Wide and multi-band reconfigurable Vivaldi antenna with slot-line feed

This paper presents a Vivaldi antenna. The antenna incorporates a slot-line feed, and is designed to operate in a wideband mode, along with four narrowband modes. The wideband mode extends from 1 to 3 GHz. A series of PIN diode switches is employed to reconfigure between the five different operating modes. Wideband-to-multiband reconfigurations are achieved by adjusting the antenna loading internally, and also by the incorporation of band-stop filters. To obtain the wideband mode, wideband matches load is used. On the other hand, to obtain the narrowband modes, two rectangular slits with narrowband matches load are used. The proposed Vivaldi antenna provides wideband operation. It is also supports operation within the LTE, GSM (L-band), GPS, and WiMAX bands.     

Scanning electron and light microscopy of foliar epidermal characters: A tool for plant taxonomists in the identification of grasses

SEM and light microscopy are of special interest for biologist to observe various features of the living bodies. In the current study we observed the foliar epidermal micro‐morphological characters of 44 grass species using SEM and Light microscopy to assess their taxonomic utility for taxonomists in the identification process. The aim of this study is to use the foliar epidermal structural variations in both upper and lower surfaces for identification of grasses. Significant diversity was observed in both qualitative and quantitative characters using SEM and Light microscopy. Variations were observed in stomatal number, size, guard cells shape, silica bodies, macro‐hairs, micro‐hairs, epidermal cell number, subsidiary cells, prickles, hooks, papillae, and short and long cells. A taxonomic key is prepared using these variations for the identification of grass species. Based on these findings, SEM and Light microscopy of foliar epidermal features can be of special interest for taxonomists in the identification of complex grass taxa.     

A churn prediction model for prepaid customers in telecom using fuzzy classifiers

The incredible growth of telecom data and fierce competition among telecommunication operators for customer retention demand continues improvements, both strategically and analytically, in the current customer relationship management (CRM) systems. One of the key objectives of a typical CRM system is to classify and predict a group of potential churners form a large set of customers to devise profitable and targeted retention campaigns for keeping a long-term relationship with valued customers. For achieving the aforementioned objective, several churn prediction models have been proposed in the past for the accurate identification of the customers who are prone to churn. However, these previously proposed models suffer from a number of limitations which place strong barriers towards the direct applicability of such models for accurate prediction. Firstly, the feature selection methods adopted in majority of the past work neglected the information rich variables present in call details record for model development. Secondly, selection of important features was done through statistical methods only. Although statistical methods have been applied successfully in diverse domains, however, these methods alone without the augmentation of domain knowledge have the tendency to yield erroneous results. Thirdly, the previous models have been validated mainly with benchmark datasets which do not provide a true representation of real world telecom data consisting of noise and large number of missing values. Fourthly, the evaluation measures used in the past neglected the True Positive (TP) rate, which actually highlights the ability of a model to correctly classify the percentage of churners as compared to non-churners. Finally, the classifiers used in the previous models completely neglected the use of fuzzy classification methods which perform reasonably well for data sets with noise. In this paper, a fuzzy based churn prediction model has been proposed and validated using a real data from a telecom company in South Asia. A number of predominant classifiers namely, Neural Network, Linear regression, C4.5, SVM, AdaBoost, Gradient Boosting and Random Forest have been compared with fuzzy classifiers to highlight the superiority of fuzzy classifiers in predicting the accurate set of churners.     

A computationally efficient selected mapping technique for reducing PAPR of OFDM

In orthogonal frequency division multiplexing (OFDM) system, high value of peak-to-average power ratio (PAPR) is an operational problem that may cause non-linear distortion resulting in high bit error rate. Selected mapping (SLM) is a well known technique that shows good PAPR reduction capability but inflicts added computational overhead. In this paper, using Riemann sequence based SLM method, we applied reverse searching technique to find out low PAPR yielding phase sequences with significant reduction in computational complexity. Additionally, we explored side-information free transmission that achieves higher throughput but sacrifices PAPR reduction. Finally, to overcome this loss in PAPR reduction, we proposed application of Square-rooting companding technique over the output OFDM transmitted signal. Simulation results show that the proposed method is able to compensate the sacrifice in PAPR and achieved PAPR reduction of 8.9 dB with very low computational overhead.     

Search me if you can: Multiple mix zones with location privacy protection for mapping services

The mobile vehicle is gaining popularity nowadays using map services like Google Maps and other mapping services. However, map services users have to expose sensitive information like geographic locations (GPS coordinates) or address to personal privacy concerns as users share their locations and queries to obtain desired services. Existing mix zones location privacy protection methods are most general purposed and theoretical value while not applicable when applied to provide location privacy for map service users. In this paper, we present new (multiple mix zones location privacy protection) MMLPP method specially designed for map services on mobile vehicles over the road network. This method enables mobile vehicle users to query a route between 2 endpoints on the map, without revealing any confidential location and queries information. The basic idea is to strategically endpoints to nearby ones, such that (1) the semantic meanings encoded in these endpoints (eg, their GPS coordinates) change much, ie, location privacy is protected; (2) the routes returned by map services little change, ie, services usability are maintained. Specifically, a mobile client first privately retrieves point of interest close to the original endpoints, and then selects 2 points of interest as the shifted endpoints satisfying the property of geoindistinguishability. We evaluate our MMLPP approach road network application for GTMobiSim on different scales of map services and conduct experiments with real traces. Results show that MMLPP strikes a good balance between location privacy and service usability.     

