Physics

Diluted magnetic semiconductors (DMS) have been of great interest in the last decade due to the combination of properties of the doped magnetic elements and the carriers of the host. Novel functions can be achieved using DMS, for example, in spin-FETs or spin LEDs, if the injection, transfer and detection of the carrier spin can be controlled electrically or optically. The bandgap engineering can be applied to maximize the light output in spin LEDs 1 . DMS in which magnetic ions substituted for cations of the host semiconducting material are assumed to be ideal systems for spintronics 2 . The Curie temperatures (Tc) above room temperatures are predicted for ZnO doped with transition metals (TM= Mn, Co, Ni, etc) and have received much attention in recent years 3 . ZnO is a promising material to be used in room temperature magnetic devices and transparent optical hybrid devices due to its wide band gap (3.37 eV) 3,4 and large excitonic binding energy (60 meV) 4 . The band gap of the host material can be tuned from 3.3 to 3.7 eV 4, 5 .

Abstract Copper doped ZnO thin films have been grown by pulsed laser deposition on fused silica substrates. X-ray diffraction spectra of the Zn1-xCuxO thin films have wurtzite ZnO structure without any secondary phases. The Zn1-xCuxO films show transparency above 75% in the visible region. The first order Raman spectra of thin films shows two characteristic modes of wurtzite ZnO at 99 and 436 cm-1 corresponding to E2 low and E2 high.

The present work reports the influence of indium concentration and annealing temperature (100-400 8C) on the structural and optoelectronic properties of indium selenide thin films grown using a stack elemental layer technique. The concentration of indium in indium selenide thin films is varied by adjusting the thickness of the indium layer to 28, 42 or 56 nm while keeping the selenium layer thickness constant at 200 nm. Depending on the indium layer thickness of 42 or 56 nm, indium selenide thin films exhibited a phase transition either from a mixed phase (InSe, gIn 2 Se 3 and bIn 2 Se 3) and/or from a complete amorphous phase to a single phase gIn 2 Se 3 at annealing temperature 400 or 300 8C, respectively, with a preferential grain orientation along the c-axis. Depth-wise X-ray photoelectron spectroscopy (XPS) analysis conducted on these samples (thickness of Se ¼ 200 nm, In ¼ 56 nm) showed evidence of a phase transition from amorphous to crystalline gIn 2 Se 3 phase and formation of uniform stoichiometric (In/Se ¼ 40:60) indium selenide. On the other hand, indium selenide grown using an indium thickness of 28 nm did not exhibit any phase transition. While using X-ray diffraction (XRD) studies to analyse the structural properties, we made use of optical absorption and Raman spectroscopy in order to determine the optical energy gap and find the presence of parasitic bIn 2 Se 3 phase, respectively. The growth along the c-axis either gives rise to carrier diffusion along the c-axis or causes the appearance of a higher photosensitivity (62-72) due to the absence of dangling bonds which trap photogenerated carriers. The properties exhibited by the c-axis-grown gIn 2 Se 3 suggest the potentiality of this material as a window layer in solar cell application.

Approximately 90 per cent of the world's power is generated by heat engines that use fossil fuel combustion as a heat source and typically operate at 30-40 per cent efficiency, such that roughly 15 terawatts of heat is lost to the environment. Thermoelectric modules could potentially convert part of this low-grade waste heat to electricity. Their efficiency depends on the thermoelectric figure of merit ZT of their material components, which is a function of the Seebeck coefficient, electrical resistivity, thermal conductivity and absolute temperature. Over the past five decades it has been challenging to increase ZT . 1, since the parameters of ZT are generally interdependent 1 . While nanostructured thermoelectric materials can increase ZT . 1 (refs 2-4), the materials (Bi, Te, Pb, Sb, and Ag) and processes used are not often easy to scale to practically useful dimensions. Here we report the electrochemical synthesis of large-area, wafer-scale arrays of rough Si nanowires that are 20-300 nm in diameter. These nanowires have Seebeck coefficient and electrical resistivity values that are the same as doped bulk Si, but those with diameters of about 50 nm exhibit 100-fold reduction in thermal conductivity, yielding ZT 5 0.6 at room temperature. For such nanowires, the lattice contribution to thermal conductivity approaches the amorphous limit for Si, which cannot be explained by current theories. Although bulk Si is a poor thermoelectric material, by greatly reducing thermal conductivity without much affecting the Seebeck coefficient and electrical resistivity, Si nanowire arrays show promise as high-performance, scalable thermoelectric materials.

