Power optimised channel coding in wireless sensor networks using low-density parity-check codes

Energy consumption of low-density parity-check (LDPC) codes in different implementations is evaluated. Decoder's complexity is reduced by finite precision representation of messages, that is, quantised LDPC decoder, and replacement of function blocks with look-up tables. It is shown that the decoder's energy consumption increases exponentially with the number of quantisation bits. For the sake of low-power consumption, 3-bit magnitude and 1-sign bit representation for messages are used in the decoder. It is concluded that high-rate Gallager codes are as energy efficient as the Reed-Solomon codes, which till now have been the first choice for wireless sensor networks (WSNs). Finally, it is shown that using LDPC codes in WSNs can be justified even more by applying the idea of trading the transmitter power with the decoder energy consumption. By exploiting the trade-off inherent in iterative decoding, the network lifetime is increased up to four times with the 3-6 regular LDPC code. Hence, it is inferred that the LDPC codes are more efficient than the block and the convolutional codes.     

Chemical composition,oral toxicity and antimicrobial activity of Iranian propolis

Propolis is a resinous natural hive product derived from plant exudates collected by honeybees. Due to biological and pharmacological activities, it has been extensively used in folk medicine. The present study was designed to investigate the chemical composition, subchronic toxicity, antimicrobial activity of Iranian propolis ethanolic extract, which has not been studied previously. One hundred and nine compounds were identified by gas chromatography–mass spectrometry analysis. Forty-five days subchronic toxicity of oral propolis extract was investigated in male rats. During the study no significant behavioral and clinical toxicity has been seen in animals however, hematologic, blood biochemistry and histopathologic data studies exhibited some significant differences between the groups. The ethanolic extract of propolis inhibited the growth of all examined microorganisms including bacteria and fungi with the highest antimicrobial activity against Gram-positive bacteria such as Staphylococcus aureus and Staphylococcus epidermidis.     

Mathematical Model and Experimental Validation of Free Surface Size Segregation for Polydisperse Granular Materials

 A mathematical model of free surface size segregation of granular matter is proposed. The material is assumed to be dry, cohesionless and to consist of several populations with distinct diameter ranges. It is not necessary that one population is predominant. The extent of segregation depends mainly on the relative amount of small and large particles and on the diameter ratios but not on their absolute values. The model predicts how the fractions of these populations change from the position where the material is fed onto a heap to its bottom. It is based on ideas of L. Prigozhin} to treat binary mixtures. Introducing a new scheme to interpolate the local deposition rates from situations with one predominant population it becomes possible to handle mixtures with three and more diameters. The model may be applied to storage systems of any kind, such as bunkers, hoppers, silos or mixing beds, provided the source is sufficiently weak and concentrated. Numerical simulations are discussed for conical and ramp-like geometries and for ternary mixtures. In order to validate the model a few experiments were conducted. As measured by the natural variability of the problem and the simplicity of the model, which is sufficiently easy to be used in an industrial controller, good agreement between theory and experiment is found. Received: 2 August 2001     

Plasma Nitriding of Low Alloy Sintered Steels

Fe-3Cr-0.5Mo-0.3C and Fe-3Cr-1.4Mn-0.5Mo-0.367C sintered alloys were plasma nitrided at different temperatures. Characterization was performed by microhardness measurement, optical microscopy, SEM and XRD. Both materials had similar nitriding case properties. 1.4% manganese did not change the as-sintered microstructure considerably.It was observed that monophase compound layer, γ‘, formed with increasing temperature. Compound layer thickness increased with increasing temperature while nitriding depth increased up to a level and then decreased. Core softening was more pronounced at higher temperature owing to cementite coarsening.     

Energy-efficient data transmissions in heterogeneous ultra-dense networks using a hybrid metaheuristic optimization

The heterogeneous ultra-dense network (UDN) is a complex network environment where the small cells (SCs) are densely populated to acquire data transmission. The UDN is mainly adopted to deal with the explosive growth of mobile data and its consequential energy consumption issues. The UDN consists of mobile users, restricting the SCs from offering seamless services as the movement may disrupt the transmissions. To provide an effective solution, this paper introduces an energy-efficient framework that enables effective data transmissions irrespective of the users' mobility. The proposed model considers the clustered SC deployment where the four-tiered architecture is adopted. The architecture includes a macro base station (BS), microcells, picocells, and femtocells. The SCs are responsible for transferring the data received from the mobile users to the macro BS. The proposed model introduces the hybrid algorithm called the firefly oriented multiverse optimization (FF-MVO) algorithm to identify the most optimal path for data transmission. This algorithm works iteratively to identify the optimal path to reach the macro BS for each transmission from the user. The proposed model is simulated in the network simulator 3 (NS3) platform, and the results are evaluated with the existing models. The outcomes proved that the proposed algorithm is more optimal than the other models in finding the optimal path to result in energy-efficient transmissions. The proposed method achieved an average energy consumption of 0.24 J, an average energy efficiency of 10.965 bits/s/J and an average network throughput of 33.907 Gbps.     

