Electrical and Electronics Commons^™

Worksheet 08 - Helix, Ashanthi Maxworth Phd

Antenna Design With HFSS

This worksheet creates a helical antenna operating in the axial mode. The antenna is mounted on a ground plane. The users can change the circumference of the helix and change the operating mode to broadside or conical if they wish.

Worksheet 10 - Patch, Ashanthi Maxworth Phd

Antenna Design With HFSS

In this worksheet, steps are given to create a slotted rectangular microstrip patch antenna with an inset feed using a stripline. The patch antenna is operating in the TM10 mode.

Worksheet 02 - Meshing, Ashanthi Maxworth Phd

Antenna Design With HFSS

This worksheet is designed to oberserve the variations in results when the mesh is varied from coarse to fine, hence how to find the optimum mesh size such that we can get accurate results while not compromising computational power.

Signal Analysis Of Photovoltaic Systems For Multilevel Cybersecurity, Wesley G. Schwartz

Electrical Engineering Undergraduate Honors Theses

The cybersecurity of grid-connected power electronics is a rapidly developing field as more and more of these devices become a part of the Internet of Things. The objective of this thesis to analyze the current control signals of a photovoltaic (PV) inverter and develop an interface board for the implementation of a new cyber-secure controller.

In this thesis, the testing and in-depth analysis of the current PV inverter control system will be conducted. Using the data collected, an interface board will be developed to allow the use of the Unified Control Board (UCB), developed by Chris Farnell, in the PV …

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 …

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 …

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 …

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 …

Removing Physical Presence Requirements For A Remote And Automated World - Api Controlled Patch Panel For Conformance Testing, Hunter George Wells

Honors Theses and Capstones

Quality assurance test engineers at the UNH-InterOperability Lab must run tests that require driving and monitoring a selection of DC signals. While the number of signals is numerous, there are limited ports on the test equipment, and only a few signals need patching for any given test. The selection of signals may vary between the 209 different tests and must be re-routed frequently. Currently, testers must leave their desk to manually modify the test setup in another room. This posed a considerable issue at the onset of the COVID-19 Pandemic when physical access was not possible. In order to enable …

Meltpondnet: A Swin Transformer U-Net For Detection Of Melt Ponds On Arctic Sea Ice, Ivan Sudakow, Vijayan K. Asari, Ruixu Liu, Denis Demchev

Electrical and Computer Engineering Faculty Publications

High-resolution aerial photographs of Arctic region are a great source for different sea ice feature recognition, which are crucial to validate, tune, and improve climate models. Melt ponds on the surface of melting Arctic sea ice are of particular interest as they are sensitive and valuable indicators and are proxy to the processes in the Arctic climate system. Manual analysis of this remote sensing data is extremely difficult and time-consuming due to the complex shapes and unpredictable boundaries of the melt ponds, and that leads to the necessity for automatizing the processes. In this study, we propose a robust and …

Circuit Optimization Techniques For Efficient Ex-Situ Training Of Robust Memristor Based Liquid State Machine, Alex Henderson, Christopher Yakopcic, Cory Merkel, Steven Harbour, Tarek M. Taha, Hananel Hazan

Electrical and Computer Engineering Faculty Publications

Spiking neural network hardware offers a high performance, power-efficient and robust platform for the processing of complex data. Many of these systems require supervised learning, which poses a challenge when using gradient-based algorithms due to the discontinuous properties of SNNs. Memristor based hardware can offer gains in portability, power reduction, and throughput efficiency when compared to pure CMOS. This paper proposes a memristor-based spiking liquid state machine (LSM). The inherent dynamics of the LSM permit the use of supervised learning without backpropagation for weight updates. To carry out the design space evaluation of the LSM for optimal hardware performance, several …

"Demeter" Soil Monitoring System, Ryan Matthews, Rachel Rummer, Temilolu Fayomi, Alex Fuller

