Weighted least-squares phase unwrapping algorithm based on derivative variance correlation map

Among different phase unwrapping approaches, the weighted least-squares minimization methods are gaining attention. In these algorithms, weighting coefficient is generated from a quality map. The intrinsic drawbacks of existing quality maps constrain the application of these algorithms. They often fail to handle wrapped phase data contains error sources, such as phase discontinuities, noise and undersampling. In order to deal with those intractable wrapped phase data, a new weighted least-squares phase unwrapping algorithm based on derivative variance correlation map is proposed. In the algorithm, derivative variance correlation map, a novel quality map, can truly reflect wrapped phase quality, ensuring a more reliable unwrapped result. The definition of the derivative variance correlation map and the principle of the proposed algorithm are present in detail. The performance of the new algorithm has been tested by use of a simulated spherical surface wrapped data and an experimental interferometric synthetic aperture radar (IFSAR) wrapped data. Computer simulation and experimental results have verified that the proposed algorithm can work effectively even when a wrapped phase map contains intractable error sources. (c) 2006 Elsevier GmbH. All rights reserved.

Discussions about FFT-based two-step phase-shifting algorithm

An FFT-based two-step phase-shifting (TPS) algorithm is described in detail and implemented by use of experimental interferograms. This algorithm has been proposed to solve the TPS problem with random phase shift except pi. By comparison with the visibility-function-based TPS algorithm, it proves that the FFT-based algorithm has obvious advantages in phase extracting. Meanwhile, we present a pi-phase-shift supplement to the TPS algorithm, which combines the two interferograms and demodulates the phase map by locating the extrema of the combined fringes after removing the respective backgrounds. So combining this method and FFT-based one, one could really implement the TPS with random phase shift. Whereafter, we systematically compare the TPS with single-interferogram analysis algorithm and conventional three-step phase-shifting one. The results demonstrate that the FFT-based TPS algorithm has a satisfactory accuracy. At last, based on the polarizing interferometry, a schematic setup of two-channel TPS interferometer with random phase shift is suggested to implement the simultaneous collection of interferograms. (c) 2007 Elsevier GrnbH. All rights reserved.

Algorithm of far-field centre estimation based on phase-only matched filter

Estimation of the far-field centre is carried out in beam auto-alignment. In this paper, the features of the far-field of a square beam are presented. Based on these features, a phase-only matched filter is designed, and the algorithm of centre estimation is developed. Using the simulated images with different kinds of noise and the 40 test images that are taken in sequence, the accuracy of this algorithm is estimated. Results show that the error is no more than one pixel for simulated noise images with a 99% probability, and the stability is restricted within one pixel for test images. Using the improved algorithm, the consumed time is reduced to 0.049 s.

Calibration method for a photoelastic modulator with a peak retardation of less than a half-wavelength

A new calibration method for a photoelastic modulator is proposed. The calibration includes a coarse calibration and a fine calibration. In the coarse calibration, the peak retardation of the photoelastic modulator is set near 1.841 rad. In the fine calibration, the value of the zeroth Bessel function is obtained. The zeroth Bessel function is approximated as a linear equation to directly calculate the peak retardation. In experiments, the usefulness of the calibration method is verified and the calibration error is less than 0.014 rad. The calibration is immune to the intensity fluctuation of the light source and independent of the circuit parameters. The method specially suits the calibration of a photoelastic modulator with a peak retardation of less than a half-wavelength. (c) 2007 Optical Society of America.

Calibration of alkenone unsaturation index with growth temperature for a lacustrine species, Chrysotila lamellosa (Haptophyceae)

