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Autonomous Flow-Based Navigation in Unsteady Underwater Environments
No full textAutonomous ocean-exploring robots promise to significantly enhance the rate at which we can explore ocean environments. However, the limited range and speed of existing autonomous underwater vehicles (AUVs) are barriers to comprehensive ocean exploration. To address these limitations, the work in this thesis investigates strategies for improving the capabilities of existing AUVs, such as targeted sampling and efficient navigation through background flows. Inspired by the ability of aquatic animals to navigate via flow sensing, hydrodynamic cues are investigated as a sensory input for accomplishing these feats of autonomous navigation using only onboard sensors. First, reinforcement learning (RL) is investigated as an algorithm for accomplishing efficient point-to-point navigation in simulated cylinder flow. The algorithm entails inputting point measurements of flow quantities such as velocity and vorticity into a deep neural network, which then determines a swimmer's actions. Using point velocity as the sensory input, the RL algorithm achieved a near 100 percent success rate in reaching the target locations while approaching the time-efficiency of optimal navigation trajectories. To test RL and flow-based navigation in a physical setting, we next developed the Caltech autonomous reinforcement learning robot (CARL), a palm-sized underwater robotic platform. As proof-of-concept analogy for tracking hydrothermal vent plumes in the ocean, the robot was tasked with locating the center of turbulent jet flows in a 13,000-liter water tank using data from onboard pressure sensors. Using a navigation policy trained with RL in a simulated flow environment, CARL successfully located the turbulent plumes at more than twice the rate of random searching by detecting mean flow gradients with the onboard pressure sensors. Lastly, combing both flow sensing and efficient navigation, the accelerometer onboard CARL was used to sense and exploit the flow from a passing vortex ring for energy-efficient propulsion. Body acceleration and rotation were shown to be effective methods of indirect flow sensing, which enabled the energy-efficient vortex ring surfing strategy. Throughout this work, efforts are made to understand the governing physics behind the discovered navigation strategies to generalize the results beyond a specific navigation problem, sensor type, or robotic implementation
Adaptive Optoelectronic Systems: From Bio- Sensing to Free-Space Optical Communication
Get PDFPortable and point-of-care medical devices are becoming an essential part of today’s medical technology. An affordable personal device that can diagnose and monitor a medical condition in real-time will improve the patient’s life quality in many ways. Additionally, by autonomously providing the suitable treatment, a universal healthcare device can be accessible to most of the population at a low cost. Despite considerable efforts and great outcomes, most of the prior arts in realizing these devices have limitations that hinder their widespread use in portable applications. On the other hand, comprehensive environmental sensing has drawn great attention in the last few years. Monitoring the quality of water, soil, air, and waste is of utmost importance to study their effect on human life and also to recognize the consequence of human actions on the planet.
The most important factors in developing a compact and portable device for medical and environmental applications are their integration level, ease of use with biomarkers, and reliability of the results. Detecting a specific chemical in the biology world relies on a biochemical reaction with a transducer that can convert the resulting signal into a measurable signal in various modalities, such as electrical, magnetic, or optical. Hence, the biosensing device is often a multidisciplinary apparatus that is not readily integrable due to the need for miniaturizing otherwise bulky optical or magnetic components. The key requirement in device miniaturization, though, is to use standard technologies to avoid extra cost and processing time for the device’s mass production. The path towards achieving such a device needs revisiting the existing solutions and the capabilities of the powerful yet affordable CMOS technologies to seamlessly integrate various device components, namely electronics, biology, and optics/magnetics. This dissertation provides an overview of integrated biosensors and presents novel designs in optics and electronics to implement a fully integrated and miniaturized device for medical and environmental applications.
