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

The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many

Harrison's Principles of Internal Medicine

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

Edge detection is a key step in medical image processing. It is widely used to extract features, perform segmentation, and further assist in diagnosis. A poor quality edge map can result in false alarms and misses in cancer detection algorithms. Therefore, it is necessary to have a reliable edge measure to assist in selecting the optimal edge map. Existing reference based edge measures require a ground truth edge map to evaluate the similarity between the generated edge map and the ground truth. However, the ground truth images are not available for medical images. Therefore, a nonreference edge measure is ideal for medical image processing applications. In this paper, a nonreference reconstruction based edge map evaluation (NREM) is proposed. The theoretical basis is that a good edge map keeps the structure and details of the original image thus would yield a good reconstructed image. The NREM is based on comparing the similarity between the reconstructed image with the original image using this concept. The edge measure is used for selecting the optimal edge detection algorithm and optimal parameters for the algorithm. Experimental results show that the quantitative evaluations given by the edge measure have good correlations with human visual analysis.

/pdf/nonreference-medical-image-edge-map-measure-ork047ysj9.pdf

Nonreference medical image edge map measure

Donoho developed nonlinear techniques known as wavelet shrinkage. They have since been successfully applied for noise suppression. This paper introduces a new parametric shrinkage technique and compares its performance to the soft threshold introduced by Donoho and the differentiable shrinkage function introduced by Zhang. Termed the polynomial hard threshold this new shrinkage technique is better able to represent polynomial behavior than the previous techniques. It is also able to represent a wider class of shrinkage functions making it ideal for use in adaptive noise suppression. This class of shrinkage functions includes both Donoho’s soft and the classical hard threshold. By using a priori knowledge to adjust its parameters this threshold can be tailored to perform well for a particular signal type.

Nonlinear noise suppression using a parametric class of wavelet shrinkage functions

This paper presents a color image enhancement algorithm based on shifting the histogram of logarithmic DCT
coefficients of the luminance component. The novelty of this work lies in utilizing both the spatial domain histogram
concept and transform domain enhancement techniques to perform the enhancement. This paper also demonstrates a
quantitative measurement based upon contrast entropy to choose the optimal parameters and determines the
effectiveness of the method. In addition, different color spaces including HSV, YCbCr, and Principle Component
Analysis (PCA) have been examined for the algorithm to determine the best visual results. We also present a
comprehensive review of four commonly used color image enhancement techniques, such as mutiscale retinex with
color restoration, multi contrast enhancement, multi contrast enhancement with dynamic compression and color image
enhancement by scaling, and compare them with the proposed histogram shifting techniques. Computer simulations and
analysis show that the histogram shifting method outperforms the commonly used methods for most images.

Comparative study of color image enhancement techniques

In this paper, we introduce a new class of discrete parametric trigonometric transforms. We establish the conditions of unitarity of the discrete parametric trigonometric transform matrices, by which we construct a wide range of orthonormal transform matrices. Analysis of proposed unitary trigonometric (cosine, sine and combined sine-cosine) transforms is performed, and efficient algorithms for their computation are developed.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Parametric family of discrete trigonometric transforms

This paper presents the new Fibonacci Fourier-like transforms. The proposed transforms render the relationship between Fibonacci numbers and the conventional Discrete Fourier Transform. The fast Fibonacci Fourier transforms are also introduced with the use of the Kronecker product properties. The proposed transforms are applied to the problem of noise reduction with two new algorithms, sliding double window filtering and fusion sliding window filtering. The primary concept of sliding double window filtering is to process the noisy signals with nonoverlapped windows, while the primary concept of fusion sliding window filtering is to process the noisy signals with various weighted filtering methods and overlapped signal values. The results and analysis show the noise reduction of the given noisy gray level images. The proposed methods are compared with the well-known Wiener filtering using images that contain Gaussian noise with the range of variance between 0 and 0.3. The analysis shows by visual inspection that the noisy parts are smoothened while retaining natural edges.

Sos S. Agaian

Papers

Nonreference medical image edge map measure

Nonlinear noise suppression using a parametric class of wavelet shrinkage functions

Comparative study of color image enhancement techniques

Parametric family of discrete trigonometric transforms

Noise reduction algorithms using Fibonacci Fourier transforms