Effects of geometry of wings submerged in turbulent bluff-body wake

The effects of the aspect ratio, the sweep angle, and the leading-edge geometry of wings placed in a turbulent wake were investigated in wind-tunnel experiments at a chord Reynolds number of 10 5. The poststall lift enhancement due to the leading-edge vortex formation was studied at optimal locations in the wake. The effects of the strength of the leading-edge vortices, the ratio of the spanwise length scale of the incident vortex to the wingspan, and the degree of two-dimensionality of the wake–wing interaction were studied. The competition between the effects of the spanwise length scale of the incident wake and the strength of the leading-edge vortices determined the optimal aspect ratio, which was found to be around four. Increasing the sweep angle decreased the mean lift due to the decreased two-dimensionality of the vortex formation. Airfoils with sharp leading edge produced the strongest leading-edge vortices but further away from the wing surface, resulting in lower maximum lift. Relative to the performance in the undisturbed freestream, the increases in the stall angle and maximum lift coefficient were not significantly affected by the leading-edge shape.</p

Lift Enhancement of a Stationary Wing in a Wake

A stationary wing placed in the wake of a bluff body experiences lift enhancement. The quasi-periodic flow in the wake causes excitation of the separated flow in the post-stall conditions. The increase in the time-averaged lift force is associated with the flow separation, leading-edge vortex formation and subsequent reattachment in a process similar to the dynamic stall of oscillating wings. The lift enhancement is maximum for an optimal offset distance from the wake centerline. At the optimal location, potential flow oscillations, rather than the direct impingement of large vortices in the wake, provide the excitation. The smaller amplitude flow oscillations lead to a large separation bubble in the time-averaged sense in the post-stall regime. The delayed flow separation in the wake has a similar mechanism and frequency to those of the active flow control methods for separation. The degree of the lift enhancement is remarkable, given that the wake at a Reynolds number of 50,000 is expected to be highly three-dimensional

Compression Ratio Learning and Semantic Communications for Video Imaging

Camera sensors have been widely used in intelligent robotic systems. Developing camera sensors with high sensing efficiency has always been important to reduce the power, memory, and other related resources. Inspired by recent success on programmable sensors and deep optic methods, we design a novel video compressed sensing system with spatially-variant compression ratios, which achieves higher imaging quality than the existing snapshot compressed imaging methods with the same sensing costs. In this article, we also investigate the data transmission methods for programmable sensors, where the performance of communication systems is evaluated by the reconstructed images or videos rather than the transmission of sensor data itself. Usually, different reconstruction algorithms are designed for applications in high dynamic range imaging, video compressive sensing, or motion debluring. This task-aware property inspires a semantic communication framework for programmable sensors. In this work, a policy-gradient based reinforcement learning method is introduced to achieve the explicit trade-off between the compression (or transmission) rate and the image distortion. Numerical results show the superiority of the proposed methods over existing baselines

Semantic Communications with Variable-Length Coding for Extended Reality

Wireless extended reality (XR) has attracted wide attentions as a promising technology to improve users' mobility and quality of experience. However, the ultra-high data rate requirement of wireless XR has hindered its development for many years. To overcome this challenge, we develop a semantic communication framework, where semantically-unimportant information is highly-compressed or discarded in semantic coders, significantly improving the transmission efficiency. Besides, considering the fact that some source content may have less amount of semantic information or have higher tolerance to channel noise, we propose a universal variable-length semantic-channel coding method. In particular, we first use a rate allocation network to estimate the best code length for semantic information and then adjust the coding process accordingly. By adopting some proxy functions, the whole framework is trained in an end-to-end manner. Numerical results show that our semantic system significantly outperforms traditional transmission methods and the proposed variable-length coding scheme is superior to the fixed-length coding methods.Comment: 1. Update the performance of VL-SCC in Fig8. under new rate allocation architecture 2. Give a fair comparison between VL-SCC and SCC in Fig9. 3. fix the typo of LDPC rate (1/3 changed to 2/3) 4. Reduce L=32 to 16, and update the bp

Semantic-Aware Image Compressed Sensing

Deep learning based image compressed sensing (CS) has achieved great success. However, existing CS systems mainly adopt a fixed measurement matrix to images, ignoring the fact the optimal measurement numbers and bases are different for different images. To further improve the sensing efficiency, we propose a novel semantic-aware image CS system. In our system, the encoder first uses a fixed number of base CS measurements to sense different images. According to the base CS results, the encoder then employs a policy network to analyze the semantic information in images and determines the measurement matrix for different image areas. At the decoder side, a semantic-aware initial reconstruction network is developed to deal with the changes of measurement matrices used at the encoder. A rate-distortion training loss is further introduced to dynamically adjust the average compression ratio for the semantic-aware CS system and the policy network is trained jointly with the encoder and the decoder in an en-to-end manner by using some proxy functions. Numerical results show that the proposed semantic-aware image CS system is superior to the traditional ones with fixed measurement matrices.Comment: Modified versio

Tomato TERF1 modulates ethylene response and enhances osmotic stress tolerance by activating expression of downstream genes

AbstractThe interaction between ethylene and osmotic stress pathways modulates the expression of the genes relating to stress adaptation; however, the mechanism is not well understood. In this paper, we report a novel ethylene responsive factor, tomato ethylene responsive factor 1 (TERF1), that integrates ethylene and osmotic stress pathways. Biochemical analysis indicated that TERF1 binds to the GCC box (an element responsive to ethylene) and to the dehydration responsive element, which is responsive to the osmoticum. Expression of TERF1 was induced by ethylene and NaCl treatment. Under normal growth conditions, overexpression of TERF1 in tobacco activated the expression of GCC box-containing pathogen related genes and also caused the typical ethylene triple response. Further investigation indicated that transgenic TERF1 tobacco exhibited salt tolerance, suggesting that TERF1 might function as a linker between the ethylene and osmotic stress pathways