Performance Evaluation for Subarray-based Reconfigurable Intelligent Surface-Aided Wireless Communication Systems

Reconfigurable intelligent surfaces (RISs) have received extensive concern to improve the performance of wireless communication systems. In this paper, a subarray-based scheme is investigated in terms of its effects on ergodic spectral efficiency (SE) and energy efficiency (EE) in RIS-assisted systems. In this scheme, the adjacent elements divided into a subarray are controlled by one signal and share the same reflection coefficient. An upper bound of ergodic SE is derived and an optimal phase shift design is proposed for the subarray-based RIS. Based on the upper bound and optimal design, we obtain the maximum of the upper bound. In particular, we analytically evaluate the effect of the subarray-based RIS on EE since it reduces SE and power consumption simultaneously. Numerical results verify the tightness of the upper bound, demonstrate the effectiveness of the optimal phase shift design for the subarray-based RIS, and reveal the effects of the subarray-based scheme on SE and EE.Comment: 6 pages, 4 figures, accepted by IEEE GLOBECOM 202

Revisiting DETR Pre-training for Object Detection

Motivated by that DETR-based approaches have established new records on COCO detection and segmentation benchmarks, many recent endeavors show increasing interest in how to further improve DETR-based approaches by pre-training the Transformer in a self-supervised manner while keeping the backbone frozen. Some studies already claimed significant improvements in accuracy. In this paper, we take a closer look at their experimental methodology and check if their approaches are still effective on the very recent state-of-the-art such as $\mathcal{H}$-Deformable-DETR. We conduct thorough experiments on COCO object detection tasks to study the influence of the choice of pre-training datasets, localization, and classification target generation schemes. Unfortunately, we find the previous representative self-supervised approach such as DETReg, fails to boost the performance of the strong DETR-based approaches on full data regimes. We further analyze the reasons and find that simply combining a more accurate box predictor and Objects$365$ benchmark can significantly improve the results in follow-up experiments. We demonstrate the effectiveness of our approach by achieving strong object detection results of AP=$59.3\%$ on COCO val set, which surpasses $\mathcal{H}$-Deformable-DETR + Swin-L by +$1.4\%$. Last, we generate a series of synthetic pre-training datasets by combining the very recent image-to-text captioning models (LLaVA) and text-to-image generative models (SDXL). Notably, pre-training on these synthetic datasets leads to notable improvements in object detection performance. Looking ahead, we anticipate substantial advantages through the future expansion of the synthetic pre-training dataset

Axial wind effects on stratification and longitudinal sediment transport in a convergent estuary during wet season

Author Posting. © American Geophysical Union, 2020. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research-Oceans 125(2), (2020): e2019JC015254, doi:10.1029/2019JC015254.The Coupled Ocean‐Atmosphere‐Wave‐Sediment Transport (COAWST) modeling system was used to examine axial wind effects on vertical stratification and sediment transport in a convergent estuary. The model demonstrated that stratification dynamics in the upper estuary (Kelvin number <1; Ke= fB/√ g'hs) are dominated by longitudinal wind straining, whereas the dominant mechanism governing estuarine stratification in the lower estuary (Kelvin number ~1) is lateral wind straining. Barotropic advection contributes to seaward sediment transport and peaks during spring tides, whereas estuarine circulation causes landward sediment transport with a maximum during neap tides. Down‐estuary winds impose no obvious effects on longitudinal sediment flux, whereas up‐estuary winds contribute to enhanced seaward sediment flux by increasing the tidal oscillatory flux. The model also demonstrates that bottom friction is significantly influenced by vertical stratification over channel regions, which is indirectly affected by axial winds.This research was funded by the National Natural Science Foundation of China (Grants 41576089, 51761135021, and 41890851), the National Key Research and Development Program of China (2016YFC0402603) and the Guangdong Provincial Water Conservancy Science and Technology Innovation Project (Grant 201719). We thank Professor Liangwen Jia at the Sun Yat‐sen University for his kindly providing the surficial sediment samples data in 2011. We also thank graduate students Guang Zhang and Yuren Chen from the Sun Yat‐sen University for their help in data analysis. We are grateful to two anonymous reviewers for their insightful comments to help improve this manuscript. The data related to this article is available online at the Zenodo website (https://zenodo.org/record/3606471).2020-07-1

Can acoustic indices reflect the characteristics of public recreational behavioral in urban green spaces?

