Browsing by Author "Nolte, Loren W"

While molecular and cellular processes are often modeled as stochastic processes, such as Brownian motion, chemical reaction networks and gene regulatory networks, there are few attempts to program a molecular-scale process to physically implement stochastic processes. DNA has been used as a substrate for programming molecular interactions, but its applications are restricted to deterministic functions and unfavorable properties such as slow processing, thermal annealing, aqueous solvents and difficult readout limit them to proof-of-concept purposes. To date, whether there exists a molecular process that can be programmed to implement stochastic processes for practical applications remains unknown.

In this dissertation, a fully specified Resonance Energy Transfer (RET) network between chromophores is accurately fabricated via DNA self-assembly, and the exciton dynamics in the RET network physically implement a stochastic process, specifically a continuous-time Markov chain (CTMC), which has a direct mapping to the physical geometry of the chromophore network. Excited by a light source, a RET network generates random samples in the temporal domain in the form of fluorescence photons which can be detected by a photon detector. The intrinsic sampling distribution of a RET network is derived as a phase-type distribution configured by its CTMC model. The conclusion is that the exciton dynamics in a RET network implement a general and important class of stochastic processes that can be directly and accurately programmed and used for practical applications of photonics and optoelectronics. Different approaches to using RET networks exist with vast potential applications. As an entropy source that can directly generate samples from virtually arbitrary distributions, RET networks can benefit applications that rely on generating random samples such as 1) fluorescent taggants and 2) stochastic computing.

By using RET networks between chromophores to implement fluorescent taggants with temporally coded signatures, the taggant design is not constrained by resolvable dyes and has a significantly larger coding capacity than spectrally or lifetime coded fluorescent taggants. Meanwhile, the taggant detection process becomes highly efficient, and the Maximum Likelihood Estimation (MLE) based taggant identification guarantees high accuracy even with only a few hundred detected photons.

Meanwhile, RET-based sampling units (RSU) can be constructed to accelerate probabilistic algorithms for wide applications in machine learning and data analytics. Because probabilistic algorithms often rely on iteratively sampling from parameterized distributions, they can be inefficient in practice on the deterministic hardware traditional computers use, especially for high-dimensional and complex problems. As an efficient universal sampling unit, the proposed RSU can be integrated into a processor / GPU as specialized functional units or organized as a discrete accelerator to bring substantial speedups and power savings.

Electromagnetic scattering in a layered medium (LM) is important for many engineering applications, including the hydrocarbon exploration. Various computational methods for tackling well logging simulations are summarized. Given their advantages and limitations, main attention is devoted to the surface integral equation (SIE) and its hybridization with the finite element method (FEM).

The thin dielectric sheet (TDS) based SIE, i.e., TDS-SIE, is introduced to the simulation of fractures. Its accuracy and efficiency are extensively demonstrated by simulating both conductive and resistive fractures. Fractures of variant apertures, conductivities, dipping angles, and extensions are also simulated and analyzed. With the aid of layered medium Green's functions (LMGFs), TDS-SIE is extended into the LM, which results in the solver entitled LM-TDS-SIE.

In order to consider the borehole effect, the well-known loop and tree basis functions are utilized to overcome low-frequency breakdown of the Poggio, Miller, Chang, Harrington, Wu, and Tsai (PMCHWT) formulation. This leads to the loop-tree (LT) enhanced PMCHWT, which can be hybridized with TDS-SIE to simulate borehole and fracture together. The resultant solver referred to as LT-TDS is further extended into the LM, which leads to the solver entitled LM-LT-TDS.

For inhomogeneous or complex structures, SIE is not suitable for their scattering simulations. It becomes advantageous to hybridize FEM with SIE in the framework of domain decomposition method (DDM), which allows independent treatment of each subdomain and nonconformal meshes between them. This hybridization can be substantially enhanced by the adoption of LMGFs and loop-tree bases, leading to the solver entitled LM-LT-DDM. In comparison with LM-LT-TDS, this solver is more powerful and able to handle more general low-frequency scattering problems in layered media.

Brain-computer interfaces (BCI) have the potential to restore communication or control abilities in individuals with severe neuromuscular limitations, such as those with amyotrophic lateral sclerosis (ALS). The role of a BCI is to extract and decode relevant information that conveys a user's intent directly from brain electro-physiological signals and translate this information into executable commands to control external devices. However, the BCI decision-making process is error-prone due to noisy electro-physiological data, representing the classic problem of efficiently transmitting and receiving information via a noisy communication channel.

This research focuses on P300-based BCIs which rely predominantly on event-related potentials (ERP) that are elicited as a function of a user's uncertainty regarding stimulus events, in either an acoustic or a visual oddball recognition task. The P300-based BCI system enables users to communicate messages from a set of choices by selecting a target character or icon that conveys a desired intent or action. P300-based BCIs have been widely researched as a communication alternative, especially in individuals with ALS who represent a target BCI user population. For the P300-based BCI, repeated data measurements are required to enhance the low signal-to-noise ratio of the elicited ERPs embedded in electroencephalography (EEG) data, in order to improve the accuracy of the target character estimation process. As a result, BCIs have relatively slower speeds when compared to other commercial assistive communication devices, and this limits BCI adoption by their target user population. The goal of this research is to develop algorithms that take into account the physical limitations of the target BCI population to improve the efficiency of ERP-based spellers for real-world communication.

