (QD) technique under sine-wave excitation method, velocity estimation system using spectral analysis in square-wave excitation field and phase shift estimation based on Hilbert transformation and correlation technique in both sine-wave and square-wave excitation fields. The performances of these systems were evaluated using Monte Carlo (MC) simulations obtained from the synthesized Doppler burst signals generated from the mathematical models implemented in MATLAB. The synthesized Doppler Burst Signal (DBS) was subsequently corrupted with the added Gaussian noise. Cross validation of the results was performed using hardware signal processing system employing Arbitrary Waveform Generator and also NASA simulator to further confirm the validity of the estimation

Passive Characterization of Unknown Spaces Using Large-Volume, Pixelated CdZnTe

Radioactive material is often encountered in unknown configurations across the fields of international safeguards, treaty verification, industry and emergency response. These disparate problems, ranging from small scale, commercial waste classification to wide-spread, post-detonation response, center around the same goal: extracting as much useful information as possible about radioisotopes and their surroundings in some unknown space. These classic nuclear questions of `who', `what', `where', `why' and `how' have been asked for many decades. However, recent developments in high-performance, 3-D position-sensitive Cadmium Zinc Telluride (CdZnTe) detectors enable these old questions to be answered in new ways. Shielding-induced perturbations in photon spectra can be recorded and analyzed to characterize the non-radioactive material around a source. Directional spectra, extracted using either Compton or coded aperture imaging, can characterize complex objects containing multiple, shielded sources. Spectra from strong sources, such as radiological dispersal devices, are similarly perturbed by atmospheric shielding during transport through hundreds of meters of air. Atmosphere-induced, spectral perturbations can be used to estimate standoff and localize sources in 3-D space from a single measurement. Once localized, the effects of solid angle and atmospheric shielding can be corrected for to estimate absolute source activity. Atmospheric scatter, in the form of skyshine, can also be used to localize sources in heavily shielded scenarios without a direct line-of-sight. Strong gamma-ray sources were similarly localized in 3-D space using mobile, helicopter mounted CdZnTe detectors. Direct comparisons between imaging and na"ive, non-imaging source localization techniques are made for these mobile measurements. Neutron emitting objects, like those encountered in safeguards and treaty verification, can be detected using new, low-noise, digital CdZnTe detectors. Coarse, 1-D fast neutron source localization was demonstrated using a four crystal, CdZnTe array. Gamma rays from neutron-induced interactions were also used to generate a qualitative, spatially-resolved estimate of shielding isotopics. Finally, high-spatial resolution coded aperture imaging was used to quickly characterize plutonium objects at spatial scales smaller than 1 cm^2. High-energy resolution CdZnTe gamma-ray spectra were then coupled with the commercial software FRAM to estimate special nuclear material isotopics. When combined, these techniques enable spatially-resolved estimation of special nuclear material grade.PHDNuclear Engineering & Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149794/1/digoodma_1.pd

Psychophysical investigations of visual density discrimination

Work in spatial vision is reviewed and a new effect of spatial averaging is reported. This shows that dot separation discriminations are improved if the cue is represented in the intervals within a collection of dots arranged in a lattice, compared to simple 2 dot separation discriminations. This phenomenon may be related to integrative processes that mediate texture density estimation. Four models for density discrimination are described. One involves measurements of spatial filter outputs. Computer simulations show that in principle, density cues can be encoded by a system of four DOG filters with peak sensitivities spanning a range of 3 octaves. Alternative models involve operations performed over representations in which spatial features are made explicit. One of these involves estimations of numerosity or coverage of the texture elements. Another involves averaging of the interval values between adjacent elements. A neural model for measuring the relevant intervals is described. It is argued that in principle the input to a density processor does not require the full sequence of operations in the MIRAGE transformation (eg. Watt and Morgan 1985). In particular, the regions of activity in the second derivative do not need to be interpreted in terms of edges, bars and blobs in order for density estimation to commence. This also implies that explicit coding of texture elements may be unnecessary. Data for density discrimination in regular and random dot patterns are reported. These do not support the coverage and counting models and observed performance shows significant departures from predictions based on an analysis of the statistics of the interval distribution in the stimuli. But this result can be understood in relation to other factors in the interval averaging process, and there is empirical support for the hypothesized method for measuring the intervals. Other experiments show that density is scaled according to stimulus size and possibly perceived depth. It is also shown that information from density analysis can be combined with size estimations to produce highly accurate discriminations of image expansion or object depth changes

