Electrical & Computer Engineering
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Item Metamaterial Model of Tachyonic Dark Energy(MDPI, 2014-02-17) Smolyaninov, Igor I.Dark energy with negative pressure and positive energy density is believed to be responsible for the accelerated expansion of the universe. Quite a few theoretical models of dark energy are based on tachyonic fields interacting with itself and normal (bradyonic) matter. Here, we propose an experimental model of tachyonic dark energy based on hyperbolic metamaterials. Wave equation describing propagation of extraordinary light inside hyperbolic metamaterials exhibits 2 + 1 dimensional Lorentz symmetry. The role of time in the corresponding effective 3D Minkowski spacetime is played by the spatial coordinate aligned with the optical axis of the metamaterial. Nonlinear optical Kerr effect bends this spacetime resulting in effective gravitational force between extraordinary photons. We demonstrate that this model has a self-interacting tachyonic sector having negative effective pressure and positive effective energy density. Moreover, a composite multilayer SiC-Si hyperbolic metamaterial exhibits closely separated tachyonic and bradyonic sectors in the long wavelength infrared range. This system may be used as a laboratory model of inflation and late time acceleration of the universe.Item Fractional Effective Charges and Misner-Wheeler Charge without Charge Effect in Metamaterials(MDPI, 2016-07-08) Smolyaninov, IgorTransformation optics enables engineering of the effective topology and dimensionality of the optical space in metamaterials. Nonlinear optics of such metamaterials may mimic Kaluza-Klein theories having one or more kinds of effective charges. As a result, novel photon blockade devices may be realized. Here we demonstrate that an electromagnetic wormhole may be designed, which connects two points of such an optical space and changes its effective topological connectivity. Electromagnetic field configurations, which exhibit fractional effective charges, appear as a result of such topology change. Moreover, such effects as Misner-Wheeler “charge without charge” may be replicated.Item Thermally Induced Effective Spacetimes in Self-Assembled Hyperbolic Metamaterials(MDPI, 2017-03-08) Smolyaninov, Igor I.Recent developments in gravitation theory indicate that the classic general relativity is an effective macroscopic theory which will be eventually replaced with a more fundamental theory based on thermodynamics of yet unknown microscopic degrees of freedom. Here we consider thermodynamics of an effective spacetime which may be formed under the influence of an external magnetic field in a cobalt ferrofluid. It appears that the extraordinary photons propagating inside the ferrofluid perceive thermal gradients in the ferrofluid as an effective gravitational field, which obeys the Newton law. Moreover, the effective de Sitter spacetime behaviour near the metric signature transition may mimic various cosmological inflation scenarios, which may be visualized directly using an optical microscope. Thus, some features of the hypothetic microscopic theory of gravity are illustrated in the ferrofluid-based analogue models of inflation.Item Extra-Dimensional “Metamaterials”: A Model of Inflation Due to a Metric Signature Transition(MDPI, 2017-09-20) Smolyaninov, Igor I.Lattices of topological defects, such as Abrikosov lattices and domain wall lattices, often arise as metastable ground states in higher-dimensional field theoretical models. We demonstrate that such lattice states may be described as extra-dimensional “metamaterials” via higher-dimensional effective medium theory. A 4 + 1 dimensional extension of Maxwell electrodynamics with a compactified time-like dimension is considered as an example. It is demonstrated that from the point of view of macroscopic electrodynamics an Abrikosov lattice state in such a 4 + 1 dimensional spacetime may be described as a uniaxial hyperbolic medium. Extraordinary photons perceive this medium as a 3 + 1 dimensional Minkowski spacetime in which one of the original spatial dimensions plays the role of a new time-like coordinate. Since the metric signature of this effective spacetime depends on the Abrikosov lattice periodicity, the described model may be useful in studying metric signature transitions.Item Secure Degrees of Freedom in Networks with User Misbehavior(MDPI, 2019-09-26) Banawan, Karim; Ulukus, SennurWe investigate the secure degrees of freedom (s.d.o.f.) of three new channel models: broadcast channel with combating helpers, interference channel with selfish users, and multiple access wiretap channel with deviating users. The goal of introducing these channel models is to investigate various malicious interactions that arise in networks, including active adversaries. That is in contrast with the common assumption in the literature that the users follow a certain protocol altruistically and transmit both message-carrying and cooperative jamming signals in an optimum manner. In the