Different distributed generation (DG) technologies, active loads, and storage devices create an independent microgrid (MG). Scheduling of an MG is an important issue in renewable energy sources (RESs) based systems. In this paper, MGs include RESs, plug-in hybrid electric vehicles (PHEVs), and electrical energy storage systems. The proposed scheduling framework utilizes the Monte Carlo simulation (MCS) to characterize the uncertain parameters of PHEVs and RESs. Three different charging strategies are investigated for modeling the impact of different behaviors of PHEVs in MGs. These schemes are smart, controlled, and uncontrolled charging. Due to the nonlinear feature of the suggested optimization problem, it needs an efficient optimization tool to tackle the problem appropriately. So, this paper uses the backtracking search optimization (BSO) algorithm for the short-term scheduling of an MG. The proper performance of the offered scheme is investigated in two scenarios with different time horizons. The BSO algorithm and other optimization algorithms are used for comparing the results to verify the presented method in solving the energy management problem of the MGs.

No abstract

Fiber optics is an important part in the telecommunication infrastructure. Large bandwidth and low attenuation are features for the fiber optics to provide gigabit transmission. Nowadays, fiber optics are used widely in long distance communication and networking to provide the required information traffic for multimedia applications. In this paper, the optical fiber structure and the operation mechanism for multimode and single modes are analyzed. The design parameters such as core radius, numerical aperture, attenuation, dispersion and information capacity for step index and graded index fibers are studied, calculated and compared for different light sources.

The increasing number of mobile smartphone users requires additional spectrum to maintain cellular quality of service. The 800 MHz band is a good candidate to achieve this goal. It can be used as standalone spectrum or aggregated with other licensed bands to increase the available bandwidth. This paper compares through physical layer simulation the downlink throughput versus distance performance of LTE-Advanced in two different bands. We consider a high frequency band at 2.6 GHz and the 800 MHz to model bands 7 and 20 of inter-band carrier aggregation CA_7-20 respectively. The link level simulation is performed for single antenna system at three different urban locations. The channel is modelled using an enhanced 3D ITU-R channel model combined with measured 3D radiation patterns for the base station and user equipment antennas. The BER versus SNR results show that the 800 MHz band enjoys a gain of up to 1 dB as a result of higher Ricean K-factors. Moreover, for the assumed simulation parameters, at distances beyond 400 m the throughput of the 800 MHz band is significantly higher than the 2.6 GHz band. At a distance of 750 m,  the throughput for the 800 MHz band is 4.5 times greater than the 2.6 GHz band. These benefits are shown to relate to the lower path loss values observed in the 800 MHz band.

In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Vector Machine, Naïve Bayes, Decision Tree, Random Forest, Stochastic Gradient Descent, Gradient Boosting and Ada Boosting classifiers were designed. Performance-wise analysis using Confusion Matrix metric carried out and comparisons between the classifiers were a due. As a case study Information Gain, Pearson and F-test feature selection techniques were used and the obtained results compared to models that use all the features. One unique outcome is that the Random Forest classifier achieves the best performance with an accuracy of 99.96% and an error margin of 0.038%, which supersedes other classifiers. Using 80% reduction in features and parameters extraction from the packet header rather than the workload, a big performance advantage is achieved, especially in online environments.