Optics Commons^™

Design, Fabrication, And Characterization Of Advanced High-Power Single-Mode 9xxnm Semiconductor Lasers, Xiaolei Zhao

All Dissertations

This thesis presents the comprehensive design, fabrication, and demonstration of advanced high-power, high-efficiency single-mode semiconductor lasers operating at a wavelength of 9xxnm. We begin with the design of the laser epitaxial structure, serving as the cornerstone for achieving high-power high-efficiency lasers. Our methodology integrates a semi-analytical calculation model, which accounts for Longitudinal Spatial Hole Burning (LSHB) and Two-Photon Absorption (TPA) effects, facilitating a thorough exploration of how design parameters influence output power and conversion efficiency. This approach offers an effective and time-efficient epitaxial structure optimization strategy compared to conventional full 3D simulation models.

Subsequently, we demonstrate high-power, high-efficiency ridge waveguide …

Numerical Simulation Of Laser Induced Elastic Waves In Response To Short And Ultrashort Laser Pulses., Alireza Zarei

All Dissertations

In an era of intensified market competition, the demand for cost-effective, high-quality, high-performance, and reliable products continues to rise. Meeting this demand necessitates the mass production of premium products through the integration of cutting-edge technologies and advanced materials while ensuring their integrity and safety. In this context, Nondestructive Testing (NDT) techniques emerge as indispensable tools for guaranteeing the integrity, reliability, and safety of products across diverse industries.

Various NDT techniques, including ultrasonic testing, computed tomography, thermography, and acoustic emissions, have long served as cornerstones for inspecting materials and structures. Among these, ultrasonic testing stands out as the most prevalent method, …

Deep Selenium Donors In Zngep2 Crystals: An Electron Paramagnetic Resonance Study Of A Nonlinear Optical Material, Timothy D. Gustafson, Larry E. Halliburton, Nancy C. Giles, Peter G. Schunemann, Kevin T. Zawilski, J. Jesenovec, Kent L. Averett, Jeremy Slagle

Faculty Publications

Zinc germanium diphosphide (ZnGeP₂) is a ternary semiconductor best known for its nonlinear optical properties. A primary application is optical parametric oscillators operating in the mid-infrared region. Controlled donor doping provides a method to minimize the acceptor-related absorption bands that limit the output power of these devices. In the present study, a ZnGeP₂ crystal is doped with selenium during growth. Selenium substitutes for phosphorus and serves as a deep donor. Significant concentrations of native defects (zinc vacancies, germanium-on-zinc antisites, and phosphorous vacancies) are also present in the crystal. Electron paramagnetic resonance (EPR) is used to establish the …

Investigation Of Gas Dynamics In Water And Oil-Based Muds Using Das, Dts, And Dss Measurements, Temitayo S. Adeyemi

LSU Master's Theses

Reliable prediction of gas migration velocity, void fraction, and length of gas-affected region in water and oil-based muds is essential for effective planning, control, and optimization of drilling operations. However, there is a gap in our understanding of gas behavior and dynamics in water and oil-based muds. This is a consequence of the use of experimental systems that are not representative of field-scale conditions. This study seeks to bridge the gap via the well-scale deployment of distributed fiber-optic sensors for real-time monitoring of gas behavior and dynamics in water and oil-based mud. The aforementioned parameters were estimated in real-time using …

6d Single-Fluorogen Orientation-Localization Microscopy For Elucidating The Architecture Of Beta-Sheet Assemblies And Biomolecular Condensates, Tingting Wu, Weiyan Zhou, Jai S. Rudra, Rohit V. Pappu, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

We develop six-dimensional single-molecule orientation-localization microscopy (SMOLM) to measure the 3D positions and 3D orientations simultaneously of single fluorophores. We show how careful optimization of phase and polarization modulation components can encode phase, polarization, and angular spectrum information from each fluorescence photon into a microscope’s dipole-spread function. We used the transient binding and blinking of Nile red (NR) to characterize the helical structure of fibrils formed by designed amphipathic peptides, KFE8^L and KFE8^D, and the pathological amyloid-beta peptide Aβ42. We also deployed merocyanine 540 to uncover the interfacial architectures of biomolecular condensates.

Residual Optical Absorption From Native Defects In Cdsip2 Crystals, Timothy D. Gustafson, Nancy C. Giles, Elizabeth M. Scherrer, Kevin T. Zawilski, Peter G. Schunemann, Kent L. Averett, Jonathan E. Slagle, Larry E. Halliburton

Faculty Publications

CdSiP₂ crystals are used in optical parametric oscillators to produce tunable output in the mid-infrared. As expected, the performance of the OPOs is adversely affected by residual optical absorption from native defects that are unintentionally present in the crystals. Electron paramagnetic resonance (EPR) identifies these native defects. Singly ionized silicon vacancies (V^-_Si) are responsible for broad optical absorption bands peaking near 800, 1033, and 1907 nm. A fourth absorption band, peaking near 630 nm, does not involve silicon vacancies. Exposure to 1064 nm light when the temperature of the CdSiP₂ crystal is near 80K converts …

Exponential Fusion Of Interpolated Frames Network (Efif-Net): Advancing Multi-Frame Image Super-Resolution With Convolutional Neural Networks, Hamed Elwarfalli, Dylan Flaute, Russell C. Hardie

Electrical and Computer Engineering Faculty Publications

Convolutional neural networks (CNNs) have become instrumental in advancing multi-frame image super-resolution (SR), a technique that merges multiple low-resolution images of the same scene into a high-resolution image. In this paper, a novel deep learning multi-frame SR algorithm is introduced. The proposed CNN model, named Exponential Fusion of Interpolated Frames Network (EFIF-Net), seamlessly integrates fusion and restoration within an end-to-end network. Key features of the new EFIF-Net include a custom exponentially weighted fusion (EWF) layer for image fusion and a modification of the Residual Channel Attention Network for restoration to deblur the fused image. Input frames are registered with subpixel …

Intelligent Millimeter-Wave System For Human Activity Monitoring For Telemedicine, Abdullah K. Alhazmi, Mubarak A. Alanazi, Awwad H. Alshehry, Saleh M. Alshahry, Jennifer Jaszek, Cameron Djukic, Anna Brown, Kurt Jackson, Vamsy P. Chodavarapu

Electrical and Computer Engineering Faculty Publications

Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately detect falls and monitor physical activity remotely without invading privacy or remembering to wear a costly device remains an ongoing concern. Our proposed system utilizes a millimeter-wave (mmwave) radar sensor (IWR6843ISK-ODS) connected to an NVIDIA Jetson Nano board for continuous monitoring of human activity. We developed a PointNet neural network for real-time human activity monitoring that can …

Cross-Layer Design Of Highly Scalable And Energy-Efficient Ai Accelerator Systems Using Photonic Integrated Circuits, Sairam Sri Vatsavai

Theses and Dissertations--Electrical and Computer Engineering

Artificial Intelligence (AI) has experienced remarkable success in recent years, solving complex computational problems across various domains, including computer vision, natural language processing, and pattern recognition. Much of this success can be attributed to the advancements in deep learning algorithms and models, particularly Artificial Neural Networks (ANNs). In recent times, deep ANNs have achieved unprecedented levels of accuracy, surpassing human capabilities in some cases. However, these deep ANN models come at a significant computational cost, with billions to trillions of parameters. Recent trends indicate that the number of parameters per ANN model will continue to grow exponentially in the foreseeable …