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Although the role of shoe constructions on running injury and performance has been widely investigated, systematic reviews on the shoe construction effects on running biomechanics were rarely reported. Therefore, this review focuses on the relevant research studies examining the biomechanical effect of running shoe constructions on reducing running-related injury and optimising performance. Searches of five databases and Footwear Science from January 1994 to September 2018 for related biomechanical studies which investigated running footwear constructions yielded a total of 1260 articles. After duplications were removed and exclusion criteria applied to the titles, abstracts and full text, 63 studies remained and categorised into following constructions: (a) shoe lace, (b) midsole, (c) heel flare, (d) heel-toe drop, (e) minimalist shoes, (f) Masai Barefoot Technologies, (g) heel cup, (h) upper, and (i) bending stiffness. Some running shoe constructions positively affect athletic performance-related and injury-related variables: 1) increasing the stiffness of running shoes at the optimal range can benefit performance-related variables; 2) softer midsoles can reduce impact forces and loading rates; 3) thicker midsoles can provide better cushioning effects and attenuate shock during impacts but may also decrease plantar sensations of a foot; 4) minimalist shoes can improve running economy and increase the cross-sectional area and stiffness of Achilles tendon but it would increase the metatarsophalangeal and ankle joint loading compared to the conventional shoes. While shoe constructions can effectively influence running biomechanics, research on some constructions including shoe lace, heel flare, heel-toe drop, Masai Barefoot Technologies, heel cup, and upper requires further investigation before a viable scientific guideline can be made. Future research is also needed to develop standard testing protocols to determine the optimal stiffness, thickness, and heel–toe drop of running shoes to optimise performance-related variables and prevent running-related injuries.

Because the underwater acoustic communication network transmits data through the underwater acoustic wireless link, the Underwater Acoustic Communication Network is easy to suffer from the external artificial interference, in this paper, the detection algorithm of wormhole attack in Underwater Acoustic Communication Network based on Azimuth measurement technology is studied. The existence of wormhole attack is judged by Azimuth or distance outliers, and the security performance of underwater acoustic communication network is evaluated. The influence of different azimuth direction errors on the detection probability of wormhole attack is analyzed by simulation. The simulation results show that this method has a good detection effect for Underwater Acoustic Communication Network.

The data in the underwater acoustic channel (UAC) can be received through the hydrophone. If there are no encryption protection measures, the enemy can easily obtain sensitive information. According to the requirements of secure transmission of underwater network measurement and control secret information, a key generation method based on UAC response characteristics is proposed. Based on the short-term correlation of UAC, the amplitude and time delay characteristics of underwater acoustic channel multipath structure are extracted, the multi-path number with the strongest energy, the number of qualified sound rays and multipath number with maximum delay difference are quantized to generate the original key. Then, the inconsistent key is corrected by key agreement mechanism. The communication parties can generate encryption key in real time to ensure the confidentiality of information. Experimental results show that the short-term correlation of multi-path structure of UAC is good, which verifies the feasibility of key generation based on multi-path structure of UAC and the key generation scheme proposed in this paper can make the matching probability of underwater acoustic key generation not less than 80%.

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that relies on intrinsic hardware characteristics of wireless devices. We designed an RFFI scheme for Long Range (LoRa) systems based on spectrogram and convolutional neural network (CNN). Specifically, we used spectrogram to represent the fine-grained time-frequency characteristics of LoRa signals. In addition, we revealed that the instantaneous carrier frequency offset (CFO) is drifting, which will result in misclassification and significantly compromise the system stability; we demonstrated CFO compensation is an effective mitigation. Finally, we designed a hybrid classifier that can adjust CNN outputs with the estimated CFO. The mean value of CFO remains relatively stable, hence it can be used to rule out CNN predictions whose estimated CFO falls out of the range. We performed experiments in real wireless environments using 20 LoRa devices under test (DUTs) and a Universal Software Radio Peripheral (USRP) N210 receiver. By comparing with the IQ-based and FFT-based RFFI schemes, our spectrogram-based scheme can reach the best classification accuracy, i.e., 97.61% for 20 LoRa DUTs., Comment: Accepted for publication in IEEE INFOCOM 2021