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Articles 1 - 27 of 27
Full-Text Articles in Engineering
Conditional Generative Adversarial Network Demosaicing Strategy For Division Of Focal Plane Polarimeters, Garrett Sargent, Bradley M. Ratliff, Vijayan K. Asari
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
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 …
Parameter Identification For Cells, Modules, Racks, And Battery For Utility-Scale Energy Storage Systems, Oluwaseun M. Akeyo, Vandana Rallabandi, Nicholas Jewell, Aron Patrick, Dan M. Ionel
Parameter Identification For Cells, Modules, Racks, And Battery For Utility-Scale Energy Storage Systems, Oluwaseun M. Akeyo, Vandana Rallabandi, Nicholas Jewell, Aron Patrick, Dan M. Ionel
Electrical and Computer Engineering Faculty Publications
The equivalent circuit model for utility-scale battery energy storage systems (BESS) is beneficial for multiple applications including performance evaluation, safety assessments, and the development of accurate models for simulation studies. This paper evaluates and compares the performance of utility-scale equivalent circuit models developed at multiple sub-component levels, i.e. at the rack, module, and cell levels. This type of modeling is used to demonstrate that the equivalent circuit model for a reference cell, module, or rack of a BESS can be scaled to represent the entire battery system provided that the battery management system (BMS) is active and functional. Contrary to …
Cost Minimization Of Battery-Supercapacitor Hybrid Energy Storage For Hourly Dispatching Wind-Solar Hybrid Power System, Pranoy Roy, Jiangbiao He, Yuan Liao
Cost Minimization Of Battery-Supercapacitor Hybrid Energy Storage For Hourly Dispatching Wind-Solar Hybrid Power System, Pranoy Roy, Jiangbiao He, Yuan Liao
Electrical and Computer Engineering Faculty Publications
This study demonstrates a dispatching scheme of wind-solar hybrid power system (WSHPS) for a one-hour dispatching period for an entire day utilizing battery and supercapacitor hybrid energy storage subsystem (HESS). A frequency management approach is deployed to extend the longevity of the batteries through extensively utilizing the high energy density property of batteries and the high power density property of supercapacitors in the HESS framework. A low-pass filter (LPF) is employed to decouple the power between a battery and a supercapacitor (SC). The cost optimization of the HESS is computed based on the time constant of the LPF through extensive …
On Correctness, Precision, And Performance In Quantitative Verification: Qcomp 2020 Competition Report, Carlos E. Budde, Arnd Hartmanns, Michaela Klauck, Jan Křetínský, David Parker, Tim Quatmann, Andrea Turrini, Zhen Zhang
On Correctness, Precision, And Performance In Quantitative Verification: Qcomp 2020 Competition Report, Carlos E. Budde, Arnd Hartmanns, Michaela Klauck, Jan Křetínský, David Parker, Tim Quatmann, Andrea Turrini, Zhen Zhang
Electrical and Computer Engineering Faculty Publications
Quantitative verification tools compute probabilities, expected rewards, or steady-state values for formal models of stochastic and timed systems. Exact results often cannot be obtained efficiently, so most tools use floating-point arithmetic in iterative algorithms that approximate the quantity of interest. Correctness is thus defined by the desired precision and determines performance. In this paper, we report on the experimental evaluation of these trade-offs performed in QComp 2020: the second friendly competition of tools for the analysis of quantitative formal models. We survey the precision guarantees - ranging from exact rational results to statistical confidence statements - offered by the nine …
Challenges And Opportunities In Near-Threshold Dnn Accelerators Around Timing Errors, Pramesh Pandey, Noel Daniel Gundi, Prabal Basu, Tahmoures Shabanian, Mitchell Craig Patrick, Koushik Chakraborty, Sanghamitra Roy
Challenges And Opportunities In Near-Threshold Dnn Accelerators Around Timing Errors, Pramesh Pandey, Noel Daniel Gundi, Prabal Basu, Tahmoures Shabanian, Mitchell Craig Patrick, Koushik Chakraborty, Sanghamitra Roy
