Constructing developable surfaces by wrapping cones and cylinders

We model developable surfaces by wrapping a planar figure around cones and cylinders. Complicated developables can be constructed by successive mappings using cones and cylinders of different sizes and shapes. We also propose an intuitive control mechanism, which allows a user to select an arbitrary point on the planar figure and move it to a new position. Numerical techniques are then used to find a cone or cylinder that produces the required mapping. Several examples demonstrate the effectiveness of our technique.     

Robust Mapping for Mobile Robot Based on Immobile Area Grid Map Considering Potential Moving Objects

Mobile robots need Simultaneous Localization and Mapping (SLAM) for autonomous movement in human living environments. The occupancy grid map used in SLAM is a conventional method which makes a map by an occupancy probability in each grid. This method renews a map based on whether an object is observed or not. In order to remove moving objects from a map, an additional method is required. However, conventional methods deal only with actually moving objects, and potential moving objects (e.g., standing humans) are mapped as static objects. Furthermore, only binary states, used or not used, are given to each object in map updating. This paper proposes the immobility area grid map to represent a map by an immobility probability in each grid. The proposed method renews a map based on the identification of observed objects by a robot's sensors, in addition to whether an object is observed or not. We introduce the map update parameter, which is set adaptively from the certainty of identification result of the object. Observed objects can take continuous states, truly static—unknown—truly moving, according to the parameter value. Potential moving objects are not mapped if the parameter takes values corresponding to moving objects. The experimental results show robust mapping in dynamic environments including potential moving objects.     

High-Performance Unidirectional Manipulation of Microdroplets by Horizontal Vibration on Femtosecond Laser-Induced Slant Microwall Arrays

The high-performance unidirectional manipulation of microdroplets is crucial for many vital applications including water collection and bioanalysis. Among several actuation methods (e.g., electric, magnetic, light, and thermal actuation), mechanical vibration is pollution-free and biocompatible. However, it suffers from limited droplet movement mode, small volume range (V_Max/V_Min < 3), and low transport velocity (≤11.5 mm s⁻¹) because the droplet motion is a sliding state caused by the vertical vibration on the asymmetric hydrophobic microstructures. Here, an alternative strategy is proposed—horizontal vibration for multimode (rolling, bouncing/reverse bouncing, converging/diffusing, climbing, 90^o turning, and sequential transport), large-volume-range (V_Max/V_Min ≈ 100), and high-speed (≈22.86 mm s⁻¹) unidirectional microdroplet manipulation, which is ascribed to the rolling state on superhydrophobic slant microwall arrays (SMWAs) fabricated by the one-step femtosecond laser oblique ablation. The unidirectional transport mechanism relies on the variance of viscous resistance induced by the difference of contact area between the microdroplet and the slant microwalls. Furthermore, a circular, curved, and “L”-shaped SMWA is designed and fabricated for droplet motion with particular paths. Finally, sequential transport of large-volume-range droplets and chemical mixing microreaction of water-based droplets are demonstrated on the SMWA, which demonstrates the great potential in the field of microdroplet manipulation.     

融合重检测机制的卷积回归网络目标跟踪算法

针对基于人工特征的背景感知相关滤波（CACF）算法在形变、运动模糊、低分辨率情形跟踪效果较差以及跟踪器遇到严重遮挡等情形容易陷入局部最优而导致跟踪失败的问题，提出一种融合重检测机制的卷积回归网络（CRN）目标跟踪算法。在训练阶段，将相关滤波作为CRN层融入进深度神经网络，使网络成为一个整体进行端到端训练；在跟踪阶段，通过残差连接融合不同网络层及其响应值，同时引入重检测机制使算法从潜在的跟踪失败中恢复，当响应值低于给定阈值时激活检测器。在数据集OTB-2013上的实验表明，所提算法在50个视频序列上精确度达到88.1%，相比原始CACF算法提高9.7个百分点，在具有形变、运动模糊等属性的视频序列上相比原始算法表现更优秀。     

Droplets Manipulated on Photothermal Organogel Surfaces

A photoresponsive organogel surface (POS), which integrates characteristics of the photothermal property of Fe₃O₄ nanoparticles and the low hysteresis feature of lubricant‐infused organogels, is shown. A photothermally induced dynamic temperature gradient can be formed rapidly at the location of near‐infrared‐light irradiation (NIR) on POS with suitable Fe₃O₄ nanoparticles content. Thus, various droplets (e.g., water, glycerol, ethylene glycol, propylene glycol, and ethanol) can be transported effectively and nimbly (e.g., along desired trajectories with controllable velocity and direction, even run uphill and deliver solid particles). This work reveals a synergistic effect between the asymmetrical droplet deformation and the inside Marangoni flows, which forms a unique driving force for droplet transport with high efficiency. This finding offers insight into the design of novel responsive interface materials for droplet transportation, which would be significant for laboratory‐on‐a‐chip contexts, mass transportation, and microengines.     

一种直升机避障电力线检测方法

电力线是一类形状细长、特征稀疏、随着视角的变化容易混淆在大量背景信息中的特殊障碍物，常规电力线检测识别算法得到的目标框对电力线所在位置的估计不够准确。为此，提出了一种相对角度估计方法，基于常规电力线目标检测与识别算法，并结合电力线相对角度估计，从而提高电力线的检测识别过程中所在位置的精度。相比电力线绝对角度回归的方法，提出的相对角度估计方法容易训练易收敛，计算量小，适用于实时性要求较高的应用场合。     

Lysin Motif (LysM) Proteins: Interlinking Manipulation of Plant Immunity and Fungi

The proteins with lysin motif (LysM) are carbohydrate-binding protein modules that play a critical role in the host-pathogen interactions. The plant LysM proteins mostly function as pattern recognition receptors (PRRs) that sense chitin to induce the plant’s immunity. In contrast, fungal LysM blocks chitin sensing or signaling to inhibit chitin-induced host immunity. In this review, we provide historical perspectives on plant and fungal LysMs to demonstrate how these proteins are involved in the regulation of plant’s immune response by microbes. Plants employ LysM proteins to recognize fungal chitins that are then degraded by plant chitinases to induce immunity. In contrast, fungal pathogens recruit LysM proteins to protect their cell wall from hydrolysis by plant chitinase to prevent activation of chitin-induced immunity. Uncovering this coevolutionary arms race in which LysM plays a pivotal role in manipulating facilitates a greater understanding of the mechanisms governing plant-fungus interactions.     

Multi-Level Fusion Net for hand pose estimation in hand-object interaction

This work is about solving a challenging problem of estimating the full 3D hand pose when a hand interacts with an unknown object. Compared to isolated single hand pose estimation, occlusion and interference induced by the manipulated object and the clutter background bring more difficulties for this task. Our proposed Multi-Level Fusion Net focuses on extracting more effective features to overcome these disadvantages by multi-level fusion design with a new end-to-end Convolutional Neural Network (CNN) framework. It takes cropped RGBD data from a single RGBD camera at free viewpoint as input without requiring additional hand–object pre-segmentation and object or hand pre-modeling. Through extensive evaluations on public hand–object interaction dataset, we demonstrate the state-of-the-art performance of our method.