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- Genre: Masters Thesis
Description
The field of education has been immensely benefited by major breakthroughs in technology. The arrival of computers and the internet made student-teacher interaction from different parts of the world viable, increasing the reach of the educator to hitherto remote corners of the world. The arrival of mobile phones in the recent past has the potential to provide the next paradigm shift in the way education is conducted. It combines the universal reach and powerful visualization capabilities of the computer with intimacy and portability. Engineering education is a field which can exploit the benefits of mobile devices to enhance learning and spread essential technical know-how to different parts of the world. In this thesis, I present AJDSP, an Android application evolved from JDSP, providing an intuitive and a easy to use environment for signal processing education. AJDSP is a graphical programming laboratory for digital signal processing developed for the Android platform. It is designed to provide utility; both as a supplement to traditional classroom learning and as a tool for self-learning. The architecture of AJDSP is based on the Model-View-Controller paradigm optimized for the Android platform. The extensive set of function modules cover a wide range of basic signal processing areas such as convolution, fast Fourier transform, z transform and filter design. The simple and intuitive user interface inspired from iJDSP is designed to facilitate ease of navigation and to provide the user with an intimate learning environment. Rich visualizations necessary to understand mathematically intensive signal processing algorithms have been incorporated into the software. Interactive demonstrations boosting student understanding of concepts like convolution and the relation between different signal domains have also been developed. A set of detailed assessments to evaluate the application has been conducted for graduate and senior-level undergraduate students.
ContributorsRanganath, Suhas (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2013
Description
Parkinson's disease is a neurodegenerative condition diagnosed on patients with
clinical history and motor signs of tremor, rigidity and bradykinesia, and the estimated
number of patients living with Parkinson's disease around the world is seven
to ten million. Deep brain stimulation (DBS) provides substantial relief of the motor
signs of Parkinson's disease patients. It is an advanced surgical technique that is used
when drug therapy is no longer sufficient for Parkinson's disease patients. DBS alleviates the motor symptoms of Parkinson's disease by targeting the subthalamic nucleus using high-frequency electrical stimulation.
This work proposes a behavior recognition model for patients with Parkinson's
disease. In particular, an adaptive learning method is proposed to classify behavioral
tasks of Parkinson's disease patients using local field potential and electrocorticography
signals that are collected during DBS implantation surgeries. Unique patterns
exhibited between these signals in a matched feature space would lead to distinction
between motor and language behavioral tasks. Unique features are first extracted
from deep brain signals in the time-frequency space using the matching pursuit decomposition
algorithm. The Dirichlet process Gaussian mixture model uses the extracted
features to cluster the different behavioral signal patterns, without training or
any prior information. The performance of the method is then compared with other
machine learning methods and the advantages of each method is discussed under
different conditions.
clinical history and motor signs of tremor, rigidity and bradykinesia, and the estimated
number of patients living with Parkinson's disease around the world is seven
to ten million. Deep brain stimulation (DBS) provides substantial relief of the motor
signs of Parkinson's disease patients. It is an advanced surgical technique that is used
when drug therapy is no longer sufficient for Parkinson's disease patients. DBS alleviates the motor symptoms of Parkinson's disease by targeting the subthalamic nucleus using high-frequency electrical stimulation.
This work proposes a behavior recognition model for patients with Parkinson's
disease. In particular, an adaptive learning method is proposed to classify behavioral
tasks of Parkinson's disease patients using local field potential and electrocorticography
signals that are collected during DBS implantation surgeries. Unique patterns
exhibited between these signals in a matched feature space would lead to distinction
between motor and language behavioral tasks. Unique features are first extracted
from deep brain signals in the time-frequency space using the matching pursuit decomposition
algorithm. The Dirichlet process Gaussian mixture model uses the extracted
features to cluster the different behavioral signal patterns, without training or
any prior information. The performance of the method is then compared with other
machine learning methods and the advantages of each method is discussed under
different conditions.
ContributorsDutta, Arindam (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Holbert, Keith E. (Committee member) / Bliss, Daniel W. (Committee member) / Arizona State University (Publisher)
Created2015
Description
In the late 1960s, Granger published a seminal study on causality in time series, using linear interdependencies and information transfer. Recent developments in the field of information theory have introduced new methods to investigate the transfer of information in dynamical systems. Using concepts from Chaos and Markov theory, much of these methods have evolved to capture non-linear relations and information flow between coupled dynamical systems with applications to fields like biomedical signal processing. This thesis deals with the application of information theory to non-linear multivariate time series and develops measures of information flow to identify significant drivers and response (driven) components in networks of coupled sub-systems with variable coupling in strength and direction (uni- or bi-directional) for each connection. Transfer Entropy (TE) is used to quantify pairwise directional information. Four TE-based measures of information flow are proposed, namely TE Outflow (TEO), TE Inflow (TEI), TE Net flow (TEN), and Average TE flow (ATE). First, the reliability of the information flow measures on models, with and without noise, is evaluated. The driver and response sub-systems in these models are identified. Second, these measures are applied to electroencephalographic (EEG) data from two patients with focal epilepsy. The analysis showed dominant directions of information flow between brain sites and identified the epileptogenic focus as the system component typically with the highest value for the proposed measures (for example, ATE). Statistical tests between pre-seizure (preictal) and post-seizure (postictal) information flow also showed a breakage of the driving of the brain by the focus after seizure onset. The above findings shed light on the function of the epileptogenic focus and understanding of ictogenesis. It is expected that they will contribute to the diagnosis of epilepsy, for example by accurate identification of the epileptogenic focus from interictal periods, as well as the development of better seizure detection, prediction and control methods, for example by isolating pathologic areas of excessive information flow through electrical stimulation.
