Effect of PBMA on PVC‐based polymer blend electrolytes

Poly(vinyl chloride) (PVC)—poly(butyl methacrylate) (PBMA) blended polymer electrolytes with lithium perchlorate (LiClO₄) as the complexing salts are prepared by solution casting technique. The addition of PBMA into PVC matrix is found to induce considerable changes in physical and electrical properties of the polymer electrolytes. Addition of PBMA into PVC matrix is found to increase the conductivity by two orders of magnitude (1.108 × 10^?5 S cm^?1) when compared with that of the pristine PVC polymer electrolyte (10^?7 S cm^?1). Structural, thermal, mechanical, morphological, and polymer–salt interactions are ascertained from X‐ray diffraction (XRD), thermogravimetry/differential thermal analysis (TG/DTA), mechanical analysis, scanning electron microscope (SEM), and Fourier transform infrared spectroscopy (FTIR) respectively. A thermal stability upto 250 °C is asserted from the TG/DTA analysis. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134 , 44939.     

Parkinson’s disease prediction using gene expression – A projection based learning meta-cognitive neural classifier approach

In this paper, we propose a gene expression based approach for the prediction of Parkinson’s disease (PD) using ‘projection based learning for meta-cognitive radial basis function network (PBL-McRBFN)’. McRBFN is inspired by human meta-cognitive learning principles. McRBFN has two components, a cognitive component and a meta-cognitive component. The cognitive component is a radial basis function network with evolving architecture. In the cognitive component, the PBL algorithm computes the optimal output weights with least computational effort. The meta-cognitive component controls the learning process in the cognitive component by choosing the best learning strategy for the current sample and adapts the learning strategies by implementing self-regulation. The interaction of cognitive component and meta-cognitive component address the what-to-learn, when-to-learn and how-to-learn of human learning principles efficiently.PBL-McRBFN classifier is used to predict PD using micro-array gene expression data obtained from ParkDB database. The performance of PBL-McRBFN classifier has been evaluated using Independent Component Analysis (ICA) reduced features sets from the complete genes and selected genes with two different significance levels. Further, the performance of PBL-McRBFN classifier is statistically compared with existing classifiers using one-way repeated ANOVA test. Further, it is also used in PD prediction using the standard vocal and gait PD data sets. In all these data sets, the performance of PBL-McRBFN is compared against existing results in the literature. Performance results clearly highlight the superior performance of our proposed approach.     

WO3 passivation layer-coated nanostructured TiO2: An efficient defect engineered photoelectrode for dye sensitized solar cell

Major loss factors for photo-generated electrons due to the presence of surface defects in titanium dioxide (TiO2) were controlled by RF-sputtered tungsten trioxide (WO3) passivation.X-ray photoelectron spectroscopy assured the coating of WO3 on the TiO2 nanoparticle layer by showing Ti 2p,W 4f and O 1 s characteristic peaks and were further confirmed by X-ray diffraction studies.The coating of WO3 on the TiO2 nanoparticle layer did not affect dye adsorption significantly.Dye sensitized solar cells (DSSCs) fabricated using WO3-coated TiO2 showed an enhancement of ～10％ compared to DSSCs fabricated using pristine TiO2-based photo-electrodes.It is attributed to the WO3 passivation on TiO2 that creates an energy barrier which favored photo-electron injection by tunneling but blocked reverse electron recombination pathways towards holes available in highest occupied molecular orbital of the dye molecules.It was further evidenced that there is an optimum thickness (duration of coating) of WO3 to improve the DSSC performance and longer duration of WO3 suppressed photo-electron injection from dye to TiO2 as inferred from the detrimental effect in short circuit current density values.RF-sputtering yields pinhole-free,highly uniform and conformal coating of WO3 onto any area of interest,which can be considered for an effective surface passivation for nanostructured photovoltaic devices.     

