Reconfigurable Compact Wideband Circularly Polarised Dielectric Resonator Antenna for Wireless Applications

In this work, a novel compact wideband reconfigurable circularly polarised (CP) dielectric resonator antenna (DRA) is presented. The L-shaped Dielectric resonator antenna is excited by an inverted question mark shaped feed. This arrangement of feed-line helps to generate two orthogonal modes inside the DR, which makes the design circularly polarised. A thin micro-strip line placed on the defected ground plane not only helps to generate a wideband response but also assist in the positioning of the two diode switches. These switches located at the left and right of the micro-strip line helps in performing two switching operations. The novel compact design offers the reconfigurability between 2.9–3.8 GHz which can be used for different important wireless applications. For the switching operation I, the achieved impedance bandwidth is 24% while axial ratio bandwidth (ARBW) is 42%. For this switching state, the design has 100% CP performance. Similarly, the switching operation II achieves 60% impedance bandwidth and 58.88% ARBW with 76.36% CP performance. The proposed design has a maximum measured gain of 3.4 dBi and 93% radiation efficiency. The proposed design is novel in terms of compactness and performance parameters. The prototype is fabricated for the performance analysis which shows that the simulated and measured results are in close agreement.     

A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification

Diabetes or Diabetes Mellitus (DM) is the upset that happens due to high glucose level within the body. With the passage of time, this polygenic disease creates eye deficiency referred to as Diabetic Retinopathy (DR) which can cause a major loss of vision. The symptoms typically originate within the retinal space square in the form of enlarged veins, liquid dribble, exudates, haemorrhages and small scale aneurysms. In current therapeutic science, pictures are the key device for an exact finding of patients’ illness. Meanwhile, an assessment of new medicinal symbolisms stays complex. Recently, Computer Vision (CV) with deep neural networks can train models with high accuracy. The thought behind this paper is to propose a computerized learning model to distinguish the key precursors of Dimensionality Reduction (DR). The proposed deep learning framework utilizes the strength of selected models (VGG and Inception V3) by fusing the extracated features. To select the most discriminant features from a pool of features, an entropy concept is employed before the classification step. The deep learning models are fit for measuring the highlights as veins, liquid dribble, exudates, haemorrhages and miniaturized scale aneurysms into various classes. The model will ascertain the loads, which give the seriousness level of the patient’s eye. The model will be useful to distinguish the correct class of seriousness of diabetic retinopathy pictures.     

On scheduling data access with cooperative load balancing in vehicular ad hoc networks (VANETs)

For overcoming the vehicle to vehicle frequent disconnection problem in VANETs data dissemination, several approaches have been proposed, including the provision of Road Side Units (RSUs). Due to the short wireless transmission range of RSUs and vehicle mobility, a vehicle spends only a short period of time inside the range of an RSU. This limitation, together with possible overload of RSUs sited near busy road junctions, may mean that requests from vehicles are not served within the prescribed deadlines. In this paper, we propose a cooperative load balancing approach among RSUs, in which an RSU can transfer the overload requests to other RSUs. Load transfer is done based on a number of factors: request delay tolerance, current load of the transferee RSU, and the direction in which the vehicle is heading. Using a series of simulation experiments, we demonstrate that the proposed cooperative load balancing approach outperforms the non-cooperative (stand-alone) approaches in a wide range of scenarios based on our performance metrics.     

Sonication‐induced self‐assembly of polymeric porphyrin–fullerene: Formation of nanorings

In this article, we detail the sonication‐induced self‐assembly of polymeric porphyrin and fullerenes into distinct nanorings in solution form. The formation of these trenchant superstructures was the result of the delicate choice of different assembly protocols, solvents, and polymeric tails associated with porphyrin and fullerene. In this study, the sonication supposedly directed the lateral aggregation into uniform ring formation. The sonication time was found to be the key parameter in ring formation. Furthermore, the flexibility of polymeric arms and electronic interactions of porphyrin–fullerene gave rise to synergistically enhanced molecular interactions, and this resulted in discrete morphologies. Key optical data, including the absorption maxima of the complexes, and microscopic studies attested to the nature and morphology of the self‐assembled complexes. This introduction of polymeric arms and sonication protocols in the porphyrin self‐assembly was expected to allow the easy formation of diverse morphologies. Because of the facile fabrication process and uniform morphology, the resulting composite architectures might show promising applications in drug‐delivery and advance materials. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43537.     

