An Improved Delay Resistant Adaptive Multicasting Strategy for High Definition Video Delivery in Wireless Overlay Network

Wireless Personal Communications - Wireless sensor networks (WSNs) incorporate small devices known as sensors. These sensors monitor the deployment field and are responsible for communicating the...     

Performance enhanced image steganography systems using transforms and optimization techniques

Image steganography is the art of hiding highly sensitive information onto the cover image. An ideal approach to image steganography must satisfy two factors: high quality of stego image and high embedding capacity. Conventionally, transform based techniques are widely preferred for these applications. The commonly used transforms for steganography applications are Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) etc. In this work, frequency domain transforms such as Fresnelet Transform (FT) and Contourlet Transform (CT) are used for the data hiding process. The secret data is normally hidden in the coefficients of these transforms. However, data hiding in transform coefficients yield less accurate results since the coefficients used for data hiding are selected randomly. Hence, in this work, optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used for improving the performance of the steganography system. GA and PSO are used to find the best coefficients in order to hide the Quick Response (QR) coded secret data. This approach yields an average PSNR of 52.56 dB and an embedding capacity of 902,136 bits. These experimental results validate the practical feasibility of the proposed methodology for security applications.     

An ultra‐wideband rectenna using optically transparent Vivaldi antenna for radio frequency energy harvesting

This paper presents a novel ultra‐wideband rectenna which consists of a transparent Vivaldi antenna and a wideband rectifying circuit for radio frequency energy harvesting. The antenna is realized on a 2.2‐mm‐thick soda‐lime glass substrate coated with fluorine‐doped tin oxide of thickness 650 nm. It provides an optical transmittance greater than 80% in the visible region. The rectifying circuit with a cascaded matching network and the Greinacher doubler circuit are fabricated on an FR4 substrate with a thickness of 0.8 mm. The antenna provides the best matching characteristics and the realized peak gain is 3.2 dBi. The designed matching network enables maximum power transfer from the antenna to the rectifier. The rectenna provides a peak power conversion efficiency of 69% for ?10 dBm input power. The proposed antenna can be realized on the windscreen of automobiles and glass windows without causing any obstruction to normal view.     

An explainable deep learning model for prediction of early-stage chronic kidney disease

Chronic kidney disease (CKD) is a major public health concern with rising prevalence and huge costs associated with dialysis and transplantation. Early prediction of CKD can reduce the patient's risk of CKD progression to end-stage kidney failure. Artificial intelligence offers more intelligent and expert healthcare services in disease diagnosis. In this work, a deep learning model is built using deep neural networks (DNN) with an adaptive moment estimation optimization function to predict early-stage CKD. The health care applications require interpretability over the predictions of the black-box model to build conviction towards the model's prediction. Hence, the predictions of the DNN-CKD model are explained by the local interpretable model-agnostic explainer (LIME). The diagnostic patient data is trained on five layered DNN with three hidden layers. Over the unseen data, the DNN-CKD model yields an accuracy of 98.75% and a roc_auc score of 98.86% in detecting CKD risk. The explanation revealed by the LIME algorithm echoes the influence of each feature on the prediction made by the DNN-CKD model over the given CKD data. With its interpretability and accuracy, the proposed system may effectively help medical experts in the early diagnosis of CKD.     

Optimization of deposition temperature of SILAR Cu-rich CuInS2 thin films

CuInS₂ (CIS) is studied widely as a promising absorber material for high efficient and low cost thin film solar cells. CIS thin films are prepared on soda lime glass substrates using Successive Ionic Layer Adsorption and Reaction (SILAR) technique at different deposition temperatures (40 to 70 °C). The structural, compositional and optical properties are studied with x-ray diffractometer, energy dispersive x-ray analyzer and spectrophotometer. The influence of the deposition temperature on the properties of CIS thin films is discussed in this paper in detail.     

Support Vector Machine-based Denoising Technique for Removal of White Noise in Partial Discharge Signal

Abstract—Partial discharge (PD) measurement has emerged as a dominant investigative tool for condition monitoring of insulation in high voltage equipment. In general, PD signals are severely polluted by several noises like white noise, random noise, discrete spectral interferences (DSI). The challenge lies with removing these noises from PD signal effectively by preserving the signal features. In this article, support vector machine (SVM) based denoising technique has been proposed for the removal of white noise from PD signal. The proposed SVM technique retains the edge of the original signal efficiently and also pseudo Gibbs phenomenon does not exist with SVM technique. In order to evaluate the effectiveness of the proposed method, artificially simulated PD signal mixed with white noise and the measured PD readings are considered. For the purpose of comparison, other denoising techniques such as fast Fourier transform (FFT), discrete wavelet transform (DWT), and translation invariant wavelet transform (TIWT) are also considered. The results reveal that, SVM based denoising technique shows better performance in terms of higher signal to noise ratio, signal reconstruction error ratio, cross correlation coefficient and reduction in noise level, mean square error, and waveform distortion.     

Phase tuning of zirconia nanocrystals by varying the surfactant and alkaline mineralizer

The influence of cationic (CTAB)/neutral polymeric (PVP) surfactants and strong (NaOH)/weak (NH₄OH) alkaline mineralizers on phase stabilization of zirconia nanocrystals synthesized by chemical precipitation is investigated. X-ray diffraction and micro-Raman analysis of the as-prepared samples show that tetragonal zirconia is predominant as compared to monoclinic using PVP with NH₄OH. The phases are also evident from lattice fringes of TEM images and the corresponding SAED pattern. Photoluminescence spectra of samples reveal oxygen vacancies present in the zirconia nanocrystals. The group H Raman vibration modes identified are attributed to surface defects and quantum size effects of nanocrystals. The phase stabilization of zirconia nanocrystals is explained using the polymerization rate of tetramers during synthesis. The rate can be varied by proper selection of the surfactant and the mineralizer. A slow polymerization rate with PVP and NH₄OH favors the formation of tetragonal zirconia. Thus, a simple method for phase stabilization of zirconia nanocrystals at room temperature using chemical precipitation by varying the surfactant and the mineralizer is demonstrated.     

Investigation of the photodegradation properties of iron oxide doped mesoporous SBA-15 silica

Mesoporous silica (SBA-15) and iron oxide incorporated silica (Fe₂O₃-SBA-15) were synthesized by co-operative self-assembly technique. Samples were characterized using nitrogen adsorption–desorption isotherm, electron microscopic and spectroscopic techniques. The results confirm the uniform distribution of pores, presence of metal oxides in the pores as well as in the surface of the mesoporous wall and oxidation state of iron in the Fe₂O₃-SBA-15. The photocatalytic degradation of methylene blue (MB), sulphorhodamine B (SR-B) and methyl orange (MO) by Fe₂O₃-SBA-15 was investigated. It was observed that Fe₂O₃-SBA-15 degraded 98 % of MB, 96 % of SR-B and 99 % of MO within 3 h after exposure to sunlight. SBA-15 does not exhibit any photocatalytic effect. These results demonstrate the potential of Fe₂O₃-SBA-15 for environmental pollution control.     

N‐Heterocyclic Carbene‐Catalyzed Oxidative Amidation of Aldehydes with Amines

The N‐heterocyclic carbene (NHC)‐catalyzed oxidative amidation of aromatic aldehydes with amines in the presence of N‐bromosuccinimide (NBS) as an oxidant has been developed for the synthesis of amides. This amidation strategy is tolerant to both the electronic and the steric nature of the aryl aldehydes employed. The present methodology was extended to chiral amino acid derivatives to generate the corresponding amides in good yields and excellent ee values (>98%).