Other Electrical and Computer Engineering Commons^™

Centralized Control For Dc Microgrid Using Finite State Machine, Mahmoud Saleh, Yusef Esa, Ahmed Mohamed

Publications and Research

In this paper, an autonomous communication-based centralized control for DC microgrids (MG) has been developed and implemented. The proposed controller enables smooth transition between various operating modes. Finite state machine (FSM) has been used to mathematically describe the various operating modes (states), and events that may lead to mode changes (transitions). Therefore, the developed centralized controller aims at optimizing the performance of MG during all possible operational scenarios, while maintaining its reliability and stability. Results of selected cases have been presented. These results show stable transition between modes, verifying the validity and applicability of the proposed controller.

Workplace Posture Assessment And Biofeedback With Kinect, Matthew Crussell

Master's Theses

With the prevalence of computing, many workers today are confined to desk within an office. By sitting in these positions for long periods of time, workers are prone to develop one of many musculoskeletal disorders (MSDs), such as carpal tunnel syndrome. In order to prevent MSDs in the long term, workers must employ good sitting habits. One promising method to ensure good workplace posture is through camera monitoring. To date, camera systems have been used in determining posture in a clean environment. However, an occluded and cluttered background, which is typical in an office setting, imposes a great challenge for …

Feasibility Of Neural Networks For Maritime Visual Detection On A Mobile Platform, Robert Goring

Doctoral Dissertations and Master's Theses

Object detection through computer vision has traditionally been difficult to reliably implement due to various lighting conditions caused by weather and time of day. Any changes in conditions can be detrimental to the detector’s ability to accurately identify objects. A modern approach implements deep learning techniques to classify and train a neural network. While highly effective, this approach can be cumbersome and computationally intensive. This project will investigate the feasibility of using deep learning to detect, classify, and track objects in near real-time while being processed on a mobile platform. I will investigate the feasibility of these processes on a …

Physiologically-Aware Communication Architecture For Transmission Of Biomedical Signals In Basns For Emerging Iot Applications, Jose Santos, Dongming Peng, Hamid Sharif

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

This research work proposes a novel physiologicallyaware communication architecture for the transmission of biomedical signals in BASNs (Body Area Sensor Networks) and wearables for emerging IoT applications. The architecture to fulfill the following objectives:

• Reduce volume of biomedical data in IoT networks and Internet infrastructure.

• Minimize the required computational load of biomedical data on the cloud side.

• Extend the lifetime of the mobile wearable/BASN through improved energy savings.

This architecture is generalizable to class of biomedical signals. It prevents large volume of biomedical data to be generated; uses patient state to control almost any system parameter; achieves …

Reducing Graphene–Metal Contact Resistance Via Laser Nano-Welding, Kamran Keramatnejad, H. Rabiee Golgir, Y. S. Zhou, D. W. Li, X. Huang, Yongfeng Lu

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

The large graphene-metal contact resistance is a major limitation for development of graphene electronics. graphene behaves as an insulator for out-of-plane carrier transport to metallic contacts.

Laser nano-welding was developed and led to RC reductions of up to 84%.

Localized laser irradiation at the edges of graphene led to the formation of chemically active point defects.

Precise structural modifications and formation of G-M bonding led to improved carrier efficiency in graphene devices.

Experimental Validation Of A Heterogeneous Radar Clutter Statistical Estimation Method, Elliot R. Erstein

Theses and Dissertations

Radar clutter models are important for radar target detection when clutter is present. A new method for estimating single clutter type, homogeneous, radar clutter statistics through measurement of a multiple type, heterogeneous clutter was developed in 2015 by researchers at the Air Force Institute of Technology. The estimation method is greatly valued in the clutter research and modeling world for reducing clutter campaign measurement time and cost. This thesis looks at validation steps for the estimation method through the use of simulations and experimental radar clutter measurements.

Block Matching And Wiener Filtering Approach To Optical Turbulence Mitigation And Its Application To Simulated And Real Imagery With Quantitative Error Analysis, Russell C. Hardie, Michael Armand Rucci, Barry K. Karch, Alexander J. Dapore

Electrical and Computer Engineering Faculty Publications

We present a block-matching and Wiener filtering approach to atmospheric turbulence mitigation for long-range imaging of extended scenes. We evaluate the proposed method, along with some benchmark methods, using simulated and real-image sequences. The simulated data are generated with a simulation tool developed by one of the authors. These data provide objective truth and allow for quantitative error analysis. The proposed turbulence mitigation method takes a sequence of short-exposure frames of a static scene and outputs a single restored image. A block-matching registration algorithm is used to provide geometric correction for each of the individual input frames. The registered frames …

