Search

Conventional sparsity-driven synthetic aperture radar (SAR) imagery often encounters the sensitivity of nonsystematic errors and highly computational load. In this article, a cooperative multitask learning algorithm is proposed based on an autocalibrated alternating direction method of multipliers (AutoCal-ADMM) framework, by which the sparse feature of the scenes/targets-of-interests can be enhanced, and simultaneously the nonmodeled motion errors of either airborne platform or moving target can be autocalibrated in a synergistic manner. By leveraging the entropy and sparsity regularizers in the AutoCal-ADMM framework, the proposed algorithm is particularly tailored to obtain focused SAR images with enhanced sparsity. A reasonable surrogate function is designed for a convex objective function, so that an analytical proximal mapping of the entropy regularizer can be derived. Both nonsystematic range cell migration (NsRCM) and azimuthal phase errors (APEs) are concerned and coherently compensated. A linear and complex soft-thresholding operator is introduced for the sparse solution. The proposed algorithm is capable of greatly alleviating “error propagation” between multiple tasks, where an optima balance between the sparse and focusing features can be achieved. Superior performance in terms of convergence and efficiency can be guaranteed. Both raw SAR and canonical ground moving target imaging (GMTIm) data sets are processed and comparisons with conventions are performed, where the effectiveness and superiority of the proposed AutoCal-ADMM algorithm are validated. [ABSTRACT FROM AUTHOR]

The linearly constrained tensor n-rank minimization problem is an extension of matrix rank minimization. It is applicable in many fields which use the multi-way data, such as data mining, machine learning and computer vision. In this paper, we adapt operator splitting technique and convex relaxation technique to transform the original problem into a convex, unconstrained optimization problem and propose a fixed point iterative method to solve it. We also prove the convergence of the method under some assumptions. By using a continuation technique, we propose a fast and robust algorithm for solving the tensor completion problem, which is called FP-LRTC (Fixed Point for Low n-Rank Tensor Completion). Our numerical results on randomly generated and real tensor completion problems demonstrate that this algorithm is effective, especially for “easy” problems. [ABSTRACT FROM PUBLISHER]

Estimation of on–off timing of human skeletal muscles during movement is an ongoing issue in surface electromyography (sEMG) signal processing for relevant clinical applications. Widely used single threshold methods still rely on the experience of the operator to manually establish a threshold level. In this paper, a novel approach to address this issue is presented. Based on the generalized likelihood ratio test, the maximum likelihood (ML) method is improved with an adaptive threshold technique based on the signal-to-noise ratio (SNR) estimate in the initial time before accurate sEMG analyses. The dependence of optimal threshold on SNR is determined by minimizing the onset/offset estimate error on a large set of simulated signals with well-known signal parameters. Accuracy and precision of the algorithm were assessed by using a set of simulated signals and real sEMG signals recorded from two healthy subjects during elbow flexion-extension movements with and without workload. Comparison with traditional algorithms shows that with a moderate increase in the computational effort the ML algorithm performs well even for low levels of EMG activity, while the proposed adaptive method is most robust with respect to variations in SNRs. Also, we discuss the results of analyzing the sEMG recordings from the selected proximal muscles of the upper limb in two hemiparetic subjects. The detection algorithm is automatic and user-independent, managing the detection of both onset and offset activation, and is applicable in presence of noise allowing use by skilled and unskilled operators alike. [ABSTRACT FROM AUTHOR]