Electrical and Computer Engineering Faculty Publications
AI evolution is accelerating and Deep Neural Network (DNN) inference accelerators are at the forefront of ad hoc architectures that are evolving to support the immense throughput required for AI computation. However, much more energy efficient design paradigms are inevitable to realize the complete potential of AI evolution and curtail energy consumption. The Near-Threshold Computing (NTC) design paradigm can serve as the best candidate for providing the required energy efficiency. However, NTC operation is plagued with ample performance and reliability concerns arising from the timing errors. In this paper, we dive deep into DNN architecture to uncover some unique challenges …
Research And Simulation Of Dc Microgrid Three-Phase Ac-Dc Converter Control Strategy Based On Double Loop, Boning Wu, Zhiqiang Gao, Xuesong Zhou, Youjie Ma, Chenglong Wang
Research And Simulation Of Dc Microgrid Three-Phase Ac-Dc Converter Control Strategy Based On Double Loop, Boning Wu, Zhiqiang Gao, Xuesong Zhou, Youjie Ma, Chenglong Wang
Electrical and Computer Engineering Faculty Publications
The new voltage and current double loop control strategy is proposed to solve the DC microgrid bus voltage fluctuation caused by loads fluctuation, parameters perturbation and unbalanced three-phase power supply. Firstly, the dq axis mathematical model of three-phase AC-DC bidirectional converter in DC microgrid is analyzed and established, and then the controllers are designed according to the dq axis mathematical model. The outer loop is a voltage loop based on variable gain linear extended state observer (VGLESO) and sliding mode theory. VGLESO can not only effectively overcome the problem of peak output of traditional high-gain LESO in the initial stage …
Combined Numerical And Experimental Determination Of Ball Bearing Capacitances For Bearing Current Prediction, Peng Han, Greg Heins, Dean Patterson, Mark Theile, Dan M. Ionel
Combined Numerical And Experimental Determination Of Ball Bearing Capacitances For Bearing Current Prediction, Peng Han, Greg Heins, Dean Patterson, Mark Theile, Dan M. Ionel
Electrical and Computer Engineering Faculty Publications
High-frequency voltages across the steel ball bearings and the corresponding currents can cause premature bearing failures in electric machines driven by PWM converters. The bearing voltage, one of the most commonly-used failure indicators, depends heavily on the bearing capacitance. This paper presents a combined numerical and experimental approach for the calculation of ball bearing capacitances to address the uncertainty introduced by lubricant property, lubrication status and other metal parts, such as seals and ball retainers. Based on the obtained capacitance breakdown, the influences of temperature, speed and bearing load (radial, axial or combined) on the capacitance are studied. Measurements and …
Titan: Uncovering The Paradigm Shift In Security Vulnerability At Near-Threshold Computing, Prabal Basu, Pramesh Pandey, Aatreyi Bal, Chidhambaranathan Rajamanikkam, Koushik Chakraborty, Sanghamitra Roy
Titan: Uncovering The Paradigm Shift In Security Vulnerability At Near-Threshold Computing, Prabal Basu, Pramesh Pandey, Aatreyi Bal, Chidhambaranathan Rajamanikkam, Koushik Chakraborty, Sanghamitra Roy
Electrical and Computer Engineering Faculty Publications
In this paper, we investigate the emerging security threats at Near-Threshold Computing (NTC) that are poised to jeopardize the trustworthy operation of future low-power electronic devices. A substantial research effort over the last decade has bolstered energy efficient operation in low-power computing. However, innovation in low-power security has received only marginal attention, thwarting a ubiquitous adoption of critical Internet of Things applications, such as wearable gadgets. Using a cross-layer methodology, we demonstrate that the timing fault vulnerability of a circuit rapidly increases as the operating conditions of the transistor devices shift from super-threshold to near-threshold values. Exploiting this vulnerability, we …
