Engineering Commons^™

Gaining Insight Into Solar Photovoltaic Power Generation Forecasting Utilizing Explainable Artificial Intelligence Tools, Murat Kuzlu, Umit Cali, Vinayak Sharma, Özgür Güler

Engineering Technology Faculty Publications

Over the last two decades, Artificial Intelligence (AI) approaches have been applied to various applications of the smart grid, such as demand response, predictive maintenance, and load forecasting. However, AI is still considered to be a ‘‘black-box’’ due to its lack of explainability and transparency, especially for something like solar photovoltaic (PV) forecasts that involves many parameters. Explainable Artificial Intelligence (XAI) has become an emerging research field in the smart grid domain since it addresses this gap and helps understand why the AI system made a forecast decision. This article presents several use cases of solar PV energy forecasting using …

Three-Event Energy Detection With Adaptive Threshold For Spectrum Sensing In Cognitive Radio Systems, Alexandru Martian, Mahmood Jalal Ahmad Al Sammarraie, Calin Vladeanu, Dimitrie Popescu

Electrical & Computer Engineering Faculty Publications

Implementation of dynamic spectrum access (DSA) in cognitive radio (CR) systems requires the unlicensed secondary users (SU) to implement spectrum sensing to monitor the activity of the licensed primary users (PU). Energy detection (ED) is one of the most widely used methods for spectrum sensing in CR systems, and in this paper we present a novel ED algorithm with an adaptive sensing threshold. The three-event ED (3EED) algorithm for spectrum sensing is considered for which an accurate approximation of the optimal decision threshold that minimizes the decision error probability (DEP) is found using Newton’s method with forced convergence in one …

Upgrading Of A Data Communication And Computer Networks Course In Engineering Technology Program, Murat Kuzlu, Otilia Popescu

Engineering Technology Faculty Publications

Data network communications is traditionally a course offered by computer engineering technology curricula, with the primary objective to introduce to the fundamental concepts in data communication and computer networks, as well as some level of hands-on component related to this area. Typical topics in such courses are the layered model of data communication, specifically the OSI seven-layered model, Internet routing, communication standards, protocols and technologies, and learning methods used to design the network and send data over the network in a secure manner. In the last decades, the data communication and applications have grown and become ubiquitous in both industry …

Work-In-Progress: Augmented Reality System For Vehicle Health Diagnostics And Maintenance, Yuzhong Shen, Anthony W. Dean, Rafael Landaeta

Electrical & Computer Engineering Faculty Publications

This paper discusses undergraduate research to develop an augmented reality (AR) system for diagnostics and maintenance of the Joint Light Tactical Vehicle (JLTV) employed by U.S. Army and U.S. Marine Corps. The JLTV’s diagnostic information will be accessed by attaching a Bluetooth adaptor (Ford Reference Vehicle Interface) to JLTV’s On-board diagnostics (OBD) system. The proposed AR system will be developed for mobile devices (Android and iOS tablets and phones) and it communicates with the JLTV’s OBD via Bluetooth. The AR application will contain a simplistic user interface that reads diagnostic data from the JLTV, shows vehicle sensors, and allows users …

In-Situ Gold-Ceria Nanoparticles: Superior Optical Fluorescence Quenching Sensor For Dissolved Oxygen, Nader Shehata, Ishac Kandas, Effat Samir

Electrical & Computer Engineering Faculty Publications

Cerium oxide (ceria) nanoparticles (NPs) have been proved to be an efficient optical fluorescent material through generating visible emission (~530 nm) under violet excitation. This feature allowed ceria NPs to be used as an optical sensor via the fluorescence quenching Technique. In this paper, the impact of in-situ embedded gold nanoparticles (Au NPs) inside ceria nanoparticles was studied. Then, gold–ceria NPs were used for sensing dissolved oxygen (DO) in aqueous media. It was observed that both fluorescence intensity and lifetime were changed due to increased concentration of DO. Added gold was found to enhance the sensitivity of ceria to DO …

A Blockchain Simulator For Evaluating Consensus Algorithms In Diverse Networking Environments, Peter Foytik, Sachin Shetty, Sarada Prasad Gochhayat, Eranga Herath, Deepak Tosh, Laurent Njilla

