First Integral Method to Study Nonlinear Evolution Equations

In this paper, we apply the first integral method to generalized ZK-BBM equation and Drinefel’d-Sokolov- Wilson system and one-dimensional modified EW-Burgers equation. The first integral method is a powerful solution method for obtaining exact solutions of some nonlinear evolution equations. This method was first proposed by Feng [8] in solving Burgers– KdV equation which is based on the ring theory of commutative algebra. This method can be applied to nonintegrable equations as well as to integrable ones.Key words First integral method; Generalized ZK-BBM equation ; Drinefel’d-Sokolov-Wilson system; One-dimensional modified EW-Burgers equatio

Robust non-blind color video watermarking using QR decomposition and entropy analysis

Issues such as content identification, document and image security, audience measurement, ownership and copyright among others can be settled by the use of digital watermarking. Many recent video watermarking methods show drops in visual quality of the sequences. The present work addresses the aforementioned issue by introducing a robust and imperceptible non-blind color video frame watermarking algorithm. The method divides frames into moving and non-moving parts. The non-moving part of each color channel is processed separately using a block-based watermarking scheme. Blocks with an entropy lower than the average entropy of all blocks are subject to a further process for embedding the watermark image. Finally a watermarked frame is generated by adding moving parts to it. Several signal processing attacks are applied to each watermarked frame in order to perform experiments and are compared with some recent algorithms. Experimental results show that the proposed scheme is imperceptible and robust against common signal processing attacks

Data augmentation from RGB to chlorophyll fluorescence imaging Application to leaf segmentation of Arabidopsis thaliana from top view images

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Low-cost image annotation for supervised machine learning. Application to the detection of weeds in dense culture

An open problem in robotized agriculture is to detect weeds in dense culture. This problem can be addressed with computer vision and machine learning. The bottleneck of supervised approaches lay in the manual annotation of training images. We propose two different approaches for detecting weeds position to speed up this process. The first approach is using synthetic images and eye-tracking to annotated images [4] which is at least 30 times faster than manual annotation by an expert, the second approach is based on real RGB and depth images collected via Kinect v2 sensor. We generated a data set of 150 synthetic images which weeds were randomly positioned on it. Images were gazed by two observers. Eye tracker sampled eye position during the execution of this task [5, 6]. Area of interest was recorded as rectangular patches. A patch is considered as including weeds if the average fixation time in this patch exceeds 1.04 seconds. The quality of visual annotation by eye-tracking is assessed by two ways. First, direct comparison of visual annotation with ground-truth which is shown an average 94.7% of all fixations on an image which fell within ground-truth bounding-boxes. Second, as shown in fig.1 eye-tracked annotated data is used as a training data set in four machine learning approaches and compare the recognition rate with the ground-truth. These four machine learning methods are tested in order to assess the quality of the visual annotation. These methods correspond to handcrafted features adapted to texture characterization. They are followed by a linear support vector machine binary classifier. The table 1 gives the average accuracy and standard deviation. Experimental results prove that visual eye-tracked annotated data are almost the same as in-silico ground-truth and performances of supervised machine learning on eye-tracked annotated data are very close to the one obtained with ground-truth

New cost and bottleneck in the Era of machine learning-based bioimage analysis

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Cultivons notre jardin avec Fourier

Cultivons notre jardin avec Fourie

Pulmonary arterial pressure detects functional mitral stenosis after annuloplasty for primary mitral regurgitation: An exercise stress echocardiographic study

Introduction: The restrictive mitral valve annuloplasty (RMA) is the treatment of choice for degenerative mitral regurgitation (MR), but postoperative functional mitral stenosis remains a matter of debate. In this study, we sought to determine the impact of mitral stenosis on the functional capacity of patients. Methods: In a cross-sectional study, 32 patients with degenerative MR who underwent RMA using a complete ring were evaluated. All participants performed treadmill exercise test and underwent echocardiographic examinations before and after exercise. Results: The patients� mean age was 50.1 ± 12.5 years. After a mean follow-up of 14.1 ± 5.9 months (6�32 months), the number of patients with a mitral valve peak gradient >7.5 mm Hg, a mitral valve mean gradient >3 mm Hg, and a pulmonary arterial pressure (PAP) �25 mm Hg at rest were 50, 40.6, and 62.5, respectively. 13 patients (40.6) had incomplete treadmill exercise test. All hemodynamic parameters were higher at peak exercise compared with at rest levels (all P <.05). The PAP at rest and at peak exercise as well as peak transmitral gradient at peak exercise were higher in patients with incomplete exercise compared with complete exercise test (all P <.05). The PAP at rest (a sensitivity and a specificity of 84.6 and 52.6, respectively; area under the curve AUC =.755) and at peak exercise (a sensitivity and a specificity of 100% and 47.4%, respectively; AUC =.755) discriminated incomplete exercise test. Conclusion: The RMA for degenerative MR was associated with a functional stenosis and the PAP at rest and at peak exercise discriminated low exercise capacity. © 2017, Wiley Periodicals, Inc