Blind recognition of k/n rate convolutional encoders from noisy observation

Blind recognition of convolutional codes is not only essential for cognitive radio, but also for non-cooperative context. This paper is dedicated to the blind identification of rate k/n convolutional encoders in a noisy context based on Walsh-Hadamard transformation and block matrix (WHT-BM). The proposed algorithm constructs a system of noisy linear equations and utilizes all its coefficients to recover parity check matrix. It is able to make use of fault-tolerant feature of WHT, thus providing more accurate results and achieving better error performance in high raw bit error rate (BER) regions. Moreover, it is more computationally efficient with the use of the block matrix (BM) method. © 2017 Beijing Institute of Aerospace Information.

Fractional Envelope Analysis for Rolling Element Bearing Weak Fault Feature Extraction

The bearing weak fault feature extraction is crucial to mechanical fault diagnosis and machine condition monitoring. Envelope analysis based on Hilbert transform has been widely used in bearing fault feature extraction. A generalization of the Hilbert transform, the fractional Hilbert transform is defined in the frequency domain, it is based upon the modification of spatial filter with a fractional parameter, and it can be used to construct a new kind of fractional analytic signal. By performing spectrum analysis on the fractional envelope signal, the fractional envelope spectrum can be obtained. When weak faults occur in a bearing, some of the characteristic frequencies will clearly appear in the fractional envelope spectrum. These characteristic frequencies can be used for bearing weak fault feature extraction. The effectiveness of the proposed method is verified through simulation signal and experiment data. © 2017 Chinese Association of Automation.

Distributed tracking control of unmanned aerial vehicles under wind disturbance and model uncertainty

A distributed robust method is developed for cooperative tracking control of unmanned aerial vehicles under unknown wind disturbance and model uncertainty. The communication network among vehicles is a directed graph with switching topology. Each vehicle can only share its states with its neighbors. Dynamics of the vehicles are nonlinear and affected by the wind disturbance and model uncertainty. Feedback linearization is adopted to transform the dynamics of vehicles into linear systems. To account for the wind disturbance and model uncertainty, a robust controller is designed for each vehicle such that all vehicles ultimately synchronize to the virtual leader in the three-dimensional path. It is theoretically shown that the position states of the vehicles will converge to that of the virtual leader if the communication network has a directed spanning tree rooted at the virtual leader. Furthermore, the robust controller is extended to address the formation control problem. Simulation examples are also given to illustrate the effectiveness of the proposed method. © 2016 Beijing Institute of Aerospace Information.

Near field 3-D imaging approach for joint high-resolution imaging and phase error correction

This paper combines compressed sensing (CS) imaging theory and range migration algorithm (RMA), and then proposes a near-field three-dimensional (3-D) imaging approach for joint high-resolution imaging and phase error correction. Firstly, a sparse measurement matrix construction method based on a logistic sequence is proposed, which conducts nonlinear transformation for the determined logistic sequence, making it obey uniform distribution, then conducts sign function mapping, and generates the pseudorandom sequence with Bernoulli distribution, thus leading to good signal recovery under down-sampling and easy availability for engineering realization. Secondly, in combination with the RMA imaging approach, the dictionary with all scene information and phase error correction is constructed for CS signal recovery and error correction. Finally, the non-quadratic solution model jointing imaging and phase error correction based on regularization is built, and it is solved by two steps - the separable surrogate functionals (SSF) iterative shrinkage algorithm is adopted to realize target scattering estimate; the iteration mode is adopted for the correction of the dictionary model, so as to achieve the goal of error correction and highly-focused imaging. The proposed approach proves to be effective through numerical simulation and real measurement in anechoic chamber. The results show that, the proposed approach can realize high-resolution imaging in the case of less data; the designed measurement matrix has better non-coherence and easy availability for engineering realization. The proposed approach can effectively correct the phase error, and achieve highly-focused target image. © 2017 Beijing Institute of Aerospace Information.

An Algorithm for Ship Wake Detection from the SAR Images Using the Radon Transform and Morphological Image Processing

Using the Radon transform and morphological image processing, an algorithm for ship's wake detection in the SAR (synthetic aperture radar) image is developed. Being manipulated in the Radon space to invert the gray-level and binary images, the linear texture of ship wake in oceanic clutter can be well detected. It has been applied to the automatic detection of a moving ship from the SEASAT SAR image. The results show that this algorithm is well robust in a strong noisy background and is not very sensitive to the threshold parameter and the working window size.

