Varied edge bundling methods have been used to reduce visual clutter in parallel coordinates plots (PCP). However, existing edge-bundled PCP do not scale well for visual analysis of multidimensional big data and often overplot the bundles in the area near the axes. In this study, we propose a scalable lightweight bundling method to support visual analysis of multidimensional big data in PCP. It helps the users discover trends and detect outliers in the data by bundling the edges between each two adjacent axes independently. We integrate human judgments into the two-dimensional data binning by novel interactions to accelerate the clustering process of the data. We use the frequency-based representation to render the clusters as histogram-like bundles to reveal the distribution of the data and eliminate the overplotting of the bundles. Based on our method, we build a lightweight web-based visual analytics system for exploring multidimensional big data in PCP. The scalability analysis of our method shows that its clustering time increases linearly with the size of the data. Its rendering time is independent of the size of the data. It can cluster and visualize 1 million data records with 6 dimensions in about 1 second in web-based visualization without pre-computation of the data or hardware-accelerated rendering. We conduct two case studies and a user study to compare our method with classic PCP and two state-of-the-art edge-bundled PCP. The results show that our method is more efficient and effective for visually analyzing multidimensional big data.

Maritime operations involve complex tasks that must be taught through practical training. Using modern technologies offshore simulators can serve as realistic environment for training. The degree of immersiveness can be increased by replacing conventional input devices (keyboards, joysticks) with natural user interface (NUI) devices. This paper surveys different NUI technologies and advocates that vision-based systems, such as Microsoft Kinect, can provide gesture recognition with high fidelity. The unobtrusiveness of vision-based technologies is highly important to avoid user resistance and preserve realism of the simulation.

Often software for wireless sensor networks (WSNs) is developed using a specific event based operating system (OS) such as TinyOS. However, this requires steep learning curve for the new developers. Other operating systems for embedded devices have limited support for new hardware platforms. Our goal is to provide an operating system for resource constrained devices that is easy to use for the wide range of researchers and developers familiar with C programming language and Unix operating system concepts. In addition, we provide a framework for agile portability to new hardware platforms, due to the nature of WSN systems that require specific hardware or features for the sensing tasks at hand. We propose Multiple agent netted sensor Operating System (MansOS), that demonstrates ability to execute the same application on different platforms including x86 CPU based computers for easy simulation and debugging of a single node or a whole network. The new platforms are easily added to MansOS thanks to the well defined abstraction layers. MansOS is being successfully used in real world applications, such as wild animal tracking and environmental monitoring in a fruit care research garden. It has also been used in academic environment for teaching wireless sensor networks.