first model, over a classical broadcast channel with confidential messages (BCCM), there are two helpers, each associated with one of the receivers. In the second model, over a classical interference channel with confidential messages (ICCM), there is a helper and users are selfish. By casting each problem as an extensive-form game and applying recursive real interference alignment, we show that, for the first model, the combating intentions of the helpers are neutralized and the full s.d.o.f. is retained; for the second model, selfishness precludes secure communication and no s.d.o.f. is achieved. In the third model, we consider the multiple access wiretap channel (MAC-WTC), where multiple legitimate users wish to have secure communication with a legitimate receiver in the presence of an eavesdropper. We consider the case when a subset of users deviate from the optimum protocol that attains the exact s.d.o.f. of this channel. We consider two kinds of deviation: when some of the users stop transmitting cooperative jamming signals, and when a user starts sending intentional jamming signals. For the first scenario, we investigate possible responses of the remaining users to counteract such deviation. For the second scenario, we use an extensive-form game formulation for the interactions of the deviating and well-behaving users. We prove that a deviating user can drive the s.d.o.f. to zero; however, the remaining users can exploit its intentional jamming signals as cooperative jamming signals against the eavesdropper and achieve an optimum s.d.o.f.Item The Capacity of Private Information Retrieval from Decentralized Uncoded Caching Databases(MDPI, 2019-11-28) Wei, Yi-Peng; Arasli, Batuhan; Banawan, Karim; Ulukus, SennurWe consider the private information retrieval (PIR) problem from decentralized uncoded caching databases. There are two phases in our problem setting, a caching phase, and a retrieval phase. In the caching phase, a data center containing all the K files, where each file is of size L bits, and several databases with storage size constraint 𝜇𝐾𝐿 bits exist in the system. Each database independently chooses 𝜇𝐾𝐿 bits out of the total 𝐾𝐿 bits from the data center to cache through the same probability distribution in a decentralized manner. In the retrieval phase, a user (retriever) accesses N databases in addition to the data center, and wishes to retrieve a desired file privately. We characterize the optimal normalized download cost to be 𝐷∗=∑𝑁+1𝑛=1(𝑁𝑛−1)𝜇𝑛−1(1−𝜇)𝑁+1−𝑛(1+1𝑛+⋯+1𝑛𝐾−1). We show that uniform and random caching scheme which is originally proposed for decentralized coded caching by Maddah-Ali and Niesen, along with Sun and Jafar retrieval scheme which is originally proposed for PIR from replicated databases surprisingly results in the lowest normalized download cost. This is the decentralized counterpart of the recent result of Attia, Kumar, and Tandon for the centralized case. The converse proof contains several ingredients such as interference lower bound, induction lemma, replacing queries and answering string random variables with the content of distributed databases, the nature of decentralized uncoded caching databases, and bit marginalization of joint caching distributions.Item ROBUST FACIAL LANDMARKS LOCALIZATION WITH APPLICATIONS IN FACIAL BIOMETRICS(2019) Kumar, Amit; Chellappa, Rama; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Localization of regions of interest on images and videos is a well studied prob- lem in computer vision community. Usually localization tasks imply localization of objects in a given image, such as detection and segmentation of objects in images. However, the regions of interests can be limited to a single pixel as in the task of facial landmark localization or human pose estimation. This dissertation studies ro- bust facial landmark detection algorithms for faces in the wild using learning methods based on Convolution Neural Networks. Detection of specific keypoints on face images is an integral pre-processing step in facial biometrics and numerous other applications including face verification and identification. Detecting keypoints allows to align face images to a canonical coordi- nate system using geometric transforms such as similarity or affine transformations mitigating the adverse affects of rotation and scaling. This challenging problem has become more attractive in recent years as a result of advances in deep learning and release of more unconstrained datasets. The research community is pushing bound-aries to achieve better and better performance on unconstrained images, where the images are diverse in pose, expression and lightning conditions. Over the years, researchers have developed various hand crafted techniques to extract meaningful features from features, most of them being appearance and geometry-based features. However, these features do not perform well for data col- lected in unconstrained settings due to large variations in appearance and other nui- sance factors. Convolution Neural Networks (CNNs) have become