In this work, it is hypothesised that building adaptive capabilities into the BCI framework can potentially give the BCI system the flexibility to improve performance by adjusting system parameters in response to changing user inputs. The research in this work addresses three potential areas for improvement within the P300 speller framework: information optimisation, target character estimation and error correction. The visual interface and its operation control the method by which the ERPs are elicited through the presentation of stimulus events. The parameters of the stimulus presentation paradigm can be modified to modulate and enhance the elicited ERPs. A new stimulus presentation paradigm is developed in order to maximise the information content that is presented to the user by tuning stimulus paradigm parameters to positively affect performance. Internally, the BCI system determines the amount of data to collect and the method by which these data are processed to estimate the user's target character. Algorithms that exploit language information are developed to enhance the target character estimation process and to correct erroneous BCI selections. In addition, a new model-based method to predict BCI performance is developed, an approach which is independent of stimulus presentation paradigm and accounts for dynamic data collection. The studies presented in this work provide evidence that the proposed methods for incorporating adaptive strategies in the three areas have the potential to significantly improve BCI communication rates, and the proposed method for predicting BCI performance provides a reliable means to pre-assess BCI performance without extensive online testing.

This thesis presents theoretical studies of some stochastic processes and their appli- cations in the Bayesian nonparametric methods. The stochastic processes discussed in the thesis are mainly the ones with independent increments - the Levy processes. We develop new representations for the Levy measures of two representative exam- ples of the Levy processes, the beta and gamma processes. These representations are manifested in terms of an infinite sum of well-behaved (proper) beta and gamma dis- tributions, with the truncation and posterior analyses provided. The decompositions provide new insights into the beta and gamma processes (and their generalizations), and we demonstrate how the proposed representation unifies some properties of the two, as these are of increasing importance in machine learning.

Next a new Levy process is proposed for an uncountable collection of covariate- dependent feature-learning measures; the process is called the kernel beta process. Available covariates are handled efficiently via the kernel construction, with covari- ates assumed observed with each data sample ("customer"), and latent covariates learned for each feature ("dish"). The dependencies among the data are represented with the covariate-parameterized kernel function. The beta process is recovered as a limiting case of the kernel beta process. An efficient Gibbs sampler is developed for computations, and state-of-the-art results are presented for image processing and music analysis tasks.

Last is a non-Levy process example of the multiplicative gamma process applied in the low-rank representation of tensors. The multiplicative gamma process is applied along the super-diagonal of tensors in the rank decomposition, with its shrinkage property nonparametrically learns the rank from the multiway data. This model is constructed as conjugate for the continuous multiway data case. For the non- conjugate binary multiway data, the Polya-Gamma auxiliary variable is sampled to elicit closed-form Gibbs sampling updates. This rank decomposition of tensors driven by the multiplicative gamma process yields state-of-art performance on various synthetic and benchmark real-world datasets, with desirable model scalability.

In this dissertation, novel uses of millimeter-wave (mmW) radars are developed and analyzed. While automotive mmW radars have been ubiquitous in advanced driver assistance systems (ADAS), their ability to sense motions at sub-millimeter scale allows them to also find application in systems that require accurate measurements of surface vibrations. While laser Doppler vibrometers (LDVs) are routinely used to measure such vibrations, the lower size, weight, power, and cost (SWAPc) of mmW radars make vibrometry viable for a variety of new applications. In this work, we consider two such applications: everything-to-vehicle (X2V) wireless communications and non-acoustic human speech analysis.

Within this dissertation, a wireless communication system that uses the radar as a vibrometer is introduced. This system, termed vibrational radar backscatter communications (VRBC), receives messages by observing phase modulations on the radar signal that are caused by vibrations on the surface of a transponder over time. It is shown that this form of wireless communication provides the ability to simultaneously detect, isolate, and decode messages from multiple sources thanks to the spatial resolution of the radar. Additionally, VRBC requires no RF emission on the end of the transponder. Since automotive radars and the conventional X2V solutions are often at odds for spectrum allocations, this characteristic of VRBC is incredibly valuable.

Using an off-the-shelf, resonant transponder, a real VRBC data collection is presented and used to demonstrate the signal processing techniques necessary to decode a VRBC message. This real data collection proves to achieve a data rate just under 100 bps at approximately 5 meters distance. Rates of this scale can provide warning messages or concise situational awareness information in applications such as X2V, but naturally higher rates are desirable. For that reason, this dissertation includes discussion on how to design a more optimal VRBC system via transponder design, messaging scheme choice, and using any afforded flexibility in radar parameter choice.

Through the use of an analytical upper bound on VRBC rate and simulation results, we see that rates closer to 1 kbps should be achievable for a transponder approximately the size of a license plate at ranges under 200 meters. The added benefits of requiring no RF spectrum or network scheduling protocols uniquely positions VRBC as a desirable solution in spaces like X2V over commonly considered, higher rate solutions such as direct short range communications (DSRC).

Upon implementing a VRBC system, a handful of complications were encountered. This document designates a full chapter to solving these cases. This includes properly modeling intersymbol interference caused by resonant surfaces and utilizing sequence detection methods rather than single symbol maximum likelihood methods to improve detection in these cases. Additionally, an analysis on what an ideal clutter filter should look like and how it can begin to be achieved is presented. Lastly, a method for mitigating platform vibrational noise at both the radar and the transponder are presented. Using these methods, message detection errors are better avoided, though more optimal system design fundamentally proves to limit what rates are achievable.