We report the discovery of a pristine crystalline 3D carbon that is magnetic, electrically conductive and stable under ambient conditions. This carbon material, which has remained elusive for decades, is synthesized by using the chemical vapor deposition (CVD) technique with a particular organic molecular precursor 3,3-dimethyl-1-butene (C6H12). An exhaustive computational search of the potential energy surface reveals its unique sp2-sp3 hybrid bonding topology. Synergistic studies involving a large number of experimental techniques and multi-scale first-principles calculations reveal the origin of its novel properties due to the special arrangement of sp2 carbon atoms in lattice. The discovery of this U-carbon, named such because of its unusual structure and properties, can open a new chapter in carbon science.

A learning algorithm based on primary school teaching and learning is presented. The methodology is to continuously evaluate the performance of the network and to train it on the examples for which they repeatedly fail, until, all the examples are correctly classified. Empirical analysis on UCI data show that the algorithm produces a good training data and improves the generalization ability of the network on unseen data. The algorithm has interesting applications in data mining, model evaluations and rare objects discovery.

We use automated surface photometry and pattern classification techniques to morphologically classify galaxies. The two-dimensional light distribution of a galaxy is reconstructed using Fourier series fits to azimuthal profiles computed in concentric elliptical annuli centered on the galaxy. Both the phase and amplitude of each Fourier component have been studied as a function of radial bin number for a large collection of galaxy images using principal component analysis. We find that up to 90 percent of the variance in many of these Fourier profiles may be characterized in as few as 3 principal components and their use substantially reduces the dimensionality of the classification problem. We use supervised learning methods in the form of artificial neural networks to train galaxy classifiers that detect morphological bars at the 85-90 percent confidence level and can identify the Hubble type with a 1-sigma scatter of 1.5 steps on the 16-step stage axis of the revised Hubble system. Finally, we systematically characterize the adverse effects of decreasing resolution and S/N on the quality of morphological information predicted by these classifiers.

We use automated surface photometry and pattern classification techniques to morphologically classify galaxies. The two-dimensional light distribution of a galaxy is reconstructed using Fourier series fits to azimuthal profiles computed in concentric elliptical annuli centered on the galaxy. Both the phase and amplitude of each Fourier component have been studied as a function of radial bin number for a large collection of galaxy images using principal-component analysis. We find that up to 90% of the variance in many of these Fourier profiles may be characterized in as few as three principal components and that their use substantially reduces the dimensionality of the classification problem. We use supervised learning methods in the form of artificial neural networks to train galaxy classifiers that detect morphological bars at the 85%-90% confidence level and can identify the Hubble type with a 1 σ scatter of 1.5 steps on the 16 step stage axis of the revised Hubble system. Finally, we systematically characterize the adverse effects of decreasing resolution and signal-to-noise ratio on the quality of morphological information predicted by these classifiers.

We present a catalogue of about 6 million unresolved photometric detections in the Sloan Digital Sky Survey Seventh Data Release classifying them into stars, galaxies and quasars. We use a machine learning classifier trained on a subset of spectroscopically confirmed objects from 14th to 22nd magnitude in the SDSS {\it i}-band. Our catalogue consists of 2,430,625 quasars, 3,544,036 stars and 63,586 unresolved galaxies from 14th to 24th magnitude in the SDSS {\it i}-band. Our algorithm recovers 99.96% of spectroscopically confirmed quasars and 99.51% of stars to i $\sim$21.3 in the colour window that we study. The level of contamination due to data artefacts for objects beyond $i=21.3$ is highly uncertain and all mention of completeness and contamination in the paper are valid only for objects brighter than this magnitude. However, a comparison of the predicted number of quasars with the theoretical number counts shows reasonable agreement.

