Estimation of Optimized Energy and Latency Constraints for Task Allocation in 3d Network on Chip

In Network on Chip (NoC) rooted system, energy consumption is affected by task scheduling and allocation schemes which affect the performance of the system. In this paper we test the pre-existing proposed algorithms and introduced a new energy skilled algorithm for 3D NoC architecture. An efficient dynamic and cluster approaches are proposed along with the optimization using bio-inspired algorithm. The proposed algorithm has been implemented and evaluated on randomly generated benchmark and real life application such as MMS, Telecom and VOPD. The algorithm has also been tested with the E3S benchmark and has been compared with the existing mapping algorithm spiral and crinkle and has shown better reduction in the communication energy consumption and shows improvement in the performance of the system. On performing experimental analysis of proposed algorithm results shows that average reduction in energy consumption is 49%, reduction in communication cost is 48% and average latency is 34%. Cluster based approach is mapped onto NoC using Dynamic Diagonal Mapping (DDMap), Crinkle and Spiral algorithms and found DDmap provides improved result. On analysis and comparison of mapping of cluster using DDmap approach the average energy reduction is 14% and 9% with crinkle and spiral.Comment: 20 Pages,17 Figure, International Journal of Computer Science & Information Technology. arXiv admin note: substantial text overlap with arXiv:1404.251

Energy and Latency Aware Application Mapping Algorithm & Optimization for Homogeneous 3D Network on Chip

Energy efficiency is one of the most critical issue in design of System on Chip. In Network On Chip (NoC) based system, energy consumption is influenced dramatically by mapping of Intellectual Property (IP) which affect the performance of the system. In this paper we test the antecedently extant proposed algorithms and introduced a new energy proficient algorithm stand for 3D NoC architecture. In addition a hybrid method has also been implemented using bioinspired optimization (particle swarm optimization) technique. The proposed algorithm has been implemented and evaluated on randomly generated benchmark and real life application such as MMS, Telecom and VOPD. The algorithm has also been tested with the E3S benchmark and has been compared with the existing algorithm (spiral and crinkle) and has shown better reduction in the communication energy consumption and shows improvement in the performance of the system. Comparing our work with spiral and crinkle, experimental result shows that the average reduction in communication energy consumption is 19% with spiral and 17% with crinkle mapping algorithms, while reduction in communication cost is 24% and 21% whereas reduction in latency is of 24% and 22% with spiral and crinkle. Optimizing our work and the existing methods using bio-inspired technique and having the comparison among them an average energy reduction is found to be of 18% and 24%.Comment: 15 pages, 11 figure, CCSEA 201

Spectral domain ghost imaging

In the last few years,the field of ghost imaging has seen many new developments. From computational ghost imaging to 3D ghost imaging, this field has shown many interesting applications. But the method of obtaining an image in ghost imaging experiments still requires data to be recorded over long duration of time due to averaging over many shots of data. We propose a method to get the intensity correlated images in one shot by averaging over different wavelength components rather than different time components

A Nonparametric Bayesian Method for Clustering of High-Dimensional Mixed Dataset

The paper is motivated from clustering problem in high-throughput mixed datasets. Clustering of such datasets can provide much insight into biological associations. An open problem in this context is to simultaneously cluster high-dimensional mixed dataset. This paper fills that gap and proposes a nonparametric Bayesian method called Gen-VariScan for biclustering of high-dimensional mixed dataset. Gen-VariScan utilizes Generalized Linear Models (GLM), and latent variable approaches to integrate mixed dataset. We make use of Poisson Dirichlet Process (PDP) to identify a lower dimensional structure of mixed covariates. We show that covariate co-cluster detection is aposteriori consistent, as the number of subject and covariates grows. The advantage of Gen-VariScan is also demonstrated through numerical simulation and data analysis. As a byproduct, we derive a working value approach to perform beta regression. Supplementary materials for this article are available online

Statistical Analysis of Privacy and Anonymity Guarantees in Randomized Security Protocol Implementations

