Physical Sciences and Mathematics Commons^™

Optical Switching Performance Of Thermally Oxidized Vanadium Dioxide With An Integrated Thin Film Heater, Andrew M. Sarangan, Gamini Ariyawansa, Ilya Vitebskiy, Igor Anisimov

Electro-Optics and Photonics Faculty Publications

Optical switching performance of vanadium dioxide produced by thermal oxidation of vanadium is presented in this paper. A 100nm thick vanadium was oxidized under controlled conditions in a quartz tube furnace to produce approximately 200nm thick VO2. The substrate was appropriately coated on the front and back side to reduce reflection in the cold state, and an integrated thin film heater was fabricated to allow in-situ thermal cycling. Electrical measurements show a greater than three orders of magnitude change in resistivity during the phase transition. Optical measurements exhibit 70% transparency at 1500nm and about 15dB extinction across a wide spectral …

A Study Of Magnetism And Possible Mixed-State Superconductivity In Phosphorus-Doped Graphene, Julian E. Gil Pinzon

FIU Electronic Theses and Dissertations

Evidence of superconducting vortices, and consequently mixed-state superconductivity, has been observed in phosphorus-doped graphene at temperatures as high as 260 K. The evidence includes transport measurements in the form of resistance versus temperature curves, and magnetic measurements in the form of susceptibility and magnetic Nernst effect measurements. The drops in resistance, periodic steps in resistance, the appearance of Nernst peaks and hysteresis all point to phosphorus-doped graphene having a broad resistive region due to flux flow as well as a Berezinskii-Kosterlitz-Thouless (BKT) transition at lower temperatures.

The observation of irreversible behavior in phosphorus-doped graphene under the influence of a thermal …

Universal Biological Motions For Educational Robot Theatre And Games, Rajesh Venkatachalapathy, Martin Zwick, Adam Slowik, Kai Brooks, Mikhail Mayers, Roman Minko, Tyler Hull, Bliss Brass, Marek Perkowski

Systems Science Faculty Publications and Presentations

Paper presents a concept that is new to robotics education and social robotics. It is based on theatrical games, in motions for social robots and animatronic robots. Presented here motion model is based on Drift Differential Model from biology and Fokker-Planck equations. This model is used in various areas of science to describe many types of motion. The model was successfully verified on various simulated mobile robots and a motion game of three robots called "Mouse and Cheese."

Flying Free: A Research Overview Of Deep Learning In Drone Navigation Autonomy, Thomas Lee, Susan Mckeever, Jane Courtney

Articles

With the rise of Deep Learning approaches in computer vision applications, significant strides have been made towards vehicular autonomy. Research activity in autonomous drone navigation has increased rapidly in the past five years, and drones are moving fast towards the ultimate goal of near-complete autonomy. However, while much work in the area focuses on specific tasks in drone navigation, the contribution to the overall goal of autonomy is often not assessed, and a comprehensive overview is needed. In this work, a taxonomy of drone navigation autonomy is established by mapping the definitions of vehicular autonomy levels, as defined by the …

A Quantitative Validation Of Multi-Modal Image Fusion And Segmentation For Object Detection And Tracking, Nicholas Lahaye, Michael J. Garay, Brian D. Bue, Hesham El-Askary, Erik Linstead

Mathematics, Physics, and Computer Science Faculty Articles and Research

In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and qualitative analysis of the output data in comparison with reference data. In this paper, we quantitatively validate the methodology against datasets currently being generated and used within the remote sensing community, as well as show the capabilities and benefits of the data fusion methodologies used. The experiments run take the output of our unsupervised fusion and segmentation methodology and map them to various labeled datasets at different levels of global …

Optical Phonon Modes, Static And High-Frequency Dielectric Constants, And Effective Electron Mass Parameter In Cubic In2O3, Megan Stokey, Rafal Korlacki, Sean Knight, Alexander Ruder, Matthew J. Hilfiker, Zbigniew Galazka, Klaus Irmscher, Yuxuan Zhang, Hongping Zhao, Vanya Darakchieva, Mathias Schubert