prominent because of their ability to extract discriminating features. Unlike the hand crafted features, DCNNs perform feature extraction and feature classification from the data itself in an end-to-end fashion. This enables the DCNNs to be robust to variations present in the data and at the same time improve their discriminative ability. In this dissertation, we discuss three different methods for facial keypoint de- tection based on Convolution Neural Networks. The methods are generic and can be extended to a related problem of keypoint detection for human pose estimation. The first method called Cascaded Local Deep Descriptor Regression uses deep features ex- tracted around local points to learn linear regressors for incrementally correcting the initial estimate of the keypoints. In the second method, called KEPLER, we develop efficient Heatmap CNNs to directly learn the non-linear mapping between the input and target spaces. We also apply different regularization techniques to tackle the effects of imbalanced data and vanishing gradients. In the third method, we model the spatial correlation between different keypoints using Pose Conditioned Convo- lution Deconvolution Networks (PCD-CNN) while at the same time making it pose agnostic by disentangling pose from the face image. Next, we show an applicationof facial landmark localization used to align the face images for the task of apparent age estimation of humans from unconstrained images. In the fourth part of this dissertation we discuss the impact of good quality landmarks on the task of face verification. Previously proposed methods perform with reasonable accuracy on high resolution and good quality images, but fail when the input image suffers from degradation. To this end, we propose a semi-supervised method which aims at predicting landmarks in the low quality images. This method learns to predict landmarks in low resolution images by learning to model the learning process of high resolution images. In this algorithm, we use Generative Adversarial Networks, which first learn to model the distribution of real low resolution images after which another CNN learns to model the distribution of heatmaps on the images. Additionally, we also propose another high quality facial landmark detection method, which is currently state of the art. Finally, we also discuss the extension of ideas developed for facial keypoint localization for the task of human pose estimation, which is one of the important cues for Human Activity Recognition. As in PCD-CNN, the parts of human body can also be modelled in a tree structure, where the relationship between these parts are learnt through convolutions while being conditioned on the 3D pose and orientation. Another interesting avenue for research is extending facial landmark localization to naturally degraded images.Item Nighttime Photovoltaic Cells: Electrical power generation by optically couping with deep space(2019) Deppe, Tristan; Munday, Jeremy N; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Photovoltaics possess significant potential due to the abundance of solar power incident on earth; however, they can only generate electricity during daylight hours. In order to produce electrical power after the sun has set, we consider an alternative photovoltaic concept that uses the earth as a heat source and the night sky as a heat sink, resulting in a “nighttime photovoltaic cell” that employs thermoradiative photovoltaics and radiative cooling to output as much as 10 W/m^2 from ambient radiation. This thesis will discuss the principles of thermoradiative photovoltaics, the theoretical limits of coupling a device with deep space, the potential of advanced radiative cooling techniques to enhance their performance, and a discussion of the practical limits, scalability, and integrability of this nighttime photovoltaic concept.Item INTEGRATED QUANTUM PHOTONIC CIRCUITS WITH QUANTUM DOTS(2019) Aghaeimeibodi, Shahriar; Waks, Edo; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Scalable quantum photonics require efficient single-photon emitters as well as low-loss reconfigurable photonic platforms that connect and manipulate these single photons. Quantum dots are excellent sources of on-demand single photons and can act as stable quantum memories. Therefore, integration of quantum dots with photonic platforms is crucial for many applications in quantum information processing. In this thesis, we first describe hybrid integration of InAs quantum dots hosted in InP to silicon photonic waveguides. We demonstrate an efficient transition of quantum emission to silicon. Quantum nature of the emission is confirmed through photon correlation measurements. Secondly, we present a micro-disk resonator device based on silicon photonics that enables on-chip filtering and routing of single photons generated by quantum dots. The tunability of silicon photonics decreases at low temperatures due to “carrier freeze-out”. Because of a strong electro-optic effect in lithium niobate, this material is the ideal platform for