Williams Honors College, Honors Research Projects

The purpose of this project is to develop a soil monitoring system that can remotely sense and relay soil conditions back to a user. The deMETER soil probe, Demeter is the Greek goddess of the harvest, is designed to aid hobbyist gardeners, small-scale farms, and nurseries to monitor their dynamic soil conditions and maximize their harvest. The probe is a self-powered system that can monitor the moisture and essential nutrients of the soil profile to determine which areas should receive water and fertilizer. This would significantly cut water and fertilizer waste. The solution will include an embedded system with sensors …

Use Of Battery Systems For Var Support In Con Edison’S Distribution Network/Substation, Elihu Nyemah

Dissertations and Theses

Battery Energy Storage System (BESS) can facilitate the integration of Distributed Energy Resources (DER) and help create a more reliable grid by providing multiple services including reactive power (VAR) support. This research will investigate the use of smart inverters to provide VAR support, assess the impact it has on the lifetime of a BESS and determine how the adverse effects (if any) can be mitigated/eliminated. To achieved this, a 7.5MW/30MWh grid connected BESS located at Con Edison substations have been modeled in MATLAB/Simulink. Preliminary assessment of the system showed that DC current to/from the battery is oscillating (non-zero) during reactive …

Low-Noise, Low-Power Analog Front End For Dual Detector, Event-Driven Radioactive Isotope Identification, Joseph Medinger

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

An analog front end (AFE) design for a low-noise, low-power, event-driven radioactive spectroscopy system is implemented in a 65 nm CMOS technology. The AFE is optimized for use with two scintillation based detectors, CsI(Na) and LaBr3(Ce), that utilize photo-multiplier tubes for charge amplification. The amplification within the AFE is accomplished through charge sensitive amplifier designs that are tailored to each detector type. The AFE includes adjustable bias generation circuits to allow amplifier tuning for process, voltage, and temperature variations. The presented AFE is implemented along with analog to digital acquisition circuits and a microcontroller to provide a single-chip radioactive spectroscopy …

Evaluating Deep-Learning Models For Debris-Covered Glacier Mapping, Zhiyuan Xie, Vijayan K. Asari, Umesh K. Haritashya

Electrical and Computer Engineering Faculty Publications

In recent decades, mountain glaciers have experienced the impact of climate change in the form of accelerated glacier retreat and other glacier-related hazards such as mass wasting and glacier lake outburst floods. Since there are wide-ranging societal consequences of glacier retreat and hazards, monitoring these glaciers as accurately and repeatedly as possible is important. However, the accurate glacier boundary, especially the debriscovered glacier (DCG) boundary, which is one of the primary inputs in many glacier analyses, remains a challenge even after many years of research using conventional remote sensing methods or machine-learning methods. The GlacierNet, a deep-learning-based approach, utilized the …

Machine Learning For Unmanned Aerial System (Uas) Networking, Jian Wang

Doctoral Dissertations and Master's Theses

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex …

Resampling And Super-Resolution Of Hexagonally Sampled Images Using Deep Learning, Dylan Flaute, Russell C. Hardie, Hamed Elwarfalli

Electrical and Computer Engineering Faculty Publications

Super-resolution (SR) aims to increase the resolution of imagery. Applications include security, medical imaging, and object recognition. We propose a deep learning-based SR system that takes a hexagonally sampled low-resolution image as an input and generates a rectangularly sampled SR image as an output. For training and testing, we use a realistic observation model that includes optical degradation from diffraction and sensor degradation from detector integration. Our SR approach first uses non-uniform interpolation to partially upsample the observed hexagonal imagery and convert it to a rectangular grid. We then leverage a state-of-the-art convolutional neural network (CNN) architecture designed for SR …

Parents’ Perspectives On A Smartwatch Intervention For Children With Adhd: Rapid Deployment And Feasibility Evaluation Of A Pilot Intervention To Support Distance Learning During Covid-19, Franceli L. Cibrian, Elissa Monteiro, Elizabeth Ankrah, Jesus A. Beltran, Arya Tavakoulnia, Sabrina E. B. Schuck, Gillian R. Hayes, Kimberley D. Lakes

