A Rank-Deficient and Sparse Penalized Optimization Model for Compressive Indoor Radar Target Localization

This paper proposes a rank-deficient and sparse penalized optimization method for addressing the problem of through-wall radar imaging (TWRI) in the presence of structured wall clutter. Compressive TWRI enables fast data collection and accurate target localization, but faces with the challenges of incomplete data measurements and strong wall clutter. This paper handles these challenges by formulating the task of wall-clutter removal and target image reconstruction as a joint low-rank and sparse regularized minimization problem. In this problem,  the low-rank regularization is used to capture the low-dimensional structure of the wall signals and the sparse penalty is employed to represent the image of the indoor targets. We introduce an iterative algorithm based on the forward-backward proximal gradient technique to solve the large-scale optimization problem, which simultaneously removes unwanted wall clutter and reconstruct an image of indoor targets. Simulated and real radar data are used to validate the effectiveness of the proposed rank-deficient and sparse regularized optimization approach

Generalized Discernibility Function Based Attribute Reduction in Incomplete Decision Systems

A rough set approach for attribute reduction is an important research subject in data mining and machine learning. However, most attribute reduction methods are performed on a complete decision system table. In this paper, we propose methods for attribute reduction in static incomplete decision systems and dynamic incomplete decision systems with dynamically-increasing and decreasing conditional attributes. Our methods use generalized discernibility matrix and function in tolerance-based rough sets

SOME DEFINITIONS FOR CONVOLUTIONS AND THE CONVOLUTIONS FOR THE FOURIER TRANSFORMS WITH GEOMETRIC VARIABLES

Joint Research on Environmental Science and Technology for the Eart

Study the appropriate conditions to obtain germinated brown rice with high biological activity

ABSTRACT – QMFS 2019Germinated brown rice strains contain more bioactive substances than germinated regular rice ones, however germination conditions play an important role in the activity and the content of those substances. The proper germination process provides the optimized active ingredients from rice that can be used for the production of nutritious beverages. In this study, we investigated the effects of pH, temperature and incubation time in microaerobic culture condition on the change of bioactive substances in AnhDao brown rice. The optimal germination condition with pH at 3, temperature of 35 0C and time for 36h release 109.11U/g of the α-amylase activity, 17.22(U/g) of the enzyme glutamate decacboxylase (GAD), 1.38(U/g) of protease, 231.76mg/100g of GABA content and 21.9 (mgGAE/100g) of polyphenol from germinated AnhDao brown rice. In nutrient evaluation, germinated AnhDao brown rice contains 65.53% of starch, 2.49% of lipid, 9.13%of protein, 2.04% of reducing sugar, and 1.26% of ash.Key words: Germinated brown rice, bioactive substances, α-amylase, protease, glutamate decacboxylase (GAD), Gamma aminobutyric acid (GABA)

The application of split step fourier migration to interpreting GPR data in Vietnam

Migration methods play an essential role in processing ground penetrating radar data. For estimating electromagnetic propagation velocity distribution, the finite - difference migration is used because of its reliable performance with high noise conditions. To optimize this migration algorithm, we propose using energy diagram as a criterion of looking for the correct velocity. If the velocity varies laterally and vertically, split step Fourier migration is used for creating a true image of subsurface structures. We applied these steps to real data in Vietnam. The results verified on field data show that migrated images with calculated velocity from energy diagram have the best quality

Quantum Gauss Jordan Elimination

In this paper we construct the Quantum Gau\ss Jordan Elimination (QGJE) Algorithm and estimate the complexity time of computation of Reduced Row Echelon Form (RREF) of an $N\times N$ matrix using QGJE procedure. The main theorem asserts that QGJE has computation time of order $2^{N/2}$

On solutions of integral equations with reflections

Приводится алгебраический метод решения сингулярных интегральных уравнений в замкнутой форме

A Method to Construct an Extension of Fuzzy Information Granularity Based on Fuzzy Distance

In fuzzy granular computing, a fuzzy granular structure is the collection of fuzzy information granules and fuzzy information granularity is used to measure the granulation degree of a fuzzy granular structure. In general, the fuzzy information granularity characterizes discernibility ability among fuzzy information granules in a fuzzy granular structure. In recent years, researchers have proposed some concepts of fuzzy information granularity based on partial order relations. However, the existing forms of fuzzy information granularity have some limitations when evaluating the fineness/coarseness between two fuzzy granular structures. In this paper, we propose an extension of fuzzy information granularity based on a fuzzy distance measure. We prove theoretically and experimentally that the proposed fuzzy information granularity is the best one to distinguish fuzzy granular structures. ACM Computing Classification System (1998): I.5.2, I.2.6

KINETIC STUDY OF SYNTHESIS REACTION OF LIGNOSULFONATE USING ISOTHERMAL DIFFERENTIAL SCANNING CALORIMETRY METHOD

The kinetics of lignin methylsulfonation were studied in solution by using differential scanning calorimetry (DSC) techniques under an isothermal program, at 55, 65, 75 and 85°C, respectively. It was found that activation energy, Eα =  41.26 kJ/mol, and preexponential factor A was 1.85×103 s-1