Fast Desynchronization For Decentralized Multichannel Medium Access Control

Distributed desynchronization algorithms are key to wireless sensor networks as they allow for medium access control in a decentralized manner. In this paper, we view desynchronization primitives as iterative methods that solve optimization problems. In particular, by formalizing a well established desynchronization algorithm as a gradient descent method, we establish novel upper bounds on the number of iterations required to reach convergence. Moreover, by using Nesterov's accelerated gradient method, we propose a novel desynchronization primitive that provides for faster convergence to the steady state. Importantly, we propose a novel algorithm that leads to decentralized time-synchronous multichannel TDMA coordination by formulating this task as an optimization problem. Our simulations and experiments on a densely-connected IEEE 802.15.4-based wireless sensor network demonstrate that our scheme provides for faster convergence to the steady state, robustness to hidden nodes, higher network throughput and comparable power dissipation with respect to the recently standardized IEEE 802.15.4e-2012 time-synchronized channel hopping (TSCH) scheme.Comment: to appear in IEEE Transactions on Communication

Modeling Camera Effects to Improve Visual Learning from Synthetic Data

Recent work has focused on generating synthetic imagery to increase the size and variability of training data for learning visual tasks in urban scenes. This includes increasing the occurrence of occlusions or varying environmental and weather effects. However, few have addressed modeling variation in the sensor domain. Sensor effects can degrade real images, limiting generalizability of network performance on visual tasks trained on synthetic data and tested in real environments. This paper proposes an efficient, automatic, physically-based augmentation pipeline to vary sensor effects --chromatic aberration, blur, exposure, noise, and color cast-- for synthetic imagery. In particular, this paper illustrates that augmenting synthetic training datasets with the proposed pipeline reduces the domain gap between synthetic and real domains for the task of object detection in urban driving scenes

Analytic Conditions for Energy Neutrality in Uniformly-Formed Wireless Sensor Networks

Future deployments of wireless sensor network (WSN) infrastructures for environmental or event monitoring are expected to be equipped with energy harvesters (e.g. piezoelectric, thermal, photovoltaic) in order to substantially increase their autonomy. In this paper we derive conditions for energy neutrality, i.e. perpetual energy autonomy per sensor node, by balancing the node's expected energy consumption with its expected energy harvesting capability. Our analysis assumes a uniformly-formed WSN, i.e. a network comprising identical transmitter sensor nodes and identical receiver/relay sensor nodes with a balanced cluster-tree topology. The proposed framework is parametric to: (i) the duty cycle for the network activation; (ii) the number of nodes in the same tier of the cluster-tree topology; (iii) the consumption rate of the receiver node(s) that collect (and possibly relay) data along with their own; (iv) the marginal probability density function (PDF) characterizing the data transmission rate per node; (v) the expected amount of energy harvested by each node. Based on our analysis, we obtain the number of nodes leading to the minimumenergy harvestingrequirement for each tier of the WSN cluster-tree topology. We also derive closed-form expressions for the difference in the minimum energy harvesting requirements between four transmission rate PDFs in function of the WSN parameters. Our analytic results are validated via experiments using TelosB sensor nodes and an energy measurement testbed. Our framework is useful for feasibility studies on energy harvesting technologies in WSNs and for optimizing the operational settings of hierarchical WSN-based monitoring infrastructures prior to time-consuming testing and deployment within the application environment

Electron-hadron shower discrimination in a liquid argon time projection chamber

By exploiting structural differences between electromagnetic and hadronic showers in a multivariate analysis we present an efficient Electron-Hadron discrimination algorithm for liquid argon time projection chambers, validated using Geant4 simulated data

Neutrino Quasielastic Scattering on Nuclear Targets: Parametrizing Transverse Enhancement (Meson Exchange Currents)

We present a parametrization of the observed enhancement in the transverse electron quasielastic (QE) response function for nucleons bound in carbon as a function of the square of the four momentum transfer ($Q^2$) in terms of a correction to the magnetic form factors of bound nucleons. The parametrization should also be applicable to the transverse cross section in neutrino scattering. If the transverse enhancement originates from meson exchange currents (MEC), then it is theoretically expected that any enhancement in the longitudinal or axial contributions is small. We present the predictions of the "Transverse Enhancement" model (which is based on electron scattering data only) for the $\nu_\mu, \bar{\nu}_\mu$ differential and total QE cross sections for nucleons bound in carbon. The $Q^2$ dependence of the transverse enhancement is observed to resolve much of the long standing discrepancy in the QE total cross sections and differential distributions between low energy and high energy neutrino experiments on nuclear targets.Comment: Revised Version- July 21, 2011: 17 pages, 20 Figures. To be published in Eur. Phys. J.

