Matching Items (65)
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
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
High Impedance Surfaces (HISs), which have been investigated extensively, have proven to be very efficient ground planes for low profile antenna applications due to their unique reflection phase characteristics. Another emerging research field among the microwave and antenna technologies is the design of flexible antennas and microwave circuits to be utilized in conformal applications. The combination of those two research topics gives birth to a third one, namely the design of Conformal or Flexible HISs (FHISs), which is the main subject of this dissertation. The problems associated with the FHISs are twofold: characterization and physical realization. The characterization involves the analysis of scattering properties of FHISs in the presence of plane wave and localized sources. For this purpose, an approximate analytical method is developed to characterize the reflection properties of a cylindrically curved FHIS. The effects of curvature on the reflection phase of the curved FHISs are examined. Furthermore, the effects of different types of currents, specifically the ones inherent to finite sized periodic structures, on the reflection phase characteristics are observed. After the reflection phase characterization of curved HISs, the performance of dipole antennas located in close proximity to a curved HIS are investigated, and the results are compared with the flat case. Different types of resonances that may occur for such a low-profile antenna application are discussed. The effects of curvature on the radiation performance of antennas are examined. Commercially available flexible materials are relatively thin which degrades the bandwidth of HISs. Another practical aspect, which is related to the substrate thickness, is the compactness of the surface. Because of the design limitations of conventional HISs, it is not possible to miniaturize the HIS and increase the bandwidth, simultaneously. To overcome this drawback, a novel HIS is proposed with a periodically perforated ground plane. Copper plated through holes are extremely vulnerable to bending and should be avoided at the bending parts of flexible circuits. Fortunately, if designed properly, the perforations on the ground plane may result in suppression of surface waves. Hence, metallic posts can be eliminated without hindering the surface wave suppression properties of HISs.
ContributorsDurgun, Ahmet Cemal (Author) / Balanis, Constantine A (Thesis advisor) / Aberle, James T (Committee member) / Yu, Hongyu (Committee member) / Bakkaloglu, Bertan (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
This thesis examines the application of statistical signal processing approaches to data arising from surveys intended to measure psychological and sociological phenomena underpinning human social dynamics. The use of signal processing methods for analysis of signals arising from measurement of social, biological, and other non-traditional phenomena has been an important and growing area of signal processing research over the past decade. Here, we explore the application of statistical modeling and signal processing concepts to data obtained from the Global Group Relations Project, specifically to understand and quantify the effects and interactions of social psychological factors related to intergroup conflicts. We use Bayesian networks to specify prospective models of conditional dependence. Bayesian networks are determined between social psychological factors and conflict variables, and modeled by directed acyclic graphs, while the significant interactions are modeled as conditional probabilities. Since the data are sparse and multi-dimensional, we regress Gaussian mixture models (GMMs) against the data to estimate the conditional probabilities of interest. The parameters of GMMs are estimated using the expectation-maximization (EM) algorithm. However, the EM algorithm may suffer from over-fitting problem due to the high dimensionality and limited observations entailed in this data set. Therefore, the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) are used for GMM order estimation. To assist intuitive understanding of the interactions of social variables and the intergroup conflicts, we introduce a color-based visualization scheme. In this scheme, the intensities of colors are proportional to the conditional probabilities observed.
ContributorsLiu, Hui (Author) / Taylor, Thomas (Thesis advisor) / Cochran, Douglas (Thesis advisor) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2012
Description
This thesis summarizes the research work carried out on design, modeling and simulation of semiconductor nanophotonic devices. The research includes design of nanowire (NW) lasers, modeling of active plasmonic waveguides, design of plasmonic nano-lasers, and design of all-semiconductor plasmonic systems. For the NW part, a comparative study of electrical injection in the longitudinal p-i-n and coaxial p-n core-shell NWs was performed. It is found that high density carriers can be efficiently injected into and confined in the core-shell structure. The required bias voltage and doping concentrations in the core-shell structure are smaller than those in the longitudinal p-i-n structure. A new device structure with core-shell configuration at the p and n contact regions for electrically driven single NW laser was proposed. Through a comprehensive design trade-off between threshold gain and threshold voltage, room temperature lasing has been proved in the laser with low threshold current and large output efficiency. For the plasmonic part, the propagation of surface plasmon polariton (SPP) in a metal-semiconductor-metal structure where semiconductor is highly excited to have an optical gain was investigated. It is shown that near the resonance the SPP mode experiences an unexpected giant modal gain that is 1000 times of the material gain in the semiconductor and the corresponding confinement factor is as high as 105. The physical origin of the giant modal gain is the slowing down of the average energy propagation in the structure. Secondly, SPP modes lasing in a metal-insulator-semiconductor multi-layer structure was investigated. It is shown that the lasing threshold can be reduced by structural optimization. A specific design example was optimized using AlGaAs/GaAs/AlGaAs single quantum well sandwiched between silver layers. This cavity has a physical volume of 1.5×10-4 λ03 which is the smallest nanolaser reported so far. Finally, the all-semiconductor based plasmonics was studied. It is found that InAs is superior to other common semiconductors for plasmonic application in mid-infrared range. A plasmonic system made of InAs, GaSb and AlSb layers, consisting of a plasmonic source, waveguide and detector was proposed. This on-chip integrated system is realizable in a single epitaxial growth process.
