Matching Items (169)
Description
Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from video data by decomposing various key contributing factors, such as pose, view angle, and body shape, in the generation of the image observations. Experimental results have shown that the resulting pose features extracted using the proposed methods exhibit excellent invariance properties to changes in view angles and body shapes. Furthermore, using the proposed invariant multifactor pose features, a suite of simple while effective algorithms have been developed to solve the movement recognition and pose estimation problems. Using these proposed algorithms, excellent human movement analysis results have been obtained, and most of them are superior to those obtained from state-of-the-art algorithms on the same testing datasets. Moreover, a number of key movement analysis challenges, including robust online gesture spotting and multi-camera gesture recognition, have also been addressed in this research. To this end, an online gesture spotting framework has been developed to automatically detect and learn non-gesture movement patterns to improve gesture localization and recognition from continuous data streams using a hidden Markov network. In addition, the optimal data fusion scheme has been investigated for multicamera gesture recognition, and the decision-level camera fusion scheme using the product rule has been found to be optimal for gesture recognition using multiple uncalibrated cameras. Furthermore, the challenge of optimal camera selection in multi-camera gesture recognition has also been tackled. A measure to quantify the complementary strength across cameras has been proposed. Experimental results obtained from a real-life gesture recognition dataset have shown that the optimal camera combinations identified according to the proposed complementary measure always lead to the best gesture recognition results.
ContributorsPeng, Bo (Author) / Qian, Gang (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Spanias, Andreas (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
The demand for handheld portable computing in education, business and research has resulted in advanced mobile devices with powerful processors and large multi-touch screens. Such devices are capable of handling tasks of moderate computational complexity such as word processing, complex Internet transactions, and even human motion analysis. Apple's iOS devices, including the iPhone, iPod touch and the latest in the family - the iPad, are among the well-known and widely used mobile devices today. Their advanced multi-touch interface and improved processing power can be exploited for engineering and STEM demonstrations. Moreover, these devices have become a part of everyday student life. Hence, the design of exciting mobile applications and software represents a great opportunity to build student interest and enthusiasm in science and engineering. This thesis presents the design and implementation of a portable interactive signal processing simulation software on the iOS platform. The iOS-based object-oriented application is called i-JDSP and is based on the award winning Java-DSP concept. It is implemented in Objective-C and C as a native Cocoa Touch application that can be run on any iOS device. i-JDSP offers basic signal processing simulation functions such as Fast Fourier Transform, filtering, spectral analysis on a compact and convenient graphical user interface and provides a very compelling multi-touch programming experience. Built-in modules also demonstrate concepts such as the Pole-Zero Placement. i-JDSP also incorporates sound capture and playback options that can be used in near real-time analysis of speech and audio signals. All simulations can be visually established by forming interactive block diagrams through multi-touch and drag-and-drop. Computations are performed on the mobile device when necessary, making the block diagram execution fast. Furthermore, the extensive support for user interactivity provides scope for improved learning. The results of i-JDSP assessment among senior undergraduate and first year graduate students revealed that the software created a significant positive impact and increased the students' interest and motivation and in understanding basic DSP concepts.
ContributorsLiu, Jinru (Author) / Spanias, Andreas (Thesis advisor) / Tsakalis, Kostas (Committee member) / Qian, Gang (Committee member) / Arizona State University (Publisher)
Created2011
Description
Great advances have been made in the construction of photovoltaic (PV) cells and modules, but array level management remains much the same as it has been in previous decades. Conventionally, the PV array is connected in a fixed topology which is not always appropriate in the presence of faults in the array, and varying weather conditions. With the introduction of smarter inverters and solar modules, the data obtained from the photovoltaic array can be used to dynamically modify the array topology and improve the array power output. This is beneficial especially when module mismatches such as shading, soiling and aging occur in the photovoltaic array. This research focuses on the topology optimization of PV arrays under shading conditions using measurements obtained from a PV array set-up. A scheme known as topology reconfiguration method is proposed to find the optimal array topology for a given weather condition and faulty module information. Various topologies such as the series-parallel (SP), the total cross-tied (TCT), the bridge link (BL) and their bypassed versions are considered. The topology reconfiguration method compares the efficiencies of the topologies, evaluates the percentage gain in the generated power that would be obtained by reconfiguration of the array and other factors to find the optimal topology. This method is employed for various possible shading patterns to predict the best topology. The results demonstrate the benefit of having an electrically reconfigurable array topology. The effects of irradiance and shading on the array performance are also studied. The simulations are carried out using a SPICE simulator. The simulation results are validated with the experimental data provided by the PACECO Company.
