Computer Engineering Commons^™

Edge-Cloud Computing For Iot Data Analytics: Embedding Intelligence In The Edge With Deep Learning, Ananda Mohon M. Ghosh, Katarina Grolinger

Electrical and Computer Engineering Publications

Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …

Deep Learning For Load Forecasting With Smart Meter Data: Online Adaptive Recurrent Neural Network, Mohammad Navid Fekri, Harsh Patel, Katarina Grolinger, Vinay Sharma

Electrical and Computer Engineering Publications

No abstract provided.

Water Conservation Potential Of Self-Funded Foam-Based Flexible Surface-Mounted Floatovoltaics, Koami Soulemane Hayibo, Pierce Mayville, Ravneet Kaur Kailey, Joshua M. Pearce

Electrical and Computer Engineering Publications

A potential solution to the coupled water–energy–food challenges in land use is the concept of floating photovoltaics or floatovoltaics (FPV). In this study, a new approach to FPV is investigated using a flexible crystalline silicon-based photovoltaic (PV) module backed with foam, which is less expensive than conventional pontoon-based FPV. This novel form of FPV is tested experimentally for operating temperature and performance and is analyzed for water-savings using an evaporation calculation adapted from the Penman–Monteith model. The results show that the foam-backed FPV had a lower operating temperature than conventional pontoon-based FPV, and thus a 3.5% higher energy output per …

Comparative Analysis Of Single-Cell Transcriptomics In Human And Zebrafish Oocytes, Handan Can, Sree K. Chanumolu, Elena Gonzalez-Muñoz, Sukumal Prukudom, Hasan H. Otu, Jose Cibelli

Department of Electrical and Computer Engineering: Faculty Publications

Background: Zebrafish is a popular model organism, which is widely used in developmental biology research. Despite its general use, the direct comparison of the zebrafish and human oocyte transcriptomes has not been well studied. It is significant to see if the similarity observed between the two organisms at the gene sequence level is also observed at the expression level in key cell types such as the oocyte.

Results: We performed single-cell RNA-seq of the zebrafish oocyte and compared it with two studies that have performed single-cell RNA-seq of the human oocyte. We carried out a comparative analysis of genes expressed …

Cdc42-Dependent Transfer Of Mir301 From Breast Cancer-Derived Extracellular Vesicles Regulates The Matrix Modulating Ability Of Astrocytes At The Blood–Brain Barrier, Golnaz Morad, Cassandra C. Daisy, Hasan H. Otu, Towia A. Libermann, Simon T. Dillon, Marsha A. Moses

Department of Electrical and Computer Engineering: Faculty Publications

Breast cancer brain metastasis is a major clinical challenge and is associated with a dismal prognosis. Understanding the mechanisms underlying the early stages of brain metastasis can provide opportunities to develop efficient diagnostics and therapeutics for this significant clinical challenge. We have previously reported that breast cancer-derived extracellular vesicles (EVs) breach the blood–brain barrier (BBB) via transcytosis and can promote brain metastasis. Here, we elucidate the functional consequences of EV transport across the BBB. We demonstrate that brain metastasis-promoting EVs can be internalized by astrocytes and modulate the behavior of these cells to promote extracellular matrix remodeling in vivo. We …

Ieee Access Special Section Editorial: Green Signal Processing For Wireless Communicationsand Networking, Wen-Long Chin, David Shiung, Yi Qian, Woongsup Lee, Andres Kwasinki, Yansha Deng

Department of Electrical and Computer Engineering: Faculty Publications

No abstract provided.

Topical Review Of Vulnerability Management For Local Hampton Roads Industry, Gregory W. Hubbard Jr., Matthew Eunice

OUR Journal: ODU Undergraduate Research Journal

The progress towards an interconnected digital world offers an exciting level of advancement for humanity. Unfortunately, this “online” connection is not safe from the threats and dangers typically associated with physical operations. With the foundation of Cyber Command of DoD cyberspace, the United States Government is taking a prominent stance in cyberspace operations. Like the federal government, both industries and individuals are not immune and are oftentimes unknowingly at risk to cyberattack. This report hopes to bring awareness to common vulnerabilities in multi-user networks by describing a historical background on cyber security as well as outlining current methods of vulnerability …

Assessment Of Dobhoff Tube Malposition On Radiographs Using Deep Learning, Kevin George, Paras Lakhani, Md

Phase 1

Introduction: Dobhoff tubes (DHT) are narrow-bore flexible devices that deliver enteral nutrition for critically ill patients. Tracheobronchial insertion of DHTs presents a significant risk for pulmonary complications. Thus, DHT insertion requires radiologist confirmation of correct placement with chest x-ray (CXR), increasing clinical delays. To address this, we demonstrate the novel application of Deep Convolutional Neural Networks (DCNNs) to automatically and accurately identify DHTs in CXRs in real time.

