This paper demonstrates a series of 1g model tests and numerical modelling in order to investigate the pushout extraction process of suction anchors in sand. The suction anchor models used in this ...study have aspect ratios of 1 and 2, and the pumping rate and soil permeability are varied to reveal their influence on the extraction process. It is shown that changes in the pumping rate and soil permeability rarely alter the overpressure during the extraction process but can affect the displacement rate and final extracted length of the suction anchor. The ratio of the final extracted length over the total length of the suction anchor is also found to be different for suction anchors with different aspect ratios in the 1g model tests. Eventually, a calculation method for this pushout extraction process is proposed and validated to determine the peak overpressure with some basic parameters of the suction anchor and sands. The relationship between the pumping rate and the displacement rate of the suction anchor is also revealed in the calculation method.
•Pumping rate and soil permeability barely affect the overpressure during extraction.•The final extracted length is dependent on the pumping rate and anchor geometry.•The initial displacement rate has a linear relationship with the pumping rate.•The overpressure can be obtained with basic parameters using the proposed method.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
WiFi-based gesture recognition emerges in recent years and attracts extensive attention from researchers. Recognizing gestures via WiFi signal is feasible because a human gesture introduces a time ...series of variations to the received raw signal. The major challenge for building a ubiquitous gesture recognition system is that the mapping between each gesture and the series of signal variations is not unique, exact the same gesture but performed at different locations or with different orientations towards the transceivers generates entirely different gesture signals (variations). To remove the location dependency, prior work proposes to use gesture-level location-independent features to characterize the gesture instead of directly matching the signal variation pattern. We observe that gesture-level features cannot fully remove the location dependency since the signal qualities inside each gesture are different and also depends on the location. Therefore, we divide the signal time series of each gesture into segments according to their qualities and propose customized signal processing techniques to handle them separately. To realize this goal, we characterize signal's sensing quality by building a mathematical model that links the gesture signal with the ambient noise, from which we further derive a unique metric i.e., error of dynamic phase index (EDP-index) to quantitatively describe the sensing quality of signal segments of each gesture. We then propose a quality-oriented signal processing framework that maximizes the contribution of the high-quality signal segments and minimizes the impact of low-quality signal segments to improve the performance of gesture recognition applications. We develop a prototype on COTS WiFi devices. The extensive experimental results demonstrate that our system can recognize gestures with an accuracy of more than 94% on average, and significant improvements compared with state-of-arts.
WiFi-CSI Difference Paradigm Li, Wenwei; Gao, Ruiyang; Xiong, Jie ...
Proceedings of ACM on interactive, mobile, wearable and ubiquitous technologies,
05/2024, Volume:
8, Issue:
2
Journal Article
Peer reviewed
Passive tracking plays a fundamental role in numerous applications such as elderly care, security surveillance, and smart home. To utilize ubiquitous WiFi signals for passive tracking, the Doppler ...speed extracted from WiFi CSI (Channel State Information) is the key information. Despite the progress made, existing approaches still require a large number of samples to achieve accurate Doppler speed estimation. To enable WiFi sensing with minimum amount of interference on WiFi communication, accurate Doppler speed estimation with fewer CSI samples is crucial. To achieve this, we build a passive WiFi tracking system which employs a novel CSI difference paradigm instead of CSI for Doppler speed estimation. In this paper, we provide the first deep dive into the potential of CSI difference for fine-grained Doppler speed estimation. Theoretically, our new design allows us to estimate Doppler speed with just three samples. While conventional methods only adopt phase information for Doppler estimation, we creatively fuse both phase and amplitude information to improve Doppler estimation accuracy. Extensive experiments show that our solution outperforms the state-of-the-art approaches, achieving higher accuracy with fewer CSI samples. Based on this proposed WiFi-CSI difference paradigm, we build a prototype passive tracking system which can accurately track a person with a median error lower than 34 cm, achieving similar accuracy compared to the state-of-the-art systems, while significantly reducing the required number of samples to only 5%.