Evolutionary Programming for IP/MIP Problems with Linear Constraints

In this paper, we propose a modified evolutionary programming with dynamic domain for solving nonlinear IP/MIP problems with linear constraints, without involving penalty function or any transformation for the problem to a linear model or others. The numerical results show that the new algorithm gives a satisfactory performance in which it works of high speed, and accuracy in IP/MIP problems.

Computation of the Simplest Normal Forms for Resonant Double Hopf Bifurcations System Based on Lie Transform

The simplest normal form of resonant double Hopf bifurcation was studied based on Lie operator. The coefficients of the simplest normal forms of resonant double Hopf bifurcation and the nonlinear transformations in terms of the original system coefficients were given explicitly. The nonlinear transformations were used for reducing the lower- and higher-order normal forms, and the rank of system matrix was used to determine the coefficient of normal form which could be reduced. These make the gained normal form simpler than the traditional one. A general program was compiled with Mathematica. This program can compute the simplest normal form of resonant double Hopf bifurcation and the non-resonant form up to the 7th order.

Integrated Velocity Transformation from Side-looking SAR Data to Squint Mode

Side-looking SAR raw data can be obtained from the commercial market. Comparatively, in other modes such as squint SAR and spotlight SAR and the real flight echo data are hardly acquired. A new approach called the integrated velocity transformation is presented in this paper, which can approximately transform the side-looking raw data to a squint mode. The simulation proves the method is acceptable.

UNSTEADY TRANSONIC AERODYNAMIC LOADINGS ON THE AIRFOIL CAUSED BY HEAVING, PITCHING OSCILLATIONS AND CONTROL SURFACE

An implicit upwind finite volume solver for the Euler equations using the improved flux - splitting method is established and used to calculate the transonic flow past the airfoils with heaving, pitching oscillations and the control surface. Results are given for the NACA64A - 10 airfoil which is in harmonic heaving and pitching oscillation and with the control surface in the transonic flow field. Some computational results are compared with the experiment data and the good agreements are shown in the paper.

Some Equivalence Transform Methods for Simulating Models in Control Systems

In this paper two classes of equivalence transform methods for solving ordinary differential equations are proposed. One class of method is the equivalence integral transform method for special differential algebraic problems. The advantage of this class of method is such that the amount of work calculating one integration with parameters becomes that of two interpolations, when the system of nonlinear equations is solved on the right hand side function. The other class of method is the equivalence substitution method for avoiding calculating derivative on the right hand side function. In order to avoid calculation derivatives, two equivalence substitution methods are proposed here. The application instances of some special effect of the equivalence substitution methods are given.

Yes, Science is Confronted by a Great Revolution

An equation is derived to explain the General Theory of Relativity and the effects of GTR: the rotations of planets' perihilion, deflects of star light by a gravitational mass, and the existence of gravitational waves. Differentiation was used in the derivation but without the dependence of mass, space and time on velocity. The general postulates that are the bases of the new approach to electrodynamics were stated.

Decision Making as Theorem Proving

We present a method for using type theory to solve decision making problem. Our method is based on the view that decision making is a special kind of theorem proving activity. An isomorphism between problems and types, and solutions and programs has been established to support this view which is much similar to the Curry-Howard isomorphism between propositions and types, and proofs and programs. To support our method, a proof development system called PowerEpsilon has been developed, and the synthesis of a decision procedure for validity of first-order propositional logic is discussed to show the power of the system.

Conversion Technique of Vector Wave Functions:Formulas and Applications

The conversion theory of vector wave function is one of important problems in electromagnetic. This paper presents a systematic treatment of the conversion technique and some applications. In this paper, the conversion relations of standard and non-standard spherical vector wave functions, standard and non-standard cylindrical vector wave functions, and spherical and cylindrical vector wave functions are developed. As an example of application of vector wave function expansion, the expansion of plane wave and dipole field in two-medium half-spaces are given. As an example of application of vector wave function conversion, the scattering patterns of buried conducting and dielectric spheres are presented. Inspection on the numerical results shows that the technique and associated programs presented in this paper are efficient.

