Engineering Commons^™

Investigation Of The Use Of 3-D Printer Platform As Building Block For Rapid Design Of Research And Manufacturing Tool, Handy Chandra

Dissertations, Master's Theses and Master's Reports

This thesis attempts to show how an open source 3-D printer platform, the self replicating rapid prototype (RepRap), could be used to accelerate the development of research and manufacturing tools. Two projects are shown as examples, both utilizing components of the 3-D printer platform.

The first project is to develop an instrument capable of performing automated large-area four-point probe measurements. A modified RepRap 3-D Printer with a four-point probe in place of the 3-D printer head is utilized as a precision positioning platform. The printer together with custom designed measurement circuit and software performs automated measurement on multiple points on …

Implantable Wireless Sensor Networks: Application To Measuring Temperature For In Vivo Detection Of Infections, Praharsh Madappaly Veetil

Dissertations, Master's Theses and Master's Reports

It is has been proven that infection in the body cause a local temperature increase due to localized inflammation. Therefore, a method to provide early diagnostic or long-term tracking of this infection will provide great benefits to patients with diabetic foot ulcers or sickle cell disease, and those receiving hemodialysis where they suffer from a weakened immune system. The goal of this project is to develop an implantable wireless temperature sensor based on a wireless sensor network system for monitoring infections in situ. The analog signals from the thermistors are digitized and wirelessly transmitted to a computer with an ez430-rf2500 …

An Optimal Energy Management Strategy For Hybrid Electric Vehicles, Amir Rezaei

Dissertations, Master's Theses and Master's Reports

Hybrid Electric Vehicles (HEVs) are used to overcome the short-range and long charging time problems of purely electric vehicles. HEVs have at least two power sources. Therefore, the Energy Management (EM) strategy for dividing the driver requested power between the available power sources plays an important role in achieving good HEV performance.

This work, proposes a novel real-time EM strategy for HEVs which is named ECMS-CESO. ECMS-CESO is based on the Equivalent Consumption Minimization Strategy (ECMS) and is designed to Catch Energy Saving Opportunities (CESO) while operating the vehicle. ECMS-CESO is an instantaneous optimal controller, i. e., it does not …

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 …

Control Of A Powered Ankle-Foot Prosthesis: From Perception To Impedance Modulation, Guilherme Aramizo Ribeiro

Dissertations, Master's Theses and Master's Reports

Active ankle prostheses controllers are demonstrating gaining smart features to improve the safety and comfort offor users. The perception of user intention to modulate the ankle dynamics is a well-known example of such feature. But not much work focused on the perception of the environment, nor how the environment should be included in the mechanical design and control of the prosthesisprostheses. The proposed work aims to improve the feasibility of integrate the environment perception integration intoto the prostheses controllersler, and to define the desired ankle dynamics, as mechanical impedance, duringof the human walk on different environmental settings. As a preliminary …

A Host Board Design For An Experiment System Of Underwater Wireless Sensor Networks, He Huang

Dissertations, Master's Theses and Master's Reports

Evaluation of theoretical innovation in field experiments plays an important role for research in Underwater Wireless Sensor Networks (UWSNs). During the experiments, the problems of power consumption, costs, and assembly of underwater nodes concern researchers all the time. This project develops a host board for an UWSNs field experiment system. This host board solves the above problems by deploying power management module, utilizing cheap and low power consumption chips (Raspberry Pi and MSP430), and rationalizing layout.

The main functions of the host board include battery monitoring, on-board DC converters, remotely power supply control, data ports bridge, and pluggable module for …

Using Lower Extremity Muscle Activations To Estimate Human Ankle Impedance In The External-Internal Direction, Lauren N. Knop

Dissertations, Master's Theses and Master's Reports

For millions of people, mobility has been afflicted by lower limb amputation. Lower extremity prostheses have been used to improve the mobility of an amputee; however, they often require additional compensation from other joints and do not allow for natural maneuverability. To improve upon the functionality of ankle-foot prostheses, it is necessary to understand the role of different muscle activations in the modulation of mechanical impedance of a healthy human ankle. This report presents the results of using artificial neural networks (ANN) to determine the functional relationship between lower extremity electromyography (EMG) signals and ankle impedance in the transverse plane. …

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 …

Multi-Time Scale Control Of Demand Flexibility In Smart Distribution Networks, Bishnu P. Bhattarai, Kurt S. Myers, Birgitte Bak-Jensen, Sumit Paudyal

Michigan Tech Publications

This paper presents a multi-timescale control strategy to deploy electric vehicle (EV) demand flexibility for simultaneously providing power balancing, grid congestion management, and economic benefits to participating actors. First, an EV charging problem is investigated from consumer, aggregator, and distribution system operator’s perspectives. A hierarchical control architecture (HCA) comprising scheduling, coordinative, and adaptive layers is then designed to realize their coordinative goal. This is realized by integrating multi-time scale controls that work from a day-ahead scheduling up to real-time adaptive control. The performance of the developed method is investigated with high EV penetration in a typical residential distribution grid. The …