Simulation Of Anisoplanatic Imaging Through Optical Turbulence Using Numerical Wave Propagation With New Validation Analysis, Russell C. Hardie, Jonathan D. Power, Daniel A. Lemaster, Douglas R. Droege, Szymon Gladysz, Santasri Bose-Pillai

Electrical and Computer Engineering Faculty Publications

We present a numerical wave propagation method for simulating imaging of an extended scene under anisoplanatic conditions. While isoplanatic simulation is relatively common, few tools are specifically designed for simulating the imaging of extended scenes under anisoplanatic conditions. We provide a complete description of the proposed simulation tool, including the wave propagation method used. Our approach computes an array of point spread functions (PSFs) for a two-dimensional grid on the object plane. The PSFs are then used in a spatially varying weighted sum operation, with an ideal image, to produce a simulated image with realistic optical turbulence degradation. The degradation …

Optimized Trajectory Generation For Car-Like Robots On A Closed Loop Track, Tyler Friedl

Electronic Theses and Dissertations

This thesis presents a method for generating an optimized path through a given track. The path is generated by choosing waypoints throughout the track then iteratively optimizing the position of these waypoints. The waypoints are then connected by optimized paths represented by curvature polynomials. The end result is a path through the track represented as a spline of curvature polynomials. This method is applied to multiple simulated tracks and the results are presented. By generating and representing the paths in the continuous domain, the method has improved computational efficiency from many of the discrete methods used to generate an optimal …

Data-Centric Situational Awareness And Management In Intelligent Power Systems, Xiaoxiao Dai

Electronic Theses and Dissertations

The rapid development of technology and society has made the current power system a much more complicated system than ever. The request for big data based situation awareness and management becomes urgent today. In this dissertation, to respond to the grand challenge, two data-centric power system situation awareness and management approaches are proposed to address the security problems in the transmission/distribution grids and social benefits augmentation problem at the distribution-customer lever, respectively.

To address the security problem in the transmission/distribution grids utilizing big data, the first approach provides a fault analysis solution based on characterization and analytics of the synchrophasor …

Automatic Power Management For Instructional Computers At Humboldt State University: A Calculation Of Potential Energy Savings And Greenhouse Gas Emissions Reductions, Nicholas F. Flenghi

Projects

Computers consume an estimated 5,610 GWh per year in California alone. Much of that energy is consumed by computers that are not being used. In this project, detailed user login data are used to estimate the energy consumption of instructional computers at Humboldt State University (HSU) over the course of one semester. The data are also used to estimate the potential energy savings from automating the shutdown process. Potential cost savings and greenhouse gas (GHG) emissions reductions resulting from implementing an automatic-shutdown power management plan are also calculated.

There are approximately 1,000 computers used for teaching and learning purposes at …

Design, Modeling, Fabrication, And Testing Of A Multistage Micro Gas Compressor With Piezoelectric Unimorph Diaphragm And Passive Microvalves For Microcooling Applications, Shawn Thanhson Le

Doctoral Dissertations

This dissertation investigates the development of a multistage micro gas compressor utilizing multiple pump stages cascaded in series to increase the pressure rise with passive microvalves and piezoelectric unimorph diaphragms. This research was conducted through modeling, simulation, design, and fabrication of the microcompressor and its components. A single-stage and a two-stage microcompressor were developed to demonstrate and compare the performance and effectiveness of using a cascaded multistage design.

Steady fluid flow through static microvalves structure was studied to gain insight on its gas flow dynamics and characteristics. Transient analysis combined with the structure's interaction was investigated with an analytical model …

Performance Comparison Of Binarized Neural Network With Convolutional Neural Network, Lopamudra Baruah

Dissertations, Master's Theses and Master's Reports

Deep learning is a trending topic widely studied by researchers due to increase in the abundance of data and getting meaningful results with them. Convolutional Neural Networks (CNN) is one of the most popular architectures used in deep learning. Binarized Neural Network (BNN) is also a neural network which consists of binary weights and activations. Neural Networks has large number of parameters and overfitting is a common problem to these networks. To overcome the overfitting problem, dropout is a solution. Randomly dropping some neurons along with its connections helps to prevent co-adaptations which finally help in reducing overfitting. Many researchers …

Substrate Integrated E-Plane Horn Antenna And Antenna Array, Zhewei Gu

All Graduate Theses, Dissertations, and Other Capstone Projects

In this paper, a novel SMA fed substrate integrated E-plane horn antenna and antenna array are proposed. The substrate integrated E-plane waveguide (SIEW) is not only able to integrate conventional waveguide circuits on printed circuit boards (PCB), but also especially falls into all E-plane circuits which substrate integrated waveguide (SIW) technique fails to. Essential copper strips are sandwiched in the middle by two PCBs to support longitudinal current in the E-plane fed through an SIEW-to-SMA transition. Copper plated holes aligned along the wave propagation direction are drilled through the substrate and copper strips, so that vertical current in the H-plane …