Acoustic indicators serve as an effective means of assessing the quality of urban green space soundscape. The informative, easy accessibility and non-invasive nature of acoustic monitoring renders it an excellent tool for studying the interaction among the natural environment, wildlife, and human activities. Urban green space is essential in the urban ecosystem and constitutes the primary location for public outdoor recreation. However, the existing methods for monitoring public recreational behavior, such as on-site observation, drone observation, or questionnaire interviews, require significant labor or professional expertise. All of these methods have their limitations, so there is still much to be researched in the acoustic indices and recreational behavior. As a result, the potential for using acoustic characteristics to monitor public recreational behavior remains underexplored. To address this gap, this study investigates the potential of 5 widely used acoustic indices and acoustic intensity for monitoring public recreational behavior: Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Richness (AR), Normalized Difference Soundscape Index (NDSI), and Power Spectral Density (PSD). Data were collected from 35 monitoring points in urban green spaces during the opening hours (6:00–22:00) to analyze the relationship between these indices and public recreational behavior. The findings indicate that (1) ACI, ADI, and AR daily exhibited multi-peak daily variation characteristics similar to those of public recreational behavior, displaying a “W” shape, while NDSI exhibits opposite variation characteristics; (2) the spatial variation characteristics of ACI, ADI, and AR change in response to the green space, and these changes align with public recreational behavior; (3) the correlation analysis and generalized linear mixed model construction further demonstrate that acoustic indices are effective in capturing the dynamic activities of visitor behavior; and (4) PSD undergoes significant temporal dynamic changes along the frequency gradient, with different frequency intervals reflecting the activity information of different recreational behaviors. In conclusion, this research highlights the effectiveness of using acoustic indices to analyze both the spatial and temporal variation characteristics of public recreational behavior in urban green spaces. The results can provide valuable data support for the enhancement and renovation of urban green spaces

Rank Optimization for MIMO systems with RIS: Simulation and Measurement

Reconfigurable intelligent surface (RIS) is a promising technology that can reshape the electromagnetic environment in wireless networks, offering various possibilities for enhancing wireless channels. Motivated by this, we investigate the channel optimization for multiple-input multiple-output (MIMO) systems assisted by RIS. In this paper, an efficient RIS optimization method is proposed to enhance the effective rank of the MIMO channel for achievable rate improvement. Numerical results are presented to verify the effectiveness of RIS in improving MIMO channels. Additionally, we construct a 2$\times$2 RIS-assisted MIMO prototype to perform experimental measurements and validate the performance of our proposed algorithm. The results reveal a significant increase in effective rank and achievable rate for the RIS-assisted MIMO channel compared to the MIMO channel without RIS

Choosing legumes and perennial grasses

Many letters are received annually by the Iowa Agricultural Experiment Station requesting information on the different legumes and grasses. This publication has been prepared to answer the more important questions pertaining to the choice of legumes and grasses for different uses and conditions.2 Legumes of greatest value for different uses or soil conditions in Iowa are (1) alfalfa, (2) medium red clover, (3) mammoth red clover, (4) alsike clover, (5) white clover, (6) the biennial white and yellow sweet clovers, (7) hubam clover, (the annual white sweet clover) (8) Korean lespedeza, (9) dalea and (10) soybeans. A discussion of soybeans is largely omitted in this publication since the growing of this crop is entirely different from that of the others.

Editorial: Omics-driven crop improvement for stress tolerance, Volume II

Crops are vulnerable to biotic and abiotic stresses that lead to reduced yields. Biotic stresses, such as fungi and pests, cause crops to rot and develop diseases. In contrast, abiotic stresses—such as high temperatures, salinity and mineral toxicity, and water shortages—irreversibly affect crops at different developmental stages, such as flowering, grain filling, and maturation through signal transduction, gene expression, and protein modifications. An increase of 1°C in the global average temperature is projected to significantly reduce crop yields. Excessive soil salinity tends to inhibit plant growth, hinder photosynthesis, and require metabolic adjustments. However, some crop types and species can tolerate modest levels of salinity without affecting their growth and yield. Drought is one of the most damaging abiotic stresses affecting severely the productivity of cereal crops. Rice struggles to survive in water-deficient fields, while maize is highly susceptible to drought. Therefore, improving crop stress tolerance is crucial for yield stability and healthy growth