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Title: Dynamic network analysis with applications to functional neural connectivity
Creator: Golibagh Mahyari, Arash
Date: 2017
Collection: Electronic Theses & Dissertations
Description: "Contemporary neuroimaging techniques provide neural activity recordings with increasing spatial and temporal resolution yielding rich multichannel datasets that can be exploited for detailed description of anatomical and functional connectivity patterns in the brain. Studies indicate that the changes in functional connectivity patterns across spatial and temporal scales play an important role in a wide range of cognitive and executive processes such as memory and attention as well as in the...
Show more"Contemporary neuroimaging techniques provide neural activity recordings with increasing spatial and temporal resolution yielding rich multichannel datasets that can be exploited for detailed description of anatomical and functional connectivity patterns in the brain. Studies indicate that the changes in functional connectivity patterns across spatial and temporal scales play an important role in a wide range of cognitive and executive processes such as memory and attention as well as in the understanding the causes of many neural diseases and psychopathologies such as epilepsy, Alzheimers, Parkinsons and schizophrenia. Early work in the area was limited to the analysis of static brain networks obtained through averaging long-term functional connectivity, thus neglecting possible time-varying connections. There is growing evidence that functional networks dynamically reorganize and coordinate on millisecond scale for the execution of mental processes. Functional networks consist of distinct network states, where each state is defined as a period of time during which the network topology is quasi-stationary. For this reason, there has been an interest in characterizing the dynamics of functional networks using high temporal resolution electroencephalogram recordings. In this thesis, dynamic functional connectivity networks are represented by multiway arrays, tensors, which are able to capture the complete topological structure of the networks. This thesis proposes new methods for both tracking the changes in these dynamic networks and characterizing or summarizing the network states. In order to achieve this goal, a Tucker decomposition based approach is introduced for detecting the change points for task-based electroencephalogram (EEG) functional connectivity networks through calculating the subspace distance between consecutive time steps. This is followed by a tensor-matrix projection based approach for summarizing multiple networks within a time interval. Tensor based summarization approaches do not necessarily result in sparse network and succinct states. Moreover, subspace based summarizations tend to capture the background brain activity more than the low energy sparse activations. For this reason, we propose utilizing the sparse common component and innovations (SCCI) model which simultaneously finds the sparse common component of multiple signals. However, as the number of signals in the model increases, this becomes computationally prohibitive. In this thesis, a hierarchical algorithm to recover the common component in the SCCI model is proposed for large number of signals. The hierarchical recovery of SCCI model solves the time and memory limitations at the expense of a slight decrease in the accuracy. This hierarchical model is used to separate the common and innovation components of functional connectivity networks across time. The innovation components are tracked over time to detect the change points, and the common component of the detected network states are used to obtain the network summarization. SCCI recovery algorithm finds the sparse representation of the common and innovation components of signals with respect to pre-determined dictionaries. However, input signals are not always well-represented by pre-determined dictionaries. In this thesis, a structured dictionary learning algorithm for SCCI model is developed. The proposed method is applied to EEG data collected during a study of error monitoring where two different types of brain responses are elicited in response to the stimulus. The learned dictionaries can discriminate between the response types and extract the error-related potentials (ERP) corresponding to the two responses."--Pages ii-iii.
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Title: Unconstrained 3D face reconstruction from photo collections
Creator: Roth, Joseph (Software engineer)
Date: 2016
Collection: Electronic Theses & Dissertations
Description: This thesis presents a novel approach for 3D face reconstruction from unconstrained photo collections. An unconstrained photo collection is a set of face images captured under an unknown and diverse variation of poses, expressions, and illuminations. The output of the proposed algorithm is a true 3D face surface model represented as a watertight triangulated surface with albedo data colloquially referred to as texture information. Reconstructing a 3D understanding of a face based on 2D input...
Show moreThis thesis presents a novel approach for 3D face reconstruction from unconstrained photo collections. An unconstrained photo collection is a set of face images captured under an unknown and diverse variation of poses, expressions, and illuminations. The output of the proposed algorithm is a true 3D face surface model represented as a watertight triangulated surface with albedo data colloquially referred to as texture information. Reconstructing a 3D understanding of a face based on 2D input is a long-standing computer vision problem. Traditional photometric stereo-based reconstruction techniques work on aligned 2D images and produce a 2.5D depth map reconstruction. We extend face reconstruction to work with a true 3D model, allowing us to enjoy the benefits of using images from all poses, up to and including profiles. To use a 3D model, we propose a novel normal field-based Laplace editing technique which allows us to deform a triangulated mesh to match the observed surface normals. Unlike prior work that require large photo collections, we formulate an approach to adapt to photo collections with few images of potentially poor quality. We achieve this through incorporating prior knowledge about face shape by fitting a 3D Morphable Model to form a personalized template before using a novel analysis-by-synthesis photometric stereo formulation to complete the fine face details. A structural similarity-based quality measure allows evaluation in the absence of ground truth 3D scans. Superior large-scale experimental results are reported on Internet, synthetic, and personal photo collections.
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Title: Kernel methods for biosensing applications
Creator: Khan, Hassan Aqeel
Date: 2015
Collection: Electronic Theses & Dissertations
Description: This thesis examines the design noise robust information retrieval techniques basedon kernel methods. Algorithms are presented for two biosensing applications: (1)High throughput protein arrays and (2) Non-invasive respiratory signal estimation.Our primary objective in protein array design is to maximize the throughput byenabling detection of an extremely large number of protein targets while using aminimal number of receptor spots. This is accomplished by viewing the proteinarray as a...
Show moreThis thesis examines the design noise robust information retrieval techniques basedon kernel methods. Algorithms are presented for two biosensing applications: (1)High throughput protein arrays and (2) Non-invasive respiratory signal estimation.Our primary objective in protein array design is to maximize the throughput byenabling detection of an extremely large number of protein targets while using aminimal number of receptor spots. This is accomplished by viewing the proteinarray as a communication channel and evaluating its information transmission capacity as a function of its receptor probes. In this framework, the channel capacitycan be used as a tool to optimize probe design; the optimal probes being the onesthat maximize capacity. The information capacity is first evaluated for a small scaleprotein array, with only a few protein targets. We believe this is the first effort toevaluate the capacity of a protein array channel. For this purpose models of theproteomic channel's noise characteristics and receptor non-idealities, based on experimental prototypes, are constructed. Kernel methods are employed to extend thecapacity evaluation to larger sized protein arrays that can potentially have thousandsof distinct protein targets. A specially designed kernel which we call the ProteomicKernel is also proposed. This kernel incorporates knowledge about the biophysicsof target and receptor interactions into the cost function employed for evaluation of channel capacity.For respiratory estimation this thesis investigates estimation of breathing-rateand lung-volume using multiple non-invasive sensors under motion artifact and highnoise conditions. A spirometer signal is used as the gold standard for evaluation oferrors. A novel algorithm called the segregated envelope and carrier (SEC) estimation is proposed. This algorithm approximates the spirometer signal by an amplitudemodulated signal and segregates the estimation of the frequency and amplitude in-formation. Results demonstrate that this approach enables effective estimation ofboth breathing rate and lung volume. An adaptive algorithm based on a combination of Gini kernel machines and wavelet filltering is also proposed. This algorithm is titledthe wavelet-adaptive Gini (or WAGini) algorithm, it employs a novel wavelet trans-form based feature extraction frontend to classify the subject's underlying respiratorystate. This information is then employed to select the parameters of the adaptive kernel machine based on the subject's respiratory state. Results demonstrate significantimprovement in breathing rate estimation when compared to traditional respiratoryestimation techniques.
