Non-intrusive support of ground vehicle wind tunnel models through superconducting magnetic levitation

Wind tunnel testing of racing cars is performed with a moving ground plane to take into account the downforce generated by the low ground clearance of these vehicles. Struts and wheel stings, mounted from the roof and walls of the tunnel, are used to hold the vehicle in position within the test section. These supports disrupt the airflow around the model, thereby deviating from on-track conditions. Where the vehicle's aerodynamics are already highly refined, the effects of subtle shape changes such as those made in Formula 1, may be much smaller than the errors introduced by the supporting struts. Support interference can also lead to incorrect optimisation of aerodynamic elements. A magnet will stably levitate over a High Temperature Superconductor (HTS) cooled below its critical temperature. The magnetic flux of the magnet becomes pinned within the bulk HTS microstructure in the form of individual flux quanta, each of which is surrounded by a current vortex at sites of imperfection in the superconducting matrix. This mechanism formed the basis of the superconducting pod which achieved stable passive levitation. Finite element analysis simulation was used to optimise the effectiveness of the electromagnets providing a restoring force to the levitating magnets. To augment the superconducting levitation, without introducing excessive instability to the levitation, the magnetic rail was invented. Traverses of both the superconducting pod and the magnetic rail were performed to map the forces each produced. The feasibility of a non-intrusive method of supporting ground vehicle wind tunnel models has been investigated. The Superconducting Magnetic Levitation System combines the inherent stability and damping of superconducting levitation with the high ground clearance of magnet only levitation. Stable passive levitation has been achieved, with six degree of freedom control. The system uses a combination of type II high temperature superconductors, rare earth permanent magnets, and electromagnets to support a model under test. The final prototype of the superconducting magnetic levitation system was designed to support a 40% scale Formula 1 model. The system was capable of supporting 250N of downforce on top of- the weight of the model and 90N of drag at ground clearances comparable to 40% scale Formula 1 clearances. The Superconducting Magnetic Levitation System is the largest wind tunnel magnetic levitation system in the world and has been successfully tested at speeds of up to 20ms"' in the Durham 2m wind tunnel

Accessibility of 3D Game Environments for People with Aphasia: An Exploratory Study

People with aphasia experience difficulties with all aspects of language and this can mean that their access to technology is substantially reduced. We report a study undertaken to investigate the issues that confront people with aphasia when interacting with technology, specifically 3D game environments. Five people with aphasia were observed and interviewed in twelve workshop sessions. We report the key themes that emerged from the study, such as the importance of direct mappings between users’ interactions and actions in a virtual environment. The results of the study provide some insight into the challenges, but also the opportunities, these mainstream technologies offer to people with aphasia. We discuss how these technologies could be more supportive and inclusive for people with language and communication difficulties

Can surgical simulation be used to train detection and classification of neural networks?

Computer-assisted interventions (CAI) aim to increase the effectiveness, precision and repeatability of procedures to improve surgical outcomes. The presence and motion of surgical tools is a key information input for CAI surgical phase recognition algorithms. Vision-based tool detection and recognition approaches are an attractive solution and can be designed to take advantage of the powerful deep learning paradigm that is rapidly advancing image recognition and classification. The challenge for such algorithms is the availability and quality of labelled data used for training. In this Letter, surgical simulation is used to train tool detection and segmentation based on deep convolutional neural networks and generative adversarial networks. The authors experiment with two network architectures for image segmentation in tool classes commonly encountered during cataract surgery. A commercially-available simulator is used to create a simulated cataract dataset for training models prior to performing transfer learning on real surgical data. To the best of authors' knowledge, this is the first attempt to train deep learning models for surgical instrument detection on simulated data while demonstrating promising results to generalise on real data. Results indicate that simulated data does have some potential for training advanced classification methods for CAI systems

Computer delivery of gesture therapy for people with severe aphasia

Background: Using gesture as a compensatory communication strategy may be challenging for people with severe aphasia. Therapy can improve skills with gesture, at least in elicitation tasks, but gains ar often modest. Raising the treatment dose with technology might improve outcomes. Aims: This feasibility study developed a computer gesture therapy tool (GeST), and piloted it with nine people who have severe aphasia. It aimed to determine whether practice with GeST would improve gesture production and/or spoken naming. It also explored whether GeST encouraged independent practice and was easy to use. Methods & Procedures: Pilot participants had 6 weeks practice with GeST, flanked by pre- and post-therapy tests of gesture and word production. Usability was explored through interviews and structured observations, and the amount of time spent in the programme was monitored. Outcomes & Results: Scores on the gesture test were evaluated by 36 independent raters. Recognition scores for gestures practised with the tool improved significantly after therapy and the gain was maintained. However, gains were small and only occurred on items that were practised with regular therapist support. There was no generalisation to unpractised gestures and no effect on spoken naming. Usability results were positive. Participants undertook an average of 64.4 practice sessions with GeST, and the average session length was just under 14 minutes. Conclusions: GeST was proved to be easy and enjoyable to use and had some effect on participants’ gesturing skills. Increasing the magnitude of gains would be desirable. The effect on everyday communication needs to be explored