Availability of a two-unit standby system with switchover time and proper initialization of connect switching

A 2-unit standby redundant system with connect switching (CS) is considered. The standby unit takes random switchover-time when the operative unit fails and CS is properly initialized in a random time after each repair. Failure-time distributions of units are exponential whereas all other distributions are arbitrary. In this note we employ the method of semi-Markov process to obtain steady-state availability of the system.     

Preparation and characterization of PbS nanoclusters made by using a powder method on ionomers

By using a powder method, a new method for producing semiconductor/polymer nanocomposites, lead sulfide (PbS) nanoparticles were prepared and dispersed in either poly(methylmethacrylate-co-methacrylic acid) or poly(styrene-co-styrenesulfonic acid). The size of the nanoclusters was less than 2 nm, and the size distribution was rather sharp, determined by UV-vis absorption spectroscopy. The absorption spectra of the PbS-containing sheets showed a blue shift and the absorption edges were steep, reflecting the formation of nano-sized PbS clusters. The spectra also exhibited an absorption maximum, which is an indication of a narrow particle size distribution. The glass transition temperature (T_g) of the PbS-containing nanocomposites was higher than that of the corresponding ionomer. The effect of heat treatment on the aggregation of PbS is also discussed. The dissolution of powder samples, followed by neutralization of acidic groups, led to reduction of particle sizes, suggesting the usefulness of ionic groups for stabilizing PbS nanoclusters.     

Improving UAV imaging quality by optical sensor fusion: an initial study

The most frequent application of unmanned aerial vehicle (UAVs) is to collect optical colour images from an area of interest. Thus, high spatial resolution colour images with high amount of signal to noise ratio (SNR) are of great importance in UAV applications. Currently, most UAVs use single sensor colour filter array (CFA) cameras for image collection, within which the Bayer-pattern sensors are the most frequently used ones. Due to the limitations of the CFAs, the quality (in terms of spatial resolution, SNR, and sharpness) of UAV colour images is not optimal. In this article, a sensor fusion solution is proposed to improve the quality of UAV imaging. In the proposed solution, a high-resolution colour (HRC) Bayer-pattern sensor is replaced by a dual camera set containing a panchromatic (Pan) sensor, with the same pixel size and a Bayer-pattern colour (or a four-band multi-spectral) sensor with larger pixel size; the resulting images of the dual camera set are then fused. The enlarged pixel size of the colour sensor provides a higher SNR for the cost of lower spatial resolution. However, the accompanied Pan sensor provides single band images with high SNR and high spatial resolution. Fusing the images of the dual camera set generates colour (or MS) images with high spatial resolution, SNR, and sharpness compensating for the major problems of the Bayer-pattern filters.

This replacement solution is initially tested in a laboratory experiment. The results of quality assessments show that the SNR is increased by 2–3 times, the sharpness is improved by around 2 times, and the spatial resolution is increased up to the level of the pan images, while the colour errors remained almost as low as the original colour images. In addition, image classification capability of the images is examined using two methods: Support Vector Machine (SVM) and Maximum Likelihood (ML). The results of image classification also confirmed around 20–40% increase in accuracy. Therefore, the proposed sensor fusion can be a good alternative for UAV colour sensors.     

Linear and nonlinear analysis of normal and CAD-affected heart rate signals

Coronary artery disease (CAD) is one of the dangerous cardiac disease, often may lead to sudden cardiac death. It is difficult to diagnose CAD by manual inspection of electrocardiogram (ECG) signals. To automate this detection task, in this study, we extracted the heart rate (HR) from the ECG signals and used them as base signal for further analysis. We then analyzed the HR signals of both normal and CAD subjects using (i) time domain, (ii) frequency domain and (iii) nonlinear techniques. The following are the nonlinear methods that were used in this work: Poincare plots, Recurrence Quantification Analysis (RQA) parameters, Shannon entropy, Approximate Entropy (ApEn), Sample Entropy (SampEn), Higher Order Spectra (HOS) methods, Detrended Fluctuation Analysis (DFA), Empirical Mode Decomposition (EMD), Cumulants, and Correlation Dimension. As a result of the analysis, we present unique recurrence, Poincare and HOS plots for normal and CAD subjects. We have also observed significant variations in the range of these features with respect to normal and CAD classes, and have presented the same in this paper. We found that the RQA parameters were higher for CAD subjects indicating more rhythm. Since the activity of CAD subjects is less, similar signal patterns repeat more frequently compared to the normal subjects. The entropy based parameters, ApEn and SampEn, are lower for CAD subjects indicating lower entropy (less activity due to impairment) for CAD. Almost all HOS parameters showed higher values for the CAD group, indicating the presence of higher frequency content in the CAD signals. Thus, our study provides a deep insight into how such nonlinear features could be exploited to effectively and reliably detect the presence of CAD.     