Department of Electrical and Computer Engineering: Faculty Publications

A complete set of all optical phonon modes predicted by symmetry for bixbyite structure indium oxide is reported here from a combination of far-infrared and infrared spectroscopic ellipsometry, as well as first principles calculations. Dielectric function spectra measured on high quality, marginally electrically conductive melt grown single bulk crystals are obtained on a wavelength-by-wavelength (also known as point-by-point) basis and by numerical reduction of a subtle free charge carrier Drude model contribution. A four-parameter semi-quantum model is applied to determine all 16 pairs of infrared-active transverse and longitudinal optical phonon modes, including the high-frequency dielectric constant, ε_∞=4.05±0.05. The …

Synthetic Aperture Radar Image Recognition Of Armored Vehicles, Christopher Szul [*], Torrey J. Wagner, Brent T. Langhals

Faculty Publications

Synthetic Aperture Radar (SAR) imagery is not affected by weather and allows for day-and-night observations, however it can be difficult to interpret. This work applies classical and neural network machine learning techniques to perform image classification of SAR imagery. The Moving and Stationary Target Acquisition and Recognition dataset from the Air Force Research Laboratory was used, which contained 2,987 total observations of the BMP-2, BTR-70, and T-72 vehicles. Using a 75%/25% train/test split, the classical model achieved an average multi-class image recognition accuracy of 70%, while a convolutional neural network was able to achieve a 97% accuracy with lower model …

Establishing Suitable Metrics To Encourage Broader Use Of Atomic Requirements, William L. Honig

Computer Science: Faculty Publications and Other Works

Despite the apparent benefits of singular, individual, or atomic requirements, their use remains limited, and teaching their creation is difficult. The acceptance of a set of requirements metrics specifically designed to evaluate atomic requirements may lead to their better utilization and improved requirements engineering. Twelve metrics designed to measure atomic requirements are presented here: six used on individual requirements statements and six applied to a requirements document or set of requirement statements. Example metrics for individual requirements are Requirement Completeness and Requirement Atomicity; examples to measure multiple requirements include Requirements Traceablity and Requirements Purity. These metrics are designed to work …

Requirements Metrics - A Working List, William L. Honig

Computer Science: Faculty Publications and Other Works

A working set of metrics for review of requirements materials including documents.

Internet Of Medical Things (Iomt): Overview, Emerging Technologies, And Case Studies, Sahshanu Razdan, Sachin Sharma

Articles

No abstract provided.

Development And Implementation Of A Pressure-Temperature Control System For The Physical Vapor Deposition Of Copper And Niobium From A Molybdenum Filament In The Development Of Superconducting 3d Printed Rf Cavity Particle Accelerators, Chandler J. Fleuette

Student Research Projects

This report covers the development of the pressure-temperature control system used in the production of small superconducting RF cavities for particle accelerators. To test the validity of the created program, a model for the process was created and tested. The model was used to fine tune the control system before integrating it into the lab. The end goal of the control system is to measure the pressure inside of a deposition vacuum chamber, convert that pressure to a temperature, and use that temperature in tandem with a PID controller to control the current passing though a molybdenum filament which is …

Suppressing Bias Stress Degradation In High Performance Solution Processed Organic Transistors Operating In Air, Hamna F. Iqbal, Qianxiang Ai, Karl J. Thorley, Hu Chen, Iain Mcculloch, Chad Risko, John E. Anthony, Oana D. Jurchescu

Chemistry Faculty Publications

Solution processed organic field effect transistors can become ubiquitous in flexible optoelectronics. While progress in material and device design has been astonishing, low environmental and operational stabilities remain longstanding problems obstructing their immediate deployment in real world applications. Here, we introduce a strategy to identify the most probable and severe degradation pathways in organic transistors and then implement a method to eliminate the main sources of instabilities. Real time monitoring of the energetic distribution and transformation of electronic trap states during device operation, in conjunction with simulations, revealed the nature of traps responsible for performance degradation. With this information, we …

Twisted Spatiotemporal Optical Vortex Random Fields, Milo W. Hyde Iv

Faculty Publications

We present twisted spatiotemporal optical vortex (STOV) beams, which are partially coherent light sources that possess a coherent optical vortex and a random twist coupling their space and time dimensions. These beams have controllable partial coherence and transverse orbital angular momentum (OAM), which distinguishes them from the more common spatial vortex and twisted beams (known to carry longitudinal OAM) in the literature and should ultimately make them useful in applications such as optical communications and optical tweezing. We present the mathematical analysis of twisted STOV beams, deriving the mutual coherence function and linear and angular momentum densities. We simulate the …

