Search
Now showing items 1-10 of 20
SECOND ORDER ALGORITHM FOR SPARSELY CONNECTED NEURAL NETWORKS
(2016-08-17)
A systematic two-step batch approach for constructing a sparse neural network is presented. Unlike other sparse neural networks, the proposed paradigm uses orthogonal least squares (OLS) to train the network. OLS based ...
BUILDING 3D SHAPE PRIMITIVE BASED OBJECT MODELS FROM RANGE IMAGES
(2016-09-16)
Most pattern recognition approaches to object identification work in the image
domain. However this is ignoring potential information that can be provided by depth
information. Using range images, ...
Group Assignment and Annual Average Daily Traffic Estimation of Short-term Traffic Counts Using Gaussian Mixture Modeling and Neural Network Models
(2016-09-13)
The grouping of similar traffic patterns and cluster assignment process represent the most critical steps in AADT estimation from short-term traffic counts. Incorrect grouping and assignment often become a significant ...
INTUITIVE HUMAN ROBOT INTERFACES FOR UPPER LIMB PROSTHETICS
(2016-08-19)
Modern robotic prosthetic devices for upper limb amputees promise to alleviate an important disability, but are underutilized due to the inability to properly control them. Specifically, the devices afford more degrees of ...
MULTILAYER PERCEPTRON WITH ADAPTIVE ACTIVATION FUNCTIONS
(2016-05-26)
A Multilayer perceptron typically has a fixed nonlinear activation function for each hidden unit. In this thesis, an adaptive activation function for individual hidden unit is designed, where the network learns these ...
Hyper-optimized Machine Learning and Deep Learning Methods For Geo-spatial and Temporal Function Estimation
(2018-08-13)
Owing to a high degree of freedom in human mobility, accurate modelling/estimation of human mobility function remains a challenge. Numerous work in the literature have tried to address the challenge using various traditional ...
Classification of Factual and Non-Factual Statements Using Adversarially Trained LSTM Networks
(2020-06-08)
Being able to determine which statements are factual and therefore likely candidates for further verification is a key value-add in any automated fact-checking system. For this task, it has been shown that LSTMs outperform ...
DEEPSIGN: A DEEP-LEARNING ARCHITECTURE FOR SIGN LANGUAGE
(2018-11-13)
Sign languages are used by deaf people for communication. In sign languages, humans use hand gestures, body, facial expressions and movements to convey meaning. Humans can easily learn and understand sign languages, but ...
MACHINE LEARNING FOR TARGET DETECTION USING UWB RADAR SENSOR NETWORKS
(2022-12-05)
Machine learning (ML) has recently been used to solve critical problems. This dissertation focuses on developing systems using Ultra-Wideband (UWB) wireless sensor networks and machine learning to solve critical tasks such ...
Machine Learning-based Methods for the Segmentation of Scanning Electron Microscopy Images of Fine-Grained Shale Samples
(2022-12-15)
The segmentation of scanning electron microscopy (SEM) images is critical yet time-consuming for geological studies, as it will need to differentiate the boundaries for different mineral objects to facilitate subsequent ...