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In this paper, we explore the technical challenges to automatically generate computer‐aided design models of existing vehicle parts using laser range imaging techniques. We propose a complete system that integrates data acquisition and model reconstruction. We discuss methods to resolve the occlusion problem and the associated registration problem. We also present our reconstruction algorithm. This range image‐based, computer‐aided reverse engineering system has a potential for faster model reconstruction over traditional reverse engineering technologies. Finally, we present results derived from the system.

The purpose of the paper is to present a novel cross-modal sensor whose tactile is computed by the visual information. The proposed sensor can measure the forces of robotic grasping.

The proposed cross-modal tactile sensor consists of a transparent elastomer with markers, a camera, an LED circuit board and supporting structures. The model and performance of the elastomer are analyzed. Then marker recognition method is proposed to determine the movements of the marker on the surface, and the force calculation algorithm is presented to compute the three-dimension force.

Experimental results demonstrate that the proposed tactile sensor can accurately measure robotic grasping forces.

The proposed cross-modal tactile sensor determines the robotic grasping forces by the images of markers. It can give more information of the force than traditional tactile sensors. Meanwhile, the proposed algorithms for forces calculation determine the superior results.

The purpose of this paper is to present a novel tactile sensor and a visual-tactile recognition framework to reduce the uncertainty of the visual recognition of transparent objects.

A multitask learning model is used to recognize intuitive appearance attributes except texture in the visual mode. Tactile mode adopts a novel vision-based tactile sensor via the level-regional feature extraction network (LRFE-Net) recognition framework to acquire high-resolution texture information and temperature information. Finally, the attribute results of the two modes are integrated based on integration rules.

The recognition accuracy of attributes, such as style, handle, transparency and temperature, is near 100%, and the texture recognition accuracy is 98.75%. The experimental results demonstrate that the proposed framework with a vision-based tactile sensor can improve attribute recognition.

Transparency and visual differences make the texture of transparent glass hard to recognize. Vision-based tactile sensors can improve the texture recognition effect and acquire additional attributes. Integrating visual and tactile information is beneficial to acquiring complete attribute features.

The purpose of this paper is to review the key issues surrounding foreign direct investment (FDI) in agriculture, and examine the potential impacts of FDI in African agriculture.

The dynamic Global Trade Analysis Project model (GDyn) is used to analyse the potential impacts of improvements in land productivity and FDI in Africa.

The results illustrate that combined efforts to improve land productivity and growth in FDI could potentially increase Africa’s share in global agricultural output and exports, particularly with respect to oil seeds, sugar, and cotton.

The authors employ a global economy-wide modelling framework to simulate the effects of growth in FDI in African agriculture.