Computer Engineering Commons^™

Evaluating Deep-Learning Models For Debris-Covered Glacier Mapping, Zhiyuan Xie, Vijayan K. Asari, Umesh K. Haritashya

Electrical and Computer Engineering Faculty Publications

In recent decades, mountain glaciers have experienced the impact of climate change in the form of accelerated glacier retreat and other glacier-related hazards such as mass wasting and glacier lake outburst floods. Since there are wide-ranging societal consequences of glacier retreat and hazards, monitoring these glaciers as accurately and repeatedly as possible is important. However, the accurate glacier boundary, especially the debriscovered glacier (DCG) boundary, which is one of the primary inputs in many glacier analyses, remains a challenge even after many years of research using conventional remote sensing methods or machine-learning methods. The GlacierNet, a deep-learning-based approach, utilized the …

Resampling And Super-Resolution Of Hexagonally Sampled Images Using Deep Learning, Dylan Flaute, Russell C. Hardie, Hamed Elwarfalli

Electrical and Computer Engineering Faculty Publications

Super-resolution (SR) aims to increase the resolution of imagery. Applications include security, medical imaging, and object recognition. We propose a deep learning-based SR system that takes a hexagonally sampled low-resolution image as an input and generates a rectangularly sampled SR image as an output. For training and testing, we use a realistic observation model that includes optical degradation from diffraction and sensor degradation from detector integration. Our SR approach first uses non-uniform interpolation to partially upsample the observed hexagonal imagery and convert it to a rectangular grid. We then leverage a state-of-the-art convolutional neural network (CNN) architecture designed for SR …

A Unified Framework Of Deep Learning-Based Facial Expression Recognition System For Diversified Applications, Sanoar Hossain, Saiyed Umer, Vijayan K. Asari, Ranjeet Kumar Rout

Electrical and Computer Engineering Faculty Publications

This work proposes a facial expression recognition system for a diversified field of appli- cations. The purpose of the proposed system is to predict the type of expressions in a human face region. The implementation of the proposed method is fragmented into three components. In the first component, from the given input image, a tree-structured part model has been applied that predicts some landmark points on the input image to detect facial regions. The detected face region was normalized to its fixed size and then down-sampled to its varying sizes such that the advantages, due to the effect of multi-resolution …

Physically Based Rendering Techniques To Visualize Thin-Film Smoothed Particle Hydrodynamics Fluid Simulations, Aditya H. Prasad

Dartmouth College Undergraduate Theses

This thesis introduces a methodology and workflow I developed to visualize smoothed hydrodynamic particle based simulations for the research paper ’Thin-Film Smoothed Particle Hydrodynamics Fluid’ (2021), that I co-authored. I introduce a physically based rendering model which allows point cloud simulation data representing thin film fluids and bubbles to be rendered in a photorealistic manner. This includes simulating the optic phenomenon of thin-film interference and rendering the resulting iridescent patterns. The key to the model lies in the implementation of a physically based surface shader that accounts for the interference of infinitely many internally reflected rays in its bidirectional surface …

A Fully-Automated, Deep Learning-Based Framework For Ct-Based Localization, Segmentation, Verification And Planning Of Metastatic Vertebrae, Tucker Netherton, Tucker James Netherton

Dissertations & Theses (Open Access)

Palliative radiotherapy is an effective treatment for the palliation of symptoms caused by vertebral metastases. Visible evidence of disease is localized on medical images as part of the treatment planning process. However, complicating factors such as time pressures, anatomic variants in the spine, and similarities in adjacent vertebrae are associated with wrong level treatments of the spine. In addition, erroneous manual contouring of anatomic structures is a major failure mode in radiotherapy treatment planning.

