Low-field portable magnetic resonance imaging (pMRI) devices address a crucial requirement in the realm of healthcare by offering the capability for on-demand and timely access to MRI, especially in the context of routine stroke emergency. Nevertheless, images acquired by these devices often exhibit poor clarity and low resolution, resulting in their reduced potential to support precise diagnostic evaluations and lesion quantification. In this paper, we propose a 3D deep learning based model, named Stroke-Aware CycleGAN (SA-CycleGAN), to enhance the quality of low-field images for further improving diagnosis of routine stroke. Firstly, based on traditional CycleGAN, SA-CycleGAN incorporates a prior of stroke lesions by applying a novel spatial feature transform mechanism. Secondly, gradient difference losses are combined to deal with the problem that the synthesized images tend to be overly smooth. We present a dataset comprising 101 paired high-field and low-field diffusion-weighted imaging (DWI), which were acquired through dual scans of the same patient in close temporal proximity. Our experiments demonstrate that SA-CycleGAN is capable of generating images with higher quality and greater clarity compared to the original low-field DWI. Additionally, in terms of quantifying stroke lesions, SA-CycleGAN outperforms existing methods. The lesion volume exhibits a strong correlation between the generated images and the high-field images, with R=0.852. In contrast, the lesion volume correlation between the low-field images and the high-field images is notably lower, with R=0.462. Furthermore, the mean absolute difference in lesion volumes between the generated images and high-field images ( $1.73\pm 2.03$ mL) was significantly smaller than the difference between the low-field images and high-field images ( $2.53\pm 4.24$ mL). It shows that the synthesized images not only exhibit superior visual clarity compared to the low-field acquired images, but also possess a high degree of consistency with high-field images. In routine clinical practice, the proposed SA-CycleGAN offers an accessible and cost-effective means of rapidly obtaining higher-quality images, holding the potential to enhance the efficiency and accuracy of stroke diagnosis in routine clinical settings. The code and trained models will be released on GitHub: SA-CycleGAN.

Mobile phone with high accessibility and usability is regarded as the ideal interface for the users to monitor and control the approaching smart home environment. Moreover, networking technologies and protocols have been advanced enough to support a universal monitoring and controlling interface on smart phones. This paper presents HouseGenie, an interactive, direct manipulation application on mobile, which supports a range of basic home monitoring and controlling functionalities as a replacement of individual remotes of smart home appliances. HouseGenie also addresses several common requirements that may be behind the vision, such as scenario, short-delay alarm, area restriction and so on. We demonstrate that HouseGenie not only provides intuitive presentations and interactions for smart home management, but also improves user experience comparing to present solutions.

We present the satellite cursor - a novel technique that uses multiple cursors to improve pointing performance by reducing input movement. The satellite cursor associates every target with a separate cursor in its vicinity for pointing, which realizes the MAGIC (manual and gaze input cascade) pointing method without gaze tracking. We discuss the problem of visual clutter caused by multiple cursors and propose several designs to mitigate it. Two controlled experiments were conducted to evaluate satellite cursor performance in a simple reciprocal pointing task and a complex task with multiple targets of varying layout densities. Results show the satellite cursor can save significant mouse movement and consequently pointing time, especially for sparse target layouts, and that satellite cursor performance can be accurately modeled by Fitts' Law.

Real-time interactive virtual classroom with teleeducation experience is an important approach in distance learning. However, most current systems fail to meet new challenges in extensibility and scalability, which mainly lie with three issues. First, an open system architecture is required to better support the integration of increasing human-computer interfaces and personal mobile devices in the classroom. Second, the learning system should facilitate opening its interfaces, which will help easy deployment that copes with different circumstances and allows other learning systems to talk to each other. Third, problems emerge on binding existing systems of classrooms together in different places or even different countries such as tackling systems intercommunication and distant intercultural learning in different languages. To address these issues, we build a prototype application called Open Smart Classroom built on our software infrastructure based on the multiagent system architecture using Web Service technology in Smart Space. Besides the evaluation of the extensibility and scalability of the system, an experiment connecting two Open Smart Classrooms deployed in different countries is also undertaken, which demonstrates the influence of these new features on the educational effect. Interesting and optimistic results obtained show a significant research prospect for developing future distant learning systems.