Maha Elsabrouty

Receiver-based packet loss concealment for pulse code modulation (PCM G.711) coder

This paper introduces a high-performance concealment algorithm for packetized PCM-coded speech as in ITU-T Recommendation G.711. The proposed prediction algorithm implements a combination of linear prediction model and reverse-order replicated pitch period technique as implemented in the ITU-T G.711 Appendix A (ITUT Recommendation G.117, November 2000). The new algorithm is compared to the ITU-T G.711 Appendix A standard and to the commercial tool of packet repetition. It is shown to produce better concealment quality in almost all cases. ? 2003 Elsevier B.V. All rights reserved.

A new hybrid long-term and short-term prediction algorithm for packet loss erasure over IP-networks

Packet loss is a common problem in Internet protocol (IP) networks. Delayed, misrouted or corrupted packets all introduce a gap in the information stream being transmitted. This gap is even more critical in the case of real time voice transmission that does not tolerate delay. The receiver in this case is obliged to generate a signal to play instead of the missing speech segment. This paper introduces a high performance speech concealment algorithm for PCM coded speech. The proposed algorithm implements a combination of linear prediction model and reverse order replicated pitch period (RORPP) implemented as in the ITU-T G.711. The new algorithm produced better objective MOS scores when compared to both the commercial tool of packet repetition and to the above mentioned ITU-T long term prediction standard.

Modified Iterative Two-Stage Hybrid Decoding Algorithm for Low-Density Parity-Check (LDPC) Codes

This paper considers a modified iterative version of the two-stage hybrid algorithm for decoding low-density parity- check (LDPC) codes. The hybrid-decision scheme is a decoding scheme used that combines two iterative decoding algorithms for decoding LDPC codes. This scheme is suitable for many applications such as audio and video transmission that are sensitive to time. The hybrid-decision scheme mixes between the characteristics of the soft-decision decoding scheme and the hard- decision decoding scheme to reduce the computational complexity of the whole decoding algorithm. The modification proposed in this paper is applied to the implementation-efficient reliability ratio weighted bit-flipping (IERRWBF) algorithm which represents hard-decision scheme in the hybrid algorithm. This modification is capable of achieving better performance than that of the hybrid decoding algorithm with reducing the number of iterations required at each SNR and approaching more to the performance of the SPA. This reduction is more observable as the maximum number of iterations assigned for the algorithm increases or as the code length increases with improving the error performance as proved by simulation results.

Face detection using PCA and skin-tone extraction for drowsy driver application

Face detection plays a huge role in many applications such as security, surveillance and human-computer interface. This paper presents a new face detection for the purpose of drowsy driver assistant system. The algorithm is based on a combination two different principles of detection, namely detection of skin color and modified PCA analysis. The algorithm has shown improved performance compared to using either of the principles alone and is performing well under different lighting conditions.

A new on-line negentropy-based algorithm for blind source separation

Negentropy is one of the principal techniques for independent component analysis. It serves as a multi-purpose tool for both blind signal separation (BSS) and blind signal extraction (BSE). However, the main and most widely used algorithm based on negentropy, namely Fast-IC works in batch mode. A fast on-line operation with a fast convergence rate is very useful in tracking non-stationary sources. In this paper, we modify the cost function of negentropy to produce an improved on-line algorithm. Simulation results of the proposed algorithm prove its good performance and remarkable convergence rate.

Low complexity iterative decoding algorithm for low-density parity-check (LDPC) codes

The reliability ratio weighted based bit-flipping (RRWBF) algorithm for decoding low-density parity-check (LDPC) codes has recently been developed to provide the best performance among all existing bit-flipping based algorithms. The implementation efficient reliability ratio weighted based bit-flipping (IERRWBF) algorithm speedup the original algorithm to decrease the processing time used. A drawback for this algorithm is the decrease in the improvement as the maximum number of iterations assigned for the algorithm increase as a large percentage of decoding time is spent on the iteration part without any change in the performance. In this paper, a modified version for this algorithm is proposed to solve this drawback by reducing the number of iterations required to achieve the same performance of the existing IERRWBF algorithm using efficient number of iterations instead of using the maximum number of iterations for decoding without any change in the performance of the IERRWBF.

Two-Stage Hybrid decoding for Low-Density Parity-Check codes

Low-density parity check (LDPC) codes are gaining increased attention in information theory field. However one of the main problems facing usage of these codes in communication systems is its high complexity decoding scheme that results in high decoding delay. Such a delay is not acceptable in many applications. This paper presents, a new hybrid decoding scheme. The proposed scheme mixes between soft-decision and hard-decision algorithms and provides a good tradeoff between error performance and decoding delay as proved by simulation results.

A New Diagonal Hessian Algorithm for Blind Signal Separation

A new algorithm for blind signal separation of speech signals that does not require pre-whitening is proposed in this paper. The algorithm is based on second order optimization using Riemannian geometry. The algorithm employs several practical approximations to the Hessian matrix of the maximum-likelihood blind separation cost function, to produce a computationally efficient algorithm that is capable of working on-line. Simulation results show the improved performance of the proposed algorithm with different mixing data

A Classical Adaptive Filtering Blind Signal Separation

A classical adaptive filtering view of the problem of instantaneous blind signal separation is presented. This classical form enables an easy understanding of the natural gradient algorithm. A new RLS-based algorithm is developed using this classical interpretation. The algorithm provides improved on-line separation speed under the same steady state error compared to the natural gradient algorithm without requiring pre-whitening

An RLS-Iterative Inversion Approach for Blind Signal Separation

A new algorithm for blind signal separation that does not require pre-whitening is proposed in this paper. The algorithm is based on an iterative inversion of the mixing matrix. The algorithm is capable of working on-line and provides improved convergence speed and steady state error compared to the popular natural gradient algorithm, with a low additional computational cost