Towards non-acoustic human speech analysis, it is shown in this dissertation that the vibrations of a person's throat during speech generation can be accurately captured using a mmW radar. These measurements prove to be similar to those achieved by the more expensive vibrometry alternative of an LDV with less than 10 dB of SNR depreciation at the first two speech harmonics in the signal's spectrogram. Furthermore, we find that mmW radar vibrometry data resembles a low-pass filtered version of its corresponding acoustic data. We show that this type of data achieves 53% performance in a speaker identification system as opposed to 11\% in a speech recognition system. This performance suggests potential for a mmW radar vibrometry in context-blind speaker identification systems if the performance of the speaker identification system can be further improved without causing the context of the speech more recognizable.

In this dissertation, mmW radar vibrational returns are modelled and signal processing chains are provided to allow for these vibrations to be estimated and used in application. In many cases, the work outlined could be used in other areas of mmW radar vibrometry even though it was originally motivated by potentially unrelated applications. It is the hope of this dissertation that the provided models, signal processing methods, visualizations, analytical bound, and results not only justify mmW radar in human speech analysis and backscatter communications, but that they also contribute to the community's understanding of how certain vibrational movements can be best observed, processed, and made useful more broadly.

Automated acoustic sensing systems are required to detect, classify and localize acoustic signals in real-time. Despite the fact that humans are capable of performing acoustic sensing tasks with ease in a variety of situations, the performance of current automated acoustic sensing algorithms is limited by seemingly benign changes in environmental or operating conditions. In this work, a framework for acoustic surveillance that is capable of accounting for changing environmental and operational conditions, is developed and analyzed. The algorithms employed in this work utilize non-stationary and nonparametric Bayesian inference techniques to allow the resulting framework to adapt to varying background signals and allow the system to characterize new signals of interest when additional information is available. The performance of each of the two stages of the framework is compared to existing techniques and superior performance of the proposed methodology is demonstrated. The algorithms developed operate on the time-domain acoustic signals in a nonparametric manner, thus enabling them to operate on other types of time-series data without the need to perform application specific tuning. This is demonstrated in this work as the developed models are successfully applied, without alteration, to landmine signatures resulting from ground penetrating radar data. The nonparametric statistical models developed in this work for the characterization of acoustic signals may ultimately be useful not only in acoustic surveillance but also other topics within acoustic sensing.

This dissertation presents methods for calibrating and beamforming a distributed array for detecting and localizing sources of interest using passive sonar. Passive sonar is critical for underwater acoustic surveillance, marine life tracking, and environmental monitoring but is increasingly difficult with greater shipping traffic and other man-made noise sources. Large aperture hydrophone arrays are needed to suppress these sources of interference and find weak targets of interest. Traditionally, large hydrophone arrays are densely sampled uniform arrays which are expensive and time-consuming to deploy and maintain. There is growing interest instead in forming distributed arrays out of low-cost, individually small arrays which are coherently processed to achieve high gain and resolution. Conventional array processing methods are not well suited to this end and this dissertation develops new methods for array calibration and beamforming which ultimately enable high resolution passive sonar at low-cost. This work develops estimation methods for array parameters in uncalibrated, unsynchronized collections of acoustic sensors and also develops adaptive beamforming techniques on such arrays in complex and uncertain ocean environments.

Methods for estimating sampling rate offset (SRO) are developed using a single narrowband source of opportunity whose parameters need not be estimated. A search-free method which jointly estimates all SRO parameters in an acoustic sensor network is presented and shown to improve as the network size increases. A second SRO estimation method is developed for unsynchronized sub-arrays to enable SRO estimation with a source that has a bearing rate. This is of particular value in ocean environments where transiting cargo ships are the most prevalent calibration sources.

Next, a technique for continuously estimating multiple sub-array positions using a single, tonal moving source is presented. Identical, well-calibrated sub-arrays with unknown relative positions exhibit a rotational invariance in the signal structure which is exploited to blindly estimate the inter-array spatial wavefronts. These wavefront measurements are used in an Unscented Kalman Filter (UKF) to continuously improve sub-array position estimates.

Lastly, this work addresses adaptive beamforming in uncertain, complex propagation environments where the modeled wavefronts will inevitably not match the true wavefronts. Adaptive beamforming techniques are developed which maintain gain even with significant signal mismatch due to unknown or uncertain source wavefronts by estimating a target-free covariance matrix from the received data and using just a single gain constraint in the beamformer optimization. Target-free covariances are estimated using an eigendecomposition of the received data and assuming that modes which potentially contain sources of interest can be identified. This method is applied to a distributed array where only part of the array wavefront is explicitly modeled and shown to improve interference suppression and the output signal-to-interference-plus-noise ratio (SINR).

This idea is then extended to realistic environments and a method for finding potential target components is developed. Blind source separation (BSS) methods using second-order statistics are adopted for wideband source separation in shallow-water environments. BSS components are associated with either target or interference based on their received temporal spectra and are automatically labeled with a convolutional neural network (CNN). This method is applicable when sources have overlapping but distinct transmitted spectra, but also when the channel itself colors the received spectra due to range-dependent frequency-selective fading. Simulations in realistic shallow-water environments demonstrate the ability to blindly separate and label uncorrelated components based on frequency-selective fading patterns. These simulations then validate the robustness of the developed wavefront adaptive sensing (WAS) beamformer compared to a standard minimum variance distortionless response (MVDR) beamformer. Finally, this method is demonstrated using real shallow-water data from the SWellEx96 S59 experiment off the coast of Southern California. A simulated target is injected into this data and masked behind a loud towed source. It is shown that the WAS beamformer is able to suppress the towed source and achieve an target output SINR which is close to that of the optimal beamformer.

