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In this letter, a novel compact frequency beam-scanning antenna (FBSA) using composite waveguide based on folded half-mode substrate integrated waveguide (FHMSIW) and spoof surface plasmon polaritons (SSPP) is proposed. The novel composite FHMSIW/SSPP waveguide with compact size and high field-confinement is initiated by embedding dual SSPP structures into the FHMSIW. Its transmission characteristic has flexible features from both the FHMSIW and SSPP. To excite the radiation mode from the composite slow-wave waveguide, a simple single-sided sinusoidal modulation method is designed and used. In this way, the common open stop-band problem around broadside is effectively eliminated, and the beam-scanning range of the antenna can be flexibly designed without changing the modulation structure. The presented scanning antenna owns a compact size and a simple feeding network. The experimental results indicate that the proposed FBSA can achieve continuous beam scanning from backward to forward in a wide scanning range of 60° within a bandwidth of 7.7–10.1 GHz. The stable unidirectional radiation patterns and appropriate gains are also realized in the entire operating frequency band. [ABSTRACT FROM AUTHOR]

Gentle suction flow at the fingertips of a compliant hand can enhance object acquisition and increase the robustness of pinch grasps under water. The approach adds a low-pressure pump and flexible tubes that terminate at the distal phalanges. The light flow rate does not create a powerful suction force, nor does it stir up significant sediment. The method works on porous and rough objects in addition to smooth objects as it does not require forming a seal. It changes contact conditions—normal force and coefficient of friction—and enlarges the acquisition region when grasping free objects under water. A simple hydrodynamic model matches empirical force measurements adequately for incorporation in a dynamic simulation to explore the effects of flow rate and object mass. Simulations and experiments show that effects of fingertip suction flow are most pronounced for acquiring objects on the order of 1 kg or less and when pinching large objects. Gentle suction flow is an effective, versatile, and convenient addition for robots that must grasp and manipulate objects under water. [ABSTRACT FROM AUTHOR]

This paper presents a method for efficiently testing the stability of an object under contact that accommodates empirical models of admissible forces at individual contact points. It handles a diverse range of possible geometries of the admissible force volume, including anisotropy, adhesion, and even nonconvexity. The method discretizes the contact region into patches, performs a convex decomposition of a polyhedral approximation to each admissible force volume, and then formulates the problem as a mixed integer linear program. The model can also accommodate articulated robot hands with torque limits and joint frictions. Predictions of our method are evaluated experimentally in object lifting tasks using a gripper that exploits microspines to exert strongly anisotropic forces. The method is applied to calculate gripper loading capabilities and equilibrium predictions for a quadruped climbing robot on steep and overhanging terrain. [ABSTRACT FROM AUTHOR]

The promise of oceanic discovery has long intrigued scientists and explorers, whether with the idea of studying underwater ecology and climate change or with the hope of uncovering natural resources and historic secrets buried deep in archaeological sites. This quest to explore the oceans requires skilled human access, yet much of the oceans are inaccessible to human divers; nearly ninetenths of the ocean floor is at 1 km or deeper [1]. Accessing these depths is imperative since factors such as pollution and deep-sea trawling threaten ecology and archaeological sites. While remotely operated vehicles (ROVs) are inadequate for the task, a robotic avatar could go where humans cannot and still embody human intelligence and intentions through immersive interfaces. [ABSTRACT FROM AUTHOR]

Human behavior analysis is an important area of research in computer vision and is also driven by a wide spectrum of applications, such as smart video surveillance and human-computer interface. In this paper, we present a novel approach for human behavior analysis. Two research challenges, motion representation and behavior recognition, are addressed. A novel motion descriptor, which is an improved feature based on optical flow, is proposed for motion representation. Optical flow is improved with a motion filter, and feature fusion with the shape and trajectory information. To recognize the behavior, the support vector machine is employed to train the classifier where the concatenation of histograms is formed as the input features. Experimental results on the Weizmann behavior database and the Institute of Automation, Chinese Academy of Science real- world multiview behavior database demonstrate the robustness and effectiveness of our method. [ABSTRACT FROM AUTHOR]