SAMU: Design and Implementation of Frequency Selectivity-Aware Multi-User MIMO for WLANs

The traffic demand of wireless networks is expected to increase 1000-fold over the next decade. In anticipation of such increasing data demand for dense networks with a large number of stations, IEEE 802.11ax has introduced key technologies for capacity improvement including Orthogonal Frequency-Division Multiple Access (OFDMA), multi-user multi-input multi-output (MU-MIMO), and greater bandwidth. However, IEEE 802.11ax has yet to fully define a specific scheduling framework, on which the throughput improvement of networks significantly depends. Even within a 20 MHz of bandwidth, users experience heterogeneous channel orthogonality characteristics across sub-carriers, which prevents access points (APs) from achieving the ideal multi-user gain. Moreover, frequency selectivity increases as bandwidth scales and correspondingly severely deteriorates multi-user MIMO performance. In this work, we develop a novel channel adaptation scheme, named selectivity-aware multi-user MIMO (SAMU), to combat the issue of frequency selectivity and support coexistence among users in the network by jointly assigning subsets of sub-carriers to selected users and implementing downlink MU-MIMO. To do so, we first investigate the channel characteristics of an indoor environment. We then consider the frequency selectivity of current and emerging WiFi channel bandwidths to optimize multi-user MIMO by dividing the occupied sub-carrier resources into equally sized sub-channels according to the level of frequency selectivity. In our design, each sub-channel is allocated according to the largest bandwidth that can be considered frequency-flat, and an optimal subset of users is chosen to serve in each sub-channel according to spatial orthogonality. As a result, we support more simultaneous users than current 802.11 designs and achieve a significant performance improvement for all users in the network. Additionally, we propose a selectivity-aware high efficiency (SA-HE) mode, which is based on and fully backward compatible with the existing IEEE 802.11ax standard. Finally, over emulated and real indoor channels, we show that SAMU can achieve as much as 84.8% throughput improvement compared to existing multi-user MIMO schemes in IEEE 802.11a

iBeam: Intelligent client-side multi-user beamforming in wireless networks

Abstract—Frequently, client-side wireless devices have a view of multiple WiFi access points, whether from open residential and commercial networks, corporate networks, or mesh networks. Given the increasing number of radios and antennas in today’s wireless devices, residual capacity from these multiple APs could be leveraged if client devices communicate with multiple APs simultaneously. In this paper, we exploit multi-user multi-input multi-output (MU-MIMO) technology to improve throughput and reliability in both directions of a wireless connection. For uplink, we use multi-user beamforming to enable the client devices to send multiple data streams to multiple APs simultaneously. For downlink, we leverage interference nulling technology to allow the client devices to decode parallel packets from multiple APs. This iBeam system requires no changes to existing APs or backhaul networks and is compatible with the IEEE 802.11 standards. We experimentally evaluate iBeam and show significant throughput improvements over both single-AP connections and multi-AP connections in a time division mode. The client’s reliability and stability are also significantly improved due to the multi-AP diversity gain

Experimental study on the mechanism of cavitation-induced ventilation

In this study, the cavitating flow and cavitation-induced ventilation flow around a surface-piercing hydrofoil were investigated to gain in-depth understanding of the interaction mechanism between the vaporous cavity and free surface at low cavitation numbers. Experiments were conducted in a constrained-launching water tank to visualize the cavity using a high-speed camera. Unsteady cloud cavitation and cavitation-induced ventilation at atmospheric pressure were observed and analyzed while piercing the free surface. The flow regime map was summarized at a fixed aspect ratio of AR(h) = 1.5. Subsequently, a physical model was proposed to predict the maximum depression depth of the water surface (H) at the trailing edge of the hydrofoil. Both the physical model and experimental results reveal that the non-dimensional depth H/c has a linear relation to Fn2 c x Re-c x sin(2)alpha. Finally, a criterion for cavitation-induced ventilation based on the improved lifting-line theory and a physical model were proposed. A new relation H/L-c similar to alpha(0.5) was obtained, where L-c is the maximum cavity length. The results of this study can guide the design and application of hydrofoils for ventilation and cavitation processes

SAMU: design and implementation of frequency selectivity-aware multi-user MIMO for WLANs

Abstract The traffic demand of wireless networks is expected to increase 1000-fold over the next decade. In anticipation of such increasing data demand for dense networks with a large number of stations, IEEE 802.11ax has introduced key technologies for capacity improvement including Orthogonal Frequency-Division Multiple Access (OFDMA), multi-user multi-input multi-output (MU-MIMO), and greater bandwidth. However, IEEE 802.11ax has yet to fully define a specific scheduling framework, on which the throughput improvement of networks significantly depends. Even within a 20 MHz of bandwidth, users experience heterogeneous channel orthogonality characteristics across sub-carriers, which prevents access points (APs) from achieving the ideal multi-user gain. Moreover, frequency selectivity increases as bandwidth scales and correspondingly severely deteriorates multi-user MIMO performance. In this work, we develop a novel channel adaptation scheme, named selectivity-aware multi-user MIMO (SAMU), to combat the issue of frequency selectivity and support coexistence among users in the network by jointly assigning subsets of sub-carriers to selected users and implementing downlink MU-MIMO. To do so, we first investigate the channel characteristics of an indoor environment. We then consider the frequency selectivity of current and emerging WiFi channel bandwidths to optimize multi-user MIMO by dividing the occupied sub-carrier resources into equally sized sub-channels according to the level of frequency selectivity. In our design, each sub-channel is allocated according to the largest bandwidth that can be considered frequency-flat, and an optimal subset of users is chosen to serve in each sub-channel according to spatial orthogonality. As a result, we support more simultaneous users than current 802.11 designs and achieve a significant performance improvement for all users in the network. Additionally, we propose a selectivity-aware high efficiency (SA-HE) mode, which is based on and fully backward compatible with the existing IEEE 802.11ax standard. Finally, over emulated and real indoor channels, we show that SAMU can achieve as much as 84.8% throughput improvement compared to existing multi-user MIMO schemes in IEEE 802.11ax

