A Graphical-based educational simulation tool for Wireless Sensor Networks

Many routing protocols have been developed to improve the lifetime, bandwidth reusability and scalability of the Wireless Sensor Networks (WSNs). The operation of routing protocols is difficult to understand and some problems may occur while developing these protocols. Simulation is a relatively fast way of estimating these protocols and understating what is happening in the network. Thus, this paper presents an open source Graphical-based educational simulation tool called Gbest-WSN for simulating routing protocols of the static and mobile, homogeneous and heterogeneous WSNs. Gbest-WSN tool has a user-friendly interface that helps the user to select the routing protocol and define the network configuration. It is provided with four routing protocols; namely LEACH, LEACH-Mobile, immune algorithm-based and genetic algorithm-based routing protocols. Also, it allows the user to update the existing routing protocols and add a new routing protocol. Gbest-WSN is provided with radio, coverage and mobility models for modeling the hardware of the sensor node. It shows a detailed 2D and 3D graphical perception for what is happing during the routing process. Also, it has the ability to compare the simulation results of different simulation methods or different network configurations. In addition, it allows the user to save and load simulation scenarios and also exports the graphical results on PDF files and the statistical results on excel or mat files. Moreover, Gbest-WSN is provided with html help documents to help the user how to use it. The illustrative simulation examples clarified that the Gbest-WSN is a helpful tool for the students, teachers and researchers who work in the field of WSNs.     

Determining the flow correlation for an orifice with a non-dimensional number

Traditional devices like orifice meters play a crucial function as a flow measuring device because there is inaccuracy in the measurement of the flow measuring device concern. The pressure drop (Δp) between the upside and downsides of the orifice-pipe flow passage is calculated using Bernoulli's principle. Orifice meter produces errors and uncertainty in the downstream of the flow because of wake or backflow. The proposed study provides the procedure to calculate the Δp and flow characteristics for a circular orifice for a compressible fluid (Air) with CFD analysis. The numerical study was carried out by considering combined parameters such as area ratio (σ) and space ratio (s) as geometrical parameters and Reynolds number as flow parameters to minimize the errors of the numerical calculation. The input parameter σ varies from 0.2 to 0.6, and the s varies from 0.1 to 0.9. Whereas the Reynolds number (Re) varies from 10000 to 100000. A non-dimensional number is defined by the combined effect of σ and s to generated correlations with accuracy which is enhanced predicted results of the work. The correlation will make a significant contribution to the flow monitoring device design.     

Optimal multi-reservoir network control by two-phase neural network

An approach based on two-phase neural network (TPNN) is proposed for the optimal operation of multi-reservoir network control problems. The advantage of the proposed technique is that it takes into account the concurrent interaction among all the water release variables of the problem. Here, the main objective of this work is to figure out the optimal amounts of water releases from each hydro-plant during each interval in the interconnected system and to minimize and distribute uniformly the energy deficit if any. This TPNN approach is basically a two-stage solution method. In stage 1, the neural network is developed to bring the solution trajectory close to the boundary of the feasible region. In stage 2, the directional vector of the constraints is slowly shifted to the corresponding Lagrange multipliers and this moves the solution trajectory to the feasible region which satisfies all practical constraints. Application of this technique to a 10-reservoir network demonstrates efficacy of the proposed algorithm. It is concluded from the results that the proposed method with proper selection of network control parameters is very effective in providing a good optimal solution.     

Analysis of morphological, structural and electrical properties of annealed TiO2 nanowires deposited by GLAD technique

The effect of annealing on vertically aligned TiO₂ NWs deposited by glancing angle deposition (GLAD) method on Si substrate using pressed and sintered TiO₂ pellets as source material is studied.The FE-SEM images reveal the retention of vertically aligned NWs on Si substrate after annealing process.The EDS analysis of TiO₂ NWs sample annealed at 600℃ in air for 1 h shows the higher weight percentage ratio of~2.6(i.e.,72.27% oxygen and 27.73% titanium).The XRD pattern reveals that the polycrystalline nature of anatase TiO₂ dominates the annealed NWs sample.The electrical characteristics of Al/TiO₂-NWs/TiO₂-TF/p-Si (NW device) and Al/TiO₂-TF/p-Si (TF device) based on annealed samples are compared.It is riveting to observe a lower leakage current of~1.32×10^-7 A/cm² at+1 V with interface trap density of~6.71×10¹¹ eV^-1cm^-2 in NW device compared to~2.23×10^-7 A/cm² in TF device.The dominant leakage mechanism is investigated to be generally Schottky emission;however Poole-Frenkel emission also takes place during high reverse bias beyond 4 V for NWs and 3 V for TF device.     

Conjugated complexes via oxidative complexation of polyaniline derivatives to vanadium(III)

The emeraldine base of poly(o-toluidine) and N,N′-bis(4′-dimethylaminophenyl)-1,4-quinonediimine were demonstrated to undergo oxidative complexation to VCl₃, affording the corresponding conjugated complexes. The complexation proceeds via reduction of the quinonediimine moiety with oxidation of V(III)–V(IV), in which the vanadium species is considered to play an important role in both the complexation and reduction.     

A relational approach to probabilistic classification in a transductive setting

Transduction is an inference mechanism adopted from several classification algorithms capable of exploiting both labeled and unlabeled data and making the prediction for the given set of unlabeled data only. Several transductive learning methods have been proposed in the literature to learn transductive classifiers from examples represented as rows of a classical double-entry table (or relational table). In this work we consider the case of examples represented as a set of multiple tables of a relational database and we propose a new relational classification algorithm, named TRANSC, that works in a transductive setting and employs a probabilistic approach to classification. Knowledge on the data model, i.e., foreign keys, is used to guide the search process. The transductive learning strategy iterates on a k-NN based re-classification of labeled and unlabeled examples, in order to identify borderline examples, and uses the relational probabilistic classifier Mr-SBC to bootstrap the transductive algorithm. Experimental results confirm that TRANSC outperforms its inductive counterpart (Mr-SBC).     

Design and Analysis of Gate Stack Silicon-on-Insulator Nanosheet FET for Low Power Applications

Silicon - Since the introduction of fast integrated circuits, semiconductor manufacturers have concentrated their efforts on reducing the size of transistors. Increased working frequencies (shorter...     

Analytic hierarchy prioritization process in the AHP application development: A prioritization operator selection approach

In the analytic hierarchy process, prioritization of the reciprocal matrix is a core issue to influence the final decision choice. Various prioritization methods have been proposed, but none of prioritization methods performs better than others in every inconsistent case. To address the prioritation operator selection problem, this paper proposes the analytic hierarchy prioritization process, which is an objective hierarchy model (without using subjective pairwise comparisons) to approximate the real priority vectors with selection of the most appropriate prioritization operator among the various prioritization candidates, for a reciprocal matrix, and on the basis of a list of measurement criteria. Nine important prioritization operators and seven measurement criteria are illustrated in AHPP. Two previous applications are revised and illustrate the validity and usability of the proposed model. The results show that the most appropriate prioritization operator is dependent of the content of the reciprocal matrix and AHPP is an appropriate method to address the prioritization problem to make better decisions.