Othman Omran Khalifa

Real time lane detection for autonomous vehicles

An increasing safety and reducing road accidents, thereby saving lives are one of great interest in the context of Advanced Driver Assistance Systems. Apparently, among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. It is based on lane detection (which includes the localization of the road, the determination of the relative position between vehicle and road, and the analysis of the vehiclepsilas heading direction). One of the principal approaches to detect road boundaries and lanes using vision system on the vehicle. However, lane detection is a difficult problem because of the varying road conditions that one can encounter while driving. In this paper, a vision-based lane detection approach capable of reaching real time operation with robustness to lighting change and shadows is presented. The system acquires the front view using a camera mounted on the vehicle then applying few processes in order to detect the lanes. Using a pair of hyperbolas which are fitting to the edges of the lane, those lanes are extracted using Hough transform. The proposed lane detection system can be applied on both painted and unpainted road as well as curved and straight road in different weather conditions. This approach was tested and the experimental results show that the proposed scheme was robust and fast enough for real time requirements. Eventually, a critical overview of the methods were discussed, their potential for future deployment were assist.

Digital watermarking for digital images using wavelet transform

The field of digital watermarking has recently seen vast interests covering theoretical studies, novel techniques, attacks and analysis. This is due to the fact that over the last 15 years, the watermarking community has focused on developing and introducing new techniques for watermark embedding and detection. Analysis of these techniques leads to methods for attack and development of countermeasures which then used to discover faults and limitations in applications, encouraging the development of better ones. In this paper, comprehensive overview of digital watermarking are discussed. This includes the general model, types, applications and future trends of current implementations. The proposed technique is described and analyzed. The paper concludes with future plans of the chosen method in digital watermarking.

English digits speech recognition system based on Hidden Markov Models

This paper aims to design and implement English digits speech recognition system using Matlab (GUI). This work was based on the Hidden Markov Model (HMM), which provides a highly reliable way for recognizing speech. The system is able to recognize the speech waveform by translating the speech waveform into a set of feature vectors using Mel Frequency Cepstral Coefficients (MFCC) technique This paper focuses on all English digits from (Zero through Nine), which is based on isolated words structure. Two modules were developed, namely the isolated words speech recognition and the continuous speech recognition. Both modules were tested in both clean and noisy environments and showed a successful recognition rates. In clean environment and isolated words speech recognition module, the multi-speaker mode achieved 99.5% whereas the speaker-independent mode achieved 79.5%. In clean environment and continuous speech recognition module, the multi-speaker mode achieved 72.5% whereas the speaker-independent mode achieved 56.25%. However in noisy environment and isolated words speech recognition module, the multi-speaker mode achieved 88% whereas the speaker-independent mode achieved 67%. In noisy environment and continuous speech recognition module, the multi-speaker mode achieved 82.5% whereas the speaker-independent mode achieved 76.67%. These recognition rates are relatively successful if compared to similar systems.

Mathematical Model for the Prediction of Microwave Signal Attenuation Due to Duststorm

Signal attenuation caused by duststorm is one of the major problems in utilization of microwave bands for terrestrial and space communication especially at desert and semi desert area. This paper presented a mathematical model developed to predict the microwave signal attenuation due to duststorm. The proposed model enables the convenient calculation of the microwave signal path attenuation which relates visibility, frequency, particle size and complex permittivity of duststorm. The predicted values from the mathematical model are compared with the measured values observed in Sudan and Saudi Arabia shows relatively close agreement.

An overview of video encryption techniques

With the rapid development of various multimedia technologies, more and more multimedia data are generated and transmitted in the medical, commercial, and military fields, which may include some sensitive information which should not be accessed by or can only be partially exposed to the general users. Therefore, security and privacy has become an important. Over the last few years several encryption algorithms have applied to secure video transmission. While a large number of multimedia encryption schemes have been proposed in the literature and some have been used in real products, cryptanalytic work has shown the existence of security problems and other weaknesses in most of the proposed multimedia encryption schemes. In this paper, a description and comparison between encryption methods and representative video algorithms were presented. With respect not only to their encryption speed but also their security level and stream size. A trade-off between quality of video streaming and choice of encryption algorithm were shown. Achieving an efficiency, flexibility and security is a challenge of researchers.

Human computer interaction using isolated-words speech recognition technology

This research paper aims to develop an isolated-word automatic speech recognition (IWASR) system based on vector quantization (VQ). This system receives, analyzes, searches and matches an input speech signal with the trained set of speech signals which are stored in the database/codebook, and returns matching results to users. IWASR is meant to assist customers calling a universitypsilas telephone operator to respond to their enquiries in a convenient way using their natural speech. Callers are assisted to select language, faculty and the staff name they wish to contact. To extract features from speech signals, Mel-frequency cepstral coefficients (MFCC) algorithm was applied. Subsequently, vector quantization was used for all feature vectors generated from the MFCC. A codebook was resulted from training the VQ initial codebook and experimental results showed that the recognition rate has been improved with the increase of codebook size and showed that the codebook size of 81 feature vectors had a recognition rate exceeded 85%.

