Modulation Based Combination of High Level Features Generated from SCCF & Contourlet Transforms for CBIR Applications

The need for suitable and cost-effective technologies rise with the growth of the internet of things (IoT) applications. These aim at handling voluminous data transmission in addition to minimum energy and latency cost constraints. LoRa networks are recommended for applications in confined spaces, long ranges, and less battery consumption requirements. However, the end devices in these networks communicate to all gateways in their ranges, thereby expediting energy unproductively in redundant transmissions. In our article, we explore the possibilities of whether LoRa networks could employ the advantages of clustering and propose two algorithms, path-based and data-centric, for such networks. We suggest that LoRaWAN technology with clustering can be apt for long-range, low power consumption IoT applications in the future. We study the impact of network density, node range, and cluster range on the energy consumption in data transmissions. The algorithms are compared with the inherent star-based communication of LoRa networks based on energy consumed, and our results show that, for dense deployments, clustering becomes advantageous.

     

Selection of regression models for predicting strength and deformability properties of rocks using GA

Recently, many regression models have been presented for prediction of mechanical parameters of rocks regarding to rock index properties. Although statistical analysis is a common method for developing regression models, but still selection of suitable transformation of the independent variables in a regression model is difficult. In this paper, a genetic algorithm (GA) has been employed as a heuristic search method for selection of best transformation of the independent variables (some index properties of rocks) in regression models for prediction of uniaxial compressive strength (UCS) and modulus of elasticity (E). Firstly, multiple linear regression (MLR) analysis was performed on a data set to establish predictive models. Then, two GA models were developed in which root mean squared error (RMSE) was defined as fitness function. Results have shown that GA models are more precise than MLR models and are able to explain the relation between the intrinsic strength/elasticity properties and index properties of rocks by simple formulation and accepted accuracy.     

A pseudo‐differential current‐reuse structure for opamp‐sharing pipelined analog‐to‐digital converters

In this paper, a power efficient pseudo‐differential (PD) current‐reuse structure is presented to alleviate the memory effects of opamp‐sharing in pipelined analog‐to‐digital converters. To implement the PD current‐reuse structure, a switched‐capacitor circuit is introduced for multiplying digital‐to‐analog converter, which has a slight modification compared with the conventional switching scheme with no power penalty. In the proposed multiplying digital‐to‐analog converter circuit, the common‐mode offset amplification of the PD structures is eliminated. Moreover, a PD current‐reuse amplifier is developed from the telescopic structure with an inverter‐based gain‐boosting circuit. The effectiveness of the proposed structure is evaluated in comparison with existing current‐reuse techniques. Copyright © 2014 John Wiley & Sons, Ltd.     

Specific heavy metal ion recovery with ion‐imprinted cryogels

In this study, the functional monomers, N‐methacryloyl‐l ‐aspartic acid and N‐methacryloyl‐l ‐cysteine were synthesized through a reaction between appropriate amino acids and methacryloyl chloride. Then, Pb(II) or Cd(II) ion‐imprinted 2‐hydroxyethyl methacrylate based cryogels were prepared by free radical polymerization method under partially frozen conditions. Following the characterization of matrices, adsorption of heavy metal ions was examined in batch mode from aqueous solution considering several parameters affecting the adsorption performance. The actual adsorption capacities were 44.5, 65.3, and 86.7 mg/g for Cd‐1, Cd‐2, and Cd‐3 cryogels meanwhile those were 41.9, 86.3, and 122.7 mg/g for Pb‐1, Pb‐2, and Pb‐3 cryogels, respectively at optimum pH: 5.5. By increasing temperature, adsorption capabilities of both cryogels were inhibited because of the electrostatic nature of coordinated covalent bonds and collapsing of coordination spheres. The adsorption process was very fast, the equilibrium adsorption was achieved in about 60 min, which was directly related to macroporous structure and interconnected flow‐channels of cryogels. Kinetics and adsorption isotherms were also studied. Langmuir isotherms and pseudo‐second order kinetic model were well suited to adsorption data, which also indicated that the process occurred without any diffusion restrictions or steric hindrances. Finally, the competitive adsorption studies were performed using multi‐ion containing synthetic wastewater to show whether the cryogels developed are suitable for specific heavy metal recycling or not. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43095.     

