Title: Automated Spore Analysis Using Bright-Field Imaging and Raman Microscopy
Abstract: In 2015, it was determined that the United States Department of Defense had been shipping samples of B. anthracis spores which had undergone gamma irradiation but were not fully inactivated. In the aftermath of this event alternative and orthogonal methods were investigated to analyze spores determine their viability. In this thesis we demonstrate a novel analysis technique that combines bright-field microscopy images with Raman chemical microscopy.
We first developed an image segmentation routine based on the watershed method to locate individual spores within bright-field images. This routine was able to effectively demarcate 97.4% of the Bacillus spores within the bright-field images with minimal over-segmentation. Size and shape measurements, to include major and minor axis and area, were then extracted for 4048 viable spores which showed very good agreement with previously published values. When similar measurements were taken on 3627 gamma-irradiated spores, a statistically significant difference was noted for the minor axis length, ratio of major to minor axis, and total area when compared to the non-irradiated spores. Classification results show the ability to correctly classify 67% of viable spores with an 18% misclassification rate using the bright-field image by thresholding the minimum classification length.
Raman chemical imaging microscopy (RCIM) was then used to measure populations of viable, gamma irradiated, and autoclaved spores of B. anthracis Sterne, B. atrophaeus. B. megaterium, and B. thuringensis kurstaki. Significant spectral differences were observed between viable and inactivated spores due to the disappearance of features associated with calcium dipicolinate after irradiation. Principal component analysis was used which showed the ability to distinguish viable spores of B. anthracis Sterne and B. atrophaeus from each other and the other two Bacillus species.
Finally, Raman microscopy was used to classify mixtures of viable and gamma inactivated spores. A technique was developed that fuses the size and shape characteristics obtained from the bright-field image to preferentially target viable spores. Simulating a scenario of a practical demonstration of the technique was performed on a field of view containing approximately 7,000 total spores of which are only 12 were viable to simulate a sample that was not fully irradiated. Ten of these spores are properly classified while interrogating just 25% of the total spores.
Title: Robust Adaptive Strategies for Myographic Prosthesis Movement Decoding
Abstract: Improving the condition-tolerance, stability, response time, and dexterity of neural prosthesis control strategies are major clinical goals to aid amputees in achieving natural restorative upper-limb function. Currently, the dominant noninvasive neural source for prosthesis motor control is the skin-surface recorded electromyographic (EMG) signal. Decoding movement intentions from EMG is a challenging problem because this signal type is subject to a high degree of interference from noise and conditional influences. As a consequence, much of the movement intention information contained within the EMG signal has remained significantly under-utilized for the purposes of controlling robotic prostheses. We sought to overcome this information deficit through the use of adaptive strategies for machine learning, sparse representations, and signal processing to significantly improve myographic prosthesis control. This body of research represents the current state-of-the-art in condition-tolerant EMG movement classification (Chapter 3), stable and responsive EMG sequence decoding during movement transitions (Chapter 4), and positional regression to reliably control 7 wrist and finger degrees-of-freedom (Chapter 5). To our knowledge, the methods we describe in Chapter 5 elicit the most dexterous, biomimetic, and natural prosthesis control performance ever obtained from the surface EMG signal.
Title: Loss Landscapes of Neural Networks and their Generalization: Theory and Applications
Abstract: In the last decade or so, deep learning has revolutionized entire domains of machine learning. Neural networks have helped achieve significant improvements in computer vision, machine translation, speech recognition, etc. These powerful empirical demonstrations leave a wide gap between our current theoretical understanding of neural networks and their practical performance. The theoretical questions in deep learning can be put under three broad but inter-related themes: 1) Architecture/Representation, 2) Optimization, and 3) Generalization. In this dissertation, we study the landscapes of different deep learning problems to answer questions in the above themes.
First, in order to understand what representations can be learned by neural networks, we study simple Autoencoder networks with one hidden layer of rectified linear units. We connect autoencoders to the well-known problem in signal processing of Sparse Coding. We show that the squared reconstruction error loss function has a critical point at the ground truth dictionary under an appropriate generative model.