Guest Editorial: Edge Intelligence For Beyond 5g Networks, Yan Zhang, Zhiyong Feng, Hassnaa Moustafa, Feng Ye, Usman Javaid, Chunfen Cui

Electrical and Computer Engineering Faculty Publications

Beyond fifth-generation (B5G) networks, or so-called "6G", is the next-generation wireless communications systems that will radically change how Society evolves. Edge intelligence is emerging as a new concept and has extremely high potential in addressing the new challenges in B5G networks by providing mobile edge computing and edge caching capabilities together with Artificial Intelligence (AI) to the proximity of end users. In edge intelligence empowered B5G networks, edge resources are managed by AI systems for offering powerful computational processing and massive data acquisition locally at edge networks. AI helps to obtain efficient resource scheduling strategies in a complex environment with …

Polymer Based Triboelectric Nanogenerator For Cost‐Effective Green Energy Generation And Implementation Of Surface‐Charge Engineering, Diana Lopez, Aminur Rashid Chowdhury, Abu Masa Abdullah, Muhtasim Ul Karim Sadaf, Isaac Martinez, Brishty Deb Chowdhury, Serena Danti, Christopher J. Ellison, Karen Lozano, Mohammed Jasim Uddin

Chemistry Faculty Publications and Presentations

Performance of triboelectric nanogenerators for harvesting mechanical energy from the ambient environment has been limited by structural complexity, cost-effectiveness, and mechanical weakness of materials. Herein, a cost-effective vertical contact separation mode triboelectric nanogenerator using polyethylene (PE) and polycarbonate (PC) in a regular digital versatile disc is reported. This cost-effective nanogenerator with simplified structures is able to generate an open-circuit voltage of 215.3 V and short-circuit current of 80 μA. The effects of the distance of impact and the air gap between the triboelectric layers have also been tested from 3 to 9 cm, and 0.25 to 1 cm, respectively. It …

Integrated Cyberattack Detection And Handling For Nonlinear Systems With Evolving Process Dynamics Under Lyapunov-Based Economic Model Predictive Control, Keshav Kasturi Rangan, Henrique Oyama, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Safety-critical processes are becoming increasingly automated and connected. While automation can increase effciency, it brings new challenges associated with guaranteeing safety in the presence of uncertainty especially in the presence of control system cyberattacks. One of the challenges for developing control strategies with guaranteed safety and cybersecurity properties under suffcient conditions is the development of appropriate detection strategies that work with control laws to prevent undetected attacks that have immediate closed-loop stability consequences. Achieving this, in the presence of uncertainty brought about by plant/model mismatch and process dynamics that can change with time, requires a fundamental understanding of the characteristics …

Development Of A Real-Time Single-Lead Single-Beat Frequency-Independent Myocardial Infarction Detector, Harold Martin

FIU Electronic Theses and Dissertations

The central aim of this research is the development and deployment of a novel multilayer machine learning design with unique application for the diagnosis of myocardial infarctions (MIs) from individual heartbeats of single-lead electrocardiograms (EKGs) irrespective of their sampling frequencies over a given range. To the best of our knowledge, this design is the first to attempt inter-patient myocardial infarction detection from individual heartbeats of single-lead (lead II) electrocardiograms that achieves high accuracy and near real-time diagnosis. The processing time of 300 milliseconds to a diagnosis is just at the time range in between extremely fast heartbeats of around 300 …

Virtual Network Function Embedding Under Nodal Outage Using Deep Q-Learning, Swarna Bindu Chetty, Hamed Ahmadi, Sachin Sharma, Avishek Nag

Articles

With the emergence of various types of applications such as delay-sensitive applications, future communication networks are expected to be increasingly complex and dynamic. Network Function Virtualization (NFV) provides the necessary support towards efficient management of such complex networks, by virtualizing network functions and placing them on shared commodity servers. However, one of the critical issues in NFV is the resource allocation for the highly complex services; moreover, this problem is classified as an NP-Hard problem. To solve this problem, our work investigates the potential of Deep Reinforcement Learning (DRL) as a swift yet accurate approach (as compared to integer linear …

Temperature-Immune Self-Referencing Fabry–Pérot Cavity Sensors, Hengky Chandrahalim, Jonathan W. Smith

AFIT Patents

A passive microscopic Fabry-Pérot Interferometer (FPI) sensor an optical fiber a three-dimensional microscopic optical structure formed on a cleaved tip of an optical fighter that reflects a light signal back through the optical fiber. The reflected light is altered by refractive index changes in the three-dimensional structure that is subject to at least one of: (i) thermal radiation; and (ii) volatile organic compounds.

