Matching Items (118)
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- All Subjects: Electrical Engineering
- Creators: Papandreou-Suppappola, Antonia
Description
Immunosignaturing is a medical test for assessing the health status of a patient by applying microarrays of random sequence peptides to determine the patient's immune fingerprint by associating antibodies from a biological sample to immune responses. The immunosignature measurements can potentially provide pre-symptomatic diagnosis for infectious diseases or detection of biological threats. Currently, traditional bioinformatics tools, such as data mining classification algorithms, are used to process the large amount of peptide microarray data. However, these methods generally require training data and do not adapt to changing immune conditions or additional patient information. This work proposes advanced processing techniques to improve the classification and identification of single and multiple underlying immune response states embedded in immunosignatures, making it possible to detect both known and previously unknown diseases or biothreat agents. Novel adaptive learning methodologies for un- supervised and semi-supervised clustering integrated with immunosignature feature extraction approaches are proposed. The techniques are based on extracting novel stochastic features from microarray binding intensities and use Dirichlet process Gaussian mixture models to adaptively cluster the immunosignatures in the feature space. This learning-while-clustering approach allows continuous discovery of antibody activity by adaptively detecting new disease states, with limited a priori disease or patient information. A beta process factor analysis model to determine underlying patient immune responses is also proposed to further improve the adaptive clustering performance by formatting new relationships between patients and antibody activity. In order to extend the clustering methods for diagnosing multiple states in a patient, the adaptive hierarchical Dirichlet process is integrated with modified beta process factor analysis latent feature modeling to identify relationships between patients and infectious agents. The use of Bayesian nonparametric adaptive learning techniques allows for further clustering if additional patient data is received. Significant improvements in feature identification and immune response clustering are demonstrated using samples from patients with different diseases.
ContributorsMalin, Anna (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Bliss, Daniel (Committee member) / Chakrabarti, Chaitali (Committee member) / Kovvali, Narayan (Committee member) / Lacroix, Zoé (Committee member) / Arizona State University (Publisher)
Created2013
Description
Structural health management (SHM) is emerging as a vital methodology to help engineers improve the safety and maintainability of critical structures. SHM systems are designed to reliably monitor and test the health and performance of structures in aerospace, civil, and mechanical engineering applications. SHM combines multidisciplinary technologies including sensing, signal processing, pattern recognition, data mining, high fidelity probabilistic progressive damage models, physics based damage models, and regression analysis. Due to the wide application of carbon fiber reinforced composites and their multiscale failure mechanisms, it is necessary to emphasize the research of SHM on composite structures. This research develops a comprehensive framework for the damage detection, localization, quantification, and prediction of the remaining useful life of complex composite structures. To interrogate a composite structure, guided wave propagation is applied to thin structures such as beams and plates. Piezoelectric transducers are selected because of their versatility, which allows them to be used as sensors and actuators. Feature extraction from guided wave signals is critical to demonstrate the presence of damage and estimate the damage locations. Advanced signal processing techniques are employed to extract robust features and information. To provide a better estimate of the damage for accurate life estimation, probabilistic regression analysis is used to obtain a prediction model for the prognosis of complex structures subject to fatigue loading. Special efforts have been applied to the extension of SHM techniques on aerospace and spacecraft structures, such as UAV composite wings and deployable composite boom structures. Necessary modifications of the developed SHM techniques were conducted to meet the unique requirements of the aerospace structures. The developed SHM algorithms are able to accurately detect and quantify impact damages as well as matrix cracking introduced.
