Matching Items (69)
Description
The temperature of a planet's surface depends on numerous physical factors, including thermal inertia, albedo and the degree of insolation. Mars is a good target for thermal measurements because the low atmospheric pressure combined with the extreme dryness results in a surface dominated by large differences in thermal inertia, minimizing the effect of other physical properties. Since heat is propagated into the surface during the day and re-radiated at night, surface temperatures are affected by sub-surface properties down to several thermal skin depths. Because of this, orbital surface temperature measurements combined with a computational thermal model can be used to determine sub-surface structure. This technique has previously been applied to estimate the thickness and thermal inertia of soil layers on Mars on a regional scale, but the Mars Odyssey Thermal Emission Imaging System "THEMIS" instrument allows much higher-resolution thermal imagery to be obtained. Using archived THEMIS data and the KRC thermal model, a process has been developed for creating high-resolution maps of Martian soil layer thickness and thermal inertia, allowing investigation of the distribution of dust and sand at a scale of 100 m/pixel.
ContributorsHeath, Simon (Author) / Christensen, Philip R. (Philip Russel) (Thesis advisor) / Bel, James (Thesis advisor) / Hervig, Richard (Committee member) / Arizona State University (Publisher)
Created2013
Description
In this thesis I model the thermal and structural evolution of Kuiper Belt Objects (KBOs) and explore their ability to retain undifferentiated crusts of rock and ice over geologic timescales. Previous calculations by Desch et al. (2009) predicted that initially homogenous KBOs comparable in size to Charon (R ~ 600 km) have surfaces too cold to permit the separation of rock and ice, and should always retain thick (~ 85 km) crusts, despite the partial differentiation of rock and ice inside the body. The retention of a thermally insulating, undifferentiated crust is favorable to the maintenance of subsurface liquid and potentially cryovolcanism on the KBO surface. A potential objection to these models is that the dense crust of rock and ice overlying an ice mantle represents a gravitationally unstable configuration that should overturn by Rayleigh-Taylor (RT) instabilities. I have calculated the growth rate of RT instabilities at the ice-crust interface, including the effect of rock on the viscosity. I have identified a critical ice viscosity for the instability to grow significantly over the age of the solar system. I have calculated the viscosity as a function of temperature for conditions relevant to marginal instability. I find that RT instabilities on a Charon-sized KBO require temperatures T > 143 K. Including this effect in thermal evolution models of KBOs, I find that the undifferentiated crust on KBOs is thinner than previously calculated, only ~ 50 km. While thinner, this crustal thickness is still significant, representing ~ 25% of the KBO mass, and helps to maintain subsurface liquid throughout most of the KBO's history.
ContributorsRubin, Mark (Author) / Desch, Steven J (Thesis advisor) / Sharp, Thomas (Committee member) / Christensen, Philip R. (Philip Russel) (Committee member) / Arizona State University (Publisher)
Created2013
Description
The cyber-physical systems (CPS) are emerging as the underpinning technology for major industries in the 21-th century. This dissertation is focused on two fundamental issues in cyber-physical systems: network interdependence and information dynamics. It consists of the following two main thrusts. The first thrust is targeted at understanding the impact of network interdependence. It is shown that a cyber-physical system built upon multiple interdependent networks are more vulnerable to attacks since node failures in one network may result in failures in the other network, causing a cascade of failures that would potentially lead to the collapse of the entire infrastructure. There is thus a need to develop a new network science for modeling and quantifying cascading failures in multiple interdependent networks, and to develop network management algorithms that improve network robustness and ensure overall network reliability against cascading failures. To enhance the system robustness, a "regular" allocation strategy is proposed that yields better resistance against cascading failures compared to all possible existing strategies. Furthermore, in view of the load redistribution feature in many physical infrastructure networks, e.g., power grids, a CPS model is developed where the threshold model and the giant connected component model are used to capture the node failures in the physical infrastructure network and the cyber network, respectively. The second thrust is centered around the information dynamics in the CPS. One speculation is that the interconnections over multiple networks can facilitate information diffusion since information propagation in one network can trigger further spread in the other network. With this insight, a theoretical framework is developed to analyze information epidemic across multiple interconnecting networks. It is shown that the conjoining among networks can dramatically speed up message diffusion. Along a different avenue, many cyber-physical systems rely on wireless networks which offer platforms for information exchanges. To optimize the QoS of wireless networks, there is a need to develop a high-throughput and low-complexity scheduling algorithm to control link dynamics. To that end, distributed link scheduling algorithms are explored for multi-hop MIMO networks and two CSMA algorithms under the continuous-time model and the discrete-time model are devised, respectively.
