Buckled nano rod - a two state system: quantum effects on its dynamics

We consider a suspended elastic rod under longitudinal compression. The compression can be used to adjust potential energy for transverse displacements from the harmonic to the double well regime. The two minima in potential energy curve describe two possible buckled states. Using transition state theory (TST) we have calculated the rate of conversion from one state to other. If the strain epsilon = 4 epsilon c the simple TST rate diverges. We suggest a method to correct this divergence for quantum calculations. We also find that zero point energy contributions can be quite large so that single mode calculations can lead to large errors in the rate.

Critical lattice size limit for synchronized chaotic state in one- and two-dimensional diffusively coupled map lattices

We consider diffusively coupled map lattices with P neighbors (where P is arbitrary) and study the stability of the synchronized state. We show that there exists a critical lattice size beyond which the synchronized state is unstable. This generalizes earlier results for nearest neighbor coupling. We confirm the analytical results by performing numerical simulations on coupled map lattices with logistic map at each node. The above analysis is also extended to two-dimensional P-neighbor diffusively coupled map lattices.

Gaussian mixture model based switched split vector quantization of LSF parameters

We address the issue of rate-distortion (R/D) performance optimality of the recently proposed switched split vector quantization (SSVQ) method. The distribution of the source is modeled using Gaussian mixture density and thus, the non-parametric SSVQ is analyzed in a parametric model based framework for achieving optimum R/D performance. Using high rate quantization theory, we derive the optimum bit allocation formulae for the intra-cluster split vector quantizer (SVQ) and the inter-cluster switching. For the wide-band speech line spectrum frequency (LSF) parameter quantization, it is shown that the Gaussian mixture model (GMM) based parametric SSVQ method provides 1 bit/vector advantage over the non-parametric SSVQ method.

Computationally efficient optimum weighting function for vector quantization of LSF parameters

We propose a new weighting function which is computationally simple and an approximation to the theoretically derived optimum weighting function shown in the literature. The proposed weighting function is perceptually motivated and provides improved vector quantization performance compared to several weighting functions proposed so far, for line spectrum frequency (LSF) parameter quantization of both clean and noisy speech data.

Cluster based training for scaling non-linear support vector machines

Support Vector Machines(SVMs) are hyperplane classifiers defined in a kernel induced feature space. The data size dependent training time complexity of SVMs usually prohibits its use in applications involving more than a few thousands of data points. In this paper we propose a novel kernel based incremental data clustering approach and its use for scaling Non-linear Support Vector Machines to handle large data sets. The clustering method introduced can find cluster abstractions of the training data in a kernel induced feature space. These cluster abstractions are then used for selective sampling based training of Support Vector Machines to reduce the training time without compromising the generalization performance. Experiments done with real world datasets show that this approach gives good generalization performance at reasonable computational expense.

Possible high-temperature superconducting state with a d plus id pairing symmetry in doped graphene

Motivated by a suggestion in our earlier work [G. Baskaran, Phys. Rev. B 65, 212505 (2002)], we study electron correlation driven superconductivity in doped graphene where on-site correlations are believed to be of intermediate strength. Using an extensive variational Monte Carlo study of the repulsive Hubbard model and a correlated ground state wave function, we show that doped graphene supports a superconducting ground state with a d+id pairing symmetry. We estimate superconductivity reaching room temperatures at an optimal doping of about 15%-20%. Our work suggests that correlations can stabilize superconductivity even in systems with intermediate coupling.

OCR Prediction Using Support Vector Machine Based on Piezocone Data

The determination of the overconsolidation ratio (OCR) of clay deposits is an important task in geotechnical engineering practice. This paper examines the potential of a support vector machine (SVM) for predicting the OCR of clays from piezocone penetration test data. SVM is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. The five input variables used for the SVM model for prediction of OCR are the corrected cone resistance (qt), vertical total stress (sigmav), hydrostatic pore pressure (u0), pore pressure at the cone tip (u1), and the pore pressure just above the cone base (u2). Sensitivity analysis has been performed to investigate the relative importance of each of the input parameters. From the sensitivity analysis, it is clear that qt=primary in situ data influenced by OCR followed by sigmav, u0, u2, and u1. Comparison between SVM and some of the traditional interpretation methods is also presented. The results of this study have shown that the SVM approach has the potential to be a practical tool for determination of OCR.

Ordered spin-ice state in the geometrically frustrated metallic ferromagnet Sm2Mo2O7

The recent discovery of spin ice is a spectacular example of the noncoplanar spin arrangements that can arise in the pyrochlore A2B2O7 structure. We present magnetic and thermodynamic studies on the metallic ferromagnet pyrochlore Sm2Mo2O7. Our studies, carried out on oriented crystals, suggest that the Sm spins have an ordered spin-ice ground state below about T*=15 K. The temperature and field evolution of the ordered spin-ice state are governed by an antiferromagnetic coupling between the Sm and Mo spins. We propose that as a consequence of a robust feature of this coupling, the tetrahedra aligned with the external field adopt a one-in, three-out spin structure as opposed to the three-in, one-out structure in dipolar spin ices, as the field exceeds a critical value.

