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

Absfract-The aim of our project is to forecast the traffc state like aweather forecast and to show the state with dynamic image. For forecasting we plan to construct the dynamic image database management system. The system needs a lot of dynamic imagcs with indices. i.e. time, date. place, congestion parameters and SO on. It is a key technique for the DBXlS to extract the paramclem from observed image automatically. I n this paper, we present a method lo acquire the parameters from real rrnffic images by image processing tcchniqucs. In order to show that our program work ils good as human. we compared the ordering by computer with lhe ordering by human drivers. The result shows that computer's ordering is almost same as human ordering within thc variation belwcen human driven' ordering.

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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.

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The Cybermedia Center (CMC), Osaka University, is a research institution that offers knowledge and technology resources obtained from advanced researches in the areas of large-scale computation, information and communication, multimedia content and education. Currently, CMC is involved in Japanese national Grid projects such as JGN II (Japan Gigabit Network), NAREGI and BioGrid. Not limited to Japan, CMC also actively takes part in international activities such as PRAGMA. In these projects and international collaborations, CMC has developed a Grid system that allows scientists to perform their analysis by remote-controlling the world's largest ultra-high voltage electron microscope located in Osaka University. In another undertaking, CMC has assumed a leadership role in BioGrid by sharing its experiences and knowledge on the system development for the area of biology. In this paper, we will give an overview of the BioGrid project and introduce the progress of the Telescience unit, which collaborates with the Telescience Project led by the National Center for Microscopy and Imaging Research (NCMIR). Furthermore, CMC collaborates with seven Computing Centers in Japan, NAREGI and National Institute of Informatics to deploy PKI base authentication infrastructure. The current status of this project and future collaboration with Grid Projects will be delineated in this paper.