Correction to: Enhanced TOA Estimation Using OFDM over Wide-Band Transmission Based on a Simulated Model

Wireless Personal Communications -     

Ti3AlC2 coated D-shaped fiber saturable absorber for Q-switched pulse generation

In this letter, a titanium aluminum carbide (Ti3AlC2) coated D-shaped fiber is proposed and demonstrated as a new saturable absorber (SA) for Q-switched laser pulse generation. In preparing the SA, the Ti3AlC2 powder is dispersed in liquid polyvinyl alcohol (PVA) before the solution is dropped and left to dry onto a polished surface of D-shape fiber. The SA is added to an erbium-doped fiber laser (EDFL) cavity to modulate the cavity loss for Q-switching. The Q-switched laser is obtained at 1 561 nm. The pulse width of the pulses can be varied between 7.4 µs and 5.1 µs with a corresponding repetition rate range from 41.26 kHz to 54.35 kHz, when the pump power is increased from 42.2 mW to 71.5 mW. At 71.5 mW pump, the pulse energy is obtained at 70.3 nJ. The signal-to-noise ratio (SNR) of the fundamental frequency is recorded at 67 dB, which indicates the stability of the laser.     

A wide axial-ratio beamwidth circularly-polarized oval patch antenna with sunlight-shaped slots for gnss and wimax applications

Wireless Networks - This paper proposes a quadruple band stacked oval patch antenna with sunlight-shaped slots supporting L1/L2/L5 GNSS bands and the 2.3 Ghz WiMAX band. The antenna produces...     

A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19

The Journal of Supercomputing - This paper designs and develops a computational intelligence-based framework using convolutional neural network (CNN) and genetic algorithm (GA) to detect COVID-19...     

Defect Prediction Using Akaike and Bayesian Information Criterion

Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the prediction. Most of the work present in software defect prediction is focused on the selection of best prediction techniques. For this purpose, deep learning and ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning the training data and selection of best parameter values from the data. Sometimes data available for training the models have high variability and this variability may cause a decrease in model accuracy. To deal with this problem we used the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for selection of the best variables to train the model. A simple ANN model with one input, one output and two hidden layers was used for the training instead of a very deep and complex model. AIC and BIC values are calculated and combination for minimum AIC and BIC values to be selected for the best model. At first, variables were narrowed down to a smaller number using correlation values. Then subsets for all the possible variable combinations were formed. In the end, an artificial neural network (ANN) model was trained for each subset and the best model was selected on the basis of the smallest AIC and BIC value. It was found that combination of only two variables’ ns and entropy are best for software defect prediction as it gives minimum AIC and BIC values. While, nm and npt is the worst combination and gives maximum AIC and BIC values.     

Optimization of process parameters affecting surface roughness in magnetic abrasive finishing process

Development in manufacturing technology enhances the mechanical behavior of machined parts and improves the surface finish with high precision, which conveys the progressive importance of magnetic abrasive finishing (MAF) process. In current research work, magnetic abrasive particles were used as finishing tools during the MAF process. However, these magnetic abrasives are fabricated by special techniques, i.e., the adhesive bonding-based method, the sintering method, the plasma-based method and so on. The present study explores the basic finishing characteristics of the magnetic abrasive produced by the sintering process. After the sintering process, improved quality of magnetic abrasives was obtained, where the abrasive particle sticks on the base metal matrix. The abrasive particle used is alumina powder and the magnetic particle is iron powder. Experiments were performed on Stainless Steel 202 to inspect the sound effects of several process parameters such as rotational speed, electromagnet voltage, machining gap and abrasive particle size on machining performance. Apart from that, surface roughness was also measured, which revealed the influence of the abrasive particle on the machined surface in terms of surface finish. It is observed from this study that appropriate size of magnetic abrasive particle optimizes the surface finish.     

PIV investigations on particle velocity distribution in uniform swirling regime of fluidization

In this study, hydrodynamics of spherical particles in uniform swirling regime of a fluidized bed were investigated using MATLAB supported particle imaging velocimetry (PIV). A least investigated mesh-type distributor was used to fluidize the bed particles, at different air entry angles, for future applications in coating and granulation industry. A quarter of the bed was photographed using high speed imaging technique and the respective velocity fields of the swirling particles were produced using PIV technique. The Gaussian distribution of the particle velocity profiles was predicted at low superficial air velocity; particles near the border of the bed showed relatively low velocity than that swirled in the middle of the test section. However, at high superficial velocity, the particles near the central cone moved with velocity comparable to the particle velocity in the middle of the test section. Contrarily, the particles in the vicinity of the outer bed-wall maintained their steady state motion at all superficial air velocities. The average particle velocity experienced monotonic increase for more angular air intake. The magnitude of the particle velocity reduced by 6.35% for each \(3^{\circ }\) increment in the air entry angle.     

Phase Distribution and Magnetic Properties of Mechanically Alloyed Hard/Soft Ferrite Nanocomposites

Improvement in the magnetic properties of hard/ soft ferrite nanocomposites was studied by varying the composition of the soft phase in SrFe₁₂O₁₉/Ni_0.5Zn_0.5Fe₂O₄ nanocomposites. The SrFe₁₂O₁₉/Ni_0.5Zn_0.5Fe₂O₄ nanocomposites were prepared using the mechanical alloying method. The samples were prepared by varying the amount of the soft phase from 10 to 50 wt% while the amount of the hard phase remained 100 wt% in the ferrite nanocomposites. X-ray diffraction (XRD), a vibrating sample magnetometer (VSM), and a transmission electron microscope (TEM) were used to characterize the samples. From the result, it was found that the nanocomposite magnet with 10 wt% of soft phase content had the highest remanence ratio, M _r / M _s , which was 0.61, while the values of the coercivity, H _c , and magnetization, M _s , measured were 4482.4 G and 9.71 emu/g, respectively, and the average particle size of the ferrite nanocomposites was < 50 nm for all the samples. It was also shown that H _c decreased as the weight percent of the soft ferrite increased, which resulted from the dipolar interaction that occurred in the ferrite nanocomposites, showing the effect of phase distribution on the magnetic properties.     

Photoacoustic‐Guided Surgery with Indocyanine Green‐Coated Superparamagnetic Iron Oxide Nanoparticle Clusters

A common cause of local tumor recurrence in brain tumor surgery results from incomplete surgical resection. Adjunctive technologies meant to facilitate gross total resection have had limited efficacy to date. Contrast agents used to delineate tumors preoperatively cannot be easily or accurately used in the real‐time operative setting. Although multimodal imaging contrast agents are developed to help the surgeon discern tumor from normal tissue in the operating room, these contrast agents are not readily translatable. This study has developed a novel contrast agent comprised solely of two Food and Drug Administration approved components, indocyanine green (ICG) and superparamagnetic iron oxide (SPIO) nanoparticles—with no additional amphiphiles or carrier materials, to enable preoperative detection by magnetic resonance (MR) imaging and intraoperative photoacoustic (PA) imaging. The encapsulation efficiency of both ICG and SPIO within the formulated clusters is ≈100%, and the total ICG payload is 20–30% of the total weight (ICG + SPIO). The ICG–SPIO clusters are stable in physiologic conditions; can be taken up within tumors by enhanced permeability and retention; and are detectable by MR. In a preclinical surgical resection model in mice, following injection of ICG–SPIO clusters, animals undergoing PA‐guided surgery demonstrate increased progression‐free survival compared to animals undergoing microscopic surgery.     

Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines

The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain.