Optics Commons^™

Conditional Generative Adversarial Network Demosaicing Strategy For Division Of Focal Plane Polarimeters, Garrett Sargent, Bradley M. Ratliff, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Division of focal plane (DoFP), or integrated microgrid polarimeters, typically consist of a 2 × 2 mosaic of linear polarization filters overlaid upon a focal plane array sensor and obtain temporally synchronized polarized intensity measurements across a scene, similar in concept to a Bayer color filter array camera. However, the resulting estimated polarimetric images suffer a loss in resolution and can be plagued by aliasing due to the spatially-modulated microgrid measurement strategy. Demosaicing strategies have been proposed that attempt to minimize these effects, but result in some level of residual artifacts. In this work we propose a conditional generative adversarial …

Transfer-To-Transfer Learning Approach For Computer Aided Detection Of Covid-19 In Chest Radiographs, Barath Narayanan Narayanan, Russell C. Hardie, Vignesh Krishnaraja, Christina Karam, Venkata Salini Priyamvada Davuluru

Electrical and Computer Engineering Faculty Publications

The coronavirus disease 2019 (COVID-19) global pandemic has severely impacted lives across the globe. Respiratory disorders in COVID-19 patients are caused by lung opacities similar to viral pneumonia. A Computer-Aided Detection (CAD) system for the detection of COVID-19 using chest radiographs would provide a second opinion for radiologists. For this research, we utilize publicly available datasets that have been marked by radiologists into two-classes (COVID-19 and non-COVID-19). We address the class imbalance problem associated with the training dataset by proposing a novel transfer-to-transfer learning approach, where we break a highly imbalanced training dataset into a group of balanced mini-sets and …

Polarization-Selective Modulation Of Supercavity Resonances Originating From Bound States In The Continuum, Chan Kyaw, Riad Yahiaoui, Joshua A. Burrow, Viet Tran, Kyron Keelen, Wesley Sims, Eddie C. Red, Willie S. Rockward, Mikkel A. Thomas, Andrew M. Sarangan, Imad Agha, Thomas A. Searles

Electro-Optics and Photonics Faculty Publications

Bound states in the continuum (BICs) are widely studied for their ability to confine light, produce sharp resonances for sensing applications and serve as avenues for lasing action with topological characteristics. Primarily, the formation of BICs in periodic photonic band gap structures are driven by symmetry incompatibility; structural manipulation or variation of incidence angle from incoming light. In this work, we report two modalities for driving the formation of BICs in terahertz metasurfaces. At normal incidence, we experimentally confirm polarization driven symmetry-protected BICs by the variation of the linear polarization state of light. In addition, we demonstrate through strong coupling …

Atmospheric Turbulence Study With Deep Machine Learning Of Intensity Scintillation Patterns, Artem V. Vorontsov, Mikhail A. Vorontsov, Grigorii A. Fillimonov, Ernst Polnau

Electro-Optics and Photonics Faculty Publications

A new paradigm for machine learning-inspired atmospheric turbulence sensing is developed and applied to predict the atmospheric turbulence refractive index structure parameter using deep neural network (DNN)-based processing of short-exposure laser beam intensity scintillation patterns obtained with both: experimental measurement trials conducted over a 7 km propagation path, and imitation of these trials using wave-optics numerical simulations. The developed DNN model was optimized and evaluated in a set of machine learning experiments. The results obtained demonstrate both good accuracy and high temporal resolution in sensing. The machine learning approach was also employed to challenge the validity of several eminent atmospheric …

Artificial Neural Network Discovery Of A Switchable Metasurface Reflector, J. R. Thompson, J. A. Burrow, P. J. Shah, J. Slagle, E. S. Harper, A. Van Rynbach, I. Agha, M. S. Mills

Electro-Optics and Photonics Faculty Publications

Optical materials engineered to dynamically and selectively manipulate electromag- netic waves are essential to the future of modern optical systems. In this paper, we simulate various metasurface configurations consisting of periodic 1D bars or 2D pillars made of the ternary phase change material Ge2Sb2Te5 (GST). Dynamic switching behavior in reflectance is exploited due to a drastic refractive index change between the crystalline and amorphous states of GST. Selectivity in the reflection and transmission spectra is manipulated by tailoring the geometrical parameters of the metasurface. Due to the immense number of possible metasurface configurations, we train deep neural networks capable of …

