Computer Engineering Commons^™

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 …

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 …

High Performance Multiview Video Coding, Caoyang Jiang

Dissertations, Master's Theses and Master's Reports

Following the standardization of the latest video coding standard High Efficiency Video Coding in 2013, in 2014, multiview extension of HEVC (MV-HEVC) was published and brought significantly better compression performance of around 50% for multiview and 3D videos compared to multiple independent single-view HEVC coding. However, the extremely high computational complexity of MV-HEVC demands significant optimization of the encoder. To tackle this problem, this work investigates the possibilities of using modern parallel computing platforms and tools such as single-instruction-multiple-data (SIMD) instructions, multi-core CPU, massively parallel GPU, and computer cluster to significantly enhance the MVC encoder performance. The aforementioned computing tools …

A Testbed Design For Monitoring The Long-Term Spatial-Temporal Dynamics Of Underwater Acoustic Channels, Krishna Chaitanya Poduru

Dissertations, Master's Theses and Master's Reports

The underwater acoustic network testbed helps to validate the theoretical results and bridge the gap between experimental results. Characterizing and modeling the spatial-temporal dynamics of underwater acoustic channels is essential to developing efficient and effective physical-layer communication algorithms and network protocols. This work dedicates to designing a testbed system to measure the spatial-temporal dynamics of underwater acoustic channels. The collected measurements will shed insights into the spatial-temporal correlation of underwater acoustic channels and will be used to evaluate the theoretical algorithms that are designed to model the spatial-temporal dynamics and to exploit the spatial-temporal dynamics for more efficient and effective …

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 …

Design Automation For Carbon Nanotube Circuits Considering Performance And Security Optimization, Lin Liu

Dissertations, Master's Theses and Master's Reports

As prevailing copper interconnect technology advances to its fundamental physical limit, interconnect delay due to ever-increasing wire resistivity has greatly limited the circuit miniaturization. Carbon nanotube (CNT) interconnects have emerged as promising replacement materials for copper interconnects due to their superior conductivity. Buffer insertion for CNT interconnects is capable of improving circuit timing of signal nets with limited buffer deployment. However, due to the imperfection of fabricating long straight CNT, there exist significant unidimensional-spatially correlated variations on the critical CNT geometric parameters such as the diameter and density, which will affect the circuit performance.

This dissertation develops a novel timing …

Heterogeneous Multi-Sensor Fusion For 2d And 3d Pose Estimation, Hanieh Deilamsalehy

Dissertations, Master's Theses and Master's Reports

Sensor fusion is a process in which data from different sensors is combined to acquire an output that cannot be obtained from individual sensors. This dissertation first considers a 2D image level real world problem from rail industry and proposes a novel solution using sensor fusion, then proceeds further to the more complicated 3D problem of multi sensor fusion for UAV pose estimation.

One of the most important safety-related tasks in the rail industry is an early detection of defective rolling stock components. Railway wheels and wheel bearings are two components prone to damage due to their interactions with the …