MINIMUM UNCERTAINTY AND ENTANGLEMENT

We address the question, does a system A being entangled with another system B, put any constraints on the Heisenberg uncertainty relation (or the Schrodinger-Robertson inequality)? We find that the equality of the uncertainty relation cannot be reached for any two noncommuting observables, for finite dimensional Hilbert spaces if the Schmidt rank of the entangled state is maximal. One consequence is that the lower bound of the uncertainty relation can never be attained for any two observables for qubits, if the state is entangled. For infinite-dimensional Hilbert space too, we show that there is a class of physically interesting entangled states for which no two noncommuting observables can attain the minimum uncertainty equality.

Assessing severe Drought and wet events over India in a future climate using a nested bias-correction approach

General circulation models (GCMs) are routinely used to simulate future climatic conditions. However, rainfall outputs from GCMs are highly uncertain in preserving temporal correlations, frequencies, and intensity distributions, which limits their direct application for downscaling and hydrological modeling studies. To address these limitations, raw outputs of GCMs or regional climate models are often bias corrected using past observations. In this paper, a methodology is presented for using a nested bias-correction approach to predict the frequencies and occurrences of severe droughts and wet conditions across India for a 48-year period (2050-2099) centered at 2075. Specifically, monthly time series of rainfall from 17 GCMs are used to draw conclusions for extreme events. An increasing trend in the frequencies of droughts and wet events is observed. The northern part of India and coastal regions show maximum increase in the frequency of wet events. Drought events are expected to increase in the west central, peninsular, and central northeast regions of India. (C) 2013 American Society of Civil Engineers.

RF plasma MOCVD of Y2O3 thin films: Effect of RF self-bias on the substrates during deposition

Yttrium oxide (Y203) thin films have been deposited by radio frequency plasma assisted metal organic chemical vapor deposition (MOCVD) process using (2,2,6,6-tetramethy1-3,5-heptanedionate) yttrium (commonly known as Y(thd)3) precursor in a plasma of argon and oxygen gases at a substrate temperature of 350 C. The films have been deposited under influence of varying RF self-bias (-50 V to 175 V) on silicon, quartz, stainless steel and tantalum substrates. The deposited coatings are characterized by glancing angle X-ray diffraction (GIXRD), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), spectroscopic ellipsometry and scanning electron microscopy (SEM). GIXRD and FTIR results indicate deposition of Y2 03 (BCC structure) in all cases. However, XPS results indicate nonstoichiometric cubic phase deposition on the surface of deposited films. The degree of nonstoichiometry varies with bias during deposition. Ellipsometry results indicate that the refractive index for the deposited films is varying from 1.70 to 1.83 that is typical for Y203. All films are transparent in the investigated wavelength range 300-1200 nm. SEM results indicate that the microstructure of the films is changing with applied bias. Results indicate that it is possible to deposit single phase cubic Y203 thin films at low substrate temperature by RF plasma MOCVD process. RF self-bias that decides about the energy of impinging ions on the substrates plays an important role in controlling the texture of deposited Y203 films on the substrates. Results indicate that to control the structure of films and its texture, it is important to control the bias on the substrate during deposition. The films deposited at high bias level show degradation in the crystallinity and reduction of thickness. (C) 2013 Elsevier B.V. All rights reserved.

Full-rate full-diversity finite feedback space-time schemes with minimum feedback and transmission duration

A Finite Feedback Scheme (FFS) for a quasi-static MIMO block fading channel with finite N-ary delay-free noise-free feedback consists of N Space-Time Block Codes (STBCs) at the transmitter, one corresponding to each possible value of feedback, and a function at the receiver that generates N-ary feedback. A number of FFSs are available in the literature that provably attain full-diversity. However, there is no known full-diversity criterion that universally applies to all FFSs. In this paper a universal necessary condition for any FFS to achieve full-diversity is given, and based on this criterion the notion of Feedback-Transmission duration optimal (FT-optimal) FFSs is introduced, which are schemes that use minimum amount of feedback N for the given transmission duration T, and minimum T for the given N to achieve full-diversity. When there is no feedback (N = 1) an FT-optimal scheme consists of a single STBC, and the proposed condition reduces to the well known necessary and sufficient condition for an STBC to achieve full-diversity. Also, a sufficient criterion for full-diversity is given for FFSs in which the component STBC yielding the largest minimum Euclidean distance is chosen, using which full-rate (N-t complex symbols per channel use) full-diversity FT-optimal schemes are constructed for all N-t > 1. These are the first full-rate full-diversity FFSs reported in the literature for T < N-t. Simulation results show that the new schemes have the best error performance among all known FFSs.