ContributorsQian, Dajun (Author) / Zhang, Junshan (Thesis advisor) / Ying, Lei (Committee member) / Zhang, Yanchao (Committee member) / Cochran, Douglas (Committee member) / Arizona State University (Publisher)
Created2012
Description
Signal processing techniques have been used extensively in many engineering problems and in recent years its application has extended to non-traditional research fields such as biological systems. Many of these applications require extraction of a signal or parameter of interest from degraded measurements. One such application is mass spectrometry immunoassay (MSIA) which has been one of the primary methods of biomarker discovery techniques. MSIA analyzes protein molecules as potential biomarkers using time of flight mass spectrometry (TOF-MS). Peak detection in TOF-MS is important for biomarker analysis and many other MS related application. Though many peak detection algorithms exist, most of them are based on heuristics models. One of the ways of detecting signal peaks is by deploying stochastic models of the signal and noise observations. Likelihood ratio test (LRT) detector, based on the Neyman-Pearson (NP) lemma, is an uniformly most powerful test to decision making in the form of a hypothesis test. The primary goal of this dissertation is to develop signal and noise models for the electrospray ionization (ESI) TOF-MS data. A new method is proposed for developing the signal model by employing first principles calculations based on device physics and molecular properties. The noise model is developed by analyzing MS data from careful experiments in the ESI mass spectrometer. A non-flat baseline in MS data is common. The reasons behind the formation of this baseline has not been fully comprehended. A new signal model explaining the presence of baseline is proposed, though detailed experiments are needed to further substantiate the model assumptions. Signal detection schemes based on these signal and noise models are proposed. A maximum likelihood (ML) method is introduced for estimating the signal peak amplitudes. The performance of the detection methods and ML estimation are evaluated with Monte Carlo simulation which shows promising results. An application of these methods is proposed for fractional abundance calculation for biomarker analysis, which is mathematically robust and fundamentally different than the current algorithms. Biomarker panels for type 2 diabetes and cardiovascular disease are analyzed using existing MS analysis algorithms. Finally, a support vector machine based multi-classification algorithm is developed for evaluating the biomarkers' effectiveness in discriminating type 2 diabetes and cardiovascular diseases and is shown to perform better than a linear discriminant analysis based classifier.
ContributorsBuddi, Sai (Author) / Taylor, Thomas (Thesis advisor) / Cochran, Douglas (Thesis advisor) / Nelson, Randall (Committee member) / Duman, Tolga (Committee member) / Arizona State University (Publisher)
Created2012
Description
Since Duffin and Schaeffer's introduction of frames in 1952, the concept of a frame has received much attention in the mathematical community and has inspired several generalizations. The focus of this thesis is on the concept of an operator-valued frame (OVF) and a more general concept called herein an operator-valued frame associated with a measure space (MS-OVF), which is sometimes called a continuous g-frame. The first of two main topics explored in this thesis is the relationship between MS-OVFs and objects prominent in quantum information theory called positive operator-valued measures (POVMs). It has been observed that every MS-OVF gives rise to a POVM with invertible total variation in a natural way. The first main result of this thesis is a characterization of which POVMs arise in this way, a result obtained by extending certain existing Radon-Nikodym theorems for POVMs. The second main topic investigated in this thesis is the role of the theory of unitary representations of a Lie group G in the construction of OVFs for the L^2-space of a relatively compact subset of G. For G=R, Duffin and Schaeffer have given general conditions that ensure a sequence of (one-dimensional) representations of G, restricted to (-1/2,1/2), forms a frame for L^{2}(-1/2,1/2), and similar conditions exist for G=R^n. The second main result of this thesis expresses conditions related to Duffin and Schaeffer's for two more particular Lie groups: the Euclidean motion group on R^2 and the (2n+1)-dimensional Heisenberg group. This proceeds in two steps. First, for a Lie group admitting a uniform lattice and an appropriate relatively compact subset E of G, the Selberg Trace Formula is used to obtain a Parseval OVF for L^{2}(E) that is expressed in terms of irreducible representations of G. Second, for the two particular Lie groups an appropriate set E is found, and it is shown that for each of these groups, with suitably parametrized unitary duals, the Parseval OVF remains an OVF when perturbations are made to the parameters of the included representations.
