In OFDM systems, it is necessary to estimate the channel to overcome the distortion caused by channel fading’s which can be induced by many phenomena such as: delay spread, multipath effect, mobility and Doppler shift. Most of the channel estimation techniques are proposed in frequency domain using the pilot symbols. One of them which has less complicated is well-known as Least-Squares (LS) method which is widely used in channel estimation but it is more sensitive to noise respected to the other reported techniques. In this paper, a new threshold based method using wavelet decomposition will be proposed which is based on an initial LS estimation technique. The reported simulation results show that the proposed method has better performance compared to the other methods such as Lee Method that has been published recently.

In the past few years, the smart grids have attracted more and more attention. The fact that these systems are based on the pervasive use of new Information and Communications Technologies (ICTs) opens new perspectives in an aim to improve the electrical grid performances. One of the most commonly deployed technologies in such a real environment is the wireless sensor networks (WSNs) technology. WSNs are thus used to monitor and control the electrical grid components. In this paper we will give an overview on WSNs applications and challenges for smart grids. One promising research topic, when speaking about WSNs in smart grids, is the co-simulation which allows studying and evaluating any new technique in a near real grid behavior. Hence, in this paper, we will also present and discuss the architectures, the advantages and the drawbacks of the most important smart grids co-simulation platforms used to combine the network behavior with the power grid systems models.

Simple rules and laws are necessary for the understanding of the meaning and of the content. This is a fundamental claim of all citizens. The instruments of legislation are deeply connected in many countries to the principles of the Roman law and to the ideas of the former centuries. Modern effective aspects, such as visualization, structuring and object-orientation are not focused. One method for handling complex projects is UML, the „Unified Modeling Language“. This tool has served for some years as an instrument in understanding the complex structures of the law, but could also be applied to the complete establishing, admini-stration and development of a new and modern building of law.

Wireless sensor networks (WSNs) are prone to node/link failures, message losses, and dynamic node joins and departures. For instance, in data gathering applications that constitute a common type of applications in WSNs, links between nodes and their parent in the data gathering tree may be broken. Protocols supporting such applications should adapt their behaviour to guarantee reliable wireless communications while keeping a low overhead. In particular, this paper focuses on the optimization of a known coloring algorithm called SERENA (‘SchEdule RoutEr Node Activity’). SERENA assigns colors to nodes such that no two interfering nodes share the same color. Each color is mapped to a time slot during which nodes having the associated color can transmit data. To ensure collision free communications, SERENA should be aware about the set of interfering nodes. However, in case of topology changes, this set may vary. Consequently, SERENA should adapt to this. Our solutions proactively select one or more parent backups and guarantee that the coloring remains valid if a parent is replaced by its backup. Simulation results show that reliability is obtained at the price of a small increase in the number of colors used to color the network.

This paper examines the use of social network media at three aspects in African and Libyan perspective. Firstly, to use social network media as an open network learning environment that provide service for interaction necessary for learners to support socialization and collaboration during problem solving. Secondly, to use social media as a tool to support blended learning in e-learning system and encourage non-native English students to express their ideas and fill the gap of communication problems. Thirdly, to analyze the interaction of the learner in social media threaded messages and its relation to group and individual performance using different social schema and social network analyses. Quasi-experimental results indicate that there is an increase on the cognitive level of students at different level while qualitative results reveal that it helps deepen learning, memorable, have freedom to express opinions and lessen pressure and increase communication and socialization.

With a massive arrival of information technologies in the past quarter century there has been a change of the set processes in the society. These changes concerns also education. Online courses are nowadays well established forms of learning and teaching all over the world. They are used as supporting courses of traditional classes, as complementary courses of hybrid or blended courses or pure online courses. Although they cannot fully replace a teacher, they are now becoming a powerful tool in the educational process, both formal and informal. Nevertheless, in all cases they must be designed well to attract and motivate students to work and study the uploaded information. Therefore, this article focuses on the analysis of online materials and their impact on the process of learning.