Implementing Inverse Design Tools For Plasmonic Digital Logic Devices, Krishna Narayan, Mark C. Harrison

Engineering Faculty Articles and Research

Despite the benefits that optics and photonics have brought to improving communications, there remains a lack of commercialized optical computing devices and systems, which reduces the benefits of using light as an information-carrying medium. We are developing architectures and designs of photonic logic gates for creating larger-scale functional photonic logic circuits. In contrast to other approaches, we are focusing on the development of logic devices which can be cascaded in arbitrary ways to allow for more complex photonic integrated circuit design. Additionally, optical computing often uses on-off keying, which fails to take advantage of denser encoding schemes often used to …

Emerging Challenges In Smart Grid Cybersecurity Enhancement: A Review, Fazel Mohammadi

Electrical and Computer Engineering Publications

In this paper, a brief survey of measurable factors affecting the adoption of cybersecurity enhancement methods in the smart grid is provided. From a practical point of view, it is a key point to determine to what degree the cyber resilience of power systems can be improved using cost-effective resilience enhancement methods. Numerous attempts have been made to the vital resilience of the smart grid against cyber-attacks. The recently proposed cybersecurity methods are considered in this paper, and their accuracies, computational time, and robustness against external factors in detecting and identifying False Data Injection (FDI) attacks are evaluated. There is …

Zinc Gallate Spinel Dielectric Function, Band-To-Band Transitions, And Γ-Point Effective Mass Parameters, Matthew J. Hilfiker, Megan Stokey, Rafal Korlacki, Ufuk Kilic, Zbigniew Galazka, Klaus Irmscher, Stefan Zollner, Mathias Schubert

Department of Electrical and Computer Engineering: Faculty Publications

We determine the dielectric function of the emerging ultrawide bandgap semiconductor ZnGa₂O₄ from the near-infrared (0.75 eV) into the vacuum ultraviolet (8.5 eV) spectral regions using spectroscopic ellipsometry on high quality single crystal substrates. We perform density functional theory calculations and discuss the band structure and the Brillouin zone Γ-point band-to-band transition energies, their transition matrix elements, and effective band mass parameters. We find an isotropic effective mass parameter (0.24m_e) at the bottom of the Γ-point conduction band, which equals the lowest valence band effective mass parameter at the top of the highly anisotropic …

Color-Compressive Bilateral Filter And Nonlocal Means For High-Dimensional Images, Christina Karam, Kenjiro Sugimoto, Keigo Hirakawa

Electrical and Computer Engineering Faculty Publications

We propose accelerated implementations of bilateral filter (BF) and nonlocal means (NLM) called color-compressive bilateral filter (CCBF) and color-compressive nonlocal means (CCNLM). CCBF and CCNLM are random filters, whose Monte-Carlo averaged output images are identical to the output images of conventional BF and NLM, respectively. However, CCBF and CCNLM are considerably faster because the spatial processing of multiple color channels are combined into a single random filtering process. This implies that the complexity of CCBF and CCNLM is less sensitive to color dimension (e.g., hyperspectral images) relatively to other BF and NLM methods. We experimentally verified that the execution time …

Deep Learning For Anisoplanatic Optical Turbulence Mitigation In Long-Range Imaging, Matthew A. Hoffmire, Russell C. Hardie, Michael A. Rucci, Richard Van Hook, Barry K. Karch

Electrical and Computer Engineering Faculty Publications

We present a deep learning approach for restoring images degraded by atmospheric optical turbulence. We consider the case of terrestrial imaging over long ranges with a wide field-of-view. This produces an anisoplanatic imaging scenario where turbulence warping and blurring vary spatially across the image. The proposed turbulence mitigation (TM) method assumes that a sequence of short-exposure images is acquired. A block matching (BM) registration algorithm is applied to the observed frames for dewarping, and the resulting images are averaged. A convolutional neural network (CNN) is then employed to perform spatially adaptive restoration. We refer to the proposed TM algorithm as …

