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.
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Showing 1 to 20 of 193 for “"CNNs"”.
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Efficient CNNs and Energy Efficient SRAM Design for ubiquitous medical devices
Intermittent monitoring of urinary bladder volume aids management of common conditions such as post-operative urinary retention. Urinary retention is prevented by catheterization, an invasive procedure that greatly increases urinary tract infection. Ultrasound imaging has been used to estimate …
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Assessment of Frontal Cephalogram Tracing Accuracy Generated by CNNs and Machine Learning
Accurate diagnosis, precise treatment planning, and reliable prognosis are essential for successful orthodontic outcomes. Frontal (postero-anterior) cephalograms enhance diagnostic accuracy when properly traced and measured, however their can be variability between providers. Frontal cephalograms …
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Sensorless ultrasound probe 6DoF pose estimation through the use of CNNs on image data
… the use of Convolutional Neural Networks (CNNs) to provide sensorless pose estimation. The Ultrasound CNN model proposed in this paper learns to regress the six degree of freedom (6-DoF) camera pose from a single ultrasound image in an end-to-end manner. Ultrasound images are easier to …
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Human action recognition with 3D convolutional neural networks
Convolutional neural networks (CNNs) adapt the regular fully-connected neural network (NN) algorithm to facilitate image classification. Recently, CNNs have been demonstrated to provide superior performance across numerous image classification databases including large natural images (Krizhevsky et …
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Efficient fixed-radius near neighbors for machine learning
… performance. Convolutional neural networks (CNNs) have especially demonstrated super-human performance in many vision tasks. One big reason for the success of CNNs is due to the use of parallelizable software and hardware to run these models, making their use computationally practical. This …
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Deep learning convolutional neural networks for Landsat-derived land cover mapping
… the accuracy of convolutional neural networks (CNNs) for computer vision image classification occurred in 2012, supported by advances in processing such as GPUs. This was subsequently adopted by the remote sensing community from 2014 for a variety of tasks including: image classification, …
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Deep learning convolutional neural networks for Landsat-derived land cover mapping
… the accuracy of convolutional neural networks (CNNs) for computer vision image classification occurred in 2012, supported by advances in processing such as GPUs. This was subsequently adopted by the remote sensing community from 2014 for a variety of tasks including: image classification, …
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On foveation of deep neural networks
… state-of- the-art convolutional neural networks (CNNs), and are much more prominent in CNNs. We found many cases where CNNs classified one region correctly and the other incorrectly, though they only differed by one row or column of pixels, and were often bigger than the average human minimal …
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Computational Face Recognition Using Machine Learning Models
… built based on Convolutional Neural Networks (CNNs) and those models are named Eyes-CNNs, Nose-CNNs, Mouth-CNNs, Forehead-CNNs, and combined EyesNose-CNNs. The experimental results illustrate a high recognition rate when it comes to small parts, for example, eyes increased up to about 90.83% …
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Computational Face Recognition Using Machine Learning Models
… built based on Convolutional Neural Networks (CNNs) and those models are named Eyes-CNNs, Nose-CNNs, Mouth-CNNs, Forehead-CNNs, and combined EyesNose-CNNs. The experimental results illustrate a high recognition rate when it comes to small parts, for example, eyes increased up to about 90.83% …
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Regularization, Uncertainty Estimation and Out of Distribution Detection in Convolutional Neural Networks
… trends of using convolutional neural networks (CNNs) for various machine learning tasks has borne many successes and CNNs are surprisingly expressive in their learning ability due to a large number of parameters and numerous stacked layers in the CNNs. This increased model complexity also …
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Benchmarking convolutional neural networks for object segmentation and pose estimation
Convolutional neural networks (CNNs), particularly those designed for object segmentation and pose estimation, are now applied to robotics applications involving mobile manipulation. For these robotic applications to be successful, robust and accurate performance from the CNNs is critical. …
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On the Use of Convolutional Neural Networks for Specific Emitter Identification
… (RFML) and Convolutional Neural Networks (CNNs) has shown the capability to perform signal processing tasks such as modulation classification, without the need for pre-defined expert features. Given this success, the work presented in this thesis investigates the ability to use CNNs, in …
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Beyond the pixels: learning and utilising video compression features for localisation of digital tampering.
… usage of deep convolutional neural networks (CNNs) in the fields of computer vision, video analysis and video tampering detection, it is important to investigate how patterns invisible to human eyes may be influencing modern computer vision techniques and how they can be used advantageously. …
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Deep learning-based seagrass detection and classification from underwater digital images
… in particular Convolutional Neural Networks (CNNs), have rapidly become a method of choice for analysing seagrass image data. Deep learning-based seagrass classification and detection are very challenging due to the limited labelled data, intraclass similarities between species, lighting …
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Sensitivity of Feedforward Neural Networks to Harsh Computing Environments
… experiments show that MLPs are more robust than CNNs. Within MLPs, deeper MLPs are more robust and for CNNs larger kernels are more robust. Additionally, the CNNs displayed bimodal failure behavior, where memory errors would either not affect the performance of the network, or they would degrade …
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A Study on Deep Learning: Training, Models and Applications
… (DNNs) and convolutional neural networks (CNNs), from scratch becomes practical, and using well-trained deep models to deal with real-world large scale problems also becomes possible. This dissertation mainly focuses on three important problems in deep learning, i.e., training algorithm, …
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Neural Network Pruning for ECG Arrhythmia Classification
<p>Convolutional Neural Networks (CNNs) are a widely accepted means of solving complex classification and detection problems in imaging and speech. However, problem complexity often leads to considerable increases in computation and parameter storage costs. Many successful attempts have been made …
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Measuring and modifying the intrinsic memorability of images
… it memorable. Convolutional neural networks (CNNs) trained on the data could predict an image's relative memorability with high accuracy. CNNs could also generate memorability heat maps which pinpoint which parts of an image are memorable. Finally, with additional usage of a massive image …
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