Broadband RCS Reduction of Antenna with AMC Using Gradually Concentric Ring Arrangement

This paper proposes a new method to the broadband RCS reduction with the artificial magnetic conductor (AMC) surfaces. The AMC surfaces can introduce a zero-degree reflection phase shift to incident waves. The phase difference between the antenna and AMC structures is 180°. Therefore, the AMC structures can be used to achieve RCS reduction. However, the bandwidth of zero-degree reflection phase of AMC structures is very narrow. In light of this, a novel gradually concentric ring arrangement AMC (GCRA-AMC) which can be applied to achieve the broadband RCS reduction is presented. The simulated and measured results show that the radiation performance of antennas is preserved when the GCRA-AMC is used. The RCS of the antenna with GCRA-AMC has been considerably reduced in a broad frequency band. The largest RCS reduction is more than 17 dB

Target material identification with commodity RFID devices

Singapore National Research Foundation under IDM Futures Funding Initiativ

detrex: Benchmarking Detection Transformers

The DEtection TRansformer (DETR) algorithm has received considerable attention in the research community and is gradually emerging as a mainstream approach for object detection and other perception tasks. However, the current field lacks a unified and comprehensive benchmark specifically tailored for DETR-based models. To address this issue, we develop a unified, highly modular, and lightweight codebase called detrex, which supports a majority of the mainstream DETR-based instance recognition algorithms, covering various fundamental tasks, including object detection, segmentation, and pose estimation. We conduct extensive experiments under detrex and perform a comprehensive benchmark for DETR-based models. Moreover, we enhance the performance of detection transformers through the refinement of training hyper-parameters, providing strong baselines for supported algorithms.We hope that detrex could offer research communities a standardized and unified platform to evaluate and compare different DETR-based models while fostering a deeper understanding and driving advancements in DETR-based instance recognition. Our code is available at https://github.com/IDEA-Research/detrex. The project is currently being actively developed. We encourage the community to use detrex codebase for further development and contributions.Comment: project link: https://github.com/IDEA-Research/detre

Undoped Strained Ge Quantum Well with Ultrahigh Mobility Grown by Reduce Pressure Chemical Vapor Deposition

We fabricate an undoped Ge quantum well under 30 nm Ge0.8Si0.2 shallow barrier with reverse grading technology. The under barrier is deposited by Ge0.8Si0.2 followed by Ge0.9Si0.1 so that the variation of Ge content forms a sharp interface which can suppress the threading dislocation density penetrating into undoped Ge quantum well. And the Ge0.8Si0.2 barrier introduces enough in-plane parallel strain -0.41% in the Ge quantum well. The heterostructure field-effect transistors with a shallow buried channel get a high two-dimensional hole gas (2DHG) mobility over 2E6 cm2/Vs at a low percolation density of 2.51 E-11 cm2. We also discover a tunable fractional quantum Hall effect at high densities and high magnetic fields. This approach defines strained germanium as providing the material basis for tuning the spin-orbit coupling strength for fast and coherent quantum computation.Comment: 11 pages, 5 figure

High Fat Diet Induces Formation of Spontaneous Liposarcoma in Mouse Adipose Tissue with Overexpression of Interleukin 22

Interleukin 22 (IL-22) is a T-cell secreted cytokine that modulates inflammatory response in nonhematopoietic tissues such as epithelium and liver. The function of IL-22 in adipose tissue is currently unknown. We generated a transgenic mouse model with overexpression of IL-22 specifically in adipose tissue. The IL-22 transgenic mice had no apparent changes in obesity and insulin resistance after feeding with high fat diet (HFD). Unexpectedly, all the IL-22 transgenic mice fed with HFD for four months developed spontaneous tumors in epididymal adipose tissue. Histological analysis indicated that the tumors were well-differentiated liposarcomas with infiltration of inflammatory cells. IL-22 overexpression promotes production of inflammatory cytokines such as IL-1β and IL-10 and stimulates ERK phosphorylation in adipose tissue. Furthermore, IL-22 treatment in differentiated 3T3-L1 adipocytes could induce IL-1β and IL-10 expression, together with stimulation of ERK phosphorylation. Taken together, our study not only established a novel mouse model with spontaneous liposarcoma, but also revealed that IL-22 overexpression may collaborate with diet-induced obesity to impact on tumor development in mouse

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Transformer-Based Feature Aggregation and Stitching Network for Crowd Counting

With the rapid development of society, crowded scenes can be seen almost everywhere. Therefore, it is important to accurately predict the number and density distribution of people in those crowded regions by utilizing image analysis. In recent years, most of those studies based on various deep learning technologies for pedestrian image analysis have been based on mature convolution neural networks (CNN). Nowadays, vision transformer has demonstrated its competitive performance compared with CNNs in many computer vision domains, and provides a novel idea for density distribution in image. In this paper, we modify the Swin-Transformer and integrate CNN to propose a feature aggregation and stitching network (FASNet), which effectively improves the counting accuracy. The hierarchical vision transformer backbone captures the global multi-scale features of the image, and encode the interaction information among different pedestrians in the deep network. Feature Aggregation Module (FAM) is used to fuse the deep and shallow features, and then Density Regression Module (DRM) upsamples the output of FAM and finally produces the predicted crowd density map and the final count. In addition, we propose the Feature Stitching Mechanism (FSM) to cope with the feature damage or loss caused by image cropping during the model testing. The experimental results on three benchmark datasets (UCF_CC_50, UCF_QNRF, Shanghaitech) demonstrate the effectiveness of our proposed scheme