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Electrical and Computer Engineering Faculty Publications

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Articles 61 - 90 of 691

Full-Text Articles in Electrical and Computer Engineering

Wireless Smart Electric Meter Operation Under Data Security Attacks, Patrick Nnaji, Harsh Kumar, Sanjeev Kumar Dec 2022

Wireless Smart Electric Meter Operation Under Data Security Attacks, Patrick Nnaji, Harsh Kumar, Sanjeev Kumar

Electrical and Computer Engineering Faculty Publications

Wireless Smart meters are increasingly being deployed by the utility companies to remotely collect power consumption data from customer premise periodically in real time. It gives utility companies the information needed to efficiently administer their customers for their electricity usage. Knowing how critical these data are, it is important to ensure that data recorded locally at the smart meters or communicated remotely or wirelessly are both confidential and reliable. Despite the efforts being made to secure the smart meter data, security threats on smart meters continue to evolve at the same time, with an increasing number of security breaches happening …

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Effective Short Text Classification Via The Fusion Of Hybrid Features For Iot Social Data, Xiong Luo, Zhijian Yu, Zhigang Zhao, Wenbing Zhao, Jenq-Haur Wang Dec 2022

Effective Short Text Classification Via The Fusion Of Hybrid Features For Iot Social Data, Xiong Luo, Zhijian Yu, Zhigang Zhao, Wenbing Zhao, Jenq-Haur Wang

Electrical and Computer Engineering Faculty Publications

Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive …

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A Patient-Specific Algorithm For Lung Segmentation In Chest Radiographs, Manawaduge Supun De Silva, Barath Narayanan Narayanan, Russell C. Hardie Nov 2022

A Patient-Specific Algorithm For Lung Segmentation In Chest Radiographs, Manawaduge Supun De Silva, Barath Narayanan Narayanan, Russell C. Hardie

Electrical and Computer Engineering Faculty Publications

Lung segmentation plays an important role in computer-aided detection and diagnosis using chest radiographs (CRs). Currently, the U-Net and DeepLabv3+ convolutional neural network architectures are widely used to perform CR lung segmentation. To boost performance, ensemble methods are often used, whereby probability map outputs from several networks operating on the same input image are averaged. However, not all networks perform adequately for any specific patient image, even if the average network performance is good. To address this, we present a novel multi-network ensemble method that employs a selector network. The selector network evaluates the segmentation outputs from several networks; on …

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Resource Allocation For Mec System With Multi-Users Resource Competition Based On Deep Reinforcement Learning Approach, Bin Qu, Yan Bai, Yul Chu, Li-E Wang, Feng Yu, Xianxian Li Oct 2022

Resource Allocation For Mec System With Multi-Users Resource Competition Based On Deep Reinforcement Learning Approach, Bin Qu, Yan Bai, Yul Chu, Li-E Wang, Feng Yu, Xianxian Li

Electrical and Computer Engineering Faculty Publications

Mobile edge computing (MEC) is an effective computing paradigm for mobile devices in the 5G era to reduce computing delay and energy consumption. However, in a multi-user resource competition environment, the revenue-driven behavior of edge servers will cause some users to increase delays or fail tasks. Considering this situation, we take the success rate of computation offloading as the trust value of the edge server, and build a system model from the user’s perspective, taking delay and energy consumption as the multi-objective task of joint optimization. In the optimization goal, we consider three factors: offloading delay, energy consumption, and queuing …

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Hydrogel And Graphene Embedded Piezoresistive Microcantilever Sensor For Solvent And Gas Flow Detection, Dipannita Ghosh, Md. Ashiqur Rahman, Ali Ashraf, Nazmul Islam Sep 2022

Hydrogel And Graphene Embedded Piezoresistive Microcantilever Sensor For Solvent And Gas Flow Detection, Dipannita Ghosh, Md. Ashiqur Rahman, Ali Ashraf, Nazmul Islam