ContributorsLi, Debin (Author) / Ning, Cun-Zheng (Thesis advisor) / Zhang, Yong-Hang (Committee member) / Balanis, Constantine A (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2012
Description
This dissertation describes a novel, low cost strategy of using particle streak (track) images for accurate micro-channel velocity field mapping. It is shown that 2-dimensional, 2-component fields can be efficiently obtained using the spatial variation of particle track lengths in micro-channels. The velocity field is a critical performance feature of many microfluidic devices. Since it is often the case that un-modeled micro-scale physics frustrates principled design methodologies, particle based velocity field estimation is an essential design and validation tool. Current technologies that achieve this goal use particle constellation correlation strategies and rely heavily on costly, high-speed imaging hardware. The proposed image/ video processing based method achieves comparable accuracy for fraction of the cost. In the context of micro-channel velocimetry, the usability of particle streaks has been poorly studied so far. Their use has remained restricted mostly to bulk flow measurements and occasional ad-hoc uses in microfluidics. A second look at the usability of particle streak lengths in this work reveals that they can be efficiently used, after approximately 15 years from their first use for micro-channel velocimetry. Particle tracks in steady, smooth microfluidic flows is mathematically modeled and a framework for using experimentally observed particle track lengths for local velocity field estimation is introduced here, followed by algorithm implementation and quantitative verification. Further, experimental considerations and image processing techniques that can facilitate the proposed methods are also discussed in this dissertation. Unavailability of benchmarked particle track image data motivated the implementation of a simulation framework with the capability to generate exposure time controlled particle track image sequence for velocity vector fields. This dissertation also describes this work and shows that arbitrary velocity fields designed in computational fluid dynamics software tools can be used to obtain such images. Apart from aiding gold-standard data generation, such images would find use for quick microfluidic flow field visualization and help improve device designs.
ContributorsMahanti, Prasun (Author) / Cochran, Douglas (Thesis advisor) / Taylor, Thomas (Thesis advisor) / Hayes, Mark (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis considers the application of basis pursuit to several problems in system identification. After reviewing some key results in the theory of basis pursuit and compressed sensing, numerical experiments are presented that explore the application of basis pursuit to the black-box identification of linear time-invariant (LTI) systems with both finite (FIR) and infinite (IIR) impulse responses, temporal systems modeled by ordinary differential equations (ODE), and spatio-temporal systems modeled by partial differential equations (PDE). For LTI systems, the experimental results illustrate existing theory for identification of LTI FIR systems. It is seen that basis pursuit does not identify sparse LTI IIR systems, but it does identify alternate systems with nearly identical magnitude response characteristics when there are small numbers of non-zero coefficients. For ODE systems, the experimental results are consistent with earlier research for differential equations that are polynomials in the system variables, illustrating feasibility of the approach for small numbers of non-zero terms. For PDE systems, it is demonstrated that basis pursuit can be applied to system identification, along with a comparison in performance with another existing method. In all cases the impact of measurement noise on identification performance is considered, and it is empirically observed that high signal-to-noise ratio is required for successful application of basis pursuit to system identification problems.
ContributorsThompson, Robert C. (Author) / Platte, Rodrigo (Thesis advisor) / Gelb, Anne (Committee member) / Cochran, Douglas (Committee member) / Arizona State University (Publisher)
Created2012
Description
Multiport antennas offer greater design flexibility than traditional one-port designs. An antenna array is a special case of a multiport antenna. If the antenna's inter-element spacing is electrically small, the antenna is capable of achieving superdirectivity. Superdirective antenna arrays are known to be narrow band and have low radiation resistance which leads to low radiation efficiency and high VSWR. However, by increasing the self-impedance of the antenna elements, the radiation resistance is increased but the bandwidth remains narrow. A design methodology is developed using the ability to superimpose electric fields and multi-objective optimization to design antenna feed networks. While the emphasis in this dissertation is on antenna arrays and superdirectivity, the design methodology is general and can be applied to other multiport antennas. The design methodology is used to design a multiport impedance-matching network and optimize both the input impedance and radiation pattern of a two-port superdirective antenna array. It is shown that the multiport impedance-matching network is capable of improving the input impedance of the antenna array while maintaining high directionality. The antenna design is critical for the methodology to improve the bandwidth and radiation characteristics of the array. To double the bandwidth of the two-port impedance matched superdirective antenna array, a three-port Yagi-Uda antenna design is demonstrated. The addition of the extra antenna element does not increase the footprint of the antenna array. The design methodology is then used to design a symmetrical antenna array capable of steering its main beam in two directions.
ContributorsArceo, Diana (Author) / Balanis, Constantine A (Thesis advisor) / Aberle, James T., 1961- (Committee member) / Moeller, Karl (Committee member) / Palais, Joseph (Committee member) / Arizona State University (Publisher)
Created2012