Other Electrical and Computer Engineering Commons^™

Design, Construction, And Characterization Of A Combined Mini-Co₂/Voc Sensor And Gas Chromatograph For Field Research, Rishi Basdeo, Michael Hampton

Digital Repository: Showcase of Undergraduate Research Excellence

No abstract provided.

Ev Charging Behavior Analysis Using Hybrid Intelligence For 5g Smart Grid, Yi Shen, Wei Fang, Feng Ye, Michel Kadoch

Electrical and Computer Engineering Faculty Publications

With the development of the Internet of Things (IoT) and the widespread use of electric vehicles (EV), vehicle-to-grid (V2G) has sparked considerable discussion as an energy-management technology. Due to the inherently high maneuverability of EVs, V2G systems must provide on-demand service for EVs. Therefore, in this work, we propose a hybrid computing architecture based on fog and cloud with applications in 5G-based V2G networks. This architecture allows the bi-directional flow of power and information between schedulable EVs and smart grids (SGs) to improve the quality of service and cost-effectiveness of energy service providers. However, it is very important to select …

Glaciernet: A Deep-Learning Approach For Debris-Covered Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Brennan W. Young, Michael P. Bishop, Jeffrey S. Kargel

Electrical and Computer Engineering Faculty Publications

Rising global temperatures over the past decades is directly affecting glacier dynamics. To understand glacier fluctuations and document regional glacier-state trends, glacier-boundary detection is necessary. Debris-covered glacier (DCG) mapping, however, is notoriously difficult using conventional geospatial technology methods. Therefore, in this research for automated DCG mapping, we evaluate the utility of a convolutional neural network (CNN), which is a deep learning feed-forward neural network. The CNN inputs include Landsat satellite images, an Advanced Land Observation Satellite (ALOS) digital elevation model (DEM) and DEM-derived land-surface parameters. Our CNN based deep-learning approach named GlacierNet was designed by appropriately choosing the type, number …

Mitosisnet: End-To-End Mitotic Cell Detection By Multi-Task Learning, Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, Tj Bowen, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Mitotic cell detection is one of the challenging problems in the field of computational pathology. Currently, mitotic cell detection and counting are one of the strongest prognostic markers for breast cancer diagnosis. The clinical visual inspection on histology slides is tedious, error prone, and time consuming for the pathologist. Thus, automatic mitotic cell detection approaches are highly demanded in clinical practice. In this paper, we propose an end-to-end multi-task learning system for mitosis detection from pathological images which is named"MitosisNet". MitosisNet consist of segmentation, detection, and classification models where the segmentation, and detection models are used for mitosis reference region …

Augmented Reality Billiards Assistant, Sarah Medved

Williams Honors College, Honors Research Projects

The game of pool is an extremely popular activity, with approximately 36 million participants yearly. As with any game of pool, the most basic skill the player must possess is the ability to accurately aim and hit the cue ball. It would be helpful to the player if they had a system which would assist with aiming by predicting the trajectories of a shot before the shot is actually made. These trajectory predictions can serve as a guide to the player and allow them to adjust their cue position before each shot. This lets the player explore many different options …

E-Z Door: Hands-Free Front Door Unlocking And Opening Mechanism, Caleb Dyck

Williams Honors College, Honors Research Projects

The E-Z Door Senior Design project is a project with the aim of designing a hands-free system to unlock and open the front door of a home using two-factor security authentication.

The main goal of this project is to help people who may have physical limitations to be able to take advantage of recent technology, making it significantly easier to enter their homes.

Estimation Of Atmospheric Conditions Over A Long Horizontal Path Using Multi-Frame Blind Deconvolution (Mfbd) Techniques In Comparison With Delayed Tilt Anisoplanatism (Delta) Software, Hannah Stoll

Dissertations, Master's Theses and Master's Reports

The potential to track and view objects in space from the ground with greater near real time knowledge of the intervening turbulence would be a revolutionary capability. The objective of this thesis is to cross-validate two separate methods used to estimate the Fried parameter. This verification is a step toward a commercial grade product that would make real-time estimates of the turbulence strength along an optical path from a ground-based observatory to a satellite in orbit around the Earth. Michigan Technological University has developed a multi-frame blind deconvolution (MFBD) algorithm used to estimate r0 and it was tested against MZA’s …

Unique Image Representation As A Tensor, Bharat Rosanlall

Dissertations and Theses

This thesis presents a two dimensional orthonormal transform that represents an image as coefficients in 4 independent channels. The salient feature of these coefficients is that they contain complete position spatial frequency information about the image, in a sense that the original image can be reconstructed from these coefficients with negligible error. These coefficients can be used in various machine learning, AI , and other tasks where data features are used. Popular convolutional layer used in various neural networks reduces information and can not reconstruct original image. In this thesis , we present several examples where these coefficients are used …