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Title: Theory, synthesis and implementation of current-mode CMOS piecewise-linear circuits using margin propagation
Creator: Gu, Ming
Date: 2012
Collection: Electronic Theses & Dissertations
Description: Achieving high energy-efficiency is a key requirement for many emerging smart sensors and portable computing systems. While digital signal processing (DSP) has been the de-facto technique for implementing ultra-low power systems, analog signal processing (ASP) provides an attractive and alternate approach that can not only achieve high energy efficiency but also high computational density. Conventional ASP techniques are based on a top-down design approach, where proven mathematical...
Show moreAchieving high energy-efficiency is a key requirement for many emerging smart sensors and portable computing systems. While digital signal processing (DSP) has been the de-facto technique for implementing ultra-low power systems, analog signal processing (ASP) provides an attractive and alternate approach that can not only achieve high energy efficiency but also high computational density. Conventional ASP techniques are based on a top-down design approach, where proven mathematical principles and related algorithms are mapped and emulated using computational primitives inherent in the device physics. An example being the translinear principle, which is the state-of-the-art ASP technique, that uses the exponential current-to-voltage characteristics for designing ultra-low-power analog processors. However, elegant formulations could result from a bottom-up approach where device and bias independent computational primitives (e.g. current and charge conservation principles) are used for designing "approximate" analog signal processors. The hypothesis of this proposal is that many signal processing algorithms exhibit an inherent calibration ability due to which their performance remains unaffected by the use of "approximate" analog computing techniques. In this research, we investigate the theory, synthesis and implementation of high performance analog processors using a novel piecewise-linear (PWL) approximation algorithm called margin propagation (MP). MP principle utilizes only basic conservation laws of physical quantities (current, charge, mass, energy) for computing and therefore is scalable across devices (silicon, MEMS, microfluidics). However, there are additional advantages of MP-based processors when implemented using CMOS current-mode circuits, which includes: 1) the operation of the MP processor requires only addition, subtraction and threshold operations and hence is independent of transistor biasing (weak, moderate and strong inversion) and robust to variations in environmental conditions (e.g. temperature); and 2) improved dynamic range and faster convergence as compared to the translinear implementations. We verify our hypothesis using two analog signal processing applications: (a) design of high-performance analog low-density parity check (LDPC) decoders for applications in sensor networks; and (b) design of ultra-low-power analog support vector machines (SVM) for smart sensors. Our results demonstrate that an algorithmic framework for designing margin propagation (MP) based LDPC decoders can be used to trade-off its BER performance with its energy efficiency, making the design attractive for applications with adaptive energy-BER constraints. We have verified this trade-off using an analog current-mode implementation of an MP-based (32,8) LDPC decoder. Measured results from prototypes fabricated in a 0.5 μm CMOS process show that the BER performance of the MP-based decoder outperforms a benchmark state-of-the-art min-sum decoder at SNR levels greater than 3.5 dB and can achieve energy efficiencies greater than 100pJ/bit at a throughput of 12.8 Mbps. In the second part of this study, MP principle is used for designing an energy-scalable support vector machine (SVM) whose power and speed requirements can be configured dynamically without any degradation in performance. We have verified the energy-scaling property using a current-mode implementation of an SVM operating with 8 dimensional feature vectors and 18 support vectors. The prototype fabricated in a 0.5μm CMOS process has integrated an array of floating gate transistors that serve as storage for up to 2052 SVM parameters. The SVM prototype also integrates novel circuits that have been designed for interfacing with an external digital processor. This includes a novel current-input current-output logarithmic amplifier circuit that can achieve a dynamic range of 120dB while consuming nanowatts of power. Another novel circuit includes a varactor based temperature compensated floating-gate memory that demonstrates a superior programming range than other temperature compensated floating-gate memories.
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Title: Assessment of functional connectivity in the human brain : multivariate and graph signal processing methods
Creator: Villafañe-Delgado, Marisel
Date: 2017
Collection: Electronic Theses & Dissertations
Description: "Advances in neurophysiological recording have provided a noninvasive way of inferring cognitive processes. Recent studies have shown that cognition relies on the functional integration or connectivity of segregated specialized regions in the brain. Functional connectivity quantifies the statistical relationships among different regions in the brain. However, current functional connectivity measures have certain limitations in the quantification of global integration and characterization of...
Show more"Advances in neurophysiological recording have provided a noninvasive way of inferring cognitive processes. Recent studies have shown that cognition relies on the functional integration or connectivity of segregated specialized regions in the brain. Functional connectivity quantifies the statistical relationships among different regions in the brain. However, current functional connectivity measures have certain limitations in the quantification of global integration and characterization of network structure. These limitations include the bivariate nature of most functional connectivity measures, the computational complexity of multivariate measures, and graph theoretic measures that are not robust to network size and degree distribution. Therefore, there is a need of computationally efficient and novel measures that can quantify the functional integration across brain regions and characterize the structure of these networks. This thesis makes contributions in three different areas for the assessment of multivariate functional connectivity. First, we present a novel multivariate phase synchrony measure for quantifying the common functional connectivity within different brain regions. This measure overcomes the drawbacks of bivariate functional connectivity measures and provides insights into the mechanisms of cognitive control not accountable by bivariate measures. Following the assessment of functional connectivity from a graph theoretic perspective, we propose a graph to signal transformation for both binary and weighted networks. This provides the means for characterizing the network structure and quantifying information in the graph by overcoming some drawbacks of traditional graph based measures. Finally, we introduce a new approach to studying dynamic functional connectivity networks through signals defined over networks. In this area, we define a dynamic graph Fourier transform in which a common subspace is found from the networks over time based on the tensor decomposition of the graph Laplacian over time."--Pages ii-iii.
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Title: Smartphone-based sensing systems for data-intensive applications
Creator: Moazzami, Mohammad-Mahdi
Date: 2017
Collection: Electronic Theses & Dissertations
Description: "Supported by advanced sensing capabilities, increasing computational resources and the advances in Artificial Intelligence, smartphones have become our virtual companions in our daily life. An average modern smartphone is capable of handling a wide range of tasks including navigation, advanced image processing, speech processing, cross app data processing and etc. The key facet that is common in all of these applications is the data intensive computation. In this dissertation we have taken...
