A Novel Adaptive Nonlinear Filter-Based Pipelined Feedforward Second-Order Volterra Architecture

Si-Chuan Province Key Lab. of Signal & Inf. Process., Southwest Jiaotong Univ., Chengdu
IEEE Transactions on Signal Processing (Impact Factor: 3.2). 02/2009; DOI: 10.1109/TSP.2008.2007105
Source: IEEE Xplore

ABSTRACT Due to the computational complexity of the Volterra filter, there are limitations on the implementation in practice. In this paper, a novel adaptive joint process filter using pipelined feedforward second-order Volterra architecture (JPPSOV) to reduce the computational burdens of the Volterra filter is proposed. The proposed architecture consists of two subsections: nonlinear subsection performing a nonlinear mapping from the input space to an intermediate space by the feedforward second-order Volterra (SOV), and a linear combiner performing a linear mapping from the intermediate space to the output space. The corresponding adaptive algorithms are deduced for the nonlinear and linear combiner subsections, respectively. Moreover, the analysis of theory shows that these adaptive algorithms based on the pipelined architecture are stable and convergence under a certain condition. To evaluate the performance of the JPPSOV, a series of simulation experiments are presented including nonlinear system identification and predicting of speech signals. Compared with the conventional SOV filter, adaptive JPPSOV filter exhibits a litter better convergence performance with less computational burden in terms of convergence speed and steady-state error.

  • [Show abstract] [Hide abstract]
    ABSTRACT: Sparse representation based classification (SRC) could not well classify the sample belonging to different classes distribute on the same direction. To solve the problem, a Volterra kernel sparse representation based classification (Volterra-SRC) algorithm is proposed in this paper. Firstly, the original face images are divided into non overlapped patches and then mapped into a high dimensional space by utilizing the Volterra kernels. During the training stage, following by the Fisher criteria, the objective function is defined to obtain the optimal Volterra kernels via maximizing inter-class distances and minimizing intra-class distances simultaneously. During the testing stage, a voting procedure is introduced in conjunction with a sparse representation based classification to decide to which class each individual patch belongs. Finally, the aggregate classification results of all patches in a face are used to determine the overall recognition outcome for the given face image. We demonstrate the experiments on ORL and Extended Yale B benchmark face databases and show that our proposed Volterra-SRC algorithm consistently outperforms the original SRC and the proposed has some advantages and robustness in case of small train number samples.
  • [Show abstract] [Hide abstract]
    ABSTRACT: Rapid advances in the field of signal processing are revolutionizing algorithms. This paper describes the concept of adaptive noise cancellation, an alternative method of estimating signals corrupted by additive noise or interference. The Adaptive algorithms are used to improve the convergence rate, signal to noise ratio, stability, mean square error, steady state behavior, tracking, misadjustment has become a focus on digital signal processing. Accurate cancellation of noise in signal processing is a key step of adaptive filter algorithms. In this paper, Acoustic echo cancellation problem was discussed out of different noise cancellation techniques by concerning different parameters with their comparative results. The results shown are using some specific algorithms. The results show, improving convergence rate with less no of taps is the most difficult phase in signal processing applications for the perfect working of any system.
    Computational Intelligence & Computing Research (ICCIC), 2012 IEEE International Conference on; 01/2012
  • [Show abstract] [Hide abstract]
    ABSTRACT: The electroencephalogram (EEG) is the most widely used method for diagnosis of brain diseases, where a good quality of recordings allows the proper interpretation and identification of physiological and pathological phenomena. However, EEG recordings are often contaminated by different kinds of noise. These annoying signals limit severely brain recording utility and, hence, have to be removed. To deal with this problem, in this paper an adaptive filtering framework is proposed for the enhancing of brain signal recordings. This new method is capable of reducing muscle and baseline noise in EEG signals with low EEG distortion and high noise cancellation. The advantages of the proposed method are demonstrated on real and synthetic brain signals with comparisons made to several benchmark methods. Results show that the proposed approach is preferable to the other systems by achieving a better trade-off between deleting noises and preserving inherent brain activities.
    Computers & Electrical Engineering 07/2013; 39(5):1561-1570. · 0.99 Impact Factor