Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain. Relying on the sparsity of the signals, CS allows us to sample the signal at a rate much below the Nyquist sampling rate. Also, the varied reconstruction algorithms of CS can faithfully reconstruct the original signal back from fewer compressive measurements. This fact has stimulated research interest toward the use of CS in several fields, such as magnetic resonance imaging, high-speed video acquisition, and ultrawideband communication. This paper reviews the basic theoretical concepts underlying CS. To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper. The major application areas where CS is currently being used are reviewed here. This paper also highlights some of the challenges and research directions in this field.

A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications

Most image encryption algorithms based on low-dimensional chaos systems bear security risks and suffer encryption data expansion when adopting nonlinear transformation directly. To overcome these weaknesses and reduce the possible transmission burden, an efficient image compression–encryption scheme based on hyper-chaotic system and 2D compressive sensing is proposed. The original image is measured by the measurement matrices in two directions to achieve compression and encryption simultaneously, and then the resulting image is re-encrypted by the cycle shift operation controlled by a hyper-chaotic system. Cycle shift operation can change the values of the pixels efficiently. The proposed cryptosystem decreases the volume of data to be transmitted and simplifies the keys distribution simultaneously as a nonlinear encryption system. Simulation results verify the validity and the reliability of the proposed algorithm with acceptable compression and security performance.

Image compression–encryption scheme based on hyper-chaotic system and 2D compressive sensing

The existing ways to encrypt images based on compressive sensing usually treat the whole measurement matrix as the key, which renders the key too large to distribute and memorize or store. To solve this problem, a new image compression–encryption hybrid algorithm is proposed to realize compression and encryption simultaneously, where the key is easily distributed, stored or memorized. The input image is divided into 4 blocks to compress and encrypt, then the pixels of the two adjacent blocks are exchanged randomly by random matrices. The measurement matrices in compressive sensing are constructed by utilizing the circulant matrices and controlling the original row vectors of the circulant matrices with logistic map. And the random matrices used in random pixel exchanging are bound with the measurement matrices. Simulation results verify the effectiveness, security of the proposed algorithm and the acceptable compression performance.

Novel image compression–encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing

In this paper, compressive sensing is combined with a chaotic key based generation of measurement matrix to provide an effective encryption algorithm for multimedia security. Block-based compressive sensing provides a better way in the field of image and video transmission by reducing the memory requirements and complexity, where as multiple hypothesis prediction provides a competent way in improving PSNR during reconstruction of block based compressive sensed images and videos. The measurement matrix Φ place a crucial role in this compressive sensing and as well as in the reconstruction process. A possibility to generate secure measurement matrix using piecewise linear chaotic map (PWLCM) as the seed and then hiding initial condition, system parameter, number of iterations of PWLCM as the key enable the sender to incorporate room for encryption along with the compression in a single step. The above mentioned scheme provides high level of data security, reduced complexity, compression with a good reconstruction quality and beside all it reduce the burden of sending the measurement matrix along with the data which further reduces the complexity in over all compressive sensing framework.

PWLCM based image encryption through compressive sensing

Single image super resolution using neighbor embedding and statistical prediction model

Integrity verification is verifying the originality of an object. A number of robust and secure image hashing methods are existing in literature, which are suited for integrity verification of images. However, a complete system for integrity verification which clearly distinguishes between acceptable and malicious manipulations is still in research. This paper presents a complete system for integrity verification of images using, an image hashing method based on Fast Johnson Linden Strauss Transform (FJLT) and a DCT based blind watermarking method. The proposed method is robust against JPEG compression of quality level more than 60 and is able to distinguish from all other possible manipulations.

An Integrity Verification System for Images Using Hashing and Watermarking

In this paper, entropy of singular values is used to promote the low rank property which accounts the inherent spatial redundancy and spectral correlation. Inspired from the connection between randomness and compactness of signals, we minimise ENtropy for low rank Tensor Completion (ENTC) using tensor-Singular Value Decomposition (t-SVD) framework. This approach assures better performance in excessive loss and complex low rank structure scenarios. As opposed to state-of- the-art-methods, the proposed approach outperforms with a fewer number of reliable samples and extends the region of recovery. The significant performance improvement is observed in terms of Inverse Relative Squared Error (iRSE), Average Structural SIMilarity (ASSIM), and Average Feature SIMilarity (AFSIM) measures.

Entropy-Based Reweighted Tensor Completion Technique for Video Recovery

Chaos based encryption techniques have been gaining immense attraction for the past few years with a view of enhancing security coupled with reduction in computational complexity. This paper explores the drawbacks of chaotic key based algorithm (CKBA) which was primarily designed for monochrome images. We put forth an improved chaotic key based image encryption algorithm for monochrome and color images. The new algorithm was subjected to the known forms of attack and has been found resistant to the same

Sudhish N. George

Papers

PWLCM based image encryption through compressive sensing

Single image super resolution using neighbor embedding and statistical prediction model

An Integrity Verification System for Images Using Hashing and Watermarking

Entropy-Based Reweighted Tensor Completion Technique for Video Recovery

An expeditious chaos based digital image encryption algorithm