Salaheldin M. Diab

An SVD audio watermarking approach using chaotic encrypted images

This paper presents a new approach for audio watermarking using the Singular Value Decomposition (SVD) mathematical technique. The proposed approach can be used for data hiding in the audio signals transmitted over wireless networks and for multi-level security systems as will be stated in the applications section. This approach is based on embedding a chaotic encrypted watermark in the singular values of the audio signal after transforming it into a 2-D format. The selection of the chaotic encryption algorithm for watermark encryption is attributed to its permutation nature, which resists noise, filtering, and compression attacks. After watermark embedding, the audio signal is transformed again into a 1-D format. The transformation between the 1-D and 2-D formats is performed in the well-known lexicographic ordering method used in image processing. The proposed approach can be implemented on the audio signal as a whole or on a segment-by-segment basis. The segment-by-segment implementation allows embedding the same watermark several times in the audio signal, which enhances the detectability of the watermark in the presence of severe attacks. Experimental results show that the proposed audio watermarking approach maintains the high quality of the audio signal and that the watermark extraction and decryption are possible even in the presence of attacks.

Speech enhancement with an adaptive Wiener filter

This paper proposes an adaptive Wiener filtering method for speech enhancement. This method depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. It is implemented in the time domain rather than in the frequency domain to accommodate for the time-varying nature of the speech signals. The proposed method is compared to the traditional frequency-domain Wiener filtering, spectral subtraction and wavelet denoising methods using different speech quality metrics. The simulation results reveal the superiority of the proposed Wiener filtering method in the case of Additive White Gaussian Noise (AWGN) as well as colored noise.

Fingerprint recognition using mel-frequency cepstral coefficients

This paper presents a new fingerprint recognition method based on mel-frequency cepstral coefficients (MFCCs). In this method, cepstral features are extracted from a group of fingerprint images, which are transformed first to 1-D signals by lexicographic ordering. MFCCs and polynomial shape coefficients are extracted from these 1-D signals or their transforms to generate a database of features, which can be used to train a neural network. The fingerprint recognition can be performed by extracting features from any new fingerprint image with the same method used in the training phase. These features are tested with the neural network. The different domains are tested and compared for efficient feature extraction from the lexicographically ordered 1-D signals. Experimental results show the success of the proposed cepstral method for fingerprint recognition at low as well as high signal to noise ratios (SNRs). Results also show that the discrete cosine transform (DCT) is the most appropriate domain for feature extraction.

A hybrid algorithm for fingerprint enhancement

Quality enhancement of fingerprint images is important for a good performance of Automatic Fingerprint Identification System (AFIS). In this paper, a hybrid fingerprint enhancement algorithm is presented. This algorithm is based on morphological enhancement in the additive wavelet transform domain and Wave Atom denoising. The performance of the proposed algorithm has been evaluated on a set of images. The results are compared with the results obtained using morphological enhancement, only. Our proposed algorithm has given a better performance than using morphological enhancement, only.

A new approach for fingerprint recognition based on mel frequency cepstral coefficients

This paper presents a new fingerprint identification method based on mel frequency cepstral coefficients (MFCCs). This method is based on the transformation of the fingerprint images into a 1-D signals and the extraction of the MFCCs from these signals. Feature matching is performed in the proposed method using neural networks. Wavelet transform of the 1-D fingerprint signals can be used for extraction of much more MFCCs to assist in the recognition process. Also wavelet denoising can be used to reduce noise. Experimental results show that the proposed method is robust in the presence of noise.

