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」

Digital Coding of Waveforms

Signals and Systems, Alexander D. Poularikas, University of Alabama in Huntsville Fourier Transforms, Kenneth B. Howell, University of Alabama in Huntsville Sine and Cosine Transforms, Pat Yip, McMaster University, Ontario The Hartley Transform, Kraig J. Olejniczak, University of Arkansas, Fayetteville Laplace Transforms, Samuel Seely (deceased), Westbrook, Connecticut The Z-Transform, Alexander D. Poularikas, University of Alabama in Huntsville Hilbert Transforms, Stefan L. Hahn, Warsaw University of Technology Radon and Abel Transforms, Stanley Deans, University of South Florida The Hankel Transform, Robert Piessens , Katholieke Universieit Leuven, Belgium Wavelet Transform, Yunlong Sheng, Laval University, Quebec The Mellin Transform, Jacqueline Bertrand, Pierre Bertrand, Universite de Paris VII, and Jean-Philippe Ovarlez, ONERA/DES, France Mixed Time-Frequency Signal Transformations, G. Faye Boudreaux-Bartels, University of Rhode Island Fractional Fourier Transforms, Dr. Mustafa Abushagur, University of Alabama in Huntsvill, Dr. Ahmed M. Almanasrah, Photronix, Malaysia The Lapped Transforms, Ricardo L. de Queiroz, Xerox Corporation The Discrete Time and the Discrete Transforms, Alexander D. Poularikas, University of Alabama in Huntsville Discrete Time and the Discrete Transforms, Alexander D. Poularikas, University of Alabama in Huntsville Appendices, Alexander D. Poularikas, University of Alabama in Huntsville Index

The transforms and applications handbook

Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real-world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new super-resolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.

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Super-resolution: a comprehensive survey

A video surveillance system extracts video primitives and extracts event occurrences from the video primitives using event discriminators. The system can undertake a response, such as an alarm, based on extracted event occurrences.

Video surveillance system employing video primitives

In free-viewpoint video, there is a recent trend to represent scene objects as solids rather than using multiple depth maps. Point clouds have been used in computer graphics for a long time, and with the recent possibility of real-time capturing and rendering, point clouds have been favored over meshes in order to save computation. Each point in the cloud is associated with its 3D position and its color. We devise a method to compress the colors in point clouds, which is based on a hierarchical transform and arithmetic coding. The transform is a hierarchical sub-band transform that resembles an adaptive variation of a Haar wavelet. The arithmetic encoding of the coefficients assumes Laplace distributions, one per sub-band. The Laplace parameter for each distribution is transmitted to the decoder using a custom method. The geometry of the point cloud is encoded using the well-established octtree scanning. Results show that the proposed solution performs comparably with the current state-of-the-art, in many occasions outperforming it, while being much more computationally efficient. We believe this paper represents the state of the art in intra-frame compression of point clouds for real-time 3D video.

Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform

Dynamic point clouds are a potential new frontier in visual communication systems. A few articles have addressed the compression of point clouds, but very few references exist on exploring temporal redundancies. This paper presents a novel motion-compensated approach to encoding dynamic voxelized point clouds at low bit rates. A simple coder breaks the voxelized point cloud at each frame into blocks of voxels. Each block is either encoded in intra-frame mode or is replaced by a motion-compensated version of a block in the previous frame. The decision is optimized in a rate-distortion sense. In this way, both the geometry and the color are encoded with distortion, allowing for reduced bit-rates. In-loop filtering is employed to minimize compression artifacts caused by distortion in the geometry information. Simulations reveal that this simple motion-compensated coder can efficiently extend the compression range of dynamic voxelized point clouds to rates below what intra-frame coding alone can accommodate, trading rate for geometry accuracy.

Motion-Compensated Compression of Dynamic Voxelized Point Clouds

This paper will describe the Mixed Raster Content (MRC) method for compressing compound images, containing both binary text and continuous-tone images. A single compression algorithm that simultaneously meets the requirements for both text and image compression has been elusive. MRC takes a different approach. Rather than using a single algorithm, MRC uses a multi-layered imaging model for representing the results of multiple compression algorithms, including ones developed specifically for text and for images. As a result, MRC can combine the best of existing or new compression algorithms and offer different quality-compression ratio tradeoffs. The algorithms used by MRC set the lower bound on its compression performance. Compared to existing algorithms, MRC has some image-processing overhead to manage multiple algorithms and the imaging model. This paper will develop the rationale for the MRC approach by describing the multilayered imaging model in light of a rate-distortion trade-off. Results will be presented comparing images compressed using MRC, JPEG and state-of-the-art wavelet algorithms such as SPIHT. MRC has been approved or proposed as an architectural model for several standards, including ITU Color Fax, IETF Internet Fax, and JPEG 2000.

/pdf/mixed-raster-content-mrc-model-for-compound-image-52tshejwfr.pdf

Mixed raster content (MRC) model for compound image compression

A method for processing compressed digital data derived from an original image sequence, the data being organized as a set of image frames, each image frame comprising a set of blocks, each block including a string of bits corresponding to an area of the original image frame in the original image sequence. A cost function is derived as a number related to the amount of bits spent to encode a block, sets of blocks, an image frame, or sets of image frames. A segmentation technique is applied to the map with cost functions. Temporal segmentation is performed by analyzing cost functions associated with each image frame. In both cases auxiliary functions can be used to improve the segmentation quality. The segmented regions of a image frame or sets of image frames can be identified, replaced, printed, or processed in special manners.

Using encoding cost data for segmentation of compressed image sequences

A mixed raster content system and method improves the performance of the compression process by reducing the amount of data. The system and method uses a selector plane as a reference to aid in reducing the amount of data necessary to encode each of associated planes. A smoothing technique is used to pre-process the associated planes using the information contained in the selector plane, thereby reducing the amount of data that will be subjected to further processing. The system and method determines the useless data, smooths the boundary of the useless data, and replaces the useless data by values that improve compression.

Ricardo L. de Queiroz

Papers

Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform

Motion-Compensated Compression of Dynamic Voxelized Point Clouds

Mixed raster content (MRC) model for compound image compression

Using encoding cost data for segmentation of compressed image sequences

Generic pre-processing of mixed raster content planes