'Wet N2O oxidation' process and interface state density characterization of nanoscale nitrided SiO2 for flash memory application

In this paper we first present the 'wet N2O' furnace oxidation process to grow nitrided tunnel oxides in the thickness range 6 to 8 nm on silicon at a temperature of 800 degrees C. Electrical characteristics of MOS capacitors and MOSFETs fabricated using this oxide as gate oxide have been evaluated and the superior features of this oxide are ascertained The frequency response of the interface states, before and after subjecting the MOSFET gate oxide to constant current stress, is studied using a simple analytical model developed in this work.

Multilevel Dodecagonal Space Vector Generation for Open-end Winding Induction Motor Drive Using Conventional Three Level Inverters

A novel dodecagonal space vector structure for induction motor drive is presented in this paper. It consists of two dodecagons, with the radius of the outer one twice the inner one. Compared to existing dodecagonal space vector structures, to achieve the same PWM output voltage quality, the proposed topology lowers the switching frequency of the inverters and reduces the device ratings to half. At the same time, other benefits obtained from existing dodecagonal space vector structure are retained here. This includes the extension of the linear modulation range and elimination of all 6+/-1 harmonics (n=odd) from the phase voltage. The proposed structure is realized by feeding an open-end winding induction motor with two conventional three level inverters. A detailed calculation of the PWM timings for switching the space vector points is also presented. Simulation and experimental results indicate the possible application of the proposed idea for high power drives.

Performance of Quantized Equal Gain Transmission With Noisy Feedback Channels

In this paper, new results and insights are derived for the performance of multiple-input, single-output systems with beamforming at the transmitter, when the channel state information is quantized and sent to the transmitter over a noisy feedback channel. It is assumed that there exists a per-antenna power constraint at the transmitter, hence, the equal gain transmission (EGT) beamforming vector is quantized and sent from the receiver to the transmitter. The loss in received signal-to-noise ratio (SNR) relative to perfect beamforming is analytically characterized, and it is shown that at high rates, the overall distortion can be expressed as the sum of the quantization-induced distortion and the channel error-induced distortion, and that the asymptotic performance depends on the error-rate behavior of the noisy feedback channel as the number of codepoints gets large. The optimum density of codepoints (also known as the point density) that minimizes the overall distortion subject to a boundedness constraint is shown to be the same as the point density for a noiseless feedback channel, i.e., the uniform density. The binary symmetric channel with random index assignment is a special case of the analysis, and it is shown that as the number of quantized bits gets large the distortion approaches the same as that obtained with random beamforming. The accuracy of the theoretical expressions obtained are verified through Monte Carlo simulations.

Support vector machine applied to settlement of shallow foundations on cohesionless soils

The determination of settlement of shallow foundations on cohesionless soil is an important task in geotechnical engineering. Available methods for the determination of settlement are not reliable. In this study, the support vector machine (SVM), a novel type of learning algorithm based on statistical theory, has been used to predict the settlement of shallow foundations on cohesionless soil. SVM uses a regression technique by introducing an ε – insensitive loss function. A thorough sensitive analysis has been made to ascertain which parameters are having maximum influence on settlement. The study shows that SVM has the potential to be a useful and practical tool for prediction of settlement of shallow foundation on cohesionless soil.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

Trap-State Dynamics in Visible-Light-Emitting ZnO:MgO Nanocrystals

Oleate-capped ZnO:MgO nanocrystals have been synthesized that are soluble in nonpolar solvents and which emit strongly in the visible region (450−600 nm) on excitation by UV radiation. The visible emission involves recombination of trap states of the nanocrystalline ZnO core and has a higher quantum yield than the band gap UV exciton emission. The spectrally resolved dynamics of the trap states have been investigated by time-resolved emission spectroscopy. The time-evolution of the photoluminescence spectra show that there are, in fact, two features in the visible emission whose relative importance and efficiencies vary with time. These features originate from recombination involving trapped electrons and holes, respectively, and with efficiencies that depend on the occupancy of the trap density of states.

Vector evaluated particle swarm optimization (VEPSO) for multi-objective design optimization of composite structures

We present a generic method/model for multi-objective design optimization of laminated composite components, based on vector evaluated particle swarm optimization (VEPSO) algorithm. VEPSO is a novel, co-evolutionary multi-objective variant of the popular particle swarm optimization algorithm (PSO). In the current work a modified version of VEPSO algorithm for discrete variables has been developed and implemented successfully for the, multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are - the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria; failure mechanism based failure criteria, Maximum stress failure criteria and the Tsai-Wu failure criteria. The optimization method is validated for a number of different loading configurations - uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences, as well fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. (C) 2007 Elsevier Ltd. All rights reserved.