Next, we turn our attention to a problem at the intersection of optimization and generalization. Training deep networks through empirical risk minimization is a non-convex problem with many local minima in the loss landscape. A number of empirical studies have observed that “flat minima” for neural networks tend to generalize better than sharper minima. However, quantifying the flatness or sharpness of minima has been an issue due to possible rescaling in neural networks with positively homogenous activations. We use ideas from Riemannian geometry to define a new measure of flatness that is invariant to rescaling. We test the hypothesis that flatter minima generalize better through a number of different experiments on deep networks.
Finally, we apply deep networks to computer vision problems with compressed measurements of natural images and videos. We conduct experiments to characterize the situations in which these networks fail, and those in which they succeed. We train deep networks to perform object detection and classification directly on these compressive measurements of images, without trying to reconstruct the scene first. These experiments are conducted on public datasets as well as datasets specific to a sponsor of our research.
Title: Neural Circuit Mechanisms of Stimulus Selection Underlying Spatial Attention
Thesis Committee: Shreesh P. Mysore, Hynek Hermansky, Mounya Elhilali, Ralph Etienne-Cummings
Abstract: Humans and animals routinely encounter competing pieces of information in their environments, and must continually select the most salient in order to survive and behave adaptively. Here, using computational modeling, extracellular neural recordings, and focal, reversible silencing of neurons in the midbrain of barn owls, we uncovered how two essential computations underlying competitive selection are implemented in the brain: a) the ability to select the most salient stimulus among all pairs of stimulus locations, and b) the ability to signal the most salient stimulus categorically.
We first discovered that a key inhibitory nucleus in the midbrain attention network, called isthmi pars magnocellularis (Imc), encodes visual space with receptive fields that have multiple excitatory hotspots (‘‘lobes’’). Such (previously unknown) multilobed encoding of visual space is necessitated for selection at all location-pairs in the face of scarcity of Imc neurons. Although distributed seemingly randomly, the RF lobe-locations are optimized across the high-firing Imc neurons, allowing them to combinatorially solve selection across space. This combinatorially optimized inhibition strategy minimizes metabolic and wiring costs.
Next, we discovered that a ‘donut-like’ inhibitory mechanism in which each competing option suppresses all options except itself is highly effective at generating categorical responses. It surpasses motifs of feedback inhibition, recurrent excitation, and divisive normalization used commonly in decision-making models. We demonstrated experimentally not only that this mechanism operates in the midbrain spatial selection network in barn owls, but also that it is required for categorical signaling by it. Moreover, the pattern of inhibition in the midbrain forms an exquisitely structured ‘multi-holed’ donut consistent with this network’s combinatorial inhibitory function (computation 1).
Our work demonstrates that the vertebrate midbrain uses seemingly carefully optimized structural and functional strategies to solve challenging computational problems underlying stimulus selection and spatial attention at all location pairs. The neural motifs discovered here represent circuit-based solutions that are generalizable to other brain areas, other forms of behavior (such as decision-making, action selection) as well as for the design of artificial systems (such as robotics, self-driving cars) that rely on the selection of one among many options.
University policy at this present time: Students and faculty CAN attend dissertation defenses as long as there are fewer than 25 people.
Title: Deep Learning Based Novelty Detection
Abstract: In recent years, intelligent systems powered by artificial intelligence and computer vision that perform visual recognition have gained much attention. These systems observe instances and labels of known object classes during training and learn association patterns that can be used during inference. A practical visual recognition system should first determine whether an observed instance is from a known class. If it is from a known class, then the identity of the instance is queried through classification. The former process is commonly known as novelty detection (or novel class detection) in the literature. Given a set of image instances from known classes, the goal of novelty detection is to determine whether an observed image during inference belongs to one of the known classes.
In this thesis, deep learning-based approaches to solve novelty detection is studied under four different settings. In the first two settings, the availability of out-of-distributional data (OOD) is assumed. With this assumption, novelty detection can be studied for cases where there are multiple known classes and a single known class separately. These two problem settings are referred to as Multi-class novelty detection with OOD data and one-class novelty detection with OOD data in the literature, respectively. It is also possible to study this problem in a more constrained setting where only the data from known classes are considered for training. When there exist multiple classes in this setting novelty detection problem is known as Multiple-class novelty detection or Open-set recognition. On the other hand, when only a single class exists it is known as one-class novelty detection.
