Control algorithms for large scale adaptive optics

In this dissertation, the problem of creating effective large scale Adaptive Optics (AO) systems control algorithms for the new generation of giant optical telescopes is addressed. The effectiveness of AO control algorithms is evaluated in several respects, such as computational complexity, compensation error rejection and robustness, i.e. reasonable insensitivity to the system imperfections. The results of this research are summarized as follows: 1. Robustness study of Sparse Minimum Variance Pseudo Open Loop Controller (POLC) for multi-conjugate adaptive optics (MCAO). The AO system model that accounts for various system errors has been developed and applied to check the stability and performance of the POLC algorithm, which is one of the most promising approaches for the future AO systems control. It has been shown through numerous simulations that, despite the initial assumption that the exact system knowledge is necessary for the POLC algorithm to work, it is highly robust against various system errors. 2. Predictive Kalman Filter (KF) and Minimum Variance (MV) control algorithms for MCAO. The limiting performance of the non-dynamic Minimum Variance and dynamic KF-based phase estimation algorithms for MCAO has been evaluated by doing Monte-Carlo simulations. The validity of simple near-Markov autoregressive phase dynamics model has been tested and its adequate ability to predict the turbulence phase has been demonstrated both for single- and multiconjugate AO. It has also been shown that there is no performance improvement gained from the use of the more complicated KF approach in comparison to the much simpler MV algorithm in the case of MCAO. 3. Sparse predictive Minimum Variance control algorithm for MCAO. The temporal prediction stage has been added to the non-dynamic MV control algorithm in such a way that no additional computational burden is introduced. It has been confirmed through simulations that the use of phase prediction makes it possible to significantly reduce the system sampling rate and thus overall computational complexity while both maintaining the system stable and effectively compensating for the measurement and control latencies.

Compressed sensing based cyclic feature spectrum sensing for cognitive radios

Spectrum sensing is currently one of the most challenging design problems in cognitive radio. A robust spectrum sensing technique is important in allowing implementation of a practical dynamic spectrum access in noisy and interference uncertain environments. In addition, it is desired to minimize the sensing time, while meeting the stringent cognitive radio application requirements. To cope with this challenge, cyclic spectrum sensing techniques have been proposed. However, such techniques require very high sampling rates in the wideband regime and thus are costly in hardware implementation and power consumption. In this thesis the concept of compressed sensing is applied to circumvent this problem by utilizing the sparsity of the two-dimensional cyclic spectrum. Compressive sampling is used to reduce the sampling rate and a recovery method is developed for re- constructing the sparse cyclic spectrum from the compressed samples. The reconstruction solution used, exploits the sparsity structure in the two-dimensional cyclic spectrum do-main which is different from conventional compressed sensing techniques for vector-form sparse signals. The entire wideband cyclic spectrum is reconstructed from sub-Nyquist-rate samples for simultaneous detection of multiple signal sources. After the cyclic spectrum recovery two methods are proposed to make spectral occupancy decisions from the recovered cyclic spectrum: a band-by-band multi-cycle detector which works for all modulation schemes, and a fast and simple thresholding method that works for Binary Phase Shift Keying (BPSK) signals only. In addition a method for recovering the power spectrum of stationary signals is developed as a special case. Simulation results demonstrate that the proposed spectrum sensing algorithms can significantly reduce sampling rate without sacrifcing performance. The robustness of the algorithms to the noise uncertainty of the wireless channel is also shown.