VMASC Publications

The massive scale, heterogeneity and distributed nature of Internet-of-Things (IoT) presents challenges in realizing a practical and effective security solution. Blockchain empowered platforms and technologies have been proposed to address aspects of this challenge. In order to realize a practical Blockchain deployment for IoT, there is a need for a testing and evaluation platform to evaluate performance and security of Blockchain applications and systems. In this paper, we present a Blockchain simulator that evaluates the consensus algorithms in a realistic and configurable network environment. Though, there are several Blockchain evaluation platforms, they are either wedded to a specific consensus protocol …

Short-Term Truckload Spot Rates' Prediction In Consideration Of Temporal And Between-Route Correlations, Wei Xiao, Chuan Xu, Hongling Liu, Hong Yang, Xiaobo Liu

Computational Modeling & Simulation Engineering Faculty Publications

Truckload spot rate (TSR), defined as a price offered on the spot to transport a certain cargo by using an entire truck on a target transportation line, usually price per kilometer-ton, is a key factor in shaping the freight market. In particular, the prediction of short-term TSR is of great importance to the daily operations of the trucking industry. However, existing predictive practices have been limited largely by the availability of multilateral information, such as detailed intraday TSR information. Fortunately, the emerging online freight exchange (OFEX) platforms provide unique opportunities to access and fuse more data for probing the trucking …

Recent Developments In The General Atomic And Molecular Electronic Structure System, Guiseppe M.J. Barca, Colleen Bertoni, Laura Carrington, Dipayan Datta, Nuwan De Silva, J. Emillano Deustua, Dmitri G. Fedorov, Jeffrey R. Cour, Anastasia O. Gunina, Emilie Guidez, Taylor Harville, Stephan Irle, Joe Ivanic, Karol Kowalski, Sarom S. Leang, Wei Li, Jesse J. Lutz, Ilias Magoulas, Joani Mato, Vladimir Mironov, Hiroya Nakata, Buu Q. Pham, Piotr Piecuch, David Poole, Spencer R. Pruitt, Alistair P. Rendell, Luke B. Roskop, Klaus Ruedenberg, Tosaporn Sattasathuchana, Michael W. Schmidt, Jun Shen, Lyudmila Slipchenko, Masha Sosonkina, Vaibhav Sundriyal, Ananta Tiwari, Jorge L. Galvez Vallejo, Bryce Westheimer, Marta Włoch, Peng Xu, Federico Zahariev, Mark S. Gordon

Computational Modeling & Simulation Engineering Faculty Publications

A discussion of many of the recently implemented features of GAMESS (General Atomic and Molecular Electronic Structure System) and LibCChem (the C++ CPU/GPU library associated with GAMESS) is presented. These features include fragmentation methods such as the fragment molecular orbital, effective fragment potential and effective fragment molecular orbital methods, hybrid MPI/OpenMP approaches to Hartree-Fock, and resolution of the identity second order perturbation theory. Many new coupled cluster theory methods have been implemented in GAMESS, as have multiple levels of density functional/tight binding theory. The role of accelerators, especially graphical processing units, is discussed in the context of the new features …

Electrochemical Detection Of Neurotransmitters, Saikat Banerjee, Stephanie Mccracken, Md Faruk Hossain, Gymama Slaughter

Bioelectrics Publications

Neurotransmitters are important chemical messengers in the nervous system that play a crucial role in physiological and physical health. Abnormal levels of neurotransmitters have been correlated with physical, psychotic, and neurodegenerative diseases such as Alzheimer's, Parkinson's, dementia, addiction, depression, and schizophrenia. Although multiple neurotechnological approaches have been reported in the literature, the detection and monitoring of neurotransmitters in the brain remains a challenge and continues to garner significant attention. Neurotechnology that provides high-throughput, as well as fast and specific quantification of target analytes in the brain, without negatively impacting the implanted region is highly desired for the monitoring of the …

Evaluation Of The Mechanical Properties Of Germanium-On-Insulator (Geoi) Films By Raman Spectroscopy And Nanoindentation, Y.S. Mohammed, Kai Zhang, S. Heissler, H. Baumgart, A. A. Elmustafa

