Other Electrical and Computer Engineering 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.

Measuring The Electrical Properties Of 3d Printed Plastics In The W-Band, Noah Gregory

Electrical Engineering Undergraduate Honors Theses

3D printers are a method of additive manufacturing that consists of layering material to produce a 3D structure. There are many types of 3D printers as well as many types of materials that are capable of being printed with. The most cost-effective and well documented method of 3D printing is called Fused Deposition Modeling (FDM). FDM printers work by feeding a thin strand of plastic filament through a heated extruder nozzle. This plastic is then deposited on a flat, typically heated, surface called a print bed. The part is then built by depositing thin layers of plastic in the shape …

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 …

Integrating Fire Responses To Combat Wildfires, Jonathan Leonard Badal

Electrical Engineering

Each year wildfires cause significant loss of property, worsen people’s health, and destroy multiple ecosystems. On average, a wildfire season costs anywhere from 7.6-62.8 billion dollars due to fire containment, repair, and restoration [1]. It can take decades to recover from the crippling loss of land and take even longer to fully restore it. However, with the implementation of a robust early detection system, these losses can be significantly reduced. The current process for identifying wildfires involves visual identification, phone call alerts, and media tools primarily driven by park rangers and public reporting. Unfortunately, this system is not preventative and …

Soft Web-Based Continuum Robot Grippers, Anthony Carambia

All Theses

We discuss the potential of soft webs to enhance robotic grasping. Specifically, we explore a novel combination of compliant continuum digits interspersed with a flexible material. The resulting webbed structure offers the potential for new modes of robust and adaptive object grasping. We introduce and describe two webbed grippers featuring alternate modes of actuation: pneumatic muscles and remotely actuated tendons. Experiments with the grippers demonstrate their ability to gently capture small, fragile, and non-cooperative objects.

Identification Of Orthologous Gene Groups Using Machine Learning, Dillon Burgess

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

Identification of genes that show similarity between different organisms, a.k.a orthologous genes, is an open problem in computational biology. The purpose of this thesis is to create an algorithm to group orthologous genes using machine learning. Following an optimization step to find the best characterization based on training data, we represented sequences of genes or proteins with kmer vectors. These kmer vectors were then clustered into orthologous groups using hierarchical clustering. We optimized the clustering phase with the same training data for the method and parameter selection. Our results indicated that use of protein sequences with k=2 and scaling the …

Cognality Vr: Exploring A Mobile Vr App With Multiple Stakeholders To Reduce Meltdowns In Autistic Children, Louanne E. Boyd, Espen Garner, Ian Kim, Gianna Valencia

Engineering Faculty Articles and Research

Many autistic children can have difficulty communicating, understanding others, and interacting with new and unfamiliar environments. At times they may suffer from a meltdown. The major contributing factor to meltdowns is sensory overwhelm. Technological solutions have shown promise in improving the quality of life for autistic children-however little exists to manage meltdowns. In this work with stakeholders, we design and deploy a low cost, mobile VR application to provide relief during sensory discomfort. Through the analysis of surveys from 88 stakeholders from a variety of groups (i.e., autistic adults, children with autism, parents of autistic individuals, and medical practitioners), we …

Smart Farm, Nolan Patrick Anderson

Honors Capstone Projects and Theses

No abstract provided.

Combat Robot, Wayne Lambert, Elijah Harris, Brian Eiseman, Jordan Meyer

ONU Student Research Colloquium

The senior capstone project that was tasked to the team was the decision of choosing a challenge within a national robotics competition. The group decided to compete at the National Robotics Challenge in Marion, Ohio. The idea was to participate in the combat robot competition at this NRC event. Once this decision had been made the next steps were to get an idea of what the rules and requirements of the competition were and to try and to sketch a very rough drawing of what the ideal robot should look like. From there it was decided to start a timeline …

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 …

A Real-Time Gaze Estimation Framework For Mobile Devices, Yu Feng, Nathan Goulding-Hotta, Asif Khan, Hans Reyserhove, Yuhao Zhu

Frameless

Tracking eyes becomes an important component to unleash new ways of human-machine interactions in augmented and virtual reality (AR/VR). To make the eye tracking system responsible, eye tracking systems need to operate at a real-time rate (> 30Hz). However, from our experiments, modern gaze tracking algorithms operate at most 5 Hz on mobile processors. In this talk, we present a real-time eye tracking algorithm that operates at 30 Hz on a mobile processor. Our algorithm achieves sub-0.5° gaze accuracy, while requiring only 30K parameters, which is one to two orders of magnitude smaller than state-of-the-art algorithms.