Tropospheric ozone and CO over North Atlantic for the past decade

We investigate how declines in US emissions of CO and O3 precursors have impacted the lower free troposphere over the North Atlantic. We use seasonal observations for O3 and CO from the PICO-NARE project for the period covering 2001 to 2010. Observations are used to verify model output generated by the GEOS-Chem 3-D global chemical transport model. Additional satellite data for CO from AIRS/Aqua and for O3 from TES/Aura were also used to provide additional comparisons; particularly for fall, winter, and spring when PICO-NARE coverage is sparse. We find GEOS-Chem captures the seasonal cycle for CO and O3 well compared to PICO-NARE data. For CO, GEOS-Chem is biased low, particularly in spring which is in agreement with findings from previous studies. GEOS-Chem is 24.7 +/- 5.2 ppbv (1-σ) low compared to PICO-NARE summer CO data while AIRS is 14.2 +/- 6.6 ppbv high. AIRS does not show nearly as much variation as seen with GEOS-Chem or the Pico data, and goes from being lower than PICO-NARE data in winter and spring, to higher in summer and fall. Both TES and GEOS-Chem match the seasonal ozone cycle well for all seasons when compared with observations. Model results for O3 show GEOS-Chem is 6.67 +/- 2.63 ppbv high compared to PICO-NARE summer measurements and TES was 3.91 +/- 4.2 ppbv higher. Pico data, model results, and AIRS all show declines in CO and O3 for the summer period from 2001 to 2010. Limited availability of TES data prevents us from using it in trend analysis. For summer CO Pico, GEOS-Chem, and AIRS results show declines of 1.32, 0.368, and 0.548 ppbv/year respectively. For summer O3, Pico and GEOS-Chem show declines of -0.726 and -0.583 ppbv/year respectively. In other seasons, both model and AIRS show declining CO, particularly in the fall. GEOS-Chem results show a fall decline of 0.798 ppbv/year and AIRS shows a decline of 0.8372 ppbv/year. Winter and spring CO declines are 0.393 and 0.307 for GEOS-Chem, and 0.455 and 0.566 for AIRS. GEOS-Chem shows declining O3 in other seasons as well; with fall being the season of greatest decrease and winter being the least. Model results for fall, winter, and spring are 0.856, 0.117, and 0.570 ppbv/year respectively. Given the availability of data we are most confident in summer results and thus find that summer CO and O3 have declined in lower free troposphere of the North Atlantic region of the Azores. Sensitivity studies for CO and O3 at Pico were conducted by turning off North American fossil fuel emissions in GEOS-Chem. Model results show that North America fossil fuel emissions contribute 8.57 ppbv CO and 4.03 ppbv O3 to Pico. The magnitude of modeled trends declines in all seasons without North American fossil fuel emissions except for summer CO. The increase in summer CO declines may be due to a decline of 5.24 ppbv/year trend in biomass burning emissions over the study period; this is higher than the 2.33 ppbv/year North American anthropogenic CO model decline. Winter O3 is the only season which goes from showing a negative trend to a positive trend.

Adaptive sensing for target tracking applications

In a statistical inference scenario, the estimation of target signal or its parameters is done by processing data from informative measurements. The estimation performance can be enhanced if we choose the measurements based on some criteria that help to direct our sensing resources such that the measurements are more informative about the parameter we intend to estimate. While taking multiple measurements, the measurements can be chosen online so that more information could be extracted from the data in each measurement process. This approach fits well in Bayesian inference model often used to produce successive posterior distributions of the associated parameter. We explore the sensor array processing scenario for adaptive sensing of a target parameter. The measurement choice is described by a measurement matrix that multiplies the data vector normally associated with the array signal processing. The adaptive sensing of both static and dynamic system models is done by the online selection of proper measurement matrix over time. For the dynamic system model, the target is assumed to move with some distribution and the prior distribution at each time step is changed. The information gained through adaptive sensing of the moving target is lost due to the relative shift of the target. The adaptive sensing paradigm has many similarities with compressive sensing. We have attempted to reconcile the two approaches by modifying the observation model of adaptive sensing to match the compressive sensing model for the estimation of a sparse vector.

LARGE SCALE LANDSLIDE ON THE ONTONAGON RIVER, MICHIGAN

In 2003, a large landslide occurred along the Ontonagon River, located in the Upper Peninsula of Michigan, and adjacent to US-45 in Ontonagon County. The failure took place during the springtime, when the river reached a peak discharge that was the second highest on record. The volume of the slide has been estimated to be approximately 1,400,000 cubic yards. The colluvium blocked the river, forcing a new channel to be carved around the debris. The landslide consisted of a silt layer at its base, overlain by a coarsening upward sand sequence, and finally a varved glacio-lacustrine clay with sparse dropstone inclusions making up the upper section of hillside.

