Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 450 for “"Convolutional neural network"”.
-
Stable and symmetric convolutional neural network
DSpace SAF Submission Ingestion Package generated from Vireo submission #9397 on 2016-11-09 at 10:19:06
-
Efficient convolutional neural network inference on microcontrollers
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01
-
Classification of Variable Stars using Convolutional Neural Network
<p>This research focuses on developing Convolutional Neural Networks (CNNs), for the process of classifying and identifying variable stars through the analysis of unprocessed light curves from Transiting Exoplanet Survey Satellite (TESS). As astronomical data is becoming increasingly complex, and …
-
Electricity Price Forecasting Using a Convolutional Neural Network
… intelligence approaches using artificial neural networks dominate the landscape. With the rise in popularity of convolutional neural networks to handle problems with large numbers of inputs, and convolutional neural networks conspicuously lacking from current literature in this field, …
-
Visual Speech Recognition Using a 3D Convolutional Neural Network
… feature cubes of lip data from videos and a 3D convolutional neural network (CNN) architecture for performing classification on a dataset of 100 spoken words, recorded in an uncontrolled envi- ronment. Our 3D-CNN architecture achieves a testing accuracy of 64%, comparable with recent works, but …
-
A deep convolutional neural network approach for biomedical applications.
… subset of machine learning that uses multi layer neural networks to perform desired tasks by using trained models. Neural networks are nonlinear mapping systems whose structure and function are loosely modeled on the physical structure of the nervous systems in humans and animals. In deep …
-
Feedback convolutional neural network in applications of computer vision
"With the development of deep neural networks, especially convolutional neural networks, computer vision tasks rely on training data to an unprecedented extent. As the network goes deeper and wider, the demand for high quality supervised training data also increases exponentially with the model …
-
Learning rate optimisation of an image processing deep convolutional neural network
A dissertation submitted in fulfilment of the requirements for the degree of Master of Engineering Department of Electronics and Computer Engineering, Faculty of Engineering and the Built Environment, Durban University of Technology, 2021.
-
Comparison of distributed training architecture for convolutional neural network in cloud
… and ever increasing model complexity of deep neural networks (DNNs) have enabled breakthroughs in various artificial intelligence fields such as computer vision, natural language processing and data mining. The training process of the DNN is a computationally intensive application that can be …
-
Classification of P300 from non-invasive EEG signal using convolutional neural network
… the system's deployment for use. In this thesis Convolutional Neural Network is applied to detect the P300 signal and observe the distinguishing features of P300 and non-P300 signals extracted by the neural network. Three different shapes of the filters, namely 1-D CNN, 2-D CNN, and 3-D CNN are …
-
Muon Neutrino Disappearance in NOvA with a Deep Convolutional Neural Network Classifier
… in the field of computer vision is the advent of convolutional neural networks, which have delivered top results in the latest image recognition contests. This work presents an approach novel to particle physics analysis in which a convolutional neural network is used for classification of …
-
Neuromorphic deep convolutional neural network learning systems for FPGA in real time
… recognition, among others. In image vision, convolutional neural networks stand out, due to their relatively simple supervised training and their efficiency extracting features from a scene. Nowadays, there exist several implementations of convolutional neural networks accelerators that …
-
A novel face recognition system in unconstrained environments using a convolutional neural network
… in unconstrained environments using the convolutional neural network. Furthermore, the thesis presents a selection of hybrid features from the enhanced image that results in effective image classification. Different face datasets were selected where each face image was enhanced using the …
-
Region-based Convolutional Neural Network and Implementation of the Network Through Zedboard Zynq
… vehicles and many other new technologies, the neural network and computer vision has become extremely popular and influential. In particular, for classifying objects, convolutional neural networks (CNN) is very efficient and accurate. One version is the Region-based CNN (RCNN). This is our …
-
Size-Adaptive Convolutional Neural Network with Parameterized-Swish Activation for Enhanced Object Detection
… This research introduces a size-adaptive Convolutional Neural Network (CNN) framework to enhance detection performance across different object sizes. By dynamically adjusting the CNN’s configuration based on the observed distribution of object sizes, the framework employs statistical …
-
Estimation of Defocus Blur in Virtual Environments Comparing Graph Cuts and Convolutional Neural Network
… In this research, we have applied graph cuts and convolutional neural network (DfD-net) to estimate depth from defocus blur using a natural (Middlebury) and a virtual (Maya) dataset. Graph Cuts showed similar performance for both natural and virtual datasets in terms of NMAE and NRMSE. However, …
-
Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering with Graph Convolutional Neural Network
<p>Recently, deep graph neural networks (GNNs) have attracted significant attention for point cloud understanding tasks, including classification, segmentation, and detection. However, the training of such deep networks still requires a large amount of annotated data, which is both expensive and …
-
Design and evaluation of a novel convolutional neural network for short-term vehicle multi-traffic prediction
… congestion state. In this thesis, we propose a convolutional neural net-work model that performs traffic forecasting for all three parameters, using historical integrated traffic data over a large area. The proposed model also predicts all three parameters for all 5-minute intervals from the …
Page 1 of 23