There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

https://minerva-access.unimelb.edu.au/bitstream/handle/11343/197439/1207.0203v1.pdf

Internet of Things (IoT): A vision, architectural elements, and future directions

We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.

Image Super-Resolution Using Deep Convolutional Networks

In this paper a class of iterative image restoration algorithms is derived based on a representation theorem for the generalized inverse of a matrix. These algorithms exhibit a first or higher order of convergence and some of them consist of an "on-line" and an "off-line" computational part. The conditions for convergence and the rate of convergence of these algorithms are derived. A new iterative algorithm is also presented which exhibits a higher rate of convergence than the standard quadratic algorithm with no extra computational load. These algorithms can be applied to the restoration of signals of any dimensionality. Iterative restoration algorithms that have appeared in the literature represent special cases of the class of algorithms described here. Therefore, the approach presented here unifies a large number of iterative restoration algorithms.

D9.8 a unified approach to iterative signal restoration

5.8 – Recovery Methods for Postprocessing of Compressed Images

In this paper we propose a novel super-resolution based al­ gorithm for the pansharpening of multispectral images. Within the Bayesian formulation, the proposed methodology incorporates prior knowledge on the expected characteristics of multispectral images; that is, imposes smoothness within each band by means of the energy associated to the £1 norm of vertical and horizontal first order differences of image pixel values and also takes into ac­ count the correlation between the bands of the multispectral image. The observation process is modeled using the sensor characteris­ tics of both panchromatic and multispectral images. The method is tested on real and synthetic images, compared with other pan­ sharpening methods, and its quality is assessed both qualitatively and quantitatively.

Super resolution of multispectralimages using £1image models and interband correlations

Principles of Nonlinear Feature Engineering

We consider a system of two identical rectangular shaped water tanks. A source of constant water inflow is available, which may only be directed to one tank at a time. The objective is to find a control policy to maximize the final sum of the water levels at some terminal time T, subject to minimum water level constraints on each tank. Water exits each tank corresponding to Toricelli’s law (i.e., the velocity depends on the current water level). We derive a closed form dynamic programming solution in discrete time to this problem without the water-level threshold constraints. Subsequently, we implement the value iteration algorithm on a set of support points to find a control policy with the threshold constraints, where a random forest regressor is iteratively used to update the value function. Our results show consistency between the dynamic programming solution and the value iteration solution. 

Aggelos K. Katsaggelos

Papers

D9.8 a unified approach to iterative signal restoration

5.8 – Recovery Methods for Postprocessing of Compressed Images

Super resolution of multispectralimages using £1image models and interband correlations

Principles of Nonlinear Feature Engineering

Control of a Water Tank System with Value Function Approximation