Show more"Supported by advanced sensing capabilities, increasing computational resources and the advances in Artificial Intelligence, smartphones have become our virtual companions in our daily life. An average modern smartphone is capable of handling a wide range of tasks including navigation, advanced image processing, speech processing, cross app data processing and etc. The key facet that is common in all of these applications is the data intensive computation. In this dissertation we have taken steps towards the realization of the vision that makes the smartphone truly a platform for data intensive computations by proposing frameworks, applications and algorithmic solutions. We followed a data-driven approach to the system design. To this end, several challenges must be addressed before smartphones can be used as a system platform for data-intensive applications. The major challenge addressed in this dissertation include high power consumption, high computation cost in advance machine learning algorithms, lack of real-time functionalities, lack of embedded programming support, heterogeneity in the apps, communication interfaces and lack of customized data processing libraries. The contribution of this dissertation can be summarized as follows. We present the design, implementation and evaluation of the ORBIT framework, which represents the first system that combines the design requirements of a machine learning system and sensing system together at the same time. We ported for the first time off-the-shelf machine learning algorithms for real-time sensor data processing to smartphone devices. We highlighted how machine learning on smartphones comes with severe costs that need to be mitigated in order to make smartphones capable of real-time data-intensive processing. From application perspective we present SPOT. SPOT aims to address some of the challenges discovered in mobile-based smart-home systems. These challenges prevent us from achieving the promises of smart-homes due to heterogeneity in different aspects of smart devices and the underlining systems. We face the following major heterogeneities in building smart-homes:: (i) Diverse appliance control apps (ii) Communication interface, (iii) Programming abstraction. SPOT makes the heterogeneous characteristics of smart appliances transparent, and by that it minimizes the burden of home automation application developers and the efforts of users who would otherwise have to deal with appliance-specific apps and control interfaces. From algorithmic perspective we introduce two systems in the smartphone-based deep learning area: Deep-Crowd-Label and Deep-Partition. Deep neural models are both computationally and memory intensive, making them difficult to deploy on mobile applications with limited hardware resources. On the other hand, they are the most advanced machine learning algorithms suitable for real-time sensing applications used in the wild. Deep-Partition is an optimization-based partitioning meta-algorithm featuring a tiered architecture for smartphone and the back-end cloud. Deep-Partition provides a profile-based model partitioning allowing it to intelligently execute the Deep Learning algorithms among the tiers to minimize the smartphone power consumption by minimizing the deep models feed-forward latency. Deep-Crowd-Label is prototyped for semantically labeling user's location. It is a crowd-assisted algorithm that uses crowd-sourcing in both training and inference time. It builds deep convolutional neural models using crowd-sensed images to detect the context (label) of indoor locations. It features domain adaptation and model extension via transfer learning to efficiently build deep models for image labeling. The work presented in this dissertation covers three major facets of data-driven and compute-intensive smartphone-based systems: platforms, applications and algorithms; and helps to spurs new areas of research and opens up new directions in mobile computing research."--Pages ii-iii.
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Title: Higher-order data reduction through clustering, subspace analysis and compression for applications in functional connectivity brain networks
Creator: Ozdemir, Alp
Date: 2017
Collection: Electronic Theses & Dissertations
Description: "With the recent advances in information technology, collection and storage of higher-order datasets such as multidimensional data across multiple modalities or variables have become much easier and cheaper than ever before. Tensors, also known as multiway arrays, provide natural representations for higher-order datasets and provide a way to analyze them by preserving the multilinear relations in these large datasets. These higher-order datasets usually contain large amount of redundant...
Show more"With the recent advances in information technology, collection and storage of higher-order datasets such as multidimensional data across multiple modalities or variables have become much easier and cheaper than ever before. Tensors, also known as multiway arrays, provide natural representations for higher-order datasets and provide a way to analyze them by preserving the multilinear relations in these large datasets. These higher-order datasets usually contain large amount of redundant information and summarizing them in a succinct manner is essential for better inference. However, existing data reduction approaches are limited to vector-type data and cannot be applied directly to tensors without vectorizing. Developing more advanced approaches to analyze tensors effectively without corrupting their intrinsic structure is an important challenge facing Big Data applications. This thesis addresses the issue of data reduction for tensors with a particular focus on providing a better understanding of dynamic functional connectivity networks (dFCNs) of the brain. Functional connectivity describes the relationship between spatially separated neuronal groups and analysis of dFCNs plays a key role for interpreting complex brain dynamics in different cognitive and emotional processes. Recently, graph theoretic methods have been used to characterize the brain functionality where bivariate relationships between neuronal populations are represented as graphs or networks. In this thesis, the changes in these networks across time and subjects will be studied through tensor representations. In Chapter 2, we address a multi-graph clustering problem which can be thought as a tensor partitioning problem. We introduce a hierarchical consensus spectral clustering approach to identify the community structure underlying the functional connectivity brain networks across subjects. New information-theoretic criteria are introduced for selecting the optimal community structure. Effectiveness of the proposed algorithms are evaluated through a set of simulations comparing with the existing methods as well as on FCNs across subjects. In Chapter 3, we address the online tensor data reduction problem through a subspace tracking perspective. We introduce a robust low-rank+sparse structure learning algorithm for tensors to separate the low-rank community structure of connectivity networks from sparse outliers. The proposed framework is used to both identify change points, where the low-rank community structure changes significantly, and summarize this community structure within each time interval. Finally, in Chapter 4, we introduce a new multi-scale tensor decomposition technique to efficiently encode nonlinearities due to rotation or translation in tensor type data. In particular, we develop a multi-scale higher-order singular value decomposition (MS-HoSVD) approach where a given tensor is first permuted and then partitioned into several sub-tensors each of which can be represented as a low-rank tensor increasing the efficiency of the representation. We derive a theoretical error bound for the proposed approach as well as provide analysis of memory cost and computational complexity. Performance of the proposed approach is evaluated on both data reduction and classification of various higher-order datasets."--Pages ii-iii.
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Title: Stochastic modeling of routing protocols for cognitive radio networks
Creator: Soltani, Soroor
Date: 2013
Collection: Electronic Theses & Dissertations
Description: Cognitive radios are expected torevolutionize wireless networking because of their ability tosense, manage and share the mobile available spectrum.Efficient utilization of the available spectrum could be significantly improved by incorporating different cognitive radio based networks. Challenges are involved in utilizing the cognitive radios in a network, most of which rise from the dynamic nature of available spectrum that is not present in traditional wireless networks. The set of available...
Show moreCognitive radios are expected torevolutionize wireless networking because of their ability tosense, manage and share the mobile available spectrum.Efficient utilization of the available spectrum could be significantly improved by incorporating different cognitive radio based networks. Challenges are involved in utilizing the cognitive radios in a network, most of which rise from the dynamic nature of available spectrum that is not present in traditional wireless networks. The set of available spectrum blocks(channels) changes randomly with the arrival and departure of the users licensed to a specific spectrum band. These users are known as primary users. If a band is used by aprimary user, the cognitive radio alters its transmission power level ormodulation scheme to change its transmission range and switches to another channel.In traditional wireless networks, a link is stable if it is less prone to interference. In cognitive radio networks, however, a link that is interference free might break due to the arrival of its primary user. Therefore, links' stability forms a stochastic process with OFF and ON states; ON, if the primary user is absent. Evidently, traditional network protocols fail in this environment. New sets of protocols are needed in each layer to cope with the stochastic dynamics of cognitive radio networks.In this dissertation we present a comprehensive stochastic framework and a decision theory based model for the problem of routing packets from a source to a destination in a cognitive radio network. We begin by introducing two probability distributions called ArgMax and ArgMin for probabilistic channel selection mechanisms, routing, and MAC protocols. The ArgMax probability distribution locates the most stable link from a set of available links. Conversely, ArgMin identifies the least stable link. ArgMax and ArgMin together provide valuable information on the diversity of the stability of available links in a spectrum band. Next, considering the stochastic arrival of primary users, we model the transition of packets from one hop to the other by a Semi-Markov process and develop a Primary Spread Aware Routing Protocol (PSARP) that learns the dynamics of the environment and adapts its routing decision accordingly. Further, we use a decision theory framework. A utility function is designed to capture the effect of spectrum measurement, fluctuation of bandwidth availability and path quality. A node cognitively decides its best candidate among its neighbors by utilizing a decision tree. Each branch of the tree is quantified by the utility function and a posterior probability distribution, constructed using ArgMax probability distribution, which predicts the suitability of available neighbors. In DTCR (Decision Tree Cognitive Routing), nodes learn their operational environment and adapt their decision making accordingly. We extend the Decision tree modeling to translate video routing in a dynamic cognitive radio network into a decision theory problem. Then terminal analysis backward induction is used to produce our routing scheme that improves the peak signal-to-noise ratio of the received video.We show through this dissertation that by acknowledging the stochastic property of the cognitive radio networks' environment and constructing strategies using the statistical and mathematical tools that deal with such uncertainties, the utilization of these networks will greatly improve.
