Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 31 - 40 of 196
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- Creators: Turaga, Pavan
- Creators: Yu, Hongbin
Description
In the sport of competitive water skiing, the skill of a human boat driver can affect athletic performance. Driver influence is not necessarily inhibitive to skiers, however, it reduces the fairness and credibility of the sport overall. In response to the stated problem, this thesis proposes a vision-based real-time control system designed specifically for tournament waterski boats. The challenges addressed in this thesis include: one, the segmentation of floating objects in frame sequences captured by a moving camera, two, the identification of segmented objects which fit a predefined model, and three, the accurate and fast estimation of camera position and orientation from coplanar point correspondences. This thesis discusses current ideas and proposes new methods for the three challenges mentioned. In the end, a working prototype is produced.
ContributorsWalker, Collin (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Claveau, David (Committee member) / Arizona State University (Publisher)
Created2014
Description
As a promising solution to the problem of acquiring and storing large amounts of image and video data, spatial-multiplexing camera architectures have received lot of attention in the recent past. Such architectures have the attractive feature of combining a two-step process of acquisition and compression of pixel measurements in a conventional camera, into a single step. A popular variant is the single-pixel camera that obtains measurements of the scene using a pseudo-random measurement matrix. Advances in compressive sensing (CS) theory in the past decade have supplied the tools that, in theory, allow near-perfect reconstruction of an image from these measurements even for sub-Nyquist sampling rates. However, current state-of-the-art reconstruction algorithms suffer from two drawbacks -- They are (1) computationally very expensive and (2) incapable of yielding high fidelity reconstructions for high compression ratios. In computer vision, the final goal is usually to perform an inference task using the images acquired and not signal recovery. With this motivation, this thesis considers the possibility of inference directly from compressed measurements, thereby obviating the need to use expensive reconstruction algorithms. It is often the case that non-linear features are used for inference tasks in computer vision. However, currently, it is unclear how to extract such features from compressed measurements. Instead, using the theoretical basis provided by the Johnson-Lindenstrauss lemma, discriminative features using smashed correlation filters are derived and it is shown that it is indeed possible to perform reconstruction-free inference at high compression ratios with only a marginal loss in accuracy. As a specific inference problem in computer vision, face recognition is considered, mainly beyond the visible spectrum such as in the short wave infra-red region (SWIR), where sensors are expensive.
ContributorsLohit, Suhas Anand (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2015
Description
Fisheye cameras are special cameras that have a much larger field of view compared to
conventional cameras. The large field of view comes at a price of non-linear distortions
introduced near the boundaries of the images captured by such cameras. Despite this
drawback, they are being used increasingly in many applications of computer vision,
robotics, reconnaissance, astrophotography, surveillance and automotive applications.
The images captured from such cameras can be corrected for their distortion if the
cameras are calibrated and the distortion function is determined. Calibration also allows
fisheye cameras to be used in tasks involving metric scene measurement, metric
scene reconstruction and other simultaneous localization and mapping (SLAM) algorithms.
This thesis presents a calibration toolbox (FisheyeCDC Toolbox) that implements a collection of some of the most widely used techniques for calibration of fisheye cameras under one package. This enables an inexperienced user to calibrate his/her own camera without the need for a theoretical understanding about computer vision and camera calibration. This thesis also explores some of the applications of calibration such as distortion correction and 3D reconstruction.
conventional cameras. The large field of view comes at a price of non-linear distortions
introduced near the boundaries of the images captured by such cameras. Despite this
drawback, they are being used increasingly in many applications of computer vision,
robotics, reconnaissance, astrophotography, surveillance and automotive applications.
The images captured from such cameras can be corrected for their distortion if the
cameras are calibrated and the distortion function is determined. Calibration also allows
fisheye cameras to be used in tasks involving metric scene measurement, metric
scene reconstruction and other simultaneous localization and mapping (SLAM) algorithms.
This thesis presents a calibration toolbox (FisheyeCDC Toolbox) that implements a collection of some of the most widely used techniques for calibration of fisheye cameras under one package. This enables an inexperienced user to calibrate his/her own camera without the need for a theoretical understanding about computer vision and camera calibration. This thesis also explores some of the applications of calibration such as distortion correction and 3D reconstruction.