Exploring The Efficiency Of Self-Organizing Software Teams With Game Theory, Clay Stevens, Jared Soundy, Hau Chan

CSE Conference and Workshop Papers

Over the last two decades, software development has moved away from centralized, plan-based management toward agile methodologies such as Scrum. Agile methodologies are founded on a shared set of core principles, including self-organizing software development teams. Such teams are promoted as a way to increase both developer productivity and team morale, which is echoed by academic research. However, recent works on agile neglect to consider strategic behavior among developers, particularly during task assignment–one of the primary functions of a self-organizing team. This paper argues that self-organizing software teams could be readily modeled using game theory, providing insight into how agile …

Plasmonic Waveguides To Enhance Quantum Electrodynamic Phenomena At The Nanoscale, Ying Li, Christos Argyropoulos

Department of Electrical and Computer Engineering: Faculty Publications

The emerging field of plasmonics can lead to enhanced light-matter interactions at extremely nanoscale regions. Plasmonic (metallic) devices promise to efficiently control both classical and quantum properties of light. Plasmonic waveguides are usually used to excite confined electromagnetic modes at the nanoscale that can strongly interact with matter. The analysis of these nanowaveguides exhibits similarities with their low frequency microwave counterparts. In this article, we review ways to study plasmonic nanostructures coupled to quantum optical emitters from a classical electromagnetic perspective. These quantum emitters are mainly used to generate single-photon quantum light that can be employed as a quantum bit …

Anisotropic Dielectric Functions, Band-To-Band Transitions, And Critical Points In Α-Ga2O3, Matthew J. Hilfiker, Rafal Korlacki, Riena Jinno, Yongjin Cho, Huili Grace Xing, Debdeep Jena, Ufuk Kilic, Megan Stokey, Mathias Schubert

Department of Electrical and Computer Engineering: Faculty Publications

We use a combined generalized spectroscopic ellipsometry and density functional theory approach to determine and analyze the anisotropic dielectric functions of an α-Ga₂O₃ thin film. The sample is grown epitaxially by plasma-assisted molecular beam epitaxy on m-plane sapphire. Generalized spectroscopic ellipsometry data from multiple sample azimuths in the spectral range from 0.73 eV to 8.75 eV are simultaneously analyzed. Density functional theory is used to calculate the valence and conduction band structure. We identify, for the indirect-bandgap material, two direct band-to-band transitions with M₀-type van Hove singularities for polarization perpendicular to the c axis, …

Computational Modelling Enables Robust Multidimensional Nanoscopy, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

The following sections are included:

• Present State of Computational Modelling in Fluorescence Nanoscopy

• Recent Contributions to Computational Modelling in Fluorescence Nanoscopy

• Outlook on Computational Modelling in Fluorescence Nanoscopy

• Acknowledgments

• References

Statistical Analysis And Comparison Of Optical Classification Of Atmospheric Aerosol Lidar Data, Mohammed Alqawba, Norou Diawara, Kwasi G. Afrifa, Mohamed I. Elbakary, Mecit Cetin, Khan Iftekharuddin

Mathematics & Statistics Faculty Publications

In this article, we present a new study for the analysis and classification of atmospheric aerosols in remote sensing LIDAR data. Information on particle size and associated properties are extracted from these remote sensing atmospheric data which are collected by a ground-based LIDAR system. This study first considers optical LIDAR parameter-based classification methods for clustering and classification of different types of harmful aerosol particles in the atmosphere. Since accurate methods for aerosol prediction behaviors are based upon observed data, computational approaches must overcome design limitations, and consider appropriate calibration and estimation accuracy. Consequently, two statistical methods based on generalized linear …

Parking Recommender System Privacy Preservation Through Anonymization And Differential Privacy, Yasir Saleem Shaikh, Mubashir Husain Rehmani, Noel Crespi, Roberto Minerva

Publications

Recent advancements in the Internet of Things (IoT) have enabled the development of smart parking systems that use services of third-party parking recommender system to provide recommendations of personalized parking spot to users based on their past experience. However, the indiscriminate sharing of users’ data with an untrusted (or semitrusted) parking recommender system may breach the privacy because users’ behavior and mobility patterns could be inferred by analyzing their past history. Therefore, in this article, we present two solutions that preserve privacy of users in parking recommender systems while analyzing the past parking history using k-anonymity (anonymization) and differential privacy …