Numerical Studies of the Generalized \u3cem\u3el\u3c/em\u3e₁ Greedy Algorithm for Sparse Signals

The generalized l1 greedy algorithm was recently introduced and used to reconstruct medical images in computerized tomography in the compressed sensing framework via total variation minimization. Experimental results showed that this algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in reconstructing these medical images. In this paper the effectiveness of the generalized l1 greedy algorithm in finding random sparse signals from underdetermined linear systems is investigated. A series of numerical experiments demonstrate that the generalized l1 greedy algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in the successful recovery of randomly generated Gaussian sparse signals from data generated by Gaussian random matrices. In particular, the generalized l1 greedy algorithm performs extraordinarily well in recovering random sparse signals with nonzero small entries. The stability of the generalized l1 greedy algorithm with respect to its parameters and the impact of noise on the recovery of Gaussian sparse signals are also studied

Two Regularization Models for Computed Tomography Image Reconstruction from Limited Projection Data

Computed tomography (CT) has been widely applied in medical imaging and industry for over decades. CT reconstruction from limited projection data is of particular importance. The total variation or l1-norm regularization has been widely used for image reconstruction in computed tomography (CT). Images in computed tomography (CT) are mostly piece-wise constant so the gradient images are considered as sparse images. The l0-norm of the gradients of an image provides a measurement of the sparsity of gradients of the image. However, the l0-norm regularization problem is NP hard. In this talk, we present two new models for CT image reconstruction from limited-angle projections. In one model we propose the smoothed l0-norm and l1-norm regularization using the nonmonotone alternating direction algorithm. In the other model we propose a combined l1-norm and l0-norm regularization model for better edge preserving

The Convergence of Two Algorithms for Compressed Sensing Based Tomography

The constrained total variation minimization has been developed successfully for image reconstruction in computed tomography. In this paper, the block component averaging and diagonally-relaxed orthogonal projection methods are proposed to incorporate with the total variation minimization in the compressed sensing framework. The convergence of the algorithms under a certain condition is derived. Examples are given to illustrate their convergence behavior and noise performance

An Area Based Fan Beam Projection Model

Area based projection models for computed tomography mitigate raw data errors by treating X-Rays as beams, whereas traditional line based projection models treat an X-Ray like a line, thus generating significant error. In an existing area based fan beam projection model, a rotation matrix, Q, simulates the rotation of the emitter detector pair to reduce computational load, but this introduces approximations by using an approximated rotation matrix. We eliminate approximations by deriving an exact formula for the entries of Q. Using a rotation of axes and by considering the neighboring cells\u27 contributions to the area, the result has formulations for the exact calculation of the matrix Q. Thus, approximations are phased out, and error in projection data is minimized for image reconstruction

Media Literasi: Upaya Bijak Menyikapi Terpaan Tayangan Televisi

The television media have transformed into industry. Tight competition among TV stations demands the media people to provide programs based on the market taste. Therefore, mostly TV stations design and produce their programs based on share and rating numbers, instead of quality. On the other side, TV stations have important roles in constructing social and cultural development. Currently, TV programs are merely produced based on the business orientation so that the quality of the TV programs is often ignored. Audience must be wise and smart to protect themselves from poor-quality TV programs exposure. This can be achieved by improving their Media Literacy. In the end, Audience is no longer treated as passive object, but actively takes control on the content selection

A novel of new class II bacteriocin from Bacillus velezensis HN-Q-8 and its antibacterial activity on Streptomyces scabies

Potato common scab is a main soil-borne disease of potato that can significantly reduce its quality. At present, it is still a challenge to control potato common scab in the field. To address this problem, the 972 family lactococcin (Lcn972) was screened from Bacillus velezensis HN-Q-8 in this study, and an Escherichia coli overexpression system was used to obtain Lcn972, which showed a significant inhibitory effect on Streptomyces scabies, with a minimum inhibitory concentration of 10.58 μg/mL. The stability test showed that Lcn972 is stable against UV radiation and high temperature. In addition, long-term storage at room temperature and 4°C had limited effects on its activity level. The antibacterial activity of Lcn972 was enhanced by Cu2+ and Ca2+, but decreased by protease K. The protein was completely inactivated by Fe2+. Cell membrane staining showed that Lcn972 damaged the cell membrane integrity of S. scabies. Scanning electron microscope (SEM) and transmission electron microscope (TEM) observations revealed that the hyphae of S. scabies treated with Lcn972 were deformed and adhered, the cell membrane was incomplete, the cytoplasm distribution was uneven, and the cell appeared hollow inside, which led to the death of S. scabies. In conclusion, we used bacteriocin for controlling potato common scab for the first time in this study, and it provides theoretical support for the further application of bacteriocin in the control of plant diseases

Analysis on the Strip-Based Projection Model for Discrete Tomography

This talk was given to the Sun Yat-sen University School of Mathematics and Computational Science

The Block Diagonally-Relaxed Orthogonal Projection Algorithm for Compressed Sensing Based Tomography

The theory of compressed sensing has recently shown that signals and images that have sparse representations in some orthonormal basis can be reconstructed from much less data, at high quality, than what the Nyquist sampling theory requires. In this talk, we will introduce a block diagonally-relaxed orthogonal projection algorithm for computed tomography image reconstruction in the compressed sensing framework and derive its convergence

The Block DROP and CAV Algorithms for Compressed Sensing Computing Tomography

In this talk, we will introduce a block diagonally-relaxed orthogonal projection algorithm and a block component averaging algorithm for computed tomography image reconstruction in the compressed sensing framework and derive the convergence. Numerical experiments are shown to illustrate the convergence of the new algorithms