Ubiquitous structural monitoring (USM) of several decades. In this point of view, a risk monitoring of buildings using wireless sensor networks is one of the most buildings with wireless sensor networks was proposed [2], [3], promising emerging technologies for mitigation of seismic hazard. [4]. The risks for the buildings include not only structural This technology has the potential to change fundamentally the damages, material degradation, fatigue and corrosion of metals, traditional monitoring systems. This paper provides an but also invasion, gas leaks, fires, and others. By using introduction of wireless sensor network technology for USM, and wireless sensor networks, high density distributed sensing for identifies some of opportunities and associated challenges.all of the risks in the building could be realized and the
In this paper we present a high-density earthquake monitoring system using wireless sensor networks. For highprecision monitoring, we developed Pavenet OS, which is a hard-realtime operating system for sensor nodes, and acceleration sensor board. Sensor nodes of the system sample acceleration with less than 0.3 us jitter with Pavenet OS. The system provides earthquake engineering researchers the ability to measure vibrations of structures during earthquakes at less cost and higher node density than previous systems.
Full-duplex (FD) wireless communication is evolving into a practical technique, and many studies are being conducting in this area, especially regarding the physical layer. However, to exploit FD benefit successfully, efficient medium access control (MAC) protocols are crucial as well as physical layer advances. Numerous FD-MAC protocols have been proposed, but these MAC protocols cannot address all the issues encountered in this area. In addition, many half-duplex (HD) capable devices are present in existing wireless local area networks (WLANs), so there is an urgent need to integrate FD clients and HD clients in the same WLAN. We refer to this type of WLAN as a heterogeneous WLAN (Het-WLAN). In this paper, we propose an FD-MAC for Het-WLAN, which considers all possible types of FD transmissions. Our proposed FD-MAC protocol suppresses inter-user interference. Simulation results demonstrated that a significant throughput gain (about 96%) could be achieved by using our proposed FD-MAC compared with traditional HD communications. Moreover, our proposed MAC obtained better performance (average throughput gain of about 11%) compared with another existing FD-MAC design. In addition, probability analysis suggested that the total probability of FD transmissions increased rapidly as the WLAN approached saturation conditions.
Wireless full-duplexing enables a transmission and a reception on the same frequency channel at the same time, and has the potential to improve the end-to-end throughput of wireless multi-hop networks. In the present paper, we propose a media access control (MAC) protocol for wireless full-duplex and multi-hop networks called Relay Full-Duplex MAC (RFD-MAC). The RFD-MAC is an asynchronous full-duplex MAC protocol, which consists of a primary transmission and a secondary transmission. The RFD-MAC increases the full-duplex links by overhearing frames, which include 1-bit information concerning the existence of a successive frame, and selecting a secondary transmission node using the gathered information. The gathered information is also used to avoid a collision between the primary and secondary transmission. Simulation results reveal that the proposed RFD-MAC improves up to 68%, 49% and 56% of end-to-end throughput compared to CSMA/CA, FD-MAC and MFD-MAC, respectively.
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 proposes a new methodological approach to evaluate students’ knowledge‐building discourse. First, we discuss the knowledge–creation metaphor of learning. In the metaphor, theories mention that learners should consider their collective knowledge objects or artifacts that materialize as a result of their collaborative discourse. Second, we argue the necessity of developing new analytics to evaluate student learning. We describe how students’ ideas and their conceptual artifacts can be examined in discourse analysis. Third, we demonstrate the application of our analytics to real discourse data. We conducted a new type of social network analysis of discourse to examine how students continuously improve their ideas. Further, we conducted another network analysis of discourse, called the Epistemic Network Analysis, by coding students’ epistemic actions as conceptual artifacts to create and examine their ideas.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.