Utilization of discrete transforms to conquer the problems of multi-tone systems

Abstract This paper presents a new implementation of discrete multi-tone (DMT) systems based on different discrete transforms that include the discrete sine transform (DST), discrete cosine transform (DCT), and discrete wavelet transform (DWT). The implementation also considers time-domain equalization to mitigate channel distortion. Compared to the fast Fourier transform discrete multi-tone (FFT-DMT) system, the proposed implementations have an advantage in that their energy-compaction property helps in reducing the channel effects. The performance of the DST-DMT, DCT-DMT, DWT-DMT, and FFT-DMT systems, employing a time-domain equalizer (TEQ), is investigated in the paper. It has been demonstrated by computer simulations that the proposed implementations outperform the FFT-DMT system and that the utilization of the TEQ can lead to higher bit rates

Sensitivity of automatic speaker identification to SVD digital audio watermarking

This paper proposes the utilization of SVD digital audio watermarking to increase the security of automatic speaker identification (ASI) systems and presents a study for the effect of watermarking on the ASI system performance. The SVD audio watermarking algorithm can be implemented on audio signals in time domain or in another appropriate transform domain and can be applied to the audio signal as a whole or on a segment-by-segment basis. The speaker recognition system works by generating a database of speaker’s features using the MFCCs and polynomial shape coefficients extracted from each speaker after they are lexicographically ordered into 1-D signals. A matching process is performed for any new speaker to determine if he is belonging to the database or not, using a trained neural network. Experimental results show that the SVD audio watermarking doesn’t degrade the ASI system performance severely. So, it can be used with ASI to increase security. Also, it was shown the segment by segment watermarking in the time domain achieves the highest detectability of the watermark. So, we can say that it is recommended to use SVD segment by segment audio watermarking with ASI systems implementing features extracted from the DCT or the DWT.

Blind separation of noisy images using finite Ridgelet Transform and wavelet de-noising

This paper deals with the problem of blind separation of digital images from noisy mixtures. It proposes the application of a blind separation algorithm on Ridgelet Transform (RT) of the mixed images, instead of performing the separation on the mixtures in the time domain. Soft Wavelet thresholding denoising of the noisy mixtures is recommended in this paper as a preprocessing step for noise reduction. Ridgelet transform is a new directional multi-resolution transform and is more suitable for describing the signals with high dimensional singularities. Finite Ridgelet Transform (FRIT) is a discrete version of ridgelet transform, which is a numerical precision as the continuous ridgelet transform and has low computational complexity. Comparing with time domain, ridgelets find more application on image separation, hence it represents smooth and edge parts of image with sparsity. In addition, the representation of ridgelets contains more directional information. The mixtures images are extracted using ICA which is based on blind source separation technique. The simulation results reveal that the performance of ridgelet transform is better when compared to time domain in digital images separation. The Peak Signal-to-Noise Ratio (PSNR), Signal-to-Noise Ratio (SNR), Root Mean Square Error (RMSE) and Segmental Signal-to-Noise Ratio (SNRseg) are used to evaluate the quality of the separated images.

C34. Enhanced blind, adaptive channel shortening for multi-carrier systems

This paper proposes a new blind, adaptive channel shortening for multi-carrier systems. We use the DST; along with its inverse as a replacement for the inverse fast Fourier transform/fast Fourier transform (IFFT/FFT) stages in DMT systems. The performance of the discrete Fourier transform-DMT (DFT-DMT) system is investigated with the proposed DST-DMT system over the standard carrier serving area (CSA) loop1, the results in this paper show that employing the proposed DST-DMT system rather than the conventional DFT-DMT system can provide a higher bit rate.

New equalization approach for maximizing data rate in discrete multi tone systems

This paper presents a new time domain equalizer (TEQ), which can be used in discrete multi-tone (DMT) systems to maximize the bit rate. The proposed TEQ is tested in the fast Fourier transform based discrete multi-tone (FFT-DMT) system, and in a proposed discrete wavelet transform based discrete multi-tone (DWT-DMT) system. The objective of the proposed DWT-DMT system is to make use of the sub-band decomposition property of the DWT to reduce the channel effects on the transmitted signals. The mathematical model of the proposed TEQ is presented in the paper. Simulation experiments have been carried out to test the effect of the proposed TEQ with the FFT-DMT system and the proposed DWT-DMT system. The results of these experiments show that the performance of the DWT-DMT system with the proposed TEQ is better than the FFT-DMT system with this TEQ over the eight standard carrier serving area (CSA). The results also show that employing the proposed TEQ in the DWT-DMT system can achieve a high bit rate ranging from 2.899 Mbps to 5.369Mbps.