UniFi Liu, Yan; Yu, Anlan; Wang, Leye ...
Proceedings of ACM on interactive, mobile, wearable and ubiquitous technologies,
01/2024, Volume:
7, Issue:
4
Journal Article
Peer reviewed
In recent years, considerable endeavors have been devoted to exploring Wi-Fi-based sensing technologies by modeling the intricate mapping between received signals and corresponding human activities. ...However, the inherent complexity of Wi-Fi signals poses significant challenges for practical applications due to their pronounced susceptibility to deployment environments. To address this challenge, we delve into the distinctive characteristics of Wi-Fi signals and distill three pivotal factors that can be leveraged to enhance generalization capabilities of deep learning-based Wi-Fi sensing models: 1) effectively capture valuable input to mitigate the adverse impact of noisy measurements; 2) adaptively fuse complementary information from multiple Wi-Fi devices to boost the distinguishability of signal patterns associated with different activities; 3) extract generalizable features that can overcome the inconsistent representations of activities under different environmental conditions (e.g., locations, orientations). Leveraging these insights, we design a novel and unified sensing framework based on Wi-Fi signals, dubbed UniFi, and use gesture recognition as an application to demonstrate its effectiveness. UniFi achieves robust and generalizable gesture recognition in real-world scenarios by extracting discriminative and consistent features unrelated to environmental factors from pre-denoised signals collected by multiple transceivers. To achieve this, we first introduce an effective signal preprocessing approach that captures the applicable input data from noisy received signals for the deep learning model. Second, we propose a multi-view deep network based on spatio-temporal cross-view attention that integrates multi-carrier and multi-device signals to extract distinguishable information. Finally, we present the mutual information maximization as a regularizer to learn environment-invariant representations via contrastive loss without requiring access to any signals from unseen environments for practical adaptation. Extensive experiments on the Widar 3.0 dataset demonstrate that our proposed framework significantly outperforms state-of-the-art approaches in different settings (99% and 90%-98% accuracy for in-domain and cross-domain recognition without additional data collection and model training).
Enabling WiFi Sensing on New-generation WiFi Cards Yi, Enze; Zhang, Fusang; Xiong, Jie ...
Proceedings of ACM on interactive, mobile, wearable and ubiquitous technologies,
01/2024, Volume:
7, Issue:
4
Journal Article
Peer reviewed
Open access
The last few years have witnessed the rapid development of WiFi sensing with a large spectrum of applications enabled. However, existing works mainly leverage the obsolete 802.11n WiFi cards (i.e., ...Intel 5300 and Atheros AR9k series cards) for sensing. On the other hand, the mainstream WiFi protocols currently in use are 802.11ac/ax and commodity WiFi products on the market are equipped with new-generation WiFi chips such as Broadcom BCM43794 and Qualcomm QCN5054. After conducting some benchmark experiments, we find that WiFi sensing has problems working on these new cards. The new communication features (e.g., MU-MIMO) designed to facilitate data transmissions negatively impact WiFi sensing. Conventional CSI base signals such as CSI amplitude and/or CSI phase difference between antennas which worked well on Intel 5300 802.11n WiFi card may fail on new cards. In this paper, we propose delicate signal processing schemes to make wireless sensing work well on these new WiFi cards. We employ two typical sensing applications, i.e., human respiration monitoring and human trajectory tracking to demonstrate the effectiveness of the proposed schemes. We believe it is critical to ensure WiFi sensing compatible with the latest WiFi protocols and this work moves one important step towards real-life adoption of WiFi sensing.