ContributorsRobinson, Benjamin (Author) / Cochran, Douglas (Thesis advisor) / Moran, William (Thesis advisor) / Boggess, Albert (Committee member) / Milner, Fabio (Committee member) / Spielberg, John (Committee member) / Arizona State University (Publisher)
Created2014
Description
Much of Mars' surface is mantled by bright dust, which masks the spectral features used to interpret the mineralogy of the underlying bedrock. Despite the wealth of near-infrared (NIR) and thermal infrared data returned from orbiting spacecraft in recent decades, the detailed bedrock composition of approximately half of the martian surface remains relatively unknown due to dust cover. To address this issue, and to help gain a better understanding of the bedrock mineralogy in dusty regions, data from the Thermal Emission Spectrometer (TES) Dust Cover Index (DCI) and Mars Reconnaissance Orbiter (MRO) Mars Color Imager (MARCI) were used to identify 63 small localized areas within the classical bright dusty regions of Arabia Terra, Elysium Planitia, and Tharsis as potential "windows" through the dust; that is, areas where the dust cover is thin enough to permit infrared remote sensing of the underlying bedrock. The bedrock mineralogy of each candidate "window" was inferred using processed spectra from the Mars Express (MEx) Observatoire pour la Mineralogie, l'Eau, les Glaces et l'Activité (OMEGA) NIR spectrometer and, where possible, TES. 12 areas of interest returned spectra that are consistent with mineral species expected to be present at the regional scale, such as high- and low-calcium pyroxene, olivine, and iron-bearing glass. Distribution maps were created using previously defined index parameters for each species present within an area. High-quality TES spectra, if present within an area of interest, were deconvolved to estimate modal mineralogy and support NIR results. OMEGA data from Arabia Terra and Elysium Planitia are largely similar and indicate the presence of high-calcium pyroxene with significant contributions of glass and olivine, while TES data suggest an intermediate between the established southern highlands and Syrtis Major compositions. Limited data from Tharsis indicate low-calcium pyroxene mixed with lesser amounts of high-calcium pyroxene and perhaps glass. TES data from southern Tharsis correlate well with the previously inferred compositions of the Aonium and Mare Sirenum highlands immediately to the south.
ContributorsLai, Jason Chi-Shun (Author) / Bell, James (Thesis advisor) / Christensen, Philip R. (Philip Russel) (Committee member) / Hervig, Richard (Committee member) / Arizona State University (Publisher)
Created2014
Description
The ability to identify unoccupied resources in the radio spectrum is a key capability for opportunistic users in a cognitive radio environment. This paper draws upon and extends geometrically based ideas in statistical signal processing to develop estimators for the rank and the occupied subspace in a multi-user environment from multiple temporal samples of the signal received at a single antenna. These estimators enable identification of resources, such as the orthogonal complement of the occupied subspace, that may be exploitable by an opportunistic user. This concept is supported by simulations showing the estimation of the number of users in a simple CDMA system using a maximum a posteriori (MAP) estimate for the rank. It was found that with suitable parameters, such as high SNR, sufficient number of time epochs and codes of appropriate length, the number of users could be correctly estimated using the MAP estimator even when the noise variance is unknown. Additionally, the process of identifying the maximum likelihood estimate of the orthogonal projector onto the unoccupied subspace is discussed.
ContributorsBeaudet, Kaitlyn (Author) / Cochran, Douglas (Thesis advisor) / Turaga, Pavan (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2014
Description
This thesis describes an approach to system identification based on compressive sensing and demonstrates its efficacy on a challenging classical benchmark single-input, multiple output (SIMO) mechanical system consisting of an inverted pendulum on a cart. Due to its inherent non-linearity and unstable behavior, very few techniques currently exist that are capable of identifying this system. The challenge in identification also lies in the coupled behavior of the system and in the difficulty of obtaining the full-range dynamics. The differential equations describing the system dynamics are determined from measurements of the system's input-output behavior. These equations are assumed to consist of the superposition, with unknown weights, of a small number of terms drawn from a large library of nonlinear terms. Under this assumption, compressed sensing allows the constituent library elements and their corresponding weights to be identified by decomposing a time-series signal of the system's outputs into a sparse superposition of corresponding time-series signals produced by the library components. The most popular techniques for non-linear system identification entail the use of ANN's (Artificial Neural Networks), which require a large number of measurements of the input and output data at high sampling frequencies. The method developed in this project requires very few samples and the accuracy of reconstruction is extremely high. Furthermore, this method yields the Ordinary Differential Equation (ODE) of the system explicitly. This is in contrast to some ANN approaches that produce only a trained network which might lose fidelity with change of initial conditions or if facing an input that wasn't used during its training. This technique is expected to be of value in system identification of complex dynamic systems encountered in diverse fields such as Biology, Computation, Statistics, Mechanics and Electrical Engineering.
