Image coding based on maximum entropy partitioning for identifying improbable intensities related to facial expressions

In this paper we investigate information-theoretic image coding techniques that assign longer codes to improbable, imprecise and non-distinct intensities in the image. The variable length coding techniques when applied to cropped facial images of subjects with different facial expressions, highlight the set of low probability intensities that characterize the facial expression such as the creases in the forehead, the widening of the eyes and the opening and closing of the mouth. A new coding scheme based on maximum entropy partitioning is proposed in our work, particularly to identify the improbable intensities related to different emotions. The improbable intensities when used as a mask decode the facial expression correctly, providing an effective platform for future emotion categorization experiments.     

Effect of chromium on the mechanical properties of powder-processed Fe–0.45 wt.% P alloys

The present investigation shows the role of chromium in Fe–P binary and Fe–P–Cr ternary alloys. The compositions are characterized in terms of microstructure, porosity content, hardness and tensile properties. The alloys were made using a hot powder forging technique. In this process mild steel encapsulated powders were hot forged into slabs. Then the slabs were hot rolled and annealed to relieve the residual stresses. Densifications as high as 98.9% of theoretical density have been realized. Microstructures of these alloys consist of single-phase ferrite only. Both Fe–0.45P and Fe–0.45P–3Cr alloys showed very high strength. As forged and hot rolled Fe–0.45P alloy showed low elongation. It was observed that, the addition of Cr to Fe–P based alloys caused an increase in strength associated with the reduction in ductility. Alloys developed in the present investigation were capable of hot working to very thin gage of sheets and wires.     

Enhanced upconversion study of Er3+-Yb3+ codoped NaYF4 phosphors synthesized by the reverse microemulsion method

Herein, nanocrystals of Er³⁺ and Er³⁺, Yb³⁺ co-doped NaYF₄ upconversion (UC) phosphor were prepared via the reverse-microemulsion method. The impact of different concentrations of Er³⁺ ions on the UC emission intensity after 980?nm diode laser excitation is discussed. The structure, morphology and composition of the nanophosphors were confirmed by X-ray diffraction, transmission electron microscopy, scanning electron microscopy and the results showed the presence of NaYF₄ nanocrystals with hexagonal phases of NaYF₄. The UC spectra revealed two emission bands including a green and a red emission band and the CIE coordinate for the samples were estimated. The present research revealed that the reverse-microemulsion approach will be suitable for the synthesis of efficient upconversion nanophosphors.     

Fabrication of electrically conductive membrane electrode of gelatin-tin (IV) phosphate nanocomposite for the detection of cobalt (II) ions

Gelatin-tin (IV) phosphate nanocomposite (GT/TPNC) ion exchanger was synthesized by mixing gelatin gel into the precipitates of tin (IV) phosphate using sol–gel method. GT/TPNC was characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The ion exchange capacity of GT/TPNC was reported to be1.44?meq/g. The material was found monofunctional as indicated from pH titration curves. The distribution coefficient of different metal ions such as Zn²⁺ (42.10), Cd²⁺ (37.93), Mg²⁺ (33.33), Cu²⁺ (33.21), Al³⁺ (14.28), Pb²⁺ (6.06), Ni²⁺ (12.50) and Co²⁺ (50.0) was studied using GT/TPNC ion exchanger. The distribution studies confirmed the selectivity of GT/TPNC for Co (II). The photocatalytical degradation of MB was found to be 78% within 5 h of solar illumination using GT/TPNC. Some binary separations such as Co²⁺–Pb²⁺, Cd²⁺–Ni²⁺, Co²⁺–Mg²⁺, Mg²⁺–Zn²⁺, Pb²⁺–Zn²⁺, Cu²⁺–Al³⁺, Al³⁺–Cd²⁺, Ni²⁺–Cu²⁺ were attempted using GT/TPNC ion exchanger. GT/TPNC was explored for the fabrication of ion-sensitive membrane electrode for the determination of Co (II) in the water system. The membrane electrode was found mechanically more stable with quick response time (30?s) and a wide pH working range (4.0–7.0).     

Metal ion sensing and light activated antimicrobial activity of aloe-vera derived carbon dots

Carbon dots (CDs) have emerged out as a potential material amongst the carbon family for a wide range of applications including chemical/biological sensing, photocatalysis, bioimaging, etc. The green synthesis of these CDs from natural sources is gaining the significant interest of peer community for their wide utility. Herein, we present a facile one-step pyrolysis method for CDs synthesis from Aloe-Vera extract, which show bright blue luminescence under UV light with a quantum yield of 12.3%. Further, ex-situ morphological, structural and optical characterizations reveal their high quality and excitation independent emission behavior with the presence of carboxyl, hydroxyl functional groups. Furthermore, these CDs were studied for Fe(III) sensing in water without any surface modifications and assessed for their light activated antibacterial activity against E.Coli and Staphylococcus aureus.     