Advances in Marine Self-Powered Vibration Sensor Based on Triboelectric Nanogenerator

With the rapid development of advanced electronics/materials and manufacturing, marine vibration sensors have made great progress in the field of ship and ocean engineering, which could cater to the development trend of marine Internet of Things (IoT) and smart shipping. However, the use of conventional power supply models requires periodic recharging or replacement of batteries due to limited battery life, which greatly causes too much inconvenience and maintenance consumption, and may also pose a potential risk to the marine environment. By using the coupling effect of contact electrification and electrostatic induction, triboelectric nanogenerators (TENGs) were demonstrated to efficiently convert mechanical vibration movements into electrical signals for sensing the vibration amplitude, direction, frequency, velocity, and acceleration. In this article, according to the two working modes of harmonic vibration and non-harmonic vibration, the latest representative achievements of TENG-based vibration sensors for sensing mechanical vibration signals are comprehensively reviewed. This review not only covers the fundamental working mechanism, rational structural design, and analysis of practical application scenarios, but also investigates the characteristics of harmonic vibration and non-harmonic vibration. Finally, perspectives and challenges regarding TENG-based marine self-powered vibration sensors at present are discussed

Self-Powered and Robust Marine Exhaust Gas Flow Sensor Based on Bearing Type Triboelectric Nanogenerator

Exhaust gas flow takes a vital position in the assessment of ship exhaust emissions, and it is essential to develop a self-powered and robust exhaust gas flow sensor in such a harsh working environment. In this work, a bearing type triboelectric nanogenerator (B-TENG) for exhaust gas flow sensing is proposed. The rolling of the steel balls on PTFE film leads to an alternative current generated, which realizes self-powered gas flow sensing. The influence of ball materials and numbers is systematically studied, and the B-TENG with six steel balls is confirmed according to the test result. After design optimization, it is successfully applied to monitor the gas flow with the linear correlation coefficient higher than 0.998 and high output voltage from 25 to 106 V within the gas flow of 2.5–14 m/s. Further, the output voltage keeps stable at 70 V under particulate matter concentration of 50–120 mg/m3. And the output performance of the B-TENG after heating at 180 °C for 10 min is also surveyed. Moreover, the mean error of the gas flow velocity by the B-TENG and a commercial gas flow sensor is about 0.73%. The test result shows its robustness and promising perspective in exhaust gas flow sensing. Therefore, the present B-TENG has a great potential to apply for self-powered and robust exhaust gas flow monitoring towards Green Ship

Research on ventilation and supercavitation mechanism of high-speed surface-piercing hydrofoil

Flow structures and hydrodynamic performance of high-speed surface-piercing hydrofoils were studied by numerical simulation, with an emphasis on the interaction mechanism between supercavitation and natural ventilation. Compared with the available experimental data, the numerical method could predict the cavitation and ventilation well. The numerical simulation results show that the flow over hydrofoil with blunt trailing edge is more conducive to separating. The semi-ogive hydrofoil was used to explore the influence of angles of attack on ventilation and cavitation. The ventilation rate increases with the increase in the angles of attack. At small attack angles (alpha = 0 & DEG; and 2 & DEG;), the regional ventilated flow is found in supercavitation. The vortex street structures and twin vortices closure mode are formed in the closure region of the supercavity. At moderate attack angles (alpha = 6 & DEG; and 10 & DEG;), the thickness of the undisturbed liquid sheet (delta) becomes thinner and the natural supercavitation transits to fully ventilated supercavitation through the cavitation-induced ventilation, but the ventilation position is different because of Taylor instability. The hydrodynamic coefficients remain relatively stable in natural supercavitation and the lift coefficient reduce to half of the original value when the supercavitation is fully ventilated, which are caused by the pressure changes on the suction and pressure surfaces.& nbsp;& nbsp;(c)& nbsp;2022 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)

A Data Augmentation Strategy Combining a Modified pix2pix Model and the Copy-Paste Operator for Solid Waste Detection With Remote Sensing Images

Solid waste detection is of great significance for environmental protection. In recent years, object detection methods based on deep learning have progressed rapidly. However, it is often extremely difficult to collect sufficient data to train a model with a good performance. In this article, a data augmentation strategy was introduced to generate sufficient synthetic high-quality images for solid waste detection. First, a modified pix2pix model was proposed, in which a local-global discriminator was designed to improve the detailed and global information of the generated images, which are commonly fuzzy with the original pix2pix model. Second, a copy-paste operator was utilized, which simply pastes the bounding box of the generated objects into different images to enhance the diversity of the samples. In this manner, the expanded dataset can be utilized to train different object detection models, for which FPN and Yolo-v4 were introduced as the validation models in this article. The experimental results show that the proposed strategy outperforms the traditional pix2pix method and the generated synthetic images can effectively improve the performance of object detection methods