Breast cancer screening is currently performed by mammography, which is limited by overlying anatomy and dense breast tissue. Computer aided detection (CADe) systems can serve as a double reader to improve radiologist performance. Tomosynthesis is a limited-angle cone-beam x-ray imaging modality that is currently being investigated to overcome mammography's limitations. CADe systems will play a crucial role to enhance workflow and performance for breast tomosynthesis.

The purpose of this work was to develop unique CADe algorithms for breast tomosynthesis reconstructed volumes. Unlike traditional CADe algorithms which rely on segmentation followed by feature extraction, selection and merging, this dissertation instead adopts information theory principles which are more robust. Information theory relies entirely on the statistical properties of an image and makes no assumptions about underlying distributions and is thus advantageous for smaller datasets such those currently used for all tomosynthesis CADe studies.

The proposed algorithm has two 2 stages (1) initial candidate generation of suspicious locations (2) false positive reduction. Images were accrued from 250 human subjects. In the first stage, initial suspicious locations were first isolated in the 25 projection images per subject acquired by the tomosynthesis system. Only these suspicious locations were reconstructed to yield 3D Volumes of Interest (VOI). For the second stage of the algorithm false positive reduction was then done in three ways: (1) using only the central slice of the VOI containing the largest cross-section of the mass, (2) using the entire volume, and (3) making decisions on a per slice basis and then combining those decisions using either a linear discriminant or decision fusion. A 92% sensitivity was achieved by all three approaches with 4.4 FPs / volume for approach 1, 3.9 for the second approach and 2.5 for the slice-by-slice based algorithm using decision fusion.

We have therefore developed a novel CADe algorithm for breast tomosynthesis. The techniques uses an information theory approach to achieve very high sensitivity for cancer detection while effectively minimizing false positives.

Diffuse reflectance spectroscopy (DRS) is a simple, yet powerful technique that has the potential to offer practical, non-invasive, and cost effective information for op- tical diagnostics and therapeutics guidance. Any progress towards moving DRS systems from their current laboratory settings to clinical settings, field settings and ambitiously to home settings, is a significant contribution to society in terms of reducing ever growing healthcare expenditures of an aging society. Additionally, im- proving on the existing mathematical models used to analyze DRS signals; in terms of speed, robustness, accuracy, and capability in accounting for larger feature space dimensionality (i.e. extraction of more tissue-relevant information) is equally im- portant for real-time diagnosis in the desired settings and to enable use of DRS in as many biomedical applications (e.g. skin cancer diagnosis, diabetics care, tissue oxygenation monitoring) as possible. Improving the reflectance signal complexity and density through novel DRS instrumentation, would facilitate development of the desired models or put the existing ones built on simulations in practical use; which otherwise could not go beyond being a theoretical demonstration.

DRS studies tissue morphology and composition through quantification of one or more (ideally all of them) of the tissue- and wavelength-specific optical properties: absorption coefficient (μa), reduced scattering coefficient (μ1s), scattering anisotropy (g), tissue thickness, and scattering phase function details (e.g. higher order moments of the scattering phase function). DRS involves sampling of diffusely reflected photons which experience multiple scattering and absorption as they travel within the tissue, at the tissue surface. Spatially resolved diffuse reflectance spectroscopy (SRDRS) is a subset of general DRS technique, which involves sampling of diffuse reflectance signals at multiple distances to an illumination source. SRDRS provides additional spatial information about the photon path; yielding depth-resolved tissue information critical to layered tissue analysis and early cancer diagnostics. Exist- ing SRDRS systems use fiber optic probes, which are limited in accommodation of large number and high-density collection fibers (i.e. yielding more and dense spa- tially resolved diffuse reflectance (SRDR) measurement data) due to difficulty of fiber multiplexing. The circular shape of the fibers restricts the implementable probe ge- ometries and reduces the fill factor for a given source to detector (i.e. collection fiber) separation (SDS); resulting in reduced light collection efficiency. The finite fiber nu- merical aperture (NA) reduces the light collection efficiency well as; and prevents selective interrogation of superficial tissues where most cancers emerge. Addition- ally, SRDR systems using fiber optic probes for photon collection, require one or more photodetectors (i.e. a cooled CCD); which are often expensive components of the systems.

This thesis deals with development of an innovative silicon SRDRS probe, which partially addresses the challenge of realizing high measurement density, miniaturized, and inexpensive SRDRS systems. The probe is fabricated by conventional, flexible and inexpensive silicon fabrication technology, which demonstrates the feasibility of developing SRDRS probes in any desired geometry and complexity. Although this approach is simple and straightforward, it has been overlooked by the DRS community due to availability of the conventional fiber optic probe technology. This new probe accommodates large number and high density of detectors; and it is in the form of a concentric semi-annular photodiode (PD) array (CMPA) with a central illumination aperture. This is the first multiple source-detector spacing Si SRDRS probe reported to date, and the most densely packed SRDRS probe reported to date for all types of SRDRS systems. The closely spaced and densely packed detectors enable higher density SRDR measurements compared to fiber-based SRDR probes, and the higher PD NA compared to that of fibers results in a higher SNR increasing light collection efficiency. The higher NA of the PDs and the presence of PDs positioned at very short distances from the illumination aperture center enable superficial tissue analysis as well as depth analysis.