ContributorsLiu, Yingtao (Author) / Chattopadhyay, Aditi (Thesis advisor) / Rajadas, John (Committee member) / Dai, Lenore (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2012
Description
In this thesis, an integrated waveform-agile multi-modal tracking-beforedetect sensing system is investigated and the performance is evaluated using an experimental platform. The sensing system of adapting asymmetric multi-modal sensing operation platforms using radio frequency (RF) radar and electro-optical (EO) sensors allows for integration of complementary information from different sensors. However, there are many challenges to overcome, including tracking low signal-to-noise ratio (SNR) targets, waveform configurations that can optimize tracking performance and statistically dependent measurements. Address some of these challenges, a particle filter (PF) based recursive waveformagile track-before-detect (TBD) algorithm is developed to avoid information loss caused by conventional detection under low SNR environments. Furthermore, a waveform-agile selection technique is integrated into the PF-TBD to allow for adaptive waveform configurations. The embedded exponential family (EEF) approach is used to approximate distributions of parameters of dependent RF and EO measurements and to further improve target detection rate and tracking performance. The performance of the integrated algorithm is evaluated using real data from three experimental scenarios.
ContributorsLiu, Shubo (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Duman, Tolga (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Super-Resolution (SR) techniques are widely developed to increase image resolution by fusing several Low-Resolution (LR) images of the same scene to overcome sensor hardware limitations and reduce media impairments in a cost-effective manner. When choosing a solution for the SR problem, there is always a trade-off between computational efficiency and High-Resolution (HR) image quality. Existing SR approaches suffer from extremely high computational requirements due to the high number of unknowns to be estimated in the solution of the SR inverse problem. This thesis proposes efficient iterative SR techniques based on Visual Attention (VA) and perceptual modeling of the human visual system. In the first part of this thesis, an efficient ATtentive-SELective Perceptual-based (AT-SELP) SR framework is presented, where only a subset of perceptually significant active pixels is selected for processing by the SR algorithm based on a local contrast sensitivity threshold model and a proposed low complexity saliency detector. The proposed saliency detector utilizes a probability of detection rule inspired by concepts of luminance masking and visual attention. The second part of this thesis further enhances on the efficiency of selective SR approaches by presenting an ATtentive (AT) SR framework that is completely driven by VA region detectors. Additionally, different VA techniques that combine several low-level features, such as center-surround differences in intensity and orientation, patch luminance and contrast, bandpass outputs of patch luminance and contrast, and difference of Gaussians of luminance intensity are integrated and analyzed to illustrate the effectiveness of the proposed selective SR frameworks. The proposed AT-SELP SR and AT-SR frameworks proved to be flexible by integrating a Maximum A Posteriori (MAP)-based SR algorithm as well as a fast two-stage Fusion-Restoration (FR) SR estimator. By adopting the proposed selective SR frameworks, simulation results show significant reduction on average in computational complexity with comparable visual quality in terms of quantitative metrics such as PSNR, SNR or MAE gains, and subjective assessment. The third part of this thesis proposes a Perceptually Weighted (WP) SR technique that incorporates unequal weighting parameters in the cost function of iterative SR problems. The proposed approach is inspired by the unequal processing of the Human Visual System (HVS) to different local image features in an image. Simulation results show an enhanced reconstruction quality and faster convergence rates when applied to the MAP-based and FR-based SR schemes.
ContributorsSadaka, Nabil (Author) / Karam, Lina J (Thesis advisor) / Spanias, Andreas S (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Abousleman, Glen P (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2011
Description
Research on developing new algorithms to improve information on brain functionality and structure is ongoing. Studying neural activity through dipole source localization with electroencephalography (EEG) and magnetoencephalography (MEG) sensor measurements can lead to diagnosis and treatment of a brain disorder and can also identify the area of the brain from where the disorder has originated. Designing advanced localization algorithms that can adapt to environmental changes is considered a significant shift from manual diagnosis which is based on the knowledge and observation of the doctor, to an adaptive and improved brain disorder diagnosis as these algorithms can track activities that might not be noticed by the human eye. An important consideration of these localization algorithms, however, is to try and minimize the overall power consumption in order to improve the study and treatment of brain disorders. This thesis considers the problem of estimating dynamic parameters of neural dipole sources while minimizing the system's overall power consumption; this is achieved by minimizing the number of EEG/MEG measurements sensors without a loss in estimation performance accuracy. As the EEG/MEG measurements models are related non-linearity to the dipole source locations and moments, these dynamic parameters can be estimated using sequential Monte Carlo methods such as particle filtering. Due to the large number of sensors required to record EEG/MEG Measurements for use in the particle filter, over long period recordings, a large amounts of power is required for storage and transmission. In order to reduce the overall power consumption, two methods are proposed. The first method used the predicted mean square estimation error as the performance metric under the constraint of a maximum power consumption. The performance metric of the second method uses the distance between the location of the sensors and the location estimate of the dipole source at the previous time step; this sensor scheduling scheme results in maximizing the overall signal-to-noise ratio. The performance of both methods is demonstrated using simulated data, and both methods show that they can provide good estimation results with significant reduction in the number of activated sensors at each time step.
