Design, optimization and Real Time implementation of a new Embedded Chien Search Block for Reed-Solomon (RS) and Bose-Chaudhuri-Hocquenghem (BCH) codes on FPGA Board

The development of error correcting codes has been a major concern for communications systems. Therefore, RS and BCH (Reed-Solomon and Bose, Ray-Chaudhuri and Hocquenghem) are effective methods to improve the quality of digital transmission. In this paper a new algorithm of Chien Search block for embedded systems is proposed. This algorithm is based on a factorization of error locator polynomial. i.e, we can minimize an important number of logic gates and hardware resources using the FPGA card. Consequently, it reduces the power consumption with a percentage which can reach 40 % compared to the basic RS and BCH decoder. The proposed system is designed, simulated using the hardware description language (HDL) and Quartus development software. Also, the performance of the designed embedded Chien search block for decoder RS\BCH (255, 239) has been successfully verified by implementation on FPGA board

A Robust Embedded Non-Linear Acoustic Noise Cancellation (ANC) Using Artificial Neural Network (ANN) for Improving the Quality of Voice Communications

Embedded Acoustic Noise Cancellation (ANC) has enjoyed remarkable success in the telecommunication field, and it becomes an essential component in various communications applications, such as digital transmission. So, it is an efficient method used to enhance the quality of communications against noise phenomena which is a problem in communication systems. This paper contributes towards a new non-linear embedded ANC based Artificial Neural Network (ANN) in digital signal processing and backpropagation (BP) of the gradient algorithm. This system is usually required for non-linear adaptive processing digital signals. The neuronal ANC estimates the noise path and subtracting noise from a received signal by minimizing a cost function. It is the mean square error. Thus, also the filter weights are adaptively updated. In this work, we designed and simulated our intelligent embedded ANC model with the help of MATLAB\Simulink software. The proposed system was designed by using embedded functions in Simulink. In addition, all simulation results are performed and verified using Signal Noise to Ratio (SNR) and Mean Square Error (MSE), number of iteration, neuronal architecture, criteria and it has been compared in various scenarios.  Finally, a study and analysis on convergence of neuronal ANC based backpropagation of the gradient algorithm demonstrate that our proposed system can effectively improve the quality of voice communications against the undesired noise. It also provides faster convergence during the back propagation of the gradient. Furthermore, the best values of SNR and MSE show the effectiveness of the proposed model