Microsoft C#.NET program and electromagnetic depth sounding for large loop source

A program, in the C# (C Sharp) language with Microsoft.NET Framework, is developed to compute the normalized vertical magnetic field of a horizontal rectangular loop source placed on the surface of an n-layered earth. The field can be calculated either inside or outside the loop. Five C# classes with member functions in each class are, designed to compute the kernel, Hankel transform integral, coefficients for cubic spline interpolation between computed values and the normalized vertical magnetic field. The program computes the vertical magnetic field in the frequency domain using the integral expressions evaluated by a combination of straightforward numerical integration and the digital filter technique. The code utilizes different object-oriented programming (OOP) features. It finally computes the amplitude and phase of the normalized vertical magnetic field. The computed results are presented for geometric and parametric soundings. The code is developed in Microsoft.NET visual studio 2003 and uses various system class libraries.     

Hybrid stiff-string–polynomial basis functions for vibration analysis of high speed rotating beams

A new finite element is developed for free vibration analysis of high speed rotating beams using basis functions which use a linear combination of the solution of the governing static differential equation of a stiff-string and a cubic polynomial. These new shape functions depend on rotation speed and element position along the beam and account for the centrifugal stiffening effect. The natural frequencies predicted by the proposed element are compared with an element with stiff-string, cubic polynomial and quintic polynomial shape functions. It is found that the new element exhibits superior convergence compared to the other basis functions.     

Broadband sub-6 GHz flower-shaped MIMO antenna with high isolation using theory of characteristic mode analysis (TCMA) for 5G NR bands and WLAN applications

A small size neutralization line integrated flower-shaped MIMO antenna is designed and analyzed for sub-6 GHz type 5G NR frequency bands like n79 (4400–5000 MHz), n78 (3300–3800 MHz), n77 (3300–4200 MHz), and WLAN (5150–5825 MHz) applications. The novel approach of theory of characteristic mode analysis (TCMA) is introduced to provide physical insight of the designed structure and its characteristics behavior. Due to the suggested modifications in the geometry, the isolation among the patches is greatly increased. The overall miniaturized dimension of the MIMO antenna is 25 × 40 mm². The edge-edge spacing among the elements is 0.0233λ. The prototype antenna is fabricated and measured that shows good agreement compared with simulated results. The designed MIMO antenna without the presence of decoupling structure offers an isolation of 28 dB, gain of 3.6 dBi, and radiation efficiency of 69.7% at the resonant frequency. The proposed MIMO antenna covers a broad range of frequency band from 3.296 to 5.962 GHz with −10 dB impedance bandwidth of 2666 MHz and maintains a good isolation of greater than 50 dB for the entire operating band. The tested radiation efficiency and gain are 85.3% and 6.22 dBi at 3.5 GHz. Moreover, the diversity parameters of the neutralization line integrated MIMO antenna, that is, channel capacity loss (CCL) isolation, mean effective gain (MEG), total active reflection coefficient (TARC) diversity gain (DG), and envelope correlation coefficient (ECC), are analyzed and discussed in this article.     

Development of acoustic source localization with adaptive neural network using distance mating-based red deer algorithm

Multichannel, audio processing approaches are widely examined in human–computer interaction, autonomous robots, audio surveillance, and teleconferencing systems. The numerous applications are linked to the speech technology and acoustic analysis area. Much attention is received to the active speakers and spatial localization of acoustic sources on the acoustic sensor arrays. Baseline approaches provide negotiable performance in a real-world comprised of far-field/near-field monitoring, reverberant and noisy environments, and also the outdoor/indoor scenarios. A practical system to detect defects in complex structures is the time difference mapping (TDM) technique. The significant scope of the research is to search the location using the minimum distance point in the time difference database to be apart from the verification point. In the case of the improved “time difference mapping (I-TDM)” technique and traditional “time difference mapping (T-TDM)” technique, the denser grids and vast database permit increased accuracy. In the database, if the location points are not present, then the accurate localization of the I-TDM and T-TDM techniques is damaged. Hence, to handle these problems, this article plans to develop acoustic source localization according to the deep learning strategy. The audio dataset is gathered from the benchmark source called the SSLR dataset and is initially subjected to preprocessing, which involves artifact removal and smoothing for effective processing. Further, the adaptive convolutional neural network (CNN)-based feature set creation is performed. Here, the adaptive CNN is accomplished by the improved optimization algorithm called distance mating-based red deer algorithm (DM-RDA). With this trained feature set, the acoustic source localization is done by the weight updated deep neural network, in which the same DM-RDA is used for optimizing the training weight. The simulation outcome proves that the designed model produced enhanced performance compared to other traditional source localization estimators.     

