Because wavelet transforms have the characteristic of decomposing signals that are similar to the human acoustic system, speech enhancement algorithms that are based on wavelet shrinkage are widely used. In this paper, we propose a new speech enhancement algorithm of hearing aids based on wavelet shrinkage. The algorithm has multi-band threshold value and a new wavelet shrinkage function for recursive noise reduction. We performed experiments using various types of authorized speech and noise signals, and our results show that the proposed algorithm achieves significantly better performances compared with other recently proposed speech enhancement algorithms using wavelet shrinkage.

Least squares error (LSE) method adopted recursively can be used to track the frequency and amplitude of signals in steady states and kinds of non-steady ones in power system. Taylor expansion is used to give another version of this recursive LSE method. Aided by variable-windowed short-time discrete Fourier transform, recursive LSEs with and without Taylor expansion converge faster than the original ones in the circumstance of off-nominal input singles. Different versions of recursive LSE were analyzed under various states, such as signals of off-nominal frequency with harmonics, signals with step changes, signals modulated by a sine signal, signals with decaying DC offset and additive Gaussian white noise. Sampling rate and data window size are two main factors influencing the performance of method recursive LSE in transient states. Recursive LSE is sensitive to step changes of signals, but it is in-sensitive to signals' modulation and singles with decaying DC offset and noise.

A recursive and efficient method for generating binary vectors in non-increasing order of their likelihood for a set of all binary vectors is proposed. Numerical results on experiments show the effectiveness of this method. Efficient decoding algorithms with simulation results are also proposed as applications of the method.

We develop a novel construction method for n-dimensional Hilbert space-filling curves. The construction method includes four steps: block allocation, Gray permutation, coordinate transformation and recursive construction. We use the tensor product theory to formulate the method. An n-dimensional Hilbert space-filling curve of 2r elements on each dimension is specified as a permutation which rearranges 2rn data elements stored in the row major order as in C language or the column major order as in FORTRAN language to the order of traversing an n-dimensional Hilbert space-filling curve. The tensor product formulation of n-dimensional Hilbert space-filling curves uses stride permutation, reverse permutation, and Gray permutation. We present both recursive and iterative tensor product formulas of n-dimensional Hilbert space-filling curves. The tensor product formulas are directly translated into computer programs which can be used in various applications. The process of program generation is explained in the paper.

This paper discusses the infinite horizon static output feedback stochastic Nash games involving state-dependent noise in weakly coupled large-scale systems. In order to construct the strategy, the conditions for the existence of equilibria have been derived from the solutions of the sets of cross-coupled stochastic algebraic Riccati equations (CSAREs). After establishing the asymptotic structure along with the positive semidefiniteness for the solutions of CSAREs, recursive algorithm for solving CSAREs is derived. As a result, it is shown that the proposed algorithm attains the reduced-order computations and the reduction of the CPU time. As another important contribution, the uniqueness of the strategy set is proved for the sufficiently small parameter ε. Finally, in order to demonstrate the efficiency of the proposed algorithm, numerical example is given.

In this paper, the 1-D real-valued discrete Gabor transform (RDGT) proposed in our previous work and its relationship with the complex-valued discrete Gabor transform (CDGT) are briefly reviewed. Block time-recursive RDGT algorithms for the efficient and fast computation of the 1-D RDGT coefficients and for the fast reconstruction of the original signal from the coefficients are then developed in both the critical sampling case and the oversampling case. Unified parallel lattice structures for the implementation of the algorithms are studied. And the computational complexity analysis and comparison show that the proposed algorithms provide a more efficient and faster approach for the computation of the discrete Gabor transforms.

In many electromagnetic field problems, matrix equations were always deduced from using the method of moment. Among these matrix equations, some of them might require a large amount of computer memory storage which made them unrealistic to be solved on a personal computer. Virtually, these matrices might be too large to be solved efficiently. A fast algorithm based on a Toeplitz matrix solution was developed for solving a bordered Toeplitz matrix equation arising in electromagnetic problems applications. The developed matrix solution method can be applied to solve some electromagnetic problems having very large-scale matrices, which are deduced from the moment method procedure. In this paper, a study of a computationally efficient order-recursive algorithm for solving the linear electromagnetic problems [Z]I = V, where [Z] is a Toeplitz matrix, was presented. Upon the described Toeplitz matrix algorithm, this paper derives an efficient recursive algorithm for solving a bordered Toeplitz matrix with the matrix's major portion in the form of a Toeplitz matrix. This algorithm has remarkable advantages in reducing both the number of arithmetic operations and memory storage.

A 2-dimensional cylindrical k-within-consecutive-(r, s)-out-of-(m, n):F system consists of m n components arranged on a cylindrical grid. Each of m circles has n components, and this system fails if and only if there exists a grid of size r s within which at least k components are failed. This system may be used into reliability models of "Feelers for measuring temperature on reaction chamber," "TFT Liquid Crystal Display system with 360 degree wide area" and others. In this paper, first, we propose an efficient algorithm for the reliability of a 2-dimensional cylindrical k-within-consecutive-(r, s)-out-of-(m, n):F system. The feature of this algorithm is calculating their system reliabilities with shorter computing time and smaller memory size than Akiba and Yamamoto. Next, we show some numerical examples so that our proposed algorithm is more effective than Akiba and Yamamoto for systems with large n.

A new algorithm for the maximum a posteriori (MAP) decoding of linear block codes is presented. The proposed algorithm can be regarded as a conventional BCJR algorithm for a section trellis diagram, where branch metrics of the trellis are computed by the recursive MAP algorithm proposed by the authors. The decoding complexity of the proposed algorithm depends on the sectionalization of the trellis. A systematic way to find the optimum sectionalization which minimizes the complexity is also presented. Since the algorithm can be regarded as a generalization of both of the BCJR and the recursive MAP algorithms, the complexity of the proposed algorithm cannot be larger than those algorithms, as far as the sectionalization is chosen appropriately.

A new identification algorithm based on output over-sampling scheme is proposed for a IIR model whose input signal can not be available directly. By using only an output signal sampled at higher rate than unknown input, parameters of the IIR model can be identified. It is clarified that the consistency of the obtained parameter estimates is assured under some specified conditions. Further an efficient recursive algorithm for blind parameter estimation is also given for practical applications. Simulation results demonstrate its effectiveness in both system and channel identification.