Multi-TID detection and characterization in a dense Global Navigation Satellite System receiver network

The medium-scale traveling ionospheric disturbances (MSTIDs) constitute the most frequent ionospheric wave signatures. We propose a method for detecting the number of simultaneous MSTIDs from a time series of high-pass-filtered Vertical Total Electron Content (VTEC) maps and their parameters. The method is tested on the VTEC map corresponding to a simulated realistic scenario and on actual data from dual-frequency Global Positioning System (GPS) measurements gathered by +1200 GPS receivers of the GPS Earth Observation Network (GEONET) in Japan. The contribution consists of the detection of the number of independent MSTIDs from a nonuniform sampling of the ionospheric pierce points. The problem is set as a sparse decomposition on elements of a dictionary of atoms that span a linear space of possible MSTIDs. These atoms consist of plane waves characterized by a wavelength, direction, and phase on a surface defined, the part of the ionosphere sounded by the GEONET (i.e., 25°N to 50°N of latitude and 125°E to 155°E of longitude). The technique is related to the atomic decomposition and least absolute shrinkage and selection operator. The geophysical contribution of this paper is showing (a) the detection of several simultaneous MSTIDs of different characteristics, with a continuous change in the velocity; (b) detection of circular MSTID waves compatible by time and center with a specific earthquake; (c) simultaneous superposition of two distinct MSTIDs, with almost the same azimuth; and (d) the presence at nighttime of MSTIDs with velocities in the range 400–600 m/s.Peer ReviewedPostprint (published version

Varying-coefficients for regional quantile via KNN-based LASSO with applications to health outcome study

Health outcomes, such as body mass index and cholesterol levels, are known to be dependent on age and exhibit varying effects with their associated risk factors. In this paper, we propose a novel framework for dynamic modeling of the associations between health outcomes and risk factors using varying-coefficients (VC) regional quantile regression via K-nearest neighbors (KNN) fused Lasso, which captures the time-varying effects of age. The proposed method has strong theoretical properties, including a tight estimation error bound and the ability to detect exact clustered patterns under certain regularity conditions. To efficiently solve the resulting optimization problem, we develop an alternating direction method of multipliers (ADMM) algorithm. Our empirical results demonstrate the efficacy of the proposed method in capturing the complex age-dependent associations between health outcomes and their risk factors

Divide-and-conquer framework for image restoration and enhancement

Abstract(#br)We develop a novel divide-and-conquer framework for image restoration and enhancement based on their task-driven requirements, which takes advantage of visual importance differences of image contents (i.e., noise versus image, edge-based structures versus smoothing areas, high-frequency versus low-frequency components) and sparse prior differences of image contents for performance improvements. The proposed framework is efficient in implementation of decomposition-processing-integration. An observed image is first decomposed into different subspaces based on considering visual importance of different subspaces and exploiting their prior differences. Different models are separately established for image subspace restoration and enhancement, and existing image restoration and enhancement methods are utilized to deal with them effectively. Then a simple but effective fusion scheme with different weights is used to integrate the post-processed subspaces for the final reconstructed image. Final experimental results demonstrate that the proposed divide-and-conquer framework outperforms several restoration and enhancement algorithms in both subjective results and objective assessments. The performance improvements of image restoration and enhancement can be yielded by using the proposed divide-and-conquer strategy, which greatly benefits in terms of mixed Gaussian and salt-and-pepper noise removal, non-blind deconvolution, and image enhancement. In addition, our divide-and-conquer framework can be simply extensible to other restoration and enhancement algorithms, and can be a new way to promote their performances for image restoration and enhancement