A numerical study of variable coefficient elliptic Cauchy problem via projection method

In this paper, we investigate a numerical method for the solution of an inverse problem of recovering lacking data on some part of the boundary of a domain from the Cauchy data on other part for a variable coefficient elliptic Cauchy problem. In the process, the Cauchy problem is transformed into the problem of solving a compact linear operator equation. As a remedy to the ill-posedness of the problem, we use a projection method which allows regularization solely by discretization. The discretization level plays the role of regularization parameter in the case of projection method. The balancing principle is used for the choice of an appropriate discretization level. Several numerical examples show that the method produces a stable good approximate solution.

Effect of magnetic field on Mott's variable-range hopping parameters in multiwall carbon nanotube mat

We report the temperature and magnetic field dependence of the conductivity of multiwall carbon nanotube mat in the temperature range 1.4-150 K and in magnetic fields up to 10 T. It is observed that charge transport in this system is governed by Mott's variable-range hopping of three-dimensional type in the higher temperature range and two-dimensional type in the lower temperature range. Mott's various parameters, such as localization length, hopping length, hopping energy and density of states at the Fermi level are deduced from the variable-range hopping fit. The resistance of the sample decreases with the magnetic field applied in the direction of tube axis of the nanotubes. The magnetic field gives rise to delocalization of states with the well-known consequence of a decrease in Mott's T-0 parameter in variable-range hopping. The application of magnetic field lowers the crossover temperature at which three-dimensional variable-range hopping turns to two-dimensional variable-range hopping. The conductivity on the lower temperature side is governed by the weak localization giving rise to positive magnetoconductance. Finally, a magnetic field-temperature diagram is proposed showing different regions for different kinds of transport mechanism.

Ion transport studies in lithium phospho-molybdate glasses containing Cl- ion

Ion conducting glasses in xLiCl-20Li(2)O-(80-x) 0.80P(2)O(5)-0.20MoO(3)] glass system have been prepared over a wide range of composition (X = 5, 10, 15, 20 and 25 mol%). The electrical conductivity and dielectric relaxation of these glasses were analyzed using impedance spectroscopy in the frequency range of 10 Hz-10 MHz and in the temperature range of 313-353 K. D.c. activation energies extracted from Arrhenius plots using regression analysis, decreases with increasing LiCl mol%. A.c. conductivity data has been fitted to both single and double power law equation with both fixed and variable parameters. The increased conductivity in the present glass system has been correlated with the volume increasing effect and the coordination changes that occur due to structural modification resulting in the creation of non-bridging oxygens (NBO's) of the type O-Mo-O- bonds in the glass network. Dielectric relaxation mechanism in these glasses is analyzed using Kohlrausch-Williams-Watts (KWW) stretched exponential function and stretched exponent (beta) is found to be insensitive to temperature.

Terminal Impact Angle Constrained Guidance Laws Using Variable Structure Systems Theory

In this brief, variable structure systems theory based guidance laws, to intercept maneuvering targets at a desired impact angle, are presented. Choosing the missile's lateral acceleration (latax) to enforce sliding mode, which is the principal operating mode of variable structure systems, on a switching surface defined by the line-of-sight angle leads to a guidance law that allows the achievement of the desired terminal impact angle. As will be shown, this law does not ensure interception for all states of the missile and the target during the engagement. Hence, additional switching surfaces are designed and a switching logic is developed that allows the latax to switch between enforcing sliding mode on one of these surfaces so that the target can be intercepted at the desired impact angle. The guidance laws are designed using nonlinear engagement dynamics for the general case of a maneuvering target.

Probabilistic least squares approach to ordinal regression

This paper proposes a novel approach to solve the ordinal regression problem using Gaussian processes. The proposed approach, probabilistic least squares ordinal regression (PLSOR), obtains the probability distribution over ordinal labels using a particular likelihood function. It performs model selection (hyperparameter optimization) using the leave-one-out cross-validation (LOO-CV) technique. PLSOR has conceptual simplicity and ease of implementation of least squares approach. Unlike the existing Gaussian process ordinal regression (GPOR) approaches, PLSOR does not use any approximation techniques for inference. We compare the proposed approach with the state-of-the-art GPOR approaches on some synthetic and benchmark data sets. Experimental results show the competitiveness of the proposed approach.

