Recently, there are many autonomous navigation applications done in outdoor environment. However, safe navigation is still a daunting challenge in terrain containing vegetation. Thus, a study on vegetation detection for outdoor automobile navigation is investigated in this work. At the early state of our research, we focused on the segmentation of LADAR data into two classes by using local three-dimensional point cloud statistics. The classes are: scatter to represent vegetation such as tall grasses, bushes and tree canopy, surface to capture solid objects like ground surface, rocks or tree trunks. However, the only use of 3D features would never result a real robust vegetation detection system because of lacking color information. We, hence, propose a 2D-3D combination approach which can utilize the complement of three-dimensional point distribution and color descriptor. Firstly, 3D point cloud is segmented into regions of homogeneous distance. The local point distribution is then analyzed for each region to extract scatter features. Secondly, a coarse 2D-3D calibration needs to be implemented in order to map the regions to the corresponding color image. Then, color descriptors are studied and applied to each region and considered as color features. Those all scatter and color features will be trained by Support Vector Machine to generate vegetation classifier. Finally, we will show the out-performance of this approach in comparison with more conventional approaches.
With the number of deaths due to liver diseases increasing steadily in recent years, early detection and treatment of these diseases has been one of the most active research fields using computational intelligence techniques. In this paper, we propose a more realistic single-neuron model with synaptic nonlinearities in a dendritic tree for liver disorder diagnosis. The computation on the neuron is performed as a combination of dimensional reduction and nonlinearity, which has a neuron-pruning function that can remove useless synapses and dendrites during learning, forming a distinct synaptic and dendritic morphology. The nonlinear interactions in a dendrite tree are expressed using the Boolean logic AND (conjunction), OR (disjunction), and NOT (negation), which can be easily implemented in hardware. Furthermore, an error backpropagation (BP) algorithm is used to train the neuron model, and the performance is compared with a traditional BP neural network in terms of accuracy, sensitivity, and specificity. We use the BUPA liver disorder datasets obtained from the UCI Machine Learning Repository to verify the proposed method. Simulation results show promise for the use of this single-neuron model as an effective pattern classification method in liver disorder diagnostics.
In order to achieve good functional performance on surface texture with high processing efficiency, this article proposes a vertical continuous precession polishing method for aspheric lenses based on the analysis of abrasive trajectories on contact region. First of all, by analyzing the relative motion between bonnet tool and workpiece, the motion model of particle trajectories on contact region was established. Then, comparison and analysis of abrasive trajectories with various polishing methods were carried out. Moreover, a vertical continuous precession polishing method for aspheric lenses and related experiments were presented. The results revealed that the texture on contact zone of aspheric lens polished by vertical continuous precession was approximately random and uniform, which is appropriate for polishing curved surfaces continuously. In addition, the contact zone on different polishing spots with various curvatures can be controlled by adaptive algorithm, and the simulated results validated the feasibility of the proposed polishing method for aspheric lenses.
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