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」

IEEE Transactions on Pattern Analysis and Machine Intelligence

Communications of the ACM (CACM for short, not the best sounding acronym around) is the ACM’s flagship magazine. Started in 1957, CACM is handy for keeping up to date on current research being carried out across all topics of computer science and realworld applications. CACM has had an illustrious past with many influential pieces of work and debates started within its pages. These include Hoare’s presentation of the Quicksort algorithm; Rivest, Shamir and Adleman’s description of the first publickey cryptosystem RSA; and Dijkstra’s famous letter against the use of GOTO. In addition to the print edition, which is released monthly, there is a fantastic website (http://cacm.acm. org/) that showcases not only the most recent edition but all previous CACM articles as well, readable online as well as downloadable as a PDF. In addition, the website lets you browse for articles by subject, a handy feature if you want to focus on a particular topic. CACM is really essential reading. Pretty much guaranteed to contain content that is interesting to anyone, it keeps tabs on the latest in computer science. It is a valuable asset for us students, who tend to delve deep into a particular area of CS and forget everything that is happening around us. — Daniel Gooch U ndergraduate research is like a box of chocolates: You never know what kind of project you will get. That being said, there are still a few things you should know to get the most out of the experience.

Communications of the ACM

From the Publisher:
Real-Time Systems is both a valuable reference for professionals and an advanced text for Computer Science and Computer Engineering students. 

Real world real-time applications based on research and practice

State-of-the-art algorithms and methods for validation

Methods for end-to-end scheduling and resource management

More than 100 illustrations to enhance understanding

Comprehensive treatment of the technology known as RMA (rate-monotonic analysis) methods

A supplemental Companion Website www.prenhall.com/liu

http://user.it.uu.se/~yi/courses/rts/dvp-rts-08/notes/Course-INFO.pdf

Real-Time Systems

Digital Signature Standard (DSS) [includes Change Notice 1 from 12/30/1996] | NIST

Edge preserving filters preserve the edges and its information while blurring an image In other words they are used to smooth an image, while reducing the edge blurring effects across the edge like halos, phantom etc They are nonlinear in nature Examples are bilateral filter, anisotropic diffusion filter, guided filter, trilateral filter etc Hence these family of filters are very useful in reducing the noise in an image making it very demanding in computer vision and computational photography applications like denoising, video abstraction, demosaicing, optical-flow estimation, stereo matching, tone mapping, style transfer, relighting etc This paper provides a concrete introduction to edge preserving filters starting from the heat diffusion equation in olden to recent eras, an overview of its numerous applications, as well as mathematical analysis, various efficient and optimized ways of implementation and their interrelationships, keeping focus on preserving the boundaries, spikes and canyons in presence of noise Furthermore it provides a realistic notion for efficient implementation with a research scope for hardware realization for further acceleration

A Brief Survey of Recent Edge-Preserving Smoothing Algorithms on Digital Images.

In this article, a new methodology for denoising of Rician noise in Magnetic Resonance Images (MRI) is presented. MRI imaging creates a distinctive view into the interior of a human body and has become an essential tool of clinical diagnosis. However, Rician noise is a type of artifact inherent to the acquisition process of the magnitude MRI image, making diagnosis difficult. We proposed a moment-based Rician noise reduction technique in anisotropic diffusion filtering. We extend the work of the classical anisotropic diffusion filter and have customized it to remove Rician noise in the magnitude MRI image in 3D domain space. Our proposed scheme shows better results against various quality measures in terms of noise removal and edge preservation while retaining fine textures.

Rician noise removal in magnitude MRI images using efficient anisotropic diffusion filtering

Quantum machine learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. It generally exposes the synthesis of important machine learning algorithms in a quantum framework. Dimensionality reduction of a dataset with a suitable feature selection strategy is one of the most important tasks in knowledge discovery and data mining. The efficient feature selection strategy helps to improve the overall accuracy of a large dataset in terms of machine learning operations. In this paper, a quantum feature selection algorithm using a graph-theoretic approach has been proposed. The proposed algorithm has used the concept of correlation coefficient based graph-theoretic classical approach initially and then applied the quantum Oracle with CNOT operation to verify whether the dataset is suitable for dimensionality reduction or not. If it is suitable, then our algorithm can efficiently estimate their high correlation values by using quantum parallel amplitude estimation and amplitude amplification techniques. This paper also shows that our proposed algorithm substantially outperforms than some popular classical feature selection algorithms for supervised classification in terms of query complexity of $O(\frac {k\sqrt {N_{c}^{(k)}N_{f}^{(k)}}}{\epsilon })$, where N is the size of the feature vectors whose values are ⩾ THmin(minimum threshold), k is the number of iterations and where 𝜖 is the error for estimating those feature vectors. Compared with the classical counterpart, i.e. the performance of our quantum algorithm quadratically improves than others.

A hybrid quantum feature selection algorithm using a quantum inspired graph theoretic approach

The amalgamation of ‘Quantum computing’ with image processing represents the various ways of handling images for different purposes. In this paper,an image denoising scheme based on quantum wavelet transform is proposed.A noisy image is embedded into the wavelet coefficients of the original image. As a result,it affects the visual quality of the original image. The quantum Daubechis kernel of 4th order is used to extract wavelet coefficients from the resultant image. Then a quantum oracle is implemented with a suitable thresholding function to decompose the wavelet coefficients into a greater effect applicable for the original image and lower effect for the noisy image wavelet coefficients. However,original image wavelet coefficients are greater than the noisy wavelet coefficients.A detail computational time complexity analysis is given and compared with some state-of-art denoising techniques. The result analysis shows that the proposed quantum image denoising technique has better visual quality in terms of PSNR,MSE and QIFM values Compare to others.

An Image Denoising Technique using Quantum Wavelet Transform

Digital image processing is an exciting area of research with a variety of applications including medical, surveillance security systems, defence, and space applications. Noise removal as a preprocessing step helps to improve the performance of the signal processing algorithms, thereby enhancing image quality. Anisotropic diffusion filtering proposed by Perona and Malik can be used as an edge-preserving smoother, removing high-frequency components of images without blurring their edges. In this paper, we present the FPGA implementation of an edge-preserving anisotropic diffusion filter for digital images. The designed architecture completely replaced the convolution operation and implemented the same using simple arithmetic subtraction of the neighboring intensities within a kernel, preceded by multiple operations in parallel within the kernel. To improve the image reconstruction quality, the diffusion coefficient parameter, responsible for controlling the filtering process, has been properly analyzed. Its signal behavior has been studied by subsequently scaling and differentiating the signal. The hardware implementation of the proposed design shows better performance in terms of reconstruction quality and accelerated performance with respect to its software implementation. It also reduces computation, power consumption, and resource utilization with respect to other related works.

/pdf/an-efficient-fpga-implementation-of-optimized-anisotropic-hpzv13wjyg.pdf

Ranjan Ghosh

Papers

A Brief Survey of Recent Edge-Preserving Smoothing Algorithms on Digital Images.

Rician noise removal in magnitude MRI images using efficient anisotropic diffusion filtering

A hybrid quantum feature selection algorithm using a quantum inspired graph theoretic approach

An Image Denoising Technique using Quantum Wavelet Transform

An Efficient FPGA Implementation of Optimized Anisotropic Diffusion Filtering of Images