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Title: Network reachability : quantification, verification, troubleshooting, and optimization
Creator: Khakpour, Amir Reza
Date: 2012
Collection: Electronic Theses & Dissertations
Description: Quantifying, verifying, troubleshooting, and optimizing the network reachability is essential for network management and network security monitoring as well as various aspects of network auditing, maintenance, and design. Although attempts to model network reachability have been made, feasible solutions for computing, maintaining and optimally designing network reachability have remained unknown. Network reachability control is very critical because, on one hand, reachability errors can cause...
Show moreQuantifying, verifying, troubleshooting, and optimizing the network reachability is essential for network management and network security monitoring as well as various aspects of network auditing, maintenance, and design. Although attempts to model network reachability have been made, feasible solutions for computing, maintaining and optimally designing network reachability have remained unknown. Network reachability control is very critical because, on one hand, reachability errors can cause network security breaches or service outages, leading to millions of dollars of revenue loss for an enterprise network. On the other hand, network operators suffer from lack of tools that thoroughly examine network access control configurations and audit them to avoid such errors. Besides, finding reachability errors is by no means easy. The access control rules, by which network reachability is restricted, are often very complex and manually troubleshooting them is extremely difficult. Hence, having a tool that finds the reachability errors and fix them automatically can be very useful. Furthermore, flawed network reachability design and deployment can degrade the network performance significantly. Thus, it is crucial to have a tool that designs the network configurations such that they have the least performance impact on the enterprise network.In this dissertation, we first present a network reachability model that considers connectionless and connection-oriented transport protocols, stateless and stateful routers/firewalls, static and dynamic NAT, PAT, IP tunneling, etc. We then propose a suite of algorithms for quantifying reachability based on network configurations (mainly access control lists (ACLs)) as well as solutions for querying network reachability. We further extend our algorithms and data structures for detecting reachability errors, pinpointing faulty access control lists, and fixing them automatically and efficiently. Finally, we propose algorithms to place rules on network devices optimally so that they satisfy the networks central access policies. To this end, we define correctness and performance criteria for rule placement and in turn propose cost-based algorithms with adjustable parameters (for the network operators) to place rules such that the correctness and performance criteria are satisfied.We implemented the algorithms in our network reachability tool called Quarnet and conducted experiments on a university network. Experimental results show that the offline computation of reachability matrices takes a few hours and the online processing of a reachability query takes 75 milliseconds on average. We also examine our reachability error detection and correction algorithms on a few real-life networks to examine their performance and ensure that Quarnet is efficient enough to be practically useful. The results indicate that we can find reachability errors in order of minutes and fix them in order of seconds depending on the size of network and number of ACLs. Finally, we added the rule placement suite of algorithms to Quarnet, which can design a network ACL in based on the network central policies in order of tens of minutes for an enterprise network. We compare it with Purdue ACL placement, the state-of-the-art access policy design technique, and explain its pros and cons.
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Title: Statistical signal processing tools for analyzing large-scale neural ensembles
Creator: Aghagolzadeh, Mehdi
Date: 2012
Collection: Electronic Theses & Dissertations
Description: Understanding how a neuron processes information and communicates with other neurons is key to unravel the brain's mechanisms underlying perception, learning and motor processing. Key to characterize neurons acting in concert is the ability to simultaneously measure their firing patterns in awake behaving subjects. Microelectrode arrays (MEAs) implanted in the brain allow simultaneous monitoring of the activity of large ensembles of cells while subjects carry out specific tasks. Isolating the...
Show moreUnderstanding how a neuron processes information and communicates with other neurons is key to unravel the brain's mechanisms underlying perception, learning and motor processing. Key to characterize neurons acting in concert is the ability to simultaneously measure their firing patterns in awake behaving subjects. Microelectrode arrays (MEAs) implanted in the brain allow simultaneous monitoring of the activity of large ensembles of cells while subjects carry out specific tasks. Isolating the spike pattern characterizing each cell's signature firing requires sophisticated signal processing algorithms, particularly to track the nonstationary behavior of these waveforms over extended periods of time. In this thesis, we introduce a compressive spike sorting technique that discriminates spike patterns from individual neurons using a sparse representation of the ensemble raw data. An iterative learning algorithm is introduced to estimate and adapt a set of optimal thresholds that maximize the separability between spike classes while minimizing the average waveform reconstruction error. Once derived, spike trains are then used to infer functional connectivity patterns among the recorded neural ensemble constituents. We introduce two information-theoretic approaches within the realm of graphical models that capture the spatiotemporal dependency between neurons' spiking patterns. Specifically, the class of spatiotemporal maximum entropy models is shown to incorporate higher-order interactions. We demonstrate the richness of these techniques in improving our understanding of neural function and dysfunction at the millisecond and micron resolutions, and their potential to be applied in emerging applications of brain machine interface technology to help improve the lifestyle of people with severe disabilities.
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Title: Tracking single-units in chronic neural recordings for brain machine interface applications
Creator: Eleryan, Ahmed Ibrahim
Date: 2013
Collection: Electronic Theses & Dissertations
Description: Ensemble recording of multiple single-unit activity has been used to study the mechanisms of neural population coding over prolonged periods of time, and to perform reliable neural decoding in neuroprosthetic motor control applications. However, there are still many challenges towards achieving reliable stable single-units recordings. One primary challenge is the variability in spike waveform features and firing characteristics of single units recorded using chronically implanted...
Show moreEnsemble recording of multiple single-unit activity has been used to study the mechanisms of neural population coding over prolonged periods of time, and to perform reliable neural decoding in neuroprosthetic motor control applications. However, there are still many challenges towards achieving reliable stable single-units recordings. One primary challenge is the variability in spike waveform features and firing characteristics of single units recorded using chronically implanted microelectrodes, making it challenging to ascertain the identity of the recorded neurons across days. In this study, I present a fast and efficient algorithm that tracks multiple single-units recorded in non-human primates performing brain control of a robotic limb, based on features extracted from units' average waveforms and interspike intervals histograms. The algorithm requires a relatively short recording duration to perform the analysis and can be applied at the start of each recording session without requiring the subject to be engaged in a behavioral task. The algorithm achieves a classification accuracy of up to 90% compared to manual tracking. I also explore using the algorithm to develop an automated technique for unit selection to perform reliable decoding of movement parameters from neural activity.
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Title: Graph-based methods for inferring neuronal connectivity from spike train ensembles
Creator: El-dawlatly, Seif El-din
Date: 2011
Collection: Electronic Theses & Dissertations
Description: Understanding the brain's inner workings requires studying the underlying complex networks that bind its basic computational elements, the neurons. Advances in extracellular neural recording techniques have enabled simultaneous recording of spike trains from multiple single neurons in awake, behaving subjects. Yet, devising methods to infer connectivity among these neurons has been significantly lacking. We introduce a connectivity inference framework based on graphical models. We first infer...
