Distributed Arithmetic Coding Over Binary Erasure Channel

In this paper, the Binary Erasure Channel (BEC) is researched by Distributed Arithmetic Coding (DAC) based on Slepian-Wolf coding framework. The source and side information are modelled as a virtual BEC. The DAC decoder uses maximum a posteriori (MAP) as the criterion to recover the source. A deep residual network is used to boost the DAC decoding process. The experimental results show that our algorithm nearly achieves the same performance with LT codes under different erasure probabilities.

Source Symbol Purging-Based Distributed Conditional Arithmetic Coding

A distributed arithmetic coding algorithm based on source symbol purging and using the context model is proposed to solve the asymmetric Slepian–Wolf problem. The proposed scheme is to make better use of both the correlation between adjacent symbols in the source sequence and the correlation between the corresponding symbols of the source and the side information sequences to improve the coding performance of the source. Since the encoder purges a part of symbols from the source sequence, a shorter codeword length can be obtained. Those purged symbols are still used as the context of the subsequent symbols to be encoded. An improved calculation method for the posterior probability is also proposed based on the purging feature, such that the decoder can utilize the correlation within the source sequence to improve the decoding performance. In addition, this scheme achieves better error performance at the decoder by adding a forbidden symbol in the encoding process. The simulation results show that the encoding complexity and the minimum code rate required for lossless decoding are lower than that of the traditional distributed arithmetic coding. When the internal correlation strength of the source is strong, compared with other DSC schemes, the proposed scheme exhibits a better decoding performance under the same code rate.

Implementation of Fir Filter Using Distributed Arithmetic and Distributed Arithmetic Off-Set Binary Coding Algorithms

This paper is the study of implementation of FIR filter using Distributed Arithmetic and Distributed Arithmetic Offset Binary Coding Algorithms. Distributed Arithmetic (DA) had been used to implement an FIR filter due to its high stability and linearity by using look-up table (LUT). The performance of DA technique and DA-OBC for FIR filter design is analyzed and the results are compared to the traditional FIR filter design techniques.

ECG Noise Removal Using Modified Distributed Arithmetic Based Finite Impulse Response Filter

ECG monitoring is essential to support human life. During signal acquisition, the signals are contaminated by various noises that occur due to different sources. This paper focuses on Baseline wander and Muscle Artifact noise removal using Distributed Arithmetic (DA) based FIR filters. An area-efficient modified DA based FIR filter consists of LUT-less structure and used for noise removal. The performance of the modified DA based FIR filter is compared with the conventional DA FIR filter. An arbitrary real-time ECG record is taken from MIT-BIH database and Baseline Wander noise, Muscle artifact noises are taken from MIT-BIH noise stress test database. The performance of both filters is evaluated in terms of output Signal to Noise Ratio (SNR) and Mean Square Error (MSE). For Baseline wander noise removal, the modified DA based FIR filter produces high output SNR and also low MSE of 76.6% than the conventional filter. Similarly, for Muscle Artifact noise removal, it produces high SNR, and MSE is reduced to 73.8%. A modified DA based FIR filter is synthesized for the target FPGA device Spartan3E XC3s2000-4fg900 and hardware resource utilization is presented.