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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
 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
 Privacy and integrity preserving computation in distributed systems
 Creator
 Chen, Fei
 Date
 2011
 Collection
 Electronic Theses & Dissertations
 Description

Preserving privacy and integrity of private data has become core requirements for many distributed systems across different parties. In these systems, one party may try to compute or aggregate useful information from the private data of other parties. However, this party is not be fully trusted by other parties. Therefore, it is important to design security protocols for preserving such private data. Furthermore, one party may want to query the useful information computed from such private...
Show morePreserving privacy and integrity of private data has become core requirements for many distributed systems across different parties. In these systems, one party may try to compute or aggregate useful information from the private data of other parties. However, this party is not be fully trusted by other parties. Therefore, it is important to design security protocols for preserving such private data. Furthermore, one party may want to query the useful information computed from such private data. However, query results may be modified by a malicious party. Thus, it is important to design query protocols such that query result integrity can be verified.In this dissertation, we study four important privacy and integrity preserving problems for different distributed systems. For twotiered sensor networks, where storage nodes serve as an intermediate tier between sensors and a sink for storing data and processing queries, we proposed SafeQ, a protocol that prevents compromised storage nodes from gaining information from both sensor collected data and sink issued queries, while it still allows storage nodes to process queries over encrypted data and the sink to detect compromised storage nodes when they misbehave. For cloud computing, where a cloud provider hosts the data of an organization and replies query results to the customers of the organization, we propose novel privacy and integrity preserving schemes for multidimensional range queries such that the cloud provider can process encoded queries over encoded data without knowing the actual values, and customers can verify the integrity of query results with high probability. For distributed firewall policies, we proposed the first privacypreserving protocol for crossdomain firewall policy optimization. For any two adjacent firewalls belonging to two different administrative domains, our protocol can identify in each firewall the rules that can be removed because of the other firewall. For network reachability, one of the key factors for capturing endtoend network behavior and detecting the violation of security policies, we proposed the first crossdomain privacypreserving protocol for quantifying network reachability.
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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
 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
 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
 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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 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
 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
 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
 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
 MEASURING AND MODELING THE EFFECTS OF SEA LEVEL RISE ON NEARCOASTAL RIVERINE REGIONS : A GEOSPATIAL COMPARISON OF THE SHATT ALARAB 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 nearcoastal 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 nearcoastal 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 AlArab River (SAR) and the southern United States in Louisiana along the Mississippi River Delta (MRD). These sites are important for both their extensive lowlying 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 welldocumented 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 ArcSecond 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 atrisk regions. Chapter five applies MRDRbased model from chapter four to enhance the SRTM 1 ArcSecond 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 alArab 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 meanzero 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
 Graphbased methods for inferring neuronal connectivity from spike train ensembles
 Creator
 Eldawlatly, Seif Eldin
 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, whiskerspecific 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 biologicallyplausible models of spiketimingdependentplasticity as well as changes in S1 response maps following sensory deprivation in the awake, behaving rat.
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