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Study of the plastic flow and strain-induced phase transformations (PTs) under high pressure with diamond anvils is important for material and geophysics. We introduce rough diamond anvils and apply them to Zr, which drastically change the plastic flow, microstructure, and PTs. Multiple steady microstructures independent of pressure, plastic strain, and strain path are reached. Maximum friction equal to the yield strength in shear is achieved. This allows determination of the pressure-dependence of the yield strength and proves that ω-Zr behaves like perfectly plastic, isotropic, and strain path-independent immediately after PT. Record minimum pressure for α-ω PT was identified. Kinetics of strain-induced PT depends on plastic strain and time. Crystallite size and dislocation density in ω-Zr during PT depend solely on the volume fraction of ω-Zr.

Image registration is broadly used in various scenarios in which similar scenes in different images are to be aligned. However, image registration becomes challenging when the contrasts and backgrounds in the images are vastly different. This work proposes using the total variation of the difference map between two images (TVDM) as a dissimilarity metric in rigid registration. A method based on TVDM minimization is implemented for image rigid registration. The method is tested with both synthesized and real experimental data that have various noise and background conditions. The performance of the proposed method is compared with the results of other rigid registration methods. It is demonstrated that the proposed method is highly accurate and robust and outperforms other methods in all of the tests. The new algorithm provides a robust option for image registrations that are critical to many nano-scale X-ray imaging and microscopy applications.

As the drone becomes widespread in numerous crucial applications with many powerful functionalities (e.g., reconnaissance and mechanical trigger), there are increasing cases related to misused drones for unethical even criminal activities. Therefore, it is of paramount importance to identify these malicious drones and track their origins using digital forensics. Traditional drone identification techniques for forensics (e.g., RF communication, ID landmarks using a camera, etc.) require high compliance of drones. However, malicious drones will not cooperate or even spoof these identification techniques. Therefore, we present an exploration for a reliable and passive identification approach based on unique hardware traits in drones directly (e.g., analogous to the fingerprint and iris in humans) for forensics purposes. Specifically, we investigate and model the behavior of the parasitic electronic elements under RF interrogation, a particular passive parasitic response modulated by an electronic system on drones, which is distinctive and unlikely to counterfeit. Based on this theory, we design and implement DroneTrace, an end-to-end reliable and passive identification system toward digital drone forensics. DroneTrace comprises a cost-effective millimeter-wave (mmWave) probe, a software framework to extract and process parasitic responses, and a customized deep neural network (DNN)-based algorithm to analyze and identify drones. We evaluate the performancemore »of DroneTrace with 36 commodity drones. Results show that DroneTrace can identify drones with the accuracy of over 99% and an equal error rate (EER) of 0.009, under a 0.1-second sensing time budget. Moreover, we test the reliability, robustness, and performance variation under a set of real-world circumstances, where DroneTrace maintains accuracy of over 98%. DroneTrace is resilient to various attacks and maintains functionality. At its best, DroneTrace has the capacity to identify individual drones at the scale of 104 with less than 5% error.« less

Ferropericlase is the second most abundant mineral in the Earth’s lower mantle and its mechanical properties have a strong influence on the rheology of this region. Here, we deform polycrystalline MgO, the magnesium end-member of ferropericlase, at conditions ranging from 1.6 to 8.3 GPa and 875–1,270 K. We analyse the flow laws and microstructures of the recovered samples using electron microscopy and compare our observations with predictions from the literature. We identify a first mechanism for samples deformed at 1,270 K, attributed to a regime controlled by grain boundary sliding accommodated by diffusion, and characterized by a small grain size, an absence of texture, and no intracrystalline deformation. At 1,070 K and below, the deformation regime is controlled by dislocations. The samples show a more homogeneous grain size distribution, significant texture, and intracrystalline strains. In this regime, deformation is controlled by the ⟨110⟩{110} slip system and a combined ⟨110⟩{110} and ⟨110⟩{100} slip, depending on pressure and temperature. Based on these results, we propose an updated deformation map for polycrystalline MgO at mantle conditions. The implications for ferropericlase and seismic observations in the Earth’s lower mantle are discussed.

A transmission X-ray microscope (TXM) can investigate morphological and chemical information of a tens to hundred micrometre-thick specimen on a length scale of tens to hundreds of nanometres. It has broad applications in material sciences and battery research. TXM data processing is composed of multiple steps. A workflow software has been developed that integrates all the tools required for general TXM data processing and visualization. The software is written in Python and has a graphic user interface in Jupyter Notebook . Users have access to the intermediate analysis results within Jupyter Notebook and have options to insert extra data processing steps in addition to those that are integrated in the software. The software seamlessly integrates ImageJ as its primary image viewer, providing rich image visualization and processing routines. As a guide for users, several TXM specific data analysis issues and examples are also presented.