ContributorsKashyap Takmul Purushothama Raju, Vinay (Author) / Karam, Lina (Thesis advisor) / Turaga, Pavan (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2014
Description
In this work, a highly sensitive strain sensing technique is developed to realize in-plane strain mapping for microelectronic packages or emerging flexible or foldable devices, where mechanical or thermal strain is a major concern that could affect the performance of the working devices or even lead to the failure of the devices. Therefore strain sensing techniques to create a contour of the strain distribution is desired.
The developed highly sensitive micro-strain sensing technique differs from the existing strain mapping techniques, such as digital image correlation (DIC)/micro-Moiré techniques, in terms of working mechanism, by filling a technology gap that requires high spatial resolution while simultaneously maintaining a large field-of-view. The strain sensing mechanism relies on the scanning of a tightly focused laser beam onto the grating that is on the sample surface to detect the change in the diffracted beam angle as a result of the strain. Gratings are fabricated on the target substrates to serve as strain sensors, which carries the strain information in the form of variations in the grating period. The geometric structure of the optical system inherently ensures the high sensitivity for the strain sensing, where the nanoscale change of the grating period is amplified by almost six orders into a diffraction peak shift on the order of several hundred micrometers. It significantly amplifies the small signal measurements so that the desired sensitivity and accuracy can be achieved.
The important features, such as strain sensitivity and spatial resolution, for the strain sensing technique are investigated to evaluate the technique. The strain sensitivity has been validated by measurements on homogenous materials with well known reference values of CTE (coefficient of thermal expansion). 10 micro-strain has been successfully resolved from the silicon CTE extraction measurements. Furthermore, the spatial resolution has been studied on predefined grating patterns, which are assembled to mimic the uneven strain distribution across the sample surface. A resolvable feature size of 10 µm has been achieved with an incident laser spot size of 50 µm in diameter.
In addition, the strain sensing technique has been applied to a composite sample made of SU8 and silicon, as well as the microelectronic packages for thermal strain mappings.
The developed highly sensitive micro-strain sensing technique differs from the existing strain mapping techniques, such as digital image correlation (DIC)/micro-Moiré techniques, in terms of working mechanism, by filling a technology gap that requires high spatial resolution while simultaneously maintaining a large field-of-view. The strain sensing mechanism relies on the scanning of a tightly focused laser beam onto the grating that is on the sample surface to detect the change in the diffracted beam angle as a result of the strain. Gratings are fabricated on the target substrates to serve as strain sensors, which carries the strain information in the form of variations in the grating period. The geometric structure of the optical system inherently ensures the high sensitivity for the strain sensing, where the nanoscale change of the grating period is amplified by almost six orders into a diffraction peak shift on the order of several hundred micrometers. It significantly amplifies the small signal measurements so that the desired sensitivity and accuracy can be achieved.
The important features, such as strain sensitivity and spatial resolution, for the strain sensing technique are investigated to evaluate the technique. The strain sensitivity has been validated by measurements on homogenous materials with well known reference values of CTE (coefficient of thermal expansion). 10 micro-strain has been successfully resolved from the silicon CTE extraction measurements. Furthermore, the spatial resolution has been studied on predefined grating patterns, which are assembled to mimic the uneven strain distribution across the sample surface. A resolvable feature size of 10 µm has been achieved with an incident laser spot size of 50 µm in diameter.
In addition, the strain sensing technique has been applied to a composite sample made of SU8 and silicon, as well as the microelectronic packages for thermal strain mappings.
