Mobile Communication Voice Enhancement Under Convolutional Neural Networks and the Internet of Things

This study aims to reduce the interference of ambient noise in mobile communication,improve the accuracy and authenticity of information transmitted by sound,and guarantee the accuracy of voice information deliv-ered by mobile communication.First,the principles and techniques of speech enhancement are analyzed,and a fast lateral recursive least square method(FLRLS method)is adopted to process sound data.Then,the convolutional neural networks(CNNs)-based noise recognition CNN(NR-CNN)algorithm and speech enhancement model are proposed.Finally,related experiments are designed to verify the performance of the proposed algorithm and model.The experimental results show that the noise classification accuracy of the NR-CNN noise recognition algorithm is higher than 99.82%,and the recall rate and F1 value are also higher than 99.92.The proposed sound enhance-ment model can effectively enhance the original sound in the case of noise interference.After the CNN is incorporated,the average value of all noisy sound perception quality evaluation system values is improved by over 21%compared with that of the traditional noise reduction method.The proposed algorithm can adapt to a variety of voice environments and can simultaneously enhance and reduce noise processing on a variety of different types of voice signals,and the processing effect is better than that of traditional sound enhancement models.In addition,the sound distortion index of the proposed speech enhancement model is inferior to that of the control group,indicating that the addition of the CNN neural network is less likely to cause sound signal distortion in various sound environments and shows superior robustness.In summary,the proposed CNN-based speech enhancement model shows significant sound enhancement effects,stable performance,and strong adapt-ability.This study provides a reference and basis for research applying neural networks in speech enhancement.

Microbial community changes in the digestive tract of the clam Meretrix petechialis in response to Vibrio parahaemolyticus challenge

Disease in clams frequently occurred over the last decade and has become a serious threat to the clam aquaculture industry and natural stocks.Mass clam mortality events were reported to be associated with the presence of opportunistic pathogen vibrio.However,the complexity of infection that occurs in the natural environment remains poorly understood.In this study,we smulated a natural disease outbreak by vibrio immersion infection to study the diversity and dynamics of microbiota in the digestive tract of clam Meretrix petechialis during the infection process.Dramatic changes in operational taxonomic unit richness and phylum composition of the bacterial communities were observed during pathogen invasion.In addition,we investigated the potential relationship between microbiota dynamics and host status during disease progression.Results reveal that,at the end stage of vibrio infection,interindividual variation in the digestive tract microbiota increased,as did the diff erence in individual health status.The moribund clams displayed signs of microbial community shifts to low diversity,and the microbial community was characterized by mass proliferation of a few operational taxonomic units.

Intermolecular Radical Fluoroalkylative Olefination of Unactivated Alkenes

We have disclosed a novel,efficient radicalmediated intermolecular fluoroalkylative olefination of unactivated alkenes.The transformation proceeded through a radical docking-migration cascade,in which a portfolio of strategically designed dual-function alkenylating reagents was harnessed to afford vinylated products with exclusive E-configuration.The reaction featured mild conditions,broad functional group compatibility,and unique chemo-,regio-,and stereoselectivities.The protocol has also provided a useful approach for late-stage olefination of complex natural products and drug derivatives containing alkenyl moieties.