Model Order Estimation in the Presence of multipath Interference using Residual Convolutional Neural Networks

Model order estimation (MOE) is often a pre-requisite for Direction of Arrival (DoA) estimation. Due to limits imposed by array geometry, it is typically not possible to estimate spatial parameters for an arbitrary number of sources; an estimate of the signal model is usually required. MOE is the process of selecting the most likely signal model from several candidates. While classic methods fail at MOE in the presence of coherent multipath interference, data-driven supervised learning models can solve this problem. Instead of the classic MLP (Multiple Layer Perceptions) or CNN (Convolutional Neural Networks) architectures, we propose the application of Residual Convolutional Neural Networks (RCNN), with grouped symmetric kernel filters to deliver state-of-art estimation accuracy of up to 95.2\% in the presence of coherent multipath, and a weighted loss function to eliminate underestimation error of the model order. We show the benefit of the approach by demonstrating its impact on an overall signal processing flow that determines the number of total signals received by the array, the number of independent sources, and the association of each of the paths with those sources . Moreover, we show that the proposed estimator provides accurate performance over a variety of array types, can identify the overloaded scenario, and ultimately provides strong DoA estimation and signal association performance

Supply Chain Modeling and Green Supply Chain: Literature Revue

A green supply chain should be rethought towards the term greening, whereas greening concerns in particular the environment, a lot of research works has been carried out jointly on the supply chain and the environmental dimension, exclusively supply chain modeling. This article is intended to present, first of all a summarized literature review of supply chain, green supply chain, and its modeling. Many researchers have proposed different models of green supply chain, except that each model is specific to the studied supply chain. Tending to meet this challenge the contribution of this paper is to propose a general framework of the green supply chains modeling

On the Virtualization of CUDA Based GPU Remoting on ARM and X86 Machines in the GVirtuS Framework

The astonishing development of diverse and different hardware platforms is twofold: on one side, the challenge for the exascale performance for big data processing and management; on the other side, the mobile and embedded devices for data collection and human machine interaction. This drove to a highly hierarchical evolution of programming models. GVirtuS is the general virtualization system developed in 2009 and firstly introduced in 2010 enabling a completely transparent layer among GPUs and VMs. This paper shows the latest achievements and developments of GVirtuS, now supporting CUDA 6.5, memory management and scheduling. Thanks to the new and improved remoting capabilities, GVirtus now enables GPU sharing among physical and virtual machines based on x86 and ARM CPUs on local workstations, computing clusters and distributed cloud appliances

BFKL and CCFM evolutions with saturation boundary

We perform numerical studies of the BFKL and CCFM equations for the unintegrated gluon distribution supplemented with an absorptive boundary which mimics saturation. For the BFKL equation, this procedure yields the same results for the saturation momentum and the gluon distribution above saturation as the non-linear BK equation, for both fixed and running coupling, and for all the considered energies. This similarity goes beyond expectations based on the correspondence with statistical physics, which hold only for fixed coupling and asymptotically high energies. For the CCFM equation, whose non-linear generalization is not known, our method provides the first study of the approach towards saturation. We find that, in the running-coupling case, the CCFM and BFKL predictions for the energy dependence of the saturation momentum are identical within our numerical accuracy. A similar saturation boundary could be easily implemented in the CCFM-based Monte Carlo event generators, so like CASCADE.Comment: 13 pages, 6 figure

The dynamics of US inflation: Can monetary policy explain the changes?

We investigate the relationship between monetary policy and inflation dynamics in the US using a medium scale structural model. The specification is estimated with Bayesian techniques and fits the data reasonably well. Policy shocks account for a part of the decline in inflation volatility; they have been less effective in triggering inflation responses over time and qualitatively account for the rise and fall in the level of inflation. A number of structural parameter variations contribute to these patterns.New Keynesian model, Bayesian methods, Monetary policy, Inflation dynamics.