Design Optimization Of Coreless Axial-Flux Pm Machines With Litz Wire And Pcb Stator Windings, Murat G. Kesgin, Peng Han, Narges Taran, Damien Lawhorn, Donovin Lewis, Dan M. Ionel
Design Optimization Of Coreless Axial-Flux Pm Machines With Litz Wire And Pcb Stator Windings, Murat G. Kesgin, Peng Han, Narges Taran, Damien Lawhorn, Donovin Lewis, Dan M. Ionel
Electrical and Computer Engineering Faculty Publications
Coreless axial-flux permanent-magnet (AFPM) machines may be attractive options for high-speed and high-power-density applications due to the elimination of core losses. In order to make full use of the advantages offered by these machines and avoid excessive eddy current losses in windings, advanced technologies for winding conductors need to be employed to suppress the eddy effect, such as the Litz wire and printed circuit board (PCB). In this paper, the best practices for designing Litz wire/PCB windings are discussed and a brief survey of state of the art PCB winding technology is provided. Three coreless AFPM machines are mainly considered. …
A Hybrid Achromatic Metalens, Fatih Balli, Mansoor A. Sultan, Sarah K. Lami, J. Todd Hastings
A Hybrid Achromatic Metalens, Fatih Balli, Mansoor A. Sultan, Sarah K. Lami, J. Todd Hastings
Electrical and Computer Engineering Faculty Publications
Metalenses, ultra-thin optical elements that focus light using subwavelength structures, have been the subject of a number of recent investigations. Compared to their refractive counterparts, metalenses offer reduced size and weight, and new functionality such as polarization control. However, metalenses that correct chromatic aberration also suffer from markedly reduced focusing efficiency. Here we introduce a Hybrid Achromatic Metalens (HAML) that overcomes this trade-off and offers improved focusing efficiency over a broad wavelength range from 1000-1800 nm. HAMLs can be designed by combining recursive ray-tracing and simulated phase libraries rather than computationally intensive global search algorithms. Moreover, HAMLs can be fabricated …
Greentpu: Predictive Design Paradigm For Improving Timing Error Resilience Of A Near-Threshold Tensor Processing Unit, Pramesh Pandey, Prabal Basu, Koushik Chakraborty, Sanghamitra Roy
Greentpu: Predictive Design Paradigm For Improving Timing Error Resilience Of A Near-Threshold Tensor Processing Unit, Pramesh Pandey, Prabal Basu, Koushik Chakraborty, Sanghamitra Roy
Electrical and Computer Engineering Faculty Publications
The emergence of hardware accelerators has brought about several orders of magnitude improvement in the speed of the deep neural-network (DNN) inference. Among such DNN accelerators, the Google tensor processing unit (TPU) has transpired to be the best-in-class, offering more than 15\times speedup over the contemporary GPUs. However, the rapid growth in several DNN workloads conspires to escalate the energy consumptions of the TPU-based data-centers. In order to restrict the energy consumption of TPUs, we propose GreenTPU - a low-power near-threshold (NTC) TPU design paradigm. To ensure a high inference accuracy at a low-voltage operation, GreenTPU identifies the patterns in …
The Gridded Retarding Ion Drift Sensor For The Petitsat Cubesat Mission, Ryan L. Davidson, B. Oborn, E. F. Robertson, S. Noel, G. D. Earle, J. Green, J. Kramer
The Gridded Retarding Ion Drift Sensor For The Petitsat Cubesat Mission, Ryan L. Davidson, B. Oborn, E. F. Robertson, S. Noel, G. D. Earle, J. Green, J. Kramer
Electrical and Computer Engineering Faculty Publications
The Gridded Retarding Ion Drift Sensor (GRIDS) is a small sensor that will fly on the 6 U petitSat CubeSat. It is designed to measure the three-dimensional plasma drift velocity vector in the Earth’s ionosphere. The GRIDS also supplies information about the ion temperature, ion density, and the ratio of light to heavy ions present in the ionospheric plasma. It utilizes well-proven techniques that have been successfully validated by similar instruments on larger satellite missions while meeting CubeSat-compatible requirements for low mass, size, and power consumption. GRIDS performs the functions of a Retarding Potential Analyzer (RPA) and an Ion Drift …