Finally, we study a practical application of novelty detection in mobile Active Authentication (AA). For a practical AA-based novelty detector, latency and efficiency are as important as the detection accuracy. Solutions are presented for the problem of quickly detecting intrusions with lower false detection rates in mobile AA systems with higher resource efficiency. Bayesian and Minimax versions of the Quickest Change Detection (QCD) algorithms are introduced to quickly detect intrusions in mobile AA systems. These algorithms are extended with an update rule to facilitate low-frequency sensing which leads to low utilization of resources.
Committee Members: Vishal Patel, Trac Tran, Najim Dehak
Taking place remotely. Email Belinda Blinkoff for more information.
Title: Engineering Earth-Abundant Colloidal Plasmonic and Semiconductor Nanomaterials for Solar Energy Harvesting and Detection Applications
Abstract: Colloidal nanomaterials have shown intriguing optical and electronic properties, making them important building blocks for a variety of applications, including photocatalysis, photovoltaics, and photodetectors. Their morphology and composition are effective tuning knobs for achieving desirable spectral characteristics for specific applications. In addition, they can be synthesized using solution-processed methods which possess the advantages of low cost, facile fabrication, and compatibility with building flexible devices. There is an ongoing quest for better colloidal materials with superior properties and high natural abundance for commercial viability. This thesis focuses on three such materials classes and applications: 1) studying the photophysical properties of earth-abundant plasmonic alumionum nanoparticles, 2) tailoring the optical profiles of semiconductor quantum dot solar cells with near-infrared sensitivity, and 3) using one-dimensional nanostructures for photodetector applications. A variety of analytical techniques and simulations are employed for characterization of both the morphology and optical properties of the nanostructures and for evaluating the performance of nanomaterial-based optoelectronic devices.
The first experimental section of this thesis consists of a systematic study of electron relaxation dynamics in solution-processed large aluminum nanocrystals. Transient absorption measurement are used to obtain the important characteristic relaxation timescales for each thermalization process. We show that several of the relevant timescales in aluminum differ from those in analogous noble metal nanoparticles and proposed that surface modification could be a useful tool for tuning heat transfer rates between the nanostructures and solvent. Further systematic studies on the relaxation dynamics in aluminum nanoparticles with tunable sizes show size-dependent phonon vibrational and damping characteristics that are influenced by size polydispersity, surface oxidation, and the presence of organic capping layers on the particles. These studies are significant first steps in demonstrating the feasibility of using aluminum nanomaterials for efficient photocatalysis.
The next section summarizes studies on the design and fabrication of multicolored PbS-based quantum dot solar cells. Specifically, thin film interference effects and multi-objective optimization methods are used to generate cell designs with controlled reflection and transmission spectra resulting in programmable device colors or visible transparency. Detailed investigations into the trade-off between the attainable color or transparency and photocurrent are discussed. The results of this study could be used to enable solar cell window-coatings and other controlled-color optoelectronic devices.
The last experimental section of thesis describes work on using 1D antimony selenide nanowires for flexible photodetector applications. A one-pot solution-based synthetic method is developed for producing a molecular ink which allows fabrication of devices on flexible substrates. Thorough characterization of the nanowire composition and morphology are performed. Flexible, broadband antimony selenide nanowire photodetectors are fabricated and show fast response and good mechanical stability. With further tuning of the nanowire size, spectral selectivity should be achievable. The excellent performance of the nanowire photodetectors is promising for the broad implementation of semiconductor inks in flexible photodetectors and photoelectronic switches.
Committee Members: Susanna Thon, Amy Foster, Jin Kang
This presentation will be taking place remotely. Follow this link to enter the Zoom meeting where it will be hosted. Do not enter the meeting before 8:45 AM EST.