ContributorsLiang, Hanshuang (Author) / Yu, Hongbin (Thesis advisor) / Poon, Poh Chieh Benny (Committee member) / Jiang, Hanqing (Committee member) / Zhang, Yong-Hang (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
In this thesis we consider the problem of facial expression recognition (FER) from video sequences. Our method is based on subspace representations and Grassmann manifold based learning. We use Local Binary Pattern (LBP) at the frame level for representing the facial features. Next we develop a model to represent the video sequence in a lower dimensional expression subspace and also as a linear dynamical system using Autoregressive Moving Average (ARMA) model. As these subspaces lie on Grassmann space, we use Grassmann manifold based learning techniques such as kernel Fisher Discriminant Analysis with Grassmann kernels for classification. We consider six expressions namely, Angry (AN), Disgust (Di), Fear (Fe), Happy (Ha), Sadness (Sa) and Surprise (Su) for classification. We perform experiments on extended Cohn-Kanade (CK+) facial expression database to evaluate the expression recognition performance. Our method demonstrates good expression recognition performance outperforming other state of the art FER algorithms. We achieve an average recognition accuracy of 97.41% using a method based on expression subspace, kernel-FDA and Support Vector Machines (SVM) classifier. By using a simpler classifier, 1-Nearest Neighbor (1-NN) along with kernel-FDA, we achieve a recognition accuracy of 97.09%. We find that to process a group of 19 frames in a video sequence, LBP feature extraction requires majority of computation time (97 %) which is about 1.662 seconds on the Intel Core i3, dual core platform. However when only 3 frames (onset, middle and peak) of a video sequence are used, the computational complexity is reduced by about 83.75 % to 260 milliseconds at the expense of drop in the recognition accuracy to 92.88 %.
ContributorsYellamraju, Anirudh (Author) / Chakrabarti, Chaitali (Thesis advisor) / Turaga, Pavan (Thesis advisor) / Karam, Lina (Committee member) / Arizona State University (Publisher)
Created2014
Description
Semiconductor devices are generally analyzed with relatively simple equations or with detailed computer simulations. Most text-books use these simple equations and show device diagrams that are frequently very simplified and occasionally incorrect. For example, the carrier densities near the pinch-off point in MOSFETs and JFETs and the minority carrier density in the base near the reverse-biased base-collector junction are frequently assumed to be zero or near zero. Also the channel thickness at the pinch-off point is often shown to approach zero. None of these assumptions can be correct. The research in thesis addresses these points. I simulated the carrier densities, potentials, electric fields etc. of MOSFETs, BJTs and JFETs at and near the pinch-off regions to determine exactly what happens there. I also simulated the behavior of the quasi-Fermi levels. For MOSFETs, the channel thickness expands slightly before the pinch-off point and then spreads out quickly in a triangular shape and the space-charge region under the channel actually shrinks as the potential increases from source to drain. For BJTs, with collector-base junction reverse biased, most minority carriers diffuse through the base from emitter to collector very fast, but the minority carrier concentration at the collector-base space-charge region is not zero. For JFETs, the boundaries of the space-charge region are difficult to determine, the channel does not disappear after pinch off, the shape of channel is always tapered, and the carrier concentration in the channel decreases progressively. After simulating traditional sized devices, I also simulated typical nano-scaled devices and show that they behave similarly to large devices. These simulation results provide a more complete understanding of device physics and device operation in those regions usually not addressed in semiconductor device physics books.
ContributorsYang, Xuan (Author) / Schroder, Dieter K. (Thesis advisor) / Vasileska, Dragica (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2011
Description
Semiconductor nanowires are featured by their unique one-dimensional structure which makes them promising for small scale electronic and photonic device applications. Among them, III-V material nanowires are particularly outstanding due to their good electronic properties. In bulk, these materials reveal electron mobility much higher than conventional silicon based devices, for example at room temperature, InAs field effect transistor (FET) has electron mobility of 40,000 cm2/Vs more than 10 times of Si FET. This makes such materials promising for high speed nanowire FETs. With small bandgap, such as 0.354 eV for InAs and 1.52 eV for GaAs, it does not need high voltage to turn on such devices which leads to low power consumption devices. Another feature of direct bandgap allows their applications of optoelectronic devices such as avalanche photodiodes. However, there are challenges to face up. Due to their large surface to volume ratio, nanowire devices typically are strongly affected by the surface states. Although nanowires can be grown into single crystal structure, people observe crystal defects along the wires which can significantly affect the performance of devices. In this work, FETs made of two types of III-V nanowire, GaAs and InAs, are demonstrated. These nanowires are grown by catalyst-free MOCVD growth method. Vertically nanowires are transferred onto patterned substrates for coordinate calibration. Then electrodes are defined by e-beam lithography followed by deposition of contact metals. Prior to metal deposition, however, the substrates are dipped in ammonium hydroxide solution to remove native oxide layer formed on nanowire surface. Current vs. source-drain voltage with different gate bias are measured at room temperature. GaAs nanowire FETs show photo response while InAs nanowire FETs do not show that. Surface passivation is performed on GaAs FETs by using ammonium surfide solution. The best results on current increase is observed with around 20-30 minutes chemical treatment time. Gate response measurements are performed at room temperature, from which field effect mobility as high as 1490 cm2/Vs is extracted for InAs FETs. One major contributor for this is stacking faults defect existing along nanowires. For InAs FETs, thermal excitations observed from temperature dependent results which leads us to investigate potential barriers.