Estimation Of Autoregressive Parameters From Noisy Observations Using Iterated Covariance Updates, Todd K. Moon, Jacob H. Gunther
Estimation Of Autoregressive Parameters From Noisy Observations Using Iterated Covariance Updates, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
Estimating the parameters of the autoregressive (AR) random process is a problem that has been well-studied. In many applications, only noisy measurements of AR process are available. The effect of the additive noise is that the system can be modeled as an AR model with colored noise, even when the measurement noise is white, where the correlation matrix depends on the AR parameters. Because of the correlation, it is expedient to compute using multiple stacked observations. Performing a weighted least-squares estimation of the AR parameters using an inverse covariance weighting can provide significantly better parameter estimates, with improvement increasing with …
Hybrid Machine Learning Architecture For Automated Detection And Grading Of Retinal Images For Diabetic Retinopathy, Barath Narayanan, Barath Narayanan, Russell C. Hardie, Manawaduge Supun De Silva, Nathaniel K. Kueterman
Hybrid Machine Learning Architecture For Automated Detection And Grading Of Retinal Images For Diabetic Retinopathy, Barath Narayanan, Barath Narayanan, Russell C. Hardie, Manawaduge Supun De Silva, Nathaniel K. Kueterman
Electrical and Computer Engineering Faculty Publications
Purpose: Diabetic retinopathy is the leading cause of blindness, affecting over 93 million people. An automated clinical retinal screening process would be highly beneficial and provide a valuable second opinion for doctors worldwide. A computer-aided system to detect and grade the retinal images would enhance the workflow of endocrinologists. Approach: For this research, we make use of a publicly available dataset comprised of 3662 images. We present a hybrid machine learning architecture to detect and grade the level of diabetic retinopathy (DR) severity. We also present and compare simple transfer learning-based approaches using established networks such as AlexNet, VGG16, ResNet, …
Ensemble Malware Classification System Using Deep Neural Networks, Barath Narayanan Narayanan, Venkata Salini Priyamvada Davuluru
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 …
Reducing Road Wear While Ensuring Comfort And Charging Constraints For Dynamically Charged Passenger Vehicles Through Noise-Shaped Path Variations, Clint Jay Ferrin, Randall Christensen
Reducing Road Wear While Ensuring Comfort And Charging Constraints For Dynamically Charged Passenger Vehicles Through Noise-Shaped Path Variations, Clint Jay Ferrin, Randall Christensen
Electrical and Computer Engineering Faculty Publications
Dynamically charged vehicles suffer from power loss during wireless power transfer due to receiver and transmitter coil misalignment while driving. Autonomous, dynamically charged vehicles can maximize wireless power transfer by minimizing the misalignment, but the repeated high-precision driving increases road wear. To avoid unnecessary road wear and rutting, a noise shaping filter is proposed that adds variability to a vehicle's trajectory that complies with passenger acceleration and position constraints. However, introducing variability into an optimal charging path also risks depleting battery life prior to destination arrival. Therefore, a path planner is proposed that guarantees average charge within a specified probability …
On The Conceptualization Of Total Disturbance And Its Profound Implications, Sen Chen, Wenyan Bai, Yu Hu, Yi Huang, Wenbing Zhao
On The Conceptualization Of Total Disturbance And Its Profound Implications, Sen Chen, Wenyan Bai, Yu Hu, Yi Huang, Wenbing Zhao
Electrical and Computer Engineering Faculty Publications
No abstract provided.