Fluorescence sensing is one of the most reliable and widespread detection methods with well- established tools in synthetic biology. Specifically, bacterial-based fluorescence sensors offer unsurpassed advantages to labeled detection since bacterial cells, when engineered, can respond to various elements in their surroundings at a low cost and quite efficiently. The use of live bacterial cells is also of great importance in establishing the bidirectional link with the CMOS device. By monitoring the dynamics of the cells’ growth and their protein expression, a desired biology response can be initiated upon receiving the stimulating signal from the device. The conventional methods in fluorescence sensing involve an elaborate setup with many external optical components unsuitable for portable and in vivo applications. Hence, integrating silicon chips and live bacterial biosensors in a miniaturized "Silicon-Cell" system can enable a wide range of applications for both sensing and remediation. Such integrated systems need on-chip optical filtering in the wavelength range compatible with fluorescent proteins, which are widely used signal reporters for bacterial biosensors.
In the first part of this dissertation, we introduce a fully integrated fluorescence sensor in 65nm standard CMOS process comprising on-chip bandpass optical filters, photodiodes, and processing circuitry. The metal/dielectric layers in CMOS are employed to implement low- loss cavity-type optical filters, achieving a bandpass response at 600/700nm range suitable to work with fluorescent proteins. The sensitivity of the sensor is further improved in the electrical domain by using a C-TIA with variable switched capacitor gain, a voltage- controlled current source (VCCS), and feedback-controlled low-leakage switches, resulting in a minimum measured current of 1.05fA with SNR >18dB. The sensor can measure the statics/dynamics of the fluorescence signal as well as the growth of living E. coli bacterial cells. Using a differential design and layout, the sensor can distinguish two biochemical signals by measuring two fluorescent proteins encoded in a single bacterial strain. Furthermore, a proof of concept is demonstrated to establish bidirectional communication between living cells and the CMOS chip, using a fluorescent protein regulated by an optogenetic control.
In the second part of this dissertation, we describe a fully integrated high-bandwidth optical receiver for RF-over-free-space optics (RoFSO). This work is motivated by the availability of a wide, unregulated bandwidth at the optical frequencies and the lower cost and setup time due to using atmosphere instead of fiber optics as the communication channel. Nonetheless, the atmospheric link poses serious challenges, including severe beam intensity and phase distortions. Here we present novel solutions at the system and circuit level to make the receiver adaptive and resilient to the mentioned distortions. The chip is designed and implemented in a 28nm CMOS process, and it is shown to achieve a measured gain of 58dB and bandwidth of 18GHz. The link performance is assessed by exposing the system to more than 26dB of optical loss, equivalent to 3.5km of free space distance under moderate visibility conditions. For a proof-of-concept demonstration, an 8Gbps non-coherent DPSK signal with an RF bandwidth of 10GHz is transmitted, resulting in a BER of 1 × 10⁻⁴ for a minimum received power of -30dBm and while consuming 19.2mW power at the receiver.</p
Machine Learning and Inference Methods for Surrogate Modeling and Inexpensive Characterization of Elastodynamic Systems
Get PDFThis thesis has two main focuses: (1) surrogate modeling of elastodynamic systems, and (2) inference methods for the inexpensive characterization of elastodynamic systems. Elastodynamics is the study of how and why materials move and deform when they are subject to time-varying loads, covering a wide range of applications from architected materials, to telecommunications, seismology, sound isolation, non-destructive evaluation, and medical imaging. Here, we explore how to more efficiently model elastodynamics, and what we can infer about our environment from observing them.
The next generation of material engineering aims to leverage advanced multi-functional control over elastodynamic behaviors, but is currently limited by the large computational cost of purely physics-based modeling methods. Surrogate models aim to alleviate this cost by providing a data-driven approach to evaluate engineered material systems more efficiently. However, most current surrogate models lack certain useful traits, diminishing their potential for real-world use. This thesis begins by surveying the current state of surrogate modeling techniques, and establishes a set of state-of-the-art traits that greatly augment the utility of surrogate models, offering a perspective for the future direction of the field.
Next, a data-driven surrogate model based on Gaussian process regression for the computation of dispersion relations is developed, GPR-dispersion. The model exhibits several of the aforementioned traits, including representation invariance, data efficiency, incorporating direct use of physical theories, and the provision of both uncertainty estimates on its predictions and gradients for compatibility with gradient-based design optimization methods. GPR-dispersion is evaluated in comparison against both deep learning and traditional physics-based models.
