Mahmoud I. Abdalla

Hybrid Face Detection System using Combination of Viola - Jones Method and Skin Detection

paper, a fast, reliable automatic human face and facial feature detection is one of the initial and most important steps of face analysis and face recognition systems for the purpose of localizing and extracting the face region from the background. This paper presents a Crossed Face Detection Method that instantly detects low resolution faces in still images or video frames. Experimental results evaluated various face detection methods, providing complete solution for image based face detection with higher accuracy, showing that the present method efficiently decreased false positive rate and subsequently increased accuracy of face detection system in still images or video frames especially in complex backgrounds.

DWT and MFCCs based Feature Extraction Methods for Isolated Word Recognition

A new method for feature extraction is presented in this paper for speech recognition using a combination of discrete wavelet transform (DWT) and mel Frequency Cepstral Coefficients (MFCCs). The objective of this method is to enhance the performance of the proposed method by introducing more features from the signal. The performance of the Wavelet-based mel Frequency Cepstral Coefficients method is compared to mel Frequency Cepstral Coefficients based method for features extraction. Wavelet transform is applied to the speech signal where the input speech signal is decomposed into various frequency channels using the properties of wavelet transform. then Mel-Frequency Cepstral Coefficients (MFCCs) of the wavelet channels are calculated. A new set of features can be generated by concatenating both features. The speech signals are sampled directly from the microphone. Neural Networks (NN) are used in the proposed methods for classification. The proposed method is implemented for 15 male speakers uttering 10 isolated words each which are the digits from zero to nine. each digit is repeated 15 times.

Detect and Analyze Face Parts Information using Viola- Jones and Geometric Approaches

paper presents a new face parts information analyzer, as a promising model for detecting faces and locating the facial features in images. The main objective is to build fully automated human facial measurements systems from images with complex backgrounds. Detection of facial features such as eye, nose, and mouth is an important step for many subsequent facial image analysis tasks. The study covers the tasks detection, landmark localization and measurement facial part that have traditionally been approached as separate problems with different techniques. Different set of techniques have been introduced recently, for example; principal component analysis, geometric modeling, auto-correlation, deformable template, neural networks, color analysis, window classifiers, view-based Eigen space methods, and elastic graph models. The study present a novel and simple model approach based on a mixture of techniques and algorithms in a shared pool based on Viola-Jones object detection framework algorithm combined with geometric and symmetric information of the face parts from the image in a smart algorithm. The study is a continued part of previous work (1) the proposed model is modestly applied with hundreds of face images taken under different lighting conditions, a number of general assumptions used in this research field are identified.

Enhanced inter-access service network handover authentication scheme for IEEE 802.16m network

The 802.16m mobile worldwide interoperability for microwave access (WiMAX) standard is the amendment to the 802.16e standard. It introduced high mobility features that enable mobile broadband services at vehicular speeds beyond 120 km/h. However, handover latency in mobile WiMAX may affect real-time application sessions such as VoIP at very high vehicle speed. This makes it imperative to develop fast and secure handover schemes for such very high-speed vehicles. One of the main factors that affect handover performance in 802.16m standard is the delay introduced by the authentication procedure when a mobile user moves between base stations (BSs). In this study, a recently proposed intra-access service network (ASN) handover authentication scheme with privacy preservation is extended to a fast symmetrical key inter-ASN handover authentication scheme that avoids the involvement of third party. The proposed scheme solves the pairwise master key backward and forward security problems and provides mutual authentication between the mobile station (MS) and the target BS during inter-ASN handover event. Finally, the proposed scheme provides MS anonymity and reduces the need to a high-quality tamper-proof device in the BS that was required in the previous scheme.

An improved method for speech/speaker recognition

A new improved method for speech/speaker recognition is presented in this paper using combination of discrete wavelet transform (DWT) and Relative Spectra Perceptual Linear Prediction (RASTA-PLP) for feature extraction. A graphical processing unit (GPU) is used for increasing the speed of computations of the neural network (NN) that acts as a classifier. Wavelet transform is applied to the speech signal. Then RASTA-PLP coefficients of the wavelet channels are calculated. Two new sets of features are generated by concatenating the two extracted features and taking the average of the concatenated vector. The obtained feature vector is then fed to NN for classification where parallel computation for NN is introduced using GPU. The objective of the proposed method is to enhance the performance by introducing more features from the signal and applying parallel computations technique leading to an improvement in both the recognition rate and the computational speed whether for a clean or a noisy speech signal for the two proposed methods in comparison to using DWT and Mel-Frequency Cepstral Coefficients (MFCCs).

