Power minimisation approach is an effective interference suppression algorithm for satellite navigation systems. It forms automatically deep nulls in the direction-of-arrival (DOA) of interferences without prior infor...
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Power minimisation approach is an effective interference suppression algorithm for satellite navigation systems. It forms automatically deep nulls in the direction-of-arrival (DOA) of interferences without prior information about the DOAs of satellite signals and interferences. However, it cannot provide flat gains for other directions. Thus, the desired satellite signals may be partly suppressed when they locate in the shallow nulls. By combining eigenvalue thresholding method and l1-norm constraint, a new interference suppression algorithm is proposed for satellite navigation systems that would provide approximately flat gains in all directions except that of interferences. However, the l1-norm constraint leads to a non-smooth optimisation problem which cannot be solved by the conventional gradient-basedalgorithm. After that, by utilising the proximal operator, an iterative algorithm is proposed. The simulations demonstrate the effectiveness of the proposed algorithm.
Macroscopic traffic flow model calibration is an optimisation problem typically solved by a derivative-free population based stochastic search methods. This paper reports on the use of a gradient based algorithm using...
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We extend Stochastic Flow Models (SFMs), used for a large class of discrete event and hybrid systems, by including the delays which typically arise in flow movement. We apply this framework to the multi-intersection t...
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The paper observes the Hermite and the Fourier Transform domains in terms of Frequency Hopping Spread Spectrum signals sparsification. Sparse signals can be recovered from a reduced set of samples by using the Compres...
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ISBN:
(纸本)9781538656839
The paper observes the Hermite and the Fourier Transform domains in terms of Frequency Hopping Spread Spectrum signals sparsification. Sparse signals can be recovered from a reduced set of samples by using the Compressive Sensing approach. The under-sampling and the reconstruction of those signals are also analyzed in this paper. The number of measurements (available signal samples) is varied and reconstruction performance is tested in all considered cases and for both observed domains. The signal recovery is done using an adaptive gradient based algorithm. The theory is verified with the experimental results.
Compressive sensing is a very important field of research in signal processing as it is based on the idea that a signal, sparse in a certain transform domain, can be completely recovered based on a small set of availa...
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ISBN:
(纸本)9781728169491
Compressive sensing is a very important field of research in signal processing as it is based on the idea that a signal, sparse in a certain transform domain, can be completely recovered based on a small set of available measurements. Many algorithms, dealing with sparse signal recovery have been proposed through the years. This paper focuses on convex optimization algorithms and explores their performance in two different transform domains - discrete Fourier and discrete cosine transforms. The observed algorithms are the adaptive gradient based algorithm, primal-dual interior point method and the log barrier algorithm, all which are used to solve different formulations of the l1-minimization problem.
In this paper, we propose a new adaptive notch filter algorithm to achieve the fast and accurate narrow-band noise reduction. In the proposed algorithm, we introduce the monotonically increasing function into the grad...
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ISBN:
(纸本)9781479946037
In this paper, we propose a new adaptive notch filter algorithm to achieve the fast and accurate narrow-band noise reduction. In the proposed algorithm, we introduce the monotonically increasing function into the gradient, which provides the fast convergence far away from the optical frequency. We additionally introduce the enhancement function into the gradient to design the steepness of the gradient curve. The proposed gradient can adjust the trade off between the convergence speed and the estimation accuracy more flexibly. Several computational simulations show that the proposed algorithm can simultaneously provide fast convergence and high accurate estimation compared with the conventional NLMS algorithm.
In this paper, the Oppositional Whale Optimization algorithm (OWOA) is applied to Adaptive Noise Canceller (ANC) for the filtering of Electroencephalography/Event-Related Potentials (EEG/ERP) signals. Performance of A...
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In this paper, the Oppositional Whale Optimization algorithm (OWOA) is applied to Adaptive Noise Canceller (ANC) for the filtering of Electroencephalography/Event-Related Potentials (EEG/ERP) signals. Performance of ANC will be improved by calculating the optimal weight value and proposed OWOA technique is used to update weight value. Adaptive filter's noise reduction capability has been tested through consideration of White Gaussian Noise (WGN) over contaminated EEG signals at various SNR levels (-10 dB, -15 dB and -20 dB). The performance of the proposed OWOA algorithm is assessed in terms of Signal to Noise Ratio (SNR) in dB, mean value, and the correlation between resultant and input ERP. In this work, ANCs are also implemented by utilizing conventional gradient-based techniques like Recursive Least Square (RLS), Least Mean Square (LMS) and other optimization algorithms such as Genetic algorithm (GA), Particle Swarm Optimization (PSO) and WOA techniques. In average cases of noisy environment, comparative analysis shows that the proposed OWOA technique provides higher SNR value and significantly lower mean, and correlation as compared to gradient-based and swarm-based techniques. The comparative results show that extracting the desired EEG component is more effective in the proposed OWOA method. So, it has seen that OWOA-based noise reduction technique removing the artifacts and improving the quality of EEG signals significantly for biomedical analysis.
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