While few studies exist in the use of Internet of Things (IoT) in indoor disaster management scenarios, there is a significant absence of research and scalable products in voice command assistant based IoT solutions for public safety and emergency responder situations like mass transit accident, floods, active shooter, and other incidents where just-in-time response and immediate aid are essential. Public safety is the cornerstone of our research study. Social networks drive consumer adoption of IoT technology because users seek feedback from peers, family, and social media influencers to lessen IoT product or service uncertainty. Prior research has demonstrated that customer trust affects IoT adoption. With the advance in Industrial IoT (IIOT) in disaster management, we have solutioned Zenext-IoT (ZIOT) which runs on a core voice command technology for public safety personnel. The goal of this research is to evaluate the degree to which first responders are willing to use the Internet of Things-based disaster management framework that has been proposed. This research aims to solve an urgent and imminent solution of providing the target segment of public safety officials and rescue worker with hands-free voice command virtual assistant IoT model. Our target users are emergency responders and rescue workers for whom technology needs to be simple to use and just-in-time.

Energy-efficiency is a critical requirement for computation-intensive real-time applications on multi-core embedded systems. Multi-core processors enable intra-task parallelism, and in this work, we study energy-efficient real-time scheduling of constrained deadline sporadic parallel tasks, where each task is represented as a directed acyclic graph (DAG). We consider a clustered multi-core platform where processors within the same cluster run at the same speed at any given time. A new concept named speed-profile is proposed to model per-task and per-cluster energy-consumption variations during run-time to minimize the expected long-term energy consumption. To our knowledge, no existing work considers energy-aware real-time scheduling of DAG tasks with constrained deadlines, nor on a clustered multi-core platform. The proposed energy-aware real-time scheduler is implemented upon an ODROID XU-3 board to evaluate and demonstrate its feasibility and practicality. To complement our system experiments in large-scale, we have also conducted simulations that demonstrate a CPU energy saving of up to 67 percent through our proposed approach compared to existing methods.

With the growth of computation-intensive real-time applications on multi-core embedded systems, energy-efficient real-time scheduling becomes crucial. Multi-core processors enable intra-task parallelism, and there has been much progress on exploiting that, while there has been only a little progress on energy-efficient multi-core real-time scheduling as yet. In this work, we study energy-efficient real-time scheduling of constrained deadline sporadic parallel tasks, where each task is represented as a directed acyclic graph (DAG). We consider a clustered multi-core platform where processors within the same cluster run at the same speed at any given time. A new concept named speed-profile is proposed to model per-task and per-cluster energy-consumption variations during run-time to minimize the expected long-term energy consumption. To our knowledge, no existing work considers energy-aware real-time scheduling of DAG tasks with constrained deadlines, nor on a clustered multi-core platform. The proposed energy-aware realtime scheduler is implemented upon an ODROID XU-3 board to evaluate and demonstrate its feasibility and practicality. To complement our system experiments in large-scale, we have also conducted simulations that demonstrate a CPU energy saving of up to 57% through our proposed approach compared to existing methods.

Human-computer interaction introduces critical open door with the proceeds with improvement of wearable gadgets. Gesture recognition through smart devices is becoming a popular research direction. This paper proposes a hand gesture recognition and real-time game control system that is capable of continues human-computer interaction in view of an off-the-rack business wearable wristband. We utilize three-axis accelerator and gyroscope sensors embedded in smart band to collect hand motion information and use Kinect camera capture video information for manual segmentation during the training model phase. A continuous gesture segmentation algorithm based on sliding window and DTW algorithm is developed to detect meaningful gestures in the real-time game control stage. In addition, an android game named Fly Birds which is controlled by gesture recognition result is presented to simulate real-time human-computer interaction. Then, we classify the data in the window using common classifiers. Finally, our experimental results show that, we can accurately identify the designed gestures during the stage of static gesture recognition, and we also achieve a perfect interactive effect in the process of dynamic real-time game control. The experiment outcomes will advance the ascent of human-PC cooperation in view of hand gesture recognition and related applications will rise in vast numbers.

Data visualization has gained significant importance since the demand for software applications providing abstract view and pattern identification has increased. To meet the demand, a number of data visualization techniques and best practices have been proposed in many disciplines. However, data visualization is not as advanced in healthcare software applications as compared to other scientific fields. The result is a limited support provided to the healthcare practitioners by software applications. Moreover, poor usability of healthcare applications is another major hurdle in adoption of electronic healthcare systems rather than traditional paper based methods. In this paper, we propose an Electronic Health Record (EHR) system for Obstetrics, integrating different data visualization techniques. The proposed system has been evaluated using standard usability evaluation methods. The results indicate that using appropriate data visualization techniques in healthcare systems results in enhanced usability. The results also prove that better data visualization helps in improved quality of care and achieves high assurance in healthcare information systems.