Low-temperature polaronic relaxations with variable range hopping conductivity in FeTiMO(6) (M = Ta,Nb,Sb)

Low-temperature dielectric measurements on FeTiMO(6) (M = Ta,Nb,Sb) rutile-type oxides at frequencies from 0.1 Hz to 10 MHz revealed anomalous dielectric relaxations with frequency dispersion. Unlike the high-temperature relaxor response of these materials, the low-temperature relaxations are polaronic in nature. The relationship between frequency and temperature of dielectric loss peak follows T(-1/4) behavior. The frequency dependence of ac conductivity shows the well-known universal dielectric response, while the dc conductivity follows Mott variable range hopping (VRH) behavior, confirming the polaronic origin of the observed dielectric relaxations. The frequency domain analysis of the dielectric spectra shows evidence for two relaxations, with the high-frequency relaxations following Mott VRH behavior more closely. Significantly, the Cr- and Ga-based analogs, CrTiNbO(6) and GaTiMO(6) (M = Ta,Nb), that were also studied, did not show these anomalies.

Online character recognition using regression techniques

This paper introduces a scheme for classification of online handwritten characters based on polynomial regression of the sampled points of the sub-strokes in a character. The segmentation is done based on the velocity profile of the written character and this requires a smoothening of the velocity profile. We propose a novel scheme for smoothening the velocity profile curve and identification of the critical points to segment the character. We also porpose another method for segmentation based on the human eye perception. We then extract two sets of features for recognition of handwritten characters. Each sub-stroke is a simple curve, a part of the character, and is represented by the distance measure of each point from the first point. This forms the first set of feature vector for each character. The second feature vector are the coeficients obtained from the B-splines fitted to the control knots obtained from the segmentation algorithm. The feature vector is fed to the SVM classifier and it indicates an efficiency of 68% using the polynomial regression technique and 74% using the spline fitting method.

Optimal local polynomial regression of noisy time varying signals

We address the problem of local-polynomial modeling of smooth time-varying signals with unknown functional form, in the presence of additive noise. The problem formulation is in the time domain and the polynomial coefficients are estimated in the pointwise minimum mean square error (PMMSE) sense. The choice of the window length for local modeling introduces a bias-variance tradeoff, which we solve optimally by using the intersection-of-confidence-intervals (ICI) technique. The combination of the local polynomial model and the ICI technique gives rise to an adaptive signal model equipped with a time-varying PMMSE-optimal window length whose performance is superior to that obtained by using a fixed window length. We also evaluate the sensitivity of the ICI technique with respect to the confidence interval width. Simulation results on electrocardiogram (ECG) signals show that at 0dB signal-to-noise ratio (SNR), one can achieve about 12dB improvement in SNR. Monte-Carlo performance analysis shows that the performance is comparable to the basic wavelet techniques. For 0 dB SNR, the adaptive window technique yields about 2-3dB higher SNR than wavelet regression techniques and for SNRs greater than 12dB, the wavelet techniques yield about 2dB higher SNR.

Best Node Selection Through Distributed Fast Variable Power Multiple Access

In many wireless applications, it is highly desirable to have a fast mechanism to resolve or select the packet from the user with the highest priority. Furthermore, individual priorities are often known only locally at the users. In this paper we introduce an extremely fast, local-information-based multiple access algorithm that selects the best node in 1.8 to 2.1 slots,which is much lower than the 2.43 slot average achieved by the best algorithm known to date. The algorithm, which we call Variable Power Multiple Access Selection (VP-MAS), uses the local channel state information from the accessing nodes to the receiver, and maps the priorities into the receive power.It is inherently distributed and scales well with the number of users. We show that mapping onto a discrete set of receive power levels is optimal, and provide a complete characterization for it. The power levels are chosen to exploit packet capture that inherently occurs in a wireless physical layer. The VP-MAS algorithm adjusts the expected number of users that contend in each step and their respective transmission powers, depending on whether previous transmission attempts resulted in capture,idle channel, or collision.

An improved sapce phasor based current hysteresis controller with reduced switching frequency variations using variable parabolic bands

A current error space phasor based simple hysteresis controller is proposed in this paper to control the switching frequency variation in two-level pulsewidth-modulation (PWM) inverter-fed induction motor (IM) drives. A parabolic boundary for the current error space phasor is suggested for the first time to obtain the switching frequency spectrum for output voltage with hysteresis controller similar to the constant switching frequency voltage-controlled space vector PWM-based IM drive. A novel concept of online variation of this parabolic boundary, which depends on the operating speed of motor, is presented. A generalized technique that determines the set of unique parabolic boundaries for a two-level inverter feeding any given induction motor is described. The sector change logic is self-adaptive and is capable of taking the drive up to the six-step mode if needed. Steady-state and transient performance of proposed controller is experimentally verified on a 3.7-kW IM drive in the entire speed range. Close resemblance of the simulation and experimental results is shown.