ContributorsBuddha, Santoshi Tejasri (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Thesis advisor) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Following the success in incorporating perceptual models in audio coding algorithms, their application in other speech/audio processing systems is expanding. In general, all perceptual speech/audio processing algorithms involve minimization of an objective function that directly/indirectly incorporates properties of human perception. This dissertation primarily investigates the problems associated with directly embedding an auditory model in the objective function formulation and proposes possible solutions to overcome high complexity issues for use in real-time speech/audio algorithms. Specific problems addressed in this dissertation include: 1) the development of approximate but computationally efficient auditory model implementations that are consistent with the principles of psychoacoustics, 2) the development of a mapping scheme that allows synthesizing a time/frequency domain representation from its equivalent auditory model output. The first problem is aimed at addressing the high computational complexity involved in solving perceptual objective functions that require repeated application of auditory model for evaluation of different candidate solutions. In this dissertation, a frequency pruning and a detector pruning algorithm is developed that efficiently implements the various auditory model stages. The performance of the pruned model is compared to that of the original auditory model for different types of test signals in the SQAM database. Experimental results indicate only a 4-7% relative error in loudness while attaining up to 80-90 % reduction in computational complexity. Similarly, a hybrid algorithm is developed specifically for use with sinusoidal signals and employs the proposed auditory pattern combining technique together with a look-up table to store representative auditory patterns. The second problem obtains an estimate of the auditory representation that minimizes a perceptual objective function and transforms the auditory pattern back to its equivalent time/frequency representation. This avoids the repeated application of auditory model stages to test different candidate time/frequency vectors in minimizing perceptual objective functions. In this dissertation, a constrained mapping scheme is developed by linearizing certain auditory model stages that ensures obtaining a time/frequency mapping corresponding to the estimated auditory representation. This paradigm was successfully incorporated in a perceptual speech enhancement algorithm and a sinusoidal component selection task.
ContributorsKrishnamoorthi, Harish (Author) / Spanias, Andreas (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2011
Description
Spotlight mode synthetic aperture radar (SAR) imaging involves a tomo- graphic reconstruction from projections, necessitating acquisition of large amounts of data in order to form a moderately sized image. Since typical SAR sensors are hosted on mobile platforms, it is common to have limitations on SAR data acquisi- tion, storage and communication that can lead to data corruption and a resulting degradation of image quality. It is convenient to consider corrupted samples as missing, creating a sparsely sampled aperture. A sparse aperture would also result from compressive sensing, which is a very attractive concept for data intensive sen- sors such as SAR. Recent developments in sparse decomposition algorithms can be applied to the problem of SAR image formation from a sparsely sampled aperture. Two modified sparse decomposition algorithms are developed, based on well known existing algorithms, modified to be practical in application on modest computa- tional resources. The two algorithms are demonstrated on real-world SAR images. Algorithm performance with respect to super-resolution, noise, coherent speckle and target/clutter decomposition is explored. These algorithms yield more accu- rate image reconstruction from sparsely sampled apertures than classical spectral estimators. At the current state of development, sparse image reconstruction using these two algorithms require about two orders of magnitude greater processing time than classical SAR image formation.
