An Authentication Protocol to Track an Object with Multiple RFID Tags Using Cloud Computing Environment

Wireless Personal Communications - In supply chain management applications, detection of a large object is made efficient by attaching multiple RFID tags in that object. Each part contains an RFID...     

Dynamics of a neuron exposed to integer- and fractional-order discontinuous external magnetic flux

We propose a modified Fitzhugh-Nagumo neuron (MFNN) model. Based on this model, an integer-order MFNN system (case A) and a fractional-order MFNN system (case B) were investigated. In the presence of electromagnetic induction and radiation, memductance and induction can show a variety of distributions. Fractional-order magnetic flux can then be considered. Indeed, a fractional-order setting can be acceptable for non-uniform diffusion. In the case of an MFNN system with integer-order discontinuous magnetic flux, the system has chaotic and non-chaotic attractors. Dynamical analysis of the system shows the birth and death of period doubling, which is a sign of antimonotonicity. Such a behavior has not been studied previously in the dynamics of neurons. In an MFNN system with fractional-order discontinuous magnetic flux, different attractors such as chaotic and periodic attractors can be observed. However, there is no sign of antimonotonicity.

     

Green synthesis of TiO2 nanoparticles prepared from Phyllanthus niruri leaf extract for dye adsorption and their isotherm and kinetic studies

Herein, the green synthesis of TiO₂ nanoparticles using Phyllanthus niruri leaf extract was accomplished by the sol‐gel method. The structure and particle size of the synthesised TiO₂ nanoparticles were characterised by X‐ray diffraction (XRD) analysis and the size was found to be 20 nm. The Fourier‐transform infrared spectra determined the existence of carboxyl and hydroxyl functional groups. The images from SEM analysis recommended a porous and heterogeneous surface. The methyl orange (MO) dye removal was examined using different parameters such as pH, time, dose, temperature and dye concentration. Maximum dye elimination percentage was achieved at pH 6.0 and 0.02 g as the optimum adsorbent dose. The kinetic analysis suggested that the pseudo‐second‐order kinetic model finely defines adsorption dynamics. Langmuir adsorption isotherm studies revealed endothermic monolayer adsorption of the methyl Orange dye. The negative value of ∆G° and positive value of ∆H° showed the spontaneous and endothermic adsorption method.     

Morphology,mechanical and thermal properties of nano-structured full IPNs based on polyisoprene and PMMA

Morphology, mechanical properties, thermal stability and gas transport behaviour of interpenetrating polymer networks (IPNs) based on PI/PMMA have been investigated using various techniques. Crosslinking level of both phases and concentration of PMMA were found to have noticeable effects on the compatibility of immiscible components during IPN formation. Effect of crosslinking was studied by preparing IPNs with varying amount of crosslinker concentration in each phase. Crosslinking of both phases facilitated deeper interpenetrations between both networks, and certain degree of compatibility is attained during IPN formation. Nanometre-sized domains were observed for highly crosslinked IPN. Lower concentration of PMMA was found to favour phase mixing more effectively than others. DSC curve of 65/35 IPN showed a broad transition arising from the α and β-relaxations of PMMA due to the higher flexibility attained by mixing with the highly mobile PI chains. The mechanical properties of the IPNs were correlated to the morphology of the system and 50/50 composition showed maximum mechanical properties among the studied compositions. Mode of mechanical failure, thermal stability and gas transport behaviour were also analysed. IPNs having nanometre-sized domains showed least gas permeability among the studied samples.     

Influence of annealing temperature and oxygen atmosphere on the optical and photoluminescence properties of BaTiO3 amorphous thin films prepared by sol–gel method

Homogeneous and transparent BaTiO₃ thin films were prepared by sol–gel dip coating method. The prepared BaTiO₃ thin films were annealed in air and O₂ atmosphere at different temperatures. The annealed BaTiO₃ thin films were amorphous in nature. Scanning electron microscopy (SEM) revealed the nucleation and particle growth on the films. Energy-dispersive X-ray (EDX) analysis data revealed the adsorption of oxygen atoms in the BaTiO₃ film. The direct energy band gap was found to vary (3.84–3.58 eV) as functions of annealing atmosphere and temperature. Photoluminescence (PL) revealed intense emission peaks at 393 and 675 nm. Quenching of PL intensity was observed in films annealed at high temperature and in O₂ atmosphere. This is due to reduction in the oxygen vacancy by the adsorption of oxygen in the film. Luminescence spectra also have been related to the results obtained by SEM and EDX analysis. The change in luminescence intensity of BaTiO₃ thin films makes it suitable for optoelectronic temperature sensor applications.     

