Khaled Almustafa

On Achievable Data Rates and Optimal Power Allocation in Fading Channels with Imperfect Channel State Information

Achievable rates of wireless communication systems with pilot-based channel estimation are investigated for the case of time-selective fading. Novel analytical expressions for the maximum achievable rates of such systems are derived in terms of the system signal-to-noise ratio (SNR), fading rate and estimation scheme deployed. The frame size is optimized jointly based on the SNR and the fading rate. The maximum rate achieving coding scheme is suggested and shown to be a modified version of the classical water-filling algorithm that accounts for imperfect channel state information (CSI) at the transmitter. The impact of the estimation scheme and the angular spread of the received signal on the quality of estimation and achievable rates is evaluated. A number of numerical simulations are provided to illustrate the dependence of the optimal block length and achievable rates on SNR, fading rate, estimation scheme and angular spread of the channel.

Peak position recognizing characters in Saudi license plates

In this paper character recognition in Saudi Automobile License Plates is described. Due to special properties of Saudi license plates, simpler procedures as compared to the ones used for Lebanese plates have been developed. A limited character set for recognition enables the development of smaller recognition trees. The developed procedure was applied to different characters taken from real license plates and the recognition rate was 100% for characters supported by the algorithm. Uniformly distributed pseudo-random noise was added to simulate error in the image. The algorithm was proven to work even in cases in which the characters were extremely degraded by noise.

Recognizing characters in Saudi License Plates using character boundaries

In this paper character recognition in Saudi Automobile License Plates is described. Due to special properties of Saudi license plates, simpler procedures as compared to the ones used for Lebanese plates have been developed. A limited character set for recognition enables the development of smaller recognition trees. The developed procedure was applied to different characters taken from real license plates and the recognition rate was 100% for characters supported by the algorithm. Uniformly distributed pseudo-random noise was added to simulate error in the image. The algorithm was proven to work even in cases in which the characters were extremely degraded by noise.

On Achievable Data Rates and Optimal Power Allocation in Fading Channels with Imperfect CSI

Achievable rates of communication systems with pilot-based channel estimation are investigated. Analytical expressions for the maximum achievable rates of such systems are derived for a given quality of estimation. It is shown how the classical waterfllling algorithm should be modified for the case of imperfect channel state information at the transmitter. The impact of the estimation scheme on achievable rates is studied and optimal frame lengths are found for analytical models with varying angular spreads.

Designing of next-generation hybrid optical-wireless access network

Due to the emergence of numerous bandwidth-hungry applications, we are motivated to investigate cheaper and faster Internet access solutions to serve in a neighborhood. We concentrate on the convergence of optical and wireless networks for the deployment of Internet access networks so that we can exploit the opportunities of both technologies. We focus on network dimensioning and placement of equipment in hybrid optical-wireless access networks. A number of integrated optical-wireless architectures have been investigated for the greenfield deployment of future access networks. A novel hybrid network infrastructure, namely PON-LTE-WiFi, has been proposed where fiber will be deployed as deeply as affordable/practical and then, wireless systems will be used to extend this connectivity to a large number of locations and ultimately connect the wireless end users. We propose a 3-phase network design optimization scheme for greenfield deployment of PON-LTE-WiFi access network infrastructure. Finally, we propose an ILP model which optimizes the greenfield deployment of LTE network based on the static distribution of mobile user equipment (MUE). The proposed model takes into account various physical layer constraints of LTE network and determines the optimal clustering of MUEs as well as the location of eNBs in a neighborhood. Computational experiments have been conducted on three different data sets consisting of 128, 256 and 512 mobile user equipment in order to evaluate the performance of the proposed scheme.

Cost Analysis of SDN/NFV Architecture over 4G Infrastructure

Abstract Two complementary architectures, software defined networking (SDN) and network function virtualization (NFV) are emerging to comprehensively address several networking issues. In this work, we introduce the most embraced virtualization concepts proposed by SDN and NFV architectures. We quantitatively evaluate hardware and energy cost savings with these two SDN and NFV architectures compared to the existing state-of-the-art network 4G hardware technologies.

A new feature extraction method for license plate recognition

In this paper, character recognition found in license plates is described. The developed procedure is based on real license plates. The numbers are limited to ten classes (0-9). The character recognition problem is a very important problem and many people worked on implementing different methods. One of the successful set of methods to recognize characters from a closed set are the methods which uses lines, but this method suffers from the fact that the number of lines and the thresholds for each feature in each line are selected manually for each set of characters. Our goal is being able to develop the optimal recognition tree in the classification process automatically. Several phases are needed in order to recognize a character. In the feature extraction phase, we introduce two new features; the first feature is related to the quantization process on a specific feature, and the second feature is the combination of several features to form new features. The developed algorithm was applied to different datasets in license plates from KSA; and the recognition rate was above 95%. In this paper, we are concerned on the English Numbers in the KSA license plates.

Evolution of Hashtags on Twitter: A Case Study from Events Groups

Twitter is a micro-blogging interactive platform that captured fame due to its simple features that allowed the communication between users. As the interactive communication grew bigger and faster, a feature called Hashtags became very known, famous and recognized by most of users as it acquainted the themes behind the posted tweets. This study focuses on the factors causing people to participate on some trendy hashtags on Twitter online social network. Consequently, these factors affect the evolution of such trendy hashtags over time. In order to study this evolution, a dataset reflecting real tweets from common events occurring between December 2015 and January 2016 were crawled. The reciprocal effect of users’ topological features and activity levels has been studied. Two Influence Measures and one Topological Measure have been introduced in this work. Moreover, other measures available in the literature such as Activity Measures and Centrality Measures have been used. These measures, along with the three newly introduced measures contributed in the determination of the measures that might be influential for a user to attract other users to a certain hashtag. In this work, the focus is on the centrality levels in addition to the activity levels of users participating on the hashtags under study and the effect of those levels on the activity or the membership of other users on same hashtags.

Architecture Considerations for Big Data Management

A network architecture is concerned with holistic view of interconnection of different nodes with each other. This refers to both physical and logical ways of interconnection of all nodes in the network. The way in which they are connected influences the strategies adopted for Big Data Management. In this present day of Internet of Things (IoT), each kind of device is required and made possible for communicating with other completely different kinds of devices. The heterogeneous nature of devices in the network needs a completely new architecture to efficiently handle Big Data which is generated continually, either for providing services to end users or for study and analysis in a research process. It is thus very essential to visit various kinds of devices that are available on the Internet, their characteristics and requirements, how they communicate and process data, and eventually how the human society embraces the Big Data generation for their daily consumption. This paper is dedicated to bringing all theses aspects together in one place, bringing different technologies into one single network architecture.

The recognition of the closed-set patterns in the Saudi license plates

In this paper, four closed-set patterns in the Saudi license plates can be recognized using our system. The four closed-set patterns are the numbers and letters in both Arabic and English languages. The system is based on classifying each closed-set in a single decision tree. We applied different feature extraction methods in order to analyze each possible line crossing the images. We studied the performance of the classification by adding the Gaussian noise to the license plate images, and finally we proposed a method in order to enhance the recognition accuracy with or without the image noise.