Sidelobe Control in Collaborative Beamforming via Node Selection

Collaborative beamforming (CB) is a power efficient method for data communications in wireless sensor networks (WSNs) which aims at increasing the transmission range in the network by radiating the power from a cluster of sensor nodes in the directions of the intended base station(s) or access point(s) (BSs/APs). The CB average beampattern expresses a deterministic behavior and can be used for characterizing/controling the transmission at intended direction(s), since the mainlobe of the CB beampattern is independent on the particular random node locations. However, the CB for a cluster formed by a limited number of collaborative nodes results in a sample beampattern with sidelobes that severely depend on the particular node locations. High level sidelobes can cause unacceptable interference when they occur at directions of unintended BSs/APs. Therefore, sidelobe control in CB has a potential to increase the network capacity and wireless channel availability by decreasing the interference. Traditional sidelobe control techniques are proposed for centralized antenna arrays and, therefore, are not suitable for WSNs. In this paper, we show that distributed, scalable, and low-complexity sidelobe control techniques suitable for CB in WSNs can be developed based on node selection technique which make use of the randomness of the node locations. A node selection algorithm with low-rate feedback is developed to search over different node combinations. The performance of the proposed algorithm is analyzed in terms of the average number of trials required to select the collaborative nodes and the resulting interference. Our simulation results approve the theoretical analysis and show that the interference is significantly reduced when node selection is used with CB.Comment: 30 pages, 10 figures, submitted to the IEEE Trans. Signal Processin

Smart integration of a DC microgrid: Enhancing the power quality management of the neighborhood low-voltage distribution network

The fast development of the residential sector regarding the additional integration of renewable distributed energy sources and the modern expansion usage of essential DC electrical equipment may cause severe power quality problems. For example, the integration of rooftop photovoltaic (PV) may cause unbalance, and voltage fluctuation, which can add constraints for further PV integrations to the network, and the deployment of DC native loads with their nonlinear behavior adds harmonics to the network. This paper demonstrates the smart integration of a DC microgrid to the neighborhood low-voltage distribution network (NLVDN). The DC microgrid is connected to the NLVDN through a three-phase voltage source inverter (VSI), in which the VSI works as a distribution static compensator (DSTATCOM). Unlike previous STATCOM work in the literature, the proposed controller of the VSI of the DC smart building allows for many functions: (a) it enables bidirectional active/reactive power flow between the DC building and the AC grid at point of common coupling (PCC); (b) it compensates for the legacy unbalance in the distribution network, providing harmonics elimination and power factor correction capability at PCC; and (c) it provides voltage support at PCC. The proposed controller was validated by Matlab/Simulink and by experimental implementation at the lab

On the Effect of Correlated Measurements on the Performance of Distributed Estimation

We address the distributed estimation of an unknown scalar parameter in Wireless Sensor Networks (WSNs). Sensor nodes transmit their noisy observations over multiple access channel to a Fusion Center (FC) that reconstructs the source parameter. The received signal is corrupted by noise and channel fading, so that the FC objective is to minimize the Mean-Square Error (MSE) of the estimate. In this paper, we assume sensor node observations to be correlated with the source signal and correlated with each other as well. The correlation coefficient between two observations is exponentially decaying with the distance separation. The effect of the distance-based correlation on the estimation quality is demonstrated and compared with the case of unity correlated observations. Moreover, a closed-form expression for the outage probability is derived and its dependency on the correlation coefficients is investigated. Numerical simulations are provided to verify our analytic results.Comment: 5 page

The Study of Optical and Electrical Properties of Nanostructured Silicon Carbide Thin Films Grown by Pulsed-Laser Deposition

In this paper, nanostructured silicon carbide (SiC) thin films are deposited onto glass substrate using pulsed laser deposition technique. Electrical and optical characterizations such as conductivity, resistivity, transmission, Seeback effect, absorption, absorption coefficient, energy band gap, and extinction coefficient as a function of photon energy, and the effect of thin films thickness on transmission are carried out to characterize the prepared samples. Results showed that the prepared SiC thin film is an n-type semiconductor with an indirect bandgap of ~3 eV, 448 nm cutoff wavelength, 3.4395 × 104 cm−1 absorption coefficient and 0.154 extinction coefficient. The surface morphology of the SiC thin films is studied using scanning electron microscope at a substrate temperature of 400 °C and it is found that the grain size of the prepared SiC thin film is about 30 nm. As such, the nano thin films optical and structural characteristics enable the films to be used as gases sensors in many optoelectronic devices such as the environment and ultraviolet photodiode