Recent recognition models for remote sensing data (e.g., infrared cameras) are based upon machine learning models such as deep neural networks (DNNs) and typically require large quantities of labeled training data. However, many applications in remote sensing suffer from limited quantities of training data. To address this problem, we explore style transfer methods to leverage preexisting large and diverse datasets in more data-abundant sensing modalities (e.g., color imagery) so that they can be used to train recognition models on data-scarce target tasks. We first explore the potential efficacy of style transfer in the context of Buried Threat Detection using ground penetrating radar data. Based upon this work we found that simple pre-processing of downward-looking GPR makes it suitable to train machine learning models that are effective at recognizing threats in hand-held GPR. We then explore cross modal style transfer (CMST) for color-to-infrared stylization. We evaluate six contemporary CMST methods on four publicly-available IR datasets, the first comparison of its kind. Our analysis reveals that existing data-driven methods are either too simplistic or introduce significant artifacts into the imagery. To overcome these limitations, we propose meta-learning style transfer (MLST), which learns a stylization by composing and tuning well-behaved analytic functions. We find that MLST leads to more complex stylizations without introducing significant image artifacts and achieves the best overall performance on our benchmark datasets.

This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification.

In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information.

In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data.

Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear.

We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.

The spinal cord is an information highway continuously transmitting afferent and efferent signals to and from the brain. Although spinal cord stimulation has been used for the treatment of chronic pain for decades, its potential has not been fully explored. Spinal cord stimulation has never been used with the aim to transmit relevant information to the brain. Although, various locations along the sensory pathway have been explored for generating electrical stimulation induced sensory percepts, right from peripheral nerves, to thalamus to primary somatosensory cortex, the role of spinal cord has been largely neglected. In this dissertation, I have attempted to investigate if, electrical stimulation of dorsal columns of spinal cord called as Dorsal Column Stimulation (DCS) can be used as an effective technique to communicate therapeutic and somatosensory information to the brain.

To study the long term effects of DCS, I employed the 6-hydroxydopamine (6-OHDA) rodent model of Parkinson’s Disease (PD). Twice a week DCS for 30 minutes resulted in a dramatic recovery of weight and behavioral symptoms in rats treated with striatal infusions of 6-OHDA. The improvement in motor symptoms was accompanied by higher dopaminergic innervation in the striatum and increased cell count of dopaminergic neurons in the substantia nigra pars compacta (SNc). These results suggest that DCS has a chronic therapeutic and neuroprotective effect, increasing its potential as a new clinical option for treating PD patients. Thus, I was able to demonstrate the long-term efficacy of DCS, as a technique for therapeutic intervention.

Subsequently, I investigated if DCS can be used as a technique to transmit artificial somatosensory information to the cortex and trained rats to discriminate multiple artificial tactile sensations. Rats were able to successfully differentiate 4 different tactile percepts generated by varying temporal patterns of DCS. As the rats learnt the task, significant changes in the encoding of this artificial information were observed in multiple brain areas. Finally, I created a Brainet that interconnected two rats: an encoder and a decoder, whereby, cortical signals from the encoder rat were processed by a neural decoder while it performed a tactile discrimination task and transmitted to the spinal cord of the decoder using DCS. My study demonstrated for the first time, a cortico-spinal communication between different organisms.

My obtained results suggest that DCS, a semi-invasive technique, can be used in the future to send prosthetic somatosensory information to the brain or to enable a healthy brain to directly modulate neural activity in the nervous system of a patient, facilitating plasticity mechanism needed for efficient recovery.

Forward Looking Infrared (FLIR) cameras have recently been studied as a sensing modality for use in landmine detection systems. FLIR-based detection systems benefit from larger standoff distances and faster rates of advance than other sensing modalities, but they also present significant challenges for detection algorithm design. FLIR video typically yields multiple looks at each object in the scene, each from a different camera perspective. As a result each object in the scene appears in multiple video frames, and each time at a different shape and size. This presents questions about how best to utilize such information. Evidence in the literature suggests such multi-look information can be exploited to improve detection performance but, to date, there has been no controlled investigation of multi-look information in detection. Any results are further confounded because no precise definition exists for what constitutes multi-look information. This thesis addresses these problems by developing a precise mathematical definition of "a look", and how to quantify the multi-look content of video data. Controlled experiments are conducted to assess the impact of multi-look information on FLIR detection using several popular detection algorithms. Based on these results two novel video processing techniques are presented, the plan-view framework and the FLRX algorithm, to better exploit multi-look information. The results show that multi-look information can have a positive or negative impact on detection performance depending on how it is used. The results also show that the novel algorithms presented here are effective techniques for analyzing video and exploiting any multi-look information to improve detection performance.

This thesis examines the bandwidth shortcomings of conventional inductive coupling biotelemetry systems for implantable devices, and presents two approaches toward an end-to-end biotelemetry system for reducing the power consumption of implanted devices at increased levels of bandwidth. By leveraging the transition zone between the near and far field, scattering in the near field at UHF frequencies for increased bandwidth at low power budgets can be employed. Additionally, taking advantage of surface wave propagation permits the use of single-wire RF transmission lines in biological tissue, offering more efficient signal routing over near field coupling resulting in controlled implant depth at low power budgets.

Due to the dielectric properties of biological tissue, and the necessity to operate in the radiating near field to communicate via scattered fields, the implant depth drives the carrier frequency. The information bandwidth supplied by each sensing electrode in conventional implants also drives the operating frequency and regime. At typical implant depths, frequencies in the UHF range permit operation in the radiating near field as well as sufficient bandwidth.

