Multisource satellite-borne synthetic aperture radar (SAR) images have different probability distributions. Traditional supervised learning, consequently, cannot achieve good test performance on one novel satellite-borne SAR dataset while training a good model on another existing satellite-borne SAR dataset. In this article, a domain adaptation (DA) Transformer object detection method is proposed to solve the unlabeled multisource satellite-borne SAR image object detection problem. Unlike existing DA methods based on convolutional neural network (CNN) that focus more on multi-level local feature extraction, we choose to use Vision Transformer (ViT) Faster region CNN (FRCNN) as the baseline network to cope with the extraction of global features of SAR images. Then, two classification tokens are used to learn the mapping of different domains and fully extract domain-specific knowledge, generating two different feature spaces that rely on the original label and pseudo-label to train the source and target domains feature spaces, respectively. Besides, the pseudo-label of target domain is also refined and reconstructed by feature clustering in order to improve the accuracy of target domain knowledge. Finally, the original detection head of FRCNN is employed to detect the target domain SAR image objects. Extensive experiments on image datasets from multisource satellite-borne SAR such as Gaofen-3, TerraSAR-X, Sentinel-1, and RadarSAT-2 show that compared to the other state of the art (SOTA) methods, the proposed method can achieve the greatest object detection accuracy. Especially, taking the recently proposed Transformer-based method as an example, our method has more than 5% improvement in accuracy and more than 16% reduction in training time.
Abstract Optical bound states in the continuum (BICs) lies in side the continuum and coexists with extended waves, but it remains perfectly confined without any radiation. This unique property of BICs has led to numerous applications, such as highly surface‐sensitive and spectrally sharp resonances for photonic biosensors. However, it remains challenging to experimentally realize the BICs in a single‐particle system, especially for subwavelength structures. This study presents the existence of optical BICs in a subwavelength metallic microstructure, and quasi‐BICs are observed experimentally in a waveguide system with only a single optimized aluminum meta‐particle. This plasmonic BICs is resulting from the destructive interference of two localized surface plasmon modes. Benefiting from its strong localized field confinement and substrate‐free merit of BICs, the experimentally measured quality factor (Q‐factor) of this transmission dip reach to 273. Additionally, this meta‐particle is experimentally verified to show a good sensitivity for both solids and liquids through the spectral shift of the BICs caused transmission dip. This finding extends the optical BICs to a subwavelength scale and opens practical application opportunities for ultrasmall‐quantity detection of biochemical substances.
Inverse synthetic aperture radar (ISAR) imaging technology has been widely used in military and civilian areas due to its ability of obtaining the fine structure of a target. However, the contradiction in the use of radar resources is highlighted when facing multi-target ISAR imaging problem. Compared with a single radar, the radar network which is constituted of many dispersed radars is expected to solve the multi-target imaging problem. An efficient resource allocation method plays an important role in guaranteeing the successful completion of the multi-target imaging task and improving the resource utilization of radar network. In this paper, according to the ISAR imaging principle, the relationship between the mission time and radar resources (i.e., radar node and radar power) is analyzed first. Thus the joint node selection and power allocation optimization model for multi-target ISAR imaging in radar network is constructed and the purpose is to minimize the mission time of the multi-target ISAR imaging tasks. Then the optimal radar node selection and power allocation scheme can be further obtained by the circular iterative method of relaxed convex optimization. Simulations demonstrate the effectiveness the proposed method.
Abstract Background Maternal smoking during pregnancy is associated with a host of detrimental effects on the exposed child. However, emerging evidence suggests that the adverse effects of smoking during pregnancy may be transmitted across generations. We conducted a systematic review to comprehensively summarize evidence on the association between grandmaternal smoking during pregnancy and health outcomes in the grandchild. Methods We searched MEDLINE, EMBASE, and 10 other databases from inception to March 02, 2021, updated on October 18, 2021, to identify analytic epidemiologic studies (prospective or retrospective cohort and case-control design) investigating the association between grandmaternal smoking during pregnancy and multiple health outcomes in the grandchild in all countries and settings. Three investigators independently screened records, extracted data, and assessed study quality using the Effective Public Health Practice Project (EPHPP) tool. Random-effects robust variance estimation was used to combine effect estimates whenever possible, separately for maternal and paternal line. Results Twenty-four reports from 15 unique studies with 23 health outcomes and 231,478 grandchildren were included. The overall study quality was moderate. In maternal line, grandmaternal smoking during pregnancy was associated with an increased risk of asthma (seven studies, 148,527 grandchildren, risk ratio (RR) 1.10, 95% CI 1.02 to 1.18) and higher birth weight (four studies, 19,478 grandchildren, mean difference (g) 40.04, 95% CI 22.60 to 57.49) in the grandchild. There was suggestive evidence that maternal grandmaternal smoking was associated with increased risks of acute lymphoblastic leukaemia, any cancer, autism, and attention deficit hyperactivity disorder, decreased risks of early-onset myopia and small birth weight for gestational age, and higher birth length and body mass index at birth. In paternal line, grandmaternal smoking during pregnancy was not associated with asthma (four studies, 41,069 grandchildren, RR 1.01, 95% CI 0.85 to 1.19), but there was suggestive evidence for lower intelligence quotient scores and decreased risks of early-onset myopia and intolerance to loud sound. Conclusions The effects of maternal smoking during pregnancy may not be limited to the exposed child, but be transmitted to the grandchild, possibly through germline epigenetic inheritance. Further public health efforts are warranted to strengthen smoking cessation programs for pregnant women.
