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

The resizing of data, either upscaling or downscaling based on need for increased or decreased resolution, is an
important signal processing technique due to the variety of data sources and formats used in today's world. Image
interpolation, the 2D variation, is commonly achieved through one of three techniques: nearest neighbor, bilinear
interpolation, or bicubic interpolation. Each method comes with advantages and disadvantages and selection of the
appropriate one is dependent on output and situation specifications. Presented in this paper are algorithms for the
resizing of images based on the analysis of the sum of primary implicants representation of image data, as generated by
a logical transform. The most basic algorithm emulates the nearest neighbor technique, while subsequent variations
build on this to provide more accuracy and output comparable to the other traditional methods. Computer simulations
demonstrate the effectiveness of these algorithms on binary and grayscale images.

/pdf/algorithms-for-the-resizing-of-binary-and-grayscale-images-5f9dsl9crp.pdf

Algorithms for the resizing of binary and grayscale images using a logical transform

Facial emotion recognition technology finds numerous real-life applications in areas of virtual learning, cognitive psychology analysis, avatar animation, neuromarketing, human machine interactions, and entertainment systems. Most state-of-the-art techniques focus primarily on visible spectrum information for emotion recognition. This becomes very arduous as emotions of individuals vary significantly. Moreover, visible images are susceptible to variation in illumination. Low lighting, variation in poses, aging, and disguise have a substantial impact on the appearance of images and textural information. Even though great advances have been made in the field, facial emotion recognition using existing techniques is often not satisfactory when compared to human performance. To overcome these shortcomings, thermal images are preferred to visible images. Thermal images a) are less sensitive to lighting conditions, b) have consistent thermal signatures, and c) have a temperature distribution formed by the face vein branches. This paper proposes a robust emotion recognition system using thermal images- TERNet. To accomplish this, customized convolutional neural network(CNNs) is employed, which possess excellent generalization capabilities. The architecture adopts features obtained via transfer learning from the VGG-Face CNN model, which is further fine-tuned with the thermal expression face data from the TUFTS face database. Computer simulations demonstrate an accuracy of 96.2% when compared to the state-of-the-art models.

TERNet: A deep learning approach for thermal face emotion recognition

In this paper, a new method of image enhancement is introduced. The method is based on the tensor (or vectorial) representation of the two-dimensional image with respect to the Fourier transform. In this representation, the image is defined as a set of one-dimensional (1-D) image-signals that split the Fourier transform into a set of 1-D transforms. As a result, the problem of the image enhancement is reduced to the 1-D processing the splitting signals. 
The splitting of the image yields a simple model for the image enhancement, when by using only a few image-signals it is possible to achieve the image enhancement that is comparative to the known class of the frequency domain based parametric image enhancement algorithms, that are used widely for the object detection and visualization. A quantitative measure of image enhancement that is related to the Weber's law of the human visual system is considered. Based on the quantitative measure the best parameters for image enhancement can be found for each image-signal to be processed separately. Examples of image-signals and their contributions in process of enhancement of an image 256×256 are given.

Tensor form of image representation: enhancement by image-signals

S-Boxes play a vital role in cipher designs and have been researched for years. In this article a new way of dynamically designing S-boxes using Latin Square doubly stochastic matrix is proposed. And it is demonstrated that the enciphering/deciphering process is a mimic of a Markov chain Monte Carlo simulation. Unlike conventional dynamic S-boxes, the proposed S-boxes are not directly defined by keys, but contained in the key dependent doubly stochastic matrix. The created matrix has desired properties including: 1) it implicitly contains S-boxes and thus complicates the internal structure of S-boxes; 2) it naturally defines S-boxes with reversibility and thus any S-boxes used for encryption can be directly used for decryption; 3) it satisfies the Strict Avalanche Criterion (SAC) for S-Boxes and thus it has good resistance to differential or linear cryptanalysis; 4) it guarantees the independence of the ciphertext distribution from plaintext one; and 5) it ensures that the expected ciphertext distribution is uniform and thus attains excellent confusion properties when use these S-Boxes iteratively. Theoretical and experimental results show that the proposed S-box has a high security level and is suitable to design cryptosystem for data encryption. We also extend our S-boxes to a simple image cipher. Experimental results show that the proposed dynamical flexible structure S-boxes has cryptographic properties comparable or better than some existing image encryption methods.

Dynamic and implicit latin square doubly stochastic S-boxes with reversibility

Medical imaging systems often require image enhancement to visualize images of the human body and its organs. This would help medical professionals in irregularity or abnormality detection and diagnosis. This paper demonstrates a method to enhance medical related images. The proposed algorithm uses techniques, such as, guided filtering, edge enhancement, contrast stretching, and image fusion to enhance low resolution images. Visually, the proposed method produces better or comparable enhanced images than several state-of-the-art methods. In addition, we also test the performance of the proposed method with the method mentioned in [1].

Sos S. Agaian

Papers

Algorithms for the resizing of binary and grayscale images using a logical transform

TERNet: A deep learning approach for thermal face emotion recognition

Tensor form of image representation: enhancement by image-signals

Dynamic and implicit latin square doubly stochastic S-boxes with reversibility

A versatile edge preserving image enhancement approach for medical images using guided filter