Deep Reinforcement Learning (DRL) has become a fundamental element in advancing Autonomous Systems, significantly transforming fields like autonomous vehicles, robotics, and drones. This survey paper provides a comprehensive overview of the role of DRL in autonomous systems, focusing on recent advancements, applications, and challenges. Through a synthesis of existing literature and case studies, the paper elucidates key principles, methodologies, and implications of integrating DRL into autonomous systems. The systematic examination of selected papers reveals recurring patterns, emerging trends, and identifies gaps and opportunities for further research. By exploring the applications of DRL across different autonomous systems, commonalities, distinctions, and prevalent challenges are discussed, laying the groundwork for future advancements and practical implementations in this rapidly evolving field.

Volumetric video streaming technologies are the future of immersive media services such as virtual, augmented, and mixed-reality experiences. The challenges surrounding such technologies are tremendous due to the high network bandwidth needed to produce high-quality and low-latency streams. Many techniques and solutions have been proposed across the streaming workflow to mitigate such challenges. To better understand and organize these developments, this survey adopts an architectural framework to showcase current and emerging techniques and solutions for volumetric video streaming while highlighting some of their characteristic challenges and opportunities.

Efficient exam timetabling is a crucial aspect of academic management, particularly in a university with many programs and many students, such as the University of Sharjah. This paper addresses the challenges of exam scheduling at the University of Sharjah by proposing two metaheuristic algorithms: the simulated annealing algorithm and the genetic algorithm. Both algorithms were evaluated based on conflict resolution, location diversity, and runtime. The experimental results indicate that both algorithms performed well overall, showcasing the potential of advanced optimization techniques in enhancing academic operations and student experiences. Both algorithms optimize course assignments within capacity and scheduling constraints, ensuring fair and efficient exam scheduling. While the simulated annealing algorithm showed potential in diversifying course-location assignments, the genetic algorithm outperformed the simulated annealing algorithm in terms of runtime efficiency, contributing to efficient exam scheduling processes. Future research could explore hybrid approaches for further enhancements.

The Internet of Things (IoT) represents a pervasive system that continuously demonstrates an expanded application in various domains. The energy-efficiency problem has always been a crucial issue linked to this type of network where the system lifetime strongly depends on devices’ batteries. Numerous energy-efficient networking protocols have been proposed in the literature to increase the system lifetime. However, most of the proposed approaches deal with the short-term vision of energy consumption and omit to consider the rechargeable battery degradation when evaluating the network lifetime. Indeed, the major parts of the network devices use rechargeable batteries that age and degrade over time due to several factors (temperature, voltage, charging/discharging cycle, etc.). Therefore, it is essential to promptly detect these internal and environmental degradation factors to avoid network failures. Clustering represents one of the main wireless network protocols and plays an essential role in network self organizing. In this work, we propose a novel long-term energy optimization clustering approach based on battery State of Health (SoH) prediction, called LECA_SOH. The objective is to predict the impact of cluster heads election on the rechargeable batteries SoH before applying the clustering. LECA_SOH fosters the selection of the nodes, which will less suffer from battery degradation during the future rounds, leading to extend the system lifetime. The obtained results demonstrate that the proposed clustering approach improves the network lifetime in the long term and extends the number of recharging cycles compared to the conventional energy-efficient approaches.

Fog computing represents an extension of the Cloud infrastructure, which allows the improvement of the performance of IoT applications. The problem of task scheduling represents a challenge in this type of environment, with the aim of how to allocate the tasks to the different nodes of the Fog-Cloud infrastructure, in order to minimize makespan, cost, response time, and energy. In this paper, we propose SQGA— an algorithm to improve the workflow scheduling in Fog-Cloud environment. This algorithm is based on the quantum genetic algorithm QGA and aims to improve the makespan of applications deployed in the Fog-Cloud computing environment. The proposed SQGA scheduling algorithm is compared to the classical genetic algorithm and the First Come First Served algorithm. The experiment results show that the proposed SQGA algorithm is more efficient in makespan, and adapts better to the available resources.

