Sampled Pattern Matching Research Articles

Research of electrical impedance tomography based on multilayer artificial neural network optimized by Hadamard product for human-chest models

Electrical impedance tomography (EIT) is a non-radiation, non-invasive visual diagnostic technique. In order to improve the imaging resolution and the removing artifacts capability of the reconstruction algorithms for electrical impedance imaging in human-chest models, the HMANN algorithm was proposed using the Hadamard product to optimize multilayer artificial neural networks (MANN). The reconstructed images of the HMANN algorithm were compared with those of the generalized vector sampled pattern matching (GVSPM) algorithm, truncated singular value decomposition (TSVD) algorithm, backpropagation (BP) neural network algorithm, and traditional MANN algorithm. The simulation results showed that the correlation coefficient of the reconstructed images obtained by the HMANN algorithm was increased by 17.30% in the circular cross-section models compared with the MANN algorithm. It was increased by 13.98% in the lung cross-section models. In the lung cross-section models, some of the correlation coefficients obtained by the HMANN algorithm would decrease. Nevertheless, the HMANN algorithm retained the image information of the MANN algorithm in all models, and the HMANN algorithm had fewer artifacts in the reconstructed images. The distinguishability between the objects and the background was better compared with the traditional MANN algorithm. The algorithm could improve the correlation coefficient of the reconstructed images, and effectively remove the artifacts, which provides a new direction to effectively improve the quality of the reconstructed images for EIT.

An Improved Algorithm GVSPM-F for Electrical Impedance Tomography

The generalized vector sampled pattern matching (GVSPM) algorithm is widely utilized in the EIT (electrical impedance tomography) reconstruction to solve the ill-posed inverse problem. An improved algorithm, which is called the generalized vector sampled pattern matching-fast (GVSPM-F), is proposed to improve the spatial resolution and reduce the iteration time based on the conventional GVSPM. The GVSPM merely applied the orthogonal projections to approximate the weights in the coordinate directions. The iteration of the proposed GVSPM-F algorithm is calculated in the projection direction of the space constructed by nonlinear correlated column vectors in the column space of the original sensitive matrix. Hence, the proposed GVSPM-F algorithm could achieve stable convergence without an empirical value to preserve the shape information and reduce the time consumption of GVSPM. In the experimental results, GVSPM-F is compared with the traditional GVSPM method in terms of voltage error, iteration time, and image error. The voltage error decreases by approximately 35%, and the number of iteration decreases from thousands to fewer than 100. The image error of GVSPM-F is 6% less than that of GVSPM. The proposed GVSPM-F algorithm is confirmed to be effective for the reconstruction of EIT images.

Simulation Studies on Image Reconstruction Algorithm for Portable Electrical Capacitance Tomography

Electrical capacitance tomography (ECT) is a non-invasive and non-intrusive internal visualization tomographic modality which promising a better quantification by providing cross-sectional distribution information of any kind of multiphase flow. These studies aim to investigate the non-iterative and iterative image reconstruction for 16 channels of portable electrical capacitance tomography (ECT) through simulation work. Linear Back Projection (LBP) as a non-iterative algorithm was compared with the Landweber and generalized vector sampled pattern matching (GVSPM) iterative algorithm for four cases which are core flow, annular flow, stratified flow and two circles by evaluation of image error and image correlation. The results show Landweber algorithms produces lowest percentage error, close to phantom as higher correlation coefficient and acceptable elapsed time. Iterative image reconstruction will produce accurate results by eliminating the artifacts near actual object and enhance the capability of segmented ECT in multiphase flow identification.

Image Reconstruction Under Contact Impedance Effect in Micro Electrical Impedance Tomography Sensors.

Contact impedance has an important effect on micro electrical impedance tomography (EIT) sensors compared to conventional macro sensors. In the present work, a complex contact impedance effect ratio ξ is defined to quantitatively evaluate the effect of the contact impedance on the accuracy of the reconstructed images by micro EIT. Quality of the reconstructed image under various ξ is estimated by the phantom simulation to find the optimum algorithm. The generalized vector sampled pattern matching (GVSPM) method reveals the best image quality and the best tolerance to ξ. Moreover, the images of yeast cells sedimentary distribution in a multilayered microchannel are reconstructed by the GVSPM method under various mean magnitudes of contact impedance effect ratio |ξ|. The result shows that the best image quality that has the smallest voltage error UE = 0.581 is achieved with measurement frequency f = 1 MHz and mean magnitude |ξ| = 26. In addition, the reconstructed images of cells distribution become improper while f < 10 kHz and mean value of |ξ| > 2400.

