Physics-Based Thermal Conductivity Model for Metallic Single-Walled Carbon Nanotube Interconnects

In this letter, a closed-form analytical model for temperature-dependent longitudinal diffusive lattice thermal conductivity (kappa) of a metallic single-walled carbon nanotube (SWCNT) has been addressed. Based on the Debye theory, the second-order three-phonon Umklapp, mass difference (MD), and boundary scatterings have been incorporated to formulate. in both low-and high-temperature regimes. It is proposed that. at low temperature (T) follows the T-3 law and is independent of the second-order three-phonon Umklapp and MD scatterings. The form factor due to MD scattering also plays a key role in the significant variation of. in addition to the SWCNT length. The present diameter-independent model of. agrees well with the available experimental data on suspended intrinsic metallic SWCNTs over a wide range of temperature and can be carried forward for electrothermal analyses of CNT-based interconnects.

Hydrodynamics of R-charged D1-branes

We study the hydrodynamic properties of strongly coupled SU(N) Yang-Mills theory of the D1-brane at finite temperature and at a non-zero density of R-charge in the framework of gauge/gravity duality. The gravity dual description involves a charged black hole solution of an Einstein-Maxwell-dilaton system in 3 dimensions which is obtained by a consistent truncation of the spinning D1-brane in 10 dimensions. We evaluate thermal and electrical conductivity as well as the bulk viscosity as a function of the chemical potential conjugate to the R-charges of the D1-brane. We show that the ratio of bulk viscosity to entropy density is independent of the chemical potential and is equal to 1/4 pi. The thermal conductivity and bulk viscosity obey a relationship similar to the Wiedemann-Franz law. We show that at the boundary of thermodynamic stability, the charge diffusion mode becomes unstable and the transport coefficients exhibit critical behaviour. Our method for evaluating the transport coefficients relies on expressing the second order differential equations in terms of a first order equation which dictates the radial evolution of the transport coefficient. The radial evolution equations can be solved exactly for the transport coefficients of our interest. We observe that transport coefficients of the D1-brane theory are related to that of the M2-brane by an overall proportionality constant which sets the dimensions.

Study Of Acoustic Source Localization Algorithm For Planar Arrays

The source localization algorithms in the earlier works, mostly used non-planar arrays. If we consider scenarios like human-computer communication, or human-television communication where the microphones need to be placed on the computer monitor or television front panel, i.e we need to use the planar arrays. The algorithm proposed in 1], is a Linear Closed Form source localization algorithm (LCF algorithm) which is based on Time Difference of Arrivals (TDOAs) that are obtained from the data collected using the microphones. It assumes non-planar arrays. The LCF algorithm is applied to planar arrays in the current work. The relationship between the error in the source location estimate and the perturbation in the TDOAs is derived using first order perturbation analysis and validated using simulations. If the TDOAs are erroneous, both the coefficient matrix and the data matrix used for obtaining source location will be perturbed. So, the Total least squares solution for source localization is proposed in the current work. The sensitivity analysis of the source localization algorithm for planar arrays and non-planar arrays is done by introducing perturbation in the TDOAs and the microphone locations. It is shown that the error in the source location estimate is less when we use planar array instead of the particular non-planar array considered for same perturbation in the TDOAs or microphone location. The location of the reference microphone is proved to be important for getting an accurate source location estimate if we are using the LCF algorithm.

Chance constrained uncertain classification via robust optimization

This paper studies the problem of constructing robust classifiers when the training is plagued with uncertainty. The problem is posed as a Chance-Constrained Program (CCP) which ensures that the uncertain data points are classified correctly with high probability. Unfortunately such a CCP turns out to be intractable. The key novelty is in employing Bernstein bounding schemes to relax the CCP as a convex second order cone program whose solution is guaranteed to satisfy the probabilistic constraint. Prior to this work, only the Chebyshev based relaxations were exploited in learning algorithms. Bernstein bounds employ richer partial information and hence can be far less conservative than Chebyshev bounds. Due to this efficient modeling of uncertainty, the resulting classifiers achieve higher classification margins and hence better generalization. Methodologies for classifying uncertain test data points and error measures for evaluating classifiers robust to uncertain data are discussed. Experimental results on synthetic and real-world datasets show that the proposed classifiers are better equipped to handle data uncertainty and outperform state-of-the-art in many cases.