Search for dissertations about: "neural processing"
Showing result 1 - 5 of 273 swedish dissertations containing the words neural processing.
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1. Radar Signal Processing using Artificial Neural Networks
Abstract : This thesis combines radar signal processing, with data driven artificial neuralnetwork (ANN) methods. Signal processing algorithms are often based on modelingassumptions of how the data was formed. In some cases, such models are sufficientfor designing good, or even optimal, solutions. READ MORE
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2. Key Signal Processing Technologies for High-speed Passive Optical Networks
Abstract : With emerging technologies such as high-definition video, virtual reality, and cloud computing, bandwidth demand in the access networks is ever-increasing. Passive optical network (PON) has become a promising architecture thanks to its low cost and easy management. READ MORE
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3. Deep Neural Networks and Image Analysis for Quantitative Microscopy
Abstract : Understanding biology paves the way for discovering drugs targeting deadly diseases like cancer, and microscopy imaging is one of the most informative ways to study biology. However, analysis of large numbers of samples is often required to draw statistically verifiable conclusions. READ MORE
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4. Representation Learning and Information Fusion : Applications in Biomedical Image Processing
Abstract : In recent years Machine Learning and in particular Deep Learning have excelled in object recognition and classification tasks in computer vision. As these methods extract features from the data itself by learning features that are relevant for a particular task, a key aspect of this remarkable success is the amount of data on which these methods train. READ MORE
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5. Neural Network Architecture Design : Towards Low-complexity and Scalable Solutions
Abstract : Over the past few years, deep neural networks have been at the center of attention in machine learning literature thanks to the advances in computational capabilities of modern graphical processing units (GPUs). This progress has made it possible to train large scale neural networks by using thousands, and even millions, of training samples to achieve outstanding estimation accuracy in various applications that were not simply possible before. READ MORE