Mechanical & Aerospace Engineering Faculty Publications

Germanium-on-insulator (GeOI) films fabricated using the Smart Cut™ wafer bonding and film exfoliation technology were investigated for the mechanical properties and induced phase transformations by using nanoindentation and Raman spectroscopy experiments. The hardness and modulus results of the GeOI films are significantly different from the literature published Silicon-on-Insulator and bulk germanium results. The GeOI films are softer and more flexible as compared to bulk Ge hardness and stiffness properties. The Raman spectroscopy of the spherical indents indicates bands of metastable Ge phases @ 220 cm⁻¹, 195 cm⁻¹, and 184 cm⁻¹ wavenumbers. Our results demonstrate that …

Recent Developments In The Pyscf Program Package, Qiming Sun, Xing Zhang, Samragni Banerjee, Peng Bao, Marc Barbry, Nick S. Blunt, Nikolay A. Bogdanov, George H. Booth, Jia Chen, Zhi-Hao Cui, Janus J. Eriksen, Yang Gao, Sheng Gun, Jan Hermann, Matthew R. Hermes, Kevin Koh, Peter Koval, Susi Lehtola, Zhendong Li, Junzi Liu, Narbe Mardirossian, James D. Mcclain, Mario Motta, Bastien Mussard, Hung Q. Pham, Artem Pulkin, Wirawan Purwanto, Paul J. Robinson, Enrico Ronca, Elvira R. Sayfutyarova, Maximillian Scheurer, Henry F. Schurkus, James E.T. Smith, Chong Sun, Shi-Ning Sun, Shiv Upadhyay, Lucas K. Wagner, Xiao Wang, Alec White, James Daniel Whitfield, Mark J. Williamson, Sebastian Wouters, Jun Yang, Jason M. Yu, Tianyu Zhu, Timothy C. Berkelbach, Sandeep Sharma, Alexander Yu Sokolov, Garnet Kin-Lic Chan

University Administration Publications

PySCF is a Python-based general-purpose electronic structure platform that supports first-principles simulations of molecules and solids as well as accelerates the development of new methodology and complex computational workflows. This paper explains the design and philosophy behind PySCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PySCF as a development environment. We then summarize the capabilities of PySCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PySCF across the domains of quantum chemistry, materials science, machine learning, and …

Challenges In Design And Fabrication Of Flexible/Stretchable Carbon- And Textile-Based Wearable Sensors For Health Monitoring: A Critical Review, Jae Seng Heo, Md Faruk Hossain, Insoo Kim

Bioelectrics Publications

To demonstrate the wearable flexible/stretchable health-monitoring sensor, it is necessary to develop advanced functional materials and fabrication technologies. Among the various developed materials and fabrication processes for wearable sensors, carbon-based materials and textile-based configurations are considered as promising approaches due to their outstanding characteristics such as high conductivity, lightweight, high mechanical properties, wearability, and biocompatibility. Despite these advantages, in order to realize practical wearable applications, electrical and mechanical performances such as sensitivity, stability, and long-term use are still not satisfied. Accordingly, in this review, we describe recent advances in process technologies to fabricate advanced carbon-based materials and textile-based sensors, followed …

Quadrupoles For Remote Electrostimulation Incorporating Bipolar Cancellation, Shu Xiao, Ryo Yamada, Carol Zhou

Bioelectrics Publications

Introduction: A method that utilizes nanosecond bipolar cancellation (BPC) near a quadrupole electrodes to suppress a biological response but cancels the distal BPC at the quadrupole center, i.e., cancellation of cancellation (CANCAN), may allow for a remote focused stimulation at the quadrupole center.

Objectives: The primary object of this study was to outline the requirement of the CANCAN implementation and select an effective quadrupole configuration.

Results: We have studied three quadrupole electrode configurations, a rod quadrupole, a plate quadrupole (Plate-Q), and a resistor quadrupole. The pulse shapes of electric fields include monophasic pulses, cancellation pulses, and additive pulses. The Plate-Q …

Effects Of Plasma-Activated Water On Skin Wound Healing In Mice, Dehui Xu, Shuai Wang, Bing Li, Miao Qi, Rui Feng, Qiaosong Li, Hao Zhang, Hailan Chen, Michael G. Kong

Bioelectrics Publications

Cold atmospheric plasma (CAP) has been widely used in biomedicine during the last two decades. While direct plasma treatment has been reported to promote wound healing, its application can be uneven and inconvenient. In this study, we first activated water with a portable dielectric barrier discharge plasma device and evaluated the inactivation effect of plasma-activated water (PAW) on several kinds of bacteria that commonly infect wounds. The results show that PAW can effectively inactivate these bacteria. Then, we activated tap water and examined the efficacy of PAW on wound healing in a mouse model of full-thickness skin wounds. We found …

