A fast algorithm for computing the modulated lapped transform (MLT) is proposed. This method is based on the combination of a fast MLT via a type-IV discrete cosine transform (DCT-IV) algorithm and a fast DFT:based DC...
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A fast algorithm for computing the modulated lapped transform (MLT) is proposed. This method is based on the combination of a fast MLT via a type-IV discrete cosine transform (DCT-IV) algorithm and a fast DFT:based DCT-IV algorithm. II is achieved by defining a new data-shuffling scheme. The proposed algorithm is very suitable for efficient programmable logic device (PLD) implementation.
A new algorithm for the modulated complex lapped transform (MCLT) with a sine windowing function is presented. It is shown that by merging the windowing operation with the main computation, both the real and the imagi...
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A new algorithm for the modulated complex lapped transform (MCLT) with a sine windowing function is presented. It is shown that by merging the windowing operation with the main computation, both the real and the imaginary parts of the MCLT with 2N inputs can be obtained from two N-point discrete cosine transforms of type II (DCTs-II) of appropriate inputs. The resulting algorithm is computationally very efficient. In general, the value of N is an even number. When N is a power of 2, the proposed algorithm uses only N log N + 2 real multiplications (including the scaling factors in the DCT computation), with none of those being outside the DCT blocks.
This paper presents a new fast identification algorithm based on Chandrasekhar-type equations. By appropriately defining extended state vectors and corresponding matrices, a state-space model is obtained from the ARMA...
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This paper presents a new fast identification algorithm based on Chandrasekhar-type equations. By appropriately defining extended state vectors and corresponding matrices, a state-space model is obtained from the ARMA representation so that the Kalman filter can be used as a parameter estimator. Because the resulting system is time-invariant it is possible to apply Chandrasekhar factorization techniques to calculate the filter gain and thus produce a fast algorithm. The sparse nature of the state-space matrices greatly facilitates the initial factorization required to launch the recursive algorithm. The new algorithm is very simple and efficient. Its computational complexity per iteration requires 14N multiplications (N = number of ARMA parameters);consequently, a substantial gain in computing time is obtained compared to most other algorithms partaicularly those of lattice type.
This Letter deals with a practical signal processing problem where the filter is zero-phase and is specified by the frequency response. The authors show that if the signal is symmetrically extended, then the fast Four...
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This Letter deals with a practical signal processing problem where the filter is zero-phase and is specified by the frequency response. The authors show that if the signal is symmetrically extended, then the fast Fourier transform (FFT)-based algorithm can be efficiently implemented by using the discrete cosine transform without signal extension. They extend the proposed algorithm to 2D filters and show that computationally it is at least four times more efficient than the FFT-based algorithm. A practical application of the new algorithm is a fast Wiener inverse filter, which is used to estimate images taken under the condition of air turbulence.
A Quick method is developed to find an element or order 2p+1p in the finite field GF(q2), where q = 2p-1 is a Mersenne prime. Such an element is needed to implement complex integer transforms of length 2kp over GF(q2)...
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A Quick method is developed to find an element or order 2p+1p in the finite field GF(q2), where q = 2p-1 is a Mersenne prime. Such an element is needed to implement complex integer transforms of length 2kp over GF(q2) where 3 ≫ k ≪ p + 1.
The new generation video standard, i.e., High-efficiency Video Coding (HEVC), shows a significantly improved efficiency relative to the last standard, i.e., H.264. However, the quad tree structured coding units (CUs),...
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The new generation video standard, i.e., High-efficiency Video Coding (HEVC), shows a significantly improved efficiency relative to the last standard, i.e., H.264. However, the quad tree structured coding units (CUs), which are adopted in HEVC to improve compression efficiency, cause high computational complexity. In this study, a novel fast algorithm is proposed for CU partition in intra coding to reduce the computational complexity. A rough minimum depth prediction of the largest CU method and an early termination method for CU partition based on the total coding bits of the current CU are employed. Many approaches have been proposed to reduce the encoding complexity of HEVC, but these methods do not use the total coding bits of the current CU as the main basis for judgment to judge the CU complexity. Compared with the reference software HM16.6, the proposed algorithm reduces encoding time by 45% on average and achieves an approximately 1.1% increase in Bjntegaard delta bit rate and a negligible peak signal-to-noise ratio loss.
The problem of power and bit allocation in OFDM systems is analysed. A solution algorithm with substantially lower computational complexity than existing algorithms is proposed.
The problem of power and bit allocation in OFDM systems is analysed. A solution algorithm with substantially lower computational complexity than existing algorithms is proposed.
We consider numerical methods for solving the modified Cahn-Hilliard equation involving strong nonlinearities. A fast algorithm based on time two-mesh (TT-M) finite element (FE) scheme is proposed to overcome the time...
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We consider numerical methods for solving the modified Cahn-Hilliard equation involving strong nonlinearities. A fast algorithm based on time two-mesh (TT-M) finite element (FE) scheme is proposed to overcome the time-consuming computation of the nonlinear terms. The TT-M FE algorithm includes three main steps: Firstly, a nonlinear FE scheme is solved on a coarse time-mesh pi(c). Here, the FE method is used for spatial discretization and the implicit second-order theta scheme (containing both implicit Crank-Nicolson scheme and second-order backward difference method) is used for temporal discretization. Secondly, the Lagrange's interpolation is used to obtain the interpolation result on the fine time-mesh. Finally, a linearized FE system is solved on a fine time-mesh tau.(tau < tau(c)). The stability analysis and priori error estimates are provided in detail. Numerical examples are given to demonstrate the validity of the proposed scheme. The TT-M FE method is compared with the traditional Galerkin FE method and it is evident that the TT-M FE method can save the calculation time.
A fast algorithm for computing the two-dimensional (2-D) forward and inverse modified discrete cosine transform (MDCT and IMDCT) is proposed. The algorithm converts the 2-D MDCT and IMDCT with block size M x N into fo...
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A fast algorithm for computing the two-dimensional (2-D) forward and inverse modified discrete cosine transform (MDCT and IMDCT) is proposed. The algorithm converts the 2-D MDCT and IMDCT with block size M x N into four 2-D discrete cosine transforms (DCTs) with block size (M/4) x (N/4). It is based on an algorithm recently presented by Cho et al. [An optimized algorithm for computing the modified discrete cosine transform and its inverse transform, in: Proceedings of the IEEE TENCON, vol. A, 21-24 November 2004, pp. 626-628] for the efficient calculation of one-dimensional MDCT and IMDCT. Comparison of the computational complexity with the traditional row-column method shows that the proposed algorithm reduces significantly the number of arithmetic operations. (c) 2008 Elsevier B.V. All rights reserved.
In this paper, a fast algorithm for image deblurring is discussed. The algorithm is based on generalized inverse iteration, and linearized Bregman iteration for the basis pursuit problem. Numerical experiments show th...
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ISBN:
(纸本)9781510613058;9781510613041
In this paper, a fast algorithm for image deblurring is discussed. The algorithm is based on generalized inverse iteration, and linearized Bregman iteration for the basis pursuit problem. Numerical experiments show that the chaotic algorithm for image restoration is effective and efficient.
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