Small Amount Of Measurements Research Articles

Compressed-Sensing-based Gradient Reconstruction for Ghost Imaging

In this paper, we propose a compression sensing ghost imaging algorithm to reduce the computation time with high image quality via compression sensing based on the total variation reconstruction. A small amount of measurements can be used for shortening the sampling time. The total variation is used as criteria during the search process. It makes the ghost image achieving a high image reconstruction quality. The simulation results demonstrate that the proposed method can enhance imaging quality with the reduced computation time.

INFLUENCE OF STOCHASTIC CHARACTERISTICS OF PROPERTIES FOR MATERIALS, PRODUCTS AND PROCESSES ON EVALUATION OF REGULATORY PARAMETERS

For the last one hundred years development of mathematical statistics has reached rather high level. A universal relative and non-dimensional factor which is known as a coefficient of variation is widely used for practical calculations, analysis and comparison of various stochastic processes. A variety of methodologies for evaluation of possible variations in definienda is used in case when a small amount of measurements (less that 30) is made in order to study the required parameter. While doing that a number of measurements executed with the help of these regularities can be significantly increased that permits reliably (correctly) to assess numerical characterristics of the investigated parameters. However according to GOST (All Union State Standard) 21153.2–84 some corrections in methodology for determination of minimum required number of measurements (less 30) have appeared to be insignificant for obtaining a reliable safety, for example, in the field of civil construction (catastrophic accidents: in Moscow – Transvaal Park and Basmanny Market; assembly and other constructions: in Poland, France and others). In 2007 it has been proposed to use chi-square criterion for determination of the required number of measurements when their number is less than 30 and appropriate patents of Eurasian Patent Organization and the Republic of Belarus have been obtained for the proposal. For this purpose Brownlie’s proposal has been used to assess confidence limits of dispersion values with the help of the chi-square criterion and the proposal has been supported by A.N. Kolmogorov who is an originator of exact mathematical probability calculus. The paper presents results of calculations for the required number of measurements in accordance with four methodologies and comparative analysis of the obtained results. It has appeared that in case when we are using chi-square criterion for the reliability level of 0.9 or even more it is necessary to make the largest number of measurements (determinations). While increasing variation coefficient and predicted reliability level the number of the required measurements is also increased. An effort has been made to apply a robust methodology, which does not depend on the form of a distribution law, for determination of minimum number of the required measurements. Having multi-year experience on practical usage of stochastic characteristics for various parameters classification of non-stability has been proposed on the basis of variation coefficient values.

Compressed Sensing Precoding with Limited Feedback in Multi-carrier MIMO System

Because of the large numbers of feedback in the multiple input & multiple output (MIMO) multi-carrier system, the precoding based on full channel state information is difficult to be applied in practical. In order to significantly reduce the overhead of the feedback link, a compressed sensing precoding method with limited feedback in MIMO multi-carrier system is proposed in this paper. First, the channel matrix which results from the channel estimation is observed by compressed sensing measurement matrix at the receiver, only a small amount of measurements are fed back to the transmitter. Second, the channel matrix is reconstructed through orthogonal matching pursuit algorithm at the transmitter, and the transmitted signals are precoded using the reconstructed channel state information. Simulation results show that the proposed method can accurately reconstructed the channel state information using only a small amount of feedback data and can achieve comparable bit error rate performance with traditional precoding methods.

Surveillance video processing using compressive sensing

A compressive sensing method combined with decomposition of a matrix formed with image frames of a surveillance video into low rank and sparse matrices is proposed to segment the background and extract moving objects in a surveillance video. The video is acquired by compressive measurements, and the measurements are used to reconstruct the video by a low rank and sparse decomposition of matrix. The low rank component represents the background, and the sparse component is used to identify moving objects in the surveillance video. The decomposition is performed by an augmented Lagrangian alternating direction method. Experiments are carried out to demonstrate that moving objects can be reliably extracted with a small amount of measurements.

Dimensionality reduction via compressive sensing

Compressive sensing is an emerging field predicated upon the fact that, if a signal has a sparse representation in some basis, then it can be almost exactly reconstructed from very few random measurements. Many signals and natural images, for example under the wavelet basis, have very sparse representations, thus those signals and images can be recovered from a small amount of measurements with very high accuracy. This paper is concerned with the dimensionality reduction problem based on the compressive assumptions. We propose novel unsupervised and semi-supervised dimensionality reduction algorithms by exploiting sparse data representations. The experiments show that the proposed approaches outperform state-of-the-art dimensionality reduction methods.

Effects of linear projections on the performance of target detection and classification in hyperspectral imagery

We explore the use of several linear dimensionality reduction techniques that can be easily integrated into the hyperspectral imaging sensor. We investigate their effect on the performance of classical target detection and classification techniques for hyperspectral images. Specifically, each N -dimensional spectral pixel is embedded to an M -dimensional measurement space with M ⪡ N by a linear transformation (e.g., random measurement matrices, uniform downsampling, principal component analysis). The detectors/classifiers are then applied to the M -dimensional measurement vectors and their performances are compared to those obtained from the entire N -dimensional spectrum. Through extensive experiments on several hyperspectral imagery data sets, we demonstrate that only a small amount of measurements are necessary to achieve comparable performance to that obtained by exploiting the full N -dimensional pixels.

Region-based reconstruction method for fluorescent molecular tomography

A common difficulty for the traditional methods of fluorescent molecular tomographic (FMT) reconstruction is that only a small amount of measurements can be used to recover the image comprised of a large number of pixels. This difficulty not only leads to expensive computational cost but also likely results in an unstable solution prone to be affected by the noise in the measurement data. In this paper, we propose a region-based method for reducing the unknowns, where the target areas are determined by searching for the nearest neighbor nodes. In this method, the Hessian matrix of the second-order derivatives is incorporated to speed up the optimization process. An iteration strategy of multi-wavelength measurement is introduced to further improve the accuracy of inverse solutions. Simulation results demonstrate that the proposed approach can significantly speed up the reconstruction process and improve the image quality of FMT.

Control over the vulcanization of rubber

On the basis of investigation of dielectric characteristics, we establish that, as the number of cross links in polar-aaoutchouc-based rubbers increases, the scattering of the charge carrier on inhomogeneities of the structure of a material also increases, and, correspondingly, the dispersion of conductivity decreases. We propose a nondestructive method for the evaluation of the level of vulcanization based on the correlation between the frequency dependence of conductivity of polar rubbers and the degree of their cross linking; unlike the existing methods, the proposed method requires a small amount of measurements and calculations.