The effect of agarose content on the morphology,phase evolution,and magnetic properties of CoFe2O4 nanoparticles prepared by sol‐gel autocombustion method

Hard‐magnetic CoFe₂O₄ nanoparticles were produced with the sol‐gel autocombustion route using agarose and citric acid as a neutral organic gel and combustion agent. For this purpose, the obtained gel is annealed at 800°C for 3 hours and then the spinel structure of the samples was confirmed by X‐ray diffraction (XRD). In addition, the existence of metal‐oxygen complexes in the gel and nanoparticles was investigated by Fourier transformation infrared (FTIR) spectra. Furthermore, FESEM images showed that semispherical and rod‐like particles were obtained by variation in agarose contents. For the rod‐like sample with 2.5 g agarose, the saturation magnetization and coercivity were measured equal to 71.7 emu/g and 10076.84 Oe, respectively.     

Cost-effective multi-pulse AC-DC converter with lower than 3% current THD

In this paper, a new multi-pulse clean power converter topology is presented with lower than 3% input current total harmonic distortion (THD). The topology is based on the combination of a 36-pulse converter and a pulse doubling circuit. The 36-pulse converter consists of a “polygon” autotransformer and two 18-pulse diode bridge rectifiers. Pulse doubling circuit consists of zero-sequence-blocking transformer (ZSBT) and tapped interphase transformer (IPT). The important advantage of the proposed AC-DC converter is considerable reduction in burdens of the magnetic parts with respect to the nominal load rating. It provides more economical solution to achieve harmonic mitigation in electric power systems as compared with other 72-pulse AC-DC converters. The simulation and experimental results show that the input current THD is less than 3% using the proposed topology. The results indicate a significant reduction in the total cost and volume of the proposed 72-pulse configuration in comparison with similar configurations. Also, it is shown that the proposed converter rating is about 44% of the DC load power.     

Dynamic behavioral modeling of nonlinear circuits using a novel recurrent neural network technique

In this paper, a new method called local-global feedback recurrent neural network (LGFRNN) is proposed for dynamic behavioral modeling of nonlinear circuits. The structure of the proposed method is based on recurrent neural network and constructed by time-delayed local and global feedbacks. Adding time-delayed feedbacks has a great impact on the learning capability of previous neural network-based methods. Moreover, time-delayed local feedbacks alleviate the problem of slow convergency of the conventional neural network-based methods in the training phase. The proposed LGFRNN can be trained only by having sampled input-output waveforms of the original circuit without knowing the internal details of the circuit. A training algorithm based on real-time recurrent learning (RTRL) is used to train LGFRNN. After the training phase, the proposed LGFRNN provides accurate macromodel of a nonlinear circuit. The proposed method is more accurate compared with the conventional neural-based models (which do not benefit from time-delayed local-global feedbacks) and also significantly reduces the training time of the conventional models. Moreover, proposed LGFRNN is faster than the existing models in simulation tools. The validity of the proposed method is verified by time-domain modeling of three nonlinear devices including commercial TI's SN74AHCT540 device, five-stage complementary metal-oxide-semiconductor (CMOS) receiver, and commercial TI's LM324 power amplifier.     

Thermal management of newly developed non-noble metal-based catalytic converter to reduce cold start emissions of small internal combustion engine

In this study, thermal management of a newly developed non-noble metal-based catalytic converter, more suitable to smaller engines, has been investigated. Two different approaches are explored; first, use of phase change material (PCM) to keep the converter hot beyond light off temperature during shorter soak times and second, electrically heating up converter before or after engine cold start-up to reduce cold start emissions. The converter used had light off temperatures of 195 and 206°C for carbon monoxide and hydro carbons, respectively. Results showed that 1?kg of PCM was capable of keeping the converter hot beyond light off temperatures for a soak time of 2.5?h. Similarly, use of glow plugs also decreased the light off time of the converter to less than 2?min as compared to 13?min without thermal management measures.     

Effect of Cesium Aluminate Nanofiller on Optical Properties of Polyvinyl Pyrrolidone Nanocomposite Films

The nanocomposite films of poly vinylpyrrolidone (PVP) with different amounts viz., 2, 4, 6 and 8?wt% of cesium aluminate (CsAlO₂) have been fabricated using solvent casting technique. The effect of nanofiller content on the optical properties of PVP/CsAlO₂ nanocomposite films has been established by UV-visible spectroscopy. The UV-visible transmittance studies revealed that the UV light absorbing nature of nanocomposite films with considerable visible transparency. The Fourier Transform Infrared (FTIR) spectral studies visualizes the effect of CsAlO₂ nanofiller on the structural behaviors of PVP, while optical studies reveals an obvious change in the electronic band structure leading to a significant reduction in optical band gaps. The scanning electron microscopic (SEM) studies establish the morphological changes in PVP matrix upon doping with CsAlO₂. The measured refractive index (RI) depends on the volume fraction of CsAlO₂ nanofiller and the result indicates that a substantial increase of RI values from 1.85 to 2.64 at wave length 360 nm. The dielectric studies, optical conductivity measurements and Urbach energy analysis also supports the dopant dependent optical property, tuning of PVP/CsAlO₂ nanocomposite films to enable effective material property engineering to suit specified application requirements.     