ContributorsWerth, Nicholas (Author) / Karam, Lina (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
Description
In the last few years, significant advances in nanofabrication have allowed tailoring of structures and materials at a molecular level enabling nanofabrication with precise control of dimensions and organization at molecular length scales, a development leading to significant advances in nanoscale systems. Although, the direction of progress seems to follow the path of microelectronics, the fundamental physics in a nanoscale system changes more rapidly compared to microelectronics, as the size scale is decreased. The changes in length, area, and volume ratios due to reduction in size alter the relative influence of various physical effects determining the overall operation of a system in unexpected ways. One such category of nanofluidic structures demonstrating unique ionic and molecular transport characteristics are nanopores. Nanopores derive their unique transport characteristics from the electrostatic interaction of nanopore surface charge with aqueous ionic solutions. In this doctoral research cylindrical nanopores, in single and array configuration, were fabricated in silicon-on-insulator (SOI) using a combination of electron beam lithography (EBL) and reactive ion etching (RIE). The fabrication method presented is compatible with standard semiconductor foundries and allows fabrication of nanopores with desired geometries and precise dimensional control, providing near ideal and isolated physical modeling systems to study ion transport at the nanometer level. Ion transport through nanopores was characterized by measuring ionic conductances of arrays of nanopores of various diameters for a wide range of concentration of aqueous hydrochloric acid (HCl) ionic solutions. Measured ionic conductances demonstrated two distinct regimes based on surface charge interactions at low ionic concentrations and nanopore geometry at high ionic concentrations. Field effect modulation of ion transport through nanopore arrays, in a fashion similar to semiconductor transistors, was also studied. Using ionic conductance measurements, it was shown that the concentration of ions in the nanopore volume was significantly changed when a gate voltage on nanopore arrays was applied, hence controlling their transport. Based on the ion transport results, single nanopores were used to demonstrate their application as nanoscale particle counters by using polystyrene nanobeads, monodispersed in aqueous HCl solutions of different molarities. Effects of field effect modulation on particle transition events were also demonstrated.
ContributorsJoshi, Punarvasu (Author) / Thornton, Trevor J (Thesis advisor) / Goryll, Michael (Thesis advisor) / Spanias, Andreas (Committee member) / Saraniti, Marco (Committee member) / Arizona State University (Publisher)
Created2011
Description
For synthetic aperture radar (SAR) image formation processing, the chirp scaling algorithm (CSA) has gained considerable attention mainly because of its excellent target focusing ability, optimized processing steps, and ease of implementation. In particular, unlike the range Doppler and range migration algorithms, the CSA is easy to implement since it does not require interpolation, and it can be used on both stripmap and spotlight SAR systems. Another transform that can be used to enhance the processing of SAR image formation is the fractional Fourier transform (FRFT). This transform has been recently introduced to the signal processing community, and it has shown many promising applications in the realm of SAR signal processing, specifically because of its close association to the Wigner distribution and ambiguity function. The objective of this work is to improve the application of the FRFT in order to enhance the implementation of the CSA for SAR processing. This will be achieved by processing real phase-history data from the RADARSAT-1 satellite, a multi-mode SAR platform operating in the C-band, providing imagery with resolution between 8 and 100 meters at incidence angles of 10 through 59 degrees. The phase-history data will be processed into imagery using the conventional chirp scaling algorithm. The results will then be compared using a new implementation of the CSA based on the use of the FRFT, combined with traditional SAR focusing techniques, to enhance the algorithm's focusing ability, thereby increasing the peak-to-sidelobe ratio of the focused targets. The FRFT can also be used to provide focusing enhancements at extended ranges.