Validation based sparse Gaussian processes for ordinal regression

This paper proposes a sparse modeling approach to solve ordinal regression problems using Gaussian processes (GP). Designing a sparse GP model is important from training time and inference time viewpoints. We first propose a variant of the Gaussian process ordinal regression (GPOR) approach, leave-one-out GPOR (LOO-GPOR). It performs model selection using the leave-one-out cross-validation (LOO-CV) technique. We then provide an approach to design a sparse model for GPOR. The sparse GPOR model reduces computational time and storage requirements. Further, it provides faster inference. We compare the proposed approaches with the state-of-the-art GPOR approach on some benchmark data sets. Experimental results show that the proposed approaches are competitive.

Turbine emulator for development of variable speed microhydro power generation systems

Variable speed operation of microhydro power plants is gaining popularity due to the benefits that accrue from their use and the development of suitable generator control systems. This paper highlights the benefits of variable speed systems over conventional systems and also proposes a simple emulator for hydraulic turbines that operate in variable speed fixed flow rate mode. The emulator consists of an uncontrolled separately excited DC motor with additional resistors and has performance characteristics similar to that of the hydraulic turbine.

Variable field ion traps

This paper presents a technique to vary the electric field within a cylindrical ion trap (CIT) mass spectrometer while it is in operation. In this technique, the electrodes of the CIT are split into number of mini-electrodes and different voltages are applied to these split-electrodes to achieve the desired field. In our study we have investigated two geometries of the split-electrode CIT. In the first, we retain the flat endcap electrodes of the CIT but split the ring electrode into five mini-rings. In the second configuration, we split the ring electrode of the CIT into three mini-rings and also divide the endcaps into two mini-discs. By applying different potentials to the mini-rings and mini-discs of these geometries we have shown that the field within the trap can be optimized to desired values. In our study, two different types of fields were targeted. In the first, potentials were adjusted to obtain a linear electric field and, in the second, a controlled higher order even multipole field was obtained by adjusting the potential. We have shown that the different potentials required can be derived from a single RF generator by connecting appropriate capacitor terminations to split electrodes. The field within the trap can be modified by changing the values of the external capacitors. (C) 2013 Elsevier B.V. All rights reserved.

Large-MIMO Receiver based on Linear Regression of MMSE Residual

Multiple input multiple output (MIMO) systems with large number of antennas have been gaining wide attention as they enable very high throughputs. A major impediment is the complexity at the receiver needed to detect the transmitted data. To this end we propose a new receiver, called LRR (Linear Regression of MMSE Residual), which improves the MMSE receiver by learning a linear regression model for the error of the MMSE receiver. The LRR receiver uses pilot data to estimate the channel, and then uses locally generated training data (not transmitted over the channel), to find the linear regression parameters. The proposed receiver is suitable for applications where the channel remains constant for a long period (slow-fading channels) and performs quite well: at a bit error rate (BER) of 10(-3), the SNR gain over MMSE receiver is about 7 dB for a 16 x 16 system; for a 64 x 64 system the gain is about 8.5 dB. For large coherence time, the complexity order of the LRR receiver is the same as that of the MMSE receiver, and in simulations we find that it needs about 4 times as many floating point operations. We also show that further gain of about 4 dB is obtained by local search around the estimate given by the LRR receiver.

Spatially Adaptive Kernel Regression Using Risk Estimation

An important question in kernel regression is one of estimating the order and bandwidth parameters from available noisy data. We propose to solve the problem within a risk estimation framework. Considering an independent and identically distributed (i.i.d.) Gaussian observations model, we use Stein's unbiased risk estimator (SURE) to estimate a weighted mean-square error (MSE) risk, and optimize it with respect to the order and bandwidth parameters. The two parameters are thus spatially adapted in such a manner that noise smoothing and fine structure preservation are simultaneously achieved. On the application side, we consider the problem of image restoration from uniform/non-uniform data, and show that the SURE approach to spatially adaptive kernel regression results in better quality estimation compared with its spatially non-adaptive counterparts. The denoising results obtained are comparable to those obtained using other state-of-the-art techniques, and in some scenarios, superior.

Large-Scale Elastic Net Regularized Linear Classification SVMs and Logistic Regression

Elastic Net Regularizers have shown much promise in designing sparse classifiers for linear classification. In this work, we propose an alternating optimization approach to solve the dual problems of elastic net regularized linear classification Support Vector Machines (SVMs) and logistic regression (LR). One of the sub-problems turns out to be a simple projection. The other sub-problem can be solved using dual coordinate descent methods developed for non-sparse L2-regularized linear SVMs and LR, without altering their iteration complexity and convergence properties. Experiments on very large datasets indicate that the proposed dual coordinate descent - projection (DCD-P) methods are fast and achieve comparable generalization performance after the first pass through the data, with extremely sparse models.