The tremendous and rapid development in sensors technology allowed their application in various fields requiring monitoring, such as, transportations, rare species surveillance, agriculture, military activities, medical field, etc. Due to their intrinsic constraints and limitations, several dedicated MAC protocols have been designed for wireless sensor networks and whose main objectives are bandwidth optimization while keeping very low energy consumption. One of the newest proposals is ANT which is emerging as a widely used MAC protocol for wellness and sports sensor devices. In this paper, we give a comprehensive overview of ANT/ANT+ and a comparison between ANT and two established standards: Bluetooth Low Energy (BLE) and 802.15.4/4a. We detail also some practical experiments we conducted to evaluate some characteristics of ANT.

The separation of the searched data from the rest is an important task in data mining. Three separation/classification methods are presented. We use a singularity exponent in classifiers that are based on distances of patterns to a given (classified) pattern. The approximation of so called probability distribution mapping function of the distribution of points from the viewpoint of distances from a given point in the form of a scaling exponent power of a distance is presented together with a way how to state it. Considering data as points in a metric space, three methods are based on transformed distances of neighbors of a given point in a multidimensional space via functions that use different estimates of scaling exponent. Classifiers – data separators utilizing knowledge about explored data distribution in a space and suggested expressions of the scaling exponent are presented. Experimental results on both synthetic and real-life data show interesting behavior (classification accuracy) of classifiers in comparison with other well-known approaches.

Edge detection process has a widespread usage in computer vision applications. But it has a different output when its input image changes from color to grayscale. This changeability of results make us add modification on edge detection process procedures to correctly detect all those edges in color images that can’t be detected in gray ones. This research reviews the proposed solution of Dutta and Chaudhuri on color edge detection algorithm that works using RGB color space, detects problem of a huge set of undetected edges by their proposed algorithm and find solutions for that, and applies complexity and performance analysis and experiments to compare the proposed algorithm with Canny and Sobel edge detection algorithms.

There are two main categories of bi-clustering approaches graph-based bi-clustering, and non-graph based bi-clustering. In graph-based bi-clustering algorithm the input dataset is converted into bigraph such as bipartite graph, and apply some heuristic local searching techniques to minimize the number of crossings between edges in the Bigraph such as BaryCenter (BC) used in SPHier algorithm. The main problem of graph-based bi-clustering algorithm is to extract the best bi-clusters, and this leads to the ordering of gene expression dataset before we apply biclustering algorithm. In bipartite graph this is achieved through minimizing the number of crossings in bipartite graph. As we minimize the number of crossings in the bipartite graph, the gene expression dataset becomes more ordered, and this enhances the results of biclustering algorithm. The main goal of our proposed algorithm is the enhancement of graph-based biclustering algorithm by enhancing BaryCenter crossing minimization heuristics of bipartite graph. In the proposed algorithm we add the rank of each node to the rank of its neighbors, and using the position of each node in the calculations to give a new rank to each node, and using this rank for reordering the nodes of each layer.

This paper discusses current threats in wireless networks and some academia research reviews regarding the matters. Significant and persistent threats discussed are sniffing, Man In the Middle Attack (MITM), Rogue Access Points (RAP), Denial Of Services (DoS) and social engineering attacks. Some current developments of wireless communication technology such as short range communication, cloud computing, bring your own device policy (BYOD), devices tethering and convergences of WiFi and cellular network technology are also presented. Some practical suggestion and advanced countermeasures are also reviewed in this paper. The findings from reviewing these research papers proved that the complexity of the attacks had increased by time and the attacks in WiFi network are passive and more dangerous to the end users.

Providing a continuous service is the main requirement for many application types deployed in Wireless Sensor Networks (WSNs). In this paper, we study two maintenance strategies using a small number of mobile maintainer robots to restore the WSN coverage and connectivity upon a sensor failure: the Centralized Maintenance Strategy (CMS) and the Centralized Maintenance Strategy with Anticipation (CMSA). The CMS and CMSA are based on the Centralized Manager Algorithm [1] used to detect, report sensor failures and coordinate the movement of robots. In CMSA, the predictive version of CMS, a selected robot is chosen as a manager to anticipate the sensor failures and schedule the available maintainer robots to repair them before they happen. To predict the lifetime of a sensor node, we propose an energy state model that represents the behavior of a sensor node based on Markov Chain. We use this model, validated by simulations, to predict the energy consumption by a sensor node and consequently the lifetime of a wireless sensor node. The simulation results show that the CMSA ensures a null dysfunction network time and a message overhead lower than the classical centralized manager strategy.