Video surveillance is an important method to obtain the dynamic changes of green macroalgae along the coast. The paper proposes a coastal green macroalgae extraction method based on the SLIC superpixel segmentation, CNN and SVM to realize the automated recognition of green macroalgae from lots of high-resolution RGB video data collected by unmanned aerial vehicle (UAV) and handheld devices. Firstly, SLIC algorithm is used to generate the multi-scale patches on the original high-resolution image. Then, three classification CNN is used to divide the multi-scale patches into three types: green macroalgae, background and mixing. Finally, SVM algorithm is used to extract the green macroalgae to improve the accuracy at the pixel level in the mixed patches. In order to evaluate the performance of the proposed method, experiments are conducted on our coastal green macroalgae image dataset. Compared with the method of RGB vegetation indices (such as ExR, RGBVI, NGBDI), the overall accuracy (OA), F1 score, and Kappa of the green macroalgae extraction with the method proposed in this paper are up to 95.23%, 0.9612, 0.9436, respectively. The results show that our method is significantly better than that of RGB vegetation indices since it effectively reduces the influence of sea waves and light on the recognition results. The automated extraction method for coastal green macroalgae proposed in this paper can provide a reference for the automatic monitoring of coastal green macroalgae with high precision.

Since the shell of the hazelnuts is very hard, it is necessary to squeeze the slit in the axial direction to facilitate peeling. When the hazelnuts are automatically processed in the industry chain, the axial of the hazelnuts needs to be quickly positioned. In this paper, the sum of projection gradient in the sensitive area of the hazelnut image is used to locate the hazelnuts axial. Firstly, a search template with discrete paths is established to find the hazelnut contour and extract the hazelnut region from the image. Secondly, the sensitive area is selected to get projection histogram at diffident orientations, and then the gradient sums of the projection histograms are calculated. Thirdly, the axial orientation of the hazelnut is determined with the biggest sum. The experiments results show that the projection gradient sum method is fast enough and can meet the requirements of industrial production. The location accuracy of the projection gradient sum method is 94.2%.

The hazelnut should be cracked before it is sold. Presently, the mechanical crack is often adopted to process the hazelnut for food. In order to ensure the crack effect, it needs to manual located the axis of the hazelnuts so that the hazelnuts can be cracked along the axial direction. This paper proposes an innovative method for axial locating of hazelnut based on projection gradient statistics. Firstly the images of the hazelnut on the production line are obtained by industrial camera, and then the edge and contour of hazelnut are extracted according to the difference values of brightness between hazelnut image and the background area. Secondly, an adaptive circle is used to select the optimal projection area. The center of the adaptive circle is set on the center of gravity of the hazelnuts, and the adjustable diameter is analyzed also. The region selected by the adaptive circle in the hazelnut image is projected from different orientations. Finally, the sum of the gradients of each projection histogram is calculated and the axial orientation of the hazelnut is determined based on the statistical values. The experimental results show that the accuracy of the method for the axial recognition of the hazelnut is up to 94.1%, which satisfies the requirements for axial locating of hazelnut in automatic test system.

Notice of Violation of IEEE Publication Principles ???Performance Indexes for SCARA Type Robot??? by Wang Pin, Lu Zhen, Wang Lili, Feng Baofu, Li Dong in the Proceedings of the International Workshop on Intelligent Systems and Applications.(ISA) 2009 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE???s Publication Principles. This paper is a duplication of the original text from the papers cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following articles: "On the Performance Indexes for Robot Manipulators." by Tanio Tanev, Bogdan Stoyanov in Problems of Engineering, Cybernetics and Robotics, 49, 2000, pp. 64-71 ???Jacobian, Manipulability, Condition Number, and Accuracy of Parallel Robots??? by J.P. Merlet in the Journal of Mechanical Design, Vol 128, Issue 1, June 2005, pp. 199-206 The concepts of Jacobian matrix, dexterity, manipulability and condition number have been floating around since the early beginning of robotics. These performance indices play an important role especially for optimal robot design. In this paper, we revisit these concepts for SCARA type robot and exhibit some surprising results that show that these concepts have to be manipulated with care for a proper understanding of the kinematics behavior of a robot. All obtained results for the performance indexes are graphically visualized. The presented graphical examples for the performance of a manipulator can be easily interpreted and also they can help in application and design of manipulators.