Palmprint Gender Classification Using Deep Learning Methods, Minou Khayami

Graduate Theses, Dissertations, and Problem Reports (ETD)

Gender identification is an important technique that can improve the performance of authentication systems by reducing searching space and speeding up the matching process. Several biometric traits have been used to ascertain human gender. Among them, the human palmprint possesses several discriminating features such as principal-lines, wrinkles, ridges, and minutiae features and that offer cues for gender identification. The goal of this work is to develop novel deep-learning techniques to determine gender from palmprint images. PolyU and CASIA palmprint databases with 90,000 and 5502 images respectively were used for training and testing purposes in this research. After ROI extraction and …

On-Demand Electrically Induced Decomposition Of Thin-Film Nitrocellulose Membranes For Wearable Or Implantable Biosensor Systems, Benjamin M. Horstmann

Theses and Dissertations

Implantable or subcutaneous biosensors used for continuous health monitoring have a limited functional lifetime requiring frequent replacement and therefore may be highly discomforting to the patient and become costly. One possible solution to this problem is use of biosensor arrays where each individual reserve sensor can be activated on-demand when the previous one becomes inoperative due to biofouling or enzyme degradation. Each reserve biosensor in the array is housed in an individual Polydimethylsiloxane (PDMS) well and is protected from exposure to bodily fluids such as interstitial fluid ( ISF) by a thin-film nitrocellulose membrane. Controlled activation is achieved by decomposing …

Technology-Dependent Quantum Logic Synthesis And Compilation, Kaitlin Smith

Electrical Engineering Theses and Dissertations

The models and rules of quantum computation and quantum information processing (QIP) differ greatly from those that govern classical computation, and these differences have caused the implementation of quantum processing devices with a variety of new technologies. Many platforms have been developed in parallel, but at the time of writing, one method of quantum computing has not shown to be superior to the rest. Because of the variation that exists between quantum platforms, even between those of the same technology, there must be a way to automatically synthesize technology-independent quantum designs into forms that are capable of physical realization on …

Amodal Instance Segmentation And Multi-Object Tracking With Deep Pixel Embedding, Yanfeng Liu

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

This thesis extends upon the representational output of semantic instance segmentation by explicitly including both visible and occluded parts. A fully convolutional network is trained to produce consistent pixel-level embedding across two layers such that, when clustered, the results convey the full spatial extent and depth ordering of each instance. Results demonstrate that the network can accurately estimate complete masks in the presence of occlusion and outperform leading top-down bounding-box approaches.

The model is further extended to produce consistent pixel-level embeddings across two consecutive image frames from a video to simultaneously perform amodal instance segmentation and multi-object tracking. No post-processing …

Optimal Allocation Of Energy Storage And Wind Generation In Power Distribution Systems, Carlos Mendoza

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

The advent of energy storage technologies applications for the electric power system gives new tools for planners to cope with the operation challenges that come from the integration of renewable generation in medium voltage networks. This work proposes and implements an optimization model for Battery Energy Storage System (BESS) and distributed generation allocation in radial distribution networks. The formulation aims to assist distribution system operators in the task of making decisions on energy storage investment, BESSs' operation, and distributed generation penetration's level to minimize electricity costs. The BESSs are required to participate in energy arbitrage and voltage control. In addition, …

Bitcoin Price Prediction Using Neural Networks, Vladislav Killiakov

Electrical Engineering

In this project, I will investigate the performance of several major neural network architectures for the task of Bitcoin price prediction. Bitcoin is a cryptocurrency that is recently becoming increasingly more popular, and more widely adopted as a financial instrument. As a result, more efforts have been made in the past several years to model and predict its price. However, to this moment a large portion of work on Bitcoin price modeling was done using statistical or classical machine learning techniques. At the same time, other artificial intelligence based prediction techniques, and specifically neural networks, have not been explored to …

Free Space Optical Communication, Anuj Gohil

Electrical Engineering

Communication is key to day to day activities for all companies and people. Wireless communication has made us expect more from our tools. Radio Frequency has been the preferred medium for a couple decades, but there is a need for faster secure communication. Free Space Optical Communication is an alternate wireless communication system which uses optics to create a link. It utilizes low-power and converts an analog signal into digital pulses which are transmitted across space to a receiver. Its only caveat is its vulnerability under atmospheric obstacles. The goal of this project is to create a free space optical …

Performance Enhancement And Characterization Of An Electromagnetic Railgun, Paul M. Gilles

