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

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

This paper presents a genetic algorithm approach for routing applications. The genetic algorithm produces many possible solutions in its search process. By utilizing this feature, our method determines both the shortest route and multiple semi-shortest routes in one search. Existing methods, such as Dijkstra's algorithm, search only for the shortest route, and cannot determine any other short routes. Therefore the proposed algorithm is useful for this kind of routing. Furthermore, the method can determine the routes which are short and not similar depending on geographical conditions since the genotype structure of our method has a weighting factor which controls route length.

A genetic algorithm for determining multiple routes and its applications

[Foreword] Welcome to the Transactions on Media Technology and Applications

-In this paper, we present a fast and robust ellipse extraction method. The proposed method can extract ellipses with high accuracy and speed from images with complicated backgrounds. It consists of two parts. First, we extract arc segments from an ellipse approximated by short straight lines that are extracted by a fast line extraction algorithm. Second, the arc segments are used to calculate accurate ellipse parameters with a least squares ellipse fitting algorithm. Tests performed on synthetic and real-world images show that the method quickly and accurately extracts circles from complicated images.

Fast and Robust Ellipse Extraction from Complicated Images

There exist two main problems in currently existing measurement matrices for compressed sensing of natural images, the difficulty of hardware implementation and low sensing efficiency. In this paper, we present a novel simple and efficient measurement matrix, Binary Permuted Block Diagonal (BPBD) matrix. The BPBD matrix is binary and highly sparse (all but one or several “1”s in each column are “0”s). Therefore, it can simplify the compressed sensing procedure dramatically. The proposed measurement matrix has the following advantages, which cannot be entirely satisfied by existing measurement matrices. (1) It has easy hardware implementation because of the binary elements; (2) It has high sensing efficiency because of the highly sparse structure; (3) It is incoherent with different popular sparsity basis' like wavelet basis and gradient basis; (4) It provides fast and nearly optimal reconstructions. Moreover, the simulation results demonstrate the advantages of the proposed measurement matrix.

The simplest measurement matrix for compressed sensing of natural images

An image classification device includes a characteristic value set calculation unit 11 that calculates a characteristic value set of the whole image for each of multiple sets of image data in an image database 51, detects an edge of the set of the image data, and calculates a characteristic value set of the detected edge portions; a first clustering unit 12 that classifies the multiple sets of image data into multiple clusters on the basis of the characteristic value sets of the whole images; a second clustering unit 13 that further classifies the multiple clusters classified by the first clustering unit 12 into multiple clusters on the basis of the characteristic value sets of the edge portions; and a cluster integration unit 14 that determines which pixels constitutes a subject in each of the multiple sets of image data, based on the composition of the image, and integrates some of the multiple clusters classified by the second clustering unit 13 together based on the pixels constituting the subject.

Miki Haseyama

Papers

A genetic algorithm for determining multiple routes and its applications

[Foreword] Welcome to the Transactions on Media Technology and Applications

Fast and Robust Ellipse Extraction from Complicated Images

The simplest measurement matrix for compressed sensing of natural images

Image classification device and image classification program