Dissimilarity for functional data clustering based on smoothing parameter commutation.

Many studies measure the same type of information longitudinally on the same subject at multiple time points, and clustering of such functional data has many important applications. We propose a novel and easy method to implement dissimilarity measure for functional data clustering based on smoothing splines and smoothing parameter commutation. This method handles data observed at regular or irregular time points in the same way. We measure the dissimilarity between subjects based on varying curve estimates with pairwise commutation of smoothing parameters. The intuition is that smoothing parameters of smoothing splines reflect the inverse of the signal-to-noise ratios and that when applying an identical smoothing parameter the smoothed curves for two similar subjects are expected to be close. Our method takes into account the estimation uncertainty using smoothing parameter commutation and is not strongly affected by outliers. It can also be used for outlier detection. The effectiveness of our proposal is shown by simulations comparing it to other dissimilarity measures and by a real application to methadone dosage maintenance levels

Changes in corneal curvature after wearing the orthokeratology lens

AbstractIntroductionThe orthokeratology lens (OK lens) is designed to reshape the cornea and correct refraction error. Owing to the convenience of ceasing the use of glasses during the day, the use of the OK lens is increasing in myopic children. In this study, changes in corneal curvature and astigmatism after wearing the OK lens were analyzed.MethodsThis retrospective cohort study included 65 children (130 eyes) who underwent full and regular examinations. None of the participants had any ocular disease other than myopia and astigmatism. The OK lenses used in this study were four-zone, reverse-geometry lenses. The corneal curvature of each patient was checked annually after the patients discontinued daily wearing of the OK lens for 10 days. Student t test and repeated measures analysis of variance (ANOVA) analyses were performed to compare the results.ResultsThe radius of corneal curvature showed a progressive annual increase with significant differences, both in the steepest and flattest radius of the corneal curvature (p < 0.001 and p = 0.001, respectively). The mean radius of the steepest and flattest corneal curvature increased significantly from baseline to the following years consecutively (all p < 0.001). Nevertheless, astigmatism did not change significantly in any of the tests.ConclusionCorneal curvature changed as the patients grew older. There was a statistically significant increase in the radius of the corneal curvature in the myopic children studied. For correct fit of OK lenses, the radius of the corneal curvature should be regularly checked prior to dispensing a new set of lenses

CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization

To efficiently extract the textual information from color degraded document images is an important research topic. Long-term imperfect preservation of ancient documents has led to various types of degradation such as page staining, paper yellowing, and ink bleeding; these degradations badly impact the image processing for information extraction. In this paper, we present CCDWT-GAN, a generative adversarial network (GAN) that utilizes the discrete wavelet transform (DWT) on RGB (red, green, blue) channel splited images. The proposed method comprises three stages: image preprocessing, image enhancement, and image binarization. This work conducts comparative experiments in the image preprocessing stage to determine the optimal selection of DWT with normalization. Additionally, we perform an ablation study on the results of the image enhancement stage and the image binarization stage to validate their positive effect on the model performance. This work compares the performance of the proposed method with other state-of-the-art (SOTA) methods on DIBCO and H-DIBCO ((Handwritten) Document Image Binarization Competition) datasets. The experimental results demonstrate that CCDWT-GAN achieves a top two performance on multiple benchmark datasets, and outperforms other SOTA methods

Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a "long-lie". Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LSTM) for FD