Many animals exploit polarized light in order to calibrate their magnetic compasses for navigation. For example, some birds are equipped with biological magnetic and celestial compasses enabling them to migrate between the Western and Eastern Hemispheres. The Vikings' ability to derive true direction from polarized light is also widely accepted. However, their amazing navigational capabilities are still not completely clear. Inspired by birds' and Vikings' ancient navigational skills. Here we present a combined real-time position method based on the use of polarized light and geomagnetic field. The new method works independently of any artificial signal source with no accumulation of errors and can obtain the position and the orientation directly. The novel device simply consists of two polarized light sensors, a 3-axis compass and a computer. The field experiments demonstrate device performance.

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We apply a linear Bayesian model to seismic tomography, a highdimensional inverse problem in geophysics. The objective is to estimate the three-dimensional structure of the earth's interior from data measured at its surface. Since this typically involves estimating thousands of unknowns or more, it has always been treated as a linear(ized) optimization problem. Here we present a Bayesian hierarchical model to estimate the joint distribution of earth structural and earthquake source parameters. An ellipsoidal spatial prior allows to accommodate the layered nature of the earth's mantle. With our efficient algorithm we can sample the posterior distributions for large-scale linear inverse problems and provide precise uncertainty quantification in terms of parameter distributions and credible intervals given the data. We apply the method to a full-fledged tomography problem, an inversion for upper-mantle structure under western North America that involves more than 11,000 parameters. In studies on simulated and real data, we show that our approach retrieves the major structures of the earth's interior as well as classical least-squares minimization, while additionally providing uncertainty assessments.

Unmanned aerial vehicles (UAVs) have been widely utilized to improve the end-to-end performance of wireless communications. However, its line-of-sight makes UAV communication vulnerable to malicious eavesdroppers. In this paper, we propose two cooperative dual-UAV enabled secure data collection schemes to ensure security, with the practical propulsion energy consumption considered. We first maximize the worst-case average secrecy rate with the average propulsion power limitation, where the scheduling, the transmit power, the trajectory and the velocity of the two UAVs are jointly optimized. To solve the non-convex multivariable problem, we propose an iterative algorithm based on block coordinate descent and successive convex approximation. To further save the on-board energy and prolong the flight time, we then maximize the secrecy energy efficiency of UAV data collection, which is a fractional and mixed integer nonlinear programming problem. Based on the Dinkelbach method, we transform the objective function into an integral expression and propose an iterative algorithm to obtain a suboptimal solution to secrecy energy efficiency maximization. Numerical results show that the average secrecy rate is maximized in the first scheme with propulsion limitation, while in the second scheme, the secrecy energy efficiency is maximized with the optimal velocity to save propulsion power and improve secrecy rate simultaneously.

Non-contrast CT (NCCT) is widely employed as the first-line imaging test to evaluate intracranial hemorrhage (ICH). Advances in mutidetector CT (MDCT) technology have greatly improved the image quality of NCCT for the detection of established, relatively large, and acute ICHs. Meanwhile, the reliability of MDCT in detecting microbleeds and chronic hemorrhage, and in predicting hemorrhagic transformation needs to be further improved. The purpose of this work was to investigate the potential use of non-spectral photon counting CT (PCCT) to address these challenges in ICH imaging. Towards this goal, the NCCT protocol of an experimental PCCT system that simulates the geometry of a general-purpose MDCT was optimized. The optimization was driven by three imaging tasks: detection of a 4.0 mm intraparenchymal hemorrhage, detection of a 1.5 mm subarachnoid hemorrhage, and discrimination of a sulcus in the insular cortex from the parenchymal background. These imaging tasks were custom-built into an anthropomorphic head phantom. Under the guidance of the frequency-dependent noise equivalent quanta and the ideal observer model detectability index d′, the optimal PCD detection mode, energy threshold, and reconstruction kernel were found to be the anti-charge sharing mode, 15 keV, and an apodized ramp kernel, respectively. Compared with a clinical MDCT operated with an ICH protocol and at a matched dose level, the PCCT system provided at least 20% improvements in d′ for all three ICH imaging tasks. These results demonstrated the potential benefits of non-spectral PCCT in ICH assessment.

Purpose: In grating-based x-ray multi-contrast imaging, signals of three contrast mechanisms-absorption contrast, differential phase contrast (DPC), and dark-field contrast-can be estimated from the same set of acquired data. The estimated signals, N0 (related to absorption), N1 (related to dark-field), and φ (related to DPC) may be intrinsically biased. However, it is yet unclear how large these biases are and how the data acquisition parameters affect the biases in the extracted signals. The purpose of this paper was to address these questions. Methods: The biases of the extracted signals (i.e., N0, N1 and φ) were theoretically studied for a well-known signal estimation method. Experimental data acquired from a grating-based x-ray multi-contrast benchtop imaging system with a photon counting detector were used to validate the theoretical results for the signal biases of the three contrast mechanisms. Results: Both theoretical and experimental studies showed the following results: (1) The bias of signal estimation for the absorption contrast signal is zero; (2) The bias of signal estimation for N1 is inversely proportional to the number of phase steps and to the average fringe visibility of the grating interferometer, but the ratio between the bias and the signal level (i.e., the relative bias) is independent of the number of phase steps; (3) The bias of signal estimation for φ depends on the mean DPC signal level, the total exposure level of the multi-contrast data acquisition, and the mean fringe visibility of the interferometer. Conclusions: In grating-based x-ray multi-contrast imaging, the estimated absorption contrast signal is unbiased; the estimated dark-field contrast signal is biased, but the relative bias is only dependent on the mean fringe visibility of the interferometer and the exposure level. The estimated DPC signal may be biased, and the bias level depends on the mean signal level, the exposure level, and the interferometer performance.

Fuel or oxidant composites were trapped in the holes of poly(azide glycidyl ether) (GAP) gel skeleton network on nano-scale, which could effectively increase the contact area, decrease the transport distance, and make the energy release more close to the ideal state to achieve the maximum power of energetic materials. In this study, GAP gels with three dimensional nano-network structures were prepared by sol-gel method using GAP as precursors and hexamethylene diisocyanate (HDI) as curing agent. The obtained gels were well characterized by Brunauer-Emmett-Teller (BET).The results showed that the specific surface area and dominant pore size were about 41.78 m 2 /g and 5~30 nm, respectively. Hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) could be crystallized in the pore of GAP gel skeleton, so RDX/GAP nanocomposite materials were prepared by solute crystallization in combination with a modified drying technology. The average grain size of RDX in GAP network was 20~46 nm. With the increase of the loadings of RDX, the specific surface area of GAP/RDX nano-composite materials decreased, and the thermal decomposition temperature of RDX in RDX/GAP nanocomposite materials decreased by 33~37°C. The decomposition heat and explosion heat of RDX(40 wt%)/GAP nanocomposite materials were higher by over 13.9 and 19.3 % than those of RDX(40 wt%)/GAP physical blend materials, respectively. Furthermore, the sensitivity of RDX(40 wt%)/GAP nano-composite materials was lower than that of physical blend materials according to the results from our impact sensitivity test.