A Reliable NLP Scheme for English Text Watermarking Based on Contents Interrelationship

In this paper, a combined approach CAZWNLP (a combined approach of zero-watermarking and natural language processing) has been developed for the tampering detection of English text exchanged through the Internet. The third gram of alphanumeric of the Markov model has been used with text-watermarking technologies to improve the performance and accuracy of tampering detection issues which are limited by the existing works reviewed in the literature of this study. The third-grade level of the Markov model has been used in this method as natural language processing technology to analyze an English text and extract the textual characteristics of the given contexts. Moreover, the extracted features have been utilized as watermark information and then validated with the attacked English text to detect any suspected tampering occurred on it. The embedding mechanism of CAZWNLP method will be achieved logically without effects or modifying the original text document to embed a watermark key. CAZWNLP has been implemented using VS code IDE with PHP. The experimental and simulation results using standard datasets of varying lengths show that the proposed approach can obtain high robustness and better detection accuracy of tampering common random insertion, reorder, and deletion attacks, e.g., Comparison results with baseline approaches also show the advantages of the proposed approach.     

An energy aware event-driven routing protocol for cognitive radio sensor networks

Cognitive radio sensor network (CRSN) is an intelligent and reasonable combination of cognitive radio technology and wireless sensor networks. It poses significant challenges to the design of topology maintenance techniques due to dynamic primary-user activities, which in turn decreases the data delivery performance of the network as well as it’s lifetime. This paper aims to provide a solution to the CRSN clustering and routing problem using an energy aware event-driven routing protocol (ERP) for CRSN. Upon detection of an event, the ERP determines eligible nodes for clustering according to local positions of CRSN nodes between the event and the sink and their residual energy levels. Cluster-heads are selected from the eligible nodes according to their residual energy values, available channels, neighbors and distance to the sink. In ERP, cluster formation is based on relative spectrum awareness such that channels with lower primary user appearance probability are selected as common data channels for clusters. For data routing, ERP employs hop-by-hop data forwarding approach through the CHs and primary/secondary gateways towards the sink. Through extensive simulations, we demonstrate that the proposed ERP provides with better network performances compared to those of the state-of-the-art protocols under a dynamic spectrum-aware data transmission environment.     

No-reference image quality assessment in complex-shearlet domain

The field of image quality measure (IQM) is growing rapidly in recent years. In particular, there was a significant progress in no-reference (NR) IQM methods. Natural scenes have certain statistical properties which vary in the presence of distortion. The statistical changes represent the loss of naturalness and can be efficiently quantified using shearlet transformation of images. In this paper, a general-purpose NR IQM approach is proposed based on the statistical characteristics of natural images in shearlet domain. The method utilizes a set of distortion-sensitive features extracted from statistical properties of shearlet coefficients. Phase and amplitude of an image contain important perceptual information; therefore, a complex version of the shearlet transform is employed to take advantage of phase and amplitude features in quality estimation. In quality prediction step, the features are used to train image classification and quality prediction models using a support vector machine. The experimental results show that the proposed NR IQM is highly correlated with subjective assessment and outperforms several full-reference and state-of-art NR IQMs.     

MHD radiative ohmic heating nanofluid flow of a stretching penetrable wedge: A numerical analysis

The present numerical investigation has focused on the magnetohydrodynamics flow of a viscous nanofluid over a stretching wedge with the boundary convective conditions, thermal radiation, and ohmic heating. Buongiorno's two-component nonhomogeneous nanoscale model was used and a dilute nanofluid with spherical type particles is considered. Similarity transformations are used to render the system of governing partial differential equations into a system of coupled similarity equations. The transformed equations are solved numerically with the BVP4C method. Validation of solutions with previous studies based on special cases of the general model is included. The salient features of fluid velocity profile, temperature as well as concentration profiles are discussed in a graphical manner for various values of selected governing factors. The skin friction coefficient, mass, and heat transfer rates are calculated and summarized. It is worthwhile noticing that the validation results exhibit an excellent agreement with already existing reports. The modeling of the present problem is useful in the thermal processing of sheet-like substances that is a necessary operation in paper procurement, wire drawing, drawing of plastic films, polymeric sheets, and metal spinning.