We provide new data and insights into a 6 February 2018 $Mw$ 6.4 earthquake that shook the city of Hualien in eastern Taiwan at the leading edge of a modern arc–continent collision. Fatalities and damages were concentrated near the Milun fault and extended south to the northern Longitudinal Valley fault. Although the Hualien area has one of the highest rates of seismicity in Taiwan, the geologic structures responsible for active deformation were not well understood before this event. We analyzed Interferometric Synthetic Aperture Radar (InSAR) and Global Positioning System (GPS) data and produced a 3D displacement model with InSAR and azimuth offset of radar images to document surface deformation induced by this earthquake. The 3D displacement model was inverted to estimate slip on the Milun fault. We find that models assuming a single fault are inconsistent with observations of coseismic deformation and regional strain patterns, providing evidence for linked slip on a little‐studied offshore thrust belt. Based on data presented here and elsewhere, we propose a model for transpressive deformation in a zone of oblique convergence and left‐lateral wrench tectonics to explain this and a prior 1951 M 7.3 earthquake.

To unravel fault zone properties, high-resolution imaging and probing are pivotal. Here we present a comprehensive study utilizing Distributed Dynamic Strain Sensing of a 3D array of optical fiber with a surface-to-depth installation, traversing an active fault zone at depth, combined with a geological analysis of drilled cores. We identify an asymmetric fault zone with a narrow fault core, exhibiting a notable amplification in strain rate across a broad-band frequency range, which is attributed to the presence of weak gouge material within a much stronger host rock. This feature heightens responsiveness of the weak fault zone with low rigidity to ground motion, and intensifies strain from both nearby and remote seismic events. We demonstrate that optical fiber facilitates long-term monitoring of ground motion amplification with high spatial resolution, thus indicating where strain and deformation accumulate through repeating ruptures. This progress is pivotal to the understanding of the role of low rigidity and changes thereof within fault gouge material.

The Internet of Things (IoT) technology has revolutionized the healthcare industry by enabling a new paradigm for healthcare delivery. This paradigm is known as the Internet of Medical Things (IoMT). IoMT devices are typically connected via a wide range of wireless communication technologies, such as Bluetooth, radio-frequency identification (RFID), ZigBee, Wi-Fi, and cellular networks. The ZigBee protocol is considered to be an ideal protocol for IoMT communication due to its low cost, low power usage, easy implementation, and appropriate level of security. However, maintaining ZigBee’s high reliability is a major challenge due to multi-path fading and interference from coexisting wireless networks. This has increased the demand for more efficient channel coding schemes that can achieve a more reliable transmission of vital patient data for ZigBee-based IoMT communications. To meet this demand, a novel coding scheme called inter-multilevel super-orthogonal space–time coding (IM-SOSTC) can be implemented by combining the multilevel coding and set partitioning of super-orthogonal space–time block codes based on the coding gain distance (CGD) criterion. The proposed IM-SOSTC utilizes a technique that provides inter-level dependency between adjacent multilevel coded blocks to facilitate high spectral efficiency, which has been compromised previously by the high coding gain due to the multilevel outer code. In this paper, the performance of IM-SOSTC is compared to other related schemes via a computer simulation that utilizes the quasi-static Rayleigh fading channel. The simulation results show that IM-SOSTC outperforms other related coding schemes and is capable of providing the optimal trade-off between coding gain and spectral efficiency whilst guaranteeing full diversity and low complexity.

Differential interferometric synthetic aperture radar (DInSAR) is an effective technique to measure the surface displacement caused by strong earthquakes. However, in the area with respect to the most significant deformation along the fault zone, the coherence between pre- and post-earthquake SAR images is completely lost because of the earthquake-induced violent and chaotic destruction on the land surface and consequently, no surface displacement data can be measured. In our previous work, an adaptive local kriging (ALK) was proposed to solve this problem. However, according to the spatial distribution of the reference points for the interpolation procedure of ALK, using moving window with different sizes through all the pixels in the image is time consuming. Thus, a high-performance computing (HPC) is needed. As a result, a parallel image interpolation approach, referred as the graphics processing unit (GPU) based ALK method, to retrieving slant-range surface motion maps derived by DInSAR is proposed in this study. The proposed GPU-ALK makes use of the parallelism of the original ALK architectures to retrieve the loss of data through the fault zone. In this paper, an HPC GPU-ALK is proposed to speed up the interpolation. It makes use of the performance profiling to analyze the serial version of ALK and perform a parallel GPU computation for reducing the computation time efficiently. By employing one NVIDIA TITAN GPU, the proposed GPU-ALK achieves a speedup of 104.32× compared to its CPU counterpart.