Optimization of steel bar manufacturing process using six sigma

Optimization of a manufacturing process results in higher productivity and reduced wastes. Production parameters of a local steel bar manufacturing industry of Pakistan is optimized by using six Sigma-Define, measure, analyze, improve, and control- methodology. Production data is collected and analyzed. After analysis, experimental design result is used to identify significant factors affecting process performance. The significant factors are controlled to optimized level using two-level factorial design method. A regression model is developed that helps in the estimation of response under multi variable input values. Model is tested, verified, and validated by using industrial data collected at a local steel bar manufacturing industry of Peshawar(Khyber Pakhtunkhwa, Pakistan). The sigma level of the manufacturing process is improved to 4.01 from 3.58. The novelty of the research is the identification of the significant factors along with the optimum levels that affects the process yield, and the methodology to optimize the steel bar manufacturing process.     

Decolourisation of direct dyes with manganese peroxidase from white rot basidiomycete Ganoderma lucidum‐IBL‐5

In the current project the ability of white‐rot fungus Ganoderma lucidum IBL‐05 was investigated for the decolourisation of some direct textile dyes. In the initial time course study maximum decolourisation (83.78 ± 5%) was observed for Solar golden yellow R at pH 4 and temperature 30°C after 6th day. Various process parameters like optimum pH, incubation time, temperature and additional carbon and nitrogen additives were optimized to achieve maximum decolourisation of the dye by G. lucidum IBL‐05. Addition of starch (1%) accelerated the dye decolourisation (96 ± 3%). All the nitrogen sources showed an inhibitory effect on dye decolourisation and enzyme induction. Manganese peroxidase (MnP) was found to be the only enzyme (256 ± 5 U/mL) secreted by G. lucidum IBL‐05.     

Synthesis of biodiesel from cottonseed oil and methanol using a carbon-based solid acid catalyst

A carbon-based solid acid catalyst was prepared by the sulfonation of carbonized vegetable oil asphalt and used to catalyze the transesterification of methanol with cottonseed oil. This catalyst was characterized by scanning electron microscopy/energy dispersive spectroscopy, BET surface area and pore size measurement, thermogravimetry analysis and Fourier transform infrared spectroscopy. The sulfonated multi-walled carbon nanotubes (s-MWCNTs) was also prepared and used to catalyze the same transesterification as the asphalt catalyst. The asphalt-based catalyst shows higher activity than the s-MWCNTs for the production of biodiesel, which may be correlated to its high acid site density, its loose irregular network and large pores can provide more acid sites for the reactants. The conversion of cottonseed oil 89.93% was obtained (using the asphalt-based catalyst) when the methanol/cottonseed oil molar ratio was 18.2, reaction temperature at 260 °C, reaction time 3.0 h and catalyst/cottonseed oil mass ratio of 0.2%. Also, it can be re-used. The sulfonated polycyclic aromatic hydrocarbons provide an electron-withdrawing function to keep the acid site stable. The catalyst can substantially reduce energy consumption and waste generation in the production of biodiesel.     

3D Ellipsoidal Model for Mobile-to-Mobile Radio Propagation Environments

Geometrical models are mostly used for the study and analysis of the characteristics of radio communication channels. In this paper, a three-dimensional semi-ellipsoidal scattering model is proposed for mobile-to-mobile communication channels, where uniformly distributed scatterers are assumed to be confined within the semi-ellipsoids around mobile stations. The semi-ellipsoidal shape with adjustable dimensions is considered to model the scattering phenomenon in urban streets and canyons. Using the proposed scattering model, a closed-form expression for the joint probability density function of the Angle-of-Arrival in azimuth and elevation planes of the incoming multipath signals is derived at each mobile station. Moreover, various observations are made, which show the impact of scatterers’ elevation and streets’ orientation on the spatial characteristics of mobile-to-mobile communication channel.