Ensemble Malware Classification System Using Deep Neural Networks, Barath Narayanan Narayanan, Venkata Salini Priyamvada Davuluru

Electrical and Computer Engineering Faculty Publications

With the advancement of technology, there is a growing need of classifying malware programs that could potentially harm any computer system and/or smaller devices. In this research, an ensemble classification system comprising convolutional and recurrent neural networks is proposed to distinguish malware programs. Microsoft's Malware Classification Challenge (BIG 2015) dataset with nine distinct classes is utilized for this study. This dataset contains an assembly file and a compiled file for each malware program. Compiled files are visualized as images and are classified using Convolutional Neural Networks (CNNs). Assembly files consist of machine language opcodes that are distinguished among classes using …

Ensemble Lung Segmentation System Using Deep Neural Networks, Redha A. Ali, Russell C. Hardie, Hussin K. Ragb

Electrical and Computer Engineering Faculty Publications

Lung segmentation is a significant step in developing computer-aided diagnosis (CAD) using Chest Radiographs (CRs). CRs are used for diagnosis of the 2019 novel coronavirus disease (COVID-19), lung cancer, tuberculosis, and pneumonia. Hence, developing a Computer-Aided Detection (CAD) system would provide a second opinion to help radiologists in the reading process, increase objectivity, and reduce the workload. In this paper, we present the implementation of our ensemble deep learning model for lung segmentation. This model is based on the original DeepLabV3+, which is the extended model of DeepLabV3. Our model utilizes various architectures as a backbone of DeepLabV3+, such as …

Corrections To ‘‘Glaciernet: A Deep-Learning Approach For Debris-Covered Glacier Mapping’’, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Brennan W. Young, Michael P. Bishop, Jeffrey S. Kargel

Electrical and Computer Engineering Faculty Publications

In the above article [1], Figure 2 was incorrect. Unfortunately, we mixed the color label of "CONV $\to $ BN $\to $ ReLu" and "Unpooling" in the CNN structure section of Figure 2. The color label of "CONV $\to $ BN $\to $ ReLu" should be orange while the color label of "Unpooling" should be green. Also, the word "Decoder" is misspelled. That same figure with the same error is also used for the graphic abstract. The corrected figure is given here. None of the sections in the figure is modified. The only change is in the color label of …

Ev Charging Behavior Analysis Using Hybrid Intelligence For 5g Smart Grid, Yi Shen, Wei Fang, Feng Ye, Michel Kadoch

Electrical and Computer Engineering Faculty Publications

With the development of the Internet of Things (IoT) and the widespread use of electric vehicles (EV), vehicle-to-grid (V2G) has sparked considerable discussion as an energy-management technology. Due to the inherently high maneuverability of EVs, V2G systems must provide on-demand service for EVs. Therefore, in this work, we propose a hybrid computing architecture based on fog and cloud with applications in 5G-based V2G networks. This architecture allows the bi-directional flow of power and information between schedulable EVs and smart grids (SGs) to improve the quality of service and cost-effectiveness of energy service providers. However, it is very important to select …

Glaciernet: A Deep-Learning Approach For Debris-Covered Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Brennan W. Young, Michael P. Bishop, Jeffrey S. Kargel

Electrical and Computer Engineering Faculty Publications

Rising global temperatures over the past decades is directly affecting glacier dynamics. To understand glacier fluctuations and document regional glacier-state trends, glacier-boundary detection is necessary. Debris-covered glacier (DCG) mapping, however, is notoriously difficult using conventional geospatial technology methods. Therefore, in this research for automated DCG mapping, we evaluate the utility of a convolutional neural network (CNN), which is a deep learning feed-forward neural network. The CNN inputs include Landsat satellite images, an Advanced Land Observation Satellite (ALOS) digital elevation model (DEM) and DEM-derived land-surface parameters. Our CNN based deep-learning approach named GlacierNet was designed by appropriately choosing the type, number …

Mitosisnet: End-To-End Mitotic Cell Detection By Multi-Task Learning, Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, Tj Bowen, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Mitotic cell detection is one of the challenging problems in the field of computational pathology. Currently, mitotic cell detection and counting are one of the strongest prognostic markers for breast cancer diagnosis. The clinical visual inspection on histology slides is tedious, error prone, and time consuming for the pathologist. Thus, automatic mitotic cell detection approaches are highly demanded in clinical practice. In this paper, we propose an end-to-end multi-task learning system for mitosis detection from pathological images which is named"MitosisNet". MitosisNet consist of segmentation, detection, and classification models where the segmentation, and detection models are used for mitosis reference region …

