CS Framework Research Articles

Mathematics Approaches in Compressed Sensing

Mathematical approaches refer to make quantitative descriptions, deductions and calculations through the use of mathematics concepts, approaches and techniques, then draw some new conclusions and foresee by mathematical analysis and judgment. In recent years, Compressed Sensing theory (CS) provides solutions in alleviating the huge amount of information demand in the pressure of signal sampling, transmission and storage pressure. It is a novel signal sampling theory under the condition that the signal is compressible or sparse.In this case, the signal can be reconstructed accurately from the small amount of signal values if the signal is sparse or compressible. This paper introduces the CS theory framework and key technical issues, and focus on the analysis of the application of mathematical approaches in three aspects of the signal sparse representation, signal sparse transformation and reconstruction.In the end, Some mathematics problems of compressed sensing to solve are given and further development is pointed out. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.3303

Investigation of a Special Education Program in a Public Cyber Charter School

For students with disabilities who are at risk of leaving high school without graduating, virtual schools have become a popular alternative to traditional schools. One promising approach to increasing graduation rates is to design learning environments that serve students with disabilities through the 5 Cs framework designed to increase school completion: Connect, Climate, Control, Curriculum, and Caring Community. A utilization-focused evaluation guided our investigation to determine the presence of and application of the 5 Cs in a special education program in a public cyber charter school. Specific examples are provided, and recommendations for practice and future research are discussed.

Compressive Sampling based Single-Image Super-resolution Reconstruction by dual-sparsity and Non-local Similarity Regularizer

Recent development on Compressive Sampling (or compressive sensing, CS) theory suggests that High-Resolution (HR) images can be correctly recovered from their Low-Resolution (LR) version under mild conditions. Inspired by it, we proposed a CS based Single-Image Super-resolution Reconstruction (SISR) framework that exploits the dual-sparsity and non-local similarity constraints of images. This new framework relies on the idea that LR image patch can be regarded as the compressive measurement of its corresponding HR patch, and a sufficiently sparse coding of HR patch under some dictionary will make an accurate recovery of HR patch from its measurement possible. In order to adaptively tune the dictionary that can well represents the underlying HR patches, we reduce the SISR to a dual-sparsity constrained optimization problem with dual variables. Moreover, the pixel based recovery is incorporated as another regularization term to exploit the image non-local similarities, which is very helpful in preserving edge sharpness. The optimization is implemented in a patch-pixel-collaboration and iterative manner, via the Singular Value Decomposition (SVD) and Orthogonal Matching Pursuit (OMP) algorithm. Experiments are taken on some natural images, remote sensing images and medical images, and the results show that our proposed method can not only provide one possible way of recovering HR image under the CS framework, but also generate HR images that are competitive or even superior in quality to images produced by other similar SISR methods.

Planning a CIS education within a CS framework

The Computer Science Department at University of Mary Washington revised its curriculum to include a Computer Information System (CIS) concentration to increase the number of computer science major...

Prediction of halocarbon mixture thermodynamics using an innovative gE-EoS mixing rule in a three-parameter CS framework

Historical models with gE-EoS mixing rules, which combine a cubic equation of state (EoS) requiring only the three parameters Tc, Pc and ω as individual inputs with a gE model for the liquid phase, are only applied to the saturation surfaces. A limitation of this rule is basically the number of parameters of the one-fluid EoS considered, which cannot exceed 2 or 3.By integrating an improved Teja corresponding states EoS (T), which requires the same individual parameters as a cubic equation, in a gE-EoS mixing rule framework, a new technique is obtained with two modes, one correlative and one predictive.In the correlative mode, the liquid phase γ is generated from input VLE data using the improved T model and the historical Wong–Sandler–Teja mixing rules for the pseudocritical functions, except for Tcmix. In the predictive mode, the same general procedure is followed, but with the liquid phase γ coming from a UNIFAC gLE model, which makes the mixing rule predictive.The Tcmix values are locally generated from these new rules and are correlated as an individual function, which in both cases presents a very smooth trend for the systems studied. The two proposed rules are applied to systems of halogenated alkanes, which are known to be polar and deviating, and are compared with the dedicated EoS available for these mixtures. The results for both modes show an interesting level of accuracy in representing the whole thermodynamic behaviour of a mixture.