No abstract

Prediction methods for seismic multiples are never ideal in practice and an adaptive subtraction process is needed to account for mismatches between the predicted and the actual multiples. We are interested in the problem of separating primary and multiple seismic signals based on their statistical properties. We link recent advances in the blind-source separation problem to the multiple removal problem, and present a novel adaptive subtraction method based on an information maximization principle. Compared with previous methods, our proposed method uses higher-order statistics of the data and incorporates the filtering nature of the adaptive subtraction problem into our algorithm formulation. We use simulations to show that our proposed adaptive subtraction method outperforms the popular least-squares adaptive subtraction and the independent component analysis methods quantitatively, as measured by the mean-squared error, and qualitatively, as evaluated by the visual quality of the image reconstruction.

Drug-resistant tuberculosis (DR-TB) posed challenges to global TB control. Whole-genome sequencing (WGS) is recommended for predicting drug resistance to guide DR-TB treatment and management. Nevertheless, data are lacking in Taiwan. Phenotypic drug susceptibility testing (DST) of 12 anti-TB drugs was performed for 200 Mycobacterium tuberculosis isolates. WGS was performed using the Illumina platform. Drug resistance profiles and lineages were predicted in silico using the Total Genotyping Solution for TB (TGS-TB). Using the phenotypic DST results as a reference, WGS-based prediction demonstrated high concordance rates of isoniazid (95.0%), rifampicin (RIF) (98.0%), pyrazinamide (98.5%) and fluoroquinolones (FQs) (99.5%) and 96.0% to 99.5% for second-line injectable drugs (SLIDs); whereas, lower concordance rates of ethambutol (87.5%), streptomycin (88.0%) and ethionamide (84.0%). Furthermore, minimum inhibitory concentrations confirmed that RIF rpoB S450L, FQs gyrA D94G and SLIDs rrs a1401g conferred high resistance levels. Besides, we identified lineage-associated mutations in lineage 1 (rpoB H445Y and fabG1 c-15t) and predominant lineage 2 (rpoB S450L and rpsL K43R). The WGS-based prediction of drug resistance is highly concordant with phenotypic DST results and can provide comprehensive genetic information to guide DR-TB precision therapies in Taiwan.

Synthetic Aperture Radar (SAR) imaging can suffer from image focus degradation due to unknown platform or target motion. Autofocus algorithms use signal processing techniques to remove the undesired phase errors. The recently proposed multichannel autofocus models formulate the problem as the solution to Ae jφ φ φ ≈ 0, where A is a given matrix and φ φ φ are the unknown phases. Previous methods approximated e jφ φ φ using the null vector of A. We propose to approximate e jφ φ φ using conic optimization and call this new autofocus algorithm Semidefinite Relaxation Autofocus (SDRA). Experimental results using a simulated SAR image shows that SDRA has promising performance advantages over existing autofocus methods.

Autofocus algorithms are used to restore images in nonideal synthetic aperture radar imaging systems. In this paper, we propose a bilinear parametric model for the unknown image and the nuisance phase parameters and derive an efficient maximum-likelihood autofocus (MLA) algorithm. In the special case of a simple image model and a narrow range of look angles, MLA coincides with the successful multichannel autofocus (MCA). MLA can be interpreted as a generalization of MCA to a larger class of models with a larger range of look angles. We analyze its advantages over previous extensions of MCA in terms of identifiability conditions and noise sensitivity. As a byproduct, we also propose numerical approximations to the difficult constant modulus quadratic program that lies at the core of these algorithms. We demonstrate the superior performance of our proposed methods using computer simulations in both the correct and mismatched system models. MLA performs better than other methods, both in terms of the mean squared error and visual quality of the restored image.