ContributorsMichael, Stefanos (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Photovoltaics (PV) is an important and rapidly growing area of research. With the advent of power system monitoring and communication technology collectively known as the "smart grid," an opportunity exists to apply signal processing techniques to monitoring and control of PV arrays. In this paper a monitoring system which provides real-time measurements of each PV module's voltage and current is considered. A fault detection algorithm formulated as a clustering problem and addressed using the robust minimum covariance determinant (MCD) estimator is described; its performance on simulated instances of arc and ground faults is evaluated. The algorithm is found to perform well on many types of faults commonly occurring in PV arrays. Among several types of detection algorithms considered, only the MCD shows high performance on both types of faults.
ContributorsBraun, Henry (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2012
Description
With increased usage of green energy, the number of photovoltaic arrays used in power generation is increasing rapidly. Many of the arrays are located at remote locations where faults that occur within the array often go unnoticed and unattended for large periods of time. Technicians sent to rectify the faults have to spend a large amount of time determining the location of the fault manually. Automated monitoring systems are needed to obtain the information about the performance of the array and detect faults. Such systems must monitor the DC side of the array in addition to the AC side to identify non catastrophic faults. This thesis focuses on two of the requirements for DC side monitoring of an automated PV array monitoring system. The first part of the thesis quantifies the advantages of obtaining higher resolution data from a PV array on detection of faults. Data for the monitoring system can be gathered for the array as a whole or from additional places within the array such as individual modules and end of strings. The fault detection rate and the false positive rates are compared for array level, string level and module level PV data. Monte Carlo simulations are performed using PV array models developed in Simulink and MATLAB for fault and no fault cases. The second part describes a graphical user interface (GUI) that can be used to visualize the PV array for module level monitoring system information. A demonstration GUI is built in MATLAB using data obtained from a PV array test facility in Tempe, AZ. Visualizations are implemented to display information about the array as a whole or individual modules and locate faults in the array.
ContributorsKrishnan, Venkatachalam (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Ayyanar, Raja (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2012
Description
In this thesis, we consider the problem of fast and efficient indexing techniques for time sequences which evolve on manifold-valued spaces. Using manifolds is a convenient way to work with complex features that often do not live in Euclidean spaces. However, computing standard notions of geodesic distance, mean etc. can get very involved due to the underlying non-linearity associated with the space. As a result a complex task such as manifold sequence matching would require very large number of computations making it hard to use in practice. We believe that one can device smart approximation algorithms for several classes of such problems which take into account the geometry of the manifold and maintain the favorable properties of the exact approach. This problem has several applications in areas of human activity discovery and recognition, where several features and representations are naturally studied in a non-Euclidean setting. We propose a novel solution to the problem of indexing manifold-valued sequences by proposing an intrinsic approach to map sequences to a symbolic representation. This is shown to enable the deployment of fast and accurate algorithms for activity recognition, motif discovery, and anomaly detection. Toward this end, we present generalizations of key concepts of piece-wise aggregation and symbolic approximation for the case of non-Euclidean manifolds. Experiments show that one can replace expensive geodesic computations with much faster symbolic computations with little loss of accuracy in activity recognition and discovery applications. The proposed methods are ideally suited for real-time systems and resource constrained scenarios.
