Search

This paper proposes a novel computational efficient adaptive nonlinear equalizer based on combination of finite impulse response (FIR) filter and functional link artificial neural network (CFFLANN) to compensate linear and nonlinear distortions in nonlinear communication channel. This convex nonlinear combination results in improving the speed while retaining the lower steady-state error. In addition, since the CFFLANN needs not the hidden layers, which exist in conventional neural-net- work-based equalizers, it exhibits a simpler structure than the traditional neural networks (NNs) and can require less computational burden during the training mode. Moreover, appropriate adaptation algorithm for the proposed equalizer is derived by the modified least mean square (MLMS). Results obtained from the simulations clearly show that the proposed equalizer using the MLMS algorithm can availably eliminate various intensity linear and nonlinear distortions, and be provided with better anti-jamming performance. Furthermore, comparisons of the mean squared error (MSE), the bit error rate (BER), and the effect of eigenvalue ratio (EVR) of input correlation matrix are presented. [ABSTRACT FROM AUTHOR]

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 pro- posed. 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 (SOY), and a linear combiner performing a linear mapping from the intermediate space to the output space. The corresponding adaptive algorithms are deduced for the non- linear 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 SOY filter, adaptive JPPSOV filter exhibits a litter better convergence performance with less computational burden in terms of convergence speed and steady-state error. [ABSTRACT FROM AUTHOR]

A fast stable normalised least-mean fourth (FSNLMF) algorithm is proposed. The major drawback of the stable normalised least-mean fourth (SNLMF) algorithm is its poor convergence rate, especially in the presence of high signal-to-noise ratios (SNRs). Considering the effect of high SNRs, a corresponding constant is introduced in SNLMF algorithm to offer a fast convergence. Simulations on system identifications demonstrate that the proposed FSNLMF algorithm achieves a faster convergence speed than the SNLMF algorithm. [ABSTRACT FROM AUTHOR]

The discrepancies between geophysical measurements and forward-modelled data are commonly modelled as Gaussian errors, thereby necessitating the use of least-squares optimisation methods. However, given the many inevitable difficulties and ambiguities in data acquisition, processing, and interpretation, subsurface-property estimation from remote geophysical measurements is subject to non-Gaussian errors. We propose to minimise the misfit with a robust error measure, which is based on a generalised Gaussian distribution. A suboptimal solution is proposed through a mixed-norm functional combination of the l1 norm and l2 norm. A mixed-norm parameter is introduced to determine the relative importance between the l1 norm and l2 norm functional, which is a function of the kurtosis of the errors. The novelty of the proposed mixed-norm algorithm is that no knowledge of the seismic errors is required. The relative contributions of the l1 norm and l2 norm are adjusted based on the partially inverted elastic properties. Both synthetic and field data demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this letter, a novel pipelined decision feedback RNN equalizer (PDFRNE) with low computational complexity is proposed. Since each module is a DFRNN with the decision feedback structure so that it can eliminate the past error remaining in the network. Moreover, the performance can be further improved. At the same time, it can overcome the unstableness due to its nature of the infinite impulse response (IIR) structure. [ABSTRACT FROM PUBLISHER]

Nonlinear effects in an acoustic metamaterial with simultaneous negative modulus and density based on Helmholtz resonators and membranes periodically distributed along a pipe are studied theoretically. Analyses of the transmission coefficient and dispersion relation of the composite system are realized using the acoustic transmission line method and Bloch theory, respectively. Due to the nonlinearities of the Helmholtz resonators and membranes, the acoustic wave propagation properties vary with the different incident acoustic intensities, and the frequency band gaps of the transmission coefficient are amplitude dependent. The nonlinearities shift the double negative pass band into the adjacent modulus negative forbidden band and transform the metamaterial from an acoustic insulator into an acoustic conductor, leading to some new potential acoustic applications. [ABSTRACT FROM AUTHOR]

Objective: Small meningiomas located in the atrium of the lateral ventricle remain a challenge for neurosurgeons due to the eloquent nature of the surrounding anatomy. Functional MRI (fMRI) and diffusion tensor tractography (DTT) allow for in vivo demonstrations of eloquent cortical structures and neuronal fiber tracts, respectively. Our objective is to evaluate the contribution of functional neuronavigation combined with fMRI and DTT results to surgical outcomes. Materials and methods: we investigated 11 patients with small meningiomas located in the atrium of the lateral ventricle who underwent surgery with the aid of functional neuronavigation via the paramedian parieto-occipital approach. The patients willingly underwent assessments of neurologic deficits preoperatively and postoperatively at discharge and at three months after surgery. Results: Gross total resection was achieved in all patients, and no residual or recurrent tumors were observed on follow-up imaging. There was no mortality. Only one patient suffered from transient postoperative aphasia (mild to moderate) that was resolved one week after surgery. No novel neurologic deficits were present in any of the other patients, and no new-onset epileptic attacks were observed. Conclusions: With the aid of the neuronavigation that incorporates fMRI and DTT results, small meningiomas located in the atrium of the lateral ventricle can be safely resected through the paramedian parieto-occipital approach. [ABSTRACT FROM AUTHOR]