Backscatter modulation provides a low-power, high-bandwidth alternative to conventional low frequency inductive coupling. A prototype active implantable device presented in this thesis is capable of transmitting data at 30 Mbps over a 915 MHz link while immersed in saline, at a communication efficiency of 16.4 pJ/bit. A prototype passive device presented in this thesis is capable of operating battery-free, fully immersed in saline, while transmitting data at 5 Mbps and consuming 1.23 mW. This prototype accurately demodulates neural data while immersed in saline at a distance of 2 cm. This communication distance is extended at similar power budgets by exploiting surface wave propagation along a single-wire transmission line. Theoretical models of single-wire RF transmission lines embedded in high permittivity and conductivity dielectrics are validated by measurements. A single-wire transmission line of radius 152.4 um exhibits a loss of 1 dB/cm at 915 MHz in saline, and extends the implant depth to 6 cm while staying within SAR limits.

This work opens the door for implantable biotelemetry systems to handle the vast amount of data generated by modern sensing devices, potentially offering new insight into neurological diseases, and may aid in the development of BMI's.

A series of fusion techniques are developed and applied to EEG and pupillary recording analysis in a rapid serial visual presentation (RSVP) based image triage task, in order to improve the accuracy of capturing single-trial neural/pupillary signatures (patterns) associated with visual target detection.

The brain response to visual stimuli is not a localized pulse, instead it reflects time-evolving neurophysiological activities distributed selectively in the brain. To capture the evolving spatio-temporal pattern, we divide an extended (``global") EEG data epoch, time-locked to each image stimulus onset, into multiple non-overlapping smaller (``local") temporal windows. While classifiers can be applied on EEG data located in multiple local temporal windows, outputs from local classifiers can be fused to enhance the overall detection performance.

According to the concept of induced/evoked brain rhythms, the EEG response can be decomposed into different oscillatory components and the frequency characteristics for these oscillatory components can be evaluated separately from the temporal characteristics. While the temporal-based analysis achieves fairly accurate detection performance, the frequency-based analysis can improve the overall detection accuracy and robustness further if frequency-based and temporal-based results are fused at the decision level.

Pupillary response provides another modality for a single-trial image triage task. We developed a pupillary response feature construction and selection procedure to extract/select the useful features that help to achieve the best classification performance. The classification results based on both modalities (pupillary and EEG) are further fused at the decision level. Here, the goal is to support increased classification confidence through inherent modality complementarities. The fusion results show significant improvement over classification results using any single modality.

For crucial image triage tasks, multiple image analysts could be asked to evaluate the same set of images to improve the probability of detection and reduce the probability of false positive. We observe significant performance gain by fusing the decisions drawn by multiple analysts.

To develop a practical real-time EEG-based application system, sometimes we have to work with an EEG system that has a limited number of electrodes. We present methods of ranking the channels, identifying a reduced set of EEG channels that can deliver robust classification performance.

This thesis provides a new take on problems in multimedia times series analysis by using a shape-based perspective to quantify patterns in time, which is complementary to more traditional analysis-based time series techniques. Inspired by the dynamical systems community, we turn time series into shapes via sliding window embeddings, which we refer to as ``time-ordered point clouds'' (TOPCs). This framework has traditionally been used on a single 1D observation function for deterministic systems, but we generalize the sliding window technique so that it not only applies to multivariate data (e.g. videos), but that it also applies to data which is not stationary (e.g. music).

The geometry of our time-ordered point clouds can be quite informative. For periodic signals, the point clouds fill out topological loops, which, depending on harmonic content, reside on various high dimensional tori. For quasiperiodic signals, the point clouds are dense on a torus. We use modern tools from topological data analysis (TDA) to quantify degrees of periodicity and quasiperiodicity by looking at these shapes, and we show that this can be used to detect anomalies in videos of vibrating vocal folds. In the case of videos, this has the advantage of substantially reducing the amount of preprocessing, as no motion tracking is needed, and the technique operates on raw pixels. This is also one of the first known uses of persistent H2 in a high dimensional setting.

Periodic processes represent only a sliver of possible dynamics, and we also show that sequences of arbitrary normalized sliding window point clouds are approximately isometric between ``cover songs,'' or different versions of the same song, possibly with radically different spectral content. Surprisingly, in this application, an incredibly simple geometric descriptor based on self-similarity matrices performs the best, and it also enables us to use MFCC features for this task, which was previously thought not to be possible due to significant timbral differences that can exist between versions. When combined with traditional pitch-based features using similarity metric fusion, we obtain state of the art results on automatic cover song identification.

In addition to being used as a geometric descriptor, self-similarity matrices provide a unifying description of phenomena in time-ordered point clouds throughout our work, and we use them to illustrate properties such as recurrence, mirror symmetry in time, and harmonics in periodic processes. They also provide the base representation for designing isometry blind time warping algorithms, which we use to synchronize time-ordered point clouds that are shifted versions of each other in space without ever having to do a spatial alignment. In particular, we devise an algorithm that lower bounds the 1-stress between two time-ordered point clouds, which is related to the Gromov-Hausdorff distance.

Overall, we show a proof-of-concept and promise of the nascent field of geometric signal processing, which is worthy of further study in applications of music structure, multimodal data analysis, and video analysis.

This dissertation presents waveform design and signal processing methods for continuous active sonar (CAS). The work presented focuses on methods for achieving high range, Doppler, and angular resolution, while maintaining a high signal-to-interference plus noise ratio (SINR).

CAS systems transmit at or near 100\% duty cycle for improved update rates compared to pulsed systems. For this reason, CAS is particularly attractive for use in shallow, reverberation-limited environments to provide more ``hits'' to adequately reject false alarms due to reverberation. High resolution is particularly important for CAS systems operating in shallow water for three reasons: (1) To separate target returns from the direct blast, (2) To separate targets from reverberation, and (3) To resolve direct and multipath target returns for maximum SINR. This dissertation presents two classes of high resolution CAS waveform designs and complementary signal processing techniques.

