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

Zonal-Alpha-Rooting color image enhancement by the two-Side 2D quaternion discrete fourier transform

Maritime border protection has an important role in border security The increasing number of refugees migrating from one country to an another in illegal ways creates a potential risk for those countries having that of traffic According to the United Nations Refugee Agency, the majority of refugees and migrants are likely to be aboard unsafe boats that are prone to capsizing For these types of situations, it is useful for law enforcement agencies to analyze the images of wreckages taken underwater in order to detect unlawful materials such as firearms, rifles, explosives Furthermore, detection of the ensign of a capsized boat may give a clue about its possible departure information, which may lead to other kinds of intelligence that can be collected as well

Contrast enhancement for underwater images in maritime border protection

Image quality assessment becomes essential for autonomous systems, where processing occurs on an acquired image and is then used for detection and recognition of objects. Images exhibiting low quality and captured in the presence of noise that are used as the basis for image recognition systems can dramatically impair the overall recognition system's performance. In this paper, we will present a new distance double variance color image quality measure that does not require a reference image in order to make its evaluation of the quality of an image. The Distance Doubling Variance measure differs from existing color image quality methods, which typically attempt to extend traditional grayscale image approaches for color images. Here, we utilize the color properties in the color space, where we evaluate the difference between two color pixels by computing the distance in the color space using different weights for each of the color components. Based on this distance, we calculate the double variance of the distance matrix. This matrix consists of the maximum distance of each pixel and its corresponding neighboring pixels. To demonstrate its performance, we use the TID-2013 database, which includes 24 different types of distortions for different kinds of images. The simulations are compared with state-of-the-art methods to show the new method has high agreement with human's visual system in many types of distortions.

A no reference image quality measure using a distance doubling variance

In this paper, a new low cost Arabic vehicle license plate recognition (LPR) method is proposed which is easily extendable to other plates. A novel LP segmentation technique with three sets of feature vectors was used with template matching to form the two main modules: license plate localization module and LPR module. This method was tested on more than 238 vehicle images taken from various scenes with different fonts and backgrounds from two Arab countries. The segmentation accuracy of the implemented system was 97.5% with a recognition accuracy of 99% for fairly distorted images. The presented model shows that despite the negative impact of shadows, cracks, dirt, and character separations, the system demonstrated an overall success rate of 92% for plate localization, 95% for plates segmentation, 92% for country and city recognition, and 99% for number segmentation and recognition. Combining all rates led to an overall system accuracy of 93%. Compared to many state-of-the-art LPR systems, this newly developed system uses 3 small training sets which cut the run times of the proposed solution to less than 5 seconds using the MATLAB R2008A running on a Compaq 8510W with 4G RAM. The results are comparable, and in some cases better with restricted conditions such as skew place, plate size, illumination and background.

/pdf/arabic-license-plate-recognition-system-1oz53b9ry4.pdf

Arabic License Plate Recognition System

Multiple classifier fusion has the capability of increasing classification accuracy over individual classifier systems. This paper focuses on the development of a multi-class classification fusion based on weighted averaging of posterior class probabilities. This fusion system is applied to the steganography fingerprint domain, in which the classifier identifies the statistical patterns in an image which distinguish one steganography algorithm from another. Specifically we focus on algorithms in which jpeg images provide the cover in order to communicate covertly. The embedding methods targeted are F5, JSteg, Model Based, OutGuess, and StegHide. The developed multi-class steganalvsis system consists of three levels: (1) feature preprocessing in which a projection function maps the input vectors into a separable space, (2) classifier system using an ensemble of classifiers, and (3) two weighted fusion techniques are compared, the first is a well known variance weighted fusion and an Gaussian weighted fusion. Results show that through the novel addition of the classifier fusion step to the multi-class steganalysis system, the classification accuracy is improved by up to 12%.

Sos S. Agaian

Papers

Zonal-Alpha-Rooting color image enhancement by the two-Side 2D quaternion discrete fourier transform

Contrast enhancement for underwater images in maritime border protection

A no reference image quality measure using a distance doubling variance

Arabic License Plate Recognition System

Multi-Class Classification Averaging Fusion for Detecting Steganography