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Intelligent metasurfaces to create smart electromagnetic environments have attracted a surge of interest. The multiscale property of metasurfaces results in a huge computational challenge, further hindering their engineering development. Our previous paper has developed a fast finite difference time domain (FDTD) algorithm for patchlike metasurfaces analysis using the surface susceptibility model (SSM). This study extends that to efficient fishnetlike metasurfaces analysis using the surface porosity model (SPM), a useful complement to SSM. We introduce the SPM to FDTD, replacing brute force simulations, to accelerate fishnetlike metasurfaces analysis. At last, we conduct a series of numerical experiments, demonstrating that the proposed FDTD-SPM algorithm greatly reduces the number of computational meshing lattices and the consumed time. [ABSTRACT FROM AUTHOR]

Robot path planning in 3D space is a challenging problem for its complex configuration. Sampling-based algorithms have gained great success in solving path planning problems in 3D space, but the quality of the initial path is not guaranteed and the convergence to the optimal solution is slow. To address these problems, in this article, we present a novel sampling-based path planning framework enhanced by the deep neural network (DNN) with applications to 3D space. In the proposed framework, we first train the DNN with a number of successful path planning cases in 3D space. Then the DNN is utilized to predict the promising region where the feasible path probably exists for a given path planning problem. This predicted promising region serves as a nonuniform sampling heuristic to bias the sampling process of the path planner. In this way, the path planner can focus on the promising region in the exploration and exploitation process so that the path planning speed gets accelerated. We conduct numerical simulations to evaluate the performance of the proposed algorithm and the results show that it can perform much better than conventional path planning algorithms. Furthermore, we also investigate the performance of different DNN architectures for path planning in 3D space. Note to Practitioners—In this work, we aim to provide an efficient learning-based method to accelerate the robot path planning process in 3D space. Conventional path planning algorithms need to perceive the environment first, and then implement a series of calculations such as collision checking and data storing to generate a feasible path. When facing complex and high-dimensional environments, they do not perform well. But the proposed neural network method in this article can predict the promising region where the feasible path exists for any given environment. This prediction result is used to guide the path planning process so that the algorithm performance can get significantly improved. Apart from sampling-based algorithms, the proposed neural network model can also be extended to other types of path planning algorithms. [ABSTRACT FROM AUTHOR]

In this brief, a compact coupled-line complex impedance transformer (CIT) with a series susceptance component is presented. It is composed of acoupledlineand a susceptancecomponentseries at the edges of the coupled line. By inserting the susceptance component, arbitrary complex impedance can be transformed and the coupled line has weak coupling coefficient and small impedance value, which shows the feature of compact, easy fabrication and size miniaturization. The design equations for the proposed CIT are derived by using the port voltage-current method combined with the even-odd mode decomposition technology. For verification, two prototypes are designed at 1.0 GHz. The measured results demonstrate that good impedance matching is obtained by theproposed CIT with arbitrary terminal impedances. [ABSTRACT FROM AUTHOR]

Analysis of nonuniform metasurfaces requires enormous computational resources and even cannot be achieved in some cases, due to their multiscale property. To address this, we propose a fast analysis method in finite difference time domain (FDTD) scheme in this article. Two steps are required: first, nonuniform metasurfaces are modeled as a distribution of surface susceptibilities. Secondly, the surface susceptibility model (SSM) is inserted into FDTD equations in a form of surface polarization currents based on generalized sheet transition conditions (GSTCs). A detailed comparison is made between brute force simulation and our proposed method. Compared with the hours consumed for physical model simulation, surface susceptibility model simulation’s time consumption is in seconds while simultaneously maintaining similar field responses independent of the incident wave’s angle and polarization. [ABSTRACT FROM AUTHOR]

Polyp recognition in colonoscopy images is crucial for early colorectal cancer detection and treatment. However, the current manual review requires undivided concentration of the gastroenterologist and is prone to diagnostic errors. In this article, we present an effective, two-stage approach called PLPNet, where the abbreviation “PLP” stands for the word “polyp,” for automated pixel-accurate polyp recognition in colonoscopy images using very deep convolutional neural networks (CNNs). Compared to hand-engineered approaches and previous neural network architectures, our PLPNet model improves recognition accuracy by adding a polyp proposal stage that predicts the location box with polyp presence. Several schemes are proposed to ensure the model’s performance. First of all, we construct a polyp proposal stage as an extension of the faster R-CNN, which performs as a region-level polyp detector to recognize the lesion area as a whole and constitutes stage I of PLPNet. Second, stage II of PLPNet is built in a fully convolutional fashion for pixelwise segmentation. We define a feature sharing strategy to transfer the learned semantics of polyp proposals to the segmentation task of stage II, which is proven to be highly capable of guiding the learning process and improve recognition accuracy. Additionally, we design skip schemes to enrich the feature scales and thus allow the model to generate detailed segmentation predictions. For accurate recognition, the advanced residual nets and feature pyramids are adopted to seek deeper and richer semantics at all network levels. Finally, we construct a two-stage framework for training and run our model convolutionally via a single-stream network at inference time to efficiently output the polyp mask. Experimental results on public data sets of GIANA Challenge demonstrate the accuracy gains of our approach, which surpasses previous state-of-the-art methods on the polyp segmentation task (74.7 Jaccard Index) and establishes new top results in the polyp localization challenge (81.7 recall). Note to Practitioners—Given the current manual review of colonoscopy is laborious and time-consuming, computational methods that can assist automatic polyp recognition will enhance the outcome both in terms of efficiency and diagnostic accuracy of colonoscopy. This article suggests a new approach using a very deep convolutional neural network (CNN) architecture for polyp recognition, which gains accuracy from deeper and richer representations. The method, called PLPNet, can effectively detect polyps in colonoscopy images and generate high-quality segmentation masks in a pixel-to-pixel manner. We evaluate the proposed framework on publicly available data sets, and we show by experiments that our method surpasses the state-of-the-art polyp recognition results. The finding of this article corroborates that CNNs with very deep architecture and richer semantics are highly efficient in medical image learning and inference. We believe that the proposed method will facilitate potential computer-aided applications in clinical practice, in that it can enhance medical decision-making in cancer detection and imaging. [ABSTRACT FROM AUTHOR]