Due to the large-scale and unequal distribution of energy resources, it has become common for numerous power system elements such as producers, consumers, microgrids, and so on to be interconnected in a network structure via energy routers (ER), referred to as the Energy Internet (EI). In EI, the trading mechanism is called a peer to peer energy trading (P2PET). It allows prosumers to trade their excess energy for additional income. This new trading scheme progressively transformed the energy supply mode from single-source to multisource and multipath. As a result, the concept of energy routing is becoming particularly crucial in the realisation of P2PET system. The minimum loss path (MLP) with power system constraints is the fundamental objective in the design of P2PET. In this work, we present a Discrete-Artificial Bee Colony algorithm (D-ABC). It is an optimization approach, which is an effective variant of ABC algorithm to solve the MLP problem with capacity constraints in EI. In addition, for the congestion management, two different schemes are applied and compared. Some numerical simulations with varied MLP scenarios are used to compare the the proposed algorithm's effectiveness to existing algorithms in the literature.

Low latency live streaming over HTTP using Dynamic Adaptive Streaming over HTTP (LL-DASH) and HTTP Live Streaming (LL- HLS) has emerged as a new way to deliver live content with an respectable video quality and short end-to-end latency. Satisfying these requirements while maintaining viewer experience in practice is challenging, and adopting conventional adaptive bitrate (ABR) schemes directly to do so will not work. Therefore, recent solutions including LoL+, L2A, Stallion, and Llama re-think conventional ABR schemes to support low-latency scenarios. These solutions have been integrated with dash.js [9] that supports LL-DASH. However, their performance in LL-HLS remains in question. To bridge this gap, we implement and integrate existing LL-DASH ABR schemes in the hls.js video player [18] which supports LL-HLS. Moreover, a series of real-world trace-driven experiments have been conducted to check their efficiency under various network conditions including a comparison with results achieved for LL-DASH in dash.js. Our version of hls.js is publicly available at [3] and a demo at [4].

Due to the global outbreak of COVID-19, education was suddenly shifted to online platforms. While some institutes were ready for the transfer, others had to use available video conferencing tools for lecturing as a quick resolution. Zoom and Microsoft Teams arose to be the most used tools by institutes of higher education for online lecturing. Despite various reported challenges were attributed to lacking e-learning-related features in these platforms, other challenges were attributed to the usability of these platforms for lecturing activities. This study aims at evaluating the usability of Zoom and Teams for online lecturing activities based on a new set of heuristics focusing on cognitive styles. A thorough and systematic usability study is conducted based on the proposed heuristics. As a result of this evaluation, a set of critical, unmet user needs are identified and scored against the proposed heuristics. In addition, a set of guidelines are proposed to support informed selection of online lecturing platforms.

Recently, the advancements in technologies have enabled volumetric media techniques to capture, encode, decode, and render videos in six degree-of-freedom (6DoF) in order to make the objects highly immersive, interactive, and expressive within the scene. This is enabled by using multiple cameras around the object(s). However, streaming 6DoF videos require a huge bandwidth and computational processing. As the end-user focuses on viewport-scenes, a large portion of the consumed bandwidth is mainly introduced due to unseen video-scenes. To fill this gap, it is imperative to predict the future head-movement (future viewport) of end-user in order to avoid the waste of network bandwidth and reduce computational processing power. In this paper, we propose a holistic architecture for the future viewport prediction using deep-neural-network (DNN)-based model. Specifically, our solution uses residual long-short-term-memory (RLSTM) architecture for accurate future viewport prediction. We confirm the effectiveness of our solution through trace-driven streaming experiments using a popular public dataset over four categories of DNN models: linear, dense, convolutional, and long-short-term-memory (LSTM). Experimental results show that our solution is able to achieve the lowest possible mean absolute error of ~ 0.01 compared to its competitor.