Methods: 141 de-identified HIPAA compliant frontal view chest radiographs containing DHTs in various positions were obtained. The DHTs were first manually segmented and verified by a board certified radiologist. Images were split …

3d Convolutional Neural Networks For The Diagnosis Of 6 Unique Pathologies On Head Ct, Travis Clarke, Paras Lakhani, Md

Phase 1

Introduction: Head CT scans are a standard first-line tool used by physicians in the diagnosis of neurological pathologies. Recently, the development of deep learning models such as convolutional neural networks (CNNs) has allowed the rapid identification of bleeds and other pathologies on CT scans. This study aims to show that by training 3D CNNs with a larger, curated dataset, a more comprehensive list of potential diagnoses can be included in the detailed model.

Methods: A retrospective study was performed using a dataset of 66,000 head CT studies from the Thomas Jefferson University health system. Studies were acquired using a natural …

Comparison Of Object Detection And Patch-Based Classification Deep Learning Models On Mid- To Late-Season Weed Detection In Uav Imagery, Arun Narenthiran Veeranampalayam Sivakumar, Jiating Li, Stephen Scott, Eric T. Psota, Amit J. Jhala, Joe D. Luck, Yeyin Shi

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Mid- to late-season weeds that escape from the routine early-season weed management threaten agricultural production by creating a large number of seeds for several future growing seasons. Rapid and accurate detection of weed patches in field is the first step of site-specific weed management. In this study, object detection-based convolutional neural network models were trained and evaluated over low-altitude unmanned aerial vehicle (UAV) imagery for mid- to late-season weed detection in soybean fields. The performance of two object detection models, Faster RCNN and the Single Shot Detector (SSD), were evaluated and compared in terms of weed detection performance using mean …

Control Strategies For Multi-Controller Multi-Objective Systems, Raaed Al-Azzawi

Electronic Theses and Dissertations, 2020-2023

This dissertation's focus is control systems controlled by multiple controllers, each having its own objective function. The control of such systems is important in many practical applications such as economic systems, the smart grid, military systems, robotic systems, and others. To reap the benefits of feedback, we consider and discuss the advantages of implementing both the Nash and the Leader-Follower Stackelberg controls in a closed-loop form. However, closed-loop controls require continuous measurements of the system's state vector, which may be expensive or even impossible in many cases. As an alternative, we consider a sampled closed-loop implementation. Such an implementation requires …

Target Acquisition Performance Improvement With Boost And Restoration Filtering Using Deep-Electron-Well Infrared Detectors, Robert Short

Electronic Theses and Dissertations, 2020-2023

Recent advances in infrared focal plane fabrication have allowed for the production of sensors with small detector size (small pitch) and long integration time (deep electron wells) in large-format arrays. Individually, these are all welcome developments, but we raise the question of whether it is possible to utilize all of these technologies in concert to optimize performance. If so, a key part of such a system will be digital boost filtering, to recover the performance loss due to diffraction blur. We describe a system design concept called PWP (Pitch-Well-Processing) that uses each of these features along with Wiener filtering to …

On The Automorphisms And Isomorphisms Of Mds Matrices And Their Efficient Implementations, Muharrem Tolga Sakalli, Sedat Akleylek, Kemal Akkanat, Vincent Rijmen

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we explicitly define the automorphisms of MDS matrices over the same binary extension field. By extending this idea, we present the isomorphisms between MDS matrices over $\mathbb{F}_{2^{m}}$ and MDS matrices over $\mathbb{F}_{2^{mt}}$, where $t \ge 1$ and $m>1$, which preserves the software implementation properties in view of XOR operations and table lookups of any given MDS matrix over $\mathbb{F}_{2^{m}}$. Then we propose a novel method to obtain distinct functions related to these automorphisms and isomorphisms to be used in generating isomorphic MDS matrices (new MDS matrices in view of implementation properties) using the existing ones. The …

Wideband Patch Array Antenna Using Superstrate Configuration For Future 5gapplications, Sidra Farhat, Farzana Arshad, Yasar Amin, Jonathan Loo