Master's Theses

Collision with orbital debris poses a serious threat to spacecraft and astronauts. Hypervelocity impacts resulting from collisions mean that objects with a mass less than 1g can cause mission-ending damage to spacecraft. A means of shielding spacecraft against collisions is necessary. A means of testing candidate shielding methods for their efficacy in mitigating hypervelocity impacts is therefore also necessary. Cal Poly’s Electromagnetic Railgun was designed with the goal of creating a laboratory system capable of simulating hypervelocity (≥ 3 km/s) impacts. Due to several factors, the system was not previously capable of high-velocity (≥ 1 km/s) tests. A deficient projectile …

Pixel-Level Deep Multi-Dimensional Embeddings For Homogeneous Multiple Object Tracking, Mateusz Mittek

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

The goal of Multiple Object Tracking (MOT) is to locate multiple objects and keep track of their individual identities and trajectories given a sequence of (video) frames. A popular approach to MOT is tracking by detection consisting of two processing components: detection (identification of objects of interest in individual frames) and data association (connecting data from multiple frames). This work addresses the detection component by introducing a method based on semantic instance segmentation, i.e., assigning labels to all visible pixels such that they are unique among different instances. Modern tracking methods often built around Convolutional Neural Networks (CNNs) and additional, …

The Stability Analysis For Wind Turbines With Doubly Fed Induction Generators, Baohua Dong

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

The quickly increasing, widespread use of wind generation around the world reduces carbon emissions, decreases the effects of global warming, and lowers dependence on fossil fuels. However, the growing penetration of wind power requires more effort to maintain power systems stability.

This dissertation focuses on developing a novel algorithm which dynamically optimizes the proportional-integral (PI) controllers of a doubly fed induction generator (DFIG) driven by a wind turbine to increase the transient performance based on small signal stability analysis.

Firstly, the impact of wind generation is introduced. The stability of power systems with wind generation is described, including the different …

Combining Virtual Reality And Machine Learning For Enhancing The Resiliency Of Transportation Infrastructure In Extreme Events, Supratik Mukhopadhyay, Yimin Zhu, Ravindra Gudishala

Data

Corresponding data set for Tran-SET Project No. 18ITSLSU09. Abstract of the final report is stated below for reference:

"Traffic management models that include route choice form the basis of traffic management systems. High-fidelity models that are based on rapidly evolving contextual conditions can have significant impact on smart and energy efficient transportation. Existing traffic/route choice models are generic and are calibrated on static contextual conditions. These models do not consider dynamic contextual conditions such as the location, failure of certain portions of the road network, the social network structure of population inhabiting the region, route choices made by other drivers, …

Combining Virtual Reality And Machine Learning For Enhancing The Resiliency Of Transportation Infrastructure In Extreme Events, Supratik Mukhopadhyay, Yimin Zhu, Ravindra Gudishala

Publications

Traffic management models that include route choice form the basis of traffic management systems. High-fidelity models that are based on rapidly evolving contextual conditions can have significant impact on smart and energy efficient transportation. Existing traffic/route choice models are generic and are calibrated on static contextual conditions. These models do not consider dynamic contextual conditions such as the location, failure of certain portions of the road network, the social network structure of population inhabiting the region, route choices made by other drivers, extreme conditions, etc. As a result, the model’s predictions are made at an aggregate level and for a …

Utilizing Trajectory Optimization In The Training Of Neural Network Controllers, Nicholas Kimball

Master's Theses

Applying reinforcement learning to control systems enables the use of machine learning to develop elegant and efficient control laws. Coupled with the representational power of neural networks, reinforcement learning algorithms can learn complex policies that can be difficult to emulate using traditional control system design approaches. In this thesis, three different model-free reinforcement learning algorithms, including Monte Carlo Control, REINFORCE with baseline, and Guided Policy Search are compared in simulated, continuous action-space environments. The results show that the Guided Policy Search algorithm is able to learn a desired control policy much faster than the other algorithms. In the inverted pendulum …

Smart Charging Of Future Electric Vehicles Using Roadway Infrastructure, Sara Ahmed, Ethan Ahn, Mahmoud Reda Taha, Samer Dessouky, Moneeb Genedy, Daniel Fernandez, Ann Sebestian, Patience Raby

Data

Corresponding data set for Tran-SET Project No. 18ITSTSA03. Abstract of the final report is stated below for reference:

"Inspired by the fact that there is an immense amount of renewable energy sources available on the roadways such as mechanical pressure and frictional heat, this study presented the development and implementation of an innovative charging technique for future electric vehicles (EVs) by fully utilizing the existing roadways and the state-of-the-art nanotechnology and power electronics. The project introduced a novel wireless charging system, SIC (Smart Illuminative Charging), that uses LEDs powered by piezoelectric nanomaterials as the energy transmitter source and thin film …

