On-site Coulomb interaction and the magnetism of (GaMn)N and (GaMn)As

We use the local density approximation (LDA) and LDA+U schemes to study the magnetism of (GaMn)As and (GaMn)N for a number of Mn concentrations and varying number of holes. We show that for both systems and both calculational schemes the presence of holes is crucial for establishing ferromagnetism. For both systems, the introduction of $U$ increases delocalization of the holes and, simultaneously, decreases the p-d interaction. Since these two trends exert opposite influences on the Mn-Mn exchange interaction the character of the variation of the Curie temperature (T$_C$) cannot be predicted without direct calculation. We show that the variation of T$_C$ is different for two systems. For low Mn concentrations we obtain the tendency to increasing T$_C$ in the case of (GaMn)N whereas an opposite tendency to decreasing T$_C$ is obtained for (GaMn)As. We reveal the origin of this difference by inspecting the properties of the densities of states and holes for both systems. The main body of calculations is performed within a supercell approach. The Curie temperatures calculated within the coherent potential approximation to atomic disorder are reported for comparison. Both approaches give similar qualitative behavior. The results of calculations are related to the experimental data.Comment: to appear in Physical Review

SEAD: source encrypted authentic data for wireless sensor networks

One of the critical issues in WSNs is providing security for the secret data in military applications. It is necessary to ensure data integrity and authentication for the source data and secure end-to-end path for data transmission. Mobile sinks are suitable for data collection and localization. Mobile sinks and sensor nodes communicate with each other using their public identity, which is prone to security attacks like sink replication and node replication attack. In this work, we have proposed Source Encrypted Authentic Data algorithm (SEAD) that hides the location of mobile sink from malicious nodes. The sensed data is encrypted utilizing symmetric encryption---Advanced Encryption Standards (AES) and tracks the location of the mobile sink. When data encounters a malicious node in a path, then data transmission path is diverted through a secure path. SEAD uses public encryption---Elliptic Curve Cryptography (ECC) to verify the authenticity of the data. Simulation results show that the proposed algorithm ensures data integrity and node authenticity against malicious nodes. Double encryption in the proposed algorithm produces better results in comparison with the existing algorithms

S. N, PD Shenoy, KR Venugopal, and LM Patnaik. Moving vehicle identification using background registration technique for traffic surveillance

Real-time segmentation of moving regions in image sequences is a fundamental step in many vision systems including automated visual surveillance and human-machine interface. In this paper we present a framework for detecting some important but unknown knowledge like vehicle identification and traffic flow count. The objective is to monitor activities at traffic intersections for detecting congestions, and then predict the traffic flow which assists in regulating traffic. The present algorithm for vision-based detection and counting of vehicles in monocular image sequences for traffic scenes are recorded by a stationary camera. The method is based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence. Background subtraction is used which improves the adaptive background mixture model and makes the system learn faster and more accurately, as well as adapt effectively to changing environments. The resulting system robustly identifies vehicles at intersection, rejecting background and tracks vehicles over a specific period of time. Real-life traffic video sequences are used to illustrate the effectiveness of the proposed algorithm

A data mining approach for data generation and analysis for digital forensic application

With the rapid advancements in information and communication technology in the world, crimes committed are becoming technically intensive. When crimes committed use digital devices, forensic examiners have to adopt practical frameworks and methods to recover data for analysis which can pose as evidence. Data Generation, Data Warehousing and Data Mining, are the three essential features involved in the investigation process. This paper proposes a unique way of generating, storing and analyzing data, retrieved from digital devices which pose as evidence in forensic analysis. A statistical approach is used in validating the reliability of the pre-processed data. This work proposes a practical framework for digital forensics on flash drives

Member, IAENG, Prasanth G Rao, Abhilash VR, P. Deepa Shenoy, Venugopal KR and LM Patnaik. A Data Mining Approach for Data Generation and Analysis for Digital Forensic Application

