Electrical and Electronics Commons^™

Artificial Neural Networks And Their Applications To Intelligent Fault Diagnosis Of Power Transmission Lines, Fatemeh Mohammadi Shakiba

Dissertations

Over the past thirty years, the idea of computing based on models inspired by human brains and biological neural networks emerged. Artificial neural networks play an important role in the field of machine learning and hold the key to the success of performing many intelligent tasks by machines. They are used in various applications such as pattern recognition, data classification, stock market prediction, aerospace, weather forecasting, control systems, intelligent automation, robotics, and healthcare. Their architectures generally consist of an input layer, multiple hidden layers, and one output layer. They can be implemented on software or hardware. Nowadays, various structures with …

Evaluation Of Material Request Order To Support Sustainable Construction, Fajar Susilowati, Nur Nahdiah Anggraeni

Makara Journal of Technology

This study was conducted on one of Indonesia’s apartment projects, in which reinforced concrete is the main structure. Based on existing project data, this project experienced a decline in the construction work’s progress, thus reducing the project’s overall progress. This decline occurred because the delivery of concrete reinforcement material was delayed. This study aimed to determine the process of material request order and its relationship with the work implementation, as well as the factors that influenced the delayed arrival of concrete reinforcement material at the project site. The method used in this study was observation and interview. Data were analyzed …

Model-Based Deep Learning For Computational Imaging, Xiaojian Xu

McKelvey School of Engineering Theses & Dissertations

This dissertation addresses model-based deep learning for computational imaging. The motivation of our work is driven by the increasing interests in the combination of imaging model, which provides data-consistency guarantees to the observed measurements, and deep learning, which provides advanced prior modeling driven by data. Following this idea, we develop multiple algorithms by integrating the classical model-based optimization and modern deep learning to enable efficient and reliable imaging. We demonstrate the performance of our algorithms by validating their performance on various imaging applications and providing rigorous theoretical analysis.

The dissertation evaluates and extends three general frameworks, plug-and-play priors (PnP), regularized …

Reduced Fuel Emissions Through Connected Vehicles And Truck Platooning, Paul D. Brummitt

Electronic Theses and Dissertations

Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication enable the sharing, in real time, of vehicular locations and speeds with other vehicles, traffic signals, and traffic control centers. This shared information can help traffic to better traverse intersections, road segments, and congested neighborhoods, thereby reducing travel times, increasing driver safety, generating data for traffic planning, and reducing vehicular pollution. This study, which focuses on vehicular pollution, used an analysis of data from NREL, BTS, and the EPA to determine that the widespread use of V2V-based truck platooning—the convoying of trucks in close proximity to one another so as to reduce air drag …

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 …

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 …

Dual-Axis Precision Imager, Gary Huarng

Computer Engineering

The Dual-Axis Precision Imager (DAPI) is a drawing robot that processes images and draws them on a whiteboard. The system has two modes: a Sobel filter mode that finds the edges of the input image with a Sobel filter, and a tri-tone grayscale mode that approximates the shading of the input image with the colors white, gray, and black. The DAPI consists of an Arduino-controlled XY gantry system with a pen mounted on the gantry head, and a Processing IDE program that processes the original image, converts the processed image into gantry instructions, and sends them to the Arduino for …

Mars Prototype Rover Environmental Measurement System, James A. Renick

Computer Engineering

In my senior project, the problem I am trying to solve is how to efficiently design, create, and install an original library onto a Mars prototype rover operating system and to further use that library to integrate a new weather measurement sensor device into the rover system with the necessary software and hardware implementations. This is an important and highly valued problem as many aerospace and other engineering companies utilize rovers and other autonomous systems for important research, explorations, and reconnaissance missions and goals. In solving this problem, I utilized many resources that were available to me such as advisors, …

Smartphone Control Of Rc Cars, Weston R. Fitzgerald

Electrical Engineering

The smartphone-controlled RC (remote-controlled) car is an inexpensive remote-controlled car designed to be fast and portable. Instead of manufacturing, packaging, and shipping a separate controller, the remote control is implemented in a phone application, which saves time and money in both the design process and the manufacturing process. Utilizing the user’s smartphone is more cost-effective since mobile devices are a common recurrence, and packaging fewer devices results in overall better portability of the product.