Electrical and Computer Engineering Faculty Publications

Piezoresistive microcantilever sensor is widely used in sensing applications including liquid and gas flow detection. Microcantilevers can function as an embedded system if they are coated with polymers or nanomaterials to improve sensing performance. In this paper, we investigated the performance of piezoresistive microcantilevers (PMC) with and without additional coating. We studied the sensitivity of the PMC sensor after coating it with a three-dimensional porous hydrogel and piezoresistive graphene oxide layer. Hydrogel embedded piezoresistive microcantilever (EPM) showed better results than PMC during solvent sensing application. The resistance change for hydrogel embedded PMC was higher compared to bare PMC by 430% …

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Zigbee Wireless Sensor Networks: Performance Study In An Apartment-Based Indoor Environment, Biswajit Kumar Dash, Jun Peng Aug 2022

Zigbee Wireless Sensor Networks: Performance Study In An Apartment-Based Indoor Environment, Biswajit Kumar Dash, Jun Peng

Electrical and Computer Engineering Faculty Publications

Zigbee is a very popular technology for Internet of things (IoT) networks mainly because of its low power consumption and low-cost features. It shares the unlicensed 2.4 GHz Industrial, Scientific, and Medical (ISM) radio band with other wireless networks such as Wi-Fi. Usually, Zigbee and Wi-Fi networks coexist in indoor environments for their respective applications. Hence, the coexistence introduces interference for both types of networks lowering the performance of the networks, but Zigbee suffers more significant performance losses because of its lower transmission power than Wi-Fi. Since the number of IoT devices is increasing at an unprecedented rate due to …

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Towards A Low-Cost Solution For Gait Analysis Using Millimeter Wave Sensor And Machine Learning, Mubarak A. Alanazi, Abdullah K. Alhazmi, Osama Alsattam, Kara Gnau, Meghan Brown, Shannon Thiel, Kurt Jackson, Vamsy P. Chodavarapu Aug 2022

Towards A Low-Cost Solution For Gait Analysis Using Millimeter Wave Sensor And Machine Learning, Mubarak A. Alanazi, Abdullah K. Alhazmi, Osama Alsattam, Kara Gnau, Meghan Brown, Shannon Thiel, Kurt Jackson, Vamsy P. Chodavarapu

Electrical and Computer Engineering Faculty Publications

Human Activity Recognition (HAR) that includes gait analysis may be useful for various rehabilitation and telemonitoring applications. Current gait analysis methods, such as wearables or cameras, have privacy and operational constraints, especially when used with older adults. Millimeter-Wave (MMW) radar is a promising solution for gait applications because of its low-cost, better privacy, and resilience to ambient light and climate conditions. This paper presents a novel human gait analysis method that combines the micro-Doppler spectrogram and skeletal pose estimation using MMW radar for HAR. In our approach, we used the Texas Instruments IWR6843ISK-ODS MMW radar to obtain the micro-Doppler spectrogram …

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Glaciernet2: A Hybrid Multi-Model Learning Architecture For Alpine Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Michael P. Bishop, Jeffrey S. Kargel, Theus Aspiras Aug 2022

Glaciernet2: A Hybrid Multi-Model Learning Architecture For Alpine Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Michael P. Bishop, Jeffrey S. Kargel, Theus Aspiras

Electrical and Computer Engineering Faculty Publications

In recent decades, climate change has significantly affected glacier dynamics, resulting in mass loss and an increased risk of glacier-related hazards including supraglacial and proglacial lake development, as well as catastrophic outburst flooding. Rapidly changing conditions dictate the need for continuous and detailed ob-servations and analysis of climate-glacier dynamics. Thematic and quantitative information regarding glacier geometry is fundamental for understanding climate forcing and the sensitivity of glaciers to climate change, however, accurately mapping debris-cover glaciers (DCGs) is notoriously difficult based upon the use of spectral information and conventional machine-learning techniques. The objective of this research is to improve upon an …

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Student Training For Motor Performance Assessment In Industry, Jaime Ramos-Salas, Miguel Pineda Aug 2022