With the rapid advancements in information and communication technology in the world, crimes committed are becoming technically intensive. When crimes committed use digital devices, forensic examiners have to adopt practical frameworks and methods to recover data for analysis which can pose as evidence. Data Generation, Data Warehousing and Data Mining, are the three essential features involved in the investigation process. This paper proposes a unique way of generating, storing and analyzing data, retrieved from digital devices which pose as evidence in forensic analysis. A statistical approach is used in validating the reliability of the pre-processed data. This work proposes a practical framework for digital forensics on flash drive

Random field sampling for a simplified model of melt-blowing considering turbulent velocity fluctuations

In melt-blowing very thin liquid fiber jets are spun due to high-velocity air streams. In literature there is a clear, unsolved discrepancy between the measured and computed jet attenuation. In this paper we will verify numerically that the turbulent velocity fluctuations causing a random aerodynamic drag on the fiber jets -- that has been neglected so far -- are the crucial effect to close this gap. For this purpose, we model the velocity fluctuations as vector Gaussian random fields on top of a k-epsilon turbulence description and develop an efficient sampling procedure. Taking advantage of the special covariance structure the effort of the sampling is linear in the discretization and makes the realization possible

A Real Space Description of Magnetic Field Induced Melting in the Charge Ordered Manganites: I. The Clean Limit

We study the melting of charge order in the half doped manganites using a model that incorporates double exchange, antiferromagnetic superexchange, and Jahn-Teller coupling between electrons and phonons. We primarily use a real space Monte Carlo technique to study the phase diagram in terms of applied field $(h)$ and temperature $(T)$, exploring the melting of charge order with increasing $h$ and its recovery on decreasing $h$. We observe hysteresis in this response, and discover that the `field melted' high conductance state can be spatially inhomogeneous even without extrinsic disorder. The hysteretic response plays out in the background of field driven equilibrium phase separation. Our results, exploring $h$, $T$, and the electronic parameter space, are backed up by analysis of simpler limiting cases and a Landau framework for the field response. This paper focuses on our results in the `clean' systems, a companion paper studies the effect of cation disorder on the melting phenomena.Comment: 16 pages, pdflatex, 11 png fig

Fast variability from black-hole binaries

Currently available information on fast variability of the X-ray emission from accreting collapsed objects constitutes a complex phenomenology which is difficult to interpret. We review the current observational standpoint for black-hole binaries and survey models that have been proposed to interpret it. Despite the complex structure of the accretion flow, key observational diagnostics have been identified which can provide direct access to the dynamics of matter motions in the close vicinity of black holes and thus to the some of fundamental properties of curved spacetimes, where strong-field general relativistic effects can be observed.Comment: 20 pages, 11 figures. Accepted for publication in Space Science Reviews. Also to appear in hard cover in the Space Sciences Series of ISSI "The Physics of Accretion onto Black Holes" (Springer Publisher

Low Complexity Regularization of Linear Inverse Problems

Inverse problems and regularization theory is a central theme in contemporary signal processing, where the goal is to reconstruct an unknown signal from partial indirect, and possibly noisy, measurements of it. A now standard method for recovering the unknown signal is to solve a convex optimization problem that enforces some prior knowledge about its structure. This has proved efficient in many problems routinely encountered in imaging sciences, statistics and machine learning. This chapter delivers a review of recent advances in the field where the regularization prior promotes solutions conforming to some notion of simplicity/low-complexity. These priors encompass as popular examples sparsity and group sparsity (to capture the compressibility of natural signals and images), total variation and analysis sparsity (to promote piecewise regularity), and low-rank (as natural extension of sparsity to matrix-valued data). Our aim is to provide a unified treatment of all these regularizations under a single umbrella, namely the theory of partial smoothness. This framework is very general and accommodates all low-complexity regularizers just mentioned, as well as many others. Partial smoothness turns out to be the canonical way to encode low-dimensional models that can be linear spaces or more general smooth manifolds. This review is intended to serve as a one stop shop toward the understanding of the theoretical properties of the so-regularized solutions. It covers a large spectrum including: (i) recovery guarantees and stability to noise, both in terms of $\ell^2$-stability and model (manifold) identification; (ii) sensitivity analysis to perturbations of the parameters involved (in particular the observations), with applications to unbiased risk estimation ; (iii) convergence properties of the forward-backward proximal splitting scheme, that is particularly well suited to solve the corresponding large-scale regularized optimization problem