A One-Bit Programmable Multi-Functional Metasurface for Microwave Beam Shaping.

In this paper, we demonstrate a multi-functional metasurface for microwave beam-shaping application. The metasurface consists of an array of programmable unit cells, and each unit cell is integrated with one varactor diode. By turning the electrical bias on the diode on and off, the phase delay of the microwave reflected by the metasurface can be switched between 0 and π at a 6.2 GHz frequency, which makes the metasurface 1-bit-coded. By programming the 1-bit-coded metasurface, the generation of a single-focus beam, a double-focus beam and a focused vortex beam was experimentally demonstrated. Furthermore, the single-focus beam with tunable focal lengths of 54 mm, 103 mm and 152 mm was experimentally observed at 5.7 GHz. The proposed programmable metasurface manifests robust and flexible beam-shaping ability which allows its application to microwave imaging, information transmission and sensing applications.

Terahertz Metamaterials for Biosensing Applications: A Review.

In recent decades, THz metamaterials have emerged as a promising technology for biosensing by extracting useful information (composition, structure and dynamics) of biological samples from the interaction between the THz wave and the biological samples. Advantages of biosensing with THz metamaterials include label-free and non-invasive detection with high sensitivity. In this review, we first summarize different THz sensing principles modulated by the metamaterial for bio-analyte detection. Then, we compare various resonance modes induced in the THz range for biosensing enhancement. In addition, non-conventional materials used in the THz metamaterial to improve the biosensing performance are evaluated. We categorize and review different types of bio-analyte detection using THz metamaterials. Finally, we discuss the future perspective of THz metamaterial in biosensing.

Citrus green fruit detection via improved feature network extraction.

Introduction: It is crucial to accurately determine the green fruit stage of citrus and formulate detailed fruit conservation and flower thinning plans to increase the yield of citrus. However, the color of citrus green fruits is similar to the background, which results in poor segmentation accuracy. At present, when deep learning and other technologies are applied in agriculture for crop yield estimation and picking tasks, the accuracy of recognition reaches 88%, and the area enclosed by the PR curve and the coordinate axis reaches 0.95, which basically meets the application requirements.To solve these problems, this study proposes a citrus green fruit detection method that is based on improved Mask-RCNN (Mask-Region Convolutional Neural Network) feature network extraction. Methods: First, the backbone networks are able to integrate low, medium and high level features and then perform end-to-end classification. They have excellent feature extraction capability for image classification tasks. Deep and shallow feature fusion is used to fuse the ResNet(Residual network) in the Mask-RCNN network. This strategy involves assembling multiple identical backbones using composite connections between adjacent backbones to form a more powerful backbone. This is helpful for increasing the amount of feature information that is extracted at each stage in the backbone network. Second, in neural networks, the feature map contains the feature information of the image, and the number of channels is positively related to the number of feature maps. The more channels, the more convolutional layers are needed, and the more computation is required, so a combined connection block is introduced to reduce the number of channels and improve the model accuracy. To test the method, a visual image dataset of citrus green fruits is collected and established through multisource channels such as handheld camera shooting and cloud platform acquisition. The performance of the improved citrus green fruit detection technology is compared with those of other detection methods on our dataset. Results: The results show that compared with Mask-RCNN model, the average detection accuracy of the improved Mask-RCNN model is 95.36%, increased by 1.42%, and the area surrounded by precision-recall curve and coordinate axis is 0.9673, increased by 0.3%. Discussion: This research is meaningful for reducing the effect of the image background on the detection accuracy and can provide a constructive reference for the intelligent production of citrus.

An Active Electromagnetically Induced Transparency (EIT) Metamaterial Based on Conductive Coupling.

In this paper, we demonstrate an active metamaterial manifesting electromagnetically induced transparency (EIT) effect in the microwave regime. The metamaterial unit cell consists of a double-cross structure, between which a varactor diode is integrated. The capacitance of the diode is controlled by a reversed electrical bias voltage supplied through two connected strip lines. The diode behaves as a radiative resonant mode and the strip lines as a non-radiative resonant mode. The two modes destructively interference with each other through conductive coupling, which leads to a transmission peak in EIT effect. Through electrical control of the diode capacitance, the transmission peak frequency is shifted from 7.4 GHz to 8.7 GHz, and the peak-to-dip ratio is tuned from 1.02 to 1.66, demonstrating a significant tunability.

Flow Direction-Dependent Elastic Instability in a Symmetry-Breaking Microchannel.

This paper reports flow direction-dependent elastic instability in a symmetry-breaking microchannel. The microchannel consisted of a square chamber and a nozzle structure. A viscoelastic polyacrylamide solution was used for the instability demonstration. The instability was realized as the viscoelastic flow became asymmetric and unsteady in the microchannel when the flow exceeded a critical Weissenberg number. The critical Weissenberg number was found to be different for the forward-directed flow and the backward-directed flow in the microchannel.

Analysis of a Cantilevered Piezoelectric Energy Harvester in Different Orientations for Rotational Motion.

This paper investigates a piezoelectric energy harvester that consists of a piezoelectric cantilever and a tip mass for horizontal rotational motion. Rotational motion results in centrifugal force, which causes the axial load on the beam and alters the resonant frequency of the system. The piezoelectric energy harvester is installed on a rotational hub in three orientations-inward, outward, and tilted configurations-to examine their influence on the performance of the harvester. The theoretical model of the piezoelectric energy harvester is developed to explain the dynamics of the system and experiments are conducted to validate the model. Theoretical and experimental studies are presented with various tilt angles and distances between the harvester and the rotating center. The results show that the installation distance and the tilt angle can be used to adjust the resonant frequency of the system to match the excitation frequency.