Effect of chromium on the corrosion behaviour of powder-processed Fe-0.45wt% P alloys

The corrosion behaviour of Fe-0.45P with/without addition of chromium, prepared by powder forging route was studied in different environments. The corrosion studies in acidic (0.25M H₂SO₄ solution of pH 0.6) and neutral/marine (3.5% NaCl solution of pH 6.8) solutions were conducted using Tafel Extrapolation method. The rate of corrosion in alkaline medium (0.5M Na₂CO₃ + 1.0M NaHCO₃ solution of pH 9.4) was measured using linear polarization technique. The studies compare electrolytic Armco iron with Fe-P alloys. It was observed that, chromium improved the resistance to corrosion in acidic and marine environments. The corrosion rates were minimal in alkaline medium and low in neutral solution.     

Neural network‐based routing protocol for opportunistic networks with intelligent water drop optimization

Opportunistic Networks (OppNets) is a system of wirelessly connected nodes in a varying network topology. Routing in OppNets is a challenge. To overcome the problem of routing, an intelligent dynamic strategy to select next best node for forwarding a message is required. This paper proposes an intelligent routing mechanism based on Intelligent Water Drop (IWD) Algorithm which is used in tandem with Neural Networks (NNs) as an optimization technique to solve the problem of routing in such networks. The nature–inspired IWD algorithm provides robustness, whereas the neural network base of the algorithm helps it to make intelligent routing decisions. The weights in the Neural Network model are calculated by IWD Algorithm using training data consisting of inputs that are characteristic parameters of nodes, such as buffer space, number of successful deliveries and energy levels along with transitive parameters such as delivery probabilities. The proposed protocol Intelligent Water Drop Neural Network (IWDNN) is compared with other protocols that use similar ideologies such as MLProph, K‐nearest neighbour classification based routing protocol (KNNR), Cognitive Routing Protocol for Opportunistic Network (CRPO), and Inheritance Inspired Context Aware Routing Protocol (IICAR), as well as the standard protocol Prophet. IWDNN is shown to outperform all other protocols with an average message delivery ratio of 60%, which is a significant improvement of over 10% in comparison to other similarly conceived algorithms. It has one of the lowest latency among the protocols studied, in a range of 3000 to 4000 s, and incurs comparably low overhead costs in the range of 15 to 30. The drop ratios are one of the lowest, staying near six and approaching zero as buffer size is increased. Average amount of time a message stayed in the buffer was the lowest, with a mean of 1600 s.     

Experimental analogy of different texture feature extraction techniques in image retrieval systems

Content based image retrieval (CBIR) is an extrusive technique of retrieving the relevant images from vast image archives by extracting their low level features. In this research paper, the pursuance of five most prominent texture feature extraction techniques used in CBIR systems are experimentally compared in detail. The main issue with the CBIR systems is the proper selection of techniques for the extraction of low level features which comprises of color, texture and shape. Among these features, texture is one of the most decisive and dominant features. This selection of features completely depends upon the type of images to be retrieved from the database. The texture techniques explored here are Grey level co-occurrence matrix (GLCM), Discrete wavelet transform (DWT), Gabor transform, Curvelet and Local binary pattern (LBP). These are experimented on three touchstone databases which are Wang, Corel-5 K and Corel-10 K. The chief parameters of CBIR systems are evaluated here such as precision, recall and F-measure on all these databases using all the techniques. After detailed investigation it is figured out that LBP, GLCM and DWT provide highlighted and comparable results in all these datasets in terms of average precision. Besides practical implementation, the précised conceptual examination of these three texture techniques is also proposed in this article. So, this analysis is extremely beneficial for selecting the appropriate feature extraction technique by taking into consideration the experimental results along with image conditions such as noise, rotation etc.

     

Preparation and characterization of polyvinyl alcohol based biomaterials: Water sorption and in vitro blood compatibility study

Semi‐interpenetrating polymer networks (semi‐IPNs) based on polyvinyl alcohol (PVA) and crosslinked polyacrylamide (PAM) were prepared by redox polymerization and further characterized by FTIR, ESEM, and XRD techniques. The semi‐IPNs of varying compositions were investigated for their water sorption behavior, and the network parameters like average molecular weight between crosslinks (M_C) and crosslink density were evaluated from water imbibition measurements. The semi‐IPNs were also judged for their in vitro blood compatibility by blood clot formation and percent hemolysis test. It was noticed that the chemical architecture exert a profound effect on the over all performance of the biomaterials. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 2402–2408, 2006     

Fuzzy filtering for robust bioconcentration factor modelling

This study introduces a fuzzy filtering based technique for rendering robustness to the modelling methods. We consider a case study dealing with the development of a model for predicting the bioconcentration factor (BCF) of chemicals. The conventional neural/fuzzy BCF models, due to the involved uncertainties, may have a poor generalization performance (i.e. poor prediction performance for new chemicals). Our approach to improve the generalization performance of neural/fuzzy BCF models consists of (1) exploiting a fuzzy filter to filter out the uncertainties from the modelling problem, (2) utilizing the information about uncertainties, being provided by the fuzzy filter, for the identification of robust BCF models with an increased generalization performance. The approach has been illustrated with a data set of 511 chemicals (Dimitrov, S., Dimitrova, N., Parkerton, T., Comber, M., Bonnell, M., Mekenyan, O., 2005. Base-line model for identifying the bioaccumulation potential of chemicals. SAR and QSAR in Environmental Research 16 (6), 531–554) taking different types of neural/fuzzy modelling techniques.