The Smart campus is a concept of an education institute using technologies, such as information systems, internet of things (IoT), and context-aware computing, to support learning, teaching, and administrative activities. Classrooms are important building blocks of a school campus. Therefore, a feasible architecture for building and running smart classrooms is essential for a smart campus. However, most studies related to the smart classroom are focused on studying or addressing particular technical or educational issues, such as networking, AI applications, lecture quality, and user responses to technology. In this study, an architecture for building and running context-aware smart classrooms is proposed. The proposed architecture consists of three parts including a prototype of a context-aware smart classroom, a model for technology integration, and supporting measures for the operation of smart classrooms in this architecture. The classroom prototype was designed based on our study results and a smart classroom project in Ming Chuan University (MCU). The integration model was a layered model uses Raspberry Pi in the bottom layer of the model to integrate underlying technologies and provide application interfaces to the higher layer applications for the ease of building context-aware smart classroom applications. As a result, application interfaces were implemented using Raspberry Pi based on the proposed technology integration model, and a context-aware energy-saving smart classroom application was implemented based on the proposed classroom prototype and the implemented web application interface. The result shows that, in terms of technology, the proposed architecture is feasible for building context-aware smart classrooms in smart campuses.3 of 34 as follows. The related works are reviewed in the following subsections of this section. The components of the proposed architecture, including a context-aware smart classroom prototype, a technology integration model, and the supporting measures for smart classroom operations, are described in Section 2. The implementations to prove the concepts of the proposed architecture are demonstrated in Section 3. Discussions and conclusions are given in Section 4. Background and Related WorksStudies related to this study are reviewed in this section. Studies related to different topics, including classroom environment, context-aware, energy issues, and Ming Chuan University (MCU) Smart Classroom Project, are reviewed in the following four subsections respectively. Physical Environment and Smart ClassroomsSome research works focus on taking a smart classroom as a tool to measure lecture quality. In [8], authors use sensors to retrieve features, such as noise level, CO 2 level, temperature, humidity, lecturers' voice, and lecturers' motion, in smart classrooms. Collected data are evaluated by various classification algorithms. The result shows that CO 2 level, temperature, humidity, and noise level are the main environmental factors that affect lecture quality. The relations...

This evidence implementation project aimed to identify barriers leading to needle-stick injuries (NSIs) and to develop implementation strategies to prevent NSIs in the acute ward of a hospital in central Taiwan. Introduction:The incidence rate of NSIs was 5.6% in the acute ward of a hospital in Taiwan. NSIs commonly occur during the drawing of blood, intravenous insertion, needle recapping, or performing any procedure involving sharp medical devices. NSIs are critical occupational risks among healthcare workers, possibly leading to transmission of infectious diseases, especially blood-borne viruses, such as HIV, hepatitis B, and hepatitis C.Methods: A clinical audit was undertaken using the JBI Practical Application of Clinical Evidence System (PACES) and the Getting Research into Practice (GRiP) approach. Five audit criteria that represented best practice recommendations for prevention of NSIs were used. Baseline data were collected from 177 nurses in five acute wards, followed by the implementation of multiple strategies during a 20-week period of the project. Both baseline and postimplementation audits were undertaken to determine changes in practice.Results: According to the pre-audit concerning the use of safety-engineered injection devices and safe use and disposal of needles, there was 14-15% compliance, which indicated poor compliance with current best-practice criteria. Following the project implementation, the nursing staff were educated about the well tolerated use and disposal of sharps and the improved compliance rate ranged from 40 to 96.6%, with safety needle use increasing from 16 to 95.5%, safety needle operation procedure awareness increasing from 14 to 96%, needles not recapped after use increasing from 47 to 85%, and placing used needles in the sharps collection box increasing from 75 to 80%. Conclusion:This article suggests that standardized puncture prevention education and training enhanced nurses' awareness in the acute ward.

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This paper presents a model-based approach for 3D pose estimation of a single RGB image to keep the 3D scene model up-to-date using a low-cost camera. A prelearned image model of the target scene is first reconstructed using a training RGB-D video. Next, the model is analyzed using the proposed multiple principal analysis to label the viewpoint class of each training RGB image and construct a training dataset for training a deep learning viewpoint classification neural network (DVCNN). For all training images in a viewpoint class, the DVCNN estimates their membership probabilities and defines the template of the class as the one of the highest probability. To achieve the goal of scene reconstruction in a 3D space using a camera, using the information of templates, a pose estimation algorithm follows to estimate the pose parameters and depth map of a single RGB image captured by navigating the camera to a specific viewpoint. Obviously, the pose estimation algorithm is the key to success for updating the status of the 3D scene. To compare with conventional pose estimation algorithms which use sparse features for pose estimation, our approach enhances the quality of reconstructing the 3D scene point cloud using the template-to-frame registration. Finally, we verify the ability of the established reconstruction system on publicly available benchmark datasets and compare it with the state-of-the-art pose estimation algorithms. The results indicate that our approach outperforms the compared methods in terms of the accuracy of pose estimation.

Abstract:In this paper we present a novel unsupervised approach to detecting and segmenting objects as well as their constituent symmetric parts in an image. Traditional unsupervised image segmentation is limited by two obvious deficiencies: the object detection accuracy degrades with the misaligned boundaries between the segmented regions and the target, and pre-learned models are required to group regions into meaningful objects. To tackle these difficulties, the proposed approach aims at incorporating the pair-wise detection of symmetric patches to achieve the goal of segmenting images into symmetric parts. The skeletons of these symmetric parts then provide estimates of the bounding boxes to locate the target objects. Finally, for each detected object, the graphcut-based segmentation algorithm is applied to find its contour. The proposed approach has significant advantages: no a priori object models are used, and multiple objects are detected. To verify the effectiveness of the approach based on the cues that a face part contains an oval shape and skin colors, human objects are extracted from among the detected objects. The detected human objects and their parts are finally tracked across video frames to capture the object part movements for learning the human activity models from video clips. Experimental results show that the proposed method gives good performance on publicly available datasets.

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