Reevaluating the Solution Photophysics of Tetraphenylethylene at the Origin of their Aggregation-Induced Emission Properties

A detailled study on the physicochemical processes taking place at the excited state of tetrapheyleneethylene derivatives, accounting for their spectacular well known AIE properties. Fo the firts time, quantum yield measurement allow to precsiley establish the implication of the different events (nonradiative dissipation, E/Z photoisomerization, Photocyclisation) responsive for full luminescence quenching in solution. Evidence is brought that E/Z photoisomerization is the dominant and almost exclusive mechanism at the origin of the phenomenon<br>

Reevaluating the Solution Photophysics of Tetraphenylethylene at the Origin of their Aggregation-Induced Emission Properties

A detailled study on the physicochemical processes taking place at the excited state of tetrapheyleneethylene derivatives, accounting for their spectacular well known AIE properties. Fo the firts time, quantum yield measurement allow to precsiley establish the implication of the different events (nonradiative dissipation, E/Z photoisomerization, Photocyclisation) responsive for full luminescence quenching in solution. Evidence is brought that E/Z photoisomerization is the dominant and almost exclusive mechanism at the origin of the phenomenon<br>

Detection and Localization of Overlapped Fruits Application in an Apple Harvesting Robot

For yield measurement of an apple orchard or the mechanical harvesting of apples, there needs to be accurate identification of the target apple fruit. However, in a natural scene, affected by the apple’s growth posture and camera position, there are many kinds of apple images, such as overlapped apples; mutual shadows or leaves; stems; etc. It is a challenge to accurately locate overlapped apples. They will influence the positioning time and recognition efficiency and then affect the harvesting efficiency of apple-harvesting robots or the accuracy of orchard yield measurement. In response to this problem, an overlapped circle positioning method based on local maxima is proposed. First, the apple image is transformed into the Lab color space and segmented by the K-means algorithm. Second, some morphological processes, like erosion and dilation, are implemented to abstract the outline of the apples. Then image points are divided into central points; edge points; or outer points. Third, a fast algorithm is used to calculate every internal point’s minimum distance from the edge. Then, the centers of the apples are obtained by finding the maxima among these distances. Last, the radii are acquired by figuring out the minimum distance between the center and the edge. Thus, positioning is achieved. Experimental results showed that this method can locate overlapped apples accurately and quickly when the apple contour was complete; and this has certain practicability.