Sequence distance via parsing complexity: Heartbeat signals

We compare and discuss the use of different symbolic codings of electrocardiogram (ECG) signals in order to distinguish healthy patients from hospitalized ones. To this aim, we recall a parsing-based similarity distance and compare the performances of several methods of classification of data.     

Inversion algorithm based on the generalized objective functional for compressed sensing

Owing to providing a novel insight for signal and image processing, compressed sensing (CS) has attracted increasing attention. The accuracy of the reconstruction algorithms plays an important role in real applications of the CS theory. In this paper, a generalized reconstruction model that simultaneously considers the inaccuracies on the measurement matrix and the measurement data is proposed for CS reconstruction. A generalized objective functional, which integrates the advantages of the least squares (LSs) estimation and the combinational M-estimation, is proposed. An iterative scheme that integrates the merits of the homotopy method and the artificial physics optimization (APO) algorithm is developed for solving the proposed objective functional. Numerical simulations are implemented to evaluate the feasibility and effectiveness of the proposed algorithm. For the cases simulated in this paper, the reconstruction accuracy is improved, which indicates that the proposed algorithm is successful in solving CS inverse problems.     

A semi-analytic method for calculating non-probabilistic reliability index based on interval models

This paper proves: (1) non-probabilistic reliability index of a structure exists merely at one of intersection points at which normalized failure surfaces of the structure intersects the straight lines passing not only through origin of an normalized infinite space but also through vertices of a symmetric convex polyhedron with its sym-center at the origin, and (2) the non-probabilistic reliability index equals to absolute value of the coordinate components of a particular intersection point. Based on a reduction of the feasible region, a semi-analytical method for calculating the reliability index is developed. The method proves to be simple and of practical significance, and has several advantages over the existing unconstrained multivariate nonlinear optimization approach.     

A numerical exploration of compressed sampling recovery

This paper explores numerically the efficiency of ?¹ minimization for the recovery of sparse signals from compressed sampling measurements in the noiseless case. This numerical exploration is driven by a new greedy pursuit algorithm that computes sparse vectors that are difficult to recover by ?¹ minimization. The supports of these pathological vectors are also used to select sub-matrices that are ill-conditioned. This allows us to challenge theoretical identifiability criteria based on polytopes analysis and on restricted isometry conditions. We evaluate numerically the theoretical analysis without resorting to Monte-Carlo sampling, which tends to avoid worst case scenarios.     

Real-time moving object segmentation in H.264 compressed domain based on approximate reasoning

This paper presents a real-time segmentation algorithm to obtain moving objects from the H.264 compressed domain. The proposed segmentation works with very little information and is based on two features of the H.264 compressed video: motion vectors associated to the macroblocks and decision modes. The algorithm uses fuzzy logic and allows to describe position, velocity and size of the detected regions in a comprehensive way, so the proposed approach works with low level information but manages highly comprehensive linguistic concepts. The performance of the algorithm is improved using dynamic design of fuzzy sets that avoids merge and split problems. Experimental results for several traffic scenes demonstrate the real-time performance and the encouraging results in diverse situations.     

Real-time moving object segmentation in H.264 compressed domain based on approximate reasoning

This paper presents a real-time segmentation algorithm to obtain moving objects from the H.264 compressed domain. The proposed segmentation works with very little information and is based on two features of the H.264 compressed video: motion vectors associated to the macroblocks and decision modes. The algorithm uses fuzzy logic and allows to describe position, velocity and size of the detected regions in a comprehensive way, so the proposed approach works with low level information but manages highly comprehensive linguistic concepts. The performance of the algorithm is improved using dynamic design of fuzzy sets that avoids merge and split problems. Experimental results for several traffic scenes demonstrate the real-time performance and the encouraging results in diverse situations.     

A note on Weil index

Let F be a non-archimedean local field of characteristic 0 and(?)a nontrivial additive character.Weil first defined the Weil indexγ(a,(?))(a∈F~*)in his famous paper,from which we know thatγ(a,(?))γ(b,(?))=γ(ab,(?))γ(1,(?))(a,b)andγ(a,(?))~4 =(-1,-1),where(a,b)is the Hilbert symbol for F.The Weil index plays an important role in the theory of theta series and in the general representation theory.In this paper,we establish an identity relating the Weil indexγ(a,(?))and the Gauss sum.     

A cooperation index based on the Rényi entropy of correlation matrix spectrum

We propose an index to measure cooperation among different time-series based on the Rényi entropy of the eigenvalues of the signal correlation matrix and an optimization step. The index could be considered as a generalization of a previously known index, based instead on the Shannon entropy. The extension to Rényi entropy and the optimization step allow a better use of the information conveyed by the correlation matrix, especially when dealing with a small number of signals.     

Regular expression searching on compressed text

We present a solution to the problem of regular expression searching on compressed text. The format we choose is the Ziv–Lempel family, specifically the LZ78 and LZW variants. Given a text of length u compressed into length n, and a pattern of length m, we report all the R occurrences of the pattern in the text in O(2^m+mn+Rmlogm) worst case time. On average this drops to O(m²+(n+Rm)logm) or O(m²+n+Ru/n) for most regular expressions. This is the first nontrivial result for this problem. The experimental results show that our compressed search algorithm needs half the time necessary for decompression plus searching, which is currently the only alternative.     