A review of image reconstruction methods in electrical capacitance tomography

In this paper, we review image reconstruction methods and their suitability in electrical capacitance tomography measurement system. These methods can be grouped into direct and iterative methods. Direct methods include Linear back projection, Singular value decomposition, and Tikhonov regularization. Iterative methods are further divided into algebraic and optimization methods. Algebraic reconstruction methods include iterative linear back projection, iterative Tikhonov, Landweber iteration, simultaneously algebraic reconstruction, and model$ - $based reconstruction. Optimization methods include fuzzy mathematical modeling, genetic algorithms, artificial neural networks, generalized vector sampled pattern matching, total variation regularization, regularized total least squares, extended Tikhonov regularization, simulated annealing, compressed sensing principle, population entropy, adaptive differential evolution, least$ - $squares support vector machine, and self-adaptive particle swarm optimization. Some of these methods have been examined through experiments and their comparative analysis have been given. Results show that iterative methods generate high quality images compared with non-iterative ones when evaluated over full component fraction range. However, iterative methods are computationally expensive, and hence used for research and off-line investigations rather than for on-line process monitoring.

Imaging and visualization of multiphase fluid flow by different reconstruction method for electrical computer tomography

A new reconstruction method called Functional Weighted Inverse Matrix (FWIM) has been applied to an ill-posed inverse problem involving an electrical computer tomography for multiphase flow. Four image reconstruction algorithms —— FWIM, Landweber (LW), Iterative Tikhonov regularization (ITR) and Generalized Vector Sampled Pattern Matching (GVSPM) methods——are tested using four types of pseudo particle distribution images with different dispersion concentration rates (DCR). The reconstructed images, as shown in figure, are compared in terms of capacitance residual, image error and image correlation. Overall, the accuracy is strongly dependent upon the image type and the dispersion concentration rates (DCR). However, the FWIM method proved superior to the LW, the ITR and the GVSPM methods in terms of the annular and bottom pseudo images.

Electrical CT image reconstruction technique for powder flow in petroleum refinery process

A new reconstruction method called sampled pattern matching (SPM) was applied to the image reconstruction of an electrical capacitance computed tomography in powder flow in a vertical pipe for petroleum refinery process. This new method is able to achieve stable convergence without the use of an empirical value. Experiments were carried out using fluid catalytic cracking (FCC) catalysts as powder with two air volume flow rates and four powder volume flow rates to measure the capacitance of a pipe cross section. The SPM method is compared with conventional methods in terms of volume fraction, residual capacitance, and correlation capacitance. Overall, the SPM method proved superior to conventional methods without any empirical value because SPM achieves accurate reconstruction by using an objective function that is calculated as the inner product calculation between the experimental capacitance and the reconstructed image capacitance.

GVSPM image reconstruction for capacitance CT images of particles in a vertical pipe and comparison with the conventional method

A new reconstruction method called generalized vector sampled pattern matching (GVSPM) was applied to the image reconstruction of an electrical capacitance computed tomography in freely falling particles in a vertical pipe. This new method is able to achieve stable convergence without the use of an empirical value. Experiments were carried out using three particle types with various electric charges and four particle flow rates to measure the capacitance of a pipe cross section. The three particle types were polyethylene pellets (PP), silica particles (SP) and polyvinyl chloride (PVC). Four particle flow rate settings were used resulting in a volume flow rate ranging from 3.08 × 10−5 to 1.04 × 10−3 m3 s−1. The GVSPM method is compared with conventional methods in terms of volume fraction, residual capacitance and correlation capacitance. Overall, the GVSPM method proved superior to conventional methods in the case of polyethylene pellets with relatively low electric charge. GVSPM achieves accurate reconstruction by using an objective function that is calculated as the inner product calculation between the experimental capacitance and the reconstructed image capacitance.

