Radar Pulse Compression with Optimized Weighting Window for SAR Receivers

Wireless Personal Communications - This paper proposes a novel design of a software-defined matched filter (MF) for digital receivers of synthetic aperture radar (SAR). The block diagram of the...     

Thermal Evaluation of Diesel/Hydrogen Peroxide Fuel Blend

Thermal properties of fossil fuel are the key fundamental characteristics, which can distinguish any compound as a potential fuel. The performance of diesel fuel blend along with stability and solubility parameter designs are evaluated. The results from the experimental study indicate that the increase in hydrogen peroxide (H₂O₂) amount enhances the cetane number of diesel fuel blend significantly. However, the calorific value decreases as compared to pure diesel fuel. All values performed well according to the ASTM D‐975 diesel testing method. The thermodynamics of the prepared fuel blends also revealed that substantial solubility and diesel/H₂O₂ blend stability are provided even at lower temperatures. Such blends can be used as a feasible replacement of pure diesel fuel.     

Ferroelectric,dielectric, magnetic,structural and photocatalytic properties of Co and Fe doped LaCrO3 perovskite synthesized via micro-emulsion route

In the present investigation, La_1-xCo_xCr_1-yFe_yO₃ (x,y = 0.0, 0.12, 0.36, 0.60) perovskite was fabricated via a facile micro-emulsion route. The synthesized perovskites were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) techniques to examine the effect of Co and Fe ions on the physico-chemical properties. The ferroelectric, dielectric, and magnetic properties of La_1-xCo_xCr_1-yFe_yO₃ were changed significantly as a function of dopants contents (Co and Fe ions). Outcomes revealed that the dielectric, ferroelectric and magnetic properties of LaCrO₃ perovskite can be tuned significantly via Co and Fe doping and La_0.40Co_0.60Cr_0.40Fe_0.60O₃ have potential for photocatalytic dye removal under (visible) light expoure. The photocatalytic activity (PCA) of the pristine LaCrO₃ and La_0.40Co_0.60Cr_0.40Fe_0.60O₃ photocatalyst was evaluated under (visible) light irradiation for crystal violet (CV) dye. Experimental results revealed that La_0.40Co_0.60Cr_0.40Fe_0.60O₃ photocatalyst degrdae almost 77.21% CV dye with the rate constant value of 0.01475 min^?1. In the presence of isopropyl alcohol (IPA) scavenger, the PCA of the La_0.40Co_0.60Cr_0.40Fe_0.60O₃ photocatalyst and rate constant value of the photocatalytic reaction decreased to 32.5% and 0.00491 min^?1, suggesting the superoxide as main active specie. Results revealed that Co and Fe doping doped material is efficient for photocatalytic presentations under solar light expoure.     

Deep-piRNA: Bi-Layered Prediction Model for PIWI-Interacting RNA Using Discriminative Features

Piwi-interacting Ribonucleic acids (piRNAs) molecule is a well-known subclass of small non-coding RNA molecules that are mainly responsible for maintaining genome integrity, regulating gene expression, and germline stem cell maintenance by suppressing transposon elements. The piRNAs molecule can be used for the diagnosis of multiple tumor types and drug development. Due to the vital roles of the piRNA in computational biology, the identification of piRNAs has become an important area of research in computational biology. This paper proposes a two-layer predictor to improve the prediction of piRNAs and their function using deep learning methods. The proposed model applies various feature extraction methods to consider both structure information and physicochemical properties of the biological sequences during the feature extraction process. The outcome of the proposed model is extensively evaluated using the k-fold cross-validation method. The evaluation result shows that the proposed predictor performed better than the existing models with accuracy improvement of 7.59% and 2.81% at layer I and layer II respectively. It is anticipated that the proposed model could be a beneficial tool for cancer diagnosis and precision medicine.     

Ionic liquid assisted polyetheretherketone-multiwalled carbon nanotubes nanocomposites: An environmentally friendly approach

Reinforcement of PEEK by nanoparticles such as multiwalled carbon nanotubes (MWCNTs), is a promising technique to prepare PEEK nanocomposites with improved properties for promising biomedical applications. However, proper dispersion of MWCNTs in the polymer matrices is a primary processing challenge. The present study reports a novel and environmentally beneficial approach for homogeneous dispersion of MWCNT in PEEK by using ionic liquid (IL) 1-ethyl-3-methylimidazolium hydrogen sulfate ([EMIM][HSO₄]). Neat PEEK, PEEK-MWCNTs (using conventional organic solvent dimethylformamide), and PEEK-MWCNTs-IL (using [EMIM][HSO₄]) nanocomposites were fabricated via melt-compounding and compression molding techniques. The fabricated composites were characterized for morphological, thermal, and mechanical properties and compared to those of neat PEEK and PEEK-MWCNTs. Ionic liquid provoked proficient dispersion of the MWCNTs in PEEK, as confirmed by FESEM and optical micrographs. The thermal stability of PEEK-MWCNTs-IL composite was significantly superior to that of the neat PEEK and PEEK-MWCNTs. Analysis of tensile strength and nanoindentation depicted that the modulus of elasticity of PEEK-MWNCTs-IL was significantly increased by 76% as compared to that of neat PEEK. We believe that the present work could provide a new and green platform for the manufacturing of PEEK nanocomposites with enhanced dispersion of nanofillers for biomedical applications.     

An efficient grayscale image encryption scheme based on variable length row-column swapping operations

Multimedia Tools and Applications - Swapping a row with a row, a column with a column or row with a column are usual operations carried out to scramble the given input plain image. To introduce...     

Energy-efficient operation of indirect adiabatic data center cooling systems via Newton-like phasor extremum seeking control

This paper considers the problem of optimizing the operation of Indirect Adiabatic Cooling (IAC) systems with application to data centers. Optimal operation is achieved when the required cooling demand is satisfied at the minimum energy cost. For this purpose, we design a supervisory control system, where the higher layer determines the optimal set-points for the local controllers by employing an Extremum Seeking Control (ESC) scheme. In particular, we consider a Newton-like phasor ESC, which augments the derivative estimator underlying the phasor approach to capture also the Hessian of the plant index and then it uses these estimates to steer the system along a Newton-like direction. The effectiveness of the considered approach is tested in simulation by exploiting a Matlab-based simulation environment including an IAC system and a computer room.