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Pages
 Title
 Signal Processing Based Distortion Mitigation in Interferometric Radar Angular Velocity Estimation
 Creator
 Klinefelter, Eric
 Date
 2021
 Collection
 Electronic Theses & Dissertations
 Description

Interferometric angular velocity estimation is a relatively recent radar technique which uses a pair of widely spaced antenna elements and a correlation receiver to directly measure the angular velocity of a target. Traditional radar systems measure range, radial velocity (Doppler), and angle, while angular velocity is typically derived as the timerate change of the angle measurements. The noise associated with the derived angular velocity estimate is statistically correlated with the angle...
Show moreInterferometric angular velocity estimation is a relatively recent radar technique which uses a pair of widely spaced antenna elements and a correlation receiver to directly measure the angular velocity of a target. Traditional radar systems measure range, radial velocity (Doppler), and angle, while angular velocity is typically derived as the timerate change of the angle measurements. The noise associated with the derived angular velocity estimate is statistically correlated with the angle measurements, and thus provides no additional information to traditional state space trackers. Interferometric angular velocity estimation, on the other hand, provides an independent measurement, thus forming a basis in R2 for both position and velocity.While promising results have been presented for single target interferometric angular velocity estimation, there is a known issue which arises when multiple targets are present. The ideal interferometric response with multiple targets would contain only the mixing product between like targets across the antenna responses, yet instead, the mixing product between all targets is generated, resulting in unwanted `crossterms' or intermodulation distortion. To date, various hardware based methods have been presented, which are effective, though they tend to require an increased number of antenna elements, a larger physical system baseline, or signals with wide bandwidths. Presented here are novel methods for signal processing based interferometric angular velocity estimation distortion mitigation, which can be performed with only a single antenna pair and traditional continuouswave or frequencymodulated continuous wave signals.In this work, two classes of distortion mitigation methods are described: modelbased and response decomposition. Modelbased methods use a learned or analytic model with traditional nonlinear optimization techniques to arrive at angular velocity estimates based on the complete interferometric signal response. Response decomposition methods, on the other hand, aim to decompose the individual antenna responses into separate responses pertaining to each target, then associate like targets between antenna responses. By performing the correlation in this manner, the crossterms, which typically corrupt the interferometric response, are mitigated. It was found that due to the quadratic scaling of distortion terms, modelbased methods become exceedingly difficult as the number of targets grows large. Thus, the method of response decomposition is selected and results on measured radar signals are presented. For this, a custom singleboard millimeterwave interferometric radar was developed, and angular velocity measurements were performed in an enclosed environment consisting of two robotic targets. A set of experiments was designed to highlight easy, medium, and difficult cases for the response decomposition algorithm, and results are presented herein.
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 Title
 LIDAR AND CAMERA CALIBRATION USING A MOUNTED SPHERE
 Creator
 Li, Jiajia
 Date
 2020
 Collection
 Electronic Theses & Dissertations
 Description

Extrinsic calibration between lidar and camera sensors is needed for multimodal sensor data fusion. However, obtaining precise extrinsic calibration can be tedious, computationally expensive, or involve elaborate apparatus. This thesis proposes a simple, fast, and robust method performing extrinsic calibration between a camera and lidar. The only required calibration target is a handheld colored sphere mounted on a whiteboard. The convolutional neural networks are developed to automatically...
Show moreExtrinsic calibration between lidar and camera sensors is needed for multimodal sensor data fusion. However, obtaining precise extrinsic calibration can be tedious, computationally expensive, or involve elaborate apparatus. This thesis proposes a simple, fast, and robust method performing extrinsic calibration between a camera and lidar. The only required calibration target is a handheld colored sphere mounted on a whiteboard. The convolutional neural networks are developed to automatically localize the sphere relative to the camera and the lidar. Then using the localization covariance models, the relative pose between the camera and lidar is derived. To evaluate the accuracy of our method, we record image and lidar data of a sphere at a set of known grid positions by using two rails mounted on a wall. The accurate calibration results are demonstrated by projecting the grid centers into the camera image plane and finding the error between these points and the handlabeled sphere centers.
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 Title
 Harnessing lowpass filter defects for improving wireless link performance : measurements and applications
 Creator
 Renani, Alireza Ameli
 Date
 2018
 Collection
 Electronic Theses & Dissertations
 Description

