Fast and accurate taxonomic identification of invasive trans-located ladybird beetle species is essential to prevent significant impacts on biological communities, ecosystem functions, and agricultural business economics. Therefore, in this work we propose a two-step automatic detector for ladybird beetles in random environment images as the first stage towards an automated classification system. First, an image processing module composed of a saliency map representation, simple linear iterative clustering superpixels segmentation, and active contour methods allowed us to generate bounding boxes with possible ladybird beetles locations within an image. Subsequently, a deep convolutional neural network-based classifier selects only the bounding boxes with ladybird beetles as the final output. This method was validated on a 2, 300 ladybird beetle image data set from Ecuador and Colombia obtained from the iNaturalist project. The proposed approach achieved an accuracy score of 92% and an area under the receiver operating characteristic curve of 0.977 for the bounding box generation and classification tasks. These successful results enable the proposed detector as a valuable tool for helping specialists in the ladybird beetle detection problem.
This article studies the positioning problem for wireless networks when TDOA measures are used and the reference anchor node is not previously known. We carried out various experiments to show the impact on accuracy when a poor selection of this reference is achieved. Furthermore, we study the use of SNR at receivers as a mean to proper select the closest node as the reference anchor, previously to perform mobile positioning. An appropriate mechanism to perform this selection is provided within a simulation platform built to study network-based positioning using space-time diversity in realistic conditions. This approach shows that the use of a signal that measures or estimates the received power is a viable mechanism for the proper selection of the anchor node even in a shadowed environment, avoiding the severe degradation that involves a poor selection.Peer ReviewedPostprint (published version
Fast and accurate identification of biting midges is crucial in the study of
Culicoides
-borne diseases. In this work, we propose a two-stage method for automatically analyzing
Culicoides
(Diptera: Ceratopogonidae) species. First, an image preprocessing task composed of median and Wiener filters followed by equalization and morphological operations is used to improve the quality of the wing image in order to allow an adequate segmentation of particles of interest. Then, the segmentation of the zones of interest inside the biting midge wing is made using the watershed transform. The proposed method is able to produce optimal feature vectors that help to identify
Culicoides
species. A database containing wing images of
C. obsoletus
,
C. pusillus
,
C. foxi
, and
C. insignis
species was used to test its performance. Feature relevance analysis indicated that the mean of hydraulic radius and eccentricity were relevant for the decision boundary between
C. obsoletus
and
C. pusillus
species. In contrast, the number of particles and the mean of the hydraulic radius was relevant for deciding between
C. foxi
and
C. insignis
species. Meanwhile, for distinguishing among the four species, the number of particles and zones, and the mean of circularity were the most relevant features. The linear discriminant analysis classifier was the best model for the three experimental classification scenarios previously described, achieving averaged areas under the receiver operating characteristic curve of 0.98, 0.90, and 0.96, respectively.
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