Title: Enhancement of Optical Properties in Artificial Metal-Dielectric Structures
Abstract: The electromagnetic properties of materials, crucial to the operation of all electronic and optical devices, are determined by their permittivity and permeability. Thus, behavior of electromagnetic fields and currents can be controlled by manipulating permittivity and permeability. However, in the natural materials these properties cannot be changed easily. To achieve a wide range of (dielectric) permittivity and (magnetic) permeability, artificial materials with unusual properties have been introduced. This body of research represents a number of novel artificial structures with unusually attractive optical properties. We studied and achieved a series of new artificial structures with novel optical properties. The first one is the so-called hyperbolic metamaterials (HMMs), which are capable of supporting the waves with a very large k-vector and thus carry promises of large enhancement of spontaneous emission and high resolution imaging. We put these assumptions to rigorous test and show that the enhancement and resolution are severely limited by a number of factors. (Chapter 2 and 3). Then we analyzed and compared different mechanisms of achieving strong field enhancement in Mid-Infrared region of spectrum based on different metamaterials and structures. (Chapter 4). Through design and lab fabrication, we realized a planar metamaterials (metasurfaces) with the ability to modulate light reflection and absorption at the designated wavelength. (Chapter 5). Based on an origami-inspired self-folding approach, we reversibly transformed 2D MoS2 into functional 3D optoelectronic devices, which show enhanced light interaction and are capable of angle-resolved photodetection. (Chapter 6). Finally, to replace the conventional magnetic based optical isolators, we achieved two novel non-magnetic isolating schemes based on nonlinear frequency conversion in waveguides and four-wave mixing in semiconductor optical amplifiers. (Chapter 7).
Jacob Khurgin, Department of Electrical and Computer Engineering
Amy Foster, Department of Electrical and Computer Engineering
David Gracias, Department of Chemical and Biomolecular Engineering
Susanna Thon, Department of Electrical and Computer Engineering
This presentation will be taking place remotely. Follow this link to enter the Zoom meeting where it will be hosted. Do not enter the meeting before 1:45 PM EST.
Title: Sparsity and Structure in UWB Synthetic Aperture Radar
Abstract: Synthetic Aperure Radar is a form of radar that uses the motion of radar to simulate a large antenna in order to create high resolution imagery. Low frequency ultra-wideband (UWB) SARs in particular uses low frequencies and a large bandwidth that provide them with penetration capabilities and high resolution. UWB SARs are typically used for near eld imaging applications such as foliage penetration, through the wall imaging and ground penetration. SAR imaging is traditionally done by matched ltering, by applying the adjoint of the projection operator that maps from the image to SAR data.The matched lter imaging suffers disadvantages such as sidelobe artifacts, poor resolution of point targets and lack of robustness to noise and missing data. Regularized imaging with sparsity priors is found to be advantageous; however the regularized imaging is implemented as an iterative process in which projections between the image domain and data domain must be done many times. The projection operations (backprojection and reprojection) are highly complex; a brute force implementation has a complexity of O(N3). In this dissertation, a fast implementation of backprojection and reprojection is investigated. The implementation is explored in the context of regularized imaging as well as compressive sensing SAR.
The second part of the dissertation deals with a problem pertinent to UWB SAR imaging. The VHF/UHF bands used by UWB SAR are shared by other communication systems and that poses two problems; i) RF interference (RFI) from other sources and ii Missing spectral bands because transmission is prohibited in certain bands. The rst problem is addressed by using sparse and/or low-rank modeling. The SAR data is modeled to be sparse. The projection operator from above is used to capture the sparsity of the SAR data. The RFI is modeled to be either sparse with respect to an appropriate dictionary or assumed to be of low-rank. The sparse estimation or the sparse and low-rank estimation is used to estimate the SAR signal and RFI simultaneously. It is demonstrated that the new methods perform much better than the traditional RFI mitigation techniques such as notched ltering. The missing frequency problem can be modeled as a special case of compressive sensing. Sparse estimation is applied to the data to recover the missing frequencies. Simulations show that the sparse estimation is robust to large spectral gaps.
This presentation will be taking place remotely. Follow this link to enter the Zoom meeting where it will be hosted. Do not enter the meeting before 12:45 PM EDT.