The thesis then pivots to inference methods for the inexpensive characterization of material systems via partial observation of elastodynamic behaviors. Tissue stiffness is a tremendously important biomarker for a long list of health conditions, but often needs to be evaluated in a medical clinic with expensive equipment and highly trained workers. At-home health monitoring is a major next-generation goal of healthcare, but the trajectories of current consumer-grade sensor technology and biomarker inference methods have not yet fully intersected.
Inspired by a related work (Visual Vibration Tomography), Visual Surface Wave Tomography (VSWT) is proposed. VSWT observes partial information about the surface waves of layered elastodynamic systems (such as biological tissue) through monocular video to infer subsurface constitutive and geometrical information. Simulated experiments are presented to evaluate the accuracy, sensitivity, and limits of the method under ideal conditions. Real-world experimental results are presented using phantom materials that emulate biological tissue to demonstrate a practical proof of concept.</p
A Kakeya Estimate for Sticky Sets Using a Planebrush
Get PDFA Besicovitch set is defined as a compact subset of ℝⁿ which contains a line segment of length 1 in every direction. The Kakeya conjecture says that every Besicovitch set has Minkowski and Hausdorff dimensions equal to n. This thesis gives an improved Hausdorff dimension estimate, d ⩾ 0.60376707287 n + O(1), for Besicovitch sets displaying a special structural property called "stickiness." The improved estimate comes from using an incidence geometry argument called a "k-planebrush," which is a higher dimensional analogue of Wolff's "hairbrush" argument from 1995.
In addition, an x-ray transform estimate is obtained as a corollary of Zahl's k-linear estimate in 2019. The x-ray estimate, together with the estimate for sticky sets, implies that all Besicovitch sets in ℝⁿ must have Minkowski dimension greater than (2 - √2 + ε)n. Though this Minkowski dimension estimate is not as good as one previously known from Katz-Tao(2000), it provides a new proof of the same result.</p
Development and Applications of Imaginary Time Path Integral Methods
Get PDFRecent engineering advances have opened up avenues to novel technologies that bridge the gap between the quantum and the classical. In order to understand large-scale quantum systems, a variety of approximate theoretical treatments have been proposed. This thesis focuses on development and applications of path-integral methods, which have enjoyed broad applicability in recent years for exploring nuclear quantum effects in the domains that span physical, bio-, geo-, and materials chemistry.
Feynman's path-integral formulation of quantum statistical mechanics offers powerful and widely used strategies for including nuclear quantum effects in complex chemical systems. These strategies are based on the observation that the quantum Boltzmann statistical mechanics of a quantum system is exactly reproduced by the classical Boltzmann statistical mechanics of an isomorphic ring-polymer system. For the numerically exact calculation of quantum Boltzmann statistical properties, the classical Boltzmann distribution of the ring-polymer system can be sampled using Monte Carlo (i.e., path-integral Monte Carlo, or PIMC) or molecular dynamics (PIMD).
Chapters 1 and 2 of this thesis identify and — with no computational overhead — eliminate the issues in virtually all previous numerical implementations of PIMD that stem from time discretization. The resultant integration scheme requires only a small modification to existing PIMD algorithms and provides accurate statistical and dynamical data in a single-shot simulation with an up to 3-fold increase in the timestep duration.
Chapter 3 transitions from the PIMD method development to the applications of the related PIMC method to understand equilibrium of stable heavy isotopes (D, 13C, 17, and 18O in small gaseous molecules. We present a collaborative experiment-theory calibration of the temperature dependence of the clumped isotope effect in methane in Chapter 4. We continue in Chapter 5, adding the study of isotopic fractionation between methane, water, and molecular hydrogen. Here we present the first concrete example of the effect of Born-Oppenheimer approximation on PI calculations. Finally, Chapter 6 extends our treatment to ethane and propane. For propane, in addition to multiple clumped isotope effects, there is also a strong site preference for the heavy isotopes to occupy the central (methylene) group.