ContributorsAnirudh, Rushil (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2012
Description
In this thesis, an adaptive waveform selection technique for dynamic target tracking under low signal-to-noise ratio (SNR) conditions is investigated. The approach is integrated with a track-before-detect (TBD) algorithm and uses delay-Doppler matched filter (MF) outputs as raw measurements without setting any threshold for extracting delay-Doppler estimates. The particle filter (PF) Bayesian sequential estimation approach is used with the TBD algorithm (PF-TBD) to estimate the dynamic target state. A waveform-agile TBD technique is proposed that integrates the PF-TBD with a waveform selection technique. The new approach predicts the waveform to transmit at the next time step by minimizing the predicted mean-squared error (MSE). As a result, the radar parameters are adaptively and optimally selected for superior performance. Based on previous work, this thesis highlights the applicability of the predicted covariance matrix to the lower SNR waveform-agile tracking problem. The adaptive waveform selection algorithm's MSE performance was compared against fixed waveforms using Monte Carlo simulations. It was found that the adaptive approach performed at least as well as the best fixed waveform when focusing on estimating only position or only velocity. When these estimates were weighted by different amounts, then the adaptive performance exceeded all fixed waveforms. This improvement in performance demonstrates the utility of the predicted covariance in waveform design, at low SNR conditions that are poorly handled with more traditional tracking algorithms.
ContributorsPiwowarski, Ryan (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Multidimensional (MD) discrete Fourier transform (DFT) is a key kernel algorithm in many signal processing applications, such as radar imaging and medical imaging. Traditionally, a two-dimensional (2-D) DFT is computed using Row-Column (RC) decomposition, where one-dimensional (1-D) DFTs are computed along the rows followed by 1-D DFTs along the columns. However, architectures based on RC decomposition are not efficient for large input size data which have to be stored in external memories based Synchronous Dynamic RAM (SDRAM). In this dissertation, first an efficient architecture to implement 2-D DFT for large-sized input data is proposed. This architecture achieves very high throughput by exploiting the inherent parallelism due to a novel 2-D decomposition and by utilizing the row-wise burst access pattern of the SDRAM external memory. In addition, an automatic IP generator is provided for mapping this architecture onto a reconfigurable platform of Xilinx Virtex-5 devices. For a 2048x2048 input size, the proposed architecture is 1.96 times faster than RC decomposition based implementation under the same memory constraints, and also outperforms other existing implementations. While the proposed 2-D DFT IP can achieve high performance, its output is bit-reversed. For systems where the output is required to be in natural order, use of this DFT IP would result in timing overhead. To solve this problem, a new bandwidth-efficient MD DFT IP that is transpose-free and produces outputs in natural order is proposed. It is based on a novel decomposition algorithm that takes into account the output order, FPGA resources, and the characteristics of off-chip memory access. An IP generator is designed and integrated into an in-house FPGA development platform, AlgoFLEX, for easy verification and fast integration. The corresponding 2-D and 3-D DFT architectures are ported onto the BEE3 board and their performance measured and analyzed. The results shows that the architecture can maintain the maximum memory bandwidth throughout the whole procedure while avoiding matrix transpose operations used in most other MD DFT implementations. The proposed architecture has also been ported onto the Xilinx ML605 board. When clocked at 100 MHz, 2048x2048 images with complex single-precision can be processed in less than 27 ms. Finally, transpose-free imaging flows for range-Doppler algorithm (RDA) and chirp-scaling algorithm (CSA) in SAR imaging are proposed. The corresponding implementations take advantage of the memory access patterns designed for the MD DFT IP and have superior timing performance. The RDA and CSA flows are mapped onto a unified architecture which is implemented on an FPGA platform. When clocked at 100MHz, the RDA and CSA computations with data size 4096x4096 can be completed in 323ms and 162ms, respectively. This implementation outperforms existing SAR image accelerators based on FPGA and GPU.
ContributorsYu, Chi-Li (Author) / Chakrabarti, Chaitali (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Karam, Lina (Committee member) / Cao, Yu (Committee member) / Arizona State University (Publisher)
Created2012