Flux Expulsion In Niobium Superconducting Radio-Frequency Cavities Of Different Purity And Essential Contributions To The Flux Sensitivity, P. Dhakal, Gianluigi Ciovati, Alex Gurevich

Physics Faculty Publications

Magnetic flux trapped during the cooldown of superconducting radio-frequency cavities through the transition temperature due to incomplete Meissner state is known to be a significant source of radio-frequency losses. The sensitivity of flux trapping depends on the distribution and the type of defects and impurities which pin vortices, as well as the cooldown dynamics when the cavity transitions from a normal to superconducting state. Here we present the results of measurements of the flux trapping sensitivity on 1.3 GHz elliptical cavities made from large-grain niobium with different purity for different cooldown dynamics and surface treatments. The results show that lower …

Quantifying Seagrass Distribution In Coastal Water With Deep Learning Models, Daniel Perez, Kazi Islam, Victoria Hill, Richard Zimmerman, Blake Schaeffer, Yuzhong Shen, Jiang Li

OES Faculty Publications

Coastal ecosystems are critically affected by seagrass, both economically and ecologically. However, reliable seagrass distribution information is lacking in nearly all parts of the world because of the excessive costs associated with its assessment. In this paper, we develop two deep learning models for automatic seagrass distribution quantification based on 8-band satellite imagery. Specifically, we implemented a deep capsule network (DCN) and a deep convolutional neural network (CNN) to assess seagrass distribution through regression. The DCN model first determines whether seagrass is presented in the image through classification. Second, if seagrass is presented in the image, it quantifies the seagrass …

Multi-Modal Natural Frequency Response Of Utility Transmission Tapered Wood Poles Under Various Soil Foundation Conditions: Natural Frequency Response Under Various Soil Conditions, Ramani Ayakannu, Zia Razzaq

Civil & Environmental Engineering Faculty Publications

Studied herein is the multi-modal natural frequency response of utility transmission tapered wood poles under various soil foundation conditions. Strong winds and hurricanes in various parts of the world have resulted in collapse of such utility poles and have resulted in the disruption of electrical distribution systems in addition to creating hazardous conditions for the public. To avoid the development of resonance under such dynamic loading, the multi-modal natural vibration of the utility poles first needs to be understood in the presence of practical soil foundation conditions. To capture the soil-structure interaction effects on the multi-modal frequencies, a SAP2000 dynamic …

Arsenic Exposure And Methylation Efficiency In Relation To Oxidative Stress In Semiconductor Workers, Chih-Hong Pan, Ching-Yu Lin, Ching-Huang Lai, Hueiwang Anna Jeng

Community & Environmental Health Faculty Publications

This study examined associations between oxidative stress and arsenic (As) exposure and methylation efficiency in semiconductor workers. An As-exposed group (n = 427) and a control group (n = 91) were included. The As-exposure group (n = 427) included 149 maintenance staff members and 278 production staff members representing high As exposure and low As exposure, respectively. The control group included 91 administrative staff members with no or minimal As exposure. An occupational exposure assessment was conducted to assess personal As exposure by measuring As concentrations in urine, hair, and fingernails of the subjects. Urinary As(III), As(V), monomethylarsonic (MMA), and …

Semi-Supervised Adversarial Domain Adaptation For Seagrass Detection Using Multispectral Images In Coastal Areas, Kazi Aminul Islam, Victoria Hill, Blake Schaeffer, Richard Zimmerman, Jiang Li

Electrical & Computer Engineering Faculty Publications

Seagrass form the basis for critically important marine ecosystems. Previously, we implemented a deep convolutional neural network (CNN) model to detect seagrass in multispectral satellite images of three coastal habitats in northern Florida. However, a deep CNN model trained at one location usually does not generalize to other locations due to data distribution shifts. In this paper, we developed a semi-supervised domain adaptation method to generalize a trained deep CNN model to other locations for seagrass detection. First, we utilized a generative adversarial network loss to align marginal data distribution between source domain and target domain using unlabeled data from …

Bioenergetic Functions In Subpopulations Of Heart Mitochondria Are Preserved In A Non-Obese Type 2 Diabetes Rat Model (Goto-Kakizaki), Nicola Lai, C. M. Kummitha, F. Loy, R. Isola, C. L. Hoppel