"The design tradeoffs of transceiver hardware are crucial to the performance of wireless systems. The effect of such tradeoffs on individual analog and digital components are vigorously studied, but their systemic impacts beyond componentlevel remain largely unexplored. In this dissertation, we present an indepth study to characterize the surprisingly notable systemic impacts of lowpass filter design, which is a small yet indispensable component used for shaping spectrum and rejecting...
Show more"The design tradeoffs of transceiver hardware are crucial to the performance of wireless systems. The effect of such tradeoffs on individual analog and digital components are vigorously studied, but their systemic impacts beyond componentlevel remain largely unexplored. In this dissertation, we present an indepth study to characterize the surprisingly notable systemic impacts of lowpass filter design, which is a small yet indispensable component used for shaping spectrum and rejecting interference. Using a bottomup approach, we examine how signallevel distortions caused by the tradeoffs of lowpass filter design propagate to the upperlayers of wireless communication, reshaping bit error patterns and degrading link performance of today's 802.11 systems. Moreover, we propose a novel unequal error protection algorithm that harnesses lowpass filter defects for improving wireless LAN throughput, particularly to be used in forward error correction, channel coding, and applications such as video streaming. Lastly, we conduct experiments to evaluate the unequal error protection algorithm in video streaming, and we present substantial enhancements of video quality in mobile environments."Page ii.
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 Title
 Higherorder 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 higherorder 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 higherorder datasets and provide a way to analyze them by preserving the multilinear relations in these large datasets. These higherorder datasets usually contain large amount of redundant...
Show more"With the recent advances in information technology, collection and storage of higherorder 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 higherorder datasets and provide a way to analyze them by preserving the multilinear relations in these large datasets. These higherorder 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 vectortype 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 multigraph 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 informationtheoretic 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 lowrank+sparse structure learning algorithm for tensors to separate the lowrank community structure of connectivity networks from sparse outliers. The proposed framework is used to both identify change points, where the lowrank community structure changes significantly, and summarize this community structure within each time interval. Finally, in Chapter 4, we introduce a new multiscale tensor decomposition technique to efficiently encode nonlinearities due to rotation or translation in tensor type data. In particular, we develop a multiscale higherorder singular value decomposition (MSHoSVD) approach where a given tensor is first permuted and then partitioned into several subtensors each of which can be represented as a lowrank 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 higherorder datasets."Pages iiiii.
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 Title
 Smartphonebased sensing systems for dataintensive applications
 Creator
 Moazzami, MohammadMahdi
 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 datadriven approach to the system design. To this end, several challenges must be addressed before smartphones can be used as a system platform for dataintensive applications. The major challenge addressed in this dissertation include high power consumption, high computation cost in advance machine learning algorithms, lack of realtime 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 offtheshelf machine learning algorithms for realtime 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 realtime dataintensive processing. From application perspective we present SPOT. SPOT aims to address some of the challenges discovered in mobilebased smarthome systems. These challenges prevent us from achieving the promises of smarthomes due to heterogeneity in different aspects of smart devices and the underlining systems. We face the following major heterogeneities in building smarthomes:: (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 appliancespecific apps and control interfaces. From algorithmic perspective we introduce two systems in the smartphonebased deep learning area: DeepCrowdLabel and DeepPartition. 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 realtime sensing applications used in the wild. DeepPartition is an optimizationbased partitioning metaalgorithm featuring a tiered architecture for smartphone and the backend cloud. DeepPartition provides a profilebased 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 feedforward latency. DeepCrowdLabel is prototyped for semantically labeling user's location. It is a crowdassisted algorithm that uses crowdsourcing in both training and inference time. It builds deep convolutional neural models using crowdsensed 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 datadriven and computeintensive smartphonebased systems: platforms, applications and algorithms; and helps to spurs new areas of research and opens up new directions in mobile computing research."Pages iiiii.
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 Title
 Assessment of functional connectivity in the human brain : multivariate and graph signal processing methods
 Creator
 VillafañeDelgado, 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 iiiii.
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 Title
 Convolutional neural networks for automated cell detection in magnetic resonance imaging data
 Creator
 Afridi, Muhammad Jamal
 Date
 2017
 Collection
 Electronic Theses & Dissertations
 Description