This smartphone-controlled car is speedy and intuitive to learn for typical smartphone users. The user can change the car’s speed and direction wirelessly using their phone; …

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 …

A Nano-Drone Safety Architecture, Connor J. Sexton

Master's Theses

As small-form factor drones grow more intelligent, they increasingly require more sophisticated capabilities to record sensor data and system state, ensuring safe and improved operation. Already regulations for black boxes, electronic data recorders (EDRs), for determining liabilities and improving the safety of large-form factor autonomous vehicles are becoming established. Conventional techniques use hardened memory storage units that conserve all sensor (visual) and system operational state; and N-way redundant models for detecting uncertainty in system operation. For small-form factor drones, which are highly limited by weight, power, and computational resources, these techniques become increasingly prohibitive. In this paper, we propose a …

Outdoor Operations Of Multiple Quadrotors In Windy Environment, Deepan Lobo

Dissertations

Coordinated multiple small unmanned aerial vehicles (sUAVs) offer several advantages over a single sUAV platform. These advantages include improved task efficiency, reduced task completion time, improved fault tolerance, and higher task flexibility. However, their deployment in an outdoor environment is challenging due to the presence of wind gusts. The coordinated motion of a multi-sUAV system in the presence of wind disturbances is a challenging problem when considering collision avoidance (safety), scalability, and communication connectivity. Performing wind-agnostic motion planning for sUAVs may produce a sizeable cross-track error if the wind on the planned route leads to actuator saturation. In a multi-sUAV …

Automated Robotic Light Bulb Testing Platform, Agha I. Akram, Muhammad Ali Ummy

Publications and Research

The main purpose of this project is to create a functional prototype of a multilayered system that incorporates aspects of electrical, mechanical, and computer engineering technology. The main objective of the system is to be able to determine whether a light bulb is working or not. The building blocks of this system are a robotic arm that is able to slide along a rail (for added mobility), a conveyor belt, and an electromechanical device that holds and tests light bulbs. Initially, the robot arm picks up a light bulb and places it into the holder which then tests it. A …

Development Of An Automated Electronic Prototyping System, Cesar Yahir Sanchez Zambrano

Open Access Theses & Dissertations

Prototyping systems with interconnected components can be a time and resource expensive process. The process consists of three main phases (design, build and analysis) with each having their own associated cost. For the case of electronic circuits, the building phase is the costliest phase among the three, being prone to human errors which causes the circuit to fail. All three phases of the prototyping process are important. However, often a disproportionate amount of time is spent on the build phase due to the difficulty of making and troubleshooting circuits by hand. In this thesis we will discuss a system that …

Design And Implementation Of I2c Bus Protocol On Master And Slave Data Transfer Based On Fpga, Mohamad Khairi Ishak, Meenal Pradeep Kumar

Makara Journal of Technology

This paper presents the design of the inter-integrated circuit (I2C) protocol with different types of features, such as combined messages, addressing modes, different data patterns and start addresses, clock frequencies, and types of modes between the field-programmable gate array (FPGA) and test card. Moreover, all these features can be randomized and run for long hours. The FPGA and the test card respectively act as master and slave. The design architecture comprises master and slave. The master generates a START condition, in which the serial data will transact between high to low levels and the serial clock will remain high. Then, …

Control Strategy For Solar Energy-Saving Lamps For Optimized Energy Utilization And Sustainability Of Operation Durability: Indonesia Case, Burhanuddin Halimi, Agus Purwadi, Nana Heryana

