Robust Blind Adaptive Channel Equalization in Chaotic Communication Systems

Based on the bounded property and statistics of chaotic signal and the idea of set-membership identification, we propose a set-membership generalized least mean square （SM-GLMS） algorithm with variable step size for blind adaptive channel equalization in chaotic communication systems. The steady state performance of the proposed SM-GLMS algorithm is analysed, and comparison with an extended Kalman filter （EKF）-based adaptive algorithm and variable gain least mean square （VG-LMS） algorithm is performed for blind adaptive channel equalization. Simulations show that the proposed SM-GLMS algorithm can provide more significant steady state performance improvement than the EKF-based adaptive algorithm and VG-LMS algorithm.     

Application of Symplectic Algebraic Dynamics Algorithm to Circular Restricted Three-Body Problem

Symplectic algebraic dynamics algorithm （SADA ） for ordinary differential equations is applied to solve numerically the circular restricted three-body problem （CR3BP） in dynamical astronomy for both stable motion and chaotic motion. The result is compared with those of Runge~Kutta algorithm and symplectic algorithm under the fourth order, which shows that SADA has higher accuracy than the others in the long-term cMculations of the CR3BP.     

Design of X-ray super-mirrors using simulated annealing algorithm

An adaptive local search-based simulated annealing (ALSA) algorithm is proposed for the design of the wide band-pass multilayer optical elements operating in the hard and soft X-ray wavelength ranges. At present the SA algorithm has been kept as simple as possible and its performance is being studied before a series of modifications are made to tailor the SA algorithm to optimize super-mirror for particular applications. The algorithm has also been developed with two specific areas in mind: a W/C broad angle super-mirror for hard X-ray applications and a Mo/Si wide band super-mirror operating at normal incidence in the EUV spectral region. The design results show that the ALSA algorithm method has the flexibility to design a wide range of multilayer structures and in comparison to other techniques has good results although computationally more intensive.     

Algorithm Study on Reconstruction of Refractive Angles in Fan Beam Diffraction Enhanced Computed Tomography

Based on the 360° computing method of refractive angle for parallel beam diffraction enhanced imaging computed tomography （DE-CT） technique, a new algorithm used to calculate the refractive angle for fan-beam DE-CT technique is developed. The refractive index gradient can be obtained by using the new algorithm with projection data of an object in the range of 0 - 360°, and the new algorithm only needs to set the analyser at half slope position of the rocking curve.     

An Adaptive Denoising Algorithm for Noisy Chaotic Signals Based on Local Sparse Representation

An adaptive denoising algorithm based on local sparse representation （local SR） is proposed. The basic idea is applying SR locally to clusters of signals embedded in a high-dimensional space of delayed coordinates. The clusters of signals are represented by the sparse linear combinations of atoms depending on the nature of the signal. The algorithm is applied to noisy chaotic signals denoising for testing its performance. In comparison with recently reported leading alternative denoising algorithms such as kernel principle component analysis （Kernel PCA）, local independent component analysis （local ICA）, local PCA, and wavelet shrinkage （WS）, the proposed algorithm is more efficient.     

Eliminating the zero spectrum in Fourier transform profilometry using a two-dimensional continuous wavelet transform

In this paper, a filtering technique based upon two-dimensional continuous wavelet transform (2D-CWT) is used to eliminate the low frequency components of fringe patterns. The filtered fringe patterns are subsequently demodulated using a standard Fourier transform profilometry (FTP) algorithm. This image pre-filtering stage improves the noise performance of the FTP algorithm and enables the FTP method to demodulate fringe patterns with larger bandwidths. Also, the 2D-CWT technique reduces speckle noise significantly. Moreover, only a single fringe pattern is required in this technique. The 2D-CWT algorithm is capable of separating low frequency terms from the high frequency terms that contain phase-modulated fringe information, even when both interfere, greatly, in the frequency domain. The proposed algorithm is tested, both via computer simulation and using real fringe patterns. This revealed the robustness of this algorithm and also demonstrably enables the demodulation of a wider range of fringe patterns using the FTP technique.     

Defect imaging with elastic waves in inhomogeneous-anisotropic materials with composite geometries

Imaging of defects in composite structures plays an important role in non-destructive testing (NDT) with elastic waves, i.e., ultrasound. Traditionally the imaging of such defects is performed using the synthetic aperture focusing technique (SAFT) algorithm assuming homogeneous isotropic materials. However, if parts of the structure are inhomogeneous and/or anisotropic, this algorithm fail to produce correct results that are needed in order to asses the lifetime of the part under test. Here we present a modification of this algorithm which enables a correct imaging of defects in inhomogeneous and/or anisotropic composite structures, whence it is termed InASAFT. The InASAFT is based on the exact modelling of the structure in order to account for the true nature of the elastic wave propagation using travel time ray tracing techniques. The algorithm is validated upon several numerical and real life examples yielding satisfactory results for imaging of cracks. The modified algorithm suffers, though, from the same difficulties encountered in the SAFT algorithm, namely “ghost” images and eventual lack of clear focused images. However, these artifacts can be identified using a forward wave propagation analysis of the structure.     