Cellbased therapy (CBT) is emerging as a promising solution for a large number of serious health issues such as brain injuries and cancer. Recent advances in CBT, has heightened interest in the noninvasive monitoring of transplanted cells in in vivo MRI (Magnetic Resonance Imaging) data. These cells appear as dark spots in MRI scans. However, to date, these spots are manually labeled by experts, which is an extremely tedious and a time consuming process. This limits the ability to conduct...
Show moreCellbased therapy (CBT) is emerging as a promising solution for a large number of serious health issues such as brain injuries and cancer. Recent advances in CBT, has heightened interest in the noninvasive monitoring of transplanted cells in in vivo MRI (Magnetic Resonance Imaging) data. These cells appear as dark spots in MRI scans. However, to date, these spots are manually labeled by experts, which is an extremely tedious and a time consuming process. This limits the ability to conduct large scale spot analysis that is necessary for the long term success of CBT. To address this gap, we develop methods to automate the spot detection task. In this regard we (a) assemble an annotated MRI database for spot detection in MRI; (b) present a superpixel based strategy to extract regions of interest from MRI; (c) design a convolutional neural network (CNN) architecture for automatically characterizing and classifying spots in MRI; (d) propose a transfer learning approach to circumvent the issue of limited training data, and (e) propose a new CNN framework that exploits labeling behavior of the expert in the learning process. Extensive experiments convey the benefits of the proposed methods.
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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 longstanding computer vision problem. Traditional photometric stereobased 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 fieldbased 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 analysisbysynthesis photometric stereo formulation to complete the fine face details. A structural similaritybased quality measure allows evaluation in the absence of ground truth 3D scans. Superior largescale 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) Noninvasive 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) Noninvasive 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 nonidealities, 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 breathingrateand lungvolume using multiple noninvasive 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 information. 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 waveletadaptive Gini (or WAGini) algorithm, it employs a novel wavelet transform 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
 Highdimensional 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 pointwise and periodic schemes for signal sampling. In particular, the wellknown 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 pointwise and periodic schemes for signal sampling. In particular, the wellknown 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 illposed problem and is not wellstudied. 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 highdimensional signal classification. Prior efforts have shown that projecting highdimensional and redundant signal vectors onto random lowdimensional 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 highdimensional 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 highdimensional 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
 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 neuronbased circuit.The proposed architecture goal is to implement approximate nonlinear 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 integrateandfire neuronbased circuits that implement the margin propagation algorithm using integration and threshold operations embedded in the transfer function of the neuron model. The integrateandfire neuron units in the network are connected together through excitatory and inhibitory paths to impose constraints on the network firingrate. The advantages of the pulsebased, integrateandfire margin propagation (IFMP) algorithmic unit are to implement complex nonlinear 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 winnertakeall algorithm, which is important for hearing, sight and touch sensor systems.
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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 SemiMarkov 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 signaltonoise 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
 Tracking singleunits in chronic neural recordings for brain machine interface applications
 Creator
 Eleryan, Ahmed Ibrahim
 Date
 2013
 Collection
 Electronic Theses & Dissertations
 Description

Ensemble recording of multiple singleunit 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 singleunits 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 singleunit 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 singleunits 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 singleunits recorded in nonhuman 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
 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 microscale integration promises significant breakthroughs is in the field of acoustic sensing. However, separation, localization, and recognition of acoustic sources using microscale 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 microscale integration promises significant breakthroughs is in the field of acoustic sensing. However, separation, localization, and recognition of acoustic sources using microscale 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 noiserobust acoustic target recognition using miniature microphone arrays.The first part of this research focuses on designing "smart" analogtodigital conversion (ADC) algorithms that can enhance acoustic cues in subwavelength microphone arrays. Many source separation algorithms fail to deliver robust performance when applied to signals recorded using highdensity 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 analogtodigital conversion) of the sensor which is insufficientto overcome the artifacts due to large crosschannel redundancy, nonhomogeneous mixing and highdimensionality of the signal space. We propose a novel framework that overcomes these limitations by integrating statistical learning directly with the signal measurement (analogtodigital) process which enables high fidelity separation of linear instantaneous mixture. At the core of the proposed ADC approach is a minmax 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 analogtodigital frontend 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 noiserobust information in speech signal is embedded in a subspace spanned by sparse, regularized, overcomplete, nonlinear, and phaseshifted gammatone basis functions. The feature extraction algorithm involves computing kernel functions between the speech data and precomputed set of phasedshifted 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 noiserobustness 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 Melfrequency cepstral coefficients (MFCCs) based benchmark.
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 Title
 Exploiting node mobility for energy optimization in wireless sensor networks
 Creator
 ElMoukaddem, Fatme Mohammad
 Date
 2012
 Collection
 Electronic Theses & Dissertations
 Description