A sensibility study of the autobinomial model estimation methods based on a feature similarity index

The estimation of parameters in the Autobinomial model is an important task for characterizing the content of an image and generating synthetic textures. This paper compares the performance of three estimation methods of the model: coding, maximum pseudo-likelihood and conditional least squares, under textures with different levels of additive contamination, using a feature similarity image index, via Monte Carlo studies. This novel framework quantifies the similarity between the original texture and its texture regenerated by each method. Differences in performance were tested with a Repeated Measures ANOVA model design. Simulation results show that the Conditional Least Squares method is associated with the highest value of the similarity image measure in contaminated textures, while Coding and Maximum Pseudo-Likelihood methods have a comparable behavior and there is no clear pattern whether to prefer one over the other. An application for landscape classification using real Landsat images with different spatial resolutions is described.     

A grey modeling procedure based on the data smoothing index for short-term manufacturing demand forecast

Product life cycles have become increasingly shorter because of global competition. Under fierce competition, the use of small samples to establish demand forecasting models is crucial for enterprises. However, limited samples typically cannot provide sufficient information; therefore, this presents a major challenge to managers who must determine demand development trends. To overcome this problem, this paper proposes a modified grey forecasting model, called DSI–GM(1,1). Specifically, we developed a data smoothing index to analyze the data behavior and rewrite the calculation equation of the background value in the applied grey modeling, constructing a suitable model for superior forecasting performance according to data characteristics. Employing a test on monthly demand data of thin film transistor liquid crystal display panels and the monthly average price of aluminum for cash buyers, the proposed modeling procedure resulted in high prediction outcomes; therefore, it is an appropriate tool for forecasting short-term demand with small samples.     

A unification of unitary similarity transforms to compressed representations

In this paper a new framework for transforming arbitrary matrices to compressed representations is presented. The framework provides a generic way of transforming a matrix via unitary similarity transformations to, e.g., Hessenberg, Hessenberg-like form and combinations of both. The new algorithms are deduced, based on the QR-factorization of the original matrix. Relying on manipulations with rotations, all the algorithms consist of eliminating the correct set of rotations, resulting in a matrix obeying the desired structural constraints. Based on this new reduction procedure we investigate further correspondences such as irreducibility, uniqueness of the reduction procedure and the link with (rational) Krylov methods. The unitary similarity transform to Hessenberg-like form as presented here, differs significantly from the one presented in earlier work. Not only does it use less rotations to obtain the desired structure, also the convergence to rational Ritz-values is not observed in the conventional approach.     

A few typical delamination patterns of compressed thin films

In this paper we consider compressed flat thin films on rigid substrates. Residual compressive stresses arising e.g. from temperature loading are the driving quantities of the irreversible delamination process. A Reissner–Mindlin shell formulation is used as a model for the thin film, since small geometrical imperfections are considered to initiate buckling. For the interface we postulate the existence of a cohesive free energy as a function of the opening displacement vector and internal variables. The irreversible delamination process is described using a cohesive law of exponential type, where the parameters depend on the combination of the modes I, II and III. In order to analyse the delamination process exactly we use the energy criterion of the steady-state growth. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

On novel grey forecasting model based on non-homogeneous index sequence

To solve the problem that traditional grey models were constructed on the hypothesis that the original data sequence is in accord with homogeneous index trend rather than non-homogeneous index trend. A novel grey forecasting model based on non-homogeneous index sequence approximately (abbreviated as NDGM) is proposed. It is proved that the models based on homogeneous index sequence are all special cases that of non-homogeneous index sequence. The recursive function of the NDGM model is proposed and the forecasting precision of the model based on pure non-homogeneous index sequence is discussed and the affine properties of the model are further studied. Finally, one numerical case is used to show the effective results of the proposed model.     

A note on the eigenvalue consistency index

In this paper we show the relation between the eigenvalue consistency index and cycles of length k in a complete directed graph. Cardinal consistency, which requires the introduction of a scale to represent the degree of preferences among different alternatives, is discussed, and a new index to measure this concept is derived.     

A gray model for increasing sequences with nonhomogeneous index trends based on fractional‐order accumulation

By comparing the class ratio deviation and restoring error of first‐order accumulation with that of fractional‐order accumulation, a gray model for monotonically increasing sequences can obtain optimal simulation accuracy via selecting a proper cumulative order. In this study, a gray model for increasing sequences with nonhomogeneous index trends based on fractional‐order accumulation is proposed. To reduce the modeling error caused by the background value and to improve the prediction accuracy of the model, an optimized model using the 3/8 Simpson formula is constructed. Finally, the 2 proposed models are used to predict the total energy consumption in China and the monthly sales of new products in an enterprise. Compared with the GM(1,1) model based on fractional‐order accumulation, the proposed model exhibits better simulation and prediction accuracy.