HTTP adaptive streaming (HAS) has become the de-facto standard for delivering video over the Internet. More content providers like YouTube and Twitch have started generating and delivering high quality streams (usually 4k resolution) with advanced end-to-end encryption mechanisms. This huge increase in HAS encrypted traffic, creates a significant challenge for network providers in understanding what is happening on their infrastructures which limits their ability to manage network infrastructures properly. Due to such invisibility, the network providers could not take appropriate decisions for better optimizations, resulting in significant revenue lost. Inferring the quality of experience (QoE) of HAS-based streaming video services is important, but recent studies highlight that most of existing solutions that rely on packet inspections, showing low performance in inference accuracy. To address this issue, we develop a machine learning powered system that infers QoE factors such as startup delay, rebuffering and selected quality, for encrypted on-demand HAS streaming video services. Our solution uses two data-driven techniques: Deep Self Organizing Map (DSOM) and Multi Layer Perceptron Backpropagation (MLPB), allowing efficient accuracy with low error in inferring QoE factors over several public video datasets, compared to some state-of-the-art approaches.

Today's HTTP adaptive streaming solutions use a variety of algorithms to measure the available network bandwidth and predict its future values. Bandwidth prediction, which is already a difficult task, must be more accurate when lower latency is desired due to the shorter time available to react to bandwidth changes, and when mobile networks are involved due to their inherently more frequent and potentially larger bandwidth fluctuations. Any inaccuracy in bandwidth prediction results in flawed adaptation decisions, which will in turn translate into a diminished viewer experience. We propose an Automated Model for Prediction (AMP) that encompasses techniques for bandwidth prediction and model auto-selection specifically designed for low-latency live steaming with chunked transfer encoding. We first study statistical and computational intelligence techniques to implement a suite of bandwidth prediction models that can work accurately under a broad range of network conditions, and second, we introduce an automated prediction model selection method. We confirm the effectiveness of our solution through trace-driven live streaming experiments.

In recent years the Smart Grids also known as intelligent energy systems have attracted the attention of researchers and became an active area for research. Smart Grid (SG) is a new development stage of power systems that aims to increase the efficiency of the energy transmission, to balance the demand and supply in the network and to improve the use of the distributed renewable energy sources. Since the transmitted energy have some losses in transmission path, which is the major issue in energy routing protocols. Where the main question is how to decrease the energy transmission loss in other words, how to find the efficient energy transmission path with the minimum transmission cost. Different methods and protocols have been proposed to solve the energy transmission path problem. These proposed protocols are based on traditional methods such as graph theory, game theory, autonomous systems, consensus... etc. In this paper, we have considered the problem of determining the energy efficient path as an optimization problem. In order to solve this problem, we proposed the use of swarm optimization methods in particularly the Bee Colony Optimization method. We have used the principle of bee foraging behaviour and proposed an energy routing protocol based on BCO algorithm to determine the lowest cost and latency energy path using features of power transmission and peer to peer energy market in smart grids.

Nowadays, Internet traffic encryption is rapidly increasing due to privacy and security concerns. This is because of the massive usage of end-to-end security protocols over Internet such as HTTPS and QUIC. The encryption trend will continue to rapidly increase in the future, and this trend concerns video streaming applications as well. Network providers face a serious challenge in managing their networks due to such widespread deployment of end-to-end security protocols. These operators need to have a clear visibility into traffic on their networks to monitor and manage both quality of experience (QoE)-and-service (QoS) impairments in popular video streaming services, in the most effective and efficient manner. Moreover, so many factors that influence QoE need to be taken care of to get an acceptable user experience. Most of the existing solutions use the deep packet inspection to infer these factors from the encrypted traffic. However, these solutions are inefficient, most of the time, leading to low QoE inference accuracy. To bridge this gap, we propose a machine-learning based solution that leverages a random forest classifier for a better QoE inference accuracy. The proposed solution uses network-and-transport layer information to infer QoE factors such as startup delay and stall events. It helps the network providers to react quickly and in real time for any impairments in the QoE of the encrypted video traffic. We evaluate our solution using an HTTP adaptive streaming service (YouTube) that uses HTTPS and QUIC protocols. Our experimental results show that our solution achieves up to 91.1% classification accuracy for HTTPS and up to 87.3% for QUIC.