The first class of waveforms presented are co-prime comb signals that achieve high range and Doppler resolution at the cost of range ambiguities. Co-prime combs consist of multiple tones at non-uniformly spaced frequencies according to a 2-level nested co-prime array. Specialized non-matched filter processing enables recovery of a range-velocity response similar to that of a uniform comb, but using fewer tonal components. Cram\'er-Rao Bounds on range and Doppler estimation errors are derived for an arbitrary comb signal and used as a benchmark for comparing three range-velocity processing algorithms. Co-prime comb results from the littoral CAS 2015 (LCAS-15) sea trial are also presented, as well as a strategy to mitigate range ambiguities. An adaptive beamformer that achieves high angular resolution is also presented that leverages the various tonal components of the waveform for snapshot support.

The second class of waveforms presented are slow-time Costas (SLO-CO) CAS signals that achieve high range resolution, but are relatively insensitive to Doppler. SLO-CO CAS signals consist of multiple short duration linear FM (LFM) chirps that are frequency-hopped according to a Costas code. Rapid range updates can be achieved by processing each SLO-CO sub-chirp independently in a cyclical manner. Results from the LCAS-15 trial validate the performance of a SLO-CO signal in a real shallow water environment. A range processing method, novel to sonar, called bandwidth synthesis (BWS) is also presented. This method uses autoregressive modeling together with linear-predictive extrapolation to synthetically extend the bandwidth of received sonar returns. It is shown that BWS results in increased SINR and improved range resolution over conventional matched filtering in the reverberation-limited LCAS-15 environment.

In Pulmonary diseases, such as chronic obstructed pulmonary diseases (COPD), fibrosis, and asthma, are responsible for substantial health and financial burden in the world. In 2016, COPD claimed more than 3 million lives, which is also the 3rd leading cause of mortality. The treatment for pulmonary diseases continues to be hampered by the lack of reliable metrics to diagnose, as well as assess disease progression and therapeutic response. The current tools to diagnose and monitor pulmonary diseases are the pulmonary function tests (PFT) consisting of spirometry and plethysmography, and diffusing capacity of the lungs for carbon monoxide (DLCO). However, these metrics are effort-dependent, tend to have poor reproducibility, and measure lung as a whole, which allow subtle or regional diseases to be ‘hidden’. Alternatively, computed tomography (CT) is capable of characterizing lung structures in exquisite details, which is commonly applied in detecting the presence of both emphysema and pulmonary fibrosis. However, these structure details do not necessarily correlate well to how patients feel, the lung function, and the treatment effect. Thus, this information is much better assessed by characterizing the functions of the lung. Nuclear medicine, employing 133Xe ventilation and 99Tcm-macroaggregated albumin perfusion scan (ventilation/perfusion V/Q scan) can assess the inequality of airflow and blood flow in the lung. However, this V/Q scan evolves the usage of radioactive tracers and is limited by both poor temporal and spatial resolution. Thus, there has been considerable interest in developing methods that can comprehensively evaluate lung function non-invasively and can provide 3D resolution. Therefore, there has been considerable interest in developing methods that can evaluate lung function comprehensively, non-invasively, and 3-dimensionality.

In recent years, the introduction of hyperpolarized (HP) 129Xe magnetic resonance imaging (MRI) into clinical research has provided a robust and non-invasive 3D imaging technique, capable of both high-resolution imaging of pulmonary ventilation and gas exchange. Notably, gas exchange imaging is enabled by the solubility and unique frequency shifts of xenon in interstitial barrier tissues and capillary red blood cells (RBC). These features offer the potential for 129Xe MRI to be used, not only to evaluate lung obstruction, but also interstitial and vascular diseases. With the capability for both ventilation and gas exchange imaging, robust and reproducible strategies are essential for both visualizing and qualifying the resulting images. Before that, a standardized acquisition with a well-understood relationship between 129Xe dose and image quality needs to be established for efficient and cost-effective acquisitions. Moreover, we also seek to understand the origins of ventilation defects as well as alterations in barrier uptake and RBC transfer. Until such fundamental issues are addressed, it will not be possible to disseminate 129Xe MRI for multi-center clinical trials.

The objective of this work is to establish a robust and comprehensive 129Xe ventilation MRI clinical workflow to investigate pulmonary disorders, and to lay the foundation for clinical deployment and multi-center dissemination. To this end, this work describes several milestones toward establishing a routine, high signal-to-noise ratio (SNR) 129Xe ventilation MRI acquisition with the minimum sufficient volume 129Xe gas, and associated robust quantification pipeline for our clinical platforms. Moreover, we compared our quantification pipeline to other approaches in the field, as well as on different types of acquisition strategies (multi-slice GRE vs. 3D-radial).

To date, various quantification methods have been established for 129Xe ventilation MRI, yet no agreement has been reached on how to calculate the ventilation defect percentage (VDP). Thus, this work begins by developing a quantification workflow with semi-automatic delineation of the 1H thoracic cavity images, automatic pulmonary vasculature extraction, and inhomogeneity correction of the 129Xe ventilation images. It employs a robust linear binning classification that characterizes the entire ventilation distribution while being grounded in a healthy reference population. This quantification method can help evaluate, with high repeatability, how aging, diseases, and treatment influence ventilation distribution.