ContributorsNaik, Manjish Arvind (Author) / Cochran, Douglas (Thesis advisor) / Kovvali, Narayan (Committee member) / Kawski, Matthias (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Created2011
Description
Many products undergo several stages of testing ranging from tests on individual components to end-item tests. Additionally, these products may be further "tested" via customer or field use. The later failure of a delivered product may in some cases be due to circumstances that have no correlation with the product's inherent quality. However, at times, there may be cues in the upstream test data that, if detected, could serve to predict the likelihood of downstream failure or performance degradation induced by product use or environmental stresses. This study explores the use of downstream factory test data or product field reliability data to infer data mining or pattern recognition criteria onto manufacturing process or upstream test data by means of support vector machines (SVM) in order to provide reliability prediction models. In concert with a risk/benefit analysis, these models can be utilized to drive improvement of the product or, at least, via screening to improve the reliability of the product delivered to the customer. Such models can be used to aid in reliability risk assessment based on detectable correlations between the product test performance and the sources of supply, test stands, or other factors related to product manufacture. As an enhancement to the usefulness of the SVM or hyperplane classifier within this context, L-moments and the Western Electric Company (WECO) Rules are used to augment or replace the native process or test data used as inputs to the classifier. As part of this research, a generalizable binary classification methodology was developed that can be used to design and implement predictors of end-item field failure or downstream product performance based on upstream test data that may be composed of single-parameter, time-series, or multivariate real-valued data. Additionally, the methodology provides input parameter weighting factors that have proved useful in failure analysis and root cause investigations as indicators of which of several upstream product parameters have the greater influence on the downstream failure outcomes.
ContributorsMosley, James (Author) / Morrell, Darryl (Committee member) / Cochran, Douglas (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Roberts, Chell (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
Description
Dust devils have proven to be commonplace on Mars, although their occurrence is unevenly distributed across the surface. They were imaged or inferred by all six successful landed spacecraft: the Viking 1 and 2 Landers (VL-1 and VL-2), Mars Pathfinder Lander, the Mars Exploration Rovers Spirit and Opportunity, and the Phoenix Mars Lander. Comparisons of dust devil parameters were based on results from optical and meteorological (MET) detection campaigns. Spatial variations were determined based on comparisons of their frequency, morphology, and behavior. The Spirit data spanning three consecutive martian years is used as the basis of comparison because it is the most extensive on this topic. Average diameters were between 8 and 115 m for all observed or detected dust devils. The average horizontal speed for all of the studies was roughly 5 m/s. At each site dust devil densities peaked between 09:00 and 17:00 LTST during the spring and summer seasons supporting insolation-driven convection as the primary formation mechanism. Seasonal number frequency averaged ~1 dust devils/ km2/sol and spanned a total of three orders of magnitude. Extrapolated number frequencies determined for optical campaigns at the Pathfinder and Spirit sites accounted for temporal and spatial inconsistencies and averaged ~19 dust devils/km2/sol. Dust fluxes calculated from Pathfinder data (5x10-4 kg/m2/s and 7x10-5 kg/m2/s) were well with in the ranges calculated from Spirit data (4.0x10-9 to 4.6x10-4 kg/m2/s for Season One, 5.2x10-7 to 6.2x10-5 kg/m2/s during Season Two, and 1.5x10-7 to 1.6x10-4 kg/m2/s during Season Three). Based on the results a campaign is written for improvements in dust devil detection at the Mars Science Laboratory's (MSL) site. Of the four remaining candidate MSL sites, the dusty plains of Gale crater may potentially be the site with the highest probability of dust devil activity.
ContributorsWaller, Devin (Author) / Greeley, Ronald (Thesis advisor) / Christensen, Philip R. (Philip Russel) (Committee member) / Cerveny, Randall (Committee member) / Arizona State University (Publisher)
Created2011