Turkish Journal of Electrical Engineering and Computer Sciences

In this work, four distinct antenna configurations for future-centric 5G applications are proposed. Initially, a single rectangular patch is designed to operate at the frequency of 28 GHz while maintaining a wide operational band. Performance of the antenna is improved by incorporating an array of identical rectangular elements resulting in a higher gain and wider bandwidth. The proposed arrangement consists of three rectangular elements realized using 0.508-mm thick Rogers RT/Duroid 5880 laminate. The bandwidth is further enhanced by increasing the number of radiating elements in the array from three to five. Evolution of the proposed design is concluded by stacking …

Revised Polyhedral Conic Functions Algorithm For Supervised Classification, Gürhan Ceylan, Gürkan Öztürk

Turkish Journal of Electrical Engineering and Computer Sciences

In supervised classification, obtaining nonlinear separating functions from an algorithm is crucial for prediction accuracy. This paper analyzes the polyhedral conic functions (PCF) algorithm that generates nonlinear separating functions by only solving simple subproblems. Then, a revised version of the algorithm is developed that achieves better generalization and fast training while maintaining the simplicity and high prediction accuracy of the original PCF algorithm. This is accomplished by making the following modifications to the subproblem: extension of the objective function with a regularization term, relaxation of a hard constraint set and introduction of a new error term. Experimental results show that …

Cdc42-Dependent Transfer Of Mir301 From Breast Cancer-Derived Extracellular Vesicles Regulates The Matrix Modulating Ability Of Astrocytes At The Blood–Brain Barrier, Golnaz Morad, Cassandra C. Daisy, Hasan H. Otu, Towia A. Libermann, Simon T. Dillon, Marsha A. Moses

Department of Electrical and Computer Engineering: Faculty Publications

Breast cancer brain metastasis is a major clinical challenge and is associated with a dismal prognosis. Understanding the mechanisms underlying the early stages of brain metastasis can provide opportunities to develop efficient diagnostics and therapeutics for this significant clinical challenge. We have previously reported that breast cancer-derived extracellular vesicles (EVs) breach the blood–brain barrier (BBB) via transcytosis and can promote brain metastasis. Here, we elucidate the functional consequences of EV transport across the BBB. We demonstrate that brain metastasis-promoting EVs can be internalized by astrocytes and modulate the behavior of these cells to promote extracellular matrix remodeling in vivo. We …

Power-Over-Tether Uas Leveraged For Nearly Indefinite Meteorological Data Acquisition In The Platte River Basin, Daniel Rico, Carrick Detweiler, Francisco Munoz-Arriola

School of Computing: Conference and Workshop Papers

The integration of unmanned aerial systems (UASs) has increased in the field of agriculture. These systems can provide data that was previously difficult to obtain to help increase efficiency and production. Typical commercial off the shelf (COTS) UASs have significant limitations in the form of small payloads, and short flight times which inhibit their ability to provide significant quantities of useful data. We present the development of a novel power-over-tether UAS that leverages the physical presence of the tether to integrate sensors at multiple altitudes along the tether. The UAS can acquire data nearly indefinitely to sense atmospheric conditions and …

Automated Assessment Of Cardiothoracic Ratios On Chest Radiographs Using Deep Learning, Varun Danda, Paras Lakhani, Md

Phase 1

Introduction: The cardiothoracic ratio (CTR) is a quantitative measure of cardiac size that can measured from chest radiography (CXR). Although radiologists using digital workstations possess the ability to calculate CTR, clinical demands prevent calculation for every case. In this study, the efficacy of a deep convolutional neural network (dCNN) to assess CTR was evaluated.

Methods: 611 HIPAA-compliant de-identified CXRs were obtained from [institution blinded] and public databases. Using ImageJ, a board-certified radiologist (reader #1) and a medical student (reader #2), measured the CTR by marking four pixels on all CXRs: the right- and left-most chest wall, the right- and left-most …

The Influence Of Blockchain Technology On Fraud And Fake Protection, Youngju Yun

OUR Journal: ODU Undergraduate Research Journal

No abstract provided.

Hierarchical Anomaly Detection For Time Series Data, Ryan E. Sperl

Browse all Theses and Dissertations

With the rise of Big Data and the Internet of Things, there is an increasing availability of large volumes of real-time streaming data. Unusual occurrences in the underlying system will be reflected in these streams, but any human analysis will quickly become out of date. There is a need for automatic analysis of streaming data capable of identifying these anomalous behaviors as they occur, to give ample time to react. In order to handle many high-velocity data streams, detectors must minimize the processing requirements per value. In this thesis, we have developed a novel anomaly detection method which makes use …