Student Training For Motor Performance Assessment In Industry, Jaime Ramos-Salas, Miguel Pineda

Electrical and Computer Engineering Faculty Publications

Energy used by electric motors in the USA According to the US Department of Energy [1], electric motors consume more than 50 percent of all electrical energy in the USA and more than 85 percent of industrial production electrical energy [2]. Furthermore, during the estimated life of an electric motor, approximately 20 years, its initial purchase price is less than 2 percent of the total cost of owning and operating it [3]. 2- The main objective of this work is to share our experience of training university students for performing electric motors energy assessments to local industries with a limited …

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Failure Detection In Deep Neural Networks For Medical Imaging, Sabeen Ahmed, Dimah Dera, Saud Ul Hassan, Nidhal Bouaynaya, Ghulam Rasool Jul 2022

Failure Detection In Deep Neural Networks For Medical Imaging, Sabeen Ahmed, Dimah Dera, Saud Ul Hassan, Nidhal Bouaynaya, Ghulam Rasool

Electrical and Computer Engineering Faculty Publications

Deep neural networks (DNNs) have started to find their role in the modern healthcare system. DNNs are being developed for diagnosis, prognosis, treatment planning, and outcome prediction for various diseases. With the increasing number of applications of DNNs in modern healthcare, their trustworthiness and reliability are becoming increasingly important. An essential aspect of trustworthiness is detecting the performance degradation and failure of deployed DNNs in medical settings. The softmax output values produced by DNNs are not a calibrated measure of model confidence. Softmax probability numbers are generally higher than the actual model confidence. The model confidence-accuracy gap further increases for …

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Analyzing Computational Components Of Standard Block Encryption Schemes, Chu-Wen Cheng, Miranda Heather Cantu, Sanjeev Kumar Jun 2022

Analyzing Computational Components Of Standard Block Encryption Schemes, Chu-Wen Cheng, Miranda Heather Cantu, Sanjeev Kumar

Electrical and Computer Engineering Faculty Publications

Encryption is used to secure sensitive computer data which may be at rest or in motion. There are several standard encryption algorithms that have been used to encrypt and protect blocks of sensitive data to ensure confidentiality. The most popular standard block encryption schemes are the Advanced Encryption Standard (AES), Triple Data Encryption Standard (3DES), and the first standardized encryption scheme, which is no longer the standard scheme now, namely the Data Encryption Standard (DES). AES is the current standard for block encryption used worldwide and is implemented on many processors. In this work, we compare the hardware performance of …

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Robust Explainability: A Tutorial On Gradient-Based Attribution Methods For Deep Neural Networks, Ian E. Nielsen, Dimah Dera, Ghulam Rasool, Nidhal Bouaynaya, Ravi P. Ramachandran Jun 2022

Robust Explainability: A Tutorial On Gradient-Based Attribution Methods For Deep Neural Networks, Ian E. Nielsen, Dimah Dera, Ghulam Rasool, Nidhal Bouaynaya, Ravi P. Ramachandran

Electrical and Computer Engineering Faculty Publications

With the rise of deep neural networks, the challenge of explaining the predictions of these networks has become increasingly recognized. While many methods for explaining the decisions of deep neural networks exist, there is currently no consensus on how to evaluate them. On the other hand, robustness is a popular topic for deep learning research; however, it is hardly talked about in explainability until very recently. In this tutorial paper, we start by presenting gradient-based interpretability methods. These techniques use gradient signals to assign the burden of the decision on the input features. Later, we discuss how gradient-based methods can …

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Imnets: Deep Learning Using An Incremental Modular Network Synthesis Approach For Medical Imaging Applications, Redha A. Ali, Russell C. Hardie, Barath Narayanan Narayanan, Temesguen Messay Jun 2022

Imnets: Deep Learning Using An Incremental Modular Network Synthesis Approach For Medical Imaging Applications, Redha A. Ali, Russell C. Hardie, Barath Narayanan Narayanan, Temesguen Messay