Focussed Crawling with large scale Ordinal Regression Solvers

In this paper we propose a novel, scalable, clustering based Ordinal Regression formulation, which is an instance of a Second Order Cone Program (SOCP) with one Second Order Cone (SOC) constraint. The main contribution of the paper is a fast algorithm, CB-OR, which solves the proposed formulation more eficiently than general purpose solvers. Another main contribution of the paper is to pose the problem of focused crawling as a large scale Ordinal Regression problem and solve using the proposed CB-OR. Focused crawling is an efficient mechanism for discovering resources of interest on the web. Posing the problem of focused crawling as an Ordinal Regression problem avoids the need for a negative class and topic hierarchy, which are the main drawbacks of the existing focused crawling methods. Experiments on large synthetic and benchmark datasets show the scalability of CB-OR. Experiments also show that the proposed focused crawler outperforms the state-of-the-art.

Pricing a Shared Access Link for Fair and Efficient Operation with Variable User Data Rates

The literature on pricing implicitly assumes an "infinite data" model, in which sources can sustain any data rate indefinitely. We assume a more realistic "finite data" model, in which sources occasionally run out of data; this leads to variable user data rates. Further, we assume that users have contracts with the service provider, specifying the rates at which they can inject traffic into the network. Our objective is to study how prices can be set such that a single link can be shared efficiently and fairly among users in a dynamically changing scenario where a subset of users occasionally has little data to send. User preferences are modelled by concave increasing utility functions. Further, we introduce two additional elements: a convex increasing disutility function and a convex increasing multiplicative congestion-penally function. The disutility function takes the shortfall (contracted rate minus present rate) as its argument, and essentially encourages users to send traffic at their contracted rates, while the congestion-penalty function discourages heavy users from sending excess data when the link is congested. We obtain simple necessary and sufficient conditions on prices for fair and efficient link sharing; moreover, we show that a single price for all users achieves this. We illustrate the ideas using a simple experiment.

Specification Based Regression Testing Using Explicit State Space Enumeration

This paper presents a method of partial automation of specification based regression testing, which we call ESSE (Explicit State Space Enumeration). The first step in ESSE method is the extraction of a finite state model of the system making use of an already tested version of the system under test (SUT). Thereafter, the finite state model thus obtained is used to compute good test sequences that can be used to regression test subsequent versions of the system. We present two new algorithms for test sequence computation - both based on our finite state model generated by the above method. We also provide the details and results of the experimental evaluation of ESSE method. Comparison with a practically used random-testing algorithm has shown substantial improvements.

Fuzzy inference based data preprocessing for VBR video traffic prediction

Prediction of variable bit rate compressed video traffic is critical to dynamic allocation of resources in a network. In this paper, we propose a technique for preprocessing the dataset used for training a video traffic predictor. The technique involves identifying the noisy instances in the data using a fuzzy inference system. We focus on three prediction techniques, namely, linear regression, neural network and support vector regression and analyze their performance on H.264 video traces. Our experimental results reveal that data preprocessing greatly improves the performance of linear regression and neural network, but is not effective on support vector regression.

Variable Granularity Access Tracking Scheme for Improving the Performance of Software Transactional Memory

Software transactional memory (STM) has been proposed as a promising programming paradigm for shared memory multi-threaded programs as an alternative to conventional lock based synchronization primitives. Typical STM implementations employ a conflict detection scheme, which works with uniform access granularity, tracking shared data accesses either at word/cache line or at object level. It is well known that a single fixed access tracking granularity cannot meet the conflicting goals of reducing false conflicts without impacting concurrency adversely. A fine grained granularity while improving concurrency can have an adverse impact on performance due to lock aliasing, lock validation overheads, and additional cache pressure. On the other hand, a coarse grained granularity can impact performance due to reduced concurrency. Thus, in general, a fixed or uniform granularity access tracking (UGAT) scheme is application-unaware and rarely matches the access patterns of individual application or parts of an application, leading to sub-optimal performance for different parts of the application(s). In order to mitigate the disadvantages associated with UGAT scheme, we propose a Variable Granularity Access Tracking (VGAT) scheme in this paper. We propose a compiler based approach wherein the compiler uses inter-procedural whole program static analysis to select the access tracking granularity for different shared data structures of the application based on the application's data access pattern. We describe our prototype VGAT scheme, using TL2 as our STM implementation. Our experimental results reveal that VGAT-STM scheme can improve the application performance of STAMP benchmarks from 1.87% to up to 21.2%.