Hybrid wavelet measurement matrices for improving compressive imaging

Compressive sensing principle claims that a compressible signal can be recovered from a small number of random linear measurements. However, the design of efficient measurement basis in compressive imaging remains as a challenging problem. In this paper, a new set of hybrid wavelet measurement matrices is proposed to improve the quality of the compressive imaging, increase the compression ratio and reduce the processing time. The performance of these hybrid wavelet matrices for image modeling and reconstruction is evaluated and compared with other traditional measurement matrices such as the random measurement matrices, Walsh and DCT matrices. The compressive imaging approach chosen in this study is the block compressive sensing with smoothed projected Landweber reconstruction technique. The simulation results indicate that the imaging performance of the proposed hybrid wavelet measurement matrices is approximately 2–3 dB better than that obtained using Gaussian matrix especially at higher compression ratios.     

Tuning the interlaminar shear strength and thermo-mechanical properties of glass fiber composites by incorporation of (3-mercaptopropyl) trimethoxysilane-functionalized carbon black

The incorporation of functionalized nanoscale fillers into traditional glass fiber/unsaturated polyester (GF/UPE) composites provides a more robust mechanical attributes. The current study demonstrates the potential of 3-mercaptopropyl trimethoxysilane (MPTS)-functionalized carbon black (f-CB) for enhancing the thermo-mechanical properties of GF composites. The composites infused with 1, 3 and 5 wt% of pristine and MPTS-functionalized CB were fabricated by hand lay-up and hot press processing. Tensile testing, interlaminar shear strength (ILSS) testing and dynamic mechanical analysis were used to evaluate the performance of nanocomposites. Fourier transform infrared spectroscopy validated the MPTS functionalization of CB. Pristine CB-loaded nanocomposites exhibited marginal improvement in ultimate tensile strength (UTS), ILSS and thermo-mechanical properties. However, with the addition of f-CB, the improvement in all the studied properties was more substantial. The inclusion of 5 wt% f-CB increased the elastic modulus and UTS by 16 and 22%, respectively, whereas the ILSS was enhanced by 36%, in comparison to the neat GF composite. The scanning electron microscope analysis of fractured ILSS samples revealed better fiber-matrix adhesion and compatibility in f-CB-loaded nanocomposites. At the same filler weight percentage, the storage modulus at 25 °C was ~ 19% higher than that of neat composite. The f-CB inclusion resulted in increment of T _g by ~ 13 °C over the T _g of neat GF/UPE composite (~ 109 °C). These improvements were due to the chemical connection of f-CB to the UPE matrix and GF surface. With such improvements in thermal and mechanical properties, these nanocomposites can replace the conventional GF composites with prominent improvements in performance.     

Reinforcement of electroactive characteristics in polyvinylidene fluoride electrospun nanofibers by intercalation of multi-walled carbon nanotubes

This research work reports on development and characterization of multi-walled carbon nanotube (MWCNT)-doped polyvinylidene difluoride (PVDF) nanofibers by the electrospinning method. PVDF is an extensively studied polymer both theoretically and experimentally due to its appealing ferroelectric, piezoelectric, and pyroelectric properties which strongly favors its promising applications in the development of micro/nanostructure devices. The foremost reason for its ferroelectric and piezoelectric behaviors has been attributed to its crystalline structure, specifically the presence of β-phase; however, the existence of the small percentage of β-phase in pristine PVDF limits its applications. To enhance the electroactive features in the PVDF, MWCNTs have been doped in it to prepare electrospun nanofibers, as electrospinning is a single-step approach. These nonwoven nanofibers were prepared at a DC voltage of 20 kV which were subsequently calcined at 100 °C for 12 h. The estimation of crystal structure and phase identification in these nanofibers have been determined by attenuated FT-IR and XRD, while the morphology, microstructure, mean diameter, and length have been examined by FE-SEM. The observed electrical conductivity, capacitance, permittivity (ε), conductivity (δ), and impedance (Z) in these samples have been tailored by doping a range of MWCNT contents and optimizing the experimental conditions.     

Counterion-Induced Cationic Flexible Nanoparticle Catalytic of Piperidinolysis of Phenyl Salicylate

The removal of PSa^? from bulk aqueous phase to the pseudo‐micellar phase by halobenzoate counterion X is responsible for the monotonic increase in k_obs (pseudo first‐order rate constants) with the increase in the values of [MX] where MX = sodium salts of 2‐, and 4‐halobenzoic acids. The values of ion exchange constants, or for X = 2‐ and 4‐halobenzoate ions in the presence of tetradecyltrimethylammonium bromide (TTABr) were calculated from the apparent catalytic rate constants, ^Xk_cat which represent the catalytic effect of CFN. Larger values of or were observed for X = 4‐halobenzoate ions than that for X = 2‐halobenzoate ions due to isomeric factors. The values of or determined in the presence of TTABr were compared with previously determined or values in the presence of cetyltrimethylammonium bromide (CTABr). The values of or are nearly 8 ~ 9‐fold larger for 4‐IBz^?, 4‐BrBz^? and 4‐ClBz^? compared to the respective values of X = 2‐IBz^?, 2‐BrBz^? and 2‐ClBz^?. The values of or for X = 4‐FBz^? is nearly 3‐fold larger than that for X = 2‐FBz^?. The values of or for X = 2‐ and 4‐halobenzoates are significantly smaller in the presence of TTABr than these in the presence of CTABr nanoparticles.