Nyborg Elliott (maneagle7)

The prototype, which includes the hardware and a smartphone app, showed promising results with performance comparable to or better than similar commercial devices, thus potentially making the proposed system an ideal wearable solution for long-term monitoring of COVID-19 patients and other chronic diseases.This paper studies the performance of a resonant capacitive wireless power transfer (C-WPT) link for biomedical implants in the presence of non-idealities. The study emphasizes on finding an accurate electrical model of a practical C-WPT link, which can be used to investigate the performance of the link under different practical/non-ideal scenarios. A sound knowledge about these non-idealities is crucial for device optimization. For the first time, a circuit model has been presented and analyzed, which is applicable to a practical C-WPT link undergoing plate mismatch, flexion, tissue contraction, and stretching. Our model considers the finite conductivity of the body tissue and fringe fields formed by capacitor plates. Analytical and HFSSTM simulation results have been presented for different non-idealities, and are in good agreement. Additionally, we show a procedure to interpolate non-ideal case results. The study shows that plate misalignment (causing reduction in parallel plate overlap area) and skin tissue contraction (while muscle grows) are the most detrimental individual factors to the link performance. We recorded ∼32% and ∼14% power transfer efficiency decrease due to these two worst-case scenarios, respectively for a C-WPT link comprising of two pairs of 400 mm2 parallel plates (12 cm edge-to-edge separation) coated with 63.5 µm thick Kapton layer and aligned around a 3 mm tissue at 20 MHz.It has become routing work to detect and correct for population structure in genome-wide association analysis. A variety of methods have been proposed. Particularly, the methods based on spectral graph theory have shown superior performance. We discovered that the inherent nonlinear distribution of high-dimensional genotypic data was a possible source of confounding factors in population structure analysis, and was also the possible underlying reason that accounted for the superiority of these spectral-based methods. We verified this hypothesis by validating a variation of the Laplacian Eigen analysis LAPMAP. The method could faithfully reveal the underlying population structures of HapMap II and III data sets. The inferred top eigenvectors together with minor eigenvectors were used to segregate samples by their ancestries. We found that the top 3 eigenvectors differentiated the 4 populations in phase II data set; the top 3 eigenvectors clustered the populations into 4 clusters, reflecting their continental origins. 9 populations were well recognized in phase III populations. Next, we estimated admixture proportions for simulated individuals. The method showed comparable or better performance in capturing and correcting for modelled population structures. All experimental results showed that LAPMAP was robust, efficient and scalable to genome-wide association studies.The low-rank tensor representation (LRTR) has become an emerging research direction to boost the multi-view clustering performance. This is because LRTR utilizes not only the pairwise relation between data points, but also the view relation of multiple views. However, there is one significant challenge LRTR uses the tensor nuclear norm as the convex approximation but provides a biased estimation of the tensor rank function. To address this limitation, we propose the generalized nonconvex low-rank tensor approximation (GNLTA) for multi-view subspace clustering. Instead of the pairwise correlation, GNLTA adopts the low-rank tensor approximation to capture the high-order correlation among multiple views and proposes the generalized nonconvex low-rank tensor norm to well consider the physical meanings of different singular values. We develop a unified solver to solve