Comparison of 10 Multi-Sensor Image Fusion Paradigms for IKONOS Images

Fusion of multi-sensor imaging data enables a synergetic interpretation of complementary information obtained by sensors of different spectral ranges. Multi-sensor data of diverse spectral, spatial and temporal resolutions require advanced numerical techniques for analysis and interpretation. This paper reviews ten advanced pixel based image fusion techniques – Component substitution (COS), Local mean and variance matching, Modified IHS (Intensity Hue Saturation), Fast Fourier Transformed-enhanced IHS, Laplacian Pyramid, Local regression, Smoothing filter (SF), Sparkle, SVHC and Synthetic Variable Ratio. The above techniques were tested on IKONOS data (Panchromatic band at 1 m spatial resolution and Multispectral 4 bands at 4 m spatial resolution). Evaluation of the fused results through various accuracy measures, revealed that SF and COS methods produce images closest to corresponding multi-sensor would observe at the highest resolution level (1 m).

Two new 1D chains of Ni(2)Na(2) heterometallic double half-cubane building units: Synthesis, structures and variable temperature magnetic study

An equimolar mixture of Ni(NO(3))(2)center dot 6H(2)O and pyridine-2-aldehyde with two equivalents of NaN(3) in methanol in the presence of NaOMe resulted in the formation of light green precipitate which upon crystallization from dimethylformamide (DMF) yielded light green single crystals [{Ni(2)Na(2)(pic)(4)(N(3))(2)(H(2)O)(2)(MeOH)}center dot MeOH center dot 3H(2)O](n) (1) and [{Ni(2)Na(2)(pic)(4)(N(3))(2)(H(2)O)(4)}center dot 2DMF center dot H(2)O](n) (2) (pic = pyridine-2-carboxylate) at room temperature and high temperature (100 degrees C), respectively. Variable temperature magnetic studies revealed the existence of overall ferromagnetic behaviour with J approximate to + 10 cm(-1) and D approximate to -2 to -7 cm(-1) for 1 and 2, respectively. Negative D values as well as variation of D upon slight distortion of structure by varying reaction temperature were observed. The X-band Electron Paramagnetic Resonance (EPR) spectra of both 2 and 3 were recorded below 50 K. The structural distortion was also implicated from the EPR spectra. Density Functional Theory (DFT) calculations on both complexes were performed in two different ways to corroborate the magnetic results. Considering only Ni(2)(II) dimeric unit, results were J = + 20.65 cm(-1) and D = -3.16 cm(-1) for 1, and J = +24.56 cm(-1) and D = -4.67 cm(-1) for 2. However, considering Ni(2)(II)Na(2)(I) cubane as magnetic core the results were J = +16.35 cm(-1) (1), +19.54 cm(-1) (2); D = -3.05 cm(-1) (1), -4.25 cm(-1) (2).

Scale effects on buckling analysis of orthotropic nanoplates based on nonlocal two-variable refined plate theory

This article presents the buckling analysis of orthotropic nanoplates such as graphene using the two-variable refined plate theory and nonlocal small-scale effects. The two-variable refined plate theory takes account of transverse shear effects and parabolic distribution of the transverse shear strains through the thickness of the plate, hence it is unnecessary to use shear correction factors. Nonlocal governing equations of motion for the monolayer graphene are derived from the principle of virtual displacements. The closed-form solution for buckling load of a simply supported rectangular orthotropic nanoplate subjected to in-plane loading has been obtained by using the Navier's method. Numerical results obtained by the present theory are compared with first-order shear deformation theory for various shear correction factors. It has been proven that the nondimensional buckling load of the orthotropic nanoplate is always smaller than that of the isotropic nanoplate. It is also shown that small-scale effects contribute significantly to the mechanical behavior of orthotropic graphene sheets and cannot be neglected. Further, buckling load decreases with the increase of the nonlocal scale parameter value. The effects of the mode number, compression ratio and aspect ratio on the buckling load of the orthotropic nanoplate are also captured and discussed in detail. The results presented in this work may provide useful guidance for design and development of orthotropic graphene based nanodevices that make use of the buckling properties of orthotropic nanoplates.

A novel variable resolution global spectral method on the sphere

A variable resolution global spectral method is created on the sphere using High resolution Tropical Belt Transformation (HTBT). HTBT belongs to a class of map called reparametrisation maps. HTBT parametrisation of the sphere generates a clustering of points in the entire tropical belt; the density of the grid point distribution decreases smoothly in the domain outside the tropics. This variable resolution method creates finer resolution in the tropics and coarser resolution at the poles. The use of FFT procedure and Gaussian quadrature for the spectral computations retains the numerical efficiency available with the standard global spectral method. Accuracy of the method for meteorological computations are demonstrated by solving Helmholtz equation and non-divergent barotropic vorticity equation on the sphere. (C) 2011 Elsevier Inc. All rights reserved.