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

A key constraint in mobile communications is the reliance on a battery with a limited energy supply. Efficiently utilizing the available energy is therefore an important design consideration. We consider a situation where a video sequence is to be compressed and transmitted over a wireless channel. The goal is to limit the amount of distortion in the received video sequence while using the minimum required transmission energy. To accomplish this goal, we consider error resilience and concealment techniques, at the source coding level, as well as the dynamic allocation of physical layer communication resources. We consider these approaches jointly in a novel framework. We formulate an optimization problem that corresponds to minimizing the energy required to transmit a video frame with an acceptable level of distortion. We present methods for solving this problem and other extensions.

Minimizing transmission energy in wireless video communications

Ptychography is an imaging technique which aims to recover the complex-valued exit wavefront of an object from a set of its diffraction pattern magnitudes. Ptychography is one of the most popular techniques for sub-30 nanometer imaging as it does not suffer from the limitations of typical lens based imaging techniques. The object can be reconstructed from the captured diffraction patterns using iterative phase retrieval algorithms. Over time many algorithms have been proposed for iterative reconstruction of the object based on manually derived update rules. In this paper, we adapt automatic differentiation framework to solve practical and complex ptychographic phase retrieval problems and demonstrate its advantages in terms of speed, accuracy, adaptability and generalizability across different scanning techniques.

ADP: Automatic differentiation ptychography

Stereo matching is fundamental to applications such as 3D visual communications and depth measurements. There are several different approaches towards this objective, including feature-based methods, block-based methods, and pixel-based methods. Most approaches use regularization to obtain reliable fields. Generally speaking, when smoothing is applied to the estimated depth field, it results in a bias towards surfaces that are parallel to the image plane. This is called fronto-parallel bias. Recent pixel-based approaches claim that no disparity smoothing is necessary. In their approach, occlusions and objects are explicitly modeled. But these models interfere each others in the case of slanted objects and result in a fragmented disparity field. In this paper we propose a disparity estimation algorithm with explicit modeling of object orientation and occlusion. The algorithm incorporates adjustable resolution and accuracy. Smoothing can be applied without introducing the fronto-parallel bias. The experiments show that the algorithm is very promising.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Disparity estimation with modeling of occlusion and object orientation

At least one visual significance metric is determined (12) for at least some frames belonging to an original series of frames (11). Key frames are identified (13) as a function, at least in part, of the visual significance metric. Cumulative visual significance values are then determined (14) for at least some of the frames that intervene between each pair of key frames. These cumulative visual significance values are then used to identify (15) frames of additional content interest. Various frames are then selected (16) for use in a visual summary. In one embodiment, all of the key frames and frames of additional content interest are selected for inclusion in the visual summary.

Aggelos K. Katsaggelos

Papers

Minimizing transmission energy in wireless video communications

ADP: Automatic differentiation ptychography

Disparity estimation with modeling of occlusion and object orientation

Method and apparatus for reduction of visual content

Displacement Field Estimation in Noisy Image Sequences