ContributorsLiang, Hanshuang (Author) / Yu, Hongbin (Thesis advisor) / Ferry, David (Committee member) / Tracy, Clarence (Committee member) / Arizona State University (Publisher)
Created2011
Description
Semiconductor nanowires (NWs) are one dimensional materials and have size quantization effect when the diameter is sufficiently small. They can serve as optical wave guides along the length direction and contain optically active gain at the same time. Due to these unique properties, NWs are now very promising and extensively studied for nanoscale optoelectronic applications. A systematic and comprehensive optical and microstructural study of several important infrared semiconductor NWs is presented in this thesis, which includes InAs, PbS, InGaAs, erbium chloride silicate and erbium silicate. Micro-photoluminescence (PL) and transmission electron microscope (TEM) were utilized in conjunction to characterize the optical and microstructure of these wires. The focus of this thesis is on optical study of semiconductor NWs in the mid-infrared wavelengths. First, differently structured InAs NWs grown using various methods were characterized and compared. Three main PL peaks which are below, near and above InAs bandgap, respectively, were observed. The octadecylthiol self-assembled monolayer was employed to passivate the surface of InAs NWs to eliminate or reduce the effects of the surface states. The band-edge emission from wurtzite-structured NWs was completely recovered after passivatoin. The passivated NWs showed very good stability in air and under heat. In the second part, mid-infrared optical study was conducted on PbS wires of subwavelength diameter and lasing was demonstrated under optical pumping. The PbS wires were grown on Si substrate using chemical vapor deposition and have a rock-salt cubic structure. Single-mode lasing at the wavelength of ~3000-4000 nm was obtained from single as-grown PbS wire up to the temperature of 115 K. PL characterization was also utilized to demonstrate the highest crystallinity of the vertical arrays of InP and InGaAs/InP composition-graded heterostructure NWs made by a top-down fabrication method. TEM-related measurements were performed to study the crystal structures and elemental compositions of the Er-compound core-shell NWs. The core-shell NWs consist of an orthorhombic-structured erbium chloride silicate shell and a cubic-structured silicon core. These NWs provide unique Si-compatible materials with emission at 1530 nm for optical communications and solid state lasers.
ContributorsSun, Minghua (Author) / Ning, Cun-Zheng (Thesis advisor) / Yu, Hongbin (Committee member) / Carpenter, Ray W. (Committee member) / Johnson, Shane (Committee member) / Arizona State University (Publisher)
Created2011
Description
Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find better solutions. In this thesis, a novel method is proposed which uses image registration techniques to provide better image classification. This method reduces the error rate of classification by performing image registration of the images with the previously obtained images before performing classification. The motivation behind this is the fact that images that are obtained in the same region which need to be classified will not differ significantly in characteristics. Hence, registration will provide an image that matches closer to the previously obtained image, thus providing better classification. To illustrate that the proposed method works, naïve Bayes and iterative closest point (ICP) algorithms are used for the image classification and registration stages respectively. This implementation was tested extensively in simulation using synthetic images and using a real life data set called the Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) dataset. The results show that the ICP algorithm does help in better classification with Naïve Bayes by reducing the error rate by an average of about 10% in the synthetic data and by about 7% on the actual datasets used.
ContributorsMuralidhar, Ashwini (Author) / Saripalli, Srikanth (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2011