Wireless Sensor Networks (WSNs) have become increasingly available for dataintensive applications such as microclimate monitoring, precision agriculture, and audio/video surveillance. A key challenge faced by dataintensive WSNs is to transmit the sheer amount of data generated within an application's lifetime to the base station despite the fact that sensor nodes have limited power supplies such as batteries or small solar panels. The availability of numerous lowcost robotic units (e.g....
Show moreWireless Sensor Networks (WSNs) have become increasingly available for dataintensive applications such as microclimate monitoring, precision agriculture, and audio/video surveillance. A key challenge faced by dataintensive WSNs is to transmit the sheer amount of data generated within an application's lifetime to the base station despite the fact that sensor nodes have limited power supplies such as batteries or small solar panels. The availability of numerous lowcost robotic units (e.g. Robomote and Khepera) has made it possible to construct sensor networks consisting of mobile sensor nodes. It has been shown that the controlled mobility offered by mobile sensors can be exploited to improve the energy efficiency of a network.In this thesis, we propose schemes that use mobile sensor nodes to reduce the energy consumption of dataintensive WSNs. Our approaches differ from previous work in two main aspects. First, our approaches do not require complex motion planning of mobile nodes, and hence can be implemented on a number of lowcost mobile sensor platforms. Second, we integrate the energy consumption due to both mobility and wireless communications into a holistic optimization framework.We consider three problems arising from the limited energy in the sensor nodes. In the first problem, the network consists of mostly static nodes and contains only a few mobile nodes. In the second and third problems, we assume essentially that all nodes in the WSN are mobile. We first study a new problem called maxdata mobile relay configuration (MMRC) that finds the positions of a set of mobile sensors, referred to as relays, that maximize the total amount of data gathered by the network during its lifetime. We show that the MMRC problem is surprisingly complex even for a trivial network topology due to the joint consideration of the energy consumption of both wireless communication and mechanical locomotion. We present optimal MMRC algorithms and practical distributed implementations for several important network topologies and applications. Second, we consider the problem of minimizing the total energy consumption of a network. We design an iterative algorithm that improves a given configuration by relocating nodes to new positions. We show that this algorithm converges to the optimal configuration for the given transmission routes. Moreover, we propose an efficient distributed implementation that does not require explicit synchronization. Finally, we consider the problem of maximizing the lifetime of the network. We propose an approach that exploits the mobility of the nodes to balance the energy consumption throughout the network. We develop efficient algorithms for single and multiple round approaches. For all three problems, we evaluate the efficiency of our algorithms through simulations. Our simulation results based on realistic energy models obtained from existing mobile and static sensor platforms show that our approaches significantly improve the network's performance and outperform existing approaches.
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 Title
 A multivariate timefrequency based phase synchrony measure and applications to dynamic brain network analysis
 Creator
 Mutlu, Ali Yener
 Date
 2012
 Collection
 Electronic Theses & Dissertations
 Description

Irregular, nonstationary, 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, nonstationary, 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 nonstationary reciprocal interactions are the key features of functional integration. Among many linear and nonlinear measures of dependency, timevarying phase synchrony has been proposed as a promising measure of connectivity. Current stateoftheart in timevarying 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 nonuniform timefrequency 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 timefrequency 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 crossfrequency phase synchronization between two signals across different frequencies. In addition, a cross frequencyspectral 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 timevarying 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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 Title
 Directed information for complex network analysis from multivariate time series
 Creator
 Liu, Ying
 Date
 2012
 Collection
 Electronic Theses & Dissertations
 Description

