Influence of temperature, voltage and hydrogen on the reversible transition of electrical conductivity in sol–gel grown nanocrystalline TiO2 thin film

Undoped nanocrystalline p-type TiO₂ thin film was deposited by sol–gel method on a thermally oxidized p-Si (2–5 Ω cm resistivity and $ \left\langle {100} \right\rangle $ orientation) substrate. The thin film was characterized by two-dimensional X-ray Diffraction (2D-XRD) and Field Emission Scanning Electron Microscopy (FESEM) to confirm the formation of stable nano crystalline anatase titania and to determine the grain size (~10 nm). Optical absorption spectroscopy was carried out to ascertain the band gap of the material. Two lateral Pd contacts were used as the metal electrodes to TiO₂ thin film to study the electrical conductivity. A clear p- to n-type transition was observed at 240 °C and a bias voltage of 0.83 V and the effect was enhanced on exposure to H₂ gas. The thin film showed fully n-type conductivity at 275 °C and 0.1 V. However, the reversal of the type of conductivity from n- to p-type was observed below 240 °C during lowering the temperature. The creation of oxygen vacancy and the diffusion of lattice oxygen to the surface of TiO₂ thin film might be the most possible mechanism of such transitions. Presence of hydrogen enhanced the process.     

A simple unsupervised MRF model based image segmentation approach

A simple technique has been suggested to obtain optimal segmentation based on tonal and textural characteristics of an image using the Markov random field (MRF) model. The technique takes an initially over segmented image as well as the original image as its inputs and defines an MRF over the region adjacency graph (RAG) of the initially segmented regions. A tonal-region based segmentation technique due to Kartikeyan and Sarkar (1989) has been used for initial segmentation. The energy function has been defined over the first order cliques of the MRF. The essence of this approach is primarily based on quantitative values of the second order statistics, on region characteristics and consequently deciding upon the action of merging neighboring regions using the F-statistic. The effectiveness of our approach is demonstrated with wide variety of real life examples viz., indoor, outdoor and satellite and a comparison of its output with that of a previous work in the literature has been provided.     

Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization

Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop ...     

Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns

Symbolic dynamic filtering (SDF) has been recently reported in literature as a pattern recognition tool for early detection of anomalies (i.e., deviations from the nominal behavior) in complex dynamical systems. This paper presents a review of SDF and its performance evaluation relative to other classes of pattern recognition tools, such as Bayesian Filters and Artificial Neural Networks, from the perspectives of: (i) anomaly detection capability, (ii) decision making for failure mitigation and (iii) computational efficiency. The evaluation is based on analysis of time series data generated from a nonlinear active electronic system. This work has been supported in part by the U.S. Army Research Laboratory and the U.S. Army Research Office under Grant No. W911NF-07-1-0376, by the U.S. Office of Naval Research under Grant No. N00014-08-1-380, and by NASA under Cooperative Agreement No. NNX07AK49A.     

Landcover classification in MRF context using Dempster-Shafer fusion for multisensor imagery.

This work deals with multisensor data fusion to obtain landcover classification. The role of feature-level fusion using the Dempster-Shafer rule and that of data-level fusion in the MRF context is studied in this paper to obtain an optimally segmented image. Subsequently, segments are validated and classification accuracy for the test data is evaluated. Two examples of data fusion of optical images and a synthetic aperture radar image are presented, each set having been acquired on different dates. Classification accuracies of the technique proposed are compared with those of some recent techniques in literature for the same image data.     

ZnS nanocrystals decorated single-walled carbon nanotube based chemiresistive label-free DNA sensor

We fabricated ZnS nanocrystals decorated single-walled carbon nanotube (SWNT) based chemiresistive sensor for DNA. Since the charge transfer in the hybrid nanostructures is considered to be responsible for many of their unique properties, the role of ZnS nanocrystals toward its performance in DNA sensor was delineated. It was found that the free carboxyl groups surrounding the ZnS nanocrystals allowed large loading of single strand DNA (ssDNA) probe that provided an ease of hybridization with target complementary c-ssDNA resulting in large electron transfer to SWNT. Thus it provided a significant improvement in sensitivity toward c-ssDNA as compared to bare SWNT based DNA sensor.     

An Improved Method for Load Taxonomy Using Sample Shifting Technique and Signature Analysis

This paper illustrates an improved method of classification of electrical appliances, particularly for domestic loads, to construct load taxonomy on the basis of their signature analysis. Each electrical load is characterized by its own distinct signature and hence load signature analysis is useful in monitoring the health of the equipment, power quality, in determining individual energy usage etc. type of services. On the other hand, load taxonomy classifies these loads in several clusters on the basis of some features extracted from their signatures. In traditional methods of construction of load taxonomy, different signature patterns based on power metrics, V-I trajectories, Eigen vectors, etc. In this proposed method, with the adoption of sample shifting technique the required number of feature extraction is reduced to a lower value to find out various signature patterns than those are required in traditional load taxonomies. Moreover, a better taxonomy, having well separated groups of loads is achieved with lower number of extracted features.