RESEARCH ON HAAR SPECTRUM IN FAULT DIAGNOSIS OF ROTATING MACHINERY

In this paper, Haar Transform (HT) is used in the fault diagnosis of rotating machinery, and the 'Impulse Sharpness' is presented as a diagnostic index. At present, Fourier Spectrum analysis is most widely used. Compared with FFT, HT is more rapid in computation and more effective in discrete approximation. It's very suitable for the extraction of pulses in the signal. However, HT has some shortcomings. It's greatly affected by the starting point and length of the sample. Here, the authors present a method to improve the stability and comparability of Haar Spectrum. The fault imitating test of rolling bearing is carried out, and the results obtained have verified the sensitivity of Haar Spectrum and Impulse Index to the fault.

SOME NEW RESULTS ABOUT FIXED PANSYSTEMS THEOREMS

In this paper the fixed pansystems theorems concerning the composition-complement-operators are discussed. Some existence conditions of panchaos and strange panattractor are given. And using the results, some fixed point theorems for many-valued mappings are also proved, which complement and develop the results obtained by Kakutani.

Global and local contrast enhancement algorithm for image using wavelet neural network and stationary wavelet transform

A new contrast enhancement algorithm for image is proposed employing wavelet neural network （WNN） and stationary wavelet transform （SWT）. Incomplete Beta transform （IBT） is used to enhance the global contrast for image. In order to avoid the expensive time for traditional contrast enhancement algorithms, which search optimal gray transform parameters in the whole gray transform parameter space, a new criterion is proposed with gray level histogram. Contrast type for original image is determined employing the new criterion. Gray transform parameter space is given respectively according to different contrast types, which shrinks the parameter space greatly. Nonlinear transform parameters are searched by simulated annealing algorithm （SA） so as to obtain optimal gray transform parameters. Thus the searching direction and selection of initial values of simulated annealing is guided by the new parameter space. In order to calculate IBT in the whole image, a kind of WNN is proposed to approximate the IBT. Having enhanced the global contrast to input image, discrete SWT is done to the image which has been processed by previous global enhancement method, local contrast enhancement is implemented by a kind of nonlinear operator in the high frequency sub-band images of each decomposition level respectively. Experimental results show that the new algorithm is able to adaptively enhance the global contrast for the original image while it also extrudes the detail of the targets in the original image well. The computation complexity for the new algorithm is O（MN） log（MN）, where M and N are width and height of the original image, respectively.

Image fusion based on expectation maximization algorithm and steerable pyramid

In this paper, a novel image fusion method based on the expectation maximization (EM) algorithm and steerable pyramid is proposed. The registered images are first decomposed by using steerable pyramid. The EM algorithm is used to fuse the image components in the low frequency band. The selection method involving the informative importance measure is applied to those in the high frequency band. The final fused image is then computed by taking the inverse transform on the composite coefficient representations. Experimental results show that the proposed method outperforms conventional image fusion methods.

Stitching algorithm for annular subaperture interferometry

Annular subaperture interferometry （ASI） has been developed for low cost and flexible test of rotationally symmetric aspheric surfaces, in which accurately combining the subaperture measurement data corrupted by misalignments and noise into a complete surface figure is the key problem. By introducing the Zernike annular polynomials which are orthogonal over annulus, a method that eliminates the coupling problem in the earlier algorithm based on Zernike circle polynomials is proposed. Vector-matrix notation is used to simplify the description and calculations. The performance of this reduction method is evaluated by numerical simulation. The results prove this method with high precision and good anti-noise capability.

Two-step image registration by artificial immune system and chamfer matching

Image registration is the precondition and foundation in the fusion of multi-source image data. A two-step approach based on artificial immune system and chamfer matching to register images from different types of sensors is presented. In the first step, it extracts the large edges and takes chamfer distance between the input image and the reference image as similarity measure and uses artificial immune network algorithm to speed up the searching of the initial transformation parameters. In the second step, an area-based method is utilized to refine the initial transformation and enhance the registration accuracy. Experimental results show that the proposed approach is a promising method for registration of multi-sensor images.

Finger crease pattern recognition using Legendre moments and principal component analysis

The finger joint lines defined as finger creases and its distribution can identify a person. In this paper, we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis （PCA）. After obtaining the region of interest （ROI） for each finger image in the pre- processing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve （K-L） transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.