Electrical and Computer Engineering Faculty Publications

Deep learning approaches play a crucial role in computer-aided diagnosis systems to support clinical decision-making. However, developing such automated solutions is challenging due to the limited availability of annotated medical data. In this study, we proposed a novel and computationally efficient deep learning approach to leverage small data for learning generalizable and domain invariant representations in different medical imaging applications such as malaria, diabetic retinopathy, and tuberculosis. We refer to our approach as Incremental Modular Network Synthesis (IMNS), and the resulting CNNs as Incremental Modular Networks (IMNets). Our IMNS approach is to use small network modules that we call SubNets …

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Analyzing Computational Components Of Standard Block Encryption Schemes, Chu-Wen Cheng, Miranda Heather Cantu, Sanjeev Kumar Jun 2022

Analyzing Computational Components Of Standard Block Encryption Schemes, Chu-Wen Cheng, Miranda Heather Cantu, Sanjeev Kumar

Electrical and Computer Engineering Faculty Publications

Encryption is used to secure sensitive computer data which may be at rest or in motion. There are several standard encryption algorithms that have been used to encrypt and protect blocks of sensitive data to ensure confidentiality. The most popular standard block encryption schemes are the Advanced Encryption Standard (AES), Triple Data Encryption Standard (3DES), and the first standardized encryption scheme, which is no longer the standard scheme now, namely the Data Encryption Standard (DES). AES is the current standard for block encryption used worldwide and is implemented on many processors. In this work, we compare the hardware performance of …

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Tracking Control Of Uavs With Uncertainty And Input Constraints, Shihab Ahmed, Wenjie Dong Jun 2022

Tracking Control Of Uavs With Uncertainty And Input Constraints, Shihab Ahmed, Wenjie Dong

Electrical and Computer Engineering Faculty Publications

This paper considers the position and attitude tracking control problem of a vertical take-off and landing unmanned aerial vehicle with uncertainty and input constraints. Considering the parametric and non-parametric uncertainties in the dynamics of systems, a robust adaptive tracking controller is proposed with the aid of the special structure of the dynamics of the system. Considering the uncertainty and input constraints, a robust adaptive saturation controller is proposed with the aid of an auxiliary compensated system. Simulation results show the effectiveness of the proposed algorithms.

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Machine Learning Used In Biomedical Computing And Intelligence Healthcare, Volume Ii, Honghao Gao, Ying Li, Zijian Zhang, Wenbing Zhao May 2022

Machine Learning Used In Biomedical Computing And Intelligence Healthcare, Volume Ii, Honghao Gao, Ying Li, Zijian Zhang, Wenbing Zhao

Electrical and Computer Engineering Faculty Publications

No abstract provided.

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Microscopic Nuclei Classification, Segmentation, And Detection With Improved Deep Convolutional Neural Networks (Dcnn), Md Zahangir Alom, Vijayan K. Asari, Anil Parwani, Tarek M. Taha Apr 2022

Microscopic Nuclei Classification, Segmentation, And Detection With Improved Deep Convolutional Neural Networks (Dcnn), Md Zahangir Alom, Vijayan K. Asari, Anil Parwani, Tarek M. Taha

Electrical and Computer Engineering Faculty Publications

Background Nuclei classification, segmentation, and detection from pathological images are challenging tasks due to cellular heterogeneity in the Whole Slide Images (WSI). Methods In this work, we propose advanced DCNN models for nuclei classification, segmentation, and detection tasks. The Densely Connected Neural Network (DCNN) and Densely Connected Recurrent Convolutional Network (DCRN) models are applied for the nuclei classification tasks. The Recurrent Residual U-Net (R2U-Net) and the R2UNet-based regression model named the University of Dayton Net (UD-Net) are applied for nuclei segmentation and detection tasks respectively. The experiments are conducted on publicly available datasets, including Routine Colon Cancer (RCC) classification and …

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Towards Improved Inertial Navigation By Reducing Errors Using Deep Learning Methodology, Hua Chen, Tarek M. Taha, Vamsy P. Chodavarapu Apr 2022