Clustering is one of the effective techniques to conserve energy in wireless sensor networks (WSNs). Its main objective is to organize the network into clusters, and selects a leader (for each cluster) defined as cluster-head (CH). This CH is responsible to aggregate data sensed by its members and send it to the base station (BS). In this work we study the problem of energy consumption and extending the life of WSNs by proposing a new hierarchical clustering protocol C-AFSA based on behaviors: swarm, prey, follow of Artificial Fish Swarm Algorithm (AFSA) for selecting optimal CHs and forming clusters. Performance of our algorithm is compared to the protocol LEACH-C through a series of simulation using the network simulator NS2.

The dramatic growth of HTTP adaptive streaming (HAS) traffic represents a practical challenge for service providers in satisfying the demand from their customers. Achieving this in a network where multiple players share the network capacity has so far proved hard because of the bandwidth competition among the HAS players. This competition is exacerbated by the bandwidth overestimation that is introduced due to the isolated and selfish behavior of the HAS players. Each player strives individually to select the maximum bitrate without considering the co-existing players or network resource dynamics. As a result, the HAS players suffer from video quality instability, quality unfairness, and network underutilization or oversubscription, and the players observe a poor quality of experience (QoE). To address this issue, we propose a fully distributed game theory and consensus-based collaborative adaptive bitrate solution for shared network environments, termed Game Theory and consensus-based Approach for Cooperative HAS delivery systems (GTAC). Our solution consists of two-stage games that run in parallel during a streaming session. We extensively evaluate GTAC on a broad set of trace-driven and real-world experiments. Results show that GTAC enhances the viewer QoE by up to 22%, presentation quality stability by up to 24%, fairness by at least 31%, and network utilization by 28% compared to the well-known schemes.

In this paper, we present a novel approach for clustering based on quantum genetic computing and complex systems. The main idea is the use of Wireless Sensor Networks (WSNs) as complex system, and Quantum Computing algorithms (QC) as research strategy. WSNs are a set of sensors that operate in parallel and interact with their neighbors using single hop or multi-hops communication. The problem with WSNs is to find, within a large set of sensors randomly deployed, the subset of best clusters and their Cluster Heads (CHs) and ensure their balanced distribution in network. To cope with this NP-hard problem, we propose a new Quantum Genetic Clustering Algorithm (QGCA) which is based on Quantum Genetic Algorithm (QGA) for CHs selection to reduce energy consumption and extend the network lifetime. A comparison is made between classical routing protocol LEACH and the proposed QGCA. Experiments show that the efficiency of QGCA is significantly better and clearly indicate that the proposed approach outperforms random CHs selection and leads to significant increase in network lifetime.

The Internet of Things (IoT) enables the smart devices to be inter-connected. They share information with each other, with us and cloud based applications. These devices combine the physical and digital world and produce a huge amount of data to enhance the productivity of life, industries and society by providing smart services. IoT applications based on smart sensors open a new challenge which is the need of big data storage and huge computation power to provide real time data processing. IoT-Cloud solves such a problem since it provides a huge storage capacity. It also provides users on-demand access to resources at any place and any time.This work is designed to support any system where a huge data is generated and processed in real time such as a traffic monitoring system, a health system for obesity management using sensory and social data. We propose in this paper a new Infrastructure as a Service (IaaS) that provides an intelligent data storage to minimize the latency of any input and output data requests in a massive data storage and a huge number of servers. To ensure a high critical data availability, our IaaS supplies Cloud servers with high monitoring, backup and recovery services in case of a server failure. The proposed approach is termed Reliable lOad Balancing Using Specialization for ioT critical application (ROBUST). We compared the latency of an output file request and the complexity of searching the replicated version of a critical data of ROBUST to a recent IaaS architecture called Load Balancing in the Cloud Using Specialization (LBCS) and the classic one. The results shows a remarkable enhancement in terms of the complexity and the latency of an output file request.

