Matching Items (218)
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
This thesis worked towards the development of a parameterized 3D model off a cover that could go over any specific prosthesis depending on the parameters that had been entered. It also focused on gathering user inputs, which was done with the aid of the Amputee Coalition, that could be used to create an aesthetic design on this cover. The Amputee Coalition helped to recruit participants through its website and social media platforms. Finally, multiple methods of creating a design were developed to increase the amount of customization that a user could have for their cover.
ContributorsRiley, Nicholas (Co-author) / Fusaro, Gerard (Co-author) / Sugar, Thomas (Thesis director) / Redkar, Sangram (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
This thesis worked towards the development of a parameterized 3D model off a cover that could go over any specific prosthesis depending on the parameters that had been entered. It also focused on gathering user inputs, which was done with the aid of the Amputee Coalition, that could be used to create an aesthetic design on this cover. The Amputee Coalition helped to recruit participants through its website and social media platforms. Finally, multiple methods of creating a design were developed to increase the amount of customization that a user could have for their cover.
ContributorsFusaro, Gerard Anthony (Co-author) / Riley, Nicholas (Co-author) / Sugar, Thomas (Thesis director) / Redkar, Sangram (Committee member) / College of Integrative Sciences and Arts (Contributor) / Engineering Programs (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The purpose of this creative project was to create a stereo sound system in a unique medium. As a team, we decided to integrate a Tesla Coil with a bluetooth audio source. These high frequency, high voltage systems can be configured to emit their electrical discharge in a manner that resembles playing tunes. Originally the idea was to split the audio into left and right, then to further segregate the signals to have a treble, mid, and base emitter for each side. Due to time, budget, and scope constraints, we decided to complete the project with only two coils.<br/><br/>For this project, the team decided to use a solid-state coil kit. This kit was purchased from OneTelsa and would help ensure everyone’s safety and the project’s success. The team developed our own interrupting or driving circuit through reverse-engineering the interrupter provided by oneTesla and discussing with other engineers. The custom interpreter was controlled by the PSoC5 LP and communicated with an audio source through the DFRobot Bluetooth module. Utilizing the left and right audio signals it can drive the two Tesla Coils in stereo to play the music.
ContributorsPinkowski, Olivia N (Co-author) / Hutcherson, Cree (Co-author) / Jordan, Shawn (Thesis director) / Sugar, Thomas (Committee member) / Engineering Programs (Contributor, Contributor) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The purpose of this creative project was to create a stereo sound system in a unique medium. As a team, we decided to integrate a Tesla Coil with a bluetooth audio source. These high frequency, high voltage systems can be configured to emit their electrical discharge in a manner that resembles playing tunes. Originally the idea was to split the audio into left and right, then to further segregate the signals to have a treble, mid, and base emitter for each side. Due to time, budget, and scope constraints, we decided to complete the project with only two coils.<br/><br/>For this project, the team decided to use a solid-state coil kit. This kit was purchased from OneTelsa and would help ensure everyone’s safety and the project’s success. The team developed our own interrupting or driving circuit through reverse-engineering the interrupter provided by oneTesla and discussing with other engineers. The custom interpreter was controlled by the PSoC5 LP and communicated with an audio source through the DFRobot Bluetooth module. Utilizing the left and right audio signals it can drive the two Tesla Coils in stereo to play the music.
ContributorsHutcherson, Cree (Co-author) / Pinkowski, Olivia (Co-author) / Jordan, Shawn (Thesis director) / Sugar, Thomas (Committee member) / Engineering Programs (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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