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Position identification is one of the most important tasks for many real-time location-oriented applications in the Internet of Things (IoT). However, precise and robust indoor localization always suffers a lot from the high complexity of the indoor environment. In order to attack this problem, a new kernel learning paradigm named kernel adaptive filtering has come to our attention. Kernel adaptive filters (KAFs) have achieved great success except for their growing network structure, which leads to a heavy storage burden. To balance the accuracy with the size of neural networks, sparsification methods are usually initiated into the KAFs to produce a sparse structure of neural networks. Different from traditional sparse approaches, the Nyström method employs a subcollection of data sampling points to generate a fixed-size filtering structure, which can effectively approximate the space spanned by whole samples. In this work, in order to efficiently fight against the large abnormalities in the indoor environment such as impulsive noise, the Matérn kernel is applied to KAFs for the first time. Based on it, a so-called exponential weighted Matérn kernel recursive maximum correntropy (mKRMC) and its Nyström approximation version, Nyström exponential weighted mKRMC (Nys-mKRMC), are proposed to obtain the desired accuracy performance with a sparse filter structure. In addition, the convergence proof of the proposed Nys-mKRMC has also been given. Finally, extensive experimental results demonstrate that both the proposed mKRMC and Nys-mKRMC can provide high accuracy and strong robustness with the compact size of the filter structure compared with the state-of-the-art KAFs and traditional machine learning methods.

The multikernel adaptive filters (MKAFs) have been successfully applied to resolve the issue of kernel parameter selection in traditional single kernel adaptive filters. However, owing to the linear growing network structures, conventional MKAFs commonly suffer a lot from large computational and memory burdens. To solve this problem, a multiple Nyström approximation is proposed to curb the computational complexity of MKAFs in this brief. More concretely, the multiple Nyström method is incorporated into the kernel generalized Cauchy conjugate gradient algorithm, generating a novel multiple Nyström kernel generalized Cauchy conjugate gradient algorithm (MNKGCCG). It is noted that the MNKGCCG can achieve the desirable filtering performance with low computational cost in the fixed-dimensional feature space. Experimental results on Mackey-Glass time series and sunspots time series predictions in non-Gaussian noise environments demonstrate the superiorities of the proposed MNKGCCG algorithm in terms of filtering accuracy and robustness.

This paper investigates an efficient compressed sensing (CS) approach that can be used to reconstruct 2-D millimeter-wave synthetic aperture radar (SAR) images from under-sampled measurements. This approach minimizes a linear combination of four terms corresponding to a least squares data fitting, ℓ 1 norm regularization, total variation (TV) and a bounding operator. Although the strong convergence of this approach cannot be guaranteed, this approach always converges to a stable structural similarity (SSIM) value with a combination of a parallel operator splitting structure and a FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) updating stage. Simulation and experimental results demonstrate the superior performance of the proposed approach in terms of both efficiency and computation complexity. [ABSTRACT FROM AUTHOR]

A new method for detection of parametric faults occurring in analog circuits based on relative amplitude and relative phase analysis of the Circuit Under Test (CUT) is proposed. The relative amplitude is the common power change of the signals and the relative phase presents the relative phase offset of the signals. In the proposed method, the value of each component of the CUT is varied within its tolerance limit using Monte Carlo simulation. The upper and lower bounds of relative amplitude and phase of the CUT sampling series are obtained. While testing, the relative amplitude and phase value of the analog circuit are obtained. If any one of the relative amplitude and phase values exceed the bounds then the CUT is declared faulty. The effectiveness of the proposed method is validated through HSpice/MATLAB simulations of two benchmark circuits and the practical circuit test of Tow- Thomas circuit.

• A generalized Student's t-based kernel function, and a new kernel adaptive filtering (KAF) algorithm named generalized Student's t kernel adaptive filter (GStKAF) are designed under the KMPE error criterion for indoor positioning under the IoT framework. • The L1-norm is proposed as the penalty to embed into the GStKAF and the resulting sparse GStKAF (SGStKAF) can provide more compact size of the neural networks. • Three experimental results show that the proposed SGStKAF has good robustness against the mixed noise consisting of abrupt noise and Gaussian noise while maintaining the high accuracy performance. The mixed noise such as Gaussian noise together with the abrupt noise widely exists in the indoor environment, which always leads to the problem of performance degradation of the positioning system under the Internet of Things (IoT). In this paper, a novel kernel function named generalized Student's t kernel (GSt) and a resulting sparse generalized Student's t kernel adaptive filter (SGStKAF) is proposed to attack this problem. The proposed SGStKAF utilizes the kernel mean p -power error criterion (KMPE) with the L 1 -norm penalty. The proposed SGStKAF has three significant features. Firstly, the generalized Student's t kernel can suppress the abrupt noise effectively. Secondly, the L 1 -norm penalty guarantees that the fixed-point sub-iteration is available so that the more precise solution can be obtained in a few iterations. At last, a sparse structure of neural networks for the implementation of the proposed method can also be obtained via the L 1 constraint. Three experiments and comparisons are carried out to prove the effectiveness of the proposed positioning framework in terms of accuracy and robustness in both the simulation situation and the real-world indoor environments. [ABSTRACT FROM AUTHOR]