Effort: Enhancing Energy Efficiency And Error Resilience Of A Near-Threshold Tensor Processing Unit, Noel Daniel Gundi, Tahmoures Shabanian, Prabal Basu, Pramesh Pandey, Sanghamitra Roy, Koushik Chakraborty, Zhen Zhang
Effort: Enhancing Energy Efficiency And Error Resilience Of A Near-Threshold Tensor Processing Unit, Noel Daniel Gundi, Tahmoures Shabanian, Prabal Basu, Pramesh Pandey, Sanghamitra Roy, Koushik Chakraborty, Zhen Zhang
Electrical and Computer Engineering Faculty Publications
Modern deep neural network (DNN) applications demand a remarkable processing throughput usually unmet by traditional Von Neumann architectures. Consequently, hardware accelerators, comprising a sea of multiplier and accumulate (MAC) units, have recently gained prominence in accelerating DNN inference engine. For example, Tensor Processing Units (TPU) account for a lion's share of Google's datacenter inference operations. The proliferation of real-time DNN predictions is accompanied with a tremendous energy budget. In quest of trimming the energy footprint of DNN accelerators, we propose EFFORT-an energy optimized, yet high performance TPU architecture, operating at the Near-Threshold Computing (NTC) region. EFFORT promotes a better-than-worst-case design …
Two-Stage Deep Learning Architecture For Pneumonia Detection And Its Diagnosis In Chest Radiographs, Barath Narayanan, Venkata Salini Priyamvada Davuluru, Russell C. Hardie
Two-Stage Deep Learning Architecture For Pneumonia Detection And Its Diagnosis In Chest Radiographs, Barath Narayanan, Venkata Salini Priyamvada Davuluru, Russell C. Hardie
Electrical and Computer Engineering Faculty Publications
Approximately two million pediatric deaths occur every year due to Pneumonia. Detection and diagnosis of Pneumonia plays an important role in reducing these deaths. Chest radiography is one of the most commonly used modalities to detect pneumonia. In this paper, we propose a novel two-stage deep learning architecture to detect pneumonia and classify its type in chest radiographs. This architecture contains one network to classify images as either normal or pneumonic, and another deep learning network to classify the type as either bacterial or viral. In this paper, we study and compare the performance of various stage one networks such …
An Extensive Set Of Kinematic And Kinetic Data For Individuals With Intact Limbs And Transfemoral Prosthesis Users, Seyed Abolfazl Fakoorian, Arash Roshanineshat, Poya Khalaf, Vahid Azimi, Daniel J. Simon, Elizabeth Hardin
An Extensive Set Of Kinematic And Kinetic Data For Individuals With Intact Limbs And Transfemoral Prosthesis Users, Seyed Abolfazl Fakoorian, Arash Roshanineshat, Poya Khalaf, Vahid Azimi, Daniel J. Simon, Elizabeth Hardin
Electrical and Computer Engineering Faculty Publications
This paper introduces an extensive human motion data set for typical activities of daily living. These data are crucial for the design and control of prosthetic devices for transfemoral prosthesis users. This data set was collected from seven individuals, including five individuals with intact limbs and two transfemoral prosthesis users. These data include the following types of movements: (1) walking at three different speeds; (2) walking up and down a 5-degree ramp; (3) stepping up and down; (4) sitting down and standing up. We provide full-body marker trajectories and ground reaction forces (GRFs) as well as joint angles, joint velocities, …
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
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 …
Mitosisnet: End-To-End Mitotic Cell Detection By Multi-Task Learning, Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, Tj Bowen, Vijayan K. Asari
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 …
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
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 …
Ev Charging Behavior Analysis Using Hybrid Intelligence For 5g Smart Grid, Yi Shen, Wei Fang, Feng Ye, Michel Kadoch
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 …
Ensemble Lung Segmentation System Using Deep Neural Networks, Redha A. Ali, Russell C. Hardie, Hussin K. Ragb
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 …
Patch-Based Gaussian Mixture Model For Scene Motion Detection In The Presence Of Atmospheric Optical Turbulence, Richard L. Van Hook, Russell C. Hardie
Patch-Based Gaussian Mixture Model For Scene Motion Detection In The Presence Of Atmospheric Optical Turbulence, Richard L. Van Hook, Russell C. Hardie
Electrical and Computer Engineering Faculty Publications
In long-range imaging regimes, atmospheric turbulence degrades image quality. In addition to blurring, the turbulence causes geometric distortion effects that introduce apparent motion in acquired video. This is problematic for image processing tasks, including image enhancement and restoration (e.g., superresolution) and aided target recognition (e.g., vehicle trackers). To mitigate these warping effects from turbulence, it is necessary to distinguish between actual in-scene motion and apparent motion caused by atmospheric turbulence. Previously, the current authors generated a synthetic video by injecting moving objects into a static scene and then applying a well-validated anisoplanatic atmospheric optical turbulence simulator. With known per-pixel truth …