Show moreUnderstanding the brain's inner workings requires studying the underlying complex networks that bind its basic computational elements, the neurons. Advances in extracellular neural recording techniques have enabled simultaneous recording of spike trains from multiple single neurons in awake, behaving subjects. Yet, devising methods to infer connectivity among these neurons has been significantly lacking. We introduce a connectivity inference framework based on graphical models. We first infer the functional connectivity between neurons by searching for clusters of statistically dependent spike trains. We then infer the effective connectivity between neurons within each cluster by building Dynamic Bayesian Network (DBN) model fit to the spike train data. Using probabilistic models of neuronal firing, we demonstrate the utility of this framework to infer neuronal connectivity in moderate and large scale networks with a substantial gain in performance compared to classical methods. We further use this framework to examine the role of spike timing correlation in infragranular layer V of the primary somatosensory cortex (S1) of the rat during unilateral whisker stimulation in vivo. Stable, whisker-specific networks provided more information about the stimulus than individual neurons' response. We finally demonstrate how this framework enables tracking and quantifying plastic changes in connectivity in biologically-plausible models of spike-timing-dependent-plasticity as well as changes in S1 response maps following sensory deprivation in the awake, behaving rat.
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Title: MEASURING AND MODELING THE EFFECTS OF SEA LEVEL RISE ON NEAR-COASTAL RIVERINE REGIONS : A GEOSPATIAL COMPARISON OF THE SHATT AL-ARAB RIVER IN SOUTHERN IRAQ WITH THE MISSISSIPPI RIVER DELTA IN SOUTHERN LOUISIANA, USA.
Creator: Kadhim, Ameen Awad
Date: 2018
Collection: Electronic Theses & Dissertations
Description: There is a growing debate among scientists on how sea level rise (SLR) will impact coastal environments, particularly in countries where economic activities are sustained along these coasts. An important factor in this debate is how best to characterize coastal environmental impacts over time. This study investigates the measurement and modeling of SLR and effects on near-coastal riverine regions. The study uses a variety of data sources, including satellite imagery from 1975 to 2017, digital...
Show moreThere is a growing debate among scientists on how sea level rise (SLR) will impact coastal environments, particularly in countries where economic activities are sustained along these coasts. An important factor in this debate is how best to characterize coastal environmental impacts over time. This study investigates the measurement and modeling of SLR and effects on near-coastal riverine regions. The study uses a variety of data sources, including satellite imagery from 1975 to 2017, digital elevation data and previous studies. This research is focusing on two of these important regions: southern Iraq along the Shatt Al-Arab River (SAR) and the southern United States in Louisiana along the Mississippi River Delta (MRD). These sites are important for both their extensive low-lying land and for their significant coastal economic activities. The dissertation consists of six chapters. Chapter one introduces the topic. Chapter two compares and contrasts bothregions and evaluates escalating SLR risk. Chapter three develops a coupled human and natural system (CHANS) perspective for SARR to reveal multiple sources of environmental degradation in this region. Alfa century ago SARR was an important and productive region in Iraq that produced fruits like dates, crops, vegetables, and fish. By 1975 the environment of this region began to deteriorate, and since then, it is well-documented that SARR has suffered under human and natural problems. In this chapter, I use the CHANS perspective to identify the problems, and which ones (human or natural systems) are especially responsible for environmental degradation in SARR. I use several measures of ecological, economic, and social systems to outline the problems identified through the CHANS framework. SARR has experienced extreme weather changes from 1975 to 2017 resulting in lower precipitation (-17mm) and humidity (-5.6%), higher temperatures (1.6 C), and sea level rise, which are affecting the salinity of groundwater and Shatt Al Arab river water. At the same time, human systems in SARR experienced many problems including eight years of war between Iraq and Iran, the first Gulf War, UN Security Council imposed sanctions against Iraq, and the second Gulf War. I modeled and analyzed the regions land cover between 1975 and 2017 to understand how the environment has been affected, and found that climate change is responsible for what happened in this region based on other factors. Chapter four constructs and applies an error propagation model to elevation data in the Mississippi River Delta region (MRDR). This modeling both reduces and accounts for the effects of digital elevation model (DEM) error on a bathtub inundation model used to predict the SLR risk in the region. Digital elevation data is essential to estimate coastal vulnerability to flooding due to sea level rise. Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global is considered the best free global digital elevation data available. However, inundation estimates from SRTM are subject to uncertainty due to inaccuracies in the elevation data. Small systematic errors in low, flat areas can generate large errors in inundation models, and SRTM is subject to positive bias in the presence of vegetation canopy, such as along channels and within marshes. In this study, I conduct an error assessment and develop statistical error modeling for SRTM to improve the quality of elevation data in these at-risk regions. Chapter five applies MRDR-based model from chapter four to enhance the SRTM 1 Arc-Second Global DEM data in SARR. As such, it is the first study to account for data uncertainty in the evaluation of SLR risk in this sensitive region. This study transfers an error propagation model from MRDR to the Shatt al-Arab river region to understand the impact of DEM error on an inundation model in this sensitive region. The error propagation model involves three stages. First, a multiple regression model, parameterized from MRDR, is used to generate an expected DEM error surface for SARR. This surface is subtracted from the SRTM DEM for SARR to adjust it. Second, residuals from this model are simulated for SARR: these are mean-zero and spatially autocorrelated with a Gaussian covariance model matching that observed in MRDR by convolution filtering of random noise. More than 50 realizations of error were simulated to make sure a stable result was realized. These realizations were subtracted from the adjusted SRTM to produce DEM realizations capturing potential variation. Third, the DEM realizations are each used in bathtub modeling to estimate flooding area in the region with 1 m of sea level rise. The distribution of flooding estimates shows the impact of DEM error on uncertainty in inundation likelihood, and on the magnitude of total flooding. Using the adjusted DEM realizations 47 ± 2 percent of the region is predicted to flood, while using the raw SRTM DEM only 28% of the region is predicted to flood.
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Title: High-dimensional learning from random projections of data through regularization and diversification
Creator: Aghagolzadeh, Mohammad
Date: 2015
Collection: Electronic Theses & Dissertations
Description: Random signal measurement, in the form of random projections of signal vectors, extends the traditional point-wise and periodic schemes for signal sampling. In particular, the well-known problem of sensing sparse signals from linear measurements, also known as Compressed Sensing (CS), has promoted the utility of random projections. Meanwhile, many signal processing and learning problems that involve parametric estimation do not consist of sparsity constraints in their original forms. With the...