The downward-looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3D SAR) has attracted a great deal of attention, due to the ability to obtain three-dimensional (3D) images. However, if the velocity and the yaw rate of the platform are not measured with enough accuracy, the azimuth signal cannot be compressed and then the 3D image of the scene cannot be obtained. In this paper, we propose a method for platform motion parameter estimation, and downward-looking 3D SAR imaging. A DLSLA 3D SAR imaging model including yaw rate was established. We then calculated the Doppler frequency modulation, which is related to the cross-track coordinates rather than the azimuth coordinates. Thus, the cross-track signal reconstruction was realized. Furthermore, based on the minimum entropy criterion (MEC), the velocity and yaw rate of the platform were accurately estimated, and the azimuth signal compression was also realized. Moreover, a deformation correction procedure was designed to improve the quality of the image. Simulation results were given to demonstrate the validity of the proposed method.
For radar imaging of multiple targets in radar network, it is necessary to schedule the imaging tasks among various radars at suitable time to achieve high performance under limited radar resources. In this paper, based on the image quality requirement, a task schedule method is proposed for multi-target inverse synthetic aperture radar (ISAR) imaging in radar network. Due to the image resolution is an important indicator of image quality and the imaging task is time sensitive, the relationship between the imaging resolution and task time is studied firstly. Thereafter, the task scheduling problem is converted into an optimization problem with time window constrains and an improved Quantum Genetic Algorithm (IQGA) is proposed to solve the problem. Then the task scheduling strategy which contains how to allocate the targets to the radars and when to observe the targets can be obtained. Finally, simulation results verify the effectiveness of the proposed method. With the help of the propose method, the multi-target ISAR task can be completed effectively and the resource utilization of the radar network can be improved.
Envelope alignment is one of the key steps for inverse synthetic aperture radar (ISAR) translational compensation. The traditional envelope alignment method cannot be accurately completed under a low signal-to-noise ratio (SNR), which will limit the accuracy of subsequent phase focusing. We propose a deep recurrent neural network (RNN) frame to address the problem. This is an end-to-end learning approach. Radar echo pulses are input to the network one by one according to time sequence. The inputs of each layer can be divided into two parts. The one is the current pulse, and the other one, named “state,” is the outputs of the previous layer except for the aligned pulse. Moreover, the outputs of each layer contain the “state” for the next layer and the aligned result of the input pulse. The above structure is a typical RNN, and the “states” transform the time-sequence information between different pulses. Compared with the traditional methods, the experiments verify that the proposed network can not only provide better alignment accuracy under low SNR but also require a shorter alignment time.
The Ultra-performance Nanophotonic Intrachip Communication (UNIC) project aims to achieve unprecedented high-density, low-power, large-bandwidth, and low-latency optical interconnect for highly compact supercomputer systems. This project, which has started in 2008, sets extremely aggressive goals on power consumptions and footprints for optical devices and the integrated VLSI circuits. In this paper we will discuss our challenges and present some of our first-year achievements, including a 320 fJ/bit hybrid-bonded optical transmitter and a 690 fJ/bit hybrid-bonded optical receiver. The optical transmitter was made of a Si microring modulator flip-chip bonded to a 90nm CMOS driver with digital clocking. With only 1.6mW power consumption measured from the power supply voltages and currents, the transmitter exhibits a wide open eye with extinction ratio >7dB at 5Gb/s. The receiver was made of a Ge waveguide detector flip-chip bonded to a 90nm CMOS digitally clocked receiver circuit. With 3.45mW power consumption, the integrated receiver demonstrated -18.9dBm sensitivity at 5Gb/s for a BER of 10-12. In addition, we will discuss our Mux/Demux strategy and present our devices with small footprints and low tuning energy.
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