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 89 for “"Deep learning algorithms"”.
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On the use of prior knowledge in deep learning algorithms
Machine learning algorithms have seen increasing use in the field of computational imaging. In the past few decades, the rapid computing hardware developments such as in GPU, mathematical optimization and the availability of large public domain databases have made these algorithms, increasingly …
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On exploiting structures for deep learning algorithms with matrix estimation
Despite recent breakthroughs of deep learning, the intrinsic structures within tasks have not yet been fully explored and exploited for better performance. This thesis proposes to harness the structured properties of deep learning tasks using matrix estimation (ME). Motivated by the theoretical …
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DEEP LEARNING ALGORITHMS FOR OPTICAL COHERENCE TOMOGRAPHY IMAGES WITH APPLICATIONS IN GLAUCOMA
… glaucoma diagnosis by leveraging on the power of deep learning (DL) to fully exploit the 3D morphological information present in OCT images.
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Grid Power Quality with FACTS Devices and Renewable Energy Sources Using Deep Learning Algorithms
… In each method, the design is simple with high learning capability and prediction accuracy, and does not require extensive training, parameter tuning, or complex optimization. For state estimation of FACTS devices, we propose a new approach called spanning tree maximum exponential absolute value …
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Evaluation of Deep Learning Algorithms in Predicting Seismic Response of a Reinforced Concrete Structure
… of the performance of three well-established deep learning algorithms in predicting the response of a six-story instrumented reinforced concrete hotel in California to seismic excitation. Given the increasing availability of strong-motion data and expanded usage of deep learning in structural …
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Image series prediction via convolutional recurrent neural networks with limited training data
<p>This thesis focuses on developing deep learning algorithms that can be used to forecast the image series under limited training data. Specifically, we study the problem of using a pine tree's existing appearance images to predict its future appearance images.</p>
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Improving the Maximum Power Point Tracking Efficiency of Photovoltaic Arrays via Machine Learning and Deep Learning
… are many maximum power point tracking (MPPT) algorithms developed to detect the true maximum power point (MPP) of a PV array. However, in the real-world environment, limited samples of power-voltage (P-V) data might be available to quickly and accurately predict the position of the global …
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Deep learning for energy-efficient wireless communications and spectrum management.
… allocation in the systems. Nevertheless, Deep Learning methods are expert at solving sophisticated optimization problems. The key advantages of Deep Learning are the efficient learning of an enormous amount of data and the precise analysis for the hidden distribution. Therefore, Deep …
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Deep learning for grouped data
… explores the problems inherent in applying deep learning algorithms to groups of data. My claim is that groups should be represented as random variables whose values should be inferred from data. This approach has the potential to unlock solutions in many important domains of machine …
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Towards an integrated understanding of neural networks
… the brain remains largely unknown, and popular deep learning algorithms lack theoretical justification or reliability guarantees. This thesis aims towards a more rigorous understanding of neural networks. We characterize and, where possible, prove essential properties of neural algorithms: …
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Differentiable visual computing
… of computer graphics, image processing, and deep learning algorithms have tremendous use in guiding parameter space searches, or solving inverse problems. As the algorithms become more sophisticated, we no longer only need to differentiate simple mathematical functions, but have to deal with …
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Deep learning with physical and power-spectral priors for robust image inversion
… class of imaging systems that utilizes inverse algorithms to recover unknown objects of interest from physical measurements. Deep learning has been used in computational imaging, typically in the supervised mode and in an End-to-End fashion. However, treating the machine learning algorithm as a …
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Digital Signal Processing and Machine Learning Applied to Power Line Communications
… this research investigates the use of machine learning techniques as a supplement to the traditional digital signal processing techniques. We focus on testing and comparing various supervised machine learning and deep learning algorithms for the purpose of signal demodulation and bit …
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Building occupancy analytics based on deep learning through the use of environmental sensor data
… to their low cost and privacy benefits. Machine learning algorithms play a critical role in estimating the relationship between occupancy levels and environmental data. To improve performance, more complex models such as deep learning algorithms are necessary. Long Short-Term Memory (LSTM) is a …
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Secure and reliable deep learning in signal processing
… about the given problems. On the contrary, deep learning-based signal processing algorithms can discover features and patterns that would not be apparent to humans by feeding a sufficient amount of training data. In the past decade, deep learning has proved to be efficient and effective at …
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Deep Learning-Based Comprehensive Pathology Image Analysis
The advances in deep learning during the past decade have provided great tools for the analysis of histopathology images. Deep learning algorithms can aid in the routine diagnostics and have the potential to extract hidden information directly from slide images, providing valuable information for …
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Inference neural network hardware acceleration techniques
… focus on designing accelerators for popular deep learning algorithms. Most of these algorithms heavily involve matrix multiplication. As a result, building a neural processing unit (NPU) beside the CPU to accelerate matrix multiplication is a popular approach. The NPU helps reduce the work …
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Gesture Recognition in Tennis Biomechanics
… best correlate to a swing efficacy. For our learning set this work aimed to record 50 tennis athletes of similar competency with the Microsoft Kinect performing standard tennis swings in the presence of different targets. With the acquired data we extracted biomechanical features that …
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CloudCV: Deep Learning and Computer Vision on the Cloud
… Unfortunately, scaling existing computer vision algorithms to large datasets leaves researchers repeatedly solving the same algorithmic and infrastructural problems. Designing and implementing efficient and provably correct computer vision algorithms is extremely challenging. Researchers must …
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Automated histopathological analyses at scale
… system for automated screening, backed by deep learning algorithms. This cost-effective, easily-scalable solution can be operated by minimally trained health workers and would extend the reach of histopathological analyses to settings such as rural villages, mass-screening camps and mobile …
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