Title: Improved Modeling and Image Generation for Fluorescence Molecular Tomography (FMT) and Positron Emission Tomography (PET)
Abstract: In this thesis, we aim to improve quantitative medical imaging with advanced image generation algorithms. We focus on two specific imaging modalities: fluorescence molecular tomography (FMT) and positron emission tomography (PET).
In the case of FMT, we present a novel photon propagation model for its forward model, and in addition, we propose and investigate a reconstruction algorithm for its inverse problem. In the first part, we develop a novel Neumann-series-based radiative transfer equation (RTE) that incorporates reflection boundary conditions in the model. In addition, we propose a novel reconstruction technique for diffuse optical imaging that incorporates this Neumann-series-based RTE as forward model. The proposed model is assessed using a simulated 3D diffuse optical imaging setup, and the results demonstrate the importance of considering photon reflection at boundaries when performing photon propagation modeling. In the second part, we propose a statistical reconstruction algorithm for FMT. The algorithm is based on sparsity-initialized maximum-likelihood expectation maximization (MLEM), taking into account the Poisson nature of data in FMT and the sparse nature of images. The proposed method is compared with a pure sparse reconstruction method as well as a uniform-initialized MLEM reconstruction method. Results indicate the proposed method is more robust to noise and shows improved qualitative and quantitative performance.
For PET, we present an MRI-guided partial volume correction algorithm for brain imaging, aiming to recover qualitative and quantitative loss due to the limited resolution of PET system, while keeping image noise at a low level. The proposed method is based on an iterative deconvolution model with regularization using parallel level sets. A non-smooth optimization algorithm is developed so that the proposed method can be feasibly applied for 3D images and avoid additional blurring caused by conventional smooth optimization process. We evaluate the proposed method using both simulation data and in vivo human data collected from the Baltimore Longitudinal Study of Aging (BLSA). Our proposed method is shown to generate images with reduced noise and improved structure details, as well as increased number of statistically significant voxels in study of aging. Results demonstrate our method has promise to provide superior performance in clinical imaging scenarios.
This presentation will be taking place remotely. Follow this link to enter the Zoom meeting where it will be hosted. Do not enter the meeting before 9:45 AM EDT.
Title: Statistical Inference in Auditory Perception
Abstract: The human auditory system effortlessly parses complex sensory inputs despite the ever-present randomness and uncertainty in real-world scenes. To achieve this, the brain tracks sounds as they evolve in time, collecting contextual information to construct an internal model of the external world for predicting future events. Previous work has shown the brain is sensitive to many predictable (and often complex) patterns in sequential sounds. However, real-world environments exhibit a broader spectrum of predictability, and moreover, the level of predictability is constantly in flux. How does the brain build robust internal representations of such stochastic and dynamic acoustic environments?
This question is addressed through the lens of a computational model based in statistical inference. Embodying theories from Bayesian perception and predictive coding, the model posits the brain collects statistical estimates from sounds and maintains multiple hypotheses for the degree of context to include in predictive processes. As a potential computational solution for perception of complex and dynamic sounds, this model is used to connect sensory inputs with listeners’ responses in a series of human behavioral and electroencephalography (EEG) experiments incorporating uncertainty. Experimental results point toward the underlying sufficient statistics collected by the brain, and the extension of these statistical representations to multiple dimensions is examined along spectral and spatial dimensions. The computational model guides interpretation of behavioral and neural responses, revealing multiplexed responses in the brain corresponding to different levels of predictive processing. In addition, the model is used to explain individual differences across listeners highlighted by uncertainty.
The proposed computational model was developed based on first principles, and its usefulness is not limited to the experiments presented here. The model was used to replicate a range of previous findings in the literature, unifying them under a single framework. Moving forward, this general and flexible model can be used as a broad-ranging tool for studying the statistical inference processes behind auditory perception, overcoming the need to minimize uncertainty in perceptual experiments and pushing what was previously considered feasible for study in the laboratory towards what is typically encountered in the “messy” environments of everyday listening.
Mounya Elhilali, Department of Electrical and Computer Engineering
Jason Fischer, Department of Psychological & Brain Sciences
Hynek Hermansky, Department of Electrical and Computer Engineering
James West, Department of Electrical and Computer Engineering