Speaker independent isolated word recognition

The work presented in this thesis concerns the recognition of isolated words using a pattern matching approach. In such a system, an unknown speech utterance, which is to be identified, is transformed into a pattern of characteristic features. These features are then compared with a set of pre-stored reference patterns that were generated from the vocabulary words. The unknown word is identified as that vocabulary word for which the reference pattern gives the best match. One of the major difficul ties in the pattern comparison process is that speech patterns, obtained from the same word, exhibit non-linear temporal fluctuations and thus a high degree of redundancy. The initial part of this thesis considers various dynamic time warping techniques used for normalizing the temporal differences between speech patterns. Redundancy removal methods are also considered, and their effect on the recognition accuracy is assessed. Although the use of dynamic time warping algorithms provide considerable improvement in the accuracy of isolated word recognition schemes, the performance is ultimately limited by their poor ability to discriminate between acoustically similar words. Methods for enhancing the identification rate among acoustically similar words, by using common pattern features for similar sounding regions, are investigated. Pattern matching based, speaker independent systems, can only operate with a high recognition rate, by using multiple reference patterns for each of the words included in the vocabulary. These patterns are obtained from the utterances of a group of speakers. The use of multiple reference patterns, not only leads to a large increase in the memory requirements of the recognizer, but also an increase in the computational load. A recognition system is proposed in this thesis, which overcomes these difficulties by (i) employing vector quantization techniques to reduce the storage of reference patterns, and (ii) eliminating the need for dynamic time warping which reduces the computational complexity of the system. Finally, a method of identifying the acoustic structure of an utterance in terms of voiced, unvoiced, and silence segments by using fuzzy set theory is proposed. The acoustic structure is then employed to enhance the recognition accuracy of a conventional isolated word recognizer

Study, Design and Fabrication of an Analogue VLSI Ormia-Ochracea-Inspired Delay Magnification System

This Thesis entails the development of a low-power delay magnification system inspired by the mechanical structure of the ear of the parasitoid fly Ormia Ochracea (O2). The proposed system is suitable as a preprocessing unit for binaural sound localization processors equipped with miniature acoustic sensors. The core of the Thesis involves the study of a delay magnification system based on the O2 sound localization mechanism and the design and testing of a low-power analog integrated circuit based on a proposed, novel delay magnification system inspired by Ormia Ochracea. The study of the delay magnification system based on the O2 sound localization mechanism is divided into two main parts. The first part studies in detail the delay magnification mechanism of the O2 ears. This study sheds light and tries to comprehend what mechanical parameters of the O2 ears are involved in the delay magnification process and how these parameters contribute to the magnification of the delay. The study presents the signal-flow-graph of the O2 system which can be used as a generic delay magnification model for the O2 ears. We also explore the effects of the tuning of the O2 system parameters on the output interaural time difference (ITD). Inspired by the study of the O2 system, in the second part of our study, we modify the O2 system using simpler building blocks and structure which can provide a delay magnification comparable to the original O2 system. We present a new binaural sound localization system suitable for small ITDs which utilizes the new modified O2 system, cochlea filter banks, cross-correlograms and our re-mapping algorithm and show that it can be used to encode very small input delay values that could not be resolved by means of a conventional binaural processor based on the Jeffress’s coincidence detection model. We evaluate the sound localization performance of our new binaural sound localization system for a single sound source and a sound source in the presence of a competing sound source scenario through detailed simulation. The performance of the proposed system is also explored in the presence of filter bandwidth variation and cochlea filter mismatch. After the study of the O2 delay magnification system, we present an analog VLSI chip which morphs the O2 delay magnification system. To determine what topology is the best morphing platform for the O2 system, we present the design and comparative performance of the O2 system when log-domain and gm-C second order weak-inversion filters are employed. The design of the proposed low-power modified O2 system circuit based on translinear loops is detailed. Its performance is evaluated through detailed simulation. Subsequently the Thesis proceeds with the design, fabrication and testing of the new chip based on the modified O2 circuit. The synthesis and testing of the proposed circuit using 0.35μm AMS CMOS process technology parameters is discussed. Detailed measured results confirm the delay magnification ability of the modified O2 circuit and its compliance with theoretical analysis explained earlier in the Thesis. The fabricated system is tuned to operate in the 100Hz to 1kHz frequency range, is able to achieve a delay gain of approximately 3.5 to 9.5 when the input (physical) delay ranges from 0μs to 20μs, and consumes 13.1μW with a 2 V power supply

Virtual metrology for plasma etch processes.

