Mechanism of electrical conduction in plasma polymerized furfural thin films

Polyfurfural thin films lying in the thickness range of 1300–2000 A˚ were prepared by ac plasma polymerization technique. The current–voltage characteristics in symmetric and asymmetric electrode configuration were studied with a view to determining the dominant conduction mechanism.It was found that the Schottky conduction mechanism is dominant in plasma polymerized furfural thin films.The predominance of Schottky mechanism was further confirmed based on the thermally stimulated current measurements.

Thermal Conduction in Polymeric Nanofluids and Nanosolids Controlled by Interfacial Scattering:Solutions to Some Selected Problems

Dept.of Instrumentation,Cochin University of Science and Technology

Elecrical conduction and dielectric behaviour of thin films of vacuum evaporated amorphous silicon, hydrogenated amorphous silicon and chemically deposited cadmium sulphide

A brief account of the several methods used for the production of thin films is presented in this Chapter. The discussions stress on the important methods used for the fabrication of a-si:H thin films. This review' also reveals ‘that almost all the general methods, like vacuum evaporation, sputtering, glow discharge and even chemical methods are currently employed for the production of a-Si:H thin films. Each method has its own advantages and disadvantages. However, certain methods are generally preferred. Subsequently a detailed account of the method used here for the preparation of amorphous silicon thin films and their hydrogenation is presented. The metal chamber used for the electrical and dielectric measurements is also described. A brief mention is made-on the electrode structure, film area and film geometry.

Document Image Segmentation and Compression using Artificial Neural Networks and Evolutionary Methods

The work is intended to study the following important aspects of document image processing and develop new methods. (1) Segmentation ofdocument images using adaptive interval valued neuro-fuzzy method. (2) Improving the segmentation procedure using Simulated Annealing technique. (3) Development of optimized compression algorithms using Genetic Algorithm and parallel Genetic Algorithm (4) Feature extraction of document images (5) Development of IV fuzzy rules. This work also helps for feature extraction and foreground and background identification. The proposed work incorporates Evolutionary and hybrid methods for segmentation and compression of document images. A study of different neural networks used in image processing, the study of developments in the area of fuzzy logic etc is carried out in this work

MicroRNA–mRNA interaction network using TSK-type recurrent neural fuzzy network

MicroRNAs are short non-coding RNAs that can regulate gene expression during various crucial cell processes such as differentiation, proliferation and apoptosis. Changes in expression profiles of miRNA play an important role in the development of many cancers, including CRC. Therefore, the identification of cancer related miRNAs and their target genes are important for cancer biology research. In this paper, we applied TSK-type recurrent neural fuzzy network (TRNFN) to infer miRNA–mRNA association network from paired miRNA, mRNA expression profiles of CRC patients. We demonstrated that the method we proposed achieved good performance in recovering known experimentally verified miRNA–mRNA associations. Moreover, our approach proved successful in identifying 17 validated cancer miRNAs which are directly involved in the CRC related pathways. Targeting such miRNAs may help not only to prevent the recurrence of disease but also to control the growth of advanced metastatic tumors. Our regulatory modules provide valuable insights into the pathogenesis of cancer

Face Recognition using Probabilistic Neural Networks

In this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results

Face Recognition using Probabilistic Neural Networks

n this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results.

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

Artificial neural network modeling for surface roughness prediction in cylindrical grinding of Al‐SiCp metal matrix composites and ANOVA analysis

Metal matrix composites (MMC) having aluminium (Al) in the matrix phase and silicon carbide particles (SiCp) in reinforcement phase, ie Al‐SiCp type MMC, have gained popularity in the re‐cent past. In this competitive age, manufacturing industries strive to produce superior quality products at reasonable price. This is possible by achieving higher productivity while performing machining at optimum combinations of process variables. The low weight and high strength MMC are found suitable for variety of components