Areca leaves as a source of carbon: Preliminary investigation as catalyst support for electrolytic hydrogen evolution in acidic medium

Our work tends to establish Green Energy from Waste concept. Refuses/wastes can be a secondary resource for variety of materials which may find applications in domestic, industrial, medical, electronic and energy devices. We have attempted to produce activated carbon powders from a cheap waste namely biomass of areca leaves. The material has been exploited as catalyst support materials in H₂ production through water electrolysis. Catalyst powders of 10% Ni and 1% Pt by weight were supported on the carbon produced from the leaves using NaBH₄ reduction of the respective salts. Physical features of the catalyst powders were evaluated through PXRD, FTIR, density, SEM, surface area. Catalytic activity of the biocarbon supported catalyst powders was assessed by LSV & CV. The carbon produced may attract technological importance because carbon source selected is cheap and green. Further the activated carbon may find applications such as electrode materials, adsorbent for color, odor and hazardous pollutants.     

Tumor detection in T1, T2, FLAIR and MPR brain images using a combination of optimization and fuzzy clustering improved by seed‐based region growing algorithm

Tumor and Edema region present in Magnetic Resonance (MR) brain image can be segmented using Optimization and Clustering merged with seed‐based region growing algorithm. The proposed algorithm shows effectiveness in tumor detection in T1 ‐ w, T2 – w, Fluid Attenuated Inversion Recovery and Multiplanar Reconstruction type MR brain images. After an initial level segmentation exhibited by Modified Particle Swarm Optimization (MPSO) and Fuzzy C – Means (FCM) algorithm, the seed points are initialized using the region growing algorithm and based on these seed points; tumor detection in MR brain images is done. The parameters taken for comparison with the conventional techniques are Mean Square Error, Peak Signal to Noise Ratio, Jaccard (Tanimoto) index, Dice Overlap indices and Computational Time. These parameters prove the efficacy of the proposed algorithm. Heterogeneous type tumor regions present in the input MR brain images are segmented using the proposed algorithm. Furthermore, the algorithm shows augmentation in the process of brain tumor identification. Availability of gold standard images has led to the comparison of the suggested algorithm with MPSO‐based FCM and conventional Region Growing algorithm. Also, the algorithm recommended through this research is capable of producing Similarity Index value of 0.96, Overlap Fraction value of 0.97 and Extra Fraction value of 0.05, which are far better than the values articulated by MPSO‐based FCM and Region Growing algorithm. The proposed algorithm favors the segmentation of contrast enhanced images. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 33–45, 2017     

Unsupervised deep learning system for local anomaly event detection in crowded scenes

Multimedia Tools and Applications - Anomaly detection in video surveillance is a significant research subject because of its immense use in real-time applications. These days, open spots like...     

Low Latency Fog-Centric Deduplication Approach to Reduce IoT Healthcare Data Redundancy

In today’s highly computerized society, detection and recognition of text present in natural scene images is complex and difficult to be properly recognized by human vision. Most of the existing algorithms and models mainly focus on detection and recognition of text from still images. Many of the recent machine translation systems are built using the Encoder-Decoder framework which works on the format of encoding the sequence of input and then based on the encoded input, the output is decoded. Both the encoder and the decoder use an attention mechanism as an interface, making the model complex. Aiming at this situation, an alternative method for recognition of texts from videos is proposed. The proposed approach is based on a single Two-Dimensional Convolutional Neural Network (2D CNN). An algorithm for extracting features from an image called the crosswise feature extraction is also proposed. The proposed model is tested and shows that crosswise feature extraction gives better recognition accuracy by requiring a lesser period of time for training than the conventional feature extraction technique used by CNN.