Engineering Commons^™

Vessel Trajectory Prediction Using Historical Ais Data, Jagir Laxmichand Charla

Dissertations and Theses

Maritime vessel position coordinates are important information for maritime situational planning and organization. A better estimate of future locations of the maritime vessels, from their current locations, can help maritime authorities to make planned decisions, which can be helpful to avoid traffic congestion and longer waiting times. This thesis develops a method for estimating future locations of the vessels using their current and previous locations and other data.

The motivating scenario for this work is that of determining the future locations of the vessels based on their current location and previous locations for accurate modelling of underwater acoustic noise. As …

An Application Of Deep Learning Models To Automate Food Waste Classification, Alejandro Zachary Espinoza

Dissertations and Theses

Food wastage is a problem that affects all demographics and regions of the world. Each year, approximately one-third of food produced for human consumption is thrown away. In an effort to track and reduce food waste in the commercial sector, some companies utilize third party devices which collect data to analyze individual contributions to the global problem. These devices track the type of food wasted (such as vegetables, fruit, boneless chicken, pasta) along with the weight. Some devices also allow the user to leave the food in a kitchen container while it is weighed, so the container weight must also …

Design Of A Canine Inspired Quadruped Robot As A Platform For Synthetic Neural Network Control, Cody Warren Scharzenberger

Dissertations and Theses

Legged locomotion is a feat ubiquitous throughout the animal kingdom, but modern robots still fall far short of similar achievements. This paper presents the design of a canine-inspired quadruped robot named DoggyDeux as a platform for synthetic neural network (SNN) research that may be one avenue for robots to attain animal-like agility and adaptability. DoggyDeux features a fully 3D printed frame, 24 braided pneumatic actuators (BPAs) that drive four 3-DOF limbs in antagonistic extensor-flexor pairs, and an electrical system that allows it to respond to commands from a SNN comprised of central pattern generators (CPGs). Compared to the previous version …

Emulating Balance Control Observed In Human Test Subjects With A Neural Network, Wade William Hilts

Dissertations and Theses

Human balance control is a complex feedback system that must be adaptable and robust in an infinitely varying external environment. It is probable that there are many concurrent control loops occurring in the central nervous system that achieve stability for a variety of postural perturbations. Though many engineering models of human balance control have been tested, no models of how these controllers might operate within the nervous system have yet been developed. We have focused on building a model of a proprioceptive feedback loop with simulated neurons. The proprioceptive referenced portion of human balance control has been successfully modeled by …

Architectures And Algorithms For Intrinsic Computation With Memristive Devices, Jens Bürger

Dissertations and Theses

Neuromorphic engineering is the research field dedicated to the study and design of brain-inspired hardware and software tools. Recent advances in emerging nanoelectronics promote the implementation of synaptic connections based on memristive devices. Their non-volatile modifiable conductance was shown to exhibit the synaptic properties often used in connecting and training neural layers. With their nanoscale size and non-volatile memory property, they promise a next step in designing more area and energy efficient neuromorphic hardware.

My research deals with the challenges of harnessing memristive device properties that go beyond the behaviors utilized for synaptic weight storage. Based on devices that exhibit …

Information Representation And Computation Of Spike Trains In Reservoir Computing Systems With Spiking Neurons And Analog Neurons, Amin Almassian

Dissertations and Theses

Real-time processing of space-and-time-variant signals is imperative for perception and real-world problem-solving. In the brain, spatio-temporal stimuli are converted into spike trains by sensory neurons and projected to the neurons in subcortical and cortical layers for further processing.

Reservoir Computing (RC) is a neural computation paradigm that is inspired by cortical Neural Networks (NN). It is promising for real-time, on-line computation of spatio-temporal signals. An RC system incorporates a Recurrent Neural Network (RNN) called reservoir, the state of which is changed by a trajectory of perturbations caused by a spatio-temporal input sequence. A trained, non- recurrent, linear readout-layer interprets the …

Training Strategies For Critic And Action Neural Networks In Dual Heuristic Programming Method, Christian Peter Paintz

Dissertations and Theses

This thesis discusses strategies for and details of training procedures for the Dual Heuristic Programming (DHP) methodology. This and other approximate dynamic programming approaches (HDP, DHP, GDHP) have been discussed in some detail in the literature, all being members of the Adaptive Critic Design (ACD) family. The example applications used here are the inverted pendulum problem and a fully nonlinear constant velocity bicycle steering model. The inverted pendulum has been successfully controlled using DHP, as reported in the literature. This thesis suggests and investigates several alternative D HP training procedures and compares their performance with respect to convergence speed and …

Automatic Tuning Of Integrated Filters Using Neural Networks, Lutz Henning Lenz

Dissertations and Theses

Component values of integrated filters vary considerably due to· manufacturing tolerances and environmental changes. Thus it is of major importance that the components of an integrated filter be electronically tunable. The method explored in this thesis is the transconductance-C-method.

A method of realizing higher-order filters is to use a cascade structure of second-order filters. In this context, a method of tuning second-order filters becomes important.

The research objective of this thesis is to determine if the Neural Network methodology can be used to facilitate the filter tuning process for a second-order filter (realized via the transconductance-C-method). Since this thesis is, …

A Gaze-Addressing Communication System Using Artificial Neural Networks, Gabriel Baud-Bovy

Dissertations and Theses

Severe motor disabilities can render a person almost completely incapable of communication. Nevertheless, in many cases, the sensory systems are intact and the eye movements are still under good control. In these cases, one can use a device such as the Brain Response Interface (BRI) to command a remote control (e.g. room temperature, bed position), a word-processor, a speech synthesizer, and so on.

Neural Network Character Recognition With A 2-D Fourier Transform Preprocessor, Daqiao Du

Dissertations and Theses

In pattern recognition applications, it is usually important that the same identification be given for a pattern, independent of a variety of positions, rotations and /or distortions of the pattern within the recognition device's field of view. This research relates to development of a preprocessor for a neural network character recognition system, where the role of the preprocessor is to assist in minimizing the difficulties related to variations of position and rotations of a character within the field of view. The preprocessor explored here was suggested in 1970' (Lendaris & Stanly, 1970), and is implemented here with more recent advances …

A New Design Approach For Numeric-To-Symbolic Conversion Using Neural Networks, Zibin Tang

Dissertations and Theses

A new approach is proposed which uses a combination of a Backprop paradigm neural network along with some perceptron processing elements performing logic operations to construct a numeric-to-symbolic converter. The design approach proposed herein is capable of implementing a decision region defined by a multi-dimensional, non-linear boundary surface. By defining a "two-valued" subspace of the boundary surface, a Backprop paradigm neural network is used to model the boundary surf ace. An input vector is tested by the neural network boundary model (along with perceptron logic gates) to determine whether the incoming vector point is within the decision region or not. …