Multi-objective modelling of day ahead scheduling of MMG-based distribution networks accompanied by DEA considering economics,emissions and power quality

ABSTRACT

This paper presents the state of the art relating to multi-objective modelling for day ahead scheduling of multi micro grid-based distribution networks, using optimal power flow (OPF) accompanied by data envelopment analysis (DEA). In this paper eco-reliability cost function, power quality enhancement and emission reduction are treated as the objective functions and the uncertainties of renewable distributed generations (DGs), load demand and market price are incorporated into the problem. This method is able to find the optimum operation of DGs in grid-connected or isolated MGs, power transaction between each MG and upstream networks/other MGs and hourly reconfiguration instants. For this purpose, firstly OPF is applied to the problem, then the obtained optimal solutions are prioritised by DEA and ranking is done, based on the efficiencies of the optimal solutions. Finally, the provided results validate the practicability of the proposed method and accuracy of the outcomes.     

Local hardness and density variation in glass substrates machined with Spark Assisted Chemical Engraving (SACE)

Spark Assisted Chemical Engraving (SACE) is an unconventional technique for surface micro-machining of non-conductive materials specially glass. SACE offers many advantages for fabrication of microfluidic and Lab on Chip devices. However the exact mechanism of material removal in this technique is not fully understood. Besides, the changes in the properties of the machined sample have not been studied so far. In this letter, the material removed from glass surface is evaluated and the results of nano-indentation test for measurement of the hardness of machined micro-channels surface is reported. Based on the amount of removed mass during machining and results of nano-indentation test on machined samples it is concluded that hardness and density of the machined zones decrease during the process.     

Numerical Investigation of Engineering Systems: Analysis of Accuracy Reduction Due to Model Oversimplification

When faced with the mathematical modeling of any engineering system, whether for design, performance assessment, optimization, or control, the engineer has to determine the level of accuracy versus the simplicity of the mathematical formulation. Although it is universally accepted that the more complex the formulation, the more accurate the results will be, these usually come at the expense of larger CPU times, a substantial amount of computer resources, and are generally limited by the capacity of computers and computing power, which most times precludes their use in favor of simpler models. Many times, however, engineers do not realize the potential risk that oversimplification of a problem generates in terms of accuracy of the results; that is, the model solution does not resemble the system behavior. Through a demonstrative example, the present study addresses the issue of oversimplification of the resulting mathematical model and the corresponding accuracy of its solution. After constructing three sets of models of the physical system, each with a different level of detail, the solutions are then compared to experimental data. The results show that the accuracy of the numerical approximation depends directly on the level of complexity of the mathematical model used, and that oversimplification may result in up to a ninefold degradation of the results. In addition, minor changes in the inlet boundary condition and geometry result in significant changes in the flow pattern, up to a fivefold difference between different models in the recirculation bubble relative error. This information is fundamental for engineering professionals to consider during the modeling process in applications.     

Development of dispersive liquid-liquid microextraction based on deep eutectic solvent using as complexing agent and extraction solvent: application for extraction of heavy metals

ABSTRACT

A green and efficient dispersive liquid-liquid microextraction method based on a new deep eutectic solvent has been developed for the preconcentration and extraction of cobalt and nickel ions. The deep eutectic solvent is formed by mixing choline chloride (hydrogen bond acceptor) and 4-aminophenol (hydrogen bond donor). Then, it is used as a chelating agent as well as extraction solvent. Under the optimum experimental conditions, the linear ranges for Ni(II) and Co(II) were 0.80–50 and 0.50–50 µgL^?1, respectively, by flame atomic absorption spectrometry. The obtained detection limits were 0.30 and 0.22 µg L^?1 for Ni(II) and Co(II), respectively.     

Security anomaly detection in software‐defined networking based on a prediction technique

Nowadays, software‐defined networking (SDN) is regarded as the best solution for the centralized handling and monitoring of large networks. However, it should be noted that SDN architecture suffers from the same security issues, which are the case with common networks. As a case in point, one of the shortcomings of SDNs is related to its high vulnerability to distributed denial of service (DDoS) attacks and other similar ones. Indeed, anomaly detection systems have been considered to deal with these attacks. The challenges are related to designing these systems including gathering data, extracting effective features, and selecting the best model for anomaly detection. In this paper, a novel combined approach is proposed; this method uses NetFlow protocol for gathering information and generating dataset, information gain ratio (IGR), in order to select the effective and relevant features and ensemble learning scheme (Stacking) for developing a structure with desirable performance and efficiency for detecting anomaly in SDN environment. The results obtained from the experiments revealed that the proposed method performs better than other methods in terms of enhancing accuracy (AC) and detection rate (DR) and reducing classification error (CE) and false alarm rate (FAR). The AC, DR, CE, and FAR of the proposed model were measured as 99.92%, 99.83%, 0.08%, and 0.03%, respectively. Furthermore, the proposed method prevents the occurrence of excessive overload on the controller and OpenFlow.