Engineering Faculty Articles and Research

Distance learning in response to the COVID-19 pandemic presented tremendous challenges for many families. Parents were expected to support children’s learning, often while also working from home. Students with Attention Deficit Hyperactivity Disorder (ADHD) are at particularly high risk for setbacks due to difficulties with organization and increased risk of not participating in scheduled online learning. This paper explores how smartwatch technology, including timing notifications, can support children with ADHD during distance learning due to COVID-19. We implemented a 6-week pilot study of a Digital Health Intervention (DHI) with ten families. The DHI included a smartwatch and a smartphone. Google …

A Unified Framework Of Deep Learning-Based Facial Expression Recognition System For Diversified Applications, Sanoar Hossain, Saiyed Umer, Vijayan K. Asari, Ranjeet Kumar Rout

Electrical and Computer Engineering Faculty Publications

This work proposes a facial expression recognition system for a diversified field of appli- cations. The purpose of the proposed system is to predict the type of expressions in a human face region. The implementation of the proposed method is fragmented into three components. In the first component, from the given input image, a tree-structured part model has been applied that predicts some landmark points on the input image to detect facial regions. The detected face region was normalized to its fixed size and then down-sampled to its varying sizes such that the advantages, due to the effect of multi-resolution …

First Order Self-Oscillating Class-D Circuit With Triangular Wave Injection, Matthew J. Carroll

Master's Theses

An investigation into performance improvements to the modulator stage of a class-D amplifier is conducted in this thesis. Two of the standard topologies, namely class-D open-loop pulse-width modulation (PWM), and the improved self-oscillating feedback system are benchmarked against a topology which includes both a hysteretic comparator in a feedback loop and triangle wave injection. Circuit performance is analyzed by comparing how the triangle injection circuit handles known issues with open-loop and self-oscillating circuits. Using this analysis, it is shown that the triangle injection topology offers an improved power supply rejection ratio relative to open-loop PWM and reduces distortion generated by …

Pilltank, Lucas Chang, Hayden Tam, Aaron Teh, Krista Round

Electrical Engineering

Imagine an elderly family member, going through their daily routine of taking their pills. They find their pill box; however, they are having trouble identifying all the pills in there. Is there a name on the tablet? Can they read what it says? Do they just trust that the medication in their box is correct? How can they properly take care of themselves if they can not even confirm that what they are taking is the right medication? To combat this issue that many face, we present PillTank.

To decrease the risk of consuming the wrong medication, PillTank identifies the …

Wildfire Early Detection System (Weds), Mason Mciver, Vincent Liang, Jeanreno Racines

Electrical Engineering

With climate change causing an increase in temperature over the past several decades, wildfires have been burning hotter and moving quicker leaving a trail of destruction in their path. Detecting a wildfire early allows firefighters to respond efficiently and effectively to ensure containment. With the rise of advanced computer vision and algorithms, autonomous systems can be used to monitor and report any fire activity. Having multiple devices spread out across a large area will allow first responders to map out the fire location and track the fire. By utilizing smart technologies, property damage can be minimized and residents living in …

Guest Editorial: Edge Intelligence For Beyond 5g Networks, Yan Zhang, Zhiyong Feng, Hassnaa Moustafa, Feng Ye, Usman Javaid, Chunfen Cui

Electrical and Computer Engineering Faculty Publications

Beyond fifth-generation (B5G) networks, or so-called "6G", is the next-generation wireless communications systems that will radically change how Society evolves. Edge intelligence is emerging as a new concept and has extremely high potential in addressing the new challenges in B5G networks by providing mobile edge computing and edge caching capabilities together with Artificial Intelligence (AI) to the proximity of end users. In edge intelligence empowered B5G networks, edge resources are managed by AI systems for offering powerful computational processing and massive data acquisition locally at edge networks. AI helps to obtain efficient resource scheduling strategies in a complex environment with …

Color-Compressive Bilateral Filter And Nonlocal Means For High-Dimensional Images, Christina Karam, Kenjiro Sugimoto, Keigo Hirakawa