Plasma processes can present dicult control challenges due to time-varying dynamics and a lack of relevant and/or regular measurements. Virtual metrology (VM) is the use of mathematical models with accessible measurements from an operating process to estimate variables of interest. This thesis addresses the challenge of virtual metrology for plasma processes, with a particular focus on semiconductor plasma etch. Introductory material covering the essentials of plasma physics, plasma etching, plasma measurement techniques, and black-box modelling techniques is rst presented for readers not familiar with these subjects. A comprehensive literature review is then completed to detail the state of the art in modelling and VM research for plasma etch processes. To demonstrate the versatility of VM, a temperature monitoring system utilising a state-space model and Luenberger observer is designed for the variable specic impulse magnetoplasma rocket (VASIMR) engine, a plasma-based space propulsion system. The temperature monitoring system uses optical emission spectroscopy (OES) measurements from the VASIMR engine plasma to correct temperature estimates in the presence of modelling error and inaccurate initial conditions. Temperature estimates within 2% of the real values are achieved using this scheme. An extensive examination of the implementation of a wafer-to-wafer VM scheme to estimate plasma etch rate for an industrial plasma etch process is presented. The VM models estimate etch rate using measurements from the processing tool and a plasma impedance monitor (PIM). A selection of modelling techniques are considered for VM modelling, and Gaussian process regression (GPR) is applied for the rst time for VM of plasma etch rate. Models with global and local scope are compared, and modelling schemes that attempt to cater for the etch process dynamics are proposed. GPR-based windowed models produce the most accurate estimates, achieving mean absolute percentage errors (MAPEs) of approximately 1:15%. The consistency of the results presented suggests that this level of accuracy represents the best accuracy achievable for the plasma etch system at the current frequency of metrology. Finally, a real-time VM and model predictive control (MPC) scheme for control of plasma electron density in an industrial etch chamber is designed and tested. The VM scheme uses PIM measurements to estimate electron density in real time. A predictive functional control (PFC) scheme is implemented to cater for a time delay in the VM system. The controller achieves time constants of less than one second, no overshoot, and excellent disturbance rejection properties. The PFC scheme is further expanded by adapting the internal model in the controller in real time in response to changes in the process operating point

Virtual metrology for plasma etch processes.

Plasma processes can present dicult control challenges due to time-varying dynamics and a lack of relevant and/or regular measurements. Virtual metrology (VM) is the use of mathematical models with accessible measurements from an operating process to estimate variables of interest. This thesis addresses the challenge of virtual metrology for plasma processes, with a particular focus on semiconductor plasma etch. Introductory material covering the essentials of plasma physics, plasma etching, plasma measurement techniques, and black-box modelling techniques is rst presented for readers not familiar with these subjects. A comprehensive literature review is then completed to detail the state of the art in modelling and VM research for plasma etch processes. To demonstrate the versatility of VM, a temperature monitoring system utilising a state-space model and Luenberger observer is designed for the variable specic impulse magnetoplasma rocket (VASIMR) engine, a plasma-based space propulsion system. The temperature monitoring system uses optical emission spectroscopy (OES) measurements from the VASIMR engine plasma to correct temperature estimates in the presence of modelling error and inaccurate initial conditions. Temperature estimates within 2% of the real values are achieved using this scheme. An extensive examination of the implementation of a wafer-to-wafer VM scheme to estimate plasma etch rate for an industrial plasma etch process is presented. The VM models estimate etch rate using measurements from the processing tool and a plasma impedance monitor (PIM). A selection of modelling techniques are considered for VM modelling, and Gaussian process regression (GPR) is applied for the rst time for VM of plasma etch rate. Models with global and local scope are compared, and modelling schemes that attempt to cater for the etch process dynamics are proposed. GPR-based windowed models produce the most accurate estimates, achieving mean absolute percentage errors (MAPEs) of approximately 1:15%. The consistency of the results presented suggests that this level of accuracy represents the best accuracy achievable for the plasma etch system at the current frequency of metrology. Finally, a real-time VM and model predictive control (MPC) scheme for control of plasma electron density in an industrial etch chamber is designed and tested. The VM scheme uses PIM measurements to estimate electron density in real time. A predictive functional control (PFC) scheme is implemented to cater for a time delay in the VM system. The controller achieves time constants of less than one second, no overshoot, and excellent disturbance rejection properties. The PFC scheme is further expanded by adapting the internal model in the controller in real time in response to changes in the process operating point