Complex networks, ranging from gene regulatory networks in biology to social networks in sociology, havereceived growing attention from the scientific community. The analysis of complex networks employs techniquesfrom graph theory, machine learning and signal processing. In recent years, complex network analysis tools havebeen applied to neuroscience and neuroimaging studies to have a better understanding of the human brain. In thisthesis, we focus on inferring and analyzing the complex...
Show moreComplex networks, ranging from gene regulatory networks in biology to social networks in sociology, havereceived growing attention from the scientific community. The analysis of complex networks employs techniquesfrom graph theory, machine learning and signal processing. In recent years, complex network analysis tools havebeen applied to neuroscience and neuroimaging studies to have a better understanding of the human brain. In thisthesis, we focus on inferring and analyzing the complex functional brain networks underlying multichannelelectroencephalogram (EEG) recordings. Understanding this complex network requires the development of a measureto quantify the relationship between multivariate time series, algorithms to reconstruct the network based on thepairwise relationships, and identification of functional modules within the network.Functional and effective connectivity are two widely studiedapproaches to quantify the connectivity between two recordings.Unlike functional connectivity which only quantifies the statisticaldependencies between two processes by measures such as crosscorrelation, phase synchrony, and mutual information (MI), effectiveconnectivity quantifies the influence one node exerts on anothernode. Directed information (DI) measure is one of the approachesthat has been recently proposed to capture the causal relationshipsbetween two time series. Two major challenges remain with theapplication of DI to multivariate data, which include thecomputational complexity of computing DI with increasing signallength and the accuracy of estimation from limited realizations ofthe data. Expressions that can simplify the computation of theoriginal definition of DI while still quantifying the causalityrelationship are needed. In addition, the advantage of DI overconventionally causality measures such as Granger causality has notbeen fully investigated. In this thesis, we propose timelaggeddirected information and modified directed information to addressthe issue of computational complexity, and compare the performanceof this model free measure with model based measures (e.g. Grangercausality) for different realistic signal models.Once the pairwise DI between two random processes is computed,another problem is to infer the underlying structure of the complexnetwork with minimal false positive detection. We propose to useconditional directed information (CDI) proposed by Kramer to addressthis issue, and introduce the timelagged conditional directedinformation and modified conditional directed information to lowerthe computational complexity of CDI. Three network inferencealgorithms are presented to infer directed acyclic networks whichcan quantify the causality and also detect the indirect couplingssimultaneously from multivariate data.One last challenge in the study of complex networks, specifically in neuroscience applications, is to identifythe functional modules from multichannel, multiple subject recordings. Most research on community detection inthis area so far has focused on finding the association matrix based on functional connectivity, instead ofeffective connectivity, thus not capturing the causality in the network. In addition, in order to find a modularstructure that best describes all of the subjects in a group, a group analysis strategy is needed. In thisthesis, we propose a multisubject hierarchical community detection algorithm suitable for a group of weightedand asymmetric (directed) networks representing effective connectivity, and apply the algorithm to multichannelelectroencephalogram (EEG) data.
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 Title
 Biometric template security
 Creator
 Nagar, Abhishek
 Date
 2012
 Collection
 Electronic Theses & Dissertations
 Description

"This dissertation provides a thorough analysis of the vulnerabilities of a biometric recognition system with emphasis on the vulnerabilities related to the information stored in biometric systems in the form of biometric templates."From abstract.
 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, nondestructive 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 noniterative, 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, nondestructive 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 noniterative, 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 broadsidesteered linear array is shown to decrease by approximately 50% with the reduced array beam pattern closely approximating the initial array beam pattern in the farfield. While the method returns a slightly tapered spacing between elements, for the arrays considered, replacing the tapered spacing with a suitablyselected uniform spacing provides very little change in the main beam and lowangle side lobes.
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 Title
 Theory, synthesis and implementation of currentmode CMOS piecewiselinear circuits using margin propagation
 Creator
 Gu, Ming
 Date
 2012
 Collection
 Electronic Theses & Dissertations
 Description

Achieving high energyefficiency is a key requirement for many emerging smart sensors and portable computing systems. While digital signal processing (DSP) has been the defacto technique for implementing ultralow 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 topdown design approach, where proven mathematical...
Show moreAchieving high energyefficiency is a key requirement for many emerging smart sensors and portable computing systems. While digital signal processing (DSP) has been the defacto technique for implementing ultralow 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 topdown 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 stateoftheart ASP technique, that uses the exponential currenttovoltage characteristics for designing ultralowpower analog processors. However, elegant formulations could result from a bottomup 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 piecewiselinear (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 MPbased processors when implemented using CMOS currentmode 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 highperformance analog lowdensity parity check (LDPC) decoders for applications in sensor networks; and (b) design of ultralowpower 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 tradeoff its BER performance with its energy efficiency, making the design attractive for applications with adaptive energyBER constraints. We have verified this tradeoff using an analog currentmode implementation of an MPbased (32,8) LDPC decoder. Measured results from prototypes fabricated in a 0.5 μm CMOS process show that the BER performance of the MPbased decoder outperforms a benchmark stateoftheart minsum 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 energyscalable support vector machine (SVM) whose power and speed requirements can be configured dynamically without any degradation in performance. We have verified the energyscaling property using a currentmode 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 currentinput currentoutput 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 floatinggate memory that demonstrates a superior programming range than other temperature compensated floatinggate memories.
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