Estimation of Multiple Coherent Source Locations by Using SPM Method Combined with Signal Subspace Fitting Technique

A visualization method of coherent source locations based on the Sampled Pattern Matching (SPM) method is described. Modified SPM method is proposed to improve the S/N, in which the measurement of the electric field distribution is repeated in appropriate time duration and eigenvalue decomposition of the covariance matrix is introduced. A combination of the modified SPM method with the Weighted Subspace Fitting (WSF) method is also proposed to estimate accurate source locations. A calibration technique by using a reference antenna to compensate the complex pattern of the receiving antenna is proposed. Experimental investigation to estimate source location for one dipole antenna and two dipole antennas is also made to demonstrate the validity of the proposed method.

キャパシタンス・トモグラフィーによる粉粒体流動計測―概要とＧＶＳＰＭ法による画像再構成―

An electrical capacitance CT is a noninvasive technique with the potential to visualize particle behavior in a cross-section of a multiphase flow pipeline. In this review, firstly, a fundamental structure and overview of the capacitance CT are reviewed. Next, a new reconstruction method called Generalized Vector Sampled Pattern Matching (GVSPM) is explained for resolving an ill-posed inverse problem. This new method is able to achieve stable convergence without the use of an empirical value. This accurate reconstruction is accomplished using an objective function that is calculated as the inner product calculation between the experimental capacitance and the reconstructed image capacitance.

Application of GVSPM to Reconstruction of Solid-Air Two-Phase Flow CT Images (Comparison with Landweber and Iterative Tikhonov Regularization Methods)

A new reconstruction method called Generalized Vector Sampled Pattern Matching (GVSPM) has been applied to an ill-posed inverse problem involving an electrical capacitance CT for solid air two-phase flow. This new mehod is able to achieve stable convergence without the use of an empirical value. This accurate reconstruction is accomplished using an objective function that is calculated as the inner product calculation between the experimental capacitance and the reconstructed image capacitance. The GVSPM method is compared with the conventional Landweber (LW) and Iterative Tikhonov regularization (ITR) methods in terms of capacitance residual, image error and image correlation. Overall, the accuracy is strongly dependent upon the image type and the iteration number, however the GVSPM method proved superior to the LW and the ITR methods in the case of annular pseudo particle images.

Application of the generalized vector sampled pattern matching method to reconstruction of electrical capacitance CT images

A new reconstruction method called generalized vector sampled pattern matching (GVSPM) has been applied to an ill-posed inverse problem involving electrical capacitance computed tomography for solid–air two-phase flow. This new method is able to achieve stable convergence without the use of an empirical value. This accurate reconstruction is accomplished using an objective function that is calculated as the inner product calculation between the experimental capacitance and the reconstructed image capacitance. The GVSPM method is compared with the conventional Landweber (LW) and iterative Tikhonov regularization (ITR) methods in terms of capacitance residual, image error and image correlation. Overall, the accuracy is strongly dependent upon the image type and the iteration number, however the GVSPM method proved superior to the LW and the ITR methods in the case of annular pseudo particle images.

Reconstruction of Free Fall Particle CT Images Using the Generalized Vector Sampled Pattern Matching Method

A new reconstruction method, which is called Generalized Vector Sampled Pattern Matching (GVSPM) method, has been applied to an ill-posed inverse problem of a capacitance-computed tomography for solid air two-phase flow. In pseudo two-phase flow images, the correlation of the reconstructed images by GVSPM is higher than those by a conventional Newton Raphson (NR) iterative method by 32.5%. Moreover, in solid air two-phase flow images, the deviation between the particles volume fraction by experimental capacitance and that by the reconstruction methods is calculated

GVSPM for reconstruction in electrical impedance tomography

We apply the generalized vector sampled pattern matching (GVSPM) method to an inverse parameter problem for the reconstruction of electrical impedance tomography (EIT). Most of the inverse problems are reduced into solving for an ill-posed system of equations whose solution is not uniquely determined. The GVSPM introduced in this paper enables us to select the physically existing solution among possible ones. By applying GVSPM for EIT reconstruction, this point is verified by reliable reconstructed images.

