A sparse loudspeaker array for surround sound reproduction using the least absolute shrinkage and selection operator algorithm

Abstract

This letter explores a least absolute shrinkage and selection operator- (Lasso-) based beamforming algorithm for a sparse cylindrically baffled speaker array, which can be used for low-cost multi-channel surround sound reproduction. The proposed method exploits the inherent sparsity of the Lasso algorithm, and achieves both narrower beamwidth and a smaller side lobe in comparison with existing algorithms in both simulation and experiment. In addition, further study on the dependency of operating speaker sparsity on regularization parameter enables user preference-based adjustment in practice.

Comprehensive analysis of alternative splicing and transcriptome related to the prognosis of cervical squamous cell cancer

“…We used these candidate SASEs to construct prognostic signatures that could predict overall survival time. In order to avoid prognostic signatures overfitting and build an optimal prognostic model, the least absolute shrinkage and selection operator (LASSO) regression analysis was applied to screen out splicing events whose absolute value of coefficients were greater than a predetermined value by using the R package "glmnet" [29]. After excluding SASEs with zero coefficients in the LASSO regression analysis, we calculated risk score of each patient for overall survival prediction by using the formula: PSI i , that is, percent-spliced-in, is a ratio that indicates the efficiency of splicing of sequences of interest into transcripts, and can be used to undertake an intuitive quantitative comparison of splicing events [30].…”

SEC23A Regulated by Splicing Factor EIF3A may be Involved in the Development of Cervical Squamous Cell Cancer via Adherens Junction Pathway

“…We used these candidate SASEs to construct prognostic signatures that could predict overall survival time. In order to avoid prognostic signatures over tting and build an optimal prognostic model, the least absolute shrinkage and selection operator (LASSO) regression analysis was applied to screen out splicing events whose absolute value of coe cients were greater than a predetermined value by using the R package "glmnet" [27]. After excluding SASEs with zero coe cients in the LASSO regression analysis, we calculated risk score of each patient for overall survival prediction by using the formula: PSI i , that is, percent-spliced-in, is a ratio that indicates the e ciency of splicing of sequences of interest into transcripts, and can be used to undertake an intuitive quantitative comparison of splicing events [28].…”