ContributorsPrasanna, Shashank (Author) / Jassemidis, Leonidas (Thesis advisor) / Tsakalis, Konstantinos (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Advances in miniaturized sensors and wireless technologies have enabled mobile health systems for efficient healthcare. A mobile health system assists the physician to monitor the patient's progress remotely and provide quick feedbacks and suggestions in case of emergencies, which reduces the cost of healthcare without the expense of hospitalization. This work involves development of an innovative mobile health system with adaptive biofeedback mechanism and demonstrates the importance of biofeedback in accurate measurements of physiological parameters to facilitate the diagnosis in mobile health systems. Resting Metabolic Rate (RMR) assessment, a key aspect in the treatment of diet related health problems is considered as a model to demonstrate the importance of adaptive biofeedback in mobile health. A breathing biofeedback mechanism has been implemented with digital signal processing techniques for real-time visual and musical guidance to accurately measure the RMR. The effects of adaptive biofeedback with musical and visual guidance were assessed on 22 healthy subjects (12 men, 10 women). Eight RMR measurements were taken for each subject on different days under same conditions. It was observed the subjects unconsciously followed breathing biofeedback, yielding consistent and accurate measurements for the diagnosis. The coefficient of variation of the measured metabolic parameters decreased significantly (p < 0.05) for 20 subjects out of 22 subjects.
ContributorsKrishnan, Ranganath (Author) / Tao, Nongjian (Thesis advisor) / Forzani, Erica (Committee member) / Yu, Hongyu (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
Tracking targets in the presence of clutter is inevitable, and presents many challenges. Additionally, rapid, drastic changes in clutter density between different environments or scenarios can make it even more difficult for tracking algorithms to adapt. A novel approach to target tracking in such dynamic clutter environments is proposed using a particle filter (PF) integrated with Interacting Multiple Models (IMMs) to compensate and adapt to the transition between different clutter densities. This model was implemented for the case of a monostatic sensor tracking a single target moving with constant velocity along a two-dimensional trajectory, which crossed between regions of drastically different clutter densities. Multiple combinations of clutter density transitions were considered, using up to three different clutter densities. It was shown that the integrated IMM PF algorithm outperforms traditional approaches such as the PF in terms of tracking results and performance. The minimal additional computational expense of including the IMM more than warrants the benefits of having it supplement and amplify the advantages of the PF.
ContributorsDutson, Karl (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Kovvali, Narayan (Committee member) / Bliss, Daniel W (Committee member) / Arizona State University (Publisher)
Created2015
Description
For a sensor array, part of its elements may fail to work due to hardware failures. Then the missing data may distort in the beam pattern or decrease the accuracy of direction-of-arrival (DOA) estimation. Therefore, considerable research has been conducted to develop algorithms that can estimate the missing signal information. On the other hand, through those algorithms, array elements can also be selectively turned off while the missed information can be successfully recovered, which will save power consumption and hardware cost.
Conventional approaches focusing on array element failures are mainly based on interpolation or sequential learning algorithm. Both of them rely heavily on some prior knowledge such as the information of the failures or a training dataset without missing data. In addition, since most of the existing approaches are developed for DOA estimation, their recovery target is usually the co-variance matrix but not the signal matrix.
In this thesis, a new signal recovery method based on matrix completion (MC) theory is introduced. It aims to directly refill the absent entries in the signal matrix without any prior knowledge. We proposed a novel overlapping reshaping method to satisfy the applying conditions of MC algorithms. Compared to other existing MC based approaches, our proposed method can provide us higher probability of successful recovery. The thesis describes the principle of the algorithms and analyzes the performance of this method. A few application examples with simulation results are also provided.
Conventional approaches focusing on array element failures are mainly based on interpolation or sequential learning algorithm. Both of them rely heavily on some prior knowledge such as the information of the failures or a training dataset without missing data. In addition, since most of the existing approaches are developed for DOA estimation, their recovery target is usually the co-variance matrix but not the signal matrix.