Electrical & Computer Engineering Faculty Publications

A distinct bioenergetic impairment of heart mitochondrial subpopulations in diabetic cardiomyopathy is associated with obesity; however, many type 2 diabetic (T2DM) patients with high-risk for cardiovascular disease are not obese. In the absence of obesity, it is unclear whether bioenergetic function in the subpopulations of mitochondria is affected in heart with T2DM. To address this issue, a rat model of non-obese T2DM was used to study heart mitochondrial energy metabolism, measuring bioenergetics and enzyme activities of the electron transport chain (ETC). Oxidative phosphorylation in the presence of substrates for ETC and ETC activities in both populations of heart mitochondria in …

A Prototype Virginia Ground Station Network, Zach Leffke, Jonathon Black, Kevin Shinpaugh, Ian Harnett, Bryce Clegg, Nick Angle, Chris Goyne, Connor Segal, William 'Trace' Lacour, Mike Mcpherson, Dimitrie Popescu, Samuel Jensen, Jason Harris, Mary Sandy, Mike Miller

Electrical & Computer Engineering Faculty Publications

This paper provides a detailed technical description of a prototype ground station network, the Virginia Ground Station Network (VGSN), developed for the Virginia Cubesat Constellation (VCC) mission. Virginia Tech (VT), University of Virginia (UVA), and Old Dominion University (ODU) have each constructed ground stations to communicate with their respective VCC spacecraft. Initially, each university was responsible for commanding its own spacecraft via its own ground station. As the mission progressed, it was decided to network the ground stations and operations centers together to provide backup communications capability for the overall mission. The NASA Wallops Flight Facility (WFF) UHF smallsat ground …

Observation Of Reduced Thermal Conductivity In A Metal-Organic Framework Due To The Presence Of Adsorbates, Hasan Babaei, Mallory E. Decoster, Minyoung Jeong, Zeinab M. Hassan, Timur Islamoglu, Helmut Baumgart, Alan J.H. Mcgaughey, Engelbert Redel, Omar K. Farha, Patrick E. Hopkins, Jonathan A. Malen, Christopher E. Wilmer

Electrical & Computer Engineering Faculty Publications

Whether the presence of adsorbates increases or decreases thermal conductivity in metal-organic frameworks (MOFs) has been an open question. Here we report observations of thermal transport in the metal-organic framework HKUST-1 in the presence of various liquid adsorbates: water, methanol, and ethanol. Experimental thermoreflectance measurements were performed on single crystals and thin films, and theoretical predictions were made using molecular dynamics simulations. We find that the thermal conductivity of HKUST-1 decreases by 40 – 80% depending on the adsorbate, a result that cannot be explained by effective medium approximations. Our findings demonstrate that adsorbates introduce additional phonon scattering in HKUST-1, …

Effect Of Layer Thickness On Structural, Morphological And Superconducting Properties Of Nb3Sn Films Fabricated By Multilayer Sequential Sputtering, M. N. Sayeed, U. Pudasaini, C. E. Reece, G. V. Eremeev, H. E. Elsayed-Ali

Electrical & Computer Engineering Faculty Publications

Superconducting Nb₃Sn films can be synthesized by controlling the atomic concentration of Sn. Multilayer sequential sputtering of Nb and Sn thin films followed by high temperature annealing is considered as a method to fabricate Nb₃Sn films, where the Sn composition of the deposited films can be controlled by the thickness of alternating Nb and Sn layers. We report on the structural, morphological and superconducting properties of Nb₃Sn films fabricated by multilayer sequential sputtering of Nb and Sn films on sapphire substrates followed by annealing at 950 °C for 3 h. We have investigated the …

Low Temperature Plasma Jets: Characterization And Biomedical Applications, Mounir Laroussi

Electrical & Computer Engineering Faculty Publications

No abstract provided.

