Cellulose nanoparticles synthesised from potato peel for the development of active packaging film for enhancement of shelf life of raw prawns (Penaeus monodon) during frozen storage

In this study, the cellulose nanoparticles (CNP) isolated from potato peel were used for reinforcement of polyvinyl alcohol (PVA)-based active packaging film. The above film was used to pack the raw prawns (Penaeus monodon) at −20 °C, and the colour change, protein content, TVB-N, TMA and microbial analysis were done at regular interval for prawns stored in CNP-PVA active packaging film. A significant difference was observed in the quality of prawns stored in potato CNP-PVA film compared with prawns packed and stored in polyethylene film. The newly designed active packaging with CNP and fennel seed oil enhanced the shelf life of prawns up to two months for both HOSO (head on shell on) prawn and PD (peeled and deveined) prawn. Hence, the study recommends the potato peel CNP-PVA film with fennel seed oil as better choice to extend the shelf life of the prawns during storage compared with polyethylene packaging.     

A Hybrid Clustering Approach Based Q-Leach in TDMA to Optimize QOS-Parameters

An Orthogonal frequency part multiplexing suffers from a considerable challenge due to a high peak to average power ratio (PAPR). Hence, an effective method such as partial transmits sequence (PTS) can avert this defiance by limit the design of PAPR. Therefore, an improving PAPR reduction performance via a novel approach is proposed by detaching each subblock into two parts furthermore exchanges the first sample with the final selection in each portion of the subblock to generate a new partitioning scheme. Several typical traditional segmentation schemes are used to analyze and apply the presented algorithm, such as adjacent, interleaving, and pseudo-random schemes. Besides, two scenarios are adopted based on simulation software in which the number of subcarriers is set to 128 and 256. Based on the results, a superior PAPR reduction performance is achieved based on the improved segmentation schemes regarding traditional strategies in both systems. Moreover, the enhanced adjusted PTS scheme poses a low computational complexity compared with that of the conventional schemes.

     

On-line voltage stability assessment using radial basis function network model with reduced input features

In recent years, voltage instability has become a major threat for the operation of many power systems. This paper presents an artificial neural network (ANN)-based approach for on-line voltage security assessment. The proposed approach uses radial basis function (RBF) networks to estimate the voltage stability level of the system under contingency state. Maximum L-index of the load buses in the system is taken as the indicator of voltage stability. Pre-contingency state power flows are taken as the input to the neural network. The key feature of the proposed method is the use of dimensionality reduction techniques to improve the performance of the developed network. Mutual information based technique for feature selection is proposed to enhance overall design of neural network. The effectiveness of the proposed approach is demonstrated through voltage security assessment in IEEE 30-bus system and Indian practical 76 bus system under various operating conditions considering single and double line contingencies and is found to predict voltage stability index more accurate than feedforward neural networks trained by back propagation algorithm and AC load flow. Experimental results show that the proposed method reduces the training time and improves the generalization capability of the network than the multilayer perceptron networks.     

Optimization of PbS thin films using different metal ion sources for photovoltaic applications

An extensive study on the influence of metal ion sources on the properties of chemical bath deposited lead sulphide thin films is reported in this paper. Four different lead sources namely lead nitrate, lead acetate, lead chloride and lead sulphate in alkaline medium have been used for the synthesis along with thiourea as sulphur source. The influence of lead sources on structural, surface morphological optical and electrical properties is investigated for photovoltaic applications. According to X-ray diffraction studies, all the films are poly crystalline with face centered cubic structure. The average crystallite sizes are found to be in the range 13–24 nm. The SEM photographs showed diverse morphology. The optical band gap is found to be very sensitive to the metal sources used. The direct band gap energy values obtained are in the range of 1.862–2.609 eV. The electrical conductivity varies in the range 33.6 ?7.62 × 10^?9 (Ω cm)^?1. Photosensitivity is closely linked to surface morphology. In this work, we established that the cationic precursor sources have significant role in physical properties of as-deposited PbS thin films. Samples prepared using nitrate as metal source are found to be most suitable to be used as solar control coating and the samples with lead acetate can be used as absorber layers for solar cell fabrication.     