In this thesis, a new signal recovery method based on matrix completion (MC) theory is introduced. It aims to directly refill the absent entries in the signal matrix without any prior knowledge. We proposed a novel overlapping reshaping method to satisfy the applying conditions of MC algorithms. Compared to other existing MC based approaches, our proposed method can provide us higher probability of successful recovery. The thesis describes the principle of the algorithms and analyzes the performance of this method. A few application examples with simulation results are also provided.
ContributorsFan, Jie (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2016
Description
The purpose of this study was to identify acoustic markers that correlate with accurate and inaccurate /r/ production in children ages 5-8 using signal processing. In addition, the researcher aimed to identify predictive acoustic markers that relate to changes in /r/ accuracy. A total of 35 children (23 accurate, 12 inaccurate, 8 longitudinal) were recorded. Computerized stimuli were presented on a PC laptop computer and the children were asked to do five tasks to elicit spontaneous and imitated /r/ production in all positions. Files were edited and analyzed using a filter bank approach centered at 40 frequencies based on the Mel-scale. T-tests were used to compare spectral energy of tokens between accurate and inaccurate groups and additional t-tests were used to compare duration of accurate and inaccurate files. Results included significant differences between the accurate and inaccurate productions of /r/, notable differences in the 24-26 mel bin range, and longer duration of inaccurate /r/ than accurate. Signal processing successfully identified acoustic features of accurate and inaccurate production of /r/ and candidate predictive markers that may be associated with acquisition of /r/.
ContributorsBecvar, Brittany Patricia (Author) / Azuma, Tamiko (Thesis advisor) / Weinhold, Juliet (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2017
Description
Quantum computing has the potential to revolutionize the signal-processing field by providing more efficient methods for analyzing signals. This thesis explores the application of quantum computing in signal analysis synthesis for compression applications. More specifically, the study focuses on two key approaches: quantum Fourier transform (QFT) and quantum linear prediction (QLP). The research is motivated by the potential advantages offered by quantum computing in massive signal processing tasks and presents novel quantum circuit designs for QFT, quantum autocorrelation, and QLP, enabling signal analysis synthesis using quantum algorithms. The two approaches are explained as follows. The Quantum Fourier transform (QFT) demonstrates the potential for improved speed in quantum computing compared to classical methods. This thesis focuses on quantum encoding of signals and designing quantum algorithms for signal analysis synthesis, and signal compression using QFTs. Comparative studies are conducted to evaluate quantum computations for Fourier transform applications, considering Signal-to-Noise-Ratio results. The effects of qubit precision and quantum noise are also analyzed. The QFT algorithm is also developed in the J-DSP simulation environment, providing hands-on laboratory experiences for signal-processing students. User-friendly simulation programs on QFT-based signal analysis synthesis using peak picking, and perceptual selection using psychoacoustics in the J-DSP are developed. Further, this research is extended to analyze the autocorrelation of the signal using QFTs and develop a quantum linear prediction (QLP) algorithm for speech processing applications. QFTs and IQFTs are used to compute the quantum autocorrelation of the signal, and the HHL algorithm is modified and used to compute the solutions of the linear equations using quantum computing. The performance of the QLP algorithm is evaluated for system identification, spectral estimation, and speech analysis synthesis, and comparisons are performed for QLP and CLP results. The results demonstrate the following: effective quantum circuits for accurate QFT-based speech analysis synthesis, evaluation of performance with quantum noise, design of accurate quantum autocorrelation, and development of a modified HHL algorithm for efficient QLP. Overall, this thesis contributes to the research on quantum computing for signal processing applications and provides a foundation for further exploration of quantum algorithms for signal analysis synthesis.
ContributorsSharma, Aradhita (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2023
Description
Object tracking refers to the problem of estimating a moving object's time-varying parameters that are indirectly observed in measurements at each time step. Increased noise and clutter in the measurements reduce estimation accuracy as they increase the uncertainty of tracking in the field of view. Whereas tracking is performed using a Bayesian filter, a Bayesian smoother can be utilized to refine parameter state estimations that occurred before the current time. In practice, smoothing can be widely used to improve state estimation or correct data association errors, and it can lead to significantly better estimation performance as it reduces the impact of noise and clutter. In this work, a single object tracking method is proposed based on integrating Kalman filtering and smoothing with thresholding to remove unreliable measurements. As the new method is effective when the noise and clutter in the measurements are high, the main goal is to find these measurements using a moving average filter and a thresholding method to improve estimation. Thus, the proposed method is designed to reduce estimation errors that result from measurements corrupted with high noise and clutter. Simulations are provided to demonstrate the improved performance of the new method when compared to smoothing without thresholding. The root-mean-square error in estimating the object state parameters is shown to be especially reduced under high noise conditions.
ContributorsSeo, Yongho (Author) / Papandreaou-Suppappola, Antonia (Thesis advisor) / Bliss, Daniel W (Committee member) / Chakrabarti, Chaitali (Committee member) / Moraffah, Bahman (Committee member) / Arizona State University (Publisher)
Created2022