Critical Review on Waqf Experiences: Lessons From Muslim and Non-Muslim

The purpose of this paper is to critically assess current practices in waqf institution based on Middle East, Asia and some selected non-Muslim countries experience. Muslims countries have their own ways of managing waqf. Some ways are similar to one another and some are different. The methodology used in this study is in depth review analysis of the literature of waqf practices in various countries. The paper could conclude that differences of waqf institution based on legal factors, historic of establishment and current implementation. The paper identifies, that the objective establishment of Waqf institution is benefit to the society and development of the country. This paper is based on critical analysis review of the waqf experiences literature review in selected countries. Future research might integrate this review with empirical methodology. There is a limit number of countries waqf experiences have been included in this study, future research might include more experiences. In term of the implications of findings, it hopes that the findings give more comprehensive and cross countries picture of waqf experience and practice. Which is, it will assist the related waqf regulators in the evaluation process of waqf management practices and determine best practice as well set up a benchmark waqf management practices

On Completeness of Fuzzy Normed Spaces

 In this paper, a new direction has been presented between the subject of domain theory and fuzzy normed spaces to introduce the so called fuzzy domain normed spaces and proved some results related to this subject concerning the completeness of such spaces.domai

Single and Multiobjective Optimal Reactive Power Dispatch Based on Hybrid Artificial Physics–Particle Swarm Optimization

The optimal reactive power dispatch (ORPD) problem represents a noncontinuous, nonlinear, highly constrained optimization problem that has recently attracted wide research investigation. This paper presents a new hybridization technique for solving the ORPD problem based on the integration of particle swarm optimization (PSO) with artificial physics optimization (APO). This hybridized algorithm is tested and verified on the IEEE 30, IEEE 57, and IEEE 118 bus test systems to solve both single and multiobjective ORPD problems, considering three main aspects. These aspects include active power loss minimization, voltage deviation minimization, and voltage stability improvement. The results prove that the algorithm is effective and displays great consistency and robustness in solving both the single and multiobjective functions while improving the convergence performance of the PSO. It also shows superiority when compared with results obtained from previously reported literature for solving the ORPD problem

Identifying Difficult exercises in an eTextbook Using Item Response Theory and Logged Data Analysis

The growing dependence on eTextbooks and Massive Open Online Courses (MOOCs) has led to an increase in the amount of students' learning data. By carefully analyzing this data, educators can identify difficult exercises, and evaluate the quality of the exercises when teaching a particular topic. In this study, an analysis of log data from the semester usage of the OpenDSA eTextbook was offered to identify the most difficult data structure course exercises and to evaluate the quality of the course exercises. Our study is based on analyzing students' responses to the course exercises. We applied item response theory (IRT) analysis and a latent trait mode (LTM) to identify the most difficult exercises .To evaluate the quality of the course exercises we applied IRT theory. Our findings showed that the exercises that related to algorithm analysis topics represented the most difficult exercises, and there existing six exercises were classified as poor exercises which could be improved or need some attention.Comment: 6 pages,5 figure

A Predictive Model for Student Performance in Classrooms using Student Interactions with an eTextbook

With the rise of online eTextbooks and Massive Open Online Courses (MOOCs), a huge amount of data has been collected related to students’ learning. With the careful analysis of this data, educators can gain useful insights into their students’ performance and their behavior in learning a particular topic. This paper proposes a new model for predicting student performance based on an analysis of how students interact with an interactive online eTextbook. By being able to predict students’ performance early in the course, educators can easily identify students at risk and provide a suitable intervention. We considered two main issues: the prediction of good/bad performance and the prediction of the final exam grade. To build the proposed model, we evaluated the most popular classification and regression algorithms. Random Forest Regression and Multiple Linear Regression have been applied in Regression. While Logistic Regression, decision tree, Random Forest Classifier, K Nearest Neighbors, and Support Vector Machine have been applied in classification. Based on the findings of the experiments, the algorithm with the best result overall in classification was Random Forest Classifier with an accuracy equal to 91.7%, while in the regression it was Random Forest Regression with an R2 equal to 0.977