reconfigurable photonics, even at cryogenic temperatures. To this end, we demonstrate integration of quantum dots with thin-film lithium niobate photonics promising for active switching and modulating of single photons. More complex quantum photonic devices require multiple identical single-photon emitters on the chip. However, the transition wavelength of quantum dots varies because of the slightly different shape and size of each dot. To address this hurdle, we propose and characterize a quantum dot device located in an electrostatic field. The resonance wavelength of the quantum dot emission is tuned up to 8 nm, more than one order of magnitude greater than the transition linewidth, opening the possibility of tuning multiple quantum dots in resonance with each other. Finally, we discuss the application of a single quantum dot strongly coupled to a nanophotonic cavity as an efficient medium for non-linear phenomenon of optical amplification. Presence of a strong pump laser inverses the population of the quantum dot and leads to stimulated emission from the cavity-coupled quantum dot. Using this platform, we observe an optical gain of ~ 16%, significantly increased compared to previous demonstrations of gain in single solid-state quantum emitters without cavities or weakly coupled to cavities. These demonstrations are significant steps toward robust control of single photons using linear and non-linear photonic platforms.Item Planning, Monitoring and Learning with Safety and Temporal Constraints for Robotic Systems(2019) Lin, Zhenyu; Baras, John S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this thesis, we address the problem of planning, monitoring and learning in robotic systems, while considering the safety and time constraints. Motion and action planning for robotic systems is important for real, physical world applications. Robots are capable of performing repetitive tasks at speeds and accuracies that far exceed those of human operators and are widely used in manufacturing, medical fields and even transportation. Planning commonly refers to a process of converting high-level task specifications into low-level control commands that can be executed on the system of interest. Time behavior is a most important issue for the autonomous systems of interest, and it is critical for many robotic tasks. Most state of the art methods, however, are not capable of providing the framework needed for the autonomous systems to plan under finite time constraints. Safety and time constraints are two important aspects for the plan. We are interested in the safety of the plan, such as ``Can the robot reach the goal without collision?''. We are also interested in the time constraints for the plan, such as ``Can the robot finish this task after 3 minutes but no later than 5 minutes?''. These type of tasks are important to understand in robot search and rescue or cooperative robotic production line. In this thesis, we address these problems by two different approaches, the first one is a timed automata based approach, which focuses on a more high-level, abstracted result with less computational requirement. The other one involves converting the problem into a mixed integer linear programming (MILP) with more low-level control details but requires higher computational power. Both methods are able to automatically generate a plan that are guaranteed to be correct. The robotic systems may behave differently in runtime and not able to execute the task perfectly as planned. Given that a robotic system is naturally cyber-physical, and malfunctions can have safety consequences, monitoring the system’s behavior at runtime can be key to safe operation. Therefore, it is important to consider both time and space tolerances during the planning phase, and also design runtime monitors for error detection and possible self-correction. We provide an optimization-based formulation which takes the tolerances into account, and we have designed runtime monitors to monitor the status of the systems, as well as an event-triggered model predictive controller for self-correction. Learning is another very important aspect for the robotics field. We hope to only provide the robot with high-level task specifications, and the robot learns to accomplish the task. Thus, in the next part of this thesis, we discussed how the robot could learn to accomplish task specified by metric interval temporal logic, and how the robot could replan and self-correct if the initial plan fails to execute correctly. As the field of robotics is expanding from the fixed environment of a production line to complex human environments, robots are required to perform increasingly human-like manipulation tasks. Thus, for the last aspect of the thesis, we considered a manipulation task with dexterous robotic hand - Shadow Hand. We collected the multimodal haptic-vision dataset, and proposed the framework of self-assurance slippage detection and correction. We provided the simulation and also real-world implementation with a UR10 and Shadowhand robotic system.