An unsupervised classification method is developed for the coarse segmentation of Moroccan coastal upwelling using the Sea Surface Temperature (SST) satellite images. The algorithm is started with the generation of c-partitioned labeled image using Otsu's method for the purpose of finding regions of homogenous temperatures. Then two well-known validity indices are used to select the c-partition which best reproduce the shape of upwelling area. A region-growing algorithm is developed that is used to remove the noisy structures in the offshore waters not belonging to the upwelling area. The algorithm is used to provide a seasonal variability of upwelling activity in the southern Moroccan Atlantic coast using 70 SST images of the years 2007 and 2008. The performance of the proposed methodology has been validated by an oceanographer, showing its effectiveness for automatic delimitation of Moroccan upwelling region.
Analysis and study of coastal upwelling using sea surface temperature (SST) satellite images is a common procedure because of its coast effectiveness (economic, time, frequency, and manpower). Developing on the Ekman theory, we propose a robust method to identify the upwelling regions along the northwest African margin. The proposed method comes to overcome the issues encountered in a recent method devoted for the same purpose and for the same upwelling system. Afterward, we show how our method can serve as a framework to study and monitor the spatio-temporal variability of the upwelling phenomenon in the studied region.
The region along the NorthWest African coast (20 • N to 36 • N and 4 • W to 19 • W) is characterized by a persistent and variable upwelling phenomenon almost all year round. In this article, the upwelling features are investigated using an algorithm dedicated to delimit the upwelling area from thermal and biological satellite observations. This method has been developed specifically for sea-surface temperature (SST) images, since they present a high latitudinal variation, which is not present in chlorophyll-a concentration images. Developing on the proposed approach, the spatial and temporal variations of the main physical and biological upwelling patterns are studied. Moreover, a study on the upwelling dynamics, which explores the interplay between the upwelling spatiotemporal extents and intensity, is presented, based on a 14-year time archive of weekly SST and chlorophyll-a concentration data.
COVID-19 epidemic continues to threaten public health with the appearance of new, more severe mutations, and given the delay in the vaccination process, the situation becomes more complex. Thus, the implementation of rapid solutions for the early detection of this virus is an immediate priority. To this end, we provide a deep learning method called CovSeg-Unet to diagnose COVID-19 from chest CT images. The CovSeg-Unet method consists in the first time of preprocessing the CT images to eliminate the noise and make all images in the same standard. Then, CovSeg-Unet uses an end-to-end architecture to form the network. Since CT images are not balanced, we propose a loss function to balance the pixel distribution of infected/uninfected regions. CovSeg-Unet achieved high performances in localizing COVID-19 lung infections compared to others methods. We performed qualitative and quantitative assessments on two public datasets (Dataset-1 and Dataset-2) annotated by expert radiologists. The experimental results prove that our method is a real solution that can better help in the COVID-19 diagnosis process.
In this paper, we present a canonical polyadic (CP) tensor decomposition isolating the scaling matrix. This has two major implications: (i) the problem conditioning shows up explicitly and could be controlled through a constraint on the so-called coherences and (ii) a performance criterion concerning the factor matrices can be exactly calculated and is more realistic than performance metrics used in the literature. Two new algorithms optimizing the CP decomposition based on gradient descent are proposed. This decomposition is illustrated by an application to direct-sequence code division multiplexing access (DS-CDMA) systems; computer simulations are provided and demonstrate the good behavior of these algorithms, compared to others in the literature.
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