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Clock skew compensation is essential for accurate time synchronization in wireless networks. However, contemporary clock skew estimation is based on inaccurate transmission time measurement, which makes credible estimation challenging. Based on one-way broadcast synchronization, this study presents a novel maximum likelihood estimation (MLE) with an innovative implementation to minimize the clock skew estimation error caused by delay. A multiple one-way broadcast model is developed for observation and set collection, while the distribution of actual delay is discussed. A clock skew MLE based on Gaussian delay is then proposed and its Cramer-Rao lower bound provided. The developed MLE is independent of the clock offset estimation, and requires only two synchronization periods to generate an accurate estimation. Extensive experimental results indicate that the performance of the proposed MLE is better than the methods popularly used in existing time synchronization protocols., Comment: 10 pages, Journal

Average consensus theory is intensely popular for building time synchronization in wireless sensor network (WSN). However, the average consensus-based time synchronization algorithm is based on iteration that pose challenges for efficiency, as they entail high communication cost and long convergence time in large-scale WSN. Based on the suggestion that the greater the algebraic connectivity leads to the faster the convergence, a novel multi-hop average consensus time synchronization (MACTS) is developed with innovative implementation in this paper. By employing multi-hop communication model, it shows that virtual communication links among multi-hop nodes are generated and algebraic connectivity of network increases. Meanwhile, a multihop controller is developed to balance the convergence time, accuracy and communication complexity. Moreover, the accurate relative clock offset estimation is yielded by delay compensation. Implementing the MACTS based on the popular one-way broadcast model and taking multi-hop over short distances, we achieve hundreds of times the MACTS convergence rate compared to ATS., Comment: 10 pages, Journal

Accurate and fast-convergent time synchronization is very important for wireless sensor networks. The flooding time synchronization converges fast, but its transmission delay and by-hop error accumulation seriously reduce the synchronization accuracy. In this article, a rapidflooding multiple one-way broadcast time-synchronization (RMTS) protocol for large-scale wireless sensor networks is proposed. To minimize the by-hop error accumulation, the RMTS uses maximum likelihood estimations for clock skew estimation and clock offset estimation, and quickly shares the estimations among the networks. As a result, the synchronization error resulting from delays is greatly reduced, while faster convergence and higher-accuracy synchronization is achieved. Extensive experimental results demonstrate that, even over 24-hops networks, the RMTS is able to build accurate synchronization at the third synchronization period, and moreover, the by-hop error accumulation is slower when the network diameter increases., Comment: 10 pages, 10 figures, Journal

One-way-broadcast based flooding time synchronization algorithms are commonly used in wireless sensor networks (WSNs). However, the packet delay and clock drift pose challenges to accuracy, as they entail serious by-hop error accumulation problems in the WSNs. To overcome it, a rapid flooding multi-broadcast time synchronization with real-time delay compensation (RDC-RMTS) is proposed in this paper. By using a rapid-flooding protocol, flooding latency of the referenced time information is significantly reduced in the RDC-RMTS. In addition, a new joint clock skew-offset maximum likelihood estimation is developed to obtain the accurate clock parameter estimations, and the real-time packet delay estimation. Moreover, an innovative implementation of the RDC-RMTS is designed with an adaptive clock offset estimation. The experimental results indicate that, the RDC-RMTS can easily reduce the variable delay and significantly slow the growth of by-hop error accumulation. Thus, the proposed RDC-RMTS can achieve accurate time synchronization in large-scale complex WSNs., Comment: 14 pages, 11 figures

A self-adaptive differential evolution neutron spectrum unfolding algorithm (SDENUA) was established in this paper to unfold the neutron spectra obtained from a Water-pumping-injection Multi-layered concentric sphere Neutron Spectrometer (WMNS). Specifically, the neutron fluence bounds were estimated to accelerate the algorithm convergence, the minimum error between the optimal solution and the input neutron counts with relative uncertainties was limited to 10-6 to avoid useless calculation. Furthermore, the crossover probability and scaling factor were controlled self-adaptively. FLUKA Monte Carlo was used to simulate the readings of the WMNS under (1) a spectrum of Cf-252 and (2) its spectrum after being moderated, (3) a spectrum used for BNCT, and (4) a reactor spectrum, and the measured neutron counts unfolded by using the SDENUA. The uncertainties of the measured neutron count and the response matrix are considered in the SDENUA, which does not require complex parameter tuning and the priori default spectrum. Results indicate that the solutions of the SDENUA are more in agreement with the IAEA spectra than that of the MAXED and GRAVEL in UMG 3.1, and the errors of the final results calculated by SDENUA are under 12%. The established SDENUA has potential applications for unfolding spectra from the WMNS.