Electrical and Computer Engineering Faculty Publications

We propose accelerated implementations of bilateral filter (BF) and nonlocal means (NLM) called color-compressive bilateral filter (CCBF) and color-compressive nonlocal means (CCNLM). CCBF and CCNLM are random filters, whose Monte-Carlo averaged output images are identical to the output images of conventional BF and NLM, respectively. However, CCBF and CCNLM are considerably faster because the spatial processing of multiple color channels are combined into a single random filtering process. This implies that the complexity of CCBF and CCNLM is less sensitive to color dimension (e.g., hyperspectral images) relatively to other BF and NLM methods. We experimentally verified that the execution time …

Deep Learning For Anisoplanatic Optical Turbulence Mitigation In Long-Range Imaging, Matthew A. Hoffmire, Russell C. Hardie, Michael A. Rucci, Richard Van Hook, Barry K. Karch

Electrical and Computer Engineering Faculty Publications

We present a deep learning approach for restoring images degraded by atmospheric optical turbulence. We consider the case of terrestrial imaging over long ranges with a wide field-of-view. This produces an anisoplanatic imaging scenario where turbulence warping and blurring vary spatially across the image. The proposed turbulence mitigation (TM) method assumes that a sequence of short-exposure images is acquired. A block matching (BM) registration algorithm is applied to the observed frames for dewarping, and the resulting images are averaged. A convolutional neural network (CNN) is then employed to perform spatially adaptive restoration. We refer to the proposed TM algorithm as …

Analog & Digital Remote Synthesizer, Adam Brunner, Andrew Cihon-Scott, Scott Grisso, Linus Wright

Williams Honors College, Honors Research Projects

The purpose of this project is to develop and design an analog synthesizer musical instrument that integrates embedded digital hardware into the design to enable control from a remote source. The use of digital hardware enables the potential for a wide range of convenient features such as sound profile saving and loading, output recording functionality, and the ability to accept digital input from another musical instrument utilizing the Musical Instrument Digital Interface (MIDI). In addition to the synthesizer itself, this project also includes the design of a companion application that can be hosted on a wide variety of consumer computing …

Dales Objects: A Large Scale Benchmark Dataset For Instance Segmentation In Aerial Lidar, Nina M. Singer, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

We present DALES Objects, a large-scale instance segmentation benchmark dataset for aerial lidar. DALES Objects contains close to half a billion hand-labeled points, including semantic and instance segmentation labels. DALES Objects is an extension of the DALES (Varney et al., 2020) dataset, adding additional intensity and instance segmentation annotation. This paper provides an overview of the data collection, preprocessing, hand-labeling strategy, and final data format. We propose relevant evaluation metrics and provide insights into potential challenges when evaluating this benchmark dataset. Finally, we provide information about how researchers can access the dataset for their use at go.udayton.edu/dales3d.

Ieee Access Special Section Editorial: Trends And Advances In Bio-Inspired Image-Based Deep Learning Methodologies And Applications, Peter Peer, Carlos M. Travieso-Gonzalez, Vijayan K. Asari, Malay Kishore Dutta

Electrical and Computer Engineering Faculty Publications

Many of the technological advances we enjoy today have been inspired by biological systems due to their ease of operation and outstanding efficiency. Designing technological solutions based on biological inspiration has become a cornerstone of research in a variety of areas ranging from control theory and optimization to computer vision, machine learning, and artificial intelligence. Especially in the latter few areas, biologically relevant solutions are becoming increasingly important as we look for new ways to make artificial systems more efficient, intelligent, and overall effective.

Integration Of Robotic And Electro-Pneumatic Systems Using Advanced Control And Communication Schemes, Chinmay Kondekar

Dissertations, Master's Theses and Master's Reports

Modern industrial automation systems are designed by interconnecting various subsystems which work together to perform a process. The thesis project aims to integrate fragmented subsystems into a flexible and reconfigurable system through advanced communication protocols and perform a process to demonstrate the effectiveness of interconnected systems.

The system consists of three six-axis robots, one electro-pneumatic robot, and two conveyors connected using EthernetIP communication and hardwired connections. The interconnected system works together to perform machining of a workpiece using advanced control methods of CAD to robot path generation, central control through a PLC, and process control through HMI.

Standardized programming blocks …

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 …