Show moreRandom signal measurement, in the form of random projections of signal vectors, extends the traditional point-wise and periodic schemes for signal sampling. In particular, the well-known problem of sensing sparse signals from linear measurements, also known as Compressed Sensing (CS), has promoted the utility of random projections. Meanwhile, many signal processing and learning problems that involve parametric estimation do not consist of sparsity constraints in their original forms. With the increasing popularity of random measurements, it is crucial to study the generic estimation performance under the random measurement model. In this thesis, we consider two specific learning problems (named below) and present the following two generic approaches for improving the estimation accuracy: 1) by adding relevant constraints to the parameter vectors and 2) by diversification of the random measurements to achieve fast decaying tail bounds for the empirical risk function.The first problem we consider is Dictionary Learning (DL). Dictionaries are extensions of vector bases that are specifically tailored for sparse signal representation. DL has become increasingly popular for sparse modeling of natural images as well as sound and biological signals, just to name a few. Empirical studies have shown that typical DL algorithms for imaging applications are relatively robust with respect to missing pixels in the training data. However, DL from random projections of data corresponds to an ill-posed problem and is not well-studied. Existing efforts are limited to learning structured dictionaries or dictionaries for structured sparse representations to make the problem tractable. The main motivation for considering this problem is to generate an adaptive framework for CS of signals that are not sparse in the signal domain. In fact, this problem has been referred to as 'blind CS' since the optimal basis is subject to estimation during CS recovery. Our initial approach, similar to some of the existing efforts, involves adding structural constraints on the dictionary to incorporate sparse and autoregressive models. More importantly, our results and analysis reveal that DL from random projections of data, in its unconstrained form, can still be accurate given that measurements satisfy the diversity constraints defined later.The second problem that we consider is high-dimensional signal classification. Prior efforts have shown that projecting high-dimensional and redundant signal vectors onto random low-dimensional subspaces presents an efficient alternative to traditional feature extraction tools such as the principle component analysis. Hence, aside from the CS application, random measurements present an efficient sampling method for learning classifiers, eliminating the need for recording and processing high-dimensional signals while most of the recorded data is discarded during feature extraction. We work with the Support Vector Machine (SVM) classifiers that are learned in the high-dimensional ambient signal space using random projections of the training data. Our results indicate that the classifier accuracy can be significantly improved by diversification of the random measurements.
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Title: Nanoengineered tissue scaffolds for regenerative medicine in neural cell systems
Creator: Tiryaki, Volkan Mujdat
Date: 2013
Collection: Electronic Theses & Dissertations
Description: Central nervous system (CNS) injuries present one of the most challenging problems. Regeneration in the mammal CNS is often limited because the injured axons cannot regenerate beyond the lesion. Implantation of a scaffolding material is one of the possible approaches to this problem. Recent implantations by our collaborative research group using electrospun polyamide nanofibrillar scaffolds have shown promising results in vitro and in vivo. The physical properties of the tissue scaffolds have...
Show moreCentral nervous system (CNS) injuries present one of the most challenging problems. Regeneration in the mammal CNS is often limited because the injured axons cannot regenerate beyond the lesion. Implantation of a scaffolding material is one of the possible approaches to this problem. Recent implantations by our collaborative research group using electrospun polyamide nanofibrillar scaffolds have shown promising results in vitro and in vivo. The physical properties of the tissue scaffolds have been neglected for many years, and it has only recently been recognized that significant aspects include nanophysical properties such as nanopatterning, surface roughness, local elasticity, surface polarity, surface charge, and growth factor presentation as well as the better-known biochemical cues.The properties of: surface polarity, surface roughness, local elasticity and local work of adhesion were investigated in this thesis. The physical and nanophysical properties of the cell culture environments were evaluated using contact angle and atomic force microscopy (AFM) measurements. A new capability, scanning probe recognition microscopy (SPRM), was also used to characterize the surface roughness of nanofibrillar scaffolds. The corresponding morphological and protein expression responses of rat model cerebral cortical astrocytes to the polyamide nanofibrillar scaffolds versus comparative culture surfaces were investigated by AFM and immunocytochemistry. Astrocyte morphological responses were imaged using AFM and phalloidin staining for F-actin. Activation of the corresponding Rho GTPase regulators was investigated using immunolabeling with Cdc42, Rac1, and RhoA. The results supported the hypothesis that the extracellular environment can trigger preferential activation of members of the Rho GTPase family, with demonstrable morphological consequences for cerebral cortical astrocytes. Astrocytes have a special role in the formation of the glial scar in response to traumatic injury. The glial scar biomechanically and biochemically blocks axon regeneration, resulting in paralysis. Astrocytes involved in glial scar formation become reactive, with development of specific morphologies and inhibitory protein expressions. Dibutyryl cyclic adenosine monophosphate (dBcAMP) was used to induce astrocyte reactivity. The directive importance of nanophysical properties for the morphological and protein expression responses of dBcAMP-stimulated cerebral cortical astrocytes was investigated by immunocytochemistry, Western blotting, and AFM. Nanofibrillar scaffold properties were shown to reduce immunoreactivity responses, while PLL Aclar properties were shown to induce responses reminiscent of glial scar formation. Comparison of the responses for dBcAMP-treated reactive-like and untreated astrocytes indicated that the most influential directive nanophysical cues may differ in wound-healing versus untreated situations.Finally, a new cell shape index (CSI) analysis system was developed using volumetric AFM height images of cells cultured on different substrates. The new CSI revealed quantitative cell spreading information not included in the conventional CSI. The system includes a floating feature selection algorithm for cell segmentation that uses a total of 28 different textural features derived from two models: the gray level co-occurance matrix and local statistics texture features. The quantitative morphometry of untreated and dBcAMP-treated cerebral cortical astrocytes was investigated using the new and conventional CSI, and the results showed that quantitative astrocyte spreading and stellation behavior was induced by variations in nanophysical properties.
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Title: Reducing the number of ultrasound array elements with the matrix pencil method
Creator: Sales, Kirk L.
Date: 2012
Collection: Electronic Theses & Dissertations
Description: Phased arrays are diversely applied with some specific areas including biomedical imaging and therapy, non-destructive testing, radar and sonar. In this thesis, the matrix pencil method is employed to reduce the number of elements in a linear ultrasound phased array. The non-iterative, linear method begins with a specified pressure beam pattern, reduces the dimensionality of the problem, then calculates the element locations and apodization of a reduced array. Computer simulations demonstrate...
Show morePhased arrays are diversely applied with some specific areas including biomedical imaging and therapy, non-destructive testing, radar and sonar. In this thesis, the matrix pencil method is employed to reduce the number of elements in a linear ultrasound phased array. The non-iterative, linear method begins with a specified pressure beam pattern, reduces the dimensionality of the problem, then calculates the element locations and apodization of a reduced array. Computer simulations demonstrate a close comparison between the initial array beam pattern and the reduced array beam pattern for four different linear arrays. The number of elements in a broadside-steered linear array is shown to decrease by approximately 50% with the reduced array beam pattern closely approximating the initial array beam pattern in the far-field. While the method returns a slightly tapered spacing between elements, for the arrays considered, replacing the tapered spacing with a suitably-selected uniform spacing provides very little change in the main beam and low-angle side lobes.
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Title: Adaptive independent component analysis : theoretical formulations and application to CDMA communication system with electronics implementation
Creator: Albataineh, Zaid
Date: 2014
Collection: Electronic Theses & Dissertations
Description: Blind Source Separation (BSS) is a vital unsupervised stochastic area that seeks to estimate the underlying source signals from their mixtures with minimal assumptions about the source signals and/or the mixing environment. BSS has been an active area of research and in recent years has been applied to numerous domains including biomedical engineering, image processing, wireless communications, speech enhancement, remote sensing, etc. Most recently, Independent Component Analysis (ICA) has...