Channel estimation relying on the minimum bit-error-ratio criterion for BPSK and QPSK signals

The authors consider the channel estimation problem in the context of a linear equaliser designed for a frequency selective channel, which relies on the minimum bit-error-ratio (MBER) optimisation framework. Previous literature has shown that the MBER-based signal detection may outperform its minimum-mean-square-error (MMSE) counterpart in the bit-error-ratio performance sense. In this study, they develop a framework for channel estimation by first discretising the parameter space and then posing it as a detection problem. Explicitly, the MBER cost function (CF) is derived and its performance studied, when transmitting binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals. It is demonstrated that the MBER based CF aided scheme is capable of outperforming existing MMSE, least square-based solutions.

A Novel Design Method for SOGI-PLL for Minimum Settling Time and Low Unit Vector Distortion

Phase-locked loops (PLLs) are necessary in grid connected systems to obtain information about the frequency, amplitude and phase of the grid voltage. In stationary reference frame control, the unit vectors of PLLs are used for reference generation. It is important that the PLL performance is not affected significantly when grid voltage undergoes amplitude and frequency variations. In this paper, a novel design for the popular single-phase PLL topology, namely the second-order generalized integrator (SOGI) based PLL is proposed which achieves minimum settling time during grid voltage amplitude and frequency variations. The proposed design achieves a settling time of less than 27.7 ms. This design also ensures that the unit vectors generated by this PLL have a steady state THD of less than 1% during frequency variations of the grid voltage. The design of the SOGI-PLL based on the theoretical analysis is validated by experimental results.

Minimum switching loss pulse width modulation for reduced power conversion loss in reactive power compensators

Advanced bus-clamping switching sequences, which employ an active vector twice in a subcycle, are used to reduce line current distortion and switching loss in a space vector modulated voltage source converter. This study evaluates minimum switching loss pulse width modulation (MSLPWM), which is a combination of such sequences, for static reactive power compensator (STATCOM) application. It is shown that MSLPWM results in a significant reduction in device loss over conventional space vector pulse width modulation. Experimental verification is presented at different power levels of up to 150 kVA.

Multi-site downscaling of maximum and minimum daily temperature using support vector machine

Climate change impact assessment studies involve downscaling large-scale atmospheric predictor variables (LSAPVs) simulated by general circulation models (GCMs) to site-scale meteorological variables. This article presents a least-square support vector machine (LS-SVM)-based methodology for multi-site downscaling of maximum and minimum daily temperature series. The methodology involves (1) delineation of sites in the study area into clusters based on correlation structure of predictands, (2) downscaling LSAPVs to monthly time series of predictands at a representative site identified in each of the clusters, (3) translation of the downscaled information in each cluster from the representative site to that at other sites using LS-SVM inter-site regression relationships, and (4) disaggregation of the information at each site from monthly to daily time scale using k-nearest neighbour disaggregation methodology. Effectiveness of the methodology is demonstrated by application to data pertaining to four sites in the catchment of Beas river basin, India. Simulations of Canadian coupled global climate model (CGCM3.1/T63) for four IPCC SRES scenarios namely A1B, A2, B1 and COMMIT were downscaled to future projections of the predictands in the study area. Comparison of results with those based on recently proposed multivariate multiple linear regression (MMLR) based downscaling method and multi-site multivariate statistical downscaling (MMSD) method indicate that the proposed method is promising and it can be considered as a feasible choice in statistical downscaling studies. The performance of the method in downscaling daily minimum temperature was found to be better when compared with that in downscaling daily maximum temperature. Results indicate an increase in annual average maximum and minimum temperatures at all the sites for A1B, A2 and B1 scenarios. The projected increment is high for A2 scenario, and it is followed by that for A1B, B1 and COMMIT scenarios. Projections, in general, indicated an increase in mean monthly maximum and minimum temperatures during January to February and October to December.

Exchange bias in strained SrRuO3 thin films

Recently, it was found that the ferromagnetic SrRuO3 when combined with another ferromagnet in thin film form gives rise to exchange bias (EB) effect. However, we observed EB in single, strained, SrRuO3 thin films grown on diamagnetic LaAlO3 (100) substrates. It displays the training effect, which essentially confirms EB. The temperature dependence of the EB reveals the blocking temperature to be around similar to 75 K. The strength of the exchange bias decreases with the increase in thickness of the film. We observe tensile strain in the out of plane direction. Further, the presence of in-plane compressive strain is observed through asymmetric reciprocal space mapping. Finally, we find a direct link between strain and EB. The evolution of strain with thickness matches well with the nature of scaled EB. It has been shown earlier by first principle calculations that this strain can induce EB in thin films. (C) 2014 AIP Publishing LLC.

AN OPTIMUM SHRINKAGE ESTIMATOR BASED ON MINIMUM-PROBABILITY-OF-ERROR CRITERION AND APPLICATION TO SIGNAL DENOISING

We address the problem of designing an optimal pointwise shrinkage estimator in the transform domain, based on the minimum probability of error (MPE) criterion. We assume an additive model for the noise corrupting the clean signal. The proposed formulation is general in the sense that it can handle various noise distributions. We consider various noise distributions (Gaussian, Student's-t, and Laplacian) and compare the denoising performance of the estimator obtained with the mean-squared error (MSE)-based estimators. The MSE optimization is carried out using an unbiased estimator of the MSE, namely Stein's Unbiased Risk Estimate (SURE). Experimental results show that the MPE estimator outperforms the SURE estimator in terms of SNR of the denoised output, for low (0 -10 dB) and medium values (10 - 20 dB) of the input SNR.