Makara Journal of Technology

To solve the electrification ratio issue, the Indonesian government has promoted the use of solar energy-saving lamps. This study proposes a control strategy that adopts current reference time-based profiling for solar energy-saving lamps. For optimal energy consumption, the current reference profile is determined according to users’ daily energy requirements. From the aspect of user comfort, a gradual step reference profile is also introduced to provide a subtle change in lighting intensity level. The proposed control strategy can minimize energy consumption and optimize the operation durability of the system. The concept is verified by simulating four scenarios, the results of which …

Enhancement Of Biogas Production Through Solid-State Anaerobic Co-Digestion Of Food Waste And Corn Cobs, Lukhi Mulia Shitophyta, Anisa Salsabila, Firanita Angraini Putri, Siti Jamilatun

Makara Journal of Technology

Although biogas has been primarily produced through liquid anaerobic digestion, this method leads to the floating and stratification of fibers and non-homogeneous mixing, which can reduce the biogas yield. Alternatively, biogas can be produced by the solid-state anaerobic digestion (SS-AD) of organic material with a high solid content, such as corn cobs. We investigated the co-digestion of food waste and corn cobs as a biomass feedstock for SS-AD in biogas production. We measured the effects of the total solid (TS) content, percentage of food waste, and reduction in volatile solids (VS), from which we determined its appropriate kinetic model. We …

Autonomous Navigator Mobile Robot Upgrade, David Sansoucy

Thinking Matters Symposium

The mobile robot platform has been developed over the course of 10 years at USM. In Spring 2020, Belle-Isle and Werner updated the previous framework by rewriting the software to use the ROS framework running on an on-board Raspberry Pi 3. They also implemented navigation using an A* motion planning algorithm and image processing. In Summer 2021, Ames incorporated Lidar and Kinect sensors onto the robot to improve its real-time navigation capabilities. He also made improvements to the power distribution systems. This project aimed to build on the ROS frameworks developed by the previous 2 teams with the main goal …

A Low-Cost, Arduino-Based Platform For Emulating Energy Harvesting In Wireless Sensor Networks, Braden A. Miller

ONU Student Research Colloquium

This paper presents an Arduino-based platform for emulating energy harvesting in Wireless Sensor Networks (WSNs) as a form of hardware-in-the-loop simulation. The platform makes use of a battery monitoring circuit and code implemented on the Arduino as an alternative to using significantly more expensive fully equipped energy harvesting nodes. Using embedded code to emulate the energy harvesting process allows for various energy harvesting models and processes to be tested using the same platform. The main contributions of this paper are the experimental data and analyses demonstrating the energy use characterization of the Arduino-based platform in a three-node relay network using …

Deep Learning For Load Forecasting With Smart Meter Data: Online And Federated Learning, Mohammad Navid Fekri

Electronic Thesis and Dissertation Repository

Electricity load forecasting has been attracting increasing attention because of its importance for energy management, infrastructure planning, and budgeting. In recent years, the proliferation of smart meters has created new opportunities for forecasting on the building and even individual household levels. Machine learning (ML) has achieved great successes in this domain; however, conventional ML techniques require data transfer to a centralized location for model training, therefore, increasing network traffic and exposing data to privacy and security risks. Also, traditional approaches employ offline learning, which means that they are only trained once and miss out on the possibility to learn from …

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 …

Thermal Aware Design Automation Of The Electronic Control System For Autonomous Vehicles, Ajinkya Bankar

FIU Electronic Theses and Dissertations

The autonomous vehicle (AV) technology, due to its tremendous social and economical benefits, is transforming the entire world in the coming decades. However, significant technical challenges still need to be overcome until AVs can be safely, reliably, and massively deployed. Temperature plays a key role in the safety and reliability of an AV, not only because a vehicle is subjected to extreme operating temperatures but also because the increasing computations demand more powerful IC chips, which can lead to higher operating temperature and large thermal gradient. In particular, as the underpinning technology for AV, artificial intelligence (AI) requires substantially increased …