Show moreBlind Source Separation (BSS) is a vital unsupervised stochastic area that seeks to estimate the underlying source signals from their mixtures with minimal assumptions about the source signals and/or the mixing environment. BSS has been an active area of research and in recent years has been applied to numerous domains including biomedical engineering, image processing, wireless communications, speech enhancement, remote sensing, etc. Most recently, Independent Component Analysis (ICA) has become a vital analytical approach in BSS. In spite of active research in BSS, however, many foundational issues still remain in regards to convergence speed, performance quality and robustness in realistic or adverse environments. Furthermore, some of the developed BSS methods are computationally expensive, sensitive to additive and background noise, and not suitable for a real4time or real world implementation. In this thesis, we first formulate new effective ICA4based measures and their corresponding robust adaptive algorithms for the BSS in dynamic "convolutive mixture" environments. We demonstrate their superior performance to present competing algorithms. Then we tailor their application within wireless (CDMA) communication systems and Acoustic Separation Systems. We finally explore a system realization of one of the developed algorithms among ASIC or FPGA platforms in terms of real time speed, effectiveness, cost, and economics of scale. Firstly, we propose a new class of divergence measures for Independent Component Analysis (ICA) for estimating sources from mixtures. The Convex Cauchy4Schwarz Divergence (CCS4DIV) is formed by integrating convex functions into the Cauchy4Schwarz inequality. The new measure is symmetric and convex with respect to the joint probability, where the degree of convexity can be tuned by a (convexity) parameter. A non4parametric (ICA) algorithm generated from the proposed divergence is developed exploiting convexity parameters and employing the Parzen window4based distribution estimates. The new contrast function results in effective parametric and non4parametric ICA4based computational algorithms. Moreover, two pairwise iterative schemes are proposed to tackle the high dimensionality of sources. Secondly, a new blind detection algorithm, based on fourth order cumulant matrices, is presented and applied to the multi4user symbol estimation problem in Direct Sequence Code Division Multiple Access (DS4CDMA) systems. In addition, we propose three new blind receiver schemes, which are based on the state space structures. These so4called blind state4space receivers (BSSR) do not require knowledge of the propagation parameters or spreading code sequences of the users but relies on the statistical independence assumption among the source signals. Lastly, system realization of one of the developed algorithms has been explored among ASIC or FPGA platforms in terms of cost, effectiveness, and economics of scale. Based on our findings of current stat4of4the4art electronics, programmable FPGA designs are deemed to be the most effective technology to be used for ICA hardware implementation at this time.In this thesis, we first formulate new effective ICA-based measures and their corresponding robust adaptive algorithms for the BSS in dynamic "convolutive mixture" environments. We demonstrate their superior performance to present competing algorithms. Then we tailor their application within wireless (CDMA) communication systems and Acoustic Separation Systems. We finally explore a system realization of one of the developed algorithms among ASIC or FPGA platforms in terms of real time speed, effectiveness, cost, and economics of scale.We firstly investigate several measures which are more suitable for extracting different source types from different mixing environments in the learning system. ICA for instantaneous mixtures has been studied here as an introduction to the more realistic convolutive mixture environments. Convolutive mixtures have been investigated in the time/frequency domains and we demonstrate that our approaches succeed in resolving the standing problem of scaling and permutation ambiguities in present research. We propose a new class of divergence measures for Independent Component Analysis (ICA) for estimating sources from mixtures. The Convex Cauchy-Schwarz Divergence (CCS-DIV) is formed by integrating convex functions into the Cauchy-Schwarz inequality. The new measure is symmetric and convex with respect to the joint probability, where the degree of convexity can be tuned by a (convexity) parameter. A non-parametric (ICA) algorithm generated from the proposed divergence is developed exploiting convexity parameters and employing the Parzen window-based distribution estimates. The new contrast function results in effective parametric and non-parametric ICA-based computational algorithms. Moreover, two pairwise iterative schemes are proposed to tackle the high dimensionality of sources. These wo pairwise non-parametric ICA algorithms are based on the new high-performance Convex Cauchy-Schwarz Divergence (CCS-DIV). These two schemes enable fast and efficient de-mixing of sources in real-world applications where the dimensionality of the sources is higher than two.Secondly, the more challenging problem in communication signal processing is to estimate the source signals and their channels in the presence of other co-channel signals and noise without the use of a training set. Blind techniques are promising to integrate and optimize the wireless communication designs i.e. equalizers/ filters/ combiners through its potential in suppressing the inter-symbol interference (ISI), adjacent channel interference, co-channel and the multi access interference MAI. Therefore, a new blind detection algorithm, based on fourth order cumulant matrices, is presented and applied to the multi-user symbol estimation problem in Direct Sequence Code Division Multiple Access (DS-CDMA) systems. The blind detection is to estimate multiple symbol sequences in the downlink of a DS-CDMA communication system using only the received wireless data and without any knowledge of the user spreading codes. The proposed algorithm takes advantage of higher cumulant matrix properties to reduce the computational load and enhance performance. In addition, we address the problem of blind multiuser equalization in the wideband CDMA system, in the noisy multipath propagation environment. Herein, we propose three new blind receiver schemes, which are based on the state space structures. These so-called blind state-space receivers (BSSR) do not require knowledge of the propagation parameters or spreading code sequences of the users but relies on the statistical independence assumption among the source signals. We then develop and derive three update-laws in order to enhance the performance of the blind detector. Also, we upgrade three semi-blind adaptive detectors based on the incorporation of the RAKE receiver and the stochastic gradient algorithms which are used in several blind adaptive signal processing algorithms, namely FastICA, RobustICA, and principle component analysis PCA. Through simulation evidence, we verify the significant bit error rate (BER) and computational speed improvements achieved by these algorithms in comparison to other leading algorithms.Lastly, system realization of one of the developed algorithms has been explored among ASIC or FPGA platforms in terms of cost, effectiveness, and economics of scale. Based on our findings of current stat-of-the-art electronics, programmable FPGA designs are deemed to be the most effective technology to be used for ICA hardware implementation at this time.
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Title: Robust signal processing methods for miniature acoustic sensing, separation, and recognition
Creator: Fazel, Amin
Date: 2012
Collection: Electronic Theses & Dissertations
Description: One of several emerging areas where micro-scale integration promises significant breakthroughs is in the field of acoustic sensing. However, separation, localization, and recognition of acoustic sources using micro-scale microphone arrays poses a significant challenge due to fundamental limitations imposed by the physics of sound propagation. The smaller the distance between the recording elements, the more difficult it is to measure localization and separation cues and hence it is more...