Manipulating Image Luminance To Improve Eye Gaze And Verbal Behavior In Autistic Children, Louanne Boyd, Vincent Berardi, Deanna Hughes, Franceli L. Cibrian, Jazette Johnson, Viseth Sean, Eliza Delpizzo-Cheng, Brandon Mackin, Ayra Tusneem, Riya Mody, Sara Jones, Karen Lotich

Engineering Faculty Articles and Research

Autism has been characterized by a tendency to attend to the local visual details over surveying an image to understand the gist–a phenomenon called local interference. This sensory processing trait has been found to negatively impact social communication. Although much work has been conducted to understand these traits, little to no work has been conducted to intervene to provide support for local interference. Additionally, recent understanding of autism now introduces the core role of sensory processing and its impact on social communication. However, no interventions to the end of our knowledge have been explored to leverage this relationship. This work …

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 …

Learning Domain Invariant Information To Enhance Presentation Attack Detection In Visible Face Recognition Systems, Jennifer Hamblin

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

Face signatures, including size, shape, texture, skin tone, eye color, appearance, and scars/marks, are widely used as discriminative, biometric information for access control. Despite recent advancements in facial recognition systems, presentation attacks on facial recognition systems have become increasingly sophisticated. The ability to detect presentation attacks or spoofing attempts is a pressing concern for the integrity, security, and trust of facial recognition systems. Multi-spectral imaging has been previously introduced as a way to improve presentation attack detection by utilizing sensors that are sensitive to different regions of the electromagnetic spectrum (e.g., visible, near infrared, long-wave infrared). Although multi-spectral presentation attack …

A Deep Learning-Based Approach To Extraction Of Filler Morphology In Sem Images With The Application Of Automated Quality Inspection, Md. Fashiar Rahman, Tzu-Liang Bill Tseng, Jianguo Wu, Yuxin Wen, Yirong Lin

Engineering Faculty Articles and Research

Automatic extraction of filler morphology (size, orientation, and spatial distribution) in Scanning Electron Microscopic (SEM) images is essential in many applications such as automatic quality inspection in composite manufacturing. Extraction of filler morphology greatly depends on accurate segmentation of fillers (fibers and particles), which is a challenging task due to the overlap of fibers and particles and their obscure presence in SEM images. Convolution Neural Networks (CNNs) have been shown to be very effective at object recognition in digital images. This paper proposes an automatic filler detection system in SEM images, utilizing a Mask Region-based CNN architecture. The proposed system …

Three Wave Mixing In Epsilon-Near-Zero Plasmonic Waveguides For Signal Regeneration, Nicholas Mirchandani, Mark C. Harrison

Engineering Faculty Articles and Research

Vast improvements in communications technology are possible if the conversion of digital information from optical to electric and back can be removed. Plasmonic devices offer one solution due to optical computing’s potential for increased bandwidth, which would enable increased throughput and enhanced security. Plasmonic devices have small footprints and interface with electronics easily, but these potential improvements are offset by the large device footprints of conventional signal regeneration schemes, since surface plasmon polaritons (SPPs) are incredibly lossy. As such, there is a need for novel regeneration schemes. The continuous, uniform, and unambiguous digital information encoding method is phase-shift-keying (PSK), so …

Ad-Corre: Adaptive Correlation-Based Loss For Facial Expression Recognition In The Wild, Ali Pourramezan Fard, Mohammad H. Mahoor

Electrical and Computer Engineering: Faculty Scholarship

Automated Facial Expression Recognition (FER) in the wild using deep neural networks is still challenging due to intra-class variations and inter-class similarities in facial images. Deep Metric Learning (DML) is among the widely used methods to deal with these issues by improving the discriminative power of the learned embedded features. This paper proposes an Adaptive Correlation (Ad-Corre) Loss to guide the network towards generating embedded feature vectors with high correlation for within-class samples and less correlation for between-class samples. Ad-Corre consists of 3 components called Feature Discriminator, Mean Discriminator, and Embedding Discriminator. We design the Feature Discriminator component to guide …

Investigation Of Green Strawberry Detection Using R-Cnn With Various Architectures, Daniel W. Rivers

Master's Theses

Traditional image processing solutions have been applied in the past to detect and count strawberries. These methods typically involve feature extraction followed by object detection using one or more features. Some object detection problems can be ambiguous as to what features are relevant and the solutions to many problems are only fully realized when the modern approach has been applied and tested, such as deep learning.

In this work, we investigate the use of R-CNN for green strawberry detection. The object detection involves finding regions of interest (ROIs) in field images using the selective segmentation algorithm and inputting these regions …