Filtration and transport of heavy metals in graphene oxide enabled sand columns

A fixed-bed sand column with graphene oxide (GO) layer was used to remove heavy metals (Cu(II) and Pb(II)) from an aqueous solution injected under steady flow. Due to the time constrained kinetic process of heavy metal sorption to GO, removal efficiency was affected by the injection flow rate. When injection flow rate changed from 1 to 5 mL min−1, the removal efficiency of the two metals decreased from 15.3% to 10.3% and from 26.7% to 19.0% for Cu(II) and Pb(II), respectively. Provided a fixed concentration of heavy metals in the injected flow, an increase in GO in column from 10 to 30 mg resulted in an sharp increase in the removal efficiency of Pb(II) from 26.7% to 40.5%. When Cu(II) and Pb(II) were applied simultaneously, the removal efficiency of the two metals was lower than when applied by individually. GO-sand column performance was much better for the removal of Pb(II) than for Cu(II) in each corresponding treatment. When breakthrough curve (BTC) data were simulated by the convection-dispersion-reaction (CDER) model, the fittings for Cu in every treatment were better than that of Pb in corresponding treatment. Considering the small amount of GO used to enable the sand columns that resulted in a great increase in k value, compared to the GO-free sand columns, the authors propose GO as an effective adsorption media in filters and reactive barriers to remove Pb(II) from flowing water

Neural Network Architectures for Optical Channel Nonlinear Compensation in Digital Subcarrier Multiplexing Systems

In this work, we propose to use various artificial neural network (ANN) structures for modeling and compensation of intra- and inter-subcarrier fiber nonlinear interference in digital subcarrier multiplexing (DSCM) optical transmission systems. We perform nonlinear channel equalization by employing different ANN cores including convolutional neural networks (CNN) and long short-term memory (LSTM) layers. We start to compensate the fiber nonlinearity distortion in DSCM systems by a fully connected network across all subcarriers. In subsequent steps, and borrowing from fiber nonlinearity analysis, we gradually upgrade the designs towards modular structures with better performance-complexity advantages. Our study shows that putting proper macro structures in design of ANN nonlinear equalizers in DSCM systems can be crucial for practical solutions in future generations of coherent optical transceivers

Blood vessel enhancement via multi-dictionary and sparse coding: Application to retinal vessel enhancing

International audienceBlood vessel images can provide considerable information of many diseases, which are widely used by ophthalmologists for disease diagnosis and surgical planning. In this paper, we propose a novel method for the blood Vessel Enhancement via Multi-dictionary and Sparse Coding (VE-MSC). In the proposed method, two dictionaries are utilized to gain the vascular structures and details, including the Representation Dictionary (RD) generated from the original vascular images and the Enhancement Dictionary (ED) extracted from the corresponding label images. The sparse coding technology is utilized to represent the original target vessel image with RD. After that, the enhanced target vessel image can be reconstructed using the obtained sparse coefficients and ED. The proposed method has been evaluated for the retinal vessel enhancement on the DRIVE and STARE databases. Experimental results indicate that the proposed method can not only effectively improve the image contrast but also enhance the retinal vascular structures and details

Quaternion softmax classifier

International audienceFor the feature extraction of red-blue-green (RGB) colour images, researchers usually deal with R, G and B channels separately to obtain three feature vectors, and then combine them together to obtain a long real feature vector. This approach does not exploit the relationships between the three channels of the colour images. Recently, attention has been paid to quaternion features, which take the relationships between channels into consideration and seem to be more suitable for representing colour images. However, there are only a few quaternion classifiers for dealing with quaternion features. To meet this requirement, a new quaternion classifier, namely, the quaternion softmax classifier is proposed, which is an extended version of the conventional softmax classifier generally defined in the complex (or real) domain. The proposed quaternion softmax classifier is applied to two of the most common quaternion features, that is, the quaternion principal components analysis feature and the colour image pixel feature. The experimental results show that the proposed method performs better than the quaternion back propagation neural network in terms of accuracy and convergence rate