An efficient algorithm for Arabic optical font recognition using scale-invariant detector

This paper proposes a new algorithm for Arabic optical font recognition (AOFR) as the first stage for Arabic optical character recognition. The proposed algorithm uses scale-invariant detector, gradient-based descriptor, and k-means clustering. The scale-invariant detector is used to find key points that identify the font of an image of printed Arabic text. The work in this paper compares between several scale-invariant detectors and selects the best one for AOFR. A gradient-based descriptor similar to the one in the famous scale-invariant feature transform algorithm is used to describe the detected key points. In addition, k-means clustering is used for font classification. In this paper, the mean recognition rate is used to evaluate the performance of the proposed algorithm. The proposed algorithm shows superior performance when compared with recently published algorithms for AOFR.

An Efficient Source–Channel Coding for Wireless Image Transmission Over Underwater Acoustic Channel

In this paper, a complete system for image transmission in harsh underwater environment is proposed. The key to increase the performance of the system is the use of an efficient image compression algorithm with a bandwidth-efficient modulation technique. The wavelet packet (WP) decomposition is used to get the best image representation and the set partitioning in hierarchical trees is applied on the WP coefficients. The parental conflicts are resolved, the parent–child relationships are adapted and thus the similarities between cross-subbands are preserved. Reed–Solomon is used for forward error correction to combat with the errors in wireless transmission. Orthogonal frequency division multiplexing with differential quadrature phase shift keying is used to transmit the generated bit stream. Effective image quality metrics are used for objective evaluation. Results show that the proposed system manages to transmit images over the limited bandwidth, and to effectively minimize the perceptual degradation.

An IMS-based LTE-WiMAX-WLAN architecture with efficient mobility management

In this paper, a framework is proposed for interworking LTE, WiMAX and WLAN using IP Multimedia Subsystem (IMS) on top of the three technologies. The aim is to provide high quality real-time multimedia services during handoff. The proposed mobility management technique uses Mobile IP (MIP) and Session Initiation Protocol (SIP) together with IMS, which proves to maintain the continuity of an on-going session and data access during roaming between the different radio access technologies. A comparison between the proposed tight coupled architecture using MIP-SIP and the same tight coupled networks using SIP-based mobility management is presented. OPNET Modeler 17.1 is used to simulate the networks. Results show that the total average packet loss is reduced considerably during VoIP sessions using MIP-SIP in the proposed network.

Optimization of Recursive Least Square-Based Adaptive Linear Equalizer for ZigBee Transceiver

An efficient technique to compensate for the channel detrimental effects in ZigBee systems is introduced in this paper. The proposed methodology relies on adding a recursive least square (RLS) based adaptive linear equalizer (ALE) to the physical layer of the receiver side. The performance of the RLS based ALE is investigated inside the ZigBee system under different multipath fading situations: Rician and Rayleigh. Moreover, the paper proposes a methodology for deciding the RLS based ALE’s design parameters. The design procedure depends on solving multiple objectives optimizing function based on genetic algorithms (GAs). The ALE’s parameters are chosen, such that the system experiences minimum bit error rate (BER) with fast convergence response. For design verification purposes, the ZigBee transceiver is modeled in MATLAB Simulink and tested under different fading and signal to noise ratios. In addition, the performance of the RLS adaptation algorithm is compared with the least mean square (LMS) one. The results show that the RLS based ALE provides better ZigBee performance with less BER and fast adaptation response.

Energy Aware Optimized Hierarchical Routing Technique for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) ordinarily be composed of a large number of low-power sensor nodes which having several functions, that are a battery powered, and thus have very limited energy capacity. To lengthen the operational lifetime of a sensor network, energy efficiency should be considered in every aspect of sensor network design. In this paper, Enhanced Hierarchical Routing Technique (EHRT) is proposed to overcome the constraint of limited energy capacity of sensor nodes which enhancing the network lifetime and the energy efficiency. The suggested technique is a cluster-based routing which optimizes the low-energy adaptive clustering hierarchy routing technique (LEACH) by using a modified artificial fish swarm algorithm (AFSA). This modified AFSA selects the optimum clusters’ head (CHs) locations by applying a number of behaviors following, preying and swarming on each cluster separately and using a modified fitness function to compare these behaviors’ outputs to select the best CHs locations for each cluster separately. A framework for evaluating the performance is constructed and applied to verify the efficiency of the suggested technique comparing to other energy efficient routing techniques; optimized hierarchical routing technique (OHRT), low-energy adaptive clustering hierarchy (LEACH), and particle swarm optimized (PSO) routing techniques. The proposed technique yields best results than other techniques OHRT, LEACH, and PSO in terms of energy consumption and network lifetime. It reduces the energy dissipation by factor 0.7 compared with OHRT.