Sensitive Functions and Approximate Problems

Properties of functions that are good measures of the CRCW PRAM complexity of computing them are investigated. While theblock sensitivityis known to be a good measure of the CREW PRAM complexity, no such measure is known for CRCW PRAMs. It is shown that the complexity of computing a function is related to itseverywhere sensitivity, introduced by Vishkin and Wigderson. Specifically, the time required to compute a functionf: Dⁿ→Rof everywhere sensitivityes(f) withPprocessors and unbounded memory isΩ(log[log es(f)/(log(|D|+4P/es(f)))]). This improves results of Azar and of Vishkin and Wigderson. This lower bound is used to derive new lower bounds for someapproximate problems. These problems can often be solved faster than their exact counterparts and for many applications, it is sufficient to solve the approximate problem. It is shown thatapproximate selection,approximate counting,approximate compaction, andpadded sortingall require timeΩ(log log n) with a linear number of processors, if the level of accuracy desired is moderately high. For these levels of accuracy, no lower bounds were known for these problems on the PRAM model. The lower bounds for some of the problems are tight.     

Prediction of performance and emission characteristics in a biodiesel engine using WCO ester: a comparative study of neural networks

In this study, the applicability of Artificial Neural Networks (ANNs) has been investigated for predicting the performance and emission characteristics of a diesel engine fuelled with Waste cooking oil (WCO). ANN modeling was done using multilayer perception (MLP) and radial basis functions (RBF). In the radial basis functions, centers were initialized by two different methods namely random selection method and using clustering algorithm. In the clustering method, center initialization was done using FCM (Fuzzy \(c\) means) and CDWFCM (cluster dependent weighted fuzzy \(c\) means) algorithms. The networks were trained using the experimental data, wherein load percentage, compression ratio, blend percentage, injection timing and injection pressure were taken as the input parameters and brake thermal efficiency, brake specific energy consumption, exhaust gas temperature and engine emissions were used as the output parameters. The investigation showed that ANN predicted results matched well with the experimental results over a wide range of operating conditions for both models. A comparison was made between ANN models and regression models. ANN performed better than the regression models. Similarly a comparison of MLP and RBF indicated that RBF with CDWFCM performed better than MLP networks with lower Mean Relative Error (MRE) and higher accuracy of prediction.     

Likelihood of wind energy assisted hydrogen production in three selected stations of Fiji Islands

ABSTRACT

Majority of the people have been paid attention towards renewable and clean sources of energy like wind. Due to the uncertainties related to wind turbines, issues of energy storage are noteworthy. One of the aptest methods of energy storage is the production of hydrogen from the wind. The main aim of this paper is to investigate the potential of wind energy assisted hydrogen production in three selected stations of Fiji Islands using various wind turbines. From the analysis of results and discussion, Vestas V110-2.0 provides acceptable capacity factors among all inspected wind turbines with the highest value of 77.06% for the station at Labasa. Additionally, the highest energy production was from the Vestas V110-2.0 wind turbine with an annual production of 13,501,620?kWh. The conversion system used in this study resulted in a linear relationship between generated wind energy and the amount of hydrogen produced. Therefore, the highest amount of yearly hydrogen production (240.19 ton-H) is related to the largest examined wind turbine, installed in the station at Labasa.     

Threshold Switching of Ag or Cu in Dielectrics: Materials,Mechanism, and Applications

Threshold switches with Ag or Cu active metal species are volatile memristors (also termed diffusive memristors) featuring spontaneous rupture of conduction channels. The temporal dynamics of the conductance evolution is closely related to the electrochemical and diffusive dynamics of the active metals which could be modulated by electric field strength, biasing duration, temperature, and so on. Microscopic pictures by electron microscopy and quantitative thermodynamics modeling are examined to give insights into the underlying physics of the switching. Depending on the time scale of the relaxation process, such devices find a variety of novel applications in electronics, ranging from selector devices for memories to synaptic devices for neuromorphic computing.     

A relationship-based approach to model integration

A key problem in model-based development is integrating a collection of models into a single, larger, specification as a way to construct a functional system, to develop a unified understanding, or to enable automated reasoning about properties of the resulting system. In this article, we suggest that the choice of a particular model integration operator depends on the inter-model relationships that hold between individual models. Based on this observation, we distinguish three key integration operators studied in the literature—merge, composition and weaving—and describe each operator along with the notion of relationship that underlies it. We then focus on the merge activity and provide a detailed look at the factors that one must consider in defining a merge operator, particularly the way in which the relationships should be captured during merge. We illustrate these factors using two merge operators that we have developed in our earlier work for combining models that originate from distributed teams.