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.