Chitosan nanoencapsulation of Pogostemon cablin (Blanco) Benth. essential oil and its novel preservative effect for enhanced shelf life of stored Maize kernels during storage: Evaluation of its enhanced antifungal,antimycotoxin, antioxidant activities and possible mode of action

The present evaluation aimed to synthesis and characterise the Pogostemon cablin essential oil encapsulated chitosan nanoemulsion (PCEO-CN) and evaluated its antifungal antimycotoxin and antioxidant activities. A total of twenty-six different chemical compounds were identified from P. cablin essential oil (PCEO), among which patchoulol (34.93%), α-bulnesene (17.76%) and α-guaiene (15.44%) were recorded as major components. The average size of PCEO-CN was 18.20 nm. The PCEO-CN showed concentration-dependent broad-spectrum antifungal and antimycotoxin activities. The in vivo evaluation showed that PCEO-CN significantly protected maize seeds from mould-induced biodeterioration and aflatoxin B₁ biosynthesis for up to 30 days. The PCEO-CN significantly inhibits ergosterol biosynthesis and cellular integrities of A. flavus. Based on the present study, PCEO-CN could be used as a food preservative to control mould and mycotoxin contamination in stored maize.     

Optical properties of graphene based annular photonic crystals

The optical properties of a graphene based annular photonic crystal (APC) are theoretically investigated. The proposed structure is a hollow core cylindrical shell consists of the alternate dielectric layer and graphene monolayer immersed in free space. In order to study the photonic band structures of the APC, we obtained the optical spectra of the graphene based APC by employing the transfer matrix method in the cylindrical waves for both TE and TM polarizations. In this work we study the effect of different geometrical and optical parameters of the structure on the low loss high reflectance graphene induced band gap. It is found that the graphene induced band gap which appeared in the frequency below 10 THz is polarization independent and remains almost invariant with the change in the period number, the radius of the inner core region and the refractive indices of the inner core region and the surrounding medium. However, its width increases by increasing the azimuthal mode number and the chemical potential of the graphene monolayers and decreases by increasing the refractive index and the thickness of the dielectric layers.     

Analysis of Computer Experiments With Functional Response

This article is motivated by a computer experiment conducted for optimizing residual stresses in the machining of metals. Although kriging is widely used in the analysis of computer experiments, it cannot be easily applied to model the residual stresses because they are obtained as a profile. The high dimensionality caused by this functional response introduces severe computational challenges in kriging. It is well known that if the functional data are observed on a regular grid, the computations can be simplified using an application of Kronecker products. However, the case of irregular grid is quite complex. In this article, we develop a Gibbs sampling-based expectation maximization algorithm, which converts the irregularly spaced data into a regular grid so that the Kronecker product-based approach can be employed for efficiently fitting a kriging model to the functional data. Supplementary materials are available online.     

On Generalized Reduced Gradient method with multi-start and self-optimizing control structure for gas lift allocation optimization

In most of the gas lifted oil fields, multiple oil wells share lift gas from a common gas distribution source. The lift gas should be distributed optimally among the wells to maximize total oil production. In this paper, a nonlinear dynamic model of the oil field is developed from first principles modeling. One of the objectives of this paper is to solve the optimal lift gas distribution problem using the Generalized Reduced Gradient (GRG) method. In addition, multi-start search routine is developed to ensure that the local optimal solution is closer to the global solution. Sensitivity of the optimal solution to changes in oil field parameters like reservoir pressure, Productivity Indices (PI), total lift gas supply (input disturbance) and separator pressure is studied. It is shown that the available lift gas is distributed among the wells according to the PI values of the wells and the optimal values are highly sensitive to the changes in PI values. A self-optimizing control structure using simple controllers is designed which is capable of keeping the oil field in optimal operating conditions without having to re-optimize the whole process when the input disturbance occur in the system. The simulation results show that the outcome of optimization is increased total oil production which leads to increased profit.