Evidence for polaron conduction in nanostructured manganese ferrite

Nanoparticles of manganese ferrite were prepared by the chemical co-precipitation technique. The dielectric parameters, namely, real and imaginary dielectric permittivity (ε and ε ), ac conductivity (σac) and dielectric loss tangent (tan δ), were measured in the frequency range of 100 kHz–8MHz at different temperatures. The variations of dielectric dispersion (ε ) and dielectric absorption (ε ) with frequency and temperature were also investigated. The variation of dielectric permittivity with frequency and temperature followed the Maxwell–Wagner model based on interfacial polarization in consonance with Koops phenomenological theory. The dielectric loss tangent and hence ε exhibited a relaxation at certain frequencies and at relatively higher temperatures. The dispersion of dielectric permittivity and broadening of the dielectric absorption suggest the possibility of a distribution of relaxation time and the existence of multiple equilibrium states in manganese ferrite. The activation energy estimated from the dielectric relaxation is found to be high and is characteristic of polaron conduction in the nanosized manganese ferrite. The ac conductivity followed a power law dependence σac = Bωn typical of charge transport assisted by a hopping or tunnelling process. The observed minimum in the temperature dependence of the frequency exponent n strongly suggests that tunnelling of the large polarons is the dominant transport process

Conduction mechanism in plasma polymerized aniline thin films

Electrical properties of ac plasma polymerized aniline thin films are investigated with a view of determining the dominant conduction mechanism. The current–voltage (I–V) characteristics in symmetric and asymmetric electrode configuration for polyaniline thin films in the thickness range from 1300 to 2000 A ° are investigated. From the studies on asymmetric electrode configuration, it is found that the dominant conduction mechanism in these films is of Schottky type

Mechanism of ac conduction in nanostructured manganese zinc mixed ferrites

Mn1−xZnxFe2O4 nanoparticles (x = 0 to 1) were synthesized by the wet chemical co-precipitation technique. X-ray diffraction and transmission electron microscopy and high resolution transmission electron microscopy were effectively utilized to investigate the different structural parameters. The ac conductivity of nanosized Mn1−xZnxFe2O4 were investigated as a function of frequency, temperature and composition. The frequency dependence of ac conductivity is analysed by the power law σ(ω)ac = Bωn which is typical for charge transport by hopping or tunnelling processes. The temperature dependence of frequency exponent n was investigated to understand the conduction mechanism in different compositions. The conduction mechanisms are mainly based on polaron hopping conduction

On conduction mechanism in paramagnetic phase of Gd based manganites

Materials belonging to the family of manganites are technologically important since they exhibit colossal magneto resistance. A proper understanding of the transport properties is very vital in tailoring the properties. A heavy rare earth doped manganite like Gd0·7Sr0·3MnO3 is purported to be exhibiting unusual properties because of smaller ionic radius of Gd. Gd0·7Sr0·3MnO3 is prepared by a wet solid state reaction method. The conduction mechanism in such a compound has been elucidated by subjecting the material to low temperature d.c. conductivity measurement. It has been found that the low band width material follows a variable range hopping (VRH) model followed by a small polaron hopping (SPH) model. The results are presented here

A Reinforcement Learning Approach to Economic Dispatch using Neural Networks

This paper presents a Reinforcement Learning (RL) approach to economic dispatch (ED) using Radial Basis Function neural network. We formulate the ED as an N stage decision making problem. We propose a novel architecture to store Qvalues and present a learning algorithm to learn the weights of the neural network. Even though many stochastic search techniques like simulated annealing, genetic algorithm and evolutionary programming have been applied to ED, they require searching for the optimal solution for each load demand. Also they find limitation in handling stochastic cost functions. In our approach once we learn the Q-values, we can find the dispatch for any load demand. We have recently proposed a RL approach to ED. In that approach, we could find only the optimum dispatch for a set of specified discrete values of power demand. The performance of the proposed algorithm is validated by taking IEEE 6 bus system, considering transmission losses

Implementation of a Neural Network Classifier for Noise Sources in the Ocean

The paper investigates the feasibility of implementing an intelligent classifier for noise sources in the ocean, with the help of artificial neural networks, using higher order spectral features. Non-linear interactions between the component frequencies of the noise data can give rise to certain phase relations called Quadratic Phase Coupling (QPC), which cannot be characterized by power spectral analysis. However, bispectral analysis, which is a higher order estimation technique, can reveal the presence of such phase couplings and provide a measure to quantify such couplings. A feed forward neural network has been trained and validated with higher order spectral features