Dynamic properties of wood using the Split-Hopkinson Pressure Bar

Strain rate significantly affects the strength of a material. The Split-Hopkinson Pressure Bar (SHPB) was initially used to study the effects of high strain rate (~103 1/s) testing of metals. Later modifications to the original technique allowed for the study of brittle materials such as ceramics, concrete, and rock. While material properties of wood for static and creep strain rates are readily available, data on the dynamic properties of wood are sparse. Previous work using the SHPB technique with wood has been limited in scope to variability of only a few conditions and tests of the applicability of the SHPB theory on wood have not been performed. Tests were conducted using a large diameter (3.0 inch (75 mm)) SHPB. The strain rate and total strain applied to a specimen are dependent on the striker bar length and velocity at impact. Pulse shapers are used to further modify the strain rate and change the shape of the strain pulse. A series of tests were used to determine test conditions necessary to produce a strain rate, total strain, and pulse shape appropriate for testing wood specimens. Hard maple, consisting of sugar maple (Acer saccharum) and black maple (Acer nigrum), and eastern white pine (Pinus strobus) specimens were used to represent a dense hardwood and a low-density soft wood. Specimens were machined to diameters of 2.5 and 3.0 inches and an assortment of lengths were tested to determine the appropriate specimen dimensions. Longitudinal specimens of 1.5 inch length and radial and tangential specimens of 0.5 inch length were found to be most applicable to SHPB testing. Stress/strain curves were generated from the SHPB data and validated with 6061-T6 aluminum and wood specimens. Stress was indirectly corroborated with gaged aluminum specimens. Specimen strain was assessed with strain gages, digital image analysis, and measurement of residual strain to confirm the strain calculated from SHPB data. The SHPB was found to be a useful tool in accurately assessing the material properties of wood under high strain rates (70 to 340 1/s) and short load durations (70 to 150 μs to compressive failure).

Reaction Control in Quiescent Systems of Free-Radical Retrograde-Precipitation Polymerization

Free-radical retrograde-precipitation polymerization, FRRPP in short, is a novel polymerization process discovered by Dr. Gerard Caneba in the late 1980s. The current study is aimed at gaining a better understanding of the reaction mechanism of the FRRPP and its thermodynamically-driven features that are predominant in controlling the chain reaction. A previously developed mathematical model to represent free radical polymerization kinetics was used to simulate a classic bulk polymerization system from the literature. Unlike other existing models, such a sparse-matrix-based representation allows one to explicitly accommodate the chain length dependent kinetic parameters. Extrapolating from the past results, mixing was experimentally shown to be exerting a significant influence on reaction control in FRRPP systems. Mixing alone drives the otherwise severely diffusion-controlled reaction propagation in phase-separated polymer domains. Therefore, in a quiescent system, in the absence of mixing, it is possible to retard the growth of phase-separated domains, thus producing isolated polymer nanoparticles (globules). Such a diffusion-controlled, self-limiting phenomenon of chain growth was also observed using time-resolved small angle x-ray scattering studies of reaction kinetics in quiescent systems of FRRPP. Combining the concept of self-limiting chain growth in quiescent FRRPP systems with spatioselective reaction initiation of lithography, microgel structures were synthesized in a single step, without the use of molds or additives. Hard x-rays from the bending magnet radiation of a synchrotron were used as an initiation source, instead of the more statistally-oriented chemical initiators. Such a spatially-defined reaction was shown to be self-limiting to the irradiated regions following a polymerization-induced self-assembly phenomenon. The pattern transfer aspects of this technique were, therefore, studied in the FRRP polymerization of N-isopropylacrylamide (NIPAm) and methacrylic acid (MAA), a thermoreversible and ionic hydrogel, respectively. Reaction temperature increases the contrast between the exposed and unexposed zones of the formed microgels, while the irradiation dose is directly proportional to the extent of phase separation. The response of Poly (NIPAm) microgels prepared from the technique described in this study was also characterized by small angle neutron scattering.