Towards Improved Inertial Navigation By Reducing Errors Using Deep Learning Methodology, Hua Chen, Tarek M. Taha, Vamsy P. Chodavarapu

Electrical and Computer Engineering Faculty Publications

Autonomous vehicles make use of an Inertial Navigation System (INS) as part of vehicular sensor fusion in many situations including GPS-denied environments such as dense urban places, multi-level parking structures, and areas with thick tree-coverage. The INS unit incorporates an Inertial Measurement Unit (IMU) to process the linear acceleration and angular velocity data to obtain orientation, position, and velocity information using mechanization equations. In this work, we describe a novel deep-learning-based methodology, using Convolutional Neural Networks (CNN), to reduce errors from MEMS IMU sensors. We develop a CNN-based approach that can learn from the responses of a particular inertial sensor …

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An Active Learning Didactic Proposal With Human-Computer Interaction In Engineering Education: A Direct Current Motor Case Study, Alejandro Said, Luis C. Félix-Herrán, Yasser A. Davizón, Carlos Hernandez-Santos, Rogelio Soto, Ricardo A. Ramírez-Mendoza Mar 2022

An Active Learning Didactic Proposal With Human-Computer Interaction In Engineering Education: A Direct Current Motor Case Study, Alejandro Said, Luis C. Félix-Herrán, Yasser A. Davizón, Carlos Hernandez-Santos, Rogelio Soto, Ricardo A. Ramírez-Mendoza

Electrical and Computer Engineering Faculty Publications

Engineering education requires learning strategies to engage students and improve the development of disciplinary and transversal competencies. Additionally, as economic resources are generally limited, it is sought to avoid investing large sums of money in software and hardware, as well as in fitting out laboratories. This work presents a didactic proposal within the framework of active and collaborative learning that includes the flipped classroom technique to be applied in the curriculum of undergraduate engineering programs and inside a massive flexible digital master class. The activity is the mathematical modeling, simulation, and control system of a direct current motor where simulation …

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An Inkjet Printed Flexible Electrocorticography (Ecog) Microelectrode Array On A Thin Parylene-C Film, Yoontae Kim, Stella Alimperti, Paul Choi, Moses Noh Feb 2022

An Inkjet Printed Flexible Electrocorticography (Ecog) Microelectrode Array On A Thin Parylene-C Film, Yoontae Kim, Stella Alimperti, Paul Choi, Moses Noh

Electrical and Computer Engineering Faculty Publications

Electrocorticography (ECoG) is a conventional, invasive technique for recording brain signals from the cortical surface using an array of electrodes. In this study, we developed a highly flexible 22-channel ECoG microelectrode array on a thin Parylene film using novel fabrication techniques. Narrow (<40 >µm) and thin (<500 >nm) microelectrode patterns were first printed on PDMS, then the patterns were transferred onto Parylene films via vapor deposition and peeling. A custom-designed, 3D-printed connector was built and assembled with the Parylene-based flexible ECoG microelectrode array without soldering. The impedance of the assembled ECoG electrode array was measured in vitro by electrochemical impedance …

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Investigation Of The Effect And Contribution Of Process Parameters By Taguchi And Anova Analysis On The Morphological And Electrical Properties Of Rf Magnetron Sputtered Sio2 Over Si Substrate, Sajid Mahfuz Uchayash, Prosanto Biswas, Meah Imtiaz Zulkarnain, Ahmed Touhami, Nazmul Islam, Hasina Huq Jan 2022

Investigation Of The Effect And Contribution Of Process Parameters By Taguchi And Anova Analysis On The Morphological And Electrical Properties Of Rf Magnetron Sputtered Sio2 Over Si Substrate, Sajid Mahfuz Uchayash, Prosanto Biswas, Meah Imtiaz Zulkarnain, Ahmed Touhami, Nazmul Islam, Hasina Huq