A State-Of-The-Art Survey On Deep Learning Theory And Architectures, Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language …

Active Recall Networks For Multiperspectivity Learning Through Shared Latent Space Optimization, Theus Aspiras, Ruixu Liu, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Given that there are numerous amounts of unlabeled data available for usage in training neural networks, it is desirable to implement a neural network architecture and training paradigm to maximize the ability of the latent space representation. Through multiple perspectives of the latent space using adversarial learning and autoencoding, data requirements can be reduced, which improves learning ability across domains. The entire goal of the proposed work is not to train exhaustively, but to train with multiperspectivity. We propose a new neural network architecture called Active Recall Network (ARN) for learning with less labels by optimizing the latent space. This …

Deep Temporal Convolutional Networks For Short-Term Traffic Flow Forecasting, Wentian Zhao, Yanyun Gao, Tingxiang Ji, Xili Wan, Feng Ye, Guangwei Bai

Electrical and Computer Engineering Faculty Publications

To reduce the increasingly congestion in cities, it is essential for intelligent transportation system (ITS) to accurately forecast the short-term traffic flow to identify the potential congestion sites. In recent years, the emerging deep learning method has been introduced to design traffic flow predictors, such as recurrent neural network (RNN) and long short-term memory (LSTM), which has demonstrated its promising results. In this paper, different from existing work, we study the temporal convolutional network (TCN) and propose a deep learning framework based on TCN model for short-term city-wide traffic forecast to accurately capture the temporal and spatial evolution of traffic …

Recurrent Residual U-Net For Medical Image Segmentation, Md Zahangir Alom, Christopher Yakopcic, Mahmudul Hasan, Tarek M. Taha, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Deep learning (DL)-based semantic segmentation methods have been providing state-of-the-art performance in the past few years. More specifically, these techniques have been successfully applied in medical image classification, segmentation, and detection tasks. One DL technique, U-Net, has become one of the most popular for these applications. We propose a recurrent U-Net model and a recurrent residual U-Net model, which are named RU-Net and R2U-Net, respectively. The proposed models utilize the power of U-Net, residual networks, and recurrent convolutional neural networks. There are several advantages to using these proposed architectures for segmentation tasks. First, a residual unit helps when training deep …

A Survey Of Techniques For Mobile Service Encrypted Traffic Classification Using Deep Learning, Pan Wang, Xuejiao Chen, Feng Ye, Zhixin Sun

Electrical and Computer Engineering Faculty Publications

The rapid adoption of mobile devices has dramatically changed the access to various net- working services and led to the explosion of mobile service traffic. Mobile service traffic classification has been a crucial task that attracts strong interest in mobile network management and security as well as machine learning communities for past decades. However, with more and more adoptions of encryption over mobile services, it brings a lot of challenges about mobile traffic classification. Although classical machine learning approaches can solve many issues that port and payload-based methods cannot solve, it still has some limitations, such as time-consuming, costly handcrafted …

Datanet: Deep Learning Based Encrypted Network Traffic Classification In Sdn Home Gateway, Pan Wang, Feng Ye, Xuejiao Chen, And Yi Qian

Electrical and Computer Engineering Faculty Publications

A smart home network will support various smart devices and applications, e.g., home automation devices, E-health devices, regular computing devices, and so on. Most devices in a smart home access the Internet through a home gateway (HGW). In this paper, we propose a software-defined- network (SDN)-HGW framework to better manage distributed smart home networks and support the SDN controller of the core network. The SDN controller enables efficient network quality-of-service management based on real-time traffic monitoring and resource allocation of the core network. However, it cannot provide network management in distributed smart homes. Our proposed SDN-HGW extends the control to …

Handwritten Bangla Character Recognition Using The State-Of-The-Art Deep Convolutional Neural Networks, Md Zahangir Alom, Paheding Sidike, Mahmudul Hasan, Tarek M. Taha, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

In spite of advances in object recognition technology, handwritten Bangla character recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla handwritings. Even many advanced existing methods do not lead to satisfactory performance in practice that related to HBCR. In this paper, a set of the state-of-the-art deep convolutional neural networks (DCNNs) is discussed and their performance on the application of HBCR is systematically evaluated. The main advantage of DCNN approaches is that they can extract discriminative features from raw data and represent them with a high degree of invariance to object …