Design And Implementation Of Photovoltaic Energy Harvesting Automaton, Iskandar Askarov

Theses

Global domestic electricity consumption has been rapidly increasing in the past three decades. In fact, from 1990 to 2020, consumption has more than doubled from 10,120 TWh to 23,177 TWh [1]. Moreover, consumers have been turning more towards clean, renewable energy sources such as Photovoltaic. According to International Energy Agency, global Solar power generation alone in 2019 has reached almost 3% [4] of the electricity supply. Even though the efficiency of photovoltaic panels has been growing, presently, the highest efficiency solar panels available to an average consumer range only from 20%-22% [14]. Many research papers have been published to increase …

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 …

Sustainable Computing - Without The Hot Air, Noman Bashir, David Irwin, Prashant Shenoy, Abel Souza

Publications

The demand for computing is continuing to grow exponentially. This growth will translate to exponential growth in computing's energy consumption unless improvements in its energy-efficiency can outpace increases in its demand. Yet, after decades of research, further improving energy-efficiency is becoming increasingly challenging, as it is already highly optimized. As a result, at some point, increases in computing demand are likely to outpace increases in its energy-efficiency, potentially by a wide margin. Such exponential growth, if left unchecked, will position computing as a substantial contributor to global carbon emissions. While prominent technology companies have recognized the problem and sought to …

Magic: The Gathering Card Virtualizer, Vincent Garbonick, Jacen C. Conlan, Jaret A. Varn

Williams Honors College, Honors Research Projects

Any well-versed Magic: The Gathering (MTG) player or collector knows how difficult it can be to keep track of all cards in their collection. Some spend hours searching for that one specific card, and others are constantly scouring the internet for how much their collection costs. However, this issue does not only affect casual fans. Resale companies spend hours a day determining the costs of cards, and tournament judges painstakingly check players’ decks to ensure they are not cheating. To assist with these struggles, the design team proposed to create the MTG Card Virtualizer. This device scans MTG playing cards …

Biomimetic Design, Modeling, And Adaptive Control Of Robotic Gripper For Optimal Grasping, Mushtaq Al-Mohammed

Electronic Theses and Dissertations, 2020-

Grasping is an essential skill for almost every assistive robot. Variations in shape and/or weight of different objects involved in Activities of Daily Living (ADL) lead to complications, especially, when the robot is trying to grip novel objects for which it has no prior information –too much force will deform or crush the object while too little force will lead to slipping and possibly dropped objects. Thus, successful grasping requires the gripper to immobilize an object with the minimal force. In Chapter 2, we present the design, analysis, and experimental implementation of an adaptive control to facilitate 1-click grasping of …

Smart Uv-C Disinfectant Module, Nicole Baldy, Luke Rogers, Haitham Saleh

Williams Honors College, Honors Research Projects

The Smart UV Disinfectant device shall sanitize objects which are 18”x14”x8” or smaller and less than 20 lbs. using UV-C light. This device should contain many safety measures to prevent human and animal exposure to the UV-C light and have no public touchpoints to operate the interface. In order to achieve the first objective, this device shall contain a "sanitizing chamber" which completely encloses the object to be sanitized to prevent outside exposure with detection of any lifeforms inside of the chamber; for the second objective, it will contain a wireless interface to an Android application which can be used …

Garden Bot: Autonomous Home Garden Weed Removal Robot, Brendon Lovejoy, Robert Connolly, Isaac Lucas, Stevan Veselinov

Williams Honors College, Honors Research Projects

With frequent weeding being a tedious chore and an essential task for a successful garden, there is need for an automated method of handling this routine. Existing technologies utilize computer vision, GPS, multiple units and other tools to remove weeds from garden plots. However, these solutions are often complex and expensive, suited for large agricultural plots in contrast to small-scale home gardens. In addition, many of these technologies, along with manual tillers and cultivators suited for home use, are unable to perform weeding within rows of crops in a process known as intra-row weeding. The Garden Bot is an autonomous, …