Performance Across Worldview-2 And Rapideye For Reproducible Seagrass Mapping, Megan M. Coffer, Blake A. Schaeffer, Richard C. Zimmerman, Victoria Hill, Jiang Li, Kazi A. Islam, Peter J. Whitman

OES Faculty Publications

Satellite remote sensing offers an effective remedy to challenges in ground-based and aerial mapping that have previously impeded quantitative assessments of global seagrass extent. Commercial satellite platforms offer fine spatial resolution, an important consideration in patchy seagrass ecosystems. Currently, no consistent protocol exists for image processing of commercial data, limiting reproducibility and comparison across space and time. Additionally, the radiometric performance of commercial satellite sensors has not been assessed against the dark and variable targets characteristic of coastal waters. This study compared data products derived from two commercial satellites: DigitalGlobe's WorldView-2 and Planet's RapidEye. A single scene from each platform …

A New Method Of Detecting And Interrupting High Impedance Faults By Specifying The Z-Source Breaker In Dc Power Networks, Sagar Bhatta, Ruiyun Fu, Yucheng Zhang

Electrical & Computer Engineering Faculty Publications

High impedance faults (HIFs) that cause a relatively smaller current magnitude compared to the traditional low impedance faults are not easily detectable but can cause an extreme threat to electric apparatus and system operation. This paper introduces a new method of detecting and interrupting HIFs in DC power networks by specifying Z-source circuit breakers (ZCBs). The ZCB is a protective device for high power DC branches, with the capabilities of protecting bidirectional power flow and automatic/controllable turnoff function. In this new method, the operational mode of ZCB (i.e., either the detection mode or interruption mode) can be specified. Beyond previous …

Context Aware Deep Learning For Brain Tumor Segmentation, Subtype Classification, And Survival Prediction Using Radiology Images, Linmin Pei, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

A brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation and classification of tumors are critical for subsequent prognosis and treatment planning. This work proposes context aware deep learning for brain tumor segmentation, subtype classification, and overall survival prediction using structural multimodal magnetic resonance images (mMRI). We first propose a 3D context aware deep learning, that considers uncertainty of tumor location in the radiology mMRI image sub-regions, to obtain tumor segmentation. We then apply a regular 3D convolutional neural network (CNN) on the tumor segments to achieve tumor subtype classification. Finally, we perform survival prediction …

Deep Learning With Context Encoding For Semantic Brain Tumor Segmentation And Patient Survival Prediction, Linmin Pei, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

One of the most challenging problems encountered in deep learning-based brain tumor segmentation models is the misclassification of tumor tissue classes due to the inherent imbalance in the class representation. Consequently, strong regularization methods are typically considered when training large-scale deep learning models for brain tumor segmentation to overcome undue bias towards representative tissue types. However, these regularization methods tend to be computationally exhaustive, and may not guarantee the learning of features representing all tumor tissue types that exist in the input MRI examples. Recent work in context encoding with deep CNN models have shown promise for semantic segmentation of …

Mitochondrial Utilization Of Competing Fuels Is Altered In Insulin Resistant Skeletal Muscle Of Non-Obese Rats (Goto-Kakizaki), Nicola Lai, Ciarán E. Fealy, Chinna M. Kummitha, Silvia Cabras, John P. Kirwan, Charles L. Hoppel

Electrical & Computer Engineering Faculty Publications

Aim: Insulin-resistant skeletal muscle is characterized by metabolic inflexibility with associated alterations in substrate selection, mediated by peroxisome-proliferator activated receptor 𝜹 (PPAR𝜹). Although it is established that PPAR𝜹 contributes to the alteration of energy metabolism, it is not clear whether it plays a role in mitochondrial fuel competition. While nutrient overload may impair metabolic flexibility by fuel congestion within mitochondria, in absence of obesity defects at a mitochondrial level have not yet been excluded. We sought to determine whether reduced PPAR𝜹 content in insulin-resistant rat skeletal muscle of a non-obese rat model of T2DM (Goto-Kakizaki, GK) ameliorate the inhibitory effect …

Flood Detection Using Multi-Modal And Multi-Temporal Images: A Comparative Study, Kazi Aminul Islam, Mohammad Shahab Uddin, Chiman Kwan, Jiang Li

Electrical & Computer Engineering Faculty Publications

Natural disasters such as flooding can severely affect human life and property. To provide rescue through an emergency response team, we need an accurate flooding assessment of the affected area after the event. Traditionally, it requires a lot of human resources to obtain an accurate estimation of a flooded area. In this paper, we compared several traditional machine-learning approaches for flood detection including multi-layer perceptron (MLP), support vector machine (SVM), deep convolutional neural network (DCNN) with recent domain adaptation-based approaches, based on a multi-modal and multi-temporal image dataset. Specifically, we used SPOT-5 and RADAR images from the flood event that …