Past research has shown that concurrent HTTP adaptive streaming (HAS) players behave selfishly and the resulting competition for shared resources leads to underutilization or oversubscription of the network, presentation quality instability and unfairness among the players, all of which adversely impact the viewer experience. While coordination among the players, as opposed to all being selfish, has its merits and may alleviate some of these issues. A fully distributed architecture is still desirable in many deployments and better reflects the design spirit of HAS. In this study, we focus on and propose a distributed bitrate adaptation scheme for HAS that borrows ideas from consensus and game theory frameworks. Experimental results show that the proposed distributed approach provides significant improvements in terms of viewer experience, presentation quality stability, fairness and network utilization, without using any explicit communication between the players.

In streaming media, it is imperative to deliver a good viewer experience to preserve customer loyalty. Prior research has shown that this is rather difficult when shared Internet resources struggle to meet the demand from streaming clients that are largely designed to behave in their own self-interest. To date, several schemes for adaptive streaming have been proposed to address this challenge with varying success. In this paper, we take a different approach and develop a game theoretic approach. We present a practical implementation integrated in the dash.js reference player and provide substantial comparisons against the state-of-the-art methods using trace-driven and real-world experiments. Our approach outperforms its competitors in the average viewer experience by 38.5% and in video stability by 62%.

The rapid advance in technology has changed how people teach and how students learn. The affordability of smart devices and the abundant variety of mobile applications, web-based applications, movie making tools, and assessment tools has provided a new spectrum for educators to enhance the way courses are delivered, which enabled educators to implement many Smart Learning concepts such as Active learning, Cooperative learning, and Problem-based learning. This paper is the result of a Smart learning course transformation process that was done on an introductory-level programming course. Our goals were to utilize the technologies and mobile applications available to our students to overcome the main problems encountered when learning programming for the first time, which are building a mental model of the program while it executes, and visualizing abstract concepts that are hard to grasp. We added movies and animations to overcome these problems and encouraged cooperative learning. The newly adopted teaching pedagogies proved to be very effective. A survey showed that students are able to better understand the material. They are engaged during class and able to work independently. They cooperated with each other to design and implement creative projects. They are more interested in the topic of programming.

Obesity has been recognized to be among the principal causes of many chronic diseases such as diabetes, cholesterol, hypertension, and other cardiovascular diseases. Therefore, monitoring, controlling, and preventing obesity will mitigate the risks generated from the complications of these diseases. Comprehensive preventive measures are essential to control the spread of obesity, while healthcare systems should be organized on the basis of locally derived data to provide adequate and affordable care to the increasing groups of overweight and obese people. In this paper, we propose a hybrid model that relies on both data collected from sensors and participatory data collected from a social network community established to provide value-added obesity awareness, monitoring, and prevention. The model encompasses some key smart features including tracking food intake, lifestyle, and exercise activities, generating warnings and recommendations, and triggering interventions whenever needed. Our model also mines the collected data to produce statistical analysis that can be used by health authorities to have a clear picture of the health status of the population and might help in making rational and informed decisions. Moreover, we implement a prototype of our model as a set of Web services using the SOA paradigm and lightweight protocols. Promising results of our prototype are reported and analyzed.

During these last years, Intelligent Transport Systems (ITS) have experienced a great growth in both areas: academic and industrial. ITS which aim to increase safety and comfort for users' transport, are essentially governed by Vehicular Ad hoc Networks: VANETs. In such networks, nodes represent smart vehicles that can communicate either between themselves to exchange traffic information or with roadside infrastructure to disseminate or request useful information. Therefore, the congestion control remains one of the most challenging problems of these networks. This paper surveys congestion control techniques, which are divided into three categories: Rate adaptation, Media access control (MAC) and trajectory based schemes. For each technique we give its principle, its merits and its limits. A comparative study with respect to some relevant metrics is given as well.

HTTP adaptive streaming (HAS) is receiving much attention from both industry and academia as it has become the de facto approach to stream media content over the Internet. Recently, we proposed a streaming architecture called SDNDASH [1] to address HAS scalability issues including video instability, quality of experience (QoE) unfairness, and network resource underutilization, while maximizing per player QoE. While SDNDASH was a significant step forward, there were three unresolved limitations: 1) it did not scale well when the number of HAS players increased; 2) it generated communication overhead; and 3) it did not address client heterogeneity. These limitations could result in suboptimal decisions that led to viewer dissatisfaction. To that effect, we propose an enhanced intelligent streaming architecture, called SDNHAS, which leverages software defined networking (SDN) capabilities of assisting HAS players in making better adaptation decisions. This architecture accommodates large-scale deployments through a cluster-based mechanism, reduces communication overhead between the HAS players and SDN core, and allocates the network resources effectively in the presence of short- and long-term changes in the network.

