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Single‐Molecule 3d Orientation Imaging Reveals Nanoscale Compositional Heterogeneity In Lipid Membranes, Jin Lu, Hesam Mazidi, Tianben Ding, Oumeng Zhang, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

In soft matter, thermal energy causes molecules to continuously translate and rotate, even in crowded environments, thereby impacting the spatial organization and function of most molecular assemblies, such as lipid membranes. Directly measuring the orientation and spatial organization of large collections (>3000 molecules μm⁻²) of single molecules with nanoscale resolution remains elusive. In this paper, we utilize SMOLM, single‐molecule orientation localization microscopy, to directly measure the orientation spectra (3D orientation plus “wobble”) of lipophilic probes transiently bound to lipid membranes, revealing that Nile red's (NR) orientation spectra are extremely sensitive to membrane chemical composition. SMOLM images resolve …

Structural Organization And Chemical Activity Revealed By New Developments In Single-Molecule Fluorescence And Orientation Imaging, Tianben Ding

McKelvey School of Engineering Theses & Dissertations

Single-molecule (SM) fluorescence and its localization are important and versatile tools for understanding and quantifying dynamical nanoscale behavior of nanoparticles and biological systems. By actively controlling the concentration of fluorescent molecules and precisely localizing individual single molecules, it is possible to overcome the classical diffraction limit and achieve 'super-resolution' with image resolution on the order of 10 nanometers.

Single molecules also can be considered as nanoscale sensors since their fluorescence changes in response to their local nanoenvironment. This dissertation discusses extending this SM approach to resolve heterogeneity and dynamics of nanoscale materials and biophysical structures by using positions and orientations …

Convex Relaxations For Particle-Gradient Flow With Applications In Super-Resolution Single-Molecule Localization Microscopy, Hesam Mazidisharfabadi

McKelvey School of Engineering Theses & Dissertations

Single-molecule localization microscopy (SMLM) techniques have become advanced bioanalytical tools by quantifying the positions and orientations of molecules in space and time at the nanoscale. With the noisy and heterogeneous nature of SMLM datasets in mind, we discuss leveraging particle-gradient flow 1) for quantifying the accuracy of localization algorithms with and without ground truth and 2) as a basis for novel, model-driven localization algorithms with empirically robust performance. Using experimental data, we demonstrate that overlapping images of molecules, a typical consequence of densely packed biological structures, cause biases in position estimates and reconstruction artifacts. To minimize such biases, we develop …

Investigating Single Precision Floating General Matrix Multiply In Heterogeneous Hardware, Steven Harris

McKelvey School of Engineering Theses & Dissertations

The fundamental operation of matrix multiplication is ubiquitous across a myriad of disciplines. Yet, the identification of new optimizations for matrix multiplication remains relevant for emerging hardware architectures and heterogeneous systems. Frameworks such as OpenCL enable computation orchestration on existing systems, and its availability using the Intel High Level Synthesis compiler allows users to architect new designs for reconfigurable hardware using C/C++. Using the HARPv2 as a vehicle for exploration, we investigate the utility of several of the most notable matrix multiplication optimizations to better understand the performance portability of OpenCL and the implications for such optimizations on this and …

Data Processing Electronics For An Ultra-Fast Single-Photon Counting Camera, Jackson Hyde

McKelvey School of Engineering Theses & Dissertations

Localizing photon arrivals with high spatial (megapixel) and temporal (sub-nanosecond) resolution would be transformative for a number of applications, including single-molecule super-resolution fluorescence microscopy. Here, the Data Processing Field Programmable Gate Array (FPGA) is developed as an ultra-fast computational platform built on an FPGA for a microchannel plate (MCP)-photomultiplier tube (PMT) based single-photon counting camera. Each photon is converted by the MCP-PMT into an electron cloud that generates current pulses across a 50×50 cross-strip anode. The Data Processing FPGA executes a massively parallel center-of-gravity coordinate determination algorithm on the digitized current pulses to determine a 2D position and time of …

Power Delivery Network As A Physically Unclonable Function, Patrick Naughton, Robert Esswein

Electrical and Systems Engineering Capstone Design Projects

Abstract— Physically unclonable functions leverage process variation in the manufacture of silicon chips and circuit boards to map inputs to outputs in an irreversible and unpredictable but consistent manner. They have many applications as security primitives: they can serve as truly random number generators, create secret keys, and fingerprint specific chips. These primitive functions can then be used to secure confidential information and regulate access to private resources. Current approaches tend to utilize variation in the production of silicon dies as the source of variability in their function’s output. We present a PUF that leverages variation in the entire circuit …