A New Upper Limit for the Tau-Neutrino Magnetic Moment

Using a prompt neutrino beam in which a nu_tau component was identified for the first time, the nu_tau magnetic moment was measured based on a search for an anomalous increase in the number of neutrino-electron interactions. One such event was observed when 2.3 were expected from background processes, giving an upper 90% confidence limit of 3.9x10^-7 Bohr magnetons.Comment: 9 pages; 1 figur

A first measurement of the interaction cross section of the tau neutrino

The DONuT experiment collected data in 1997 and published first results in 2000 based on four observed $\nu_\tau$ charged-current (CC) interactions. The final analysis of the data collected in the experiment is presented in this paper, based on $3.6 \times 10^{17}$ protons on target using the 800 GeV Tevatron beam at Fermilab. The number of observed $\nu_\tau$ CC interactions is 9, from a total of 578 observed neutrino interactions. We calculated the energy-independent part of the tau-neutrino CC cross section ($\nu + \bar \nu$), relative to the well-known $\nu_e$ and $\nu_\mu$ cross sections. The ratio $\sigma(\nu_\tau)$/$\sigma(\nu_{e,\mu})$ was found to be $1.37\pm0.35\pm0.77$. The $\nu_\tau$ CC cross section was found to be $0.72 \pm 0.24\pm0.36 \times 10^{-38}$ cm$^{2}\rm{GeV}^{-1}$. Both results are in agreement the Standard Model.Comment: 37 pages, 15 figure

Complexity-Scalable Neural Network Based MIMO Detection With Learnable Weight Scaling

This paper introduces a framework for systematic complexity scaling of deep neural network (DNN) based MIMO detectors. The model uses a fraction of the DNN inputs by scaling their values through weights that follow monotonically non-increasing functions. This allows for weight scaling across and within the different DNN layers in order to achieve scalable complexity-accuracy results. To reduce complexity further, we introduce a regularization constraint on the layer weights such that, at inference, parts (or the entirety) of network layers can be removed with minimal impact on the detection accuracy. We also introduce trainable weight-scaling functions for increased robustness to changes in the activation patterns and a further improvement in the detection accuracy at the same inference complexity. Numerical results show that our approach is 10 and 100-fold less complex than classical approaches based on semi-definite relaxation and ML detection, respectively

Measurement of the underground atmospheric muon charge ratio using the MINOS Near Detector

The magnetized MINOS Near Detector, at a depth of 225 mwe, is used to measure the atmospheric muon charge ratio. The ratio of observed positive to negative atmospheric muon rates, using 301 days of data, is measured to be 1.266±0.001(stat)_(-0.014)^(+0.015)(syst). This measurement is consistent with previous results from other shallow underground detectors and is 0.108±0.019(stat+syst) lower than the measurement at the functionally identical MINOS Far Detector at a depth of 2070 mwe. This increase in charge ratio as a function of depth is consistent with an increase in the fraction of muons arising from kaon decay for increasing muon surface energie

Measurement of the Neutrino Mass Splitting and Flavor Mixing by MINOS

Measurements of neutrino oscillations using the disappearance of muon neutrinos from the Fermilab NuMI neutrino beam as observed by the two MINOS detectors are reported. New analysis methods have been applied to an enlarged data sample from an exposure of 7.25×10^(20) protons on target. A fit to neutrino oscillations yields values of |Δm^2|=(2.32_(-0.08)^(+0.12))×10^(-3)  eV^2 for the atmospheric mass splitting and sin^2(2θ)>0.90 (90% C.L.) for the mixing angle. Pure neutrino decay and quantum decoherence hypotheses are excluded at 7 and 9 standard deviations, respectively