This study aims to determine the upper limit of the wireless sensing capability of acquiring physical space information. This is a challenging objective because, at present, wireless sensing studies continue to succeed in acquiring novel phenomena. Thus, although we have still not obtained a complete answer, a step is taken toward it herein. To achieve this, CSI2Image, a novel channel state information (CSI)-to-image conversion method based on generative adversarial networks (GANs), is proposed. The type of physical information acquired using wireless sensing can be estimated by checking whether the reconstructed image captures the desired physical space information. We demonstrate three types of learning methods: generator-only learning, GAN-only learning, and hybrid learning. Evaluating the performance of CSI2Image is difficult because both the clarity of the image and the presence of the desired physical space information must be evaluated. To solve this problem, we propose a quantitative evaluation methodology using an image-based object detection system. CSI2Image was implemented using IEEE 802.11ac compressed CSI, and the evaluation results show that CSI2Image successfully reconstructs images. The results demonstrate that generator-only learning is sufficient for simple wireless sensing problems; however, in complex wireless sensing problems, GANs are essential for reconstructing generalized images with more accurate physical space information. INDEX TERMS wireless sensing, channel state information, deep learning, generative adversarial networks, image reconstruction
This study aims to find the upper limit of the wireless sensing capability of acquiring physical space information. This is a challenging objective, because at present, wireless sensing studies continue to succeed in acquiring novel phenomena. Thus, although a complete answer cannot be obtained yet, a step is taken towards it here. To achieve this, CSI2Image, a novel channel-state-information (CSI)to-image conversion method based on generative adversarial networks (GANs), is proposed. The type of physical information acquired using wireless sensing can be estimated by checking whether the reconstructed image captures the desired physical space information. Three types of learning methods are demonstrated: generator-only learning, GAN-only learning, and hybrid learning. Evaluating the performance of CSI2Image is difficult, because both the clarity of the image and the presence of the desired physical space information must be evaluated. To solve this problem, a quantitative evaluation methodology using an object detection library is also proposed. CSI2Image was implemented using IEEE 802.11ac compressed CSI, and the evaluation results show that the image was successfully reconstructed. The results demonstrate that generator-only learning is sufficient for simple wireless sensing problems, but in complex wireless sensing problems, GANs are important for reconstructing generalized images with more accurate physical space information.
CCS CONCEPTS• Computer systems organization → Embedded and cyber-physical systems; Sensor networks.
Wireless local area network (WLAN)-based localization is key for advanced indoor Internetof-Things and embedded sensor applications. To further improve the accuracy of indoor localization, attention has been focused on WLAN-based indoor localization using channel-state information (CSI) in addition to the existing information provided by received signal strength (RSS). For easy and low cost installation of wireless sensing, wireless sensing based on standardized protocols and commercial WLAN devices, such as IEEE 802.11ac and IEEE 802.11ax, is necessary. Much previous research used the angle of arrival (AoA), but commercial WLAN devices cannot use directly for AoA estimation. Therefore, we propose a practical method for estimating the AoA to solve four problems: 1) compressed CSI, which cannot be used for AoA estimation directly, 2) the antenna wireline, in which the phase changes depending on the length of the wireline, 3) the antenna spacing, in which the distance between antennas places a restriction on AoA estimation, and 4) antenna individuality, in which the antennas used in actual MIMO communication have different characteristics. We implemented the proposed method on IEEE 802.11ac devices and evaluated it in a lecture room and shield tent. The results indicate that the proposed method can estimate AoA with an average error of 9.1 • and reduce the estimation error by 85.4 % compared with a straightforward approach.
Non-equilibrium Pd–Ru alloys were synthesized by leaching quasicrystal (icosahedral quasicrystal, IQC: Al71Pd19Ru10) and approximants (3/2 approximant, P40: Al72Pd16.4Ru11.6, 1/1 approximant, C1: Al70.4Pd14.7Ru14.9) of Al–Pd–Ru alloys with 20 wt% NaOH aqueous solution. The Pd-Ru alloys were investigated using XRD and HAADF-STEM, which showed that Pd and Ru were mixed and formed a non-equilibrium state (atomic ratio: 0.9 < Pd/Ru < 2). The Pd–Ru alloy obtained by leaching of P40 phase was found to exhibit high catalytic performance toward CO oxidation than Pd and Ru catalysts obtained by leaching of Al3Pd (decagonal quasicrystal) and Al13Ru4 (decagonal approximant), respectively (i.e., the alloying effect). Consequently, Al-Pd-Ru quasicrystals and approximant alloys are found to be useful precursors to obtain the non-equilibrium Pd-Ru alloys.
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