Assessing GCM Convergence for India Using the Variable Convergence Score

General circulation models (GCMs) use transient climate simulations to predict climate conditions in the future. Coarse-grid resolutions and process uncertainties necessitate the use of downscaling models to simulate precipitation. However, in the downscaling models, with multiple GCMs now available, selecting an atmospheric variable from a particular model which is representative of the ensemble mean becomes an important consideration. The variable convergence score (VCS) provides a simple yet meaningful approach to address this issue, providing a mechanism to evaluate variables against each other with respect to the stability they exhibit in future climate simulations. In this study, VCS methodology is applied to 10 atmospheric variables of particular interest in downscaling precipitation over India and also on a regional basis. The nested bias-correction methodology is used to remove the systematic biases in the GCMs simulations, and a single VCS curve is developed for the entire country. The generated VCS curve is expected to assist in quantifying the variable performance across different GCMs, thus reducing the uncertainty in climate impact-assessment studies. The results indicate higher consistency across GCMs for pressure and temperature, and lower consistency for precipitation and related variables. Regional assessments, while broadly consistent with the overall results, indicate low convergence in atmospheric attributes for the Northeastern parts of India.

A VARIABLE SWITCHING FREQUENCY PWM TECHNIQUE FOR INVERTER-FED INDUCTION MOTOR TO ACHIEVE SPREAD SPECTRUM

Voltage Source Inverter (VSI) fed induction motors are widely used in variable speed applications. For inverters using fixed switching frequency PWM, the output harmonic spectra are located at a few discrete frequencies. The ac motordrives powered by these inverters cause acoustic noise. This paper proposes a new variable switching frequency pwm technique and compares its performance with constant switching frequency pwm technique. It is shown that the proposed technique leads to spread spectra of voltages and currents. Also this technique ensures that no lower order harmonics are present and the current THD is comparable to that of fixed switching frequency PWM and is even better for higher modulation indices.

Determination of Internal Structures of Heterogeneous Nanocrystals Using Variable-Energy Photoemission Spectroscopy

This article describes the determination of the internal structure of heterogeneous nanoparticle systems including inverted core-shell (CdS core and CdSe shell) and alloyed (CdSeS) quantum dots using depth-resolved, variable-energy X-ray photoelectron spectroscopy (XPS). A unique feature of this work is the combination of photoelectron spectroscopy performed at lower X-ray energies (400-700 eV), to achieve surface sensitivity, with bulk sensitive measurements at high photon energies (>2000 eV), thereby providing detailed information about the whole nanoparticle structure with a great accuracy. The use of high photon energies furthermore allows us to investigate nanoparticles much larger than those studied thus far. This capability is a consequence of the much-increased mean free path of the photoelectron achieved at high excitation energies. Our results show that the actual structures of the synthesized nanoparticles are considerably different from the nominal, targeted structures, which can be post facto rationalized in terms of the reactivity of different constituents.

Multimodal therapy for complete regression of malignant melanoma using constrained nonlinear optimal dynamic inversion

Using a realistic nonlinear mathematical model for melanoma dynamics and the technique of optimal dynamic inversion (exact feedback linearization with static optimization), a multimodal automatic drug dosage strategy is proposed in this paper for complete regression of melanoma cancer in humans. The proposed strategy computes different drug dosages and gives a nonlinear state feedback solution for driving the number of cancer cells to zero. However, it is observed that when tumor is regressed to certain value, then there is no need of external drug dosages as immune system and other therapeutic states are able to regress tumor at a sufficiently fast rate which is more than exponential rate. As model has three different drug dosages, after applying dynamic inversion philosophy, drug dosages can be selected in optimized manner without crossing their toxicity limits. The combination of drug dosages is decided by appropriately selecting the control design parameter values based on physical constraints. The process is automated for all possible combinations of the chemotherapy and immunotherapy drug dosages with preferential emphasis of having maximum possible variety of drug inputs at any given point of time. Simulation study with a standard patient model shows that tumor cells are regressed from 2 x 107 to order of 105 cells because of external drug dosages in 36.93 days. After this no external drug dosages are required as immune system and other therapeutic states are able to regress tumor at greater than exponential rate and hence, tumor goes to zero (less than 0.01) in 48.77 days and healthy immune system of the patient is restored. Study with different chemotherapy drug resistance value is also carried out. (C) 2014 Elsevier Ltd. All rights reserved.