Show moreOne of several emerging areas where micro-scale integration promises significant breakthroughs is in the field of acoustic sensing. However, separation, localization, and recognition of acoustic sources using micro-scale microphone arrays poses a significant challenge due to fundamental limitations imposed by the physics of sound propagation. The smaller the distance between the recording elements, the more difficult it is to measure localization and separation cues and hence it is more difficult to recognize the acoustic sources of interest. The objective of this research is to investigate signal processing and machine learning techniques that can be used for noise-robust acoustic target recognition using miniature microphone arrays.The first part of this research focuses on designing "smart" analog-to-digital conversion (ADC) algorithms that can enhance acoustic cues in sub-wavelength microphone arrays. Many source separation algorithms fail to deliver robust performance when applied to signals recorded using high-density sensor arrays where the distance between sensor elements is much less than the wavelength of the signals. This can be attributed to limited dynamic range (determined by analog-to-digital conversion) of the sensor which is insufficientto overcome the artifacts due to large cross-channel redundancy, non-homogeneous mixing and high-dimensionality of the signal space. We propose a novel framework that overcomes these limitations by integrating statistical learning directly with the signal measurement (analog-to-digital) process which enables high fidelity separation of linear instantaneous mixture. At the core of the proposed ADC approach is a min-max optimization of a regularized objective function that yields a sequence of quantized parameters which asymptotically tracks the statistics of the input signal. Experiments with synthetic and real recordings demonstrate consistent performance improvements when the proposed approach is used as the analog-to-digital front-end to conventional source separation algorithms.The second part of this research focuses on investigating a novel speech feature extraction algorithm that can recognize auditory targets (keywords and speakers) using noisy recordings. The features known as Sparse Auditory Reproducing Kernel (SPARK) coefficients are extracted under the hypothesis that the noise-robust information in speech signal is embedded in a subspace spanned by sparse, regularized, over-complete, non-linear, and phase-shifted gammatone basis functions. The feature extraction algorithm involves computing kernel functions between the speech data and pre-computed set of phased-shifted gammatone functions, followed by a simple pooling technique ("MAX" operation). In this work, we present experimental results for a hidden Markov model (HMM) based speech recognition system whose performance has been evaluated on a standard AURORA 2 dataset. The results demonstrate that the SPARK features deliver significant and consistent improvements in recognition accuracy over the standard ETSI STQ WI007 DSR benchmark features. We have also verified the noise-robustness of the SPARK features for the task of speaker verification. Experimental results based on the NIST SRE 2003 dataset show significant improvements when compared to a standard Mel-frequency cepstral coefficients (MFCCs) based benchmark.
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Title: Scalable pulsed mode computation architecture using integrate and fire structure based on margin propagation
Creator: Hindo, Thamira
Date: 2014
Collection: Electronic Theses & Dissertations
Description: Neuromorphic computing architectures mimic the brain to implement efficient computations for sensory applications in a different way from that of the traditional Von Neumann architecture. The goal of neuromorphic computing systems is to implement sensory devices and systems that operate as efficiently as their biological equivalents. Neuromorphic computing consists of several potential components including parallel processing instead of synchronous processing, hybrid (pulse) computation...
Show moreNeuromorphic computing architectures mimic the brain to implement efficient computations for sensory applications in a different way from that of the traditional Von Neumann architecture. The goal of neuromorphic computing systems is to implement sensory devices and systems that operate as efficiently as their biological equivalents. Neuromorphic computing consists of several potential components including parallel processing instead of synchronous processing, hybrid (pulse) computation instead of digital computation, neuron models as a basic core of the processing instead of the arithmetic logic units, and analog VLSI design instead of digital VLSI design. In this work a new neuromorphic computing architecture is proposed and investigated for the implementation of algorithms based on using the pulsed mode with a neuron-based circuit.The proposed architecture goal is to implement approximate non-linear functions that are important components of signal processing algorithms. Some of the most important signal processing algorithms are those that mimic biological systems such as hearing, sight and touch. The designed architecture is pulse mode and it maps the functions into an algorithm called margin propagation. The designed structure is a special network of integrate-and-fire neuron-based circuits that implement the margin propagation algorithm using integration and threshold operations embedded in the transfer function of the neuron model. The integrate-and-fire neuron units in the network are connected together through excitatory and inhibitory paths to impose constraints on the network firing-rate. The advantages of the pulse-based, integrate-and-fire margin propagation (IFMP) algorithmic unit are to implement complex non-linear and dynamic programming functions in a scalable way; to implement functions using cascaded design in parallel or serial architecture; to implement the modules in low power and small size circuits of analog VLSI; and to achieve a wide dynamic range since the input parameters of IFMP module are mapped in the logarithmic domain.The newly proposed IFMP algorithmic unit is investigated both on a theoretically basis and an experimental performance basis. The IFMP algorithmic unit is implemented with a low power analog circuit. The circuit is simulated using computer aided design tools and it is fabricated in a 0.5 micron CMOS process. The hardware performance of the fabricated IFMP algorithmic architecture is also measured. The application of the IFMP algorithmic architecture is investigate for three signal processing algorithms including sequence recognition, trace recognition using hidden Markov model and binary classification using a support vector machine. Additionally, the IFMP architecture is investigated for the application of the winner-take-all algorithm, which is important for hearing, sight and touch sensor systems.
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Title: A multivariate time-frequency based phase synchrony measure and applications to dynamic brain network analysis
Creator: Mutlu, Ali Yener
Date: 2012
Collection: Electronic Theses & Dissertations
Description: Irregular, non-stationary, and noisy multichannel data are abound in many fields of research. Observations of multichannel data in nature include changes in weather, the dynamics of satellites in the solar system, the time evolution of the magnetic field of celestial bodies, population growth in ecology and the dynamics of the action potentials in neurons.One particular application of interest is the functional integration of neuronal networks in the human brain. Human brain is known to be...
Show moreIrregular, non-stationary, and noisy multichannel data are abound in many fields of research. Observations of multichannel data in nature include changes in weather, the dynamics of satellites in the solar system, the time evolution of the magnetic field of celestial bodies, population growth in ecology and the dynamics of the action potentials in neurons.One particular application of interest is the functional integration of neuronal networks in the human brain. Human brain is known to be one of the most complex biological systems and quantifying functional neural coordination in the brain is a fundamental problem. It has been recently proposed that networks of highly nonlinear and non-stationary reciprocal interactions are the key features of functional integration. Among many linear and nonlinear measures of dependency, time-varying phase synchrony has been proposed as a promising measure of connectivity. Current state-of-the-art in time-varying phase estimation uses either the Hilbert transform or the complex wavelet transform of the signals. Both of these methods have some major drawbacks such as the assumption that the signals are narrowband for the Hilbert transform and the non-uniform time-frequency resolution inherent to the wavelet analysis. Furthermore, the current phase synchrony measures are limited to quantifying bivariate relationships and do not reveal any information about multivariate synchronization patterns which are important for understanding the underlying oscillatory networks.In this dissertation, a new phase estimation method based on the Rihaczek distribution and Reduced Interference Rihaczek distribution belonging to Cohen's class is proposed. These distributions offer phase estimates with uniformly high time-frequency resolution which can be used for defining time and frequency dependent phase synchrony within the same frequency band as well as across different frequency bands. Properties of the phase estimator and the corresponding phase synchrony measure are evaluated both analytically and through simulations showing the effectiveness of the new measures compared to existing ones. The proposed distribution is then extended to quantify the cross-frequency phase synchronization between two signals across different frequencies. In addition, a cross frequency-spectral lag distribution is introducedto quantify the amount of amplitude modulation between signals. Furthermore, the notion of bivariate synchrony is extended to multivariate synchronization to quantify the relationships within and across groups of signals. Measures of multiple correlation and complexity are used as well as a more direct multivariate synchronization measure, `Hyperspherical Phase Synchrony', is proposed. This new measure is based on computing pairwise phase differences to create a multidimensional phase difference vector and mapping this vector to a high dimensional space. Hyperspherical phase synchrony offers lower computational complexity and is more robust to noise compared to the existing measures.Finally, a subspace analysis framework is proposed for studying time-varying evolution of functional brain connectivity. The proposed approach identifies event intervals accounting for the underlying neurophysiological events and extracts key graphs for describing the particular intervals with minimal redundancy. Results from the application to EEG data indicate the effectiveness of the proposed framework in determining the event intervals and summarizing brain activity with a few number of representative graphs.
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