In this paper some effective techniques for reducing the computational complexity of cost efficiency analysis models are provided. (C) 2006 Elsevier Inc. All rights reserved.
In this paper some effective techniques for reducing the computational complexity of cost efficiency analysis models are provided. (C) 2006 Elsevier Inc. All rights reserved.
Tang et al. (Eur J Oper Res 263:401-411, 2017) have recently introduced a parallel-batching machine scheduling problem with linearly deteriorating jobs of two agents and presented a computational complexity classifica...
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Tang et al. (Eur J Oper Res 263:401-411, 2017) have recently introduced a parallel-batching machine scheduling problem with linearly deteriorating jobs of two agents and presented a computational complexity classification of various special cases of this problem, including a number of NP-hardness proofs. We refine these results by demonstrating strong NP-hardness of several special cases, which are proved NP-hard in the ordinary sense in Tang et al. (Eur J Oper Res 263:401-411, 2017). Our reduction employs the problem studied in the first issue of Journal of Scheduling.
This note presents a fast stability test algorithm for two-dimensional (2-D) systems which requires max {O(m(2)), O(mn(2) log(2) mn), O(mn(4))} multiplications to test the stability of [GRAPHICS] posed design techniqu...
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This note presents a fast stability test algorithm for two-dimensional (2-D) systems which requires max {O(m(2)), O(mn(2) log(2) mn), O(mn(4))} multiplications to test the stability of [GRAPHICS] posed design technique are compared to two other proposed methods, an integer programming. technique and roundoff of the optimal floating point set of coefficients. Using the Chebyshev metric as the comparison criterion, design of the low pass filter by the parallel genetic algorithm technique proved to be superior than the other methodologies.
Object superposition is a way to derive Bayesian estimators for multiple object tracking using point processes. A low computational complexity Bayesian multiple target tracking filter, based on target superposition, i...
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Object superposition is a way to derive Bayesian estimators for multiple object tracking using point processes. A low computational complexity Bayesian multiple target tracking filter, based on target superposition, is presented. The concept of superposition is introduced and applied to the well-known Joint Probabilistic Data Association (JPDA) filter to derive the JPDA with superposition (JPDAS) filter. The JPDAS intensity function is evaluated to machine precision "for free" by computing the generating functional of the posterior process using complex arithmetic. A simulated example with eight targets is presented.
The reliability of a distributed program in a distributed computing system is the probability that a program which runs on multiple processing elements and needs to communicate with other processing elements for remot...
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The reliability of a distributed program in a distributed computing system is the probability that a program which runs on multiple processing elements and needs to communicate with other processing elements for remote data files will be executed successfully. This reliability varies according to (1) the topology of the distributed computing system, (2) the reliability of the communication links, (3) the data files and program distribution among processing elements, and (4) the data files required to execute a program. This paper shows that solving this reliability problem is NP-hard even when the distributed computing system is restricted to a series-parallel, a 2-tree, a tree, or a star structure. (C) 1997 Elsevier Science B.V.
The convolutional neural network (CNN) based power amplifier (PA) model has been proven to reduce the model complexity significantly. However, due to the calculation mode of the convolutional structure, the applicatio...
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The convolutional neural network (CNN) based power amplifier (PA) model has been proven to reduce the model complexity significantly. However, due to the calculation mode of the convolutional structure, the application of the CNN-based predistortion model still faces the problem of high computational complexity. In this letter, we use one lightweight CNN to propose a modeling method of the predistorter with low computational complexity for the wideband PA. This method first decomposes the traditional two-dimensional convolution kernels into two kinds of one-dimensional convolution kernels, to create the predesigned filter layer. These two kinds of convolution kernels are used to successively construct the nonlinear terms and the cross basis function terms required by the digital predistortion (DPD) model, respectively. Then, the unnecessary connections of the fully connected structure are removed using the pruning method based on amplitudes, to further reduce the complexity. Experimental results based on 100 MHz Doherty PA show that this predistortion model can significantly reduce the computational complexity, while ensuring that the linearization effects do not deteriorate.
A Fast linear convolution algorithm based on the Discrete Hirschman Transform (DHT) provides increased hardware flexibility and reduced computational complexity compared to those based on the Fast Fourier Transform (F...
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A Fast linear convolution algorithm based on the Discrete Hirschman Transform (DHT) provides increased hardware flexibility and reduced computational complexity compared to those based on the Fast Fourier Transform (FFT). This DHT convolution can be realized by block-processing filters. We propose a hardware-efficient structure to implement the DHT convolution filter. A digital data example is used to discuss its improvement in computational complexity. Observation indicates that our proposed DHT convolution filter either enjoys the same peak performances as its FFT competitor, or even reduces more computational load with a slightly larger output size. This performance can be further enhanced using alternative DHT-based methods.
In this paper, we propose a computationally efficient code-book search method in code-excited linear prediction. The proposed method can reduce the computational complexity by almost one half as compared to the freque...
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In this paper, we propose a computationally efficient code-book search method in code-excited linear prediction. The proposed method can reduce the computational complexity by almost one half as compared to the frequency-domain codebook search method that is currently regarded as the fastest search method. This reduction is possible as a result of the simultaneous use of frequency-domain search and code vector sparsity.
Proposed is an interpolation-free quarter-pixel motion estimation method based on a mathematical model for video encoding. In this method, only three shift operators and four compare operators are required in each hor...
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Proposed is an interpolation-free quarter-pixel motion estimation method based on a mathematical model for video encoding. In this method, only three shift operators and four compare operators are required in each horizontal and vertical direction to find a quarter-pixel resolution motion vector. Experimental results show that the proposed method reaches almost the same performance as an interpolation-based full-search for quarter-pixel motion estimation with much less computational complexity.
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