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Purpose: To assess the effectiveness of automated external defibrillator (AED) and manual external defibrillator (MED) training for third-year nurse students. Methods: We conducted post-demonstration and post-practice evaluation for MED defibrillation, and pre-training, post-demonstration, and post-practice evaluation for AED defibrillation. Results: Following MED training, time and confidence to defibrillate were improved significantly post-practice (p

Object detection is an important task in very high-resolution remote sensing image analysis. Traditional detection approaches are often not sufficiently robust in dealing with the variations of targets and sometimes suffer from limited training samples. In this paper, we tackle these two problems by proposing a novel method for object detection based on structural feature description and query expansion. The feature description combines both local and global information of objects. After initial feature extraction from a query image and representative samples, these descriptors are updated through an augmentation process to better describe the object of interest. The object detection step is implemented using a ranking support vector machine (SVM), which converts the detection task to a ranking query task. The ranking SVM is first trained on a small subset of training data with samples automatically ranked based on similarities to the query image. Then, a novel query expansion method is introduced to update the initial object model by active learning with human inputs on ranking of image pairs. Once the query expansion process is completed, which is determined by measuring entropy changes, the model is then applied to the whole target data set in which objects in different classes shall be detected. We evaluate the proposed method on high-resolution satellite images and demonstrate its clear advantages over several other object detection methods. [ABSTRACT FROM PUBLISHER]

Segmentation and classification are important tasks in remote sensing image analysis. Recent research shows that images can be described in hierarchical structure or regions. Such hierarchies can produce the state-of-the-art segmentations and can be used in the classification. However, they often contain more levels and regions than required for an efficient image description, which may cause increased computational complexity. In this letter, we propose a new hierarchical segmentation method that applies graph Laplacian energy as a generic measure for segmentation. It reduces the redundancy in the hierarchy by an order of magnitude with little or no loss of performance. In the classification stage, we apply local self-similarity feature to capture the internal geometric layouts of regions in an image. By incorporating advantages from both semantic hierarchical segmentation and local geometric region description, we have achieved better performance than those from the methods being compared. In the experimental section, we validate the effectiveness of our method by showing results on QuickBird and GeoEye-1 image data sets. [ABSTRACT FROM PUBLISHER]