Electrical and Computer Engineering Faculty Publications

In this work, we applied Taguchi Signal-to-noise (S/N) analysis to investigate the effect of varying three process parameters, namely — sputtering power, working pressure and Ar gas flow rate on the surface, morphological and electrical properties of the RF sputtered SiO2 over Si substrate. We also inspected the contribution of a particular process parameter on these properties by applying Analysis of Variance (ANOVA). SiO2 thin films were fabricated over Si substrate using RF magnetron sputtering system. Three sets of inputs for the three mentioned process parameters were chosen; for power, we chose 100W, 150W and 200W; 5mTorr, 10mTorr …

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Recent Advances Of Wind-Solar Hybrid Renewable Energy Systems For Power Generation: A Review, Pranoy Roy, Jiangbiao He, Tiefu Zhao, Yash Veer Singh Jan 2022

Recent Advances Of Wind-Solar Hybrid Renewable Energy Systems For Power Generation: A Review, Pranoy Roy, Jiangbiao He, Tiefu Zhao, Yash Veer Singh

Electrical and Computer Engineering Faculty Publications

A hybrid renewable energy source (HRES) consists of two or more renewable energy sources, such as wind turbines and photovoltaic systems, utilized together to provide increased system efficiency and improved stability in energy supply to a certain degree. The objective of this study is to present a comprehensive review of wind-solar HRES from the perspectives of power architectures, mathematical modeling, power electronic converter topologies, and design optimization algorithms. Since the uncertainty of HRES can be reduced further by including an energy storage system, this paper presents several hybrid energy storage system coupling technologies, highlighting their major advantages and disadvantages. Various …

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Cellular Service With Settlement-Free Peering, Shahzeb Mustafa, Sayanta Seth, Murat Yuksel, Mostafizur Rahman Jan 2022

Cellular Service With Settlement-Free Peering, Shahzeb Mustafa, Sayanta Seth, Murat Yuksel, Mostafizur Rahman

Electrical and Computer Engineering Faculty Publications

Despite several iconic innovations in wireless networks, cellular service still remains largely unreliable with regards to non-urban network coverage. Cellular providers often need to make roaming agreements among each other for serving their customers with basic connectivity in areas where they do not have coverage. Considering all the technical limitations of domestic roaming, we present a “wireless peering” model for settlement-free spectrum sharing. It allows providers to extend their coverage to “off-network” regions without any hardware modifications. Its software-defined nature makes the model highly scalable, easy to deploy and cost-effective. Simulation results show a significant improvement in off-network wireless speed, …

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Application Of Deep Neural Networks To Distribution System State Estimation And Forecasting, James P. Carmichael, Yuan Liao Jan 2022

Application Of Deep Neural Networks To Distribution System State Estimation And Forecasting, James P. Carmichael, Yuan Liao

Electrical and Computer Engineering Faculty Publications

Classical neural networks such as feedforward multi-layer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. The dynamic nature of distributed generation (i.e. solar and wind), vehicle to grid technology (V2G) and false data injection attacks (FDIAs), may pose significant challenges to the application of classical MLPs to state estimation (SE) and state forecasting (SF) in power distribution systems. This paper investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long-short term networks …

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Application Of Deep Neural Networks To Distribution System State Estimation And Forecasting, James P. Carmichael, Yuan Liao Jan 2022

Application Of Deep Neural Networks To Distribution System State Estimation And Forecasting, James P. Carmichael, Yuan Liao

Electrical and Computer Engineering Faculty Publications

Classical neural networks such as feedforward multi-layer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. The dynamic nature of distributed generation (i.e. solar and wind), vehicle to grid technology (V2G) and false data injection attacks (FDIAs), may pose significant challenges to the application of classical MLPs to state estimation (SE) and state forecasting (SF) in power distribution systems. This paper investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long-short term networks …

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A Deep Neural Network For Early Detection And Prediction Of Chronic Kidney Disease, Vijendra Singh, Vijayan K. Asari, Rajkumar Rajasekaran Jan 2022

A Deep Neural Network For Early Detection And Prediction Of Chronic Kidney Disease, Vijendra Singh, Vijayan K. Asari, Rajkumar Rajasekaran

