Wireless Interference Classification with Low Complexity Multi-Branch Networks

In non-cooperative communication systems,wireless interference classification(WIC)is one of the most essential technologies.Recently,deep learning(DL)based WIC methods have been proposed.However,conventional DL-based WIC methods have high computational complexity and unsatisfactory accuracy,especially when the interference-tonoise ratio(INR)is low.To this end,we propose three effective approaches.Firstly,we introduce multibranch convolutional neural networks(CNNs)for interference recognition.The multi-branch CNN is constructed by repeating a layer that aggregates several transformations with the same topology,and it notably improves the recognition ability for WIC.Our design avoids the carefully crafted selection of each transformation.Unfortunately,multi-branch CNNs are computationally expensive and memory-inefficient.To this end,we further propose Low complexity multibranch networks(LCMN),which are mathematically equivalent to multi-branch CNNs but maintain low computing costs and efficient inference.Thirdly,we present novel loss function,which encourages networks to have consistent prediction probabilities for samples with high visual similarities,resulting in increasing recognition accuracy of LCMN.Experimental results demonstrate the proposed methods consistently boost the classification performance of WIC without substantially increasing computational overhead compared to traditional DL-based methods.

Highly efficient capture of thorium ion by citric acid-modified chitosan gels from aqueous solution

A novel citric acid-modified chitosan gel(CSCA)was synthesized through a simple one-step process and was used to extract thorium ions from wastewater.The CSCA samples with varying chemical compositions were analyzed using SEM with mapping EDS,FT-IR,and static water contact angle measurements,and their adsorption behaviors were studied in detail.The results showed that the adsorption performance of CSCA improves with the increase of CA content in the sample.CSCA possesses an impressive capacity for thorium adsorption of 279.8 mg/g.Furthermore,it showed an ultra-fast adsorption rate and reached equilibrium within 30 min.In terms of recyclability,the CSCA still retained more than 86%of its initial adsorption capacity after 6 cycles of reuse.Density functional theory(DFT)analysis reveals that the good selectivity of this material towards thorium ions should be attributed to the high density of adsorption sites and strong interaction between carboxyl groups and thorium ions.This work could be beneficial in the design and synthesis of new polymer materials for extracting thorium.