Accuracy analysis on centroid estimation algorithm limited by photon noise for point object

The variance of the position of point object because of photon noise for centroid estimation algorithm is derived for the first time when the background cannot be eliminated by threshold. On this foundation, the accuracy of centroid estimation algorithm is analyzed by comparing its estimated efficiency (the ratio of the Cramer-Rao lower bound of point object and the variance for centroid algorithm) based on the classic estimation theory. The results indicated that the following factors influence the accuracy of centroid estimation algorithm: SBR (the ratio of the outputs from the signal light and the background), the Gaussian width of signal spot and the size of detected window. The effects of these factors are also described.     

Single frame blind image deconvolution by non-negative sparse matrix factorization

Novel approach to single frame multichannel blind image deconvolution has been formulated recently as non-negative matrix factorization problem with sparseness constraints imposed on the unknown mixing vector that accounts for the case of non-sparse source image. Unlike most of the blind image deconvolution algorithms, the novel approach assumed no a priori knowledge about the blurring kernel and original image. Our contributions in this paper are: (i) we have formulated generalized non-negative matrix factorization approach to blind image deconvolution with sparseness constraints imposed on either unknown mixing vector or unknown source image; (ii) the criteria are established to distinguish whether unknown source image was sparse or not as well as to estimate appropriate sparseness constraint from degraded image itself, thus making the proposed approach completely unsupervised; (iii) an extensive experimental performance evaluation of the non-negative matrix factorization algorithm is presented on the images degraded by the blur caused by the photon sieve, out-of-focus blur with sparse and non-sparse images and blur caused by atmospheric turbulence. The algorithm is compared with the state-of-the-art single frame blind image deconvolution algorithms such as blind Richardson-Lucy algorithm and single frame multichannel independent component analysis based algorithm and non-blind image restoration algorithms such as multiplicative algebraic restoration technique and Van-Cittert algorithms. It has been experimentally demonstrated that proposed algorithm outperforms mentioned non-blind and blind image deconvolution methods.     

Information Filtering via Improved Similarity Definition

Based on a new definition of user similarity, we introduce an improved collaborative filtering （ICF） algorithm, which couM improve the algorithmic accuracy and diversity simultaneously. In the ICF, instead of the standard Pearson coefficient, the user-user similarities are obtained by integrating the heat conduction and mass diffusion processes. The simulation results on a benchmark data set indicate that the corresponding algorithmic accuracy, measured by the ranking score, is improved by 6. 7% in the optimal case compared to the standard collaborative filtering （CF） algorithm. More importantly, the diversity of the recommendation lists is also improved by 63.6%. Since the user similarity is crucial for the CF algorithm, this work may shed some light on how to improve the algorithmic performance by giving accurate similarity measurement.     

Coherent Beam Combining of Three Watt-Level Fiber Amplifiers Using a DSP-Based Stochastic Parallel Gradient Descent Algorithm

We present an experimental study on coherent beam combining of three watt-level fiber amplifiers using a stochas- tic parallel gradient descent （SPGD） algorithm. Phase controlling is performed by running the SPGD algorithm on a digital-signal-processor （DSP） chip with a voltage updating rate of 16500 times per second. Energy encircled in the target pinhole is 2.62 times more than that in an open loop. The combining efficiency is as high as 87%.     

Improving the performance of DCT-based fragile watermarking using intelligent optimization algorithms

The performance of a fragile watermarking method based on discrete cosine transform (DCT) has been improved in this paper by using intelligent optimization algorithms (IOA), namely genetic algorithm, differential evolution algorithm, clonal selection algorithm and particle swarm optimization algorithm. In DCT based fragile watermarking techniques, watermark embedding can usually be achieved by modifying the least significant bits of the transformation coefficients. After the embedding process is completed, transforming the modified coefficients from the frequency domain to the spatial domain produces some rounding errors due to the conversion of real numbers to integers. The rounding errors caused by this transformation process were corrected by the use of intelligent optimization algorithms mentioned above. This paper gives experimental results which show the feasibility of using these optimization algorithms for the fragile watermarking and demonstrate the accuracy of these methods. The performance comparison of the algorithms was also realized.     

Luus-Jaakola optimization procedure for multilayer optical coatings

Designing multilayer optical coatings is a difficult optimization problem because of the huge size of the search space. In the present paper, the Luus-Jaakola (LJ) optimization procedure, a new optimization algorithm, is employed to model multilayer optical coatings in the X-ray domain. With this algorithm it is not necessary to specify initially the number of layers present in a design. Only an upper limit needs to be defined. The algorithm has been used to maximize the reflectivity over a range of incident angles at a fixed wavelength, and over a wavelength range at a fixed incident angle. The results show that the LJ algorithm can be effectively applied to the design of multilayer optical coatings resulting in fewer layers than obtained using alternative optimization methods.     