The new global economy has a great potential to shift economic power on a massive level resulting in a new and growing digital divide in the world. Over the past few decades, computers have transformed both the world and the workforce in many profound ways. As a result, computer science and associated technologies now lie at the heart of smart economies worldwide. Many reports around the world state that failure to teach Computer Science and associated technologies in the Digital Age will be disastrous. This paper summarizes the recently developed comprehensive standards and performance criteria for K-12 computer science and technology education designed to strengthen computer science fluency and competency throughout primary and secondary Schools in UAE. The paper presents a comprehensive overview of the up-to-date trends in computer science education worldwide and then demonstrates the adaptation of such practices to the design of a fully customized standards document that fit UAE culture and vision regarding transformation to knowledge based economy, innovation, and entrepreneurship.

Smart marketing models could utilize communities within the social Web to target advertisements. However, providing accurate community partitions in a reasonable time is challenging for current online large-scale social networks. In this paper, we propose an approach to enhance community detection in online social networks using node similarity techniques. We apply these techniques on unweighted social networks to detect community structure. Our proposed approach creates a virtual network based on the original social network. Virtual edges are added during this pre-processing step based on nodes' similarity in the original social network. Hence, a virtual link is established between any two similar nodes. Then the landmark CNM algorithm is applied on the generated virtual network to detect communities. This approach, labelled Similarity-CNM is expected to further maximize the quality of the inferred communities in terms of modularity and detection speed. Our experimental evaluation study asserts these gains, which accuracy is supported by a study based on Normalized Mutual Information Measure to determine how similar are the actual communities in the original network and the ones found by the proposed approach in this paper.

Digital systems are offering opportunities to find, select, adapt, and use digital educational resources for teaching and learning which are openly available in the Web. As a result, innovative technology-supported classroom-based activities are widely anticipated by key stakeholders in formal education, namely, students, parents, education managers and policy makers. This puts a tremendous pressure on teachers and instructors to exploit digital resources in their daily classroom activities. Re-using available teaching resources is currently a major trend worldwide. This creates the need for tools that can support teachers in finding, and selecting appropriate digital resources for populating their lesson plans in school classrooms. Such tools can facilitate teaching processes to adopt technology-supported instructional strategies in a cost effective manner. In order to be able to build such tools, it is important to devise and study methods that can support teachers in discovering and selecting appropriate digital resources to populate their classroom-based teaching activities, taking into consideration important parameters of classroom-based instruction, such as time constraints and class population. To this end, in this paper we propose a design-to-time classroom-based teaching model and a time-adjusted lesson planning method that incorporates a classroom-constrained selection algorithm for digital resources available in Learning Object Repositories. We also evaluate the performance of the proposed algorithm.

Mobile Ad hoc networks (MANETs) are self-organizing and self-configuring multi-hop wireless networks without any pre-existing communication infrastructures or centralized management. Scalability in MANETs is a new issue where network topology includes large number of nodes and demands a large number of packets in limited wireless bandwidth and nodes mobility that results in a high frequency of failure regarding wireless links. Clustering in MANETs is an important topic that divides the large network into several sub networks and widely used in efficient network management, improving resource management, hierarchical routing protocol design, Quality of Service and a good monitoring architecture of MANETs security. Subsequently, many clustering approaches have been proposed to divide nodes into clusters to support routing and network management. In this paper, we propose a new efficient weight based clustering algorithm. It takes into consideration the metrics: trust (T), density (D), Mobility (M) and energy (E) to choose locally the optimal cluster heads during cluster formation phase. In our proposed algorithm each cluster is supervised by its cluster head in order to ensure an acceptable level of security. It aims to improve the usage of scarce resources such as bandwidth, maintaining stable clusters structure with a lowest number of clusters formed, decreasing the total overhead during cluster formation and maintenance, maximizing lifespan of mobile nodes in the network and reduces energy consumption. Preliminary simulation experiments are conducted to compare the performance of our algorithm to Lowest ID, Highest Degree and WCA in terms of Average Number of CHs, Average Number of CH Changes, Total Number of Re-affiliations, Clusters Stability and Total Overhead. The initial results show that our scheme performs better than other clustering schemes based on the performance metrics considered.