The purpose of this study is to mitigate the challenges associated with treatment planning of the spine by automating the treatment planning process for three-dimensional conformal …

Lecture 12: Recent Advances In Time Integration Methods And How They Can Enable Exascale Simulations, Carol S. Woodward

Mathematical Sciences Spring Lecture Series

To prepare for exascale systems, scientific simulations are growing in physical realism and thus complexity. This increase often results in additional and changing time scales. Time integration methods are critical to efficient solution of these multiphysics systems. Yet, many large-scale applications have not fully embraced modern time integration methods nor efficient software implementations. Hence, achieving temporal accuracy with new and complex simulations has proved challenging. We will overview recent advances in time integration methods, including additive IMEX methods, multirate methods, and parallel-in-time approaches, expected to help realize the potential of exascale systems on multiphysics simulations. Efficient execution of these methods …

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 …

Electrostatic Design And Characterization Of A 200 Kev Photogun And Wien Spin Rotator, Gabriel G. Palacios Serrano

Electrical & Computer Engineering Theses & Dissertations

High-energy nuclear physics experiments at the Jefferson Lab Continuous Electron Beam Accelerator Facility (CEBAF) require high spin-polarization electron beams produced from strained super-lattice GaAs photocathodes activated to negative electron affinity in a high voltage photogun operating at 130 kV dc. A pair of Wien filter spin rotators in the injector provides precise control of the electron beam polarization at the end station target. An upgrade of the CEBAF injector to better support the upcoming Moller experiment requires increasing the electron beam energy to 200 keV, resulting in better transmission through injector apertures and improved photocathode lifetime. In addition, the energy …

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 …

Computation And Data Driven Discovery Of Topological Phononic Materials, Jiangxu Li, Jiaxi Liu, Stanley A. Baronett, Mingfeng Liu, Lei Wang, Ronghan Li, Yun Chen, Dianzhong Li, Qiang Zhu, Xing Qiu Chen

Physics & Astronomy Faculty Research

© 2021, The Author(s). The discovery of topological quantum states marks a new chapter in both condensed matter physics and materials sciences. By analogy to spin electronic system, topological concepts have been extended into phonons, boosting the birth of topological phononics (TPs). Here, we present a high-throughput screening and data-driven approach to compute and evaluate TPs among over 10,000 real materials. We have discovered 5014 TP materials and grouped them into two main classes of Weyl and nodal-line (ring) TPs. We have clarified the physical mechanism for the occurrence of single Weyl, high degenerate Weyl, individual nodal-line (ring), nodal-link, nodal-chain, …

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 …

Electroosmotic Mixing Of Non-Newtonian Fluid In A Microchannel With Obstacles And Zeta Potential Heterogeneity, Lanju Mei, Defu Cui, Jiayue Shen, Diganta Dutta, Willie Brown, Lei Zhang, Ibibia K. Dabipi

Computational Modeling & Simulation Engineering Faculty Publications

This paper investigates the electroosmotic micromixing of non-Newtonian fluid in a microchannel with wall-mounted obstacles and surface potential heterogeneity on the obstacle surface. In the numerical simulation, the full model consisting of the Navier–Stokes equations and the Poisson–Nernst–Plank equations are solved for the electroosmotic fluid field, ion transport, and electric field, and the power law model is used to characterize the rheological behavior of the aqueous solution. The mixing performance is investigated under different parameters, such as electric double layer thickness, flow behavior index, obstacle surface zeta potential, obstacle dimension. Due to the zeta potential heterogeneity at the obstacle surface, …

Ieee Access Special Section Editorial: Trends And Advances In Bio-Inspired Image-Based Deep Learning Methodologies And Applications, Peter Peer, Carlos M. Travieso-Gonzalez, Vijayan K. Asari, Malay Kishore Dutta

Electrical and Computer Engineering Faculty Publications

Many of the technological advances we enjoy today have been inspired by biological systems due to their ease of operation and outstanding efficiency. Designing technological solutions based on biological inspiration has become a cornerstone of research in a variety of areas ranging from control theory and optimization to computer vision, machine learning, and artificial intelligence. Especially in the latter few areas, biologically relevant solutions are becoming increasingly important as we look for new ways to make artificial systems more efficient, intelligent, and overall effective.

Dales Objects: A Large Scale Benchmark Dataset For Instance Segmentation In Aerial Lidar, Nina M. Singer, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

We present DALES Objects, a large-scale instance segmentation benchmark dataset for aerial lidar. DALES Objects contains close to half a billion hand-labeled points, including semantic and instance segmentation labels. DALES Objects is an extension of the DALES (Varney et al., 2020) dataset, adding additional intensity and instance segmentation annotation. This paper provides an overview of the data collection, preprocessing, hand-labeling strategy, and final data format. We propose relevant evaluation metrics and provide insights into potential challenges when evaluating this benchmark dataset. Finally, we provide information about how researchers can access the dataset for their use at go.udayton.edu/dales3d.