ContributorsNorthrop, Judith (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Spanias, Andreas (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2011
Description
ABSTRACT The unique structural features of deoxyribonucleic acid (DNA) that are of considerable biological interest also make it a valuable engineering material. Perhaps the most useful property of DNA for molecular engineering is its ability to self-assemble into predictable, double helical secondary structures. These interactions are exploited to design a variety of DNA nanostructures, which can be organized into both discrete and periodic structures. This dissertation focuses on studying the dynamic behavior of DNA nanostructure recognition processes. The thermodynamics and kinetics of nanostructure binding are evaluated, with the intention of improving our ability to understand and control their assembly. Presented here are a series of studies toward this goal. First, multi-helical DNA nanostructures were used to investigate how the valency and arrangement of the connections between DNA nanostructures affect super-structure formation. The study revealed that both the number and the relative position of connections play a significant role in the stability of the final assembly. Next, several DNA nanostructures were designed to gain insight into how small changes to the nanostructure scaffolds, intended to vary their conformational flexibility, would affect their association equilibrium. This approach yielded quantitative information about the roles of enthalpy and entropy in the affinity of polyvalent DNA nanostructure interactions, which exhibit an intriguing compensating effect. Finally, a multi-helical DNA nanostructure was used as a model `chip' for the detection of a single stranded DNA target. The results revealed that the rate constant of hybridization is strongly dominated by a rate-limiting nucleation step.
ContributorsNangreave, Jeanette (Author) / Yan, Hao (Thesis advisor) / Liu, Yan (Thesis advisor) / Chen, Julian J.-L. (Committee member) / Seo, Dong Kyun (Committee member) / Arizona State University (Publisher)
Created2011
Description
Natural photosynthesis features a complex biophysical/chemical process that requires sunlight to produce energy rich products. It is one of the most important processes responsible for the appearance and sustainability of life on earth. The first part of the thesis focuses on understanding the mechanisms involved in regulation of light harvesting, which is necessary to balance the absorption and utilization of light energy and in that way reduce the effect caused by photooxidative damage. In photosynthesis, carotenoids are responsible not only for collection of light, but also play a major role in protecting the photosynthetic system. To investigate the role of carotenoids in the quenching of the excited state of cyclic tetrapyrroles, two sets of dyads were studied. Both sets of dyads contain zinc phthalocyanine (Pc) covalently attached to carotenoids of varying conjugation lengths. In the first set of dyads, carotenoids were attached to the phthalocyanine via amide linkage. This set of dyads serves as a good model for understanding the molecular "gear-shift" mechanism, where the addition of one double bond can turn the carotenoid from a nonquencher to a very strong quencher of the excited state of a tetrapyrrole. In the second set of dyads, carotenoids were attached to phthalocyanine via a phenyl amino group. Two independent studies were performed on these dyads: femtosecond transient absorption and steady state fluorescence induced by two-photon excitation. In the transient absorption study it was observed that there is an instantaneous population of the carotenoid S1 state after Pc excitation, while two-photon excitation of the optically forbidden carotenoid S1 state shows 1Pc population. Both observations provide a strong indication of the existence of a shared excitonic state between carotenoid and Pc. Similar results were observed in LHC II complexes in plants, supporting the role of such interactions in photosynthetic down regulation. In the second chapter we describe the synthesis of porphyrin dyes functionalized with carboxylate and phosphonate anchoring groups to be used in the construction of photoelectrochemical cells containing a porphyrin-IrO2·nH2O complex immobilized on a TiO2 electrode. The research presented here is a step in the development of high potential porphyrin-metal oxide complexes to be used in the photooxidation of water. The last chapter focuses on developing synthetic strategies for the construction of an artificial antenna system consisting of porphyrin-silver nanoparticle conjugates, linked by DNA of varied length to study the distance dependence of the interaction between nanoparticles and the porphyrin chromophore. Preliminary studies indicate that at the distance of about 7-10 nm between porphyrin and silver nanoparticle is where the porphyrin absorption leading to fluorescence shows maximum enhancement. These new hybrid constructs will be helpful for designing efficient light harvesting systems.
ContributorsPillai, Smitha (Author) / Moore, Ana (Thesis advisor) / Moore, Thomas (Committee member) / Gould, Ian (Committee member) / Arizona State University (Publisher)
Created2011