Mustafa M. Abd Elnaby

Chaotic image encryption in transform domains

This paper presents a new approach for image encryption based on the chaotic Baker map, but in the different transform domains. The discrete cosine transform (DCT), the discrete sine transform (DST), the discrete wavelet transform (DWT) and the additive wavelet transform (AWT) are exploited in the proposed encryption approach. Chaotic encryption is performed in these transform domains to make use of the characteristics of each domain. A comparison study between all transform domain encryption schemes is held in the presence of different attacks. Results of this comparison study are in favor of encryption in the DST domain if the degree of randomness is of major concern.

Efficient implementation of chaotic image encryption in transform domains

Investigation of transform domain encryption.IWT encryption study.Study of chaotic Baker map permutation. The primary goal of this paper is security management in data image transmission and storage. Because of the increased use of images in industrial operations, it is necessary to protect the secret data of the image against unauthorized access. In this paper, we introduce a novel approach for image encryption based on employing a cyclic shift and the 2-D chaotic Baker map in different transform domains. The Integer Wavelet Transform (IWT), the Discrete Wavelet Transform (DWT), and the Discrete Cosine Transform (DCT) are exploited in the proposed encryption approach. The characteristics of the transform domains are studied and used to carry out the chaotic encryption. A comparison study between the transform-domain encryption approaches in the presence of attacks shows the superiority of encryption in the DWT domain.

Automatic Modulation Recognition in Wireless Multi-carrier Wireless Systems with Cepstral Features

Automatic digital modulation recognition (ADMR) has become an interesting problem in wireless communication systems with various civil and military applications. In this paper, an ADMR algorithm is proposed for both orthogonal frequency division multiplexing and multi-carrier code division multiple access systems using discrete transforms and mel-frequency cepstral coefficients (MFCCs). The proposed algorithm uses one of the discrete cosine transform, discrete sine transform, and discrete wavelet transform with MFCCs to extract the modulated signal coefficients, and uses also either a support vector machine (SVM) or an artificial neural network (ANN) for modulation classification. Simulation results show that the proposed algorithm provides higher recognition rates than those obtained in previous studies, in addition to a superiority of SVM performance compared to ANN performance at low signal-to-noise ratios.

Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine

PurposeBasal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identification and classification using image processing techniques is highly required to reduce the diagnosis errors.MethodsIn this study, a novel technique is applied to classify skin lesion images into two classes, namely the malignant Basal cell carcinoma and the benign nevus. A hybrid combination of bi-dimensional empirical mode decomposition and gray-level difference method features is proposed after hair removal. The combined features are further classified using quadratic support vector machine (Q-SVM).ResultsThe proposed system has achieved outstanding performance of 100% accuracy, sensitivity and specificity compared to other support vector machine procedures as well as with different extracted features.ConclusionBasal Cell Carcinoma is effectively classified using Q-SVM with the proposed combined features.

Wavelet fusion for encrypting images with a few details

This paper introduces a new scheme for encrypting images with few details based on wavelet fusion..The fusion is a pre-processing step to change the homogeneity of flat areas in the images having a few details. RC6 or chaotic Baker map encryption are then performed on the fused image.Encryption with chaotic Baker map is just a permutation algorithm that cannot perform well on flat areas of the images, because the permutation yields approximately the same intensities. So, circular shifts on pixels are performed on the fused image prior to chaotic encryption to remove flat areas or reduce the degree of homogeneity.The robustness of the suggested image ciphering schemes is tested in the presence of noise before decryption. Simulation results demonstrated that the suggested image ciphering schemes provide a secure and effective way for encrypting images with few details. This paper introduces a new scheme for encrypting images with a few details based on wavelet fusion. In this scheme, the image with a few details to be encrypted is fused with another image that is rich in details utilizing the Discrete Wavelet Transform (DWT) prior to encryption. The fusion is a pre-processing step to change the homogeneity of flat areas in the images having a few details. RC6 or chaotic Baker map encryption are then performed on the fused image. Encryption with chaotic Baker map is just a permutation algorithm that cannot perform well on flat areas of the images, because the permutation yields approximately the same intensities. So, circular shifts on pixels are performed on the fused image prior to chaotic encryption to remove flat areas or reduce the degree of homogeneity. Chaotic encryption is then performed in the wavelet domain to increase the degree of diffusion. Several metrics are used in this paper for performance evaluation of the suggested ciphering schemes like visual inspection, histogram test, encryption quality analysis, and diffusion analysis. The robustness of the suggested image ciphering schemes is tested in the presence of noise before decryption. Simulation results demonstrated that the suggested image ciphering schemes provide a secure and effective way for encrypting images with few details.