We present a catalogue of about 6 million unresolved photometric detections in the Sloan Digital Sky Survey Seventh Data Release classifying them into stars, galaxies and quasars. We use a machine learning classifier trained on a subset of spectroscopically confirmed objects from 14th to 22nd magnitude in the SDSS {\it i}-band. Our catalogue consists of 2,430,625 quasars, 3,544,036 stars and 63,586 unresolved galaxies from 14th to 24th magnitude in the SDSS {\it i}-band. Our algorithm recovers 99.96% of spectroscopically confirmed quasars and 99.51% of stars to i $\sim$21.3 in the colour window that we study. The level of contamination due to data artefacts for objects beyond $i=21.3$ is highly uncertain and all mention of completeness and contamination in the paper are valid only for objects brighter than this magnitude. However, a comparison of the predicted number of quasars with the theoretical number counts shows reasonable agreement.

In this paper we describe the use of a new artificial neural network, called the difference boosting neural network (DBNN), for automated classification problems in astronomical data analysis. We illustrate the capabilities of the network by applying it to star galaxy classification using recently released, deep imaging data. We have compared our results with classification made by the widely used Source Extractor (SExtractor) package. We show that while the performance of the DBNN in star-galaxy classification is comparable to that of SExtractor, it has the advantage of significantly higher speed and flexibility during training as well as classification.

This paper discusses mixing of chaotic systems as a dependable method for secure communication. Distribution of the entropy function for steady state as well as plaintext input sequences are analyzed. It is shown that the mixing of chaotic sequences results in a sequence that does not have any state dependence on the information encrypted by them. The generated output states of such a cipher approach the theoretical maximum for both complexity measures and cycle length. These features are then compared with some popular ciphers.

This paper discusses mixing of chaotic systems as a dependable method for secure communication. Distribution of the entropy function for steady state as well as plaintext input sequences are analyzed. It is shown that the mixing of chaotic sequences results in a sequence that does not have any state dependence on the information encrypted by them. The generated output states of such a cipher approach the theoretical maximum for both complexity measures and cycle length. These features are then compared with some popular ciphers.

The difference-boosting algorithm is used on letters dataset from the UCI repository to classify distorted raster images of English alphabets. In contrast to rather complex networks, the difference-boosting is found to produce comparable or better classification efficiency on this complex problem.

Rainfall in Kerala State, the southern part of Indian Peninsula in particular is caused by the two monsoons and the two cyclones every year. In general, climate and rainfall are highly nonlinear phenomena in nature giving rise to what is known as the ‘butterfly effect’. We however attempt to train an ABF neural network on the time series rainfall data and show for the first time that in spite of the fluctuations resulting from the nonlinearity in the system, the trends in the rainfall pattern in this corner of the globe have remained unaffected over the past 87 years from 1893 to 1980. We also successfully filter out the chaotic part of the system and illustrate that its effects are marginal over long term predictions.

Rainfall in Kerala State, the southern part of Indian Peninsula in particular is caused by the two monsoons and the two cyclones every year. In general, climate and rainfall are highly nonlinear phenomena in nature giving rise to what is known as the `butterfly effect'. We however attempt to train an ABF neural network on the time series rainfall data and show for the first time that in spite of the fluctuations resulting from the nonlinearity in the system, the trends in the rainfall pattern in this corner of the globe have remained unaffected over the past 87 years from 1893 to 1980. We also successfully filter out the chaotic part of the system and illustrate that its effects are marginal over long term predictions.