Polarization Correlations of 1S0 Proton Pairs as Tests of Bell and Wigner Inequalities

In an experiment designed to overcome the loophole of observer dependent reality and satisfying the counterfactuality condition, we measured polarization correlations of 1S0 proton pairs produced in 12C(d,2He) and 1H(d,He) reactions in one setting. The results of these measurements are used to test the Bell and Wigner inequalties against the predictions of quantum mechanics.Comment: 8 pages, 4 figure

Spin observables in deuteron-proton radiative capture at intermediate energies

A radiative deuteron-proton capture experiment was carried out at KVI using polarized-deuteron beams at incident energies of 55, 66.5, and 90 MeV/nucleon. Vector and tensor-analyzing powers were obtained for a large angular range. The results are interpreted with the help of Faddeev calculations, which are based on modern two- and three-nucleon potentials. Our data are described well by the calculations, and disagree significantly with the observed tensor anomaly at RCNP.Comment: 10 pages, 4 figures, submitted to PL

Alpha-decay branching ratios of near-threshold states in 19Ne and the astrophysical rate of 15O(alpha,gamma)19Ne

The 15O(alpha,gamma)19Ne reaction is one of two routes for breakout from the hot CNO cycles into the rp process in accreting neutron stars. Its astrophysical rate depends critically on the decay properties of excited states in 19Ne lying just above the 15O + alpha threshold. We have measured the alpha-decay branching ratios for these states using the p(21Ne,t)19Ne reaction at 43 MeV/u. Combining our measurements with previous determinations of the radiative widths of these states, we conclude that no significant breakout from the hot CNO cycle into the rp process in novae is possible via 15O(alpha,gamma)19Ne, assuming current models accurately represent their temperature and density conditions

Online extreme learning on fixed-point sensor networks

Anomaly detection is a key factor in the processing of large amounts of sensor data from Wireless Sensor Networks (WSN). Efficient anomaly detection algorithms can be devised performing online node-local computations and reducing communication overhead, thus improving the use of the limited hardware resources. This work introduces a fixed-point embedded implementation of Online Sequential Extreme Learning Machine (OS-ELM), an online learning algorithm for Single Layer Feed forward Neural Networks (SLFN). To overcome the stability issues introduced by the fixed precision, we apply correction mechanisms previously proposed for Recursive Least Squares (RLS). The proposed implementation is tested extensively with generated and real-world datasets, and compared with RLS, Linear Least Squares Estimation, and a rule-based method as benchmarks. The methods are evaluated on the prediction accuracy and on the detection of anomalies. The experimental results demonstrate that fixed-point OS-ELM can be successfully implemented on resource-limited embedded systems, with guarantees of numerical stability. Furthermore, the detection accuracy of fixed-point OS-ELM shows better generalization properties in comparison with, for instance, fixed-point RLS. © 2013 IEEE

Online extreme learning on fixed-point sensor networks

Anomaly detection is a key factor in the processing of large amounts of sensor data from Wireless Sensor Networks (WSN). Efficient anomaly detection algorithms can be devised performing online node-local computations and reducing communication overhead, thus improving the use of the limited hardware resources. This work introduces a fixed-point embedded implementation of Online Sequential Extreme Learning Machine (OS-ELM), an online learning algorithm for Single Layer Feedforward Neural Networks (SLFN). To overcome the stability issues introduced by the fixed precision, we apply correction mechanisms previously proposed for Recursive Least Squares (RLS). The proposed implementation is tested extensively with generated and real-world datasets, and compared with RLS, Linear Least Squares Estimation, and a rule-based method as benchmarks. The methods are evaluated on the prediction accuracy and on the detection of anomalies. The experimental results demonstrate that fixed-point OS-ELM can be successfully implemented on resource-limited embedded systems, with guarantees of numerical stability. Furthermore, the detection accuracy of fixed-point OS-ELM shows better generalization properties in comparison with, for instance, fixed-point RLS

Anomaly detection in sensor systems using lightweight machine learning

The maturing field of Wireless Sensor Networks (WSN) results in long-lived deployments that produce large amounts of sensor data. Lightweight online on-mote processing may improve the usage of their limited resources, such as energy, by transmitting only unexpected sensor data (anomalies). We detect anomalies by analyzing sensor reading predictions from a linear model. We use Recursive Least Squares (RLS) to estimate the model parameters, because for large datasets the standard Linear Least Squares Estimation (LLSE) is not resource friendly. We evaluate the use of fixed-point RLS with adaptive thresholding, and its application to anomaly detection in embedded systems. We present an extensive experimental campaign on generated and real-world datasets, with floating-point RLS, LLSE, and a rule-based method as benchmarks. The methods are evaluated on prediction accuracy of the models, and on detection of anomalies, which are injected in the generated dataset. The experimental results show that the proposed algorithm is comparable, in terms of prediction accuracy and detection performance, to the other LS methods. However, fixed-point RLS is efficiently implementable in embedded devices. The presented method enables online on-mote anomaly detection with results comparable to offline LS methods

Sensor motes for the exploration and monitoring of operational pipelines

We report on a first field test in which miniaturized sensor motes were used to explore and inspect an operational pipeline by performing in situ measurements. The spherical sensor motes with a diameter of 39 mm were equipped with an inertial measurement unit (IMU) measuring 3-D acceleration, rotation, and magnetic field, as well as an ultrasound emitter. The motes were injected into the pipeline and traversed a 260-m section of it with the flow of water. After the extraction of the motes from the pipeline, the recorded IMU data were read out for the off-line analysis. Unlike dead-reckoning techniques, we analyze the IMU data to reveal structural information about the pipeline and locate pipe components, such as hydrants and junctions. The recorded data show different and distinct patterns that are a result of the fluid dynamics and the interaction with the pipeline. Using the magnetic data, pipe sections made from different materials and pipe components are identified and localized. A preliminary analysis on the motes' interaction with the pipeline shows differences in pipe wall roughness and locates structural anomalies. The results of this field test show that sensor motes can be used as a versatile and cost-effective tool for exploration and inspection of a wide variety of pipelines