Efficient speaker identification from speech transmitted over Bluetooth networks

This paper studies the process of speaker identification over Bluetooth networks. Bluetooth channel degradations are considered prior to the speaker identification process. The work in this paper employs Mel-frequency cepstral coefficients for feature extraction. Features are extracted from different transforms of the received speech signals such as the discrete cosine transform (DCT), signal plus DCT, discrete sine transform (DST), signal plus DST, discrete wavelet transform (DWT), and signal plus DWT. A neural network classifier is used in the experiments, while the training phase uses clean speech signals and the testing phase uses degraded signals due to communication over the Bluetooth channel. A comparison is carried out between the different methods of feature extraction showing that the DCT achieves the highest recognition rates.

C24. Automatic modulation recognition in wireless systems using cepstral analysis and neural networks

Modulation type is one of the most important characteristics used in signal waveform identification for wireless communications. In this paper, a cepstral algorithm for Automatic Digital Modulation Recognition (ADMR) is proposed. This algorithm uses Mel-Frequency Cepstral Coefficients (MFCCs) to extract the features of the modulated signal and a multi-layer feed-forward Artificial Neural Network (ANN) to classify the modulation type and its order. The proposed algorithm is capable of recognizing the modulation scheme with high accuracy in the presence of Additive White Gaussian Noise (AWGN) over a wide Signal-to-Noise Ratio (SNR) range.

Neural modeling of polynomial image interpolation

In this paper, a neural implementation is suggested for polynomial based image interpolation techniques such as bilinear, bicubic and cubic Spline techniques. The performance of the suggested neural image interpolation algorithm is compared to those of traditional polynomial based image interpolation techniques and to warped distance image interpolation techniques. In this implementation, the modeling of the polynomial interpolation algorithm is performed with a fixed number of neurons contrary to the traditional interpolation techniques, which depend on the order of the interpolation basis function. The proposed approach is efficient from the mean square error (MSE) point of view and the computation complexity point of view by reducing the number of computations required. Simulation results show that the suggested implementation has approximately the same peak signal-to-noise ratio (PSNR) as the traditional techniques with a less computational burden due to the fixed size of the neural networks implemented.

Sensitivity Analysis of a Class of Iris Localization Algorithms to Blurring Effect

This paper presents a study of a class of iris localization algorithms in the presence of blurring. The effect of blurring is a serious problem in most image processing systems. It may originate in iris imaging systems due to out-of-focus effect. It affects the features extracted from the iris images. Hence, the objective of this paper is to study the sensitivity of three popular iris localization algorithms to the presence of blurring. Features are extracted from normal as well as blurred iris images and used for iris localization. Moreover, Wiener filter restoration is used as a tool to combat the effect of blurring. Performance of the compared iris localization algorithms with Wiener filter restoration is also studied. Simulation results reveal that Masek iris localization algorithm has the least sensitivity to the blurring effect. Its accuracy without blurring is 88.2%, and with blurring, it decreases to 68.18%. Moreover, the Wiener filter significantly improves the accuracy of iris localization.

Towards computer vision-based approach for an adaptive traffic control system

ABSTRACT In this paper, an adaptive traffic control system (ATCS) is proposed using the state of the art of video processing techniques. We illustrate how the system controls standard four-way intersections using three parameters; namely, average vehicles flow speed, level of crowdedness of vehicles, and a critical state timer. These parameters are detected from traffic videos using our computer vision algorithm. The ATCS decision-making process has been designed to adapt to predefined priorities over the traffic parameters. The validation of the proposed ATCS has been tested using four synchronized test videos in order to feed the proposed ATCS with different traffic information. Experimental results show a complete adaptation for the traffic flow.