Speckle Effects In Target-In-The-Loop Laser Beam Projection Systems, Mikhail Vorontsov

Electro-Optics and Photonics Faculty Publications

In target-in-the-loop laser beam projection scenarios typical of remote sensing, directed energy, and adaptive optics applications, a transmitted laser beam propagates through an optically inhomogeneous medium toward a target, scatters off the target’s rough surface, and returns back to the transceiver plane. Coherent beam scattering off the randomly rough surface results in strong speckle modulation in the transceiver plane. This speckle modulation has been a long-standing challenge that limits performance of remote sensing, active imaging, and adaptive optics techniques. Using physics-based models of laser beam scattering off a randomly rough surface, we show that received speckle-field spatial and temporal characteristics …

Recursive Non-Local Means Filter For Video Denoising, Redha A. Ali, Russell C. Hardie

Electrical and Computer Engineering Faculty Publications

In this paper, we propose a computationally efficient algorithm for video denoising that exploits temporal and spatial redundancy. The proposed method is based on non-local means (NLM). NLM methods have been applied successfully in various image denoising applications. In the single-frame NLM method, each output pixel is formed as a weighted sum of the center pixels of neighboring patches, within a given search window.

The weights are based on the patch intensity vector distances. The process requires computing vector distances for all of the patches in the search window. Direct extension of this method from 2D to 3D, for video …

Analysis Of The Joint Impact Of Atmospheric Turbulence And Refractivity On Laser Beam Propagation, Victor A. Kulikov, Mikhail Vorontsov

Electro-Optics and Photonics Faculty Publications

A laser beam propagation model that accounts for the joint effect of atmospheric turbulence and refractivity is introduced and evaluated through numerical simulations. In the numerical analysis of laser beam propagation, refractive index inhomogeneities along the atmospheric propagation path were represented by a combination of the turbulence-induced random fluctuations described in the framework of classical Kolmogorov turbulence theory and large-scale refractive index variations caused by the presence of an inverse temperature layer. The results demonstrate that an inverse temperature layer located in the vicinity of a laser beam’s propagation path may strongly impact the laser beam statistical characteristics including the …

On-Chip Training Of Memristor Crossbar Based Multi-Layer Neural Networks, Raqibul Hasan, Tarek M. Taha, Christopher Yakopcic

Electrical and Computer Engineering Faculty Publications

Memristor crossbar arrays carry out multiply-add operations in parallel in the analog domain, and so can enable neuromorphic systems with high throughput at low energy and area consumption. On-chip training of these systems have the significant advantage of being able to get around device variability and faults. This paper presents on-chip training circuits for multi-layer neural networks implemented using a single crossbar per layer and two memristors per synapse. Using two memristors per synapse provides double the synaptic weight precision when compared to a design that uses only one memristor per synapse. Proposed on-chip training system utilizes the back propagation …

Review Of Optical Detection Of Single Molecules Beyond The Diffraction And Diffusion Limit Using Plasmonic Nanostructures, Farzia Karim, Todd B. Smith, Chenglong Zhao

Physics Faculty Publications

Single-molecule detection has become a unique and indispensable tool for the study of molecular motions and interactions at the single-molecule level. Unlike ensemble measurement where the information is averaged, single-molecule analysis yields invaluable information on both the individual molecular properties and their microenvironment. Among the various technologies for the detection of single molecules, the detection with optical methods has many advantages in terms of its high sensitivity, electrical passiveness, and robustness. The recent advances in the engineering of either the excitation light or the solution of the molecules have paved the way for enhanced single-molecule detection. We present recent developments …

Agenda: Second International Workshop On Thin Films For Electronics, Electro-Optics, Energy And Sensors (Tfe3s), University Of Dayton Research Institute

Electro-Optics and Photonics Faculty Publications

University of Dayton’s Center of Excellence for Thin Film Research and Surface Engineering (CETRASE) is delighted to organize its second international workshop at the University of Dayton’s Research Institute (UDRI) campus in Dayton, Ohio, USA. The purpose of the new workshop is to exchange technical knowledge and boost technical and educational collaboration activities within the thin film research community through our CETRASE and the UDRI.