Energy Efficient Multi-hop Cooperative Transmission Protocol for Large Scale Mobile Ad hoc Networks

A great advancement has been made in intelligent transportation system and communication technologies in order to exchange secure information between automobiles, facilities provider have led an frame over road network. The intelligent transportation system provides an efficient traffic system for drivers, so that there must be less risk to users. In order to design a secure communication protocol among V & V and V & I is a challenging problem. In order to reduce the chance of attacks and increase privacy level, crypto graphic tools provides the feasible solution. In this paper, we proposed pseudonym changing strategy with mix zones (1) Anonymous authentication: the message should be authenticated by issuer due to mix zone and cryptographic tools secure message without any attack. (2) Privacy: Communication contents are confidential due to encrypted messages during communications also improving the scalability through address configuration scheme to reduce computational cost. (3) Efficiency: low storage requirements, The velocity and distance factors may also consider secure measurement, message delivery, overhead and coverage,packet delivery rate, reduce latency and overhead not only by computation cost and time but also compared our scheme, fast delivery rate, low latency and maximum coverage in order to enhance privacy against malicious attacks.

     

Combining contention‐based access and dynamic service period allocation for performance improvement in IEEE 802.11ad mmWave WLAN

In IEEE 802.11ad millimeter wave wireless LANs, the directional multi‐giga bit stations (DMGSTAs) use contention‐based access periods (CBAPs) and scheduled service periods (SPs) for medium access. The STAs carrying non‐QoS traffic use CBAPs, while SPs are allocated for STAs carrying high QoS applications. During CBAP, the STAs use enhanced distributed channel access (EDCA) scheme. Further, 802.11ad advocates dynamic allocation of SP for guaranteed data transmission based on a centralized polling scheme. The standard advocates that the coverage area around the access point can be divided into several sectors, and the total CBAP shall be divided among the sectors on a time sharing basis. The STAs residing within a sector simultaneously contend during the CBAP fraction of that sector. However, such STAs have to defer their transmission attempts and wait for the designated CBAP fraction of the succeeding beacon interval (BI), if the residual time in the current CBAP fraction is not sufficient for a frame transmission. This leads to very high delay and reduced throughput. The objective of this paper is to propose an efficient hybrid medium access control scheme, where the deferred STAs during CBAP are scheduled again by utilizing the unallocated slots in the SP of the same BI. We describe an analytical model for the throughput and the average frame delay, under the proposed scheme, and compare the performance against the legacy scheme, where the STAs use CBAP alone for channel access. The analytical and simulation results establish that the proposed scheme significantly improves the throughput and reduces the average frame delay.     

DISCERN: Enhanced Dynamic noISe varianCe based EneRgy sensing for cognitive radio using USRP at Wi‐Fi bands

Spectrum sensing is one of the critical tasks in a cognitive radio system that allows a secondary user to use the spectrum while the primary user does not use it. The energy detection (ED) sensing is one of the most common techniques to identify the unused portions in the spectrum bands. In ED, threshold plays a vital role in signal detection, and noise is one of the significant factors in threshold calculation. However, ED efficiency is degraded by the noise uncertainty phenomenon caused by the random changes in noise level. The adverse effects of noise uncertainty are reduced by changing its detection threshold dynamically to the noise circumstances encountered during each sensing period. In the proposed method, received random samples are arranged in M blocks, applied strong Pearson correlation to separate and estimate the variance from the noise samples. The enhanced dynamic noise variance‐based energy sensing is implemented in GNU radio processing blocks and tested on industrial, scientific, medical (ISM) 2.4 GHz frequency bands by using national instrument universal software radio peripheral (NI USRP‐2932) device. The experimental results of proposed energy detction mechanism are compared with existing sensing techniques.