To further evaluate the robustness of this linear binning quantification method, its performance was assessed against another commonly used clustering method – K-means, on quantifying ventilation images. As part of the investigation, the methods were tested on images for which SNR had been artificially degraded. Through this evaluation, the minimum image SNR was established for an adequate quantification. We have also made the SNR-degraded image sets publicly available at Harvard Dataverse. These shared image sets could be used to evaluate the robustness of various quantification methods in the field. This endeavor is intended to help the pulmonary functional MRI community to standardize the analysis methods and laid the groundwork for future multi-center comparison studies.

We further address the fact that 129Xe ventilation MRI can be and has been conducted using a variety of pulse sequences, scan duration, and 129Xe doses. With more acceptance of the general utility of 129Xe MRI, imaging protocols must be standardized to enable multi-center trials. We thus sought to establish a rational basis for understanding the dose requirements and evaluating how different pulse sequences and 129Xe doses can influence 129Xe ventilation quantification. From that, the minimum required 129Xe dose for an adequate 129Xe ventilation quantification can be derived.

Maybe the emergence and development of 129Xe gas transfer MRI has introduced not only the ability to regionally assess gas exchange, but has introduced the interesting problem that it also delivers ventilation data from the same breath. However, the gas phase is acquired differently, with low resolution and isotropically. This raises the question as to how to generalize the ventilation quantification approach previously introduced specifically for multi-slice GRE. Therefore, we sought to generalize the linear binning approach for rescaling the intensity histogram, which enables the application of linear binning analysis to any ventilation MRI acquisition. We also investigated whether, and to what extent, 3D-radial acquisition can provide similar diagnostic information as from a dedicated multi-slice GRE acquisition. Through these efforts, we evaluated the possibility to employ a more efficient scan protocol for future routine clinical application.

During the course of this work, several practical engineering challenges were raised. First, hyperpolarized MRI has so far mostly been demonstrated at 1.5 Tesla (T), while most MRI vendors are transitioning multi-nuclear platforms to 3 T. This transition from 1.5 T to 3 T requires a reconsideration of optimal imaging acquisition and further optimization of quantification method. Moreover, preparation for multi-center dissemination points to the need for future centralized processing. This leads to the interest in cloud-based processing. However, in order to make this possible, manual segmentation of the thoracic cavity must be replaced by automatic methods. This, in turn requires the use of a novel neural network-based approaches. To this end, we first optimized the sequence on the transition to our new 3 T system. After completing the transition, the linear binning quantification method was further optimized with an enhanced vasculature segmentation and a neural network based 1H thoracic cavity segmentation. We also exploited the emergence of RBC transfer and implemented a framework to interpret these images by comparing them to more well-established approaches such as Gd-enhanced dynamic contrast-enhanced (DCE) perfusion MRI. To this end, we also developed a quantitative perfusion imaging pipeline that could be used to interpret the causes of RBC defects in our gas exchange imaging.

Taken together, results presented in this dissertation provide the step by step development of our rapid clinical exam workflow for hyperpolarized 129Xe MRI. This clinical workflow, not only demonstrates a comprehensive image quantification pipeline with applications to the 129Xe ventilation images and Gd-enhanced DCE MRI, but also the considerations for the acquisition sequence and delivered 129Xe dose. Overall, the established quantification pipeline offers a robust and sensitive way for diseases phenotyping, disease monitoring, and treatment planning. Moreover, this thesis work has hopefully laid the groundwork for standardized quantification, that could be deployed for future multi-center clinical trials.

It is well established that cochlear implants (CIs) are able to provide many users with excellent speech recognition ability in quiet conditions; however, the ability to correctly identify speech in noisy conditions or appreciate music is generally poor for implant users with respect to normal-hearing listeners. This discrepancy has been hypothesized to be in part a function of the relative decrease in spectral information available to implant users (Rubinstein and Turner, 2003; Wilson et al., 2004). One method that has been proposed for increasing the amount of spectral information available to CI users is to include time-varying stimulation rate in addition to changes in the place of stimulation. However, previous implementations of multi-rate strategies have failed to result in an improvement in speech recognition over the clinically available, fixed-rate strategies (Fearn, 2001; Nobbe, 2004). It has been hypothesized that this lack of success was due to a failure to consider the underlying perceptual responses to multi-rate stimulation.

In this work, psychophysical experiments were implemented with the goal of achieving a better understanding of the interaction of place and rate of stimulation and the effects of duration and context on CI listeners' ability to detect changes in stimulation rate. Results from those experiments were utilized in the implementation of a tuned multi-rate sound processing strategy for implant users in order to potentially ``tune" multi-rate strategies and improve speech recognition performance.

In an acute study with quiet conditions, speech recognition performance with a tuned multi-rate implementation was better than performance with a clinically available, fixed-rate strategy, although the difference was not statistically significant. These results suggest that utilizing time-varying pulse rates in a subject-specific implementation of a multi-rate algorithm may offer improvements in speech recognition over clinically available strategies. A longitudinal study was also performed to investigate the potential benefit from training to speech recognition. General improvements in speech recognition ability were observed as a function of time; however, final scores with the tuned multi-rate algorithm never surpassed performance with the fixed-rate algorithm for noisy conditions.

The ability to improve upon speech recognition scores for quiet conditions with respect to the fixed-rate algorithm suggests that using time-varying stimulation rates potentially provides additional, usable information to listeners. However, performance with the fixed-rate algorithm proved to be more robust to noise, even after three weeks of training. This lack of robustness to noise may be in part a result of the frequency estimation technique used in the multi-rate strategy, and thus more sophisticated techniques for real-time frequency estimation should be explored in the future.