Comparing Multiple Turbulence Restoration Algorithms Performance On Noisy Anisoplanatic Imagery, Michael Armand Rucci, Russell C. Hardie, Alexander J. Dapore

Electrical and Computer Engineering Faculty Publications

In this paper, we compare the performance of multiple turbulence mitigation algorithms to restore imagery degraded by atmospheric turbulence and camera noise. In order to quantify and compare algorithm performance, imaging scenes were simulated by applying noise and varying levels of turbulence. For the simulation, a Monte-Carlo wave optics approach is used to simulate the spatially and temporally varying turbulence in an image sequence. A Poisson-Gaussian noise mixture model is then used to add noise to the observed turbulence image set. These degraded image sets are processed with three separate restoration algorithms: Lucky Look imaging, bispectral speckle imaging, and a …

On The Simulation And Mitigation Of Anisoplanatic Optical Turbulence For Long Range Imaging, Russell C. Hardie, Daniel A. Lemaster

Electrical and Computer Engineering Faculty Publications

We describe a numerical wave propagation method for simulating long range imaging of an extended scene under anisoplanatic conditions. Our approach computes an array of point spread functions (PSFs) for a 2D grid on the object plane. The PSFs are then used in a spatially varying weighted sum operation, with an ideal image, to produce a simulated image with realistic optical turbulence degradation. To validate the simulation we compare simulated outputs with the theoretical anisoplanatic tilt correlation and differential tilt variance. This is in addition to comparing the long- and short-exposure PSFs, and isoplanatic angle. Our validation analysis shows an …

Block Matching And Wiener Filtering Approach To Optical Turbulence Mitigation And Its Application To Simulated And Real Imagery With Quantitative Error Analysis, Russell C. Hardie, Michael Armand Rucci, Barry K. Karch, Alexander J. Dapore

Electrical and Computer Engineering Faculty Publications

We present a block-matching and Wiener filtering approach to atmospheric turbulence mitigation for long-range imaging of extended scenes. We evaluate the proposed method, along with some benchmark methods, using simulated and real-image sequences. The simulated data are generated with a simulation tool developed by one of the authors. These data provide objective truth and allow for quantitative error analysis. The proposed turbulence mitigation method takes a sequence of short-exposure frames of a static scene and outputs a single restored image. A block-matching registration algorithm is used to provide geometric correction for each of the individual input frames. The registered frames …

Simulation Of Anisoplanatic Imaging Through Optical Turbulence Using Numerical Wave Propagation With New Validation Analysis, Russell C. Hardie, Jonathan D. Power, Daniel A. Lemaster, Douglas R. Droege, Szymon Gladysz, Santasri Bose-Pillai

Electrical and Computer Engineering Faculty Publications

We present a numerical wave propagation method for simulating imaging of an extended scene under anisoplanatic conditions. While isoplanatic simulation is relatively common, few tools are specifically designed for simulating the imaging of extended scenes under anisoplanatic conditions. We provide a complete description of the proposed simulation tool, including the wave propagation method used. Our approach computes an array of point spread functions (PSFs) for a two-dimensional grid on the object plane. The PSFs are then used in a spatially varying weighted sum operation, with an ideal image, to produce a simulated image with realistic optical turbulence degradation. The degradation …

Practical Guide To The Realization Of A Convertible Optical Trapping System, Chenglong Zhao

Physics Faculty Publications

In this article, we provide a detailed guide to the construction of a convertible optical trapping system for either single-beam or counter-propagating trap. The single-beam trap maintains all the functionalities that a conventional optical tweezer has. While the counter-propagating trap allows for the trapping of particles that single-beam trap cannot handle. The counter-propagating trap can be easily switched to a single-beam trap, and vice versa. Therefore, this convertible optical trapping system allows for the trapping and manipulation of particles with a wide variety of sizes and materials.

Identity‐Based Schemes For A Secured Big Data And Cloud Ict Framework In Smart Grid System, Feng Ye, Yi Qian, Rose Qingyang Hu

Electrical and Computer Engineering Faculty Publications

Smart grid is an intelligent cyber physical system (CPS). The CPS generates a massive amount of data for efficient grid operation. In this paper, a big data‐driven, cloud‐based information and communication technology (ICT) framework for smart grid CPS is proposed. The proposed ICT framework deploys hybrid cloud servers to enhance scalability and reliability of smart grid communication infrastructure. Because the data in the ICT framework contains much privacy of customers and important data for automated controlling, the security of data transmission must be ensured. In order to secure the communications over the Internet in the system, identity‐based schemes are proposed …