DC-Bias-Superposition Characteristics of Ni0.4Zn0.2Mn0.4Fe2O4 Nanopowders Synthesized by Auto-Combustion

Ni0.4Zn0.2Mn0.4Fe2O4 nanopowders were prepared by auto-combustion method. The as-synthesized powders were characterized using X-ray diffraction (XRD) and thermo-gravimetric-differential thermal analysis (TG-DTA), and the powders were densified at different temperatures 400 degrees C, 500 degrees C, 600 degrees C and 700 degrees C/4 hrs using conventional sintering method. The sintered samples were characterized by XRD and transmission electron microscope (TEM). The bulk densities of the samples were increased with an increase of sintering temperature. The grain sizes of all the samples vary in between 18 nm to 30 nm. The hysteresis loops show high saturation magnetization and low coercivity, indicates that it is a soft material. The incremental permeability (permeability with magnetic field superposition) was influenced by both Delta M and H-c. A sample with higher initial permeability and favoured the attainment of a higher incremental permeability. The Q-factor was mainly determined by the sintered density and microstructure. To summarize, a uniform and dense microstructure with relatively small average grain size is favourable for obtaining better dc-bias-superposition characteristics, including permeability and Q-factor.

And yet it shrinks: A novel method for correcting bias in forest tree growth estimates caused by water-induced fluctuations

Accuracy in tree woody growth estimates is important to global carbon budget estimation and climate-change science. Tree growth in permanent sampling plots (PSPs) is commonly estimated by measuring stem diameter changes, but this method is susceptible to bias resulting from water-induced reversible stem shrinkage. In the absence of bias correction, temporal variability in growth is likely to be overestimated and incorrectly attributed to fluctuations in resource availability, especially in forests with high seasonal and inter-annual variability in water. We propose and test a novel approach for estimating and correcting this bias at the community level. In a 50-ha PSP from a seasonally dry tropical forest in southern India, where tape measurements have been taken every four years from 1988 to 2012, for nine trees we estimated bias due to reversible stem shrinkage as the difference between woody growth measured using tree rings and that estimated from tape. We tested if the bias estimated from these trees could be used as a proxy to correct bias in tape-based growth estimates at the PSP scale. We observed significant shrinkage-related bias in the growth estimates of the nine trees in some censuses. This bias was strongly linearly related to tape-based growth estimates at the level of the PSP, and could be used as a proxy. After bias was corrected, the temporal variance in growth rates of the PSP decreased, while the effect of exceptionally dry or wet periods was retained, indicating that at least a part of the temporal variability arose from reversible shrinkage-related bias. We also suggest that the efficacy of the bias correction could be improved by measuring the proxy on trees that belong to different size classes and census timing, but not necessarily to different species. Our approach allows for reanalysis - and possible reinterpretation of temporal trends in tree growth, above ground biomass change, or carbon fluxes in forests, and their relationships with resource availability in the context of climate change. (C) 2014 Elsevier B.V. All rights reserved.

Belief propagation for minimum weight many-to-one matchings in the random complete graph

In a complete bipartite graph with vertex sets of cardinalities n and n', assign random weights from exponential distribution with mean 1, independently to each edge. We show that, as n -> infinity, with n' = n/alpha] for any fixed alpha > 1, the minimum weight of many-to-one matchings converges to a constant (depending on alpha). Many-to-one matching arises as an optimization step in an algorithm for genome sequencing and as a measure of distance between finite sets. We prove that a belief propagation (BP) algorithm converges asymptotically to the optimal solution. We use the objective method of Aldous to prove our results. We build on previous works on minimum weight matching and minimum weight edge cover problems to extend the objective method and to further the applicability of belief propagation to random combinatorial optimization problems.

Equilibrium and equivariant triangulations of some small covers with minimum number of vertices

Small covers were introduced by Davis and Januszkiewicz in 1991. We introduce the notion of equilibrium triangulations for small covers. We study equilibrium and vertex minimal 4-equivariant triangulations of 2-dimensional small covers. We discuss vertex minimal equilibrium triangulations of RP3#RP3, S-1 x RP2 and a nontrivial S-1 bundle over RP2. We construct some nice equilibrium triangulations of the real projective space RPn with 2(n) + n 1 vertices. The main tool is the theory of small covers.

All Digital Energy Sensing for Minimum Energy Tracking

Minimizing energy consumption is of utmost importance in an energy starved system with relaxed performance requirements. This brief presents a digital energy sensing method that requires neither a constant voltage reference nor a time reference. An energy minimizing loop uses this to find the minimum energy point and sets the supply voltage between 0.2 and 0.5 V. Energy savings up to 1275% over existing minimum energy tracking techniques in the literature is achieved.