Joint Estimation Of Attenuation And Scatter For Tomographic Imaging With The Broken Ray Transform, Michael Ray Walker

McKelvey School of Engineering Theses & Dissertations

The single-scatter approximation is fundamental for many tomographic imaging problems. This class broadly includes x-ray scattering imaging and optical scatter imaging for certain media. In all cases, noisy measurements are affected by both local events and nonlocal attenuation. Related applications typically focus on reconstructing one of two images: scatter density or total attenuation. However, both images are media specific. Both images are useful for object identification. Knowledge of one image aides estimation of the other, especially when estimating images from noisy data.Joint image recovery has been demonstrated analytically in the context of the broken ray transform (BRT) for attenuation and …

Cognitive Radar Detection In Nonstationary Environments And Target Tracking, Yijian Xiang

McKelvey School of Engineering Theses & Dissertations

Target detection and tracking are the most fundamental and important problems in a wide variety of defense and civilian radar systems. In recent years, to cope with complex environments and stealthy targets, the concept of cognitive radars has been proposed to integrate intelligent modules into conventional radar systems. To achieve better performance, cognitive radars are designed to sense, learn from, and adapt to environments. In this dissertation, we introduce cognitive radars for target detection in nonstationary environments and cognitive radar networks for target tracking.For target detection, many algorithms in the literature assume a stationary environment (clutter). However, in practical scenarios, …

Self Capacitance Based Wireless Power Transfer For Wearable Electronics: Theory And Implementation, Yarub Omer Alazzawi

McKelvey School of Engineering Theses & Dissertations

Wireless power transfer (WPT)

Robust Control Of Burst Suppression Amid Physical And Neurological Uncertainty, Stephen Ampleman, Shinung Ching

McKelvey School of Engineering Theses & Dissertations

Burst suppression is a clinical term describing a phenomenon in which the electroencephalogram (EEG) of a sedated patient produces behavior that switches between higher frequency and amplitude bursting to lower frequency and lower amplitude suppression. This phenomenon can be observed during general anesthesia, hypothermia, or in an otherwise induced coma state. In a clinical setting, this phenomenon is typically induced by sedation from a drug such as propofol (2,6-diisopropylphenol). The level of sedation can be quantified by something called the burst suppression ratio (BSR), which is defined as the amount of time that a patient’s EEG is in a suppressed …

Measuring Localization Confidence For Quantifying Accuracy And Heterogeneity In Single-Molecule Super-Resolution Microscopy, Hesam Mazidi, Tianben Ding, Arye Nehorai, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

We present a computational method, termed Wasserstein-induced flux (WIF), to robustly quantify the accuracy of individual localizations within a single-molecule localization microscopy (SMLM) dataset without ground- truth knowledge of the sample. WIF relies on the observation that accurate localizations are stable with respect to an arbitrary computational perturbation. Inspired by optimal transport theory, we measure the stability of individual localizations and develop an efficient optimization algorithm to compute WIF. We demonstrate the advantage of WIF in accurately quantifying imaging artifacts in high-density reconstruction of a tubulin network. WIF represents an advance in quantifying systematic errors with unknown and complex distributions, …

A Computationally-Efficient Bound For The Variance Of Measuring The Orientation Of Single Molecules, Tingting Wu, Tianben Ding, Hesam Mazidi, Oumeng Zhang, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

Modulating the polarization of excitation light, resolving the polarization of emitted fluorescence, and point spread function (PSF) engineering have been widely leveraged for measuring the orientation of single molecules. Typically, the performance of these techniques is optimized and quantified using the Cramér-Rao bound (CRB), which describes the best possible measurement variance of an unbiased estimator. However, CRB is a local measure and requires exhaustive sampling across the measurement space to fully characterize measurement precision. We develop a global variance upper bound (VUB) for fast quantification and comparison of orientation measurement techniques. Our VUB tightly bounds the diagonal elements of the …

Investigating Patterns In Convolution Neural Network Parameters Using Probabilistic Support Vector Machines, Yuqiu Zhang

McKelvey School of Engineering Theses & Dissertations

Artificial neural networks(ANNs) are recognized as high-performance models for classification problems. They have proved to be efficient tools for many of today's applications like automatic driving, image and video recognition and restoration, big-data analysis. However, high performance deep neural networks have millions of parameters, and the iterative training procedure thus involves a very high computational cost. This research attempts to study the relationships between parameters in convolutional neural networks(CNNs). I assume there exists a certain relation between adjacent convolutional layers and proposed a machine learning model(MLM) that can be trained to represent this relation. The MLM's generalization ability is evaluated …