When evolution algorithms are used to unfold the neutron energy spectrum, fitness function design is an important fundamental work for evaluating the quality of the solution, but it has not attracted much attention. In this work, we investigated the performance of eight fitness functions attached to the genetic algorithm (GA) and the differential evolution algorithm (DEA) used for unfolding four neutron spectra selected from the IAEA 403 report. Experiments show that the fitness functions with a maximum in the GA can limit the ability of the population to percept the fitness change, but the ability can be made up in the DEA. The fitness function with a feature penalty term helps to improve the performance of solutions, and the fitness function using the standard deviation and the Chi-squared result shows the balance between the algorithm and the spectra. The results also show that the DEA has good potential for neutron energy spectrum unfolding. The purposes of this work are to provide evidence for structuring and modifying the fitness functions and to suggest some genetic operations that should receive attention when using the fitness function to unfold neutron spectra., Comment: 12 pages,5 figures

Superparamagnetic {\gamma}-Fe2O3 nanoparticles (5 nm diameter) were synthesized in water. The bare particles exhibit good colloidal stability at ~ pH 2 because of the strong electrostatic repulsion with a surface charge of +25 mV. The polyacrylic acid (PAA)-coated particles exhibit remarkable colloidal stability at ~ pH 7 with abundant free carboxyl groups as reactive sites for subsequent functionalization. In this work, we used zeta potential analysis, transmission electron microscopy, small angle X-ray scattering, and Inductively coupled plasma mass spectrometry to investigate the adsorption behavior of U (VI) on bare and coated colloidal superparamagnetic nanoparticles at pH 2 and pH 7. At pH 2, uranyl ion (UO22+) absorbed on the surface of the bare particles with decreasing particle surface charge. This induced particle agglomeration. At pH 7, uranyl ion (UO22+) hydrolyzed and formed plate-like particles of uranium hydroxide that were ~ 50 nm in diameter. The PAA-coated iron oxide nanoparticles absorbed on the surface of these U (VI) hydroxide plates to form large aggregates that precipitate to the bottom of the dispersion. At both pH 2 and pH 7, the resulting U (VI)/nanoparticle complex can be easily collected and extracted from the aqueous environment via an external magnetic field. The results show that both bare and polymer-coated superparamagnetic {\gamma}-Fe2O3 nanoparticles are potential absorbents for removing U (VI) from water.

There is a key research issue to accurately select out neutron signals and discriminate gamma signals from a mixed radiation field in the neutron detection. This paper proposes a fractal spectrum discrimination approach by means of different spectrum characteristics of neutron and gamma. Figure of merit and average discriminant error ratio are adopted together to evaluate the discriminant effects. Different neutron and gamma signals with various noises and pulse pile-ups are simulated according to real data in the literature. The proposed approach is compared with the digital charge integration and pulse gradient methods. It is found that the fractal approach exhibits the best discriminant performance among three methods. The fractal spectrum approach is not sensitive to the high frequency noises and pulse pile-ups. It means that the proposed approach takes the advantages of anti-noises and high discriminant ability, and can be used to better discriminate neutron and gamma in neutron detection., Comment: 10 pages, 8 figures, 6 tables in Chinese Physics C 2016

Monte Carlo simulations were carried out to design a prompt {\gamma}-ray neutron activation analysis (PGNAA) thermal neutron output setup using MCNP5 computer code. In these simulations the moderator materials, reflective materials and structure of the PGNAA 252Cf neutrons of thermal neutron output setup were optimized. Results of the calcuations revealed that the thin layer paraffin and the thick layer of heavy water moderated effect is best for 252Cf neutrons spectrum. The new design compared with the conventional neutron source design, the thermal neutron flux and rate were increased by 3.02 times and 3.27 times. Results indicate that the use of this design should increase the neutron flux of prompt gamma-ray neutron activation analysis significantly., Comment: 7 pages,22figures