Calcaneus fracture is the most common fracture in all types of Tarsal fracture. Early and accurate diagnosis is essential for prompt treatment. This study aims to develop an automatic detection and classification system for calcaneal fracture with deep learning techiques, in which the X-ray images of calcaneal fractures can be detected and classified clearly. In this study, we collected the X-ray image dataset of calcaneal, which was categorized as either fracture or non-fracture, and employed data augmentation techniques to expand the volume and variety of the dataset. In this work, a Deep Residual Neural Network (ResNet) model has been trained for binary fracture classification. To enhance the model interpret-ability and help non-deep learning experts understand how the model predicts. We've utilized the Grad-CAM method to generate the heatmaps. With the heatmaps, the range of calcaneal fracture can be highligted and realized more clearly and intuitively.

Falls are consistently the top cause of death among seniors. At a time when the global population is getting older and fewer births. The shortage of nursing staff seriously affects the health care of the elderly. If information and communication technology can be used, automatic detection and identification the elderly fall, we believe it can reduce the injury of the elderly due to falls. This paper proposes a method different from the previous wearable sensing device, which is based on the displacement of human relative positional parameters in the image to identify the occurrence of human fall. We implemented a system based on OpenPose and combined with the deep learning neural network model LSTM with time series, the image recognition is carried out, the human joint parameters of human posture falling and falling in the image are captured, and the identified parameters are simply filtered, and then the filtered parameters are used for model training.

Abstract The influenza problem has always been an important global issue. It not only affects people’s health problems but is also an essential topic of governments and health care facilities. Early prediction and response is the most effective control method for flu epidemics. It can effectively predict the influenza-like illness morbidity, and provide reliable information to the relevant facilities. For social facilities, it is possible to strengthen epidemic prevention and care for highly sick groups. It can also be used as a reminder for the public. This study collects information on the influenza-like illness emergency department visits to the Taiwan Centers for Disease Control, and the PM2.5 open-source data from the Taiwan Environmental Protection Administration's air quality monitoring network. By using deep learning techniques, the relevance of short-term estimates and the outbreak calculation method can be determined. The techniques are published by the WHO to determine whether the influenza-like illness situation is still in a stage of reasonable control. Finally, historical data and future forecasted data are integrated on the web page for visual presentation, to show the actual regional air quality situation and influenza-like illness data and to predict whether there is an outbreak of influenza in the region.

A phylogenetic tree is used to present the evolutionary relationships among the interesting biological species based on the similarities in their genetic sequences. The UPGMA is one of the popular algorithms to construct a phylogenetic tree according to the distance matrix created by the pairwise distances among taxa. To solve the performance issue of the UPGMA, the implementation of the UPGMA method on a single GPU has been proposed. However, it is not capable of handling the large taxa set. This work describes a novel parallel UPGMA approach on multiple GPUs that is able to build a tree from extremely large datasets. The experimental results show that the proposed approach with 4 NVIDIA GTX 980 achieves an approximately × fold speedup over the implementation of UPGMA on CPU and GPU, respectively.

In this paper, we study spectrum trading in cognitive radio (CR) networks with multiple primary services (PSs) and multiple secondary services (SSs) from a game-theoretic perspective. We propose a multistage Bayesian game-based trading model which accounts for unknown private information of players (for example, the number of user connections in PSs may be unknown to the SSs) as in practical network scenarios. The perfect Bayesian equilibrium (PBE) is derived by solving an involved sequential optimization problem. We formulate the joint Karush-Kuhn-Tucker (KKT) conditions and use the KKT translation technique to obtain the PBE at each stage. Simulation demonstrates the convergence of the sequence of strategies in the multistage Bayesian game.