Pathological brain detection in MRI scanning via Hu moment invariants and machine learning

Abstract

Background: We proposed a new automatic and rapid computer-aided diagnosis system to detect pathological brain images obtained in the scans of magnetic resonance imaging (MRI). Methods: For simplification, we transformed the problem to a binary classification task (pathological or normal). It consisted of two steps: first, Hu moment invariants (HMI) were extracted from a specific MR brain image; then, seven HMI features were fed into two classifiers: twin support vector machine (TSVM) and generalised eigenvalue proximal SVM (GEPSVM). Results: Then, a 5 × 5-fold cross validation on a data set containing 90 MR brain images, demonstrated that the proposed methods “HMI + GEPSVM” and “HMI + TSVM” achieved classification accuracy of 98.89%, higher than eight state-of-the-art methods: “DWT + PCA + BP-NN”, “DWT + PCA + RBF-NN”, “DWT + PCA + PSO-KSVM”, “WE + BP-NN”, “WE + KSVM”, “DWT + PCA + GA-KSVM”, “WE + PSO-KSVM” and “WE + BBO-KSVM”. Conclusion: The proposed methods are superior to other methods on pathological brain detection (p < 0.05).     

High performance HTLNR/epoxy blend—Phase morphology and thermo-mechanical properties

An attempt was made to toughen diglycidyl ether of bisphenol A (DGEBA) type epoxy resin with liquid natural rubber possessing hydroxyl functionality (HTLNR). Epon 250 epoxy monomer is cured using nadic methyl anhydride as hardener in presence of N, N dimethyl benzyl amine as accelerator. HTLNR of different concentrations up to 20 wt % is used as modifier for epoxy resin. The addition HTLNR to an anhydride hardener/epoxy monomer mixture has given rise to the formation of phase-separated structure, consisting of small spherical liquid natural rubber particles bonded to the surrounding epoxy matrix. The particle size increased with increase in rubber content. The viscoelastic properties of the blends were analyzed using dynamic mechanical thermal analysis. The T_g corresponding to epoxy rich phase was evident from the dynamic mechanical spectrum, while the T_g of the rubber phase was overlapped by the β relaxation of epoxy phase. Glass transition of the epoxy phase decreased linearly as a function of the amount of rubber. The mechanical properties such as impact and fracture toughness were also carefully examined. The impact and fracture toughness increase with HTLNR content. A threefold increase in impact strength was observed with 15 wt % HTLNR/epoxy blend. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Interference aware resource allocation for hybrid hierarchical wireless networks

This paper addresses the problem of interference aware resource allocation for OFDMA based hybrid hierarchical wireless networks. We develop two resource allocation algorithms considering the impact of wireless interference constraints using a weighted SINR conflict graph to quantify the interference among the various nodes: (1) interference aware routing using maximum concurrent flow optimization; and (2) rate adaptive joint subcarrier and power allocation algorithm under interference and QoS constraints. We exploit spatial reuse to allocate subcarriers in the network and show that an intelligent reuse of resources can improve throughput while mitigating interference. We provide a sub-optimal heuristic to solve the rate adaptive resource allocation problem. We demonstrate that aggressive spatial reuse and fine tuned-interference modeling garner advantages in terms of throughput, end-to-end delay and power distribution.     

Polarimetric synthetic aperture radar image segmentation by convolutional neural network using graphical processing units

Image segmentation is an important application of polarimetric synthetic aperture radar. This study aimed to create an 11-layer deep convolutional neural network for this task. The Pauli decomposition formed the RGB image and was used as the input. We created an 11-layer convolutional neural network (CNN). L-band data over the San Francisco bay area and C-band data over Flevoland area were employed as the dataset. For the San Francisco bay PSAR image, our method achieved an overall accuracy of 97.32%, which was at least 2% superior to four state-of-the-art approaches. We provided the confusion matrix over test area, and the kernel visualization. We compared the max pooling and average pooling. We validated by experiment that four convolution layers perform the best. Besides, our method gave better results than AlexNet. The GPU yields a 173× acceleration on the training samples, and a 181× acceleration on the test samples, compared to standard CPU. For the Flevoland PSAR image, our 11-layer CNN also gives better overall accuracy than five state-of-the-art approaches. The convolutional neural network is better than traditional classifiers and is effective in remote sensing image segmentation.     

Violence detection in videos for an intelligent surveillance system using MoBSIFT and movement filtering algorithm

Pattern Analysis and Applications - Action recognition is an active research area in computer vision as it has enormous applications in today’s world, out of which, recognizing violent action...