This paper develops infinite impulse response (IIR) lattice structures based on two widely known blind equalization algorithms, which are the multi-modulus algorithm (MMA) and the constant modulus algorithm (CMA). The motivation for doing so is that generally for algorithms to achieve the same performance, an IIR filter requires fewer coefficients than its corresponding finite impulse response (FIR) counterpart. Moreover, the monitoring of the stability of an IIR lattice filter is simpler and less computationally complicated than that of the direct-form IIR filter. Computer simulations that compare the IIR lattice-based blind equalization algorithm with the corresponding FIR transversal algorithms and FIR-based lattice algorithms demonstrate that the former is especially suitable for equalizing channels, yielding inter-symbol interference (ISI) that mostly results from the post-cursor term.

This paper analyzes and compares the robustness of the constant modulus algorithm (CMA) and multimodulus algorithm (MMA) in the presence of additive channel noise when the channel satisfies the zero-forcing conditions. A generalized formulation of the relocation of stationary points toward the origin in terms of signal-to-noise ratio (SNR) was developed for both the CMA and the MMA to facilitate the comparison of the sensitivity to channel noise of CMA with that of MMA for various source constellations. Relationships between the relocation of the stationary points for the MMA and CMA were derived. Simulation results appear to confirm the main results of this work, regarding the sensitivity to channel noise when the fractionally spaced CMA (FS-CMA) and fractionally spaced MMA (FS- MMA) criteria were applied with various sources.

This work investigates the effect of channel noise on the performances of the constant modulus algorithm (CMA), the multimodulus algorithm (MMA), and a phase tracker (PT), following the CMA for carrier phase recovery, when various symmetrical two-dimensional complex-valued source constellations are used. The contributions of this work are two-fold. First, a generalized formulation of the relocation of stationary points toward the origin owing to channel noise is derived for both the CMA and the MMA, allowing their sensitivity to channel noise to be explicitly compared when various constellations are used. Second, the curvatures of the error surface at the stationary points in both the radial and the angular directions are reduced in a way that is linearly proportional to the square of the distances between the stationary points and the origin as the stationary points of the CMA and the MMA relocate toward the origin in the presence of additive channel noise. The reduction of the error surface curvature in the radial direction reduces the rate of blind equalization convergence, whereas the reduction in the angular direction degrades the capacity for carrier phase recovery.

A novel decision-based fuzzy averaging filter consisting of a new Dempster-Shafer (D-S) noise detector and a two-pass noise filtering mechanism is proposed. Bodies of evidence are extracted, and the basic belief assignment is developed, avoiding the counter-intuitive problem of Dempster's combination rule. The combination belief value can be the decision rule for the D-S noise detector. A fuzzy averaging method where the weights are constructed using a predefined fuzzy set is developed to achieve noise cancellation. Besides that, a simple second-pass filter is also employed to improve the final filtering performance. Experimental results have confirmed the proposed filter outperforms other decision-based filters in terms of both noise suppression and detail preservation.

A new two-pass switching rank-ordered arithmetic mean (TSRAM) filter controlled by evidence fusion is proposed for fuzzy image restoration. The new filter mechanism is composed of an efficient impulse detector based on Dempster-Shafer evidence theory and a rank-ordered arithmetic mean filter that works by estimating the noise-free ordered mean values. A modified Dempster’s combination rule is proposed and applied to the impulse detector. In addition, to improve the filtering performance, a second pass filtering is performed using a simple switching median filter. Experimental results have demonstrated that the proposed filter outperforms other switching-based median filters in terms of both noise suppression and detail preservation. The proposed new filter also provides excellent robustness at various percentages of impulsive noises.

This work derives general formulas for the widely known constant modulus algorithm (CMA) and multimodulus algorithm (MMA) error surface curvatures at all of the stationary points with arbitrary symmetric two-dimensional signal constellations, regardless of the in-phase and quadrature dependency of the source symbols. Once these curvatures are computed, the effect of the source distributions on the capabilities of equalizer convergence and phase tracking may be accurately predicted and evaluated.

An adaptive median-type filter controlled by evidence fusion is proposed for fuzzy image restoration. The fusion of evidence based on the Dempster-Shafer evidence theory, providing a way to deal with the uncertainty in the evidence fusion, indicates to what extent an impulsive noise is considered. The setting membership function of impulsive corruption is based on the belief value of the input signal sequence, and the weight is indicated for filtering operation. The efficient step-like function is used to partition the belief space, and the least mean square (LMS) algorithm is applied to obtain the optimal weight for each block. Experimental results have demonstrated that the proposed filter can outperform many well-accepted median based filters in preserving image details while effectively suppressing impulsive noise.

This paper proposes a novel asymmetric subsethood-based neural fuzzy network (ASNFN) that identifies and controls nonlinear dynamic systems. ASNFN has the flexibility to handle both numeric and linguistic inputs. The numeric inputs in ASNFN are fuzzified by input nodes as tunable feature fuzzifiers. Connections in ASNFN are represented by Pseudo-Gaussian fuzzy sets which provide the neural fuzzy network with higher flexibility and which get more accurate optimization. An on-line self-constructing learning algorithm that is constructed and implemented in ASNFN consists of structural learning and parametric learning, and would create adaptive fuzzy logic rules. Computer simulations illustrate the performance and capability of the proposed model in identifying a dynamic system, in Iris data classification, and in backing to park the truck.

In this letter, a novel adaptive filter, the adaptive two-pass median (ATM) filter based on support vector machines (SVMs), is proposed to preserve more image details while effectively suppressing impulse noise for image restoration. The proposed filter is composed of a noise decision maker and two-pass median filters. Our new approach basically uses an SVM impulse detector to judge whether the input pixel is noise. If a pixel is detected as a corrupted pixel, the noise-free reduction median filter will be triggered to replace it. Otherwise, it remains unchanged. Then, to improve the quality of the restored image, a decision impulse filter is put to work in the second-pass filtering procedure. As for the noise suppressing both fixed-valued and random-valued impulses without degrading the quality of the fine details, the results of our extensive experiments demonstrate that the proposed filter outperforms earlier median-based filters in the literature. Our new filter also provides excellent robustness at various percentages of impulse noise.