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 342 for “"convolution"”.
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Convolution method in elastodynamics
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-08-01
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Fast algorithms for DFT and convolution
… of converting Discrete Fourier Transform to convolution and Implementing convolution efficiently, have been combined to give two algorithms viz. Nested Fourier Algorithm (NFA -- using linear multidimensional map) and Index Fourier Algorithm (IFA using a non-linear Index map). The two …
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Convolution Algebras on Locally Compact Spaces
Made available in DSpace on 2014-12-09T22:17:38Z (GMT). No. of bitstreams: 1 6801747.pdf: 1813989 bytes, checksum: bac74ff1ddeec67887057c8d27536067 (MD5) Previous issue date: 1967
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The Generalised Gaussian Process Convolution Model
… formulates the Generalised Gaussian Process Convolution Model (GGPCM), which is a generalisation of the Gaussian Process Convolution Model presented by Tobar et al. [2015b]. The GGPCM provides a theoretical framework for nonparametric kernel models of multidimensional signals defined on …
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High speed convolution using residue number systems
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1989.
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Dynamic line integral convolution for visualizing electromagnetic phenomena
… A fairly recent technique called Line Integral Convolution (LIC) has improved the level of detail that can be visualized by convolving a random input texture along the streamlines in the vector field. This thesis extends the technique to time-varying vector fields, where the motion of the field …
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Day convolution and the Hodge filtration on THH
… D, we define an oc-categorical analog of the Day convolution symmetric monoidal structure on the functor category Fun(C, D). In the second, we develop a Hodge filtration on the topological Hochschild homolgy spectrum of a commutative ring spectrum and describe its elementary properties.
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Some New Results on Difference Equations of Convolution Type
In this thesis we consider convolution type linear difference equations with coefficients satisfying some monotonicity properties. Methods from renewal theory are employed to obtain easily verified conditions for asymptotic stability of the zero solution, in terms of the coefficient sequence. …
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Quality assessment of docked protein interfaces using 3D convolution
… As a part of this work, we have developed a 3D convolutional network approach that uses raw atomic densities to address this problem. Our method achieves performance which is on par with state-of-art methods. We have evaluated our model on docked protein structures simulated from four docking …
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Pruning Convolution Neural Network (SqueezeNet) for Efficient Hardware Deployment
… focuses on reducing the model size of the Convolution Neural Network (CNN) by various compression techniques like Architectural compression, Pruning, Quantization, and Encoding (e.g., Huffman encoding). Network pruning is one of the promising technique to solve these problems. This thesis …
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The Fourier convolution-deconvolution method: its limitation and application
… electron paramagnetic resonance (EPR) Fourier Convolution-Deconvolution method of measuring distance was utilized to determine the distance between two cysteine mutated residues in the linker region of Sso1p in order to determine the conformation of this region during SNARE complex assembly. A …
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Multi-class segmentation of brain tumor using Convolution Neural Network
In this report a fully Convolution Neural Network (CNN) architecture is used to segment multi-modal Brain Tumors from Magnetic Resonance (MR) images. Due to the challenges in manual segmentation, computerized brain tumor segmentation is one of the most important challenges in medical imaging. The …
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Molecular graph Self attention and graph convolution for drug discovery
… molecules as undirected graphs and use graph convolutions and self-attention to predict molecular properties. With a series of ablation studies, we demonstrate the added value of several key components in our network. We analyze two standard datasets: BBBP, which includes classication data on …
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Understanding Adversarial Training: Improve Image Recognition Accuracy of Convolution Neural Network
… digits. Recently many researchers work on Convolution Neural Network for image recognition, and get results as good as human being. Additionally, Image recognition task is getting more popular and high demand to apply to other fields, but also there are still many problems to utilize in …
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Convergence of Convolution Operators and Weighted Averages in L(P) Spaces
… is possible for perturbed moving averages and convolution operators induced by approximate identities. Furthermore, we study weighted versions of moving averages and differentiation operators. We address the question of optimality for the classes of weights used to assure that these operators …
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Deep Learning Image Augmentation using Inpainting with Partial Convolution and GANs
… techniques: (1) inpainting using partial convolution and (2) generative adversarial network (GAN) to generate synthetic data to train deep learning image classifiers. We show that the addition of synthetic training images dramatically improved the accuracies of our defect classifiers. …
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Tardigrade: A Hardware Accelerator for Sparse Matrix Multiplication and Sparse Convolution
… matrix multiplication (SpMSpM) and sparse convolution are critical primitive operations for scientific computing and deep learning. Prior work has proposed accelerators for each of these primitives, but these systems are often specialized to run either SpMSpM or sparse convolution …
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