All the isotopic equilibrium calculations utilize accurate potential energy surfaces and are validated against experimental data in close collaboration with Daniel Stolper's experimental group at Berkeley, representing (to the best of our knowledge) the most accurate reference data available to date.</p
Gravitational Wave Exotica - Advancing the Search for Signatures of Exotic Compact Objects and Gravitational Lensing from Data-Analysis and Theoretical Perspectives
Get PDFIn this thesis, I explore two new arenas of gravitational-wave physics and advance them from both data-analysis and theoretical perspectives. I probe the nature of the remnant of a compact binary merger and study the strong gravitational lensing of gravitational waves. For probing the nature of a merger remnant, I first describe recipes of computing radiation emitted by a perturbed Kerr black hole, and in particular using the Generalized Sasaki-Nakamura formalism. Using a modified Kerr black hole spacetime as a model of a generic compact object, I then describe a prescription to compute waveforms of the repeating bursts of gravitational waves, referred to as gravitational-wave echoes, that are theorized to be emitted when a compact object with a reflective surface is formed as the remnant of a merger. Equipped with a waveform model for these echoes, I present a Bayesian model selection approach to look for echoes in data while inferring properties of the potential exotic compact object. I apply this approach to search for echoes in the data covering the first, the second, and the first half of the third observing run of the LIGO-Virgo-KAGRA network. For the strong lensing of gravitational waves, I first develop a Bayesian statistical framework that is capable of computing the probability of a given set of gravitational-wave events being the strongly-lensed counterparts of the same source or simply coming from distinct sources. If they are truly lensed, the framework can also infer the properties of the lensed source in a way unaffected by lensing. I apply this framework to search for signatures of strongly-lensed binary black hole systems in the data covering the third observing run. While we did not find any statistically significant evidence in the search for gravitational-wave echoes and strongly-lensed binary black holes, we can still place limits using the null results. Admittedly the existence of exotic compact objects is speculative and the observing rate of strongly-lensed gravitational waves is rare; however, the scientific impacts that they can bring are profound if they are proven to exist
Atomic Dynamics in Solids and Liquids from Inelastic Neutron Scattering
Get PDFAs temperature increases, atomic scale disorder, or entropy, drives the thermophysical properties of materials. One way it does this is by passing heat through materials in the form of vibrations. In solids, vibrational motions are called phonons, and their behaviors are used to predict macroscopic properties such as thermal expansion and thermal conductivity. Vibrational dynamics also exist in liquids but are traditionally less studied. Other forms of entropy include configurational and electronic entropy, which also evolve with temperature. Configurational changes in solids are often small, but in liquids, the prominence of diffusion makes this contribution significant. This dissertation addresses these atomistic components of entropy in two studies, one on bcc chromium and the other on the melting of monatomic systems.
In the first study, phonon densities of states (DOS) of body-centered cubic chromium were measured by time-of-flight inelastic neutron scattering (INS) at temperatures up to 1493 K. Density functional theory calculations with both quasi-harmonic (QH) and anharmonic (AH) methods were performed at temperatures above the Neel temperature. Features in the phonon DOS decrease in energy (soften) substantially with temperature. A Born-von Karman analysis using fits to the experimental DOS reveals a softening of almost 17% of the high transverse phonon branch between 330 and 1493 K. The low transverse branch changes by approximately half this amount. The AH calculations capture the observed behavior of the two transverse phonon branches, but the QH calculations give some inverted trends. Vibrational entropies from phonons and electrons are obtained, and their sum is in excellent agreement with the entropy of chromium obtained by calorimetry, indicating that above 330 K, no explicit temperature-dependent magnetic contributions are necessary.
