A fast parallel (23, 12, 7) QR decoder based on syndrome-weight determination

In this paper, a time-area efficient hardware decoder of the (23, 12, 7) quadratic residue code, or Golay code, is presented. The key feature of this proposed algorithm lies in fast determination of error positions through the analysis on the weight of syndromes without large operations in finite fields. Comparing with the algorithm using one syndrome, the proposed algorithm is more suitable for the parallel hardware design by using two syndromes. Based on an Altera Cyclone II field-programmable gate array (FPGA) device (EP2C35F484C6), the area cost and the time delay of the complete system are reduced by up to 86.4% and 22.5%, respectively. Using the Taiwan Semiconductor Manufacturing Company (TSMC) 0.18-μm complementary metal-oxide-semiconductor (CMOS) standard cell library, the proposed decoder is 91.8% smaller and 8.3% faster.     

Additive noise removal using a novel fuzzy-based filter

The paper proposes a new fuzzy-based two-step filter for restoring images corrupted with additive noise. The goal of the first step is to compute the difference between the central pixel and its neighborhood in a selected window and to compute a fuzzy membership degree for each difference value using a Gaussian membership function. Computed fuzzy membership values are appropriately utilized as weights for each pixel and then computes the weighted average representing the modified value for the current central pixel. The second step is used as an augmented step to the first one and its goal is to improve the result obtained in the first step by reducing the noise in the color component differences without destroying the fine details of the image. The experimental analysis shows that the proposed method gives better results compared to existing advanced filters for additive noise reduction. Both visual, quantitative and qualitative analysis have been done to prove the efficiency and effectiveness of the proposed method.     

Study of a new method for power system transients classification based on wavelet entropy and neural network

The detection and classification of transient signals are widely applied in many fields of power system. The study of transient signal detection and classification is a sustaining focus of researchers as well as a difficult issue. There are still many problems needed to be solved in this area. Based on the wavelet transform (WT), the idea of entropy and weight coefficient is introduced, and the wavelet energy entropy (WEE) and wavelet entropy weight (WEW) are defined in this paper. The distribution picture of WEE and WEW along with scales are presented for the first time. PSCAD/EMTDC models for six types of transients, namely breaker switching, capacitor switching, short circuit fault, primary arc, lightning disturbance and lightning strike fault, are constructed. With WEE and WEW, the eigenvectors for the six transients are established and a model which uses the eigenvectors as the input of the BP (back-propagation) neural network is set up to realize the classification of these transients. The simulation has been executed based on a 500 kV transmission line model in China and the results show that feature extraction based on WEE and WEW can effectively discover the useful local features. With the help of neural network classifier, it has effective classifying result. This method is applicable in the power system.