In this paper we will use the specification of a biochemistry practical work to demonstrate the use of our virtual laboratory platform. This platform has an agent based architecture which we proposed in our previous work. We describe a generic architecture of a virtual laboratory. We have chosen biochemistry major because almost all its courses require experimental work. The real experiments, in a such a field, require expensive materials and might be dangerous for students to manipulate. The objective of this work is to design and implement the remote practical work that we are interested in. We used MaSE (Multi agent System Engineering) methodology to develop the system. This tool must be capable of creating a digital work environment where learners collaborate and cooperate among themselves to achieve the various stages of this practical work (PW).

In this paper, we present the implementation of a Virtual Private Network (VPN) using a Multi-Protocol Label Switching (MPLS) protocol. Because this protocol is very promising, economically and technologically, we have adopted it as the basic protocol to simulate a case study of VPN-MPLS as part of a Multi-Services Network project of the Algerian Telecommunication Company "Algerie Telecom". The simulation was done using GNS3.

A method for recognizing handwritten Hindi numerals is proposed based on the structural descriptors of a numeral's shape. The method consists of three major steps. The first one is preprocessing, where a handwritten numeral is scanned, normalized and then thinned. Next, a robust algorithm is used to segment the scanned image into stroke(s), based on feature points, and to identify cavity features. The output of this algorithm is a syntactic representation (that is one or more syntactic terms). Finally, this syntacytic representation is matched against the set of prototype syntactic representations of handwritten numerals for a possible match. Early experimental results are not only encouraging but also proving the tolerance of the proposed system to recognize a high variability of Hindi numerals' shapes. The system attained a successful recognition rate of 96%.

In this paper the theoretical basis is presented and the implementation of a term rewriting system based on algebraic specifications is described. The input to this system is represented by an algebraic specification language, which forms not only the set of axioms but also the sorts, variables, operators and terms of a specific simulated theory or application. Rewriting and matching mechanisms provide the formal methodology for evaluating terms and proving assertions in an algebraic theory. Specifications are evaluated by interpreting terms by means of rewrite rules. The rules are described by the axioms of the specifications where the finite termination and congruence properties are assumed. A term rewriting system to recognize handwritten Hindu numerals is introduced as a case study. Besides rewriting, a robust algorithm is proposed to segment the numeral's image into strokes based on feature points and to identify cavity features. A syntactic representation (term) of the input image is matched and rewritten against a set of rules. Experimental results proved that the proposed system is tolerant to recognize a variety of numeral shapes with 96% successful recognition rate.

Presents a model of timed Petri nets which is more general than known models in terms of modeling convenience. The model consists of simple but fairly general modules. This would result in simpler and more modular codes for simulation of these systems, as compared with the known models of timed Petri nets. After discussing this model, the authors present an approach towards its distributed simulation. The well known distributed simulation schemes for discrete event systems do not directly apply to these systems due to the non-autonomous nature of place nodes in timed Petri nets. Moreover, in the authors' approach they incorporate several ideas to increase the degree of concurrency and to reduce the number of overhead messages in distributed simulation.>

The results of an experimental study on distributed simulation of three open queuing networks are reported. The distributed simulation scheme considered is a simple variation of the scheme given by K.M. Chandy and J. Misra (1979) using NULL messages. A new approach is used to study the relationship between the overhead and performance of a distributed simulator, and the approach is illustrated by studying these three example networks. Two measures of ideal speedup of distributed simulation over sequential simulation are defined and measured. These values of ideal speedup are much less than simply the number of processors, and hence provide a more realistic value for the ideal speedup.>