The past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring. However, existing approaches only work ...when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away. On the other hand, most home environments only have one WiFi access point and it may not be located in the same room as the target. This sensing range constraint greatly limits the application of the proposed approaches in real life. This paper presents FarSense--the first real-time system that can reliably monitor human respiration when the target is far away from the WiFi transceiver pair. FarSense works well even when one of the transceivers is located in another room, moving a big step towards real-life deployment. We propose two novel schemes to achieve this goal: (1) Instead of applying the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range; (2) The division operation further enables us to utilize the phase information which is not usable with one single antenna for sensing. The orthogonal amplitude and phase are elaborately combined to address the "blind spots" issue and further increase the sensing range. Extensive experiments show that FarSense is able to accurately monitor human respiration even when the target is 8 meters away from the transceiver pair, increasing the sensing range by more than 100%. We believe this is the first system to enable through-wall respiration sensing with commodity WiFi devices and the proposed method could also benefit other sensing applications.
FarSense Zeng, Youwei; Wu, Dan; Xiong, Jie ...
Proceedings of ACM on interactive, mobile, wearable and ubiquitous technologies,
09/2019, Volume:
3, Issue:
3
Journal Article
Peer reviewed
Open access
The past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring. However, existing approaches only work ...when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away. On the other hand, most home environments only have one WiFi access point and it may not be located in the same room as the target. This sensing range constraint greatly limits the application of the proposed approaches in real life. This paper presents FarSense--the first real-time system that can reliably monitor human respiration when the target is far away from the WiFi transceiver pair. FarSense works well even when one of the transceivers is located in another room, moving a big step towards real-life deployment. We propose two novel schemes to achieve this goal: (1) Instead of applying the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range; (2) The division operation further enables us to utilize the phase information which is not usable with one single antenna for sensing. The orthogonal amplitude and phase are elaborately combined to address the "blind spots" issue and further increase the sensing range. Extensive experiments show that FarSense is able to accurately monitor human respiration even when the target is 8 meters away from the transceiver pair, increasing the sensing range by more than 100%.1 We believe this is the first system to enable through-wall respiration sensing with commodity WiFi devices and the proposed method could also benefit other sensing applications.
With a big success in data communication, wireless signals are now exploited for fine-grained contactless activity sensing including human respiration monitoring, finger gesture recognition, subtle ...chin movement tracking when speaking, etc. Different from coarsegrained body and limb movements, these fine-grained movements are in the scale of millimetres and are thus difficult to be sensed. While good sensing performance can be achieved at one location, the performance degrades dramatically at a very nearby location. In this paper, by revealing the effect of static multipaths in sensing, we propose a novel method to add man-made "virtual" multipath to significantly improve the sensing performance. With carefully designed "virtual" multipath, we are able to boost the sensing performance at each location purely in software without any extra hardware.
We demonstrate the effectiveness of the proposed method on three fine-grained sensing applications with just one Wi-Fi transceiver-pair, each equipped with a single antenna. For respiration monitoring, we can remove the "blind spots" and achieve full coverage respiration sensing. For finger gesture recognition, our system can significantly increase the recognition accuracy from 33% to 81%. For chin movement tracking, we are able to count the number of spoken syllables in a sentence at an accuracy of 92.8%.
Ni-base alloy DZ468 has been joined by transient liquid phase bonding technique with a newly developed Co-based filler. The microstructures of the Co-base filler and the joint, the effects of heat ...treatment on microstructure and hardness of the joint have been investigated by various experimental methods. Results show that the Co-base filler consists of γ, M2B, M5B3 and M23B6 phases. Because of the interdiffusion between the base metal and the filler, γ, MC, M5B3 and M23B6 phases are formed in the bonding zone. And localized liquidation of substrate occurs in the diffusion affected zone, with MC and M3B2 precipitating in this area. During heat treatment, the volume of the intermetallic phases in the bonding zone resulting from incomplete isothermal solidification decreases obviously. On the contrary, the width of the diffusion affected zone increases at the solution stage and subsequently decreases at the aging stages.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPUK
The past few years have witnessed the great potential of exploiting channel state information (CSI) retrieved from COTS WiFi devices for respiration monitoring. However, existing approaches only work ...when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away. This sensing range constraint greatly limits the application of the proposed approaches in real life. Different from the existing approaches that apply the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range.1 In this demo, we will demonstrate FarSense - a CSI-ratio model based house-level real-time respiration monitoring system using COTS WiFi devices.
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