Although long chain alkenones (LCKs) occur widely in lacustrine sediments, their origin is not clear. Here, we report a lacustrine source, the non-calcifying species Chrysotila lamellosa Anand (Haptophyceae), collected and isolated from an inland saline water body, Lake Xiarinur (Inner Mongolia, China). Its alketione pattern is similar to those of coastal marine strains of C lamellosa,but the relationship between U-37(K') index and culture temperature for the lacustrine species is quite different from that of the coastal species. A significant feature of the alkenones in this strain of C lamellosa is a lack of C-38 methyl alkenones, which might be used to distinguish the species from the marine haptophyte species Emiliania huxleyi and Gephyrocapsa oceanica. The higher C-38 tetraunsaturated compound abundance might be another important feature for distinguishing the C lamellosa alkenone producer from the coastal species Isochrysis galbana. This alkenone distribution pattern has been detected in many lakes, which suggests that C lamellosa or a closely related species might be a very common alkenone precursor in lacustrine systems. We examined U-37(K') and U-37(K) values for C lamellosa as a function of culture temperature in a batch culture experiment. The calibration for U-37(K') vs. culture temperature (T) was U-37(K') = 0.0011 x T-2 - 0.0157 x T + 0.1057(n = 14, r(2) = 0.99) from 10 degrees C to 22 degrees C or U-37(K') = 0.0257 x T - 0.2608(n = 9, r(2) = 0.97) from 14 degrees C to 22 degrees C. U-37(K) vs. culture temperature was U-37(K) = 0 0377 x T - 0.5992(n = 14, r(2) = 0.98) from 10 degrees C to 22 degrees C. Our experiments show that the alkenone unsaturation index (U-37(K')) is strongly controlled by culture temperature and can be used for palaeoclimate reconstruction. (C) 2007 Elsevier Ltd. All rights reserved.

Correcting the systematic error of the density functional theory calculation: the alternate combination approach of genetic algorithm and neural network

The alternate combinational approach of genetic algorithm and neural network (AGANN) has been presented to correct the systematic error of the density functional theory (DFT) calculation. It treats the DFT as a black box and models the error through external statistical information. As a demonstration, the AGANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides. Through the AGANN correction, the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set. For comparison, the neural network approach reduces the mean value to 2.56%. And for the common combinational approach of genetic algorithm and neural network, the value drops to 2.15%. The multiple linear regression method almost has no correction effect here.

Choice of calibration equations in calibrating microwave test fixtures

The error theory of linear equation system has been applied to the calibration procedure of microwave network analyser in this article. A new explanation for the choice of the linear calibration equations is proposed and a general principle for choosing calibration equations is presented. The method can also be used to predict the occurrence of the problem of frequency limitation at some periodic frequencies. This principle is employed to the thru-short-delay (TSD) method and the solution using the chosen equations gives the most accurate results. A good agreement between the theory and the experiment has been obtained.

The multi-weight neuron with geometry algorithm and its application

A novel approach for multi-dimension signals processing, that is multi-weight neural network based on high dimensional geometry theory, is proposed. With this theory, the geometry algorithm for building the multi-weight neuron is mentioned. To illustrate the advantage of the novel approach, a Chinese speech emotion recognition experiment has been done. From this experiment, the human emotions are classified into 6 archetypal classes: fear, anger, happiness, sadness, surprise and disgust. And the amplitude, pitch frequency and formant are used as the feature parameters for speech emotion recognition. Compared with traditional GSVM model, the new method has its superiority. It is noted that this method has significant values for researches and applications henceforth.

Calibration of microwave network analyzer

This paper presents a systematic description of the methods for calibrating microwave network analyzer and test fixtures, and discusses the problems arising in the calibration. The general criteria for choosing calibration standards and corresponding algorithms are discussed and suggestions to overcome these problems and improve the calibration accuracy are also given. It has been found that for reciprocal test fixtures, the four equations obtained with the thru standard can be used at the same time. Meanwhile, the calibration accuracy can be improved. It has been shown that using the same calibration procedures but different algorithms may lead to the occurrence of frequency limitation.

Iris recognition algorithm based on point covering of high-dimensional space and neural network

In this paper, we constructed a Iris recognition algorithm based on point covering of high-dimensional space and Multi-weighted neuron of point covering of high-dimensional space, and proposed a new method for iris recognition based on point covering theory of high-dimensional space. In this method, irises are trained as "cognition" one class by one class, and it doesn't influence the original recognition knowledge for samples of the new added class. The results of experiments show the rejection rate is 98.9%, the correct cognition rate and the error rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the rejection rate of test samples excluded in the training samples class is very high. It proves the proposed method for iris recognition is effective.

A new method based on the construction of hypothetical symmetrical networks for fixture calibration in network analyzers

At least three known standards are normally required for the full two-port test fixture calibration in vector network analyzers (VNA). In this paper, a calibration procedure using only one standard, based on establishing two hypothetical symmetrical fixtures using triple-through method, is shown. The results using the calibrating method to subtract the influence of fixtures are in accord with the directly measured data of the device-under-test (DUT) without the fixtures very well, which shows that the proposed method is very simple and accurate.