Electrical and Computer Engineering Faculty Publications

Diabetes and high blood pressure are the primary causes of Chronic Kidney Disease (CKD). Glomerular Filtration Rate (GFR) and kidney damage markers are used by researchers around the world to identify CKD as a condition that leads to reduced renal function over time. A person with CKD has a higher chance of dying young. Doctors face a difficult task in diagnosing the different diseases linked to CKD at an early stage in order to prevent the disease. This research presents a novel deep learning model for the early detection and prediction of CKD. This research objectives to create a deep …

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A Progressive Learning Strategy For Large-Scale Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari Jan 2022

A Progressive Learning Strategy For Large-Scale Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

In recent years, the worldwide temperature increase has resulted in rapid deglaciation and a higher risk of glacier-related natural hazards such as flooding and debris flow. Due to the severity of these hazards, continuous observation and detailed analysis of glacier fluctuations are crucial. Many such analyses require an accurately delineated glacier boundary. However, the complexity and heterogeneity of glaciers, particularly debris-covered glaciers (DCGs), poses a challenge for glacier mapping when using conventional remote sensing or machine-learning techniques. Some examples exist about small-scale automated glacier mapping, but large or regional-scale mapping is challenging. Previously, a deep-learning-based approach named GlacierNet2 had been …

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Unsupervised Multiview Embedding Of Node Embeddings, Jia Chen, Lizeth Figueroa, Dalia Orozco, Evangelos E. Papalexakis Jan 2022

Unsupervised Multiview Embedding Of Node Embeddings, Jia Chen, Lizeth Figueroa, Dalia Orozco, Evangelos E. Papalexakis

Electrical and Computer Engineering Faculty Publications

—In this paper, we propose a comprehensive unsupervised framework that leverages existing and novel multiview learning models, towards obtaining a single node embedding from a collection of node embeddings, combining the best of all worlds. Through extensive experiments, we demonstrate that the proposed multiview node embedding is able to perform on par or better than the best of its constituents and provide reliable performance across downstream tasks including node classification and graph reconstruction

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The Effect Of Training Dataset Size On Sar Automatic Target Recognition Using Deep Learning, Weidong Kuang, Wenjie Dong, Liang Dong Jan 2022

The Effect Of Training Dataset Size On Sar Automatic Target Recognition Using Deep Learning, Weidong Kuang, Wenjie Dong, Liang Dong

Electrical and Computer Engineering Faculty Publications

Synthetic aperture radar (SAR) is an effective remote sensor for target detection and recognition. Deep learning has a great potential for implementing automatic target recognition based on SAR images. In general, Sufficient labeled data are required to train a deep neural network to avoid overfitting. However, the availability of measured SAR images is usually limited due to high cost and security in practice. In this paper, we will investigate the relationship between the recognition performance and training dataset size. The experiments are performed on three classifiers using MSTAR (Moving and Stationary Target Acquisition and Recognition) dataset. The results show us …

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Self-Assessment And Robust Anomaly Detection With Bayesian Deep Learning, Giuseppina Carannante, Dimah Dera, Orune Aminul, Nidhal Bouaynaya, Ghulam Rasool Jan 2022

Self-Assessment And Robust Anomaly Detection With Bayesian Deep Learning, Giuseppina Carannante, Dimah Dera, Orune Aminul, Nidhal Bouaynaya, Ghulam Rasool

Electrical and Computer Engineering Faculty Publications

Deep Learning (DL) models have achieved or even surpassed human-level accuracy in several areas, including computer vision and pattern recognition. The state-of-art performance of DL models has raised the interest in using them in real-world applications, such as disease diagnosis and clinical decision support systems. However, the challenge remains the lack of trustworthiness and reliability of these DL models. The detection of incorrect decisions or flagging suspicious input samples is essential for the reliability of machine learning models. Uncertainty estimation in the output decision is a key component in establishing the trustworthiness and reliability of these models. In this work, …

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