Study on a Composite Patch Antenna Based on Left Handed Material with Near Zero Index

In this paper, a composite patch antenna based on left handed material (LHM) with near zero index (NZI) is presented. This composite patch antenna is designed by assembling split resonant rings (SRRs) and metal strips on the substrates. This multilayer composite structure results in a metamaterial with NZI near 13.89 GHz. A method of finite difference time domain (FDTD) is used. The results show that the composite antenna’s gain improves 0.61 times, and its bandwidth adds 2.95 times compared to the conventional antenna’s ones. The results indicate that this composite patch antenna system can reduce return loss of the antenna and increase the gain obviously

Electrochemical biosensing of chilled seafood freshness by xanthine oxidase immobilized on copper-based metal organic framework nanofiber film

ATP degradation is an important biochemical change during decomposition of seafood. Hypoxanthine and xanthine which are formed during ATP degradation process can be used for evaluation of chilled seafood freshness. Present study successfully immobilized xanthine oxidase (XOD) onto a type of biocompatible copper-based metal organic framework nanofibers (Cu-MOF) film and used it for fabrication of a hypoxanthine and xanthine electrochemical biosensor. Cu-MOF can efficiently entrap XOD, provide a suitable atmosphere for XOD biocatalysis, and ensure good electron transfer between enzyme and electrode surface. The as-prepared XOD-electrochemical biosensor demonstrated high sensitivity for both hypoxanthine and xanthine with a wide linear range (0.01 to 10 μM) and low limit of detection. During the 3-week storage stability test at 4 °C, the fabricated biosensor demonstrated good reusability (up to 100 times) and excellent storage stability for hypoxanthine and xanthine. When applied for detection of hypoxanthine and xanthine in chilled squid and large yellow croaker, the XOD-electrochemical biosensors demonstrated good recovery rates

Development of an Optically Detected Magnetic Resonance Spectrometer under pressure

Nitrogen-vacancy color centers in diamond have attracted broad attention as quantumsensors for AC mangetic field. Here we develop a quantum diamond spectrometer for both ambient condition and under pressure in diamond anvil cells. Optically based nuclear magnetic resonance has been achieved and various AC sensing methods have been demonstrated. A clear signal from precessing 13C spins in the diamond lattice has been found. The synchronized readout sensitivity at ambient and 3.6 GPa pressure are 1.9 and 7.6 nT/√Hz, respectively. In order to decrease the pressure inhomogeneity, a novel method – double quantum resonance – has been testified and discussed. Using conventional nuclear magnetic resonance approach, a rare earth insulator TmVO4, which is a model system to study nematic order and the roles played by nematic fluctuations, has been studied as a function of temperature and magnetic field direction orientation. We find that the magnetic shift tensor agrees quantitatively with direct dipolar coupling between the V nuclear moments and the Tm 4f moments. The spin-lattice relaxation rate exhibits a steep minimum for a field oriented 90◦ to the c axis, which is inconsistent with purely magnetic fluctuations. It is likely that both quadrupolar and magnetic fluctuations are present and drive spin-lattice relaxation

Development of an Optically Detected Magnetic Resonance Spectrometer under pressure

Nitrogen-vacancy color centers in diamond have attracted broad attention as quantumsensors for AC mangetic field. Here we develop a quantum diamond spectrometer for both ambient condition and under pressure in diamond anvil cells. Optically based nuclear magnetic resonance has been achieved and various AC sensing methods have been demonstrated. A clear signal from precessing 13C spins in the diamond lattice has been found. The synchronized readout sensitivity at ambient and 3.6 GPa pressure are 1.9 and 7.6 nT/√Hz, respectively. In order to decrease the pressure inhomogeneity, a novel method – double quantum resonance – has been testified and discussed. Using conventional nuclear magnetic resonance approach, a rare earth insulator TmVO4, which is a model system to study nematic order and the roles played by nematic fluctuations, has been studied as a function of temperature and magnetic field direction orientation. We find that the magnetic shift tensor agrees quantitatively with direct dipolar coupling between the V nuclear moments and the Tm 4f moments. The spin-lattice relaxation rate exhibits a steep minimum for a field oriented 90◦ to the c axis, which is inconsistent with purely magnetic fluctuations. It is likely that both quadrupolar and magnetic fluctuations are present and drive spin-lattice relaxation

Optical Remote Sensing Image Cloud Detection with Self-Attention and Spatial Pyramid Pooling Fusion

Cloud detection is a key step in optical remote sensing image processing, and the cloud-free image is of great significance for land use classification, change detection, and long time-series landcover monitoring. Traditional cloud detection methods based on spectral and texture features have acquired certain effects in complex scenarios, such as cloud–snow mixing, but there is still a large room for improvement in terms of generation ability. In recent years, cloud detection with deep-learning methods has significantly improved the accuracy in complex regions such as high-brightness feature mixing areas. However, the existing deep learning-based cloud detection methods still have certain limitations. For instance, a few omission alarms and commission alarms still exist in cloud edge regions. At present, the cloud detection methods based on deep learning are gradually converted from a pure convolutional structure to a global feature extraction perspective, such as attention modules, but the computational burden is also increased, which is difficult to meet for the rapidly developing time-sensitive tasks, such as onboard real-time cloud detection in optical remote sensing imagery. To address the above problems, this manuscript proposes a high-precision cloud detection network fusing a self-attention module and spatial pyramidal pooling. Firstly, we use the DenseNet network as the backbone, then the deep semantic features are extracted by combining a global self-attention module and spatial pyramid pooling module. Secondly, to solve the problem of unbalanced training samples, we design a weighted cross-entropy loss function to optimize it. Finally, cloud detection accuracy is assessed. With the quantitative comparison experiments on different images, such as Landsat8, Landsat9, GF-2, and Beijing-2, the results indicate that, compared with the feature-based methods, the deep learning network can effectively distinguish in the cloud–snow confusion-prone region using only visible three-channel images, which significantly reduces the number of required image bands. Compared with other deep learning methods, the accuracy at the edge of the cloud region is higher and the overall computational efficiency is relatively optimal