Hand motion detection from EMG signals by using ANN based classifier for human computer interaction

Todays advanced muscular sensing and processing technologies have made the acquisition of electromyography (EMG) signal which is valuable. EMG signal is the measurement of electrical potentials generated by muscle cells which is an indicator of muscle activity. Other than rehabilitation engineering and clinical applications, EMG signals can also be employed in the field of human computer interaction (HCI) system. In this work, the detection of different hand movements (left, right, up and down) was obtained using artificial neural network (ANN). A back-propagation (BP) network with Levenberg-Marquardt training algorithm was utilized. The conventional time and time-frequency based feature sets have been chosen to train the neural network. The simulation results show that the designed network is able to recognize hand movements with satisfied classification efficiency in average of 88.4%.

Natural speaker-independent Arabic speech recognition system based on Hidden Markov Models using Sphinx tools

This paper reports the design, implementation, and evaluation of a research work for developing a high performance natural speaker-independent Arabic continuous speech recognition system. It aims to explore the usefulness and success of a newly developed speech corpus, which is phonetically rich and balanced, presenting a competitive approach towards the development of an Arabic ASR system as compared to the state-of-the-art Arabic ASR researches. The developed Arabic AS R mainly used the Carnegie Mellon University (CMU) Sphinx tools together with the Cambridge HTK tools. To extract features from speech signals, Mel-Frequency Cepstral Coefficients (MFCC) technique was applied producing a set of feature vectors. Subsequently, the system uses five-state Hidden Markov Models (HMM) with three emitting states for tri-phone acoustic modeling. The emission probability distribution of the states was best using continuous density 16 Gaussian mixture distributions. The state distributions were tied to 500 senons. The language model contains uni-grams, bi-grams, and tri-grams. The system was trained on 7.0 hours of phonetically rich and balanced Arabic speech corpus and tested on another one hour. For similar speakers but different sentences, the system obtained a word recognition accuracy of 92.67% and 93.88% and a Word Error Rate (WER) of 11.27% and 10.07% with and without diacritical marks respectively. For different speakers but similar sentences, the system obtained a word recognition accuracy of 95.92% and 96.29% and a Word Error Rate (WER) of 5.78% and 5.45% with and without diacritical marks respectively. Whereas different speakers and different sentences, the system obtained a word recognition accuracy of 89.08% and 90.23% and a Word Error Rate (WER) of 15.59% and 14.44% with and without diacritical marks respectively.

Design and Optimization of Levenberg-Marquardt based Neural Network Classifier for EMG Signals to Identify Hand Motions

This paper presents an application of artificial neural network for the classification of single channel EMG signal in the context of hand motion detection. Seven statistical input features that are extracted from the preprocessed single channel EMG signals recorded for four predefined hand motions have been used for neural network classifier. Different structures of neural network, based on the number of hidden neurons and two prominent training algorithms, have been considered in the research to find out their applicability for EMG signal classification. The classification performances are analyzed for different architectures of neural network by considering the number of input features, number of hidden neurons, learning algorithms, correlation between network outputs and targets, and mean square error. Between the Levenberg-Marquardt and scaled conjugate gradient learning algorithms, the aforesaid algorithm shows better classification performance. The outcomes of the research show that the optimal design of Levenberg-Marquardt based neural network classifier can perform well with an average classification success rate of 88.4%. A comparison of results has also been presented to validate the effectiveness of the designed neural network classifier to discriminate EMG signals.

Enhancing selective encryption for H.264/AVC using advanced encryption standard

Video encryption algorithms have becomes an important field of research nowadays. As an increasing rate of applying video is getting high, the security of video data becomes more important. A digital media can be transmitted easily in real time anywhere at any time due to the advanced development of communications, Internet and multimedia technology. Information availability has increased dramatically with the advent of mobile devices. However, with this availability comes a problem of maintaining the security of information that is displayed in public. Many approaches have been used or proposed to provide security for information disseminated over the networks. These include encryption, authentication, and digital signatures. For video, the method has been adopted to protect unwanted interception and viewing of any video while in transmission over the networks. In this thesis, a development of an enhanced selective video encryption scheme for H.264/AVC based on Advanced Encryption Standard (AES) was reported. A proposed scheme been used instead of encrypting the entire video stream bit by bit, only the I-Frames bitstreams were encrypted. This scheme took into consideration the good features of former selective encryption algorithms with regard to computational complexity, and data compression performance. The proposed system was tested in the simulated environment using different video sequences. The experimental results show that the proposed method provides adequate security to video streams. It has no effect on compression ratio and does not reduce the original video compression efficiency. Moreover, the proposed scheme provides a good trade-off between encryption robustness, flexibility, and real-time processing. It is an appropriate ii technique for secure H.264 bitstreams that require transmission or storage in un-trusted intermediate devices.