Ridge detection is a classical tool to extract curvilinear features in image processing. As such, it has great promise in applications to material science problems; specifically, for trend filtering relatively stable atom-shaped objects in image sequences, such as bright-field Transmission Electron Microscopy (TEM) videos. Standard analysis of TEM videos is limited to frame-by-frame object recognition. We instead harness temporal correlation across frames through simultaneous analysis of long image sequences, specified as a spatio-temporal image tensor. We define new ridge detection algorithms to non-parametrically estimate explicit trajectories of atomic-level object locations as a continuous function of time. Our approach is specially tailored to handle temporal analysis of objects that seemingly stochastically disappear and subsequently reappear throughout a sequence. We demonstrate that the proposed method is highly effective in simulation scenarios, and delivers notable performance improvements in TEM experiments compared to other material science benchmarks.

Patient management in hospital environments with the provision of effective care pathways represents a significant challenge. Hospital settings are characterized by uncertainty driven by the need to accommodate both elective treatments and emergency cases [which generally arrive without warning, this can place great strain on the healthcare system. This paper considers these issues along with the potential technological developments which may provide a solution to the challenges faced by healthcare professionals (or at least mitigate the impact of the challenging complex and dynamic hospital environment). It is postulated that technology enhanced patient management based on situational awareness in intelligent context-aware systems has the capability to improve patient experience along with improvements in resource utilization in a hospital setting.

There is a growing need to facilitate at-home care through development of smart care spaces. Such systems require significant use of sensors, often potentially in very-low-power wireless nodes. In contrast to the logical and numerical nature of sensor data, care is emotional and hence symbolic in nature, and interfaces to care-systems need to respect the need to understand emotions and appropriately respond to them. Considering emotions and symbols from the ground up can help ensure that emotional intelligence is integrated throughout smart care-space development. Therefore, this paper provides a heuristic discussion around the potential for developing pseudo-emotions into low-power wireless sensor nodes, as an illustration of the concept.

Rapid expansion of wireless technologies has provided a platform to support intelligent systems in the domain of mobile marketing. Utilizing Location Based Services (LBS) and Global Navigational Satellite Systems (GNSS) infrastructure provides the transport of real-time, scheduled, location-based advertising to individuals and business. In this paper a distributed synchronous platform is proposed called iMAS. iMAS proposes to use an open source platform independent approach to provide the services and interaction with the iMAS system. Intelligent advertising services (IAS) is an integral component of the system and uses a global positioning system (GPS) to enable information to be received depending on the longitude and latitude coordinates of the iMAS device. This allows for contextual information to be used as a means to provide intelligent context-aware location-centric targeted marketing advertisements to the general public. At this stage iMAS is a proof of concept and subsequent research is already under way to test the device in a controlled environment.

In our exciting world of pervasive computing and always-available mobile internet, meeting the educational needs of students has seen a growing trend toward collaborative electronic and mobile learning systems that build on the vision of Web 2.0. However, other trends relevant to modern students must not be ignored, including data freedom, brokerage and interconnectivity. Such factors are associated with the Internet of Things and the vision for Web 3.0, and so include the needs for greater consideration of data context and educational personalization so important to the future of campus-based, distance and vocational study. Therefore, future education can be expected to require a deeper technological connection between students and learning environments, in a manner requiring significant use of sensors, mobile devices, cloud computing and rich-media visualization. This paper considers the challenges associated with adopting such a futuristic concept as a means of enriching learning materials and environments within a university context. It will be concluded that much of the technology required to embrace the vision of Web 3.0 in education already exists, but that further research in key areas is required for the concept to achieve its full potential.