The second investigation delves into the latent heat of melting, defined as TmΔSfus where Tm is the melting temperature and ΔSfus is the entropy of fusion. At the scale of atoms and electrons, ΔSfus has components from changes of atom configurations, atom vibrations, and thermal excitations of electrons. New data analyses were developed for inelastic neutron scattering to obtain changes in vibrational spectra upon melting. Combining these INS experiments with computational work using thermodynamic integration and molecular dynamics, components of ΔSfus were obtained for a total of six elements, Ge, Si, Bi, Sn, Pb, Li. Upon melting, there is always a positive change of configurational entropy, ΔSconfig. A baseline value of ΔSconfig=1.2kB/atom, approximately the value for Richard's rule, corresponds to zero change in the vibrational part of the entropy of fusion, ΔSvib. Elements having values of ΔSfus
that depart from this value of Richard's rule have both an additional ΔSvib and an additional ΔSconfig. Surprisingly, the extra ΔSconfig is close to 77% of ΔSvib, for both positive and negative deviations from Richard's rule. This implies a correlation between the change in the number of basins in a potential energy landscape and the change in the inverse of their curvature upon melting.</p
A New Sensor for Milky-Way Particle Accelerators: The Standalone-Radio Cosmic Ray Detector at the Owens Valley Radio Observatory Long Wavelength Array
Get PDFThis thesis describes the development of a standalone radio cosmic ray detector at the Owens Valley Radio Observatory Long Wavelength Array (OVRO-LWA), for the purpose of understanding the high-energy limits of Milky Way particle accelerators. A shift from Milky Way cosmic ray sources to extragalactic accelerators likely occurs for particle energies somewhere between 1--1000 PeV. Placing the shift at the lower end of this range creates challenges explaining certain features of the cosmic ray spectrum at high energies, but placing the shift at the high-energy end of the range requires unknown types of Milky Way cosmic ray sources.
I have built a cosmic ray detection system as part of a major upgrade to the OVRO-LWA. The OVRO-LWA array layout and the fast digital signal processing hardware led to the cosmic ray search strategy chosen here. A key part of this thesis work was developing a process for rejecting radio frequency interference as well as developing a system to search for cosmic rays among subsets of antennas but save snapshots of data from all 352 dual-polarization LWA antennas at once. In this thesis, the presentation of the OVRO-LWA cosmic ray detector is bracketed by an exploration of the landscape of Milky Way cosmic rays, beginning with an overview chapter and finishing with an investigation of a specific habitat for relativistic particles: the large-scale magnetosphere of a flare star at the end of the main sequence.
Since cosmic ray trajectories do not point back to their sources, identifying their origins will require precise measurement of shifts in cosmic ray mass composition---a measurement which this thesis has set the OVRO-LWA on the path toward making.</p
Experimental and Neuroinformatic Definition of Neural Circuits in Caenorhabditis elegans
Get PDFThe free-living nematode Caenorhabditidis elegans is an established model organism for research in molecular genetics, cell and developmental biology, evolution, and neuroscience. This thesis describes two research projects in C. elegans neuroscience. The first project concerns the challenge of synthesizing the accumulating neurobiological literature in C. elegans. I describe how an established framework for semantic modelling of cellular pathways can be adapted for semantic modelling of neural circuits and functional annotation of the nervous system, and its potential applications for systems neuroscience. A second portion describes a series of experiments investigating a decision-making process in C. elegans larval development that is under neuronal control. C. elegans larvae have the ability to decide among alternative developmental trajectories based on environmental conditions, that are detected via its nervous system. In this work, I describe the contribution of several neurons to this decision-making process, and our discoveries about the response properties of two neurons to ethologically relevant chemical stimuli
On the Hecke Module of GLₙ(k[[z]])\GLₙ(k((z)))/GLₙ(k((z²)))
Get PDF[See Abstract in text of thesis for correct representation of mathematics]
Every double coset in GLₘ(k[[z]])\GLₘ(k((z)))/GLₘ(k((z²))) is uniquely represented by a block diagonal matrix with diagonal blocks in { 1,z, (11 z \\0 zⁱ \\) (i>1) } if char(k) ≠ 2 and k is a finite field. These cosets form a (spherical) Hecke module H(G,H,K) over the (spherical) Hecke algebra H(G,K) of double cosets in K\G/H, where K=GLₘ(k[[z]]) and H=GLₘ(k((z²))) and G=GLₘ(k((z))). Similarly to Hall polynomial hλ,ν^µ from the Hecke algebra H(G,K), coefficients hλ,ν^µ arise from the Hecke module. We will provide a closed formula for hλ,ν^µ, under some restrictions over λ, ν, µ.</p