Vehicle Sensing And Communications Using Led Headlights To Enhance The Performance Of Intelligent Transportation Systems: Proof Of Concept, Implementation, And Applications, Sabit Ekin, Samir Ahmed

Publications

This project investigates the use of vehicle light-emitting diode (LED) headlamp devices for improving the accuracy and reliability of traffic (sensing and communication) data measurements required for developing effective intelligent transportation systems (ITS) technologies and solutions. Vehicular communication and sensing technologies are mainly based on conventional radio frequency (RF) or laser technologies. These systems suffer from several issues such as RF interference and poor performance in scenarios where the incidence angle between the speed detector and the vehicle is rapidly varying. Introducing a new sensing technology will add diversity to these systems and enhance the reliability of the real-time data. …

Cooperative Learning For The Consensus Of Multi-Agent Systems, Qishuai Liu

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

Due to a lot of attention for the multi-agent system in recent years, the consensus algorithm gained immense popularity for building fault-tolerant systems in system and control theory. Generally, the consensus algorithm drives the swarm of agents to work as a coherent group that can reach an agreement regarding a certain quantity of interest, which depends on the state of all agents themselves. The most common consensus algorithm is the average consensus, the final consensus value of which is equal to the average of the initial values. If we want the agents to find the best area of the particular …

Embedded System Design Of Robot Control Architectures For Unmanned Agricultural Ground Vehicles, Ryan Humphrey

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

Engineering technology has matured to the extent where accompanying methods for unmanned field management is now becoming a technologically achievable and economically viable solution to agricultural tasks that have been traditionally performed by humans or human operated machines. Additionally, the rapidly increasing world population and the daunting burden it places on farmers in regards to the food production and crop yield demands, only makes such advancements in the agriculture industry all the more imperative. Consequently, the sector is beginning to observe a noticeable shift, where there exist a number of scalable infrastructural changes that are in the process of slowly …

Residential Electricity Management And Protection System, Ejimonu Kosisochukwu Gabriel

Journal of International Technology and Information Management

The Residential Electricity Monitoring System is an electricity meter connected to the Internet to provide real time data on the power system in your home. The REMS is designed to replace the distribution board allowing it to conduct a series of tests on the quality of the electricity supply to your home/facility and, within your home detect basic wiring faults and allow owners and service providers to identify potential problems with the electrical systems.

The electrical monitoring system finally puts proper electrical control into the hands of the home owner by providing him with the ability to control power distribution …

Alzheimer’S Disease Detection Using Convolutional Neural Netowork & Wavelets, Ean Hendrickson, Gabriel Amancio, Chloe Eusbio, Kaioli Bessert

Electrical Engineering

It is estimated that 5.7 million people in the United States have Alzheimer’s dementia, including about 1 in 10 people above the age of 65. Alzheimer’s disease detection can help doctors to diagnose patients earlier and help them with preventative, proactive treatment. In this project, we attempt to use two different methods to analyze MRI brain images for Alzheimer’s disease detection, one with convolutional neural network and deep learning algorithm, and the other one with discrete wavelet transform. Computer simulation results are presented in the report.

Workload Allocation In Mobile Edge Computing Empowered Internet Of Things, Qiang Fan

Dissertations

In the past few years, a tremendous number of smart devices and objects, such as smart phones, wearable devices, industrial and utility components, are equipped with sensors to sense the real-time physical information from the environment. Hence, Internet of Things (IoT) is introduced, where various smart devices are connected with each other via the internet and empowered with data analytics. Owing to the high volume and fast velocity of data streams generated by IoT devices, the cloud that can provision flexible and efficient computing resources is employed as a smart "brain" to process and store the big data generated from …

Emt 1130 - Electromechanical Manufacturing Lab, Farjana Ferdousy

Open Educational Resources

No abstract provided.

Wireless Channel Characterization Based On Crowdsourced Data And Geographical Features, Rita Enami

Electrical Engineering Theses and Dissertations

To design and plan wireless communication systems, an accurate propagation estimate is required of a deployment region. Propagation prediction models consist of two types of fading: large-scale and small-scale fading. With large-scale fading, the path loss information is crucial for cell planning, coverage estimation, and optimization. With small-scale fading, the statistical fluctuation on the local variations of the average signal level can have a dramatic effect on protocol decisions and resulting performance. To obtain accurate estimates of both types of fading, typically field measurements are needed that use drive testing, which is expensive in terms of time and cost. Recently, …