B113 パラメタ推定に関する逆問題とその関数型トモグラフィーへの応用

With the developments of modern high-speed digital computer, X-ray as well as MRI (Magnetic Resonance Imaging) tomography is widely used as one of the most effective instruments for medical diagnosis. On the other side, EIT (Electrical Impedance Tomography) is developing only for the industrial use. This is because EIT is one of the functional types of tomography, which require a solution of ill posed system of equations while the X-ray and MRI need not solve such an ill posed system of equations.In order to overcome such difficulty in EIT, this paper proposes a new deterministic method, which consists of a new solving strategy, i.e. GVSPM (Generalized Sampled Pattern Matching) for the ill posed linear system of equations and image processing tool, i.e., convolution strategy to extract the common parts in the images. As a result, it is revealed that our method promises the highly reliable tomography.

D101 ベクトル型サンプル・パターン・マッチング法によるキャパシタンスCTの画像再構成アルゴリズム&lt;SUP&gt;*&lt;/SUP&gt;

A new reconstruction method, which is called Generalized Vector Sampled Pattern Matching (GVSPM) method, has been applied to an ill-posed inverse problem of a capacitance-computed tomography for solid air two-phase flow. The time-mean correlation between the experimental capacitance and the capacitances from the reconstruction method is calculated. As a result, GVSPM method improves the correlation by 23.6% as compared with LW method. The accurate reconstruction of GVSPM results from an inner product calculation between the experimental capacitance and the capacitance from the reconstructed images as an objective function.

A universal predictor based on pattern matching

We consider a universal predictor based on pattern matching. Given a sequence X/sub 1/, ..., X/sub n/ drawn from a stationary mixing source, it predicts the next symbol X/sub n+1/ based on selecting a context of X/sub n+1/. The predictor, called the sampled pattern matching (SPM), is a modification of the Ehrenfeucht-Mycielski (1992) pseudorandom generator algorithm. It predicts the value of the most frequent symbol appearing at the so-called sampled positions. These positions follow the occurrences of a fraction of the longest suffix of the original sequence that has another copy inside X/sub 1/X/sub 2//spl middot//spl middot//spl middot/X/sub n/; that is, in SPM, the context selection consists of taking certain fraction of the longest match. The study of the longest match for lossless data compression was initiated by Wyner and Ziv in their 1989 seminal paper. Here, we estimate the redundancy of the SPM universal predictor, that is, we prove that the probability the SPM predictor makes worse decisions than the optimal predictor is O(n/sup -/spl nu//) for some 0</spl nu/< 1/2 as n/spl rarr//spl infin/. As a matter of fact, we show that we can predict K=O(1) symbols with the same probability of error.

電気インピーダンス法による導電率分布の可視化

Electrical impedance tomography (EIT) is one of the cheapest ways in order to get a tomography. Principal purpose of the EIT is to obtain a conductivity distribution of a two-dimensional cross-sectional target area by injecting the current. EIT utilizes a functional relationship between the injection current and surface potential distribution depending on the conductivity difference. This functional relationship of EIT reveals that EIT has versatile possibilities but it is essentially reduced into solving for an ill-posed inverse problem. In order to develop the EIT, we try to apply the generalized vector sampled pattern matching (GVSPM) method to the inverse problem accompanying with EIT. As an initial test experiment of EIT development, we try to evaluate a resistance distribution in a planer circuit by measuring entire nodal voltages while changing the electrodes for current injection. As a result, it is revealed that a fairly good result could be obtained as an initial test example.

Dose optimization of proton and heavy ion therapy using generalized sampled pattern matching

We have proposed a new dose optimization method for proton and heavy ion therapy using generalized sampled pattern matching, where an optimal beam weight distribution for scanning is obtained as a solution. Using water phantom models, one-dimensional lateral and depth dose distributions were separately optimized, each resulting in a uniform dose distribution within a target region and minimum dose fall-off to minimize undesired irradiation onto neighbouring tissues. Subsequently, we have applied the technique to broad beam three-dimensional proton therapy, leading to a homogeneous dose distribution inside a target and minimized distal and lateral dose fall-off for most convex tumour shapes.

Shape estimation of radioactive spent resins in a storage tank using the sampled pattern matching

A method for estimating the shape of accumulated radioactive spent resins in a storage tank has been proposed using the sampled pattern matching. In a two-dimensional simulation model, several kinds of the resin shapes are assumed, and the radiation fields outside the tank are calculated. The resin shapes were estimated using the simulated radiation field data and compared to the assumed shapes, resulting in reasonable agreements between them.