Performance of Wang-Landau algorithm in continuous spin models and a case study: Modified XY-model

Performance of Wang-Landau (WL) algorithm in two continuous spin models is tested by determining the fluctuations in energy histogram. Finite size scaling is performed on a modified XY-model using different WL sampling schemes. Difficulties faced in simulating relatively large continuous systems using WL algorithm are discussed.     

Two-scale modeling of adsorption processes at structured surfaces

We present an algorithm for the simulation of vicinal surface growth. It combines a lattice gas anisotropic Ising model with a phase-field model. The molecular behavior of individual adatoms is described by the lattice gas model. The microstructure dynamics on the vicinal surface are calculated using the phase-field method. In this way, adsorption processes on two different length scales can be described: nucleation processes on the terraces (lattice gas model) and step-flow growth (phase field model). The hybrid algorithm that is proposed here, is therefore able to describe an epitaxial layer-by-layer growth controlled by temperature and by deposition rate. This method is faster than kinetic Monte Carlo simulations and can take into account the stochastic processes in a comparable way.     

Detect overlapping and hierarchical community structure in networks

Clustering and community structure is crucial for many network systems and the related dynamic processes. It has been shown that communities are usually overlapping and hierarchical. However, previous methods investigate these two properties of community structure separately. This paper proposes an algorithm (EAGLE) to detect both the overlapping and hierarchical properties of complex community structure together. This algorithm deals with the set of maximal cliques and adopts an agglomerative framework. The quality function of modularity is extended to evaluate the goodness of a cover. The examples of application to real world networks give excellent results.     

Performance of a Chase-type decoding algorithm for Reed–Solomon codes on perpendicular magnetic recording channels

Algebraic soft-decision Reed–Solomon (RS) decoding algorithms with improved error-correcting capability and comparable complexity to standard algebraic hard-decision algorithms could be very attractive for possible implementation in the next generation of read channels. In this work, we investigate the performance of a low-complexity Chase (LCC)-type soft-decision RS decoding algorithm, recently proposed by Bellorado and Kav?i?, on perpendicular magnetic recording channels for sector-long RS codes of practical interest. Previous results for additive white Gaussian noise channels have shown that for a moderately long high-rate code, the LCC algorithm can achieve a coding gain comparable to the Koetter–Vardy algorithm with much lower complexity. We present a set of numerical results that show that this algorithm provides small coding gains, on the order of a fraction of a dB, with similar complexity to the hard-decision algorithms currently used, and that larger coding gains can be obtained if we use more test patterns, which significantly increases its computational complexity.     

An Efficient Proximity Point Algorithm for Total-Variation-Based Image Restoration

In this paper, we propose a fast proximity point algorithm and apply it to total variation (TV) based image restoration. The novel method is derived from the idea of establishing a general proximity point operator framework based on which new first-order schemes for total variation (TV) based image restoration have been proposed. Many current algorithms for TV-based image restoration, such as Chambolle's projection algorithm, the split Bregman algorithm, the Bermúdez-Moreno algorithm, the Jia-Zhao denoising algorithm, and the fixed point algorithm, can be viewed as special cases of the new first-order schemes. Moreover, the convergence of the new algorithm has been analyzed at length. Finally, we make comparisons with the split Bregman algorithm which is one of the best algorithms for solving TV-based image restoration at present. Numerical experiments illustrate the efficiency of the proposed algorithms.     

Spin dynamics in hole-doped two-dimensional S = 1/2 Heisenberg antiferromagnets: 63Cu NQR relaxation in La2-xSrxCuO4 for x ≤ 0.04

A cluster algorithm formulated in continuous (imaginary) time is presented for Ising models in a transverse field. It works directly with an infinite number of time-slices in the imaginary time direction, avoiding the necessity to take this limit explicitly. The algorithm is tested at the zero-temperature critical point of the pure two-dimensional (2d) transverse Ising model. Then it is applied to the 2d Ising ferromagnet with random bonds and transverse fields, for which the phase diagram is determined. Finite size scaling at the quantum critical point as well as the study of the quantum Griffiths-McCoy phase indicate that the dynamical critical exponent is infinite as in 1d. Received 6 November 1998     

A Time Domain Algorithm on the Reconstruction of Rough Surfaces

We consider the inverse scattering problem of a perfectly conducting one-dimensional rough surface in the case that the incident field is unnecessary to be time harmonic. Based on our previous investigation of the frequency domain algorithm, a new time domain algorithm is proposed, in which we approximate the incident pulse by a finite sum of time harmonic fields and then apply the frequency domain algorithm for time harmonic waves. Numerical experiments indicate that the time domain algorithm shows great accuracy of reconstruction of the surface profile and yields significant improvement than the frequency domain algorithm.