Fruit detection is a fundamental task for automatic yield estimation. The goal is to detect all the fruits in images. The-state of the art of fruit detection algorithm, Faster R-CNN, shows a lack of detection advantage on small fruits. One of the reasons is only that single-level features and a classifier are used for localization of proposal candidates. In this article, we propose to incorporate a multiple classifier fusion strategy into a Faster R-CNN network for small fruit detection. We utilize features from three different levels to learn three classifiers for objectness classification in the stage of proposal localization. Probabilities from classifiers are combined by a simple convolutional layer to generate final objectness classification for proposal candidates. During training, in order to train a model with strong generalization capability, we propose to use correlation coefficients to measure the diversity of multiple classifiers. A novel loss function with classifier correlation is introduced to train the region proposal network. We evaluate the proposed model on two data sets of small fruits. Extensive experiments show that the proposed model outperforms the state-of-the-art detectors for fruit detection. Note to Practitioners—This article was motivated by the problem of the detection of small fruits for yield estimation but it also applies to the detection of other small objects. Existing approaches to fruit detection experience difficulty in detecting small fruits and the overall detection accuracy suffers as a result. This article suggests a novel approach to detect fruits of all sizes by using a multiple classifier fusion strategy in the stage of fruit localization. In the learning of the classifiers, multilevel features of fruits are extracted. In addition, we achieve classifier diversity through the analysis of the correlation of classification probabilities. The diversity of classifiers also helps improve the performance of fruit localization. Using experiments on real fruit tree images, we show that the suggested approach is feasible on orange detection of various sites in different seasons. As a future work, we will integrate the suggested algorithm into a fruit yield estimation system and test it online. [ABSTRACT FROM AUTHOR]

Accurate recognition of polyps is crucial for early colorectal cancer diagnosis and treatment. Wireless capsule endoscopy (WCE) is a noninvasive, wireless imaging tool that allows direct visualization of the entire colon without discomfort to patients and has the potential to revolutionize the screening workup for colorectal diseases. However, current manual review is laborious and time consuming, requiring the undivided concentration of the gastroenterologist. Computational methods that can assist automated polyp recognition will enhance the outcome both in terms of diagnostic accuracy and efficiency of WCE. This review introduces the computer-assisted algorithms as applied to colorectal polyp screening, focusing on the successes of deep-learning-based strategies in the WCE sequences. We survey key applications of WCE polyp recognition, covering deep-learning-based image-level classification, lesion region detection, and pixel-accurate segmentation. We conclude by discussing emerging research challenges, possible trends, and future directions. [ABSTRACT FROM AUTHOR]

The bianisotropic susceptibility generalized sheet transition conditions (GSTCs) synthesis method is extended from planar to spherical metasurfaces. Properties specific to the nonzero intrinsic curvature of the spherical shape are pointed out and different types of spherical transformations are described. Finally, the susceptibility GSTC method and exotic properties of spherical metasurfaces are validated and illustrated with three examples: illusion transformation, ring focusing, and birefringence. [ABSTRACT FROM AUTHOR]

Phosphor-converted light-emitting diode (pcLED) is one of the most promising methods to generate white light. The interaction of the excitation light with the phosphor in pcLED is a complicated phenomenon, combining light absorption, light conversion, and scattering. An effective and efficient way to predict the optical behavior of pcLED is using the bidirectional scattering distribution function (BSDF). In this paper, the BSDF for pcLEDs with arbitrary phosphor concentration and thickness was established. The measured integrating energy and angular distributions of BSDF were successfully fitted by the interpolation method and the ABg model, respectively, realizing the calculation of BSDF values for arbitrary phosphor configurations. The proposed model was applied to a remote pcLED using an arbitrary phosphor configuration in Monte Carlo simulation. The output radiant power and the angular distributions of the blue and yellow light show good agreements between experiments and simulations; the errors of radiant power were all less than 4%, validating the effectiveness of the model. Consequently, this study provides a simple and useful tool to precisely predict the optical performance of pcLEDs with arbitrary concentration and thickness, it is essential for white LED design and fabrication. [ABSTRACT FROM AUTHOR]