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In this paper, a patch array antenna with wideband circular polarization and high gain is proposed by utilizing a hybrid metasurface (MS). A corner-cut slotted patch antenna was chosen as the source due to the possible generation of CP mode. The hybrid MS (HMS), consisting of a receiver MS (RMS) arranged in a 2 × 2 array of squared patches and a linear-to-circular polarization conversion (LCPC) MS surrounding it was then utilized as the superstrate driven by the source. The LCPC MS cell is a squared-corner-cut patch with a 45° oblique slot etched, which has the capability for wideband LCPC. The LCPC unit cell possesses wideband PC capabilities, as demonstrated by the surface current analysis and S-parameter simulations conducted using a Floquet–port setup. The LP EM wave radiated by the source antenna was initially received by the RMS, then converted to a CP wave as it passed through the LCPC MS, and ultimately propagated into space. To further enhance the LCPC properties, an improved HMS (IHMS) was then proposed with four cells cut at the corners, based on the original HMS design. To verify this design, both CMA and E-field were utilized to analyze the three MSs, indicating that the IHMS possessed a wideband LCPC capability compared to the other two MSs. The proposed antenna was then arranged in a 2 × 2 array with sequential rotation to further enhance its properties. As demonstrated by the measurements, the array antenna achieved an S11 bandwidth of 60.5%, a 3 dB AR bandwidth of 2.85 GHz, and a peak gain of 15.1 dBic, all while maintaining a low profile of only 0.09λ0. [ABSTRACT FROM AUTHOR]

Despite progress in the past decades, 3D shape acquisition techniques are still a threshold for various 3D face-based applications and have therefore attracted extensive research. Moreover, advanced 2D data generation models based on deep networks may not be directly applicable to 3D objects because of the different dimensionality of 2D and 3D data. In this work, we propose two novel sampling methods to represent 3D faces as matrix-like structured data that can better fit deep networks, namely (1) a geometric sampling method for the structured representation of 3D faces based on the intersection of iso-geodesic curves and radial curves, and (2) a depth-like map sampling method using the average depth of grid cells on the front surface. The above sampling methods can bridge the gap between unstructured 3D face models and powerful deep networks for an unsupervised generative 3D face model. In particular, the above approaches can obtain the structured representation of 3D faces, which enables us to adapt the 3D faces to the Deep Convolution Generative Adversarial Network (DCGAN) for 3D face generation to obtain better 3D faces with different expressions. We demonstrated the effectiveness of our generative model by producing a large variety of 3D faces with different expressions using the two novel down-sampling methods mentioned above. [ABSTRACT FROM AUTHOR]

The traditional optical fiber-based twist sensing has the disadvantage of low sensitivity and difficulty of distinguishing the twist direction. Moreover, chiral isomerism may lead to sensing errors. In this paper, a six-hole helical microstructured optical fiber (HMSF) with a thin-gold-film-coat based on the surface plasmon resonance (SPR) effect was designed. The twist sensing characteristics of this fiber were further analyzed. Numerical calculation and analysis show that the combination of helical effect and SPR effect can design an HMSF-based sensor that is very sensitive to distortion. In the torsion range of ±300°, the distortion sensitivity can reach 2470.7 pm/(rad/m), and the linear correlation coefficient is 0.99996. Based on the special sensing mechanism, it has a good linear coefficient over a large range. Additionally, the direction of the twist can be easily discerned. The HMSF in this work not only has high sensitivity, high linearity, high fault tolerance rate, and a wide range of measurement, but is also easy to manufacture. Therefore, it is promising in the field of twist sensing and has a good application prospect. [ABSTRACT FROM AUTHOR]

Catching cancer at an early stage is necessary to make it easier to treat and to save people's lives rather than just extending them. Reactive oxygen species (ROS) have sparked a huge interest owing to their vital role in various biological processes, especially in tumorigenesis, thus leading to the potential of ROS as prognostic biomarkers for cancer. Herein, a non-enzymatic biosensor for the dynamic monitoring of intracellular hydrogen peroxide (H2O2), the most important ROS, via an effective electrode composed of poly (diallyldimethylammonium chloride) (PDDA)-capped reduced graphene oxide (RGO) nanosheets with high loading trimetallic AuPtAg nanoalloy, is proposed. The designed biosensor was able to measure H2O2 released from different cancerous cells promptly and precisely owing to the impressive conductivity of RGO and PDDA and the excellent synergistic effect of the ternary alloy in boosting the electrocatalytic activity. Built upon the peroxidase-like activity of the nanoalloy, the developed sensor exhibited distinguished electrochemical performance, resulting in a low detection limit of 1.2 nM and a wide linear range from 0.05 μM to 5.5 mM. Our approach offers a significant contribution toward the further elucidation of the role of ROS in carcinogenesis and the effective screening of cancer at an early stage. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of flexible and accurate global calibration for multiple non-overlapping vision sensors in a confined workspace. Instead of using an auxiliary calibration pattern, the proposed method uses one laser tracker and only its accessory target sphere to obtain all the 3D calibration points and then accomplish the initial estimation of pose between the vision sensors. Then, the 3D calibration points and the extrinsic parameters between vision sensors are further optimized via the bundle adjustment algorithm based on the distance and reprojection constraints. Experiments were conducted to validate the performance and the experimental results demonstrate that the distance error can be decreased from 3.5 mm to 0.8 mm after introducing the distance and reprojection constraints. [ABSTRACT FROM AUTHOR]

To dynamically monitor the horizontal well, we studied the oil–water two-phase water holdup detection method based on transmission lines, and designed a micro-sensor and a sensor-array water holdup detection tool. We modeled the relationship of the dielectric constant of the transmission line filling medium and the amplitude and phase shift of the electromagnetic wave signal on the transmission line by using the time-domain analysis. We proposed a novel method to measure the water holdup of oil–water mixtures based on the phase shift of signals on the conical spiral transmission line. Furthermore, we simulated and optimized the structural parameters by software simulation, and developed a small conical spiral water holdup sensor suitable for arraying. The single sensor with detection circuits can achieve the full scale (water holdup from 0% to 100%) measurement with resolution better than 3%. On this basis, 12 sensors are used to develop a clock-like sensor-array water holdup detection tool, realizing the array detection of the distribution of the cross-section medium in horizontal wells. [ABSTRACT FROM AUTHOR]

The exact location of objects, such as infrastructure, is crucial to the systematic understanding of the built environment. The emergence and development of the Internet of Things (IoT) have attracted growing attention to the low-cost location scheme, which can respond to a dramatic increasing amount of public infrastructure in smart cities. Various Radio Frequency IDentification (RFID)-based locating systems and noise mitigation methods have been developed. However, most of them are impractical for built environments in large areas due to their high cost, computational complexity, and low noise detection capability. In this paper, we proposed a novel noise mitigation solution integrating the low-cost localization scheme with one mobile RFID reader. We designed a filter algorithm to remove the influence of abnormal data. Inspired the sampling concept, a more carefully parameters calibration was carried out for noise data sampling to improve the accuracy and reduce the computational complexity. To achieve robust noise detection results, we employed the powerful noise detection capability of the random sample consensus (RANSAC) algorithm. Our experiments demonstrate the effectiveness and advantages of the proposed method for the localization and noise mitigation in a large area. The proposed scheme has potential applications for location-based services in smart cities. [ABSTRACT FROM AUTHOR]