Optimized Endpoint Delivery Via Unmanned Aerial Vehicles, Fuad Gazal

All Graduate Theses, Dissertations, and Other Capstone Projects

Unmanned Aerial Vehicles (UAVs) are remotely piloted aircraft with a range of varying applications. Though early adoption of UAVs focused on military applications, surveillance, photography, and agricultural applications are presently on the rise. This work aims to ascertain how UAVs may be employed to elicit deceased transportation times, increased power efficiency, and improved safety. Resulting in optimized end point delivery. A combination of tools and techniques, involving a mathematical model, UAV simulations, redundant control systems, and custom designed electrical and mechanical components were used towards reaching the goal of a 10-kilogram maximum payload delivered 10 miles under 30 minutes. Two …

Feature And Decision Level Fusion Using Multiple Kernel Learning And Fuzzy Integrals, Anthony Pinar

Dissertations, Master's Theses and Master's Reports

The work collected in this dissertation addresses the problem of data fusion. In other words, this is the problem of making decisions (also known as the problem of classification in the machine learning and statistics communities) when data from multiple sources are available, or when decisions/confidence levels from a panel of decision-makers are accessible. This problem has become increasingly important in recent years, especially with the ever-increasing popularity of autonomous systems outfitted with suites of sensors and the dawn of the ``age of big data.'' While data fusion is a very broad topic, the work in this dissertation considers …

Position Control Of An Unmanned Aerial Vehicle From A Mobile Ground Vehicle, Astha Tiwari

Dissertations, Master's Theses and Master's Reports

Quadcopters have been developed with controls providing good maneuverability, simple mechanics, and the ability to hover, take-off and land vertically with precision. Due to their small size, they can get close to targets of interest and furthermore stay undetected at lower heights. The main drawbacks of a quadcopter are its high-power consumption and payload restriction, due to which, the number of onboard sensors is constrained. To overcome this limitation, vision-based localization techniques and remote control for the quadcopter are essential areas of current research. The core objective of this research is to develop a closed loop feedback system between an …

Brief Study Of Classification Algorithms In Machine Learning, Ramesh Sankara Subbu

Dissertations and Theses

The purpose of this study is to briefly learn the theory and implementation of three most commonly used Machine Learning algorithms: k-Nearest Neighbors (kNN), Decision Trees and Naïve Bayes. All these algorithms fall under the Classification algorithm category of Unsupervised Machine Learning. This paper is constructed structurally in explaining the working theory behind each algorithm and an implementation of a Machine Learning problem solved by each algorithm. KNN algorithm is designed using Euclidean distance measurement and Decision Trees make use of ID3 algorithm as a basis. We conclude the study by providing an overall picture of its strengths and weaknesses …

Omni-Directional Infrared 3d Reconstruction And Tracking Of Human Targets, Emrah Benli

Theses and Dissertations

Omni-directional (O-D) infrared (IR) vision is an effective capability for mobile systems in robotics, due to its advantages: illumination invariance, wide field-of-view, ease of identifying heat-emitting objects, and long term tracking without interruption. Unfortunately, O-D IR sensors have low resolution, low frame rates, high cost, sensor noise, and an increase in tracking time. In order to overcome these disadvantages, we propose an autonomous system application in indoor scenarios including 1) Dynamic 3D Reconstruction (D3DR) of the target view in real time images, 2) Human Behavior-based Target Tracking from O-D thermal images, 3) Thermal Multisensor Fusion (TMF), and 4) Visual Perception …

Respiratory Prediction And Image Quality Improvement Of 4d Cone Beam Ct And Mri For Lung Tumor Treatments, Seonyeong Park

Theses and Dissertations

Identification of accurate tumor location and shape is highly important in lung cancer radiotherapy, to improve the treatment quality by reducing dose delivery errors. Because a lung tumor moves with the patient's respiration, breathing motion should be correctly analyzed and predicted during the treatment for prevention of tumor miss or undesirable treatment toxicity. Besides, in Image-Guided Radiation Therapy (IGRT), the tumor motion causes difficulties not only in delivering accurate dose, but also in assuring superior quality of imaging techniques such as four-dimensional (4D) Cone Beam Computed Tomography (CBCT) and 4D Magnetic Resonance Imaging (MRI). Specifically, 4D CBCT used in CBCT …