Security protocols often use randomization to achieve probabilistic non-determinism. This non-determinism, in turn, is used in obfuscating the dependence of observable values on secret data. Since the correctness of security protocols is very important, formal analysis of security protocols has been widely studied in literature. Randomized security protocols have also been analyzed using formal techniques such as process-calculi and probabilistic model checking. In this paper, we consider the problem of validating implementations of randomized protocols. Unlike previous approaches which treat the protocol as a white-box, our approach tries to verify an implementation provided as a black box. Our goal is to infer the secrecy guarantees provided by a security protocol through statistical techniques. We learn the probabilistic dependency of the observable outputs on secret inputs using Bayesian network. This is then used to approximate the leakage of secret. In order to evaluate the accuracy of our statistical approach, we compare our technique with the probabilistic model checking technique on two examples: crowds protocol and dining crypotgrapher's protocol

Unitarity Constraints on non-minimal Universal Extra Dimensional Model

We examine the unitarity constraints in gauge and scalar sectors of non-minimal Universal Extra Dimensional model. We show that some of the tree-level two-body scattering amplitudes in gauge and scalar sectors do not respect partial wave unitarity. Unitarity analysis of this model leads to an upper bound on corresponding boundary-localized (BLT) parameter which depends on the maximum number of Kaluza-Klein (KK) mode considered in the analysis. This upper bound of the relevant BLT parameter decreases with the increasing KK-modes. The results are, in effect, independent of the inverse of compactifiaction radius. The upper bound on BLT parameter also results in a lower bound on gauge and scalar KK-masses.Comment: 40 pages, 11 figures and 3 tables; added new discussions and figures; typos fixed; references added; matches published versio

A closed form for the generalized Bernoulli polynomials via Fa\`a di Bruno's formula

We derive a closed form for the generalized Bernoulli polynomial of order $n$ in terms of Bell polynomials and Stirling numbers of the second kind using the Fa\`a di Bruno's formula.Comment: 4 pages; No figure

`f0(600)f_0 (600)' and chiral dynamics

The role of light scalar meson `$f_0 (600)$' is investigated in nuclear matter in an Effective chiral model in the mean-field approach. For the purpose, we scan the properties of the matter at various saturation densities imposing constraint such as the vacuum value of the pion decay constant $f_{\pi} \approx 131 ~MeV$. With a simple approach, the bound on the mass of the scalar meson is calculated and found in the range $m_{\sigma} = 546 \pm 10 MeV$. Further, the present analysis show that nuclear matter favor high nucleon effective mass and dominant repulsive forces at high density, the insight and the implications of which are discussed.Comment: 8 pages, 5 Figure

Using Quantum Coherence to Enhance Gain in Atomic Physics

Quantum coherence and interference effects in atomic and molecular physics has been extensively studied due to intriguing counterintuitive physics and potential important applications. Here we present one such application of using quantum coherence to generate and enhance gain in extreme ultra-violet(XUV)(@58.4nm in Helium) and infra-red(@794.76nm in Rubidium) regime of electromagnetic radiation. We show that using moderate external coherent drive, a substantial enhancement in the energy of the lasing pulse can be achieved under optimal conditions. We also discuss the role of coherence. The present paper is intended to be pedagogical on this subject of coherence-enhanced lasing.Comment: 16 pages, 16 figures. Review Articl

Numerical Simulation guided Lazy Abstraction Refinement for Nonlinear Hybrid Automata

This draft suggests a new counterexample guided abstraction refinement (CEGAR) framework that uses the combination of numerical simulation for nonlinear differential equations with linear programming for linear hybrid automata (LHA) to perform reachability analysis on nonlinear hybrid automata. A notion of $\epsilon-$ structural robustness is also introduced which allows the algorithm to validate counterexamples using numerical simulations. Keywords: verification, model checking, hybrid systems, hybrid automata, robustness, robust hybrid systems, numerical simulation, cegar, abstraction refinement.Comment: 11 pages, 2 figure