Probability weighted four-point arc imaging algorithm for time-reversed lamb wave damage detection

Damage imaging based on scattering signals of ultrasonic Lamb waves in plate structure is considered as one of the most effective ways for structural health monitoring area. To improve location accuracy and reduce the impact of artifacts, a probability weighted four-point arc imaging algorithm for time reversal Lamb wave damage detection is proposed in this paper. By taking the defect as a secondary wave source, the four-point arc positioning method is used to calculate the propagation time of the signal from transducer to defect. And the amplitude of damage signal corresponding to the time of flight is used for imaging. In order to eliminate the artifacts, a damage probability weighting is combined with four-point circular arc imaging algorithm. The effectiveness of the proposed method is experimentally verified in aluminum plate. Experimental results indicate that damage location accuracy and imaging quality has been improved in both single-flaw and double-flaw samples compared with conventional delay-and-sum method

Multi-Antenna Assisted Virtual Full-Duplex Relaying with Reliability-Aware Iterative Decoding

In this paper, a multi-antenna assisted virtual full-duplex (FD) relaying with reliability-aware iterative decoding at destination node is proposed to improve system spectral efficiency and reliability. This scheme enables two half-duplex relay nodes, mimicked as FD relaying, to alternatively serve as transmitter and receiver to relay their decoded data signals regardless the decoding errors, meanwhile, cancel the inter-relay interference with QR-decomposition. Then, by deploying the reliability-aware iterative detection/decoding process, destination node can efficiently mitigate inter-frame interference and error propagation effect at the same time. Simulation results show that, without extra cost of time delay and signalling overhead, our proposed scheme outperforms the conventional selective decode-and-forward (S-DF) relaying schemes, such as cyclic redundancy check based S-DF relaying and threshold based S-DF relaying, by up to 8 dB in terms of bit-error-rate.Comment: 6 pages, 4 figures, conference paper has been submitte

Neutrino oscillations: Quantum mechanics vs. quantum field theory

A consistent description of neutrino oscillations requires either the quantum-mechanical (QM) wave packet approach or a quantum field theoretic (QFT) treatment. We compare these two approaches to neutrino oscillations and discuss the correspondence between them. In particular, we derive expressions for the QM neutrino wave packets from QFT and relate the free parameters of the QM framework, in particular the effective momentum uncertainty of the neutrino state, to the more fundamental parameters of the QFT approach. We include in our discussion the possibilities that some of the neutrino's interaction partners are not detected, that the neutrino is produced in the decay of an unstable parent particle, and that the overlap of the wave packets of the particles involved in the neutrino production (or detection) process is not maximal. Finally, we demonstrate how the properly normalized oscillation probabilities can be obtained in the QFT framework without an ad hoc normalization procedure employed in the QM approach.Comment: LaTeX, 42 pages, 1 figure; v2: minor clarifications, matches published version; v3: Corrected the discussion of the conditions under which an oscillation probability can be sensibly defined in the QFT approach (sec. 5.2.4

On Efficiently Detecting Overlapping Communities over Distributed Dynamic Graphs

Modern networks are of huge sizes as well as high dynamics, which challenges the efficiency of community detection algorithms. In this paper, we study the problem of overlapping community detection on distributed and dynamic graphs. Given a distributed, undirected and unweighted graph, the goal is to detect overlapping communities incrementally as the graph is dynamically changing. We propose an efficient algorithm, called \textit{randomized Speaker-Listener Label Propagation Algorithm} (rSLPA), based on the \textit{Speaker-Listener Label Propagation Algorithm} (SLPA) by relaxing the probability distribution of label propagation. Besides detecting high-quality communities, rSLPA can incrementally update the detected communities after a batch of edge insertion and deletion operations. To the best of our knowledge, rSLPA is the first algorithm that can incrementally capture the same communities as those obtained by applying the detection algorithm from the scratch on the updated graph. Extensive experiments are conducted on both synthetic and real-world datasets, and the results show that our algorithm can achieve high accuracy and efficiency at the same time.Comment: A short version of this paper will be published as ICDE'2018 poste

Optimal Sensors Placement to Enhance Damage Detection in Composite Plates

This paper examines an important challenge in ultrasonic structural health monitoring (SHM), which is the problem of the optimal placement of sensors in order to accurately detect and localize damages. The goal of this study is to enhance damage detection through an optimal sensor placement (OSP) algorithm. The problem is formulated as a global optimization problem, where the objective function to be maximized is evaluated by a ray tracing approach, which approximately models Lamb waves propagation. A genetic algorithm (GA) is then used to solve this optimization problem. Simulations and experiments were conducted to validate the proposed method on a carbon epoxy composite plate. Results show the effectiveness and the advantages of the proposed method as a tool for OSP with reasonable computation time.Projet AIRCELLE (EPICE/CORALIE

Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data

Many systems are partially stochastic in nature. We have derived data driven approaches for extracting stochastic state machines (Markov models) directly from observed data. This chapter provides an overview of our approach with numerous practical applications. We have used this approach for inferring shipping patterns, exploiting computer system side-channel information, and detecting botnet activities. For contrast, we include a related data-driven statistical inferencing approach that detects and localizes radiation sources.Comment: Accepted by 2017 International Symposium on Sensor Networks, Systems and Securit