Marking code

The 74LVC1G386 provides a 3-input EXCLUSIVE-OR function. The input can be driven from either 3.3 or 5 V devices. This feature allows the use of these devices in a mixed 3.3 and 5 V environment. Schmitt-trigger action at all inputs makes the circuit tolerant of slower input rise and fall time. 2. Features 3. Ordering information This device is fully specified for partial power-down applications using IOFF. The IOFF circuitry disables the output, preventing the damaging backflow current through the device when it is powered down. ■ Wide supply voltage range from 1.65 to 5.5 V ■ High noise immunity ■ Complies with JEDEC standard

An Application of Game Technology to Virtual University Campus Touring and Interior Navigation

Many applications optimistically claim the title of virtual campus tour nowadays with a small minority deserving it. Throughout this paper various virtual campus tours are explored and analysed along with the problems of interior navigation. The technologies that support development of virtual worlds for computer games are studied and a solution to interior navigation and virtual campus tour derived from this technology is proposed

A Platform Independent Game Technology Model for Model Driven Serious Games Development

Game‑based learning (GBL) combines pedagogy and interactive entertainment to create a virtual learning environment in an effort to motivate and regain the interest of a new generation of ‘digital native’ learners. However, this approach is impeded by the limited availability of suitable ‘serious’ games and high‑level design tools to enable domain experts to develop or customise serious games. Model Driven Engineering (MDE) goes some way to provide the techniques required to generate a wide variety of interoperable serious games software solutions whilst encapsulating and shielding the technicality of the full software development process. In this paper, we present our Game Technology Model (GTM) which models serious game software in a manner independent of any hardware or operating platform specifications for use in our Model Driven Serious Game Development Framework

Passive Indoor Positioning System (PIPS) Using Near Field Communication (NFC) Technology

Travel can be an enjoyable experience but it can also be stressful when one is unable to get to the destination in timely manner. Satellite navigation systems (satnav) such as the ubiquitous Global Positioning System (GPS) provide an aid to locating unfamiliar places without hassle. However, the effectiveness of satnav stops at the doorstep of the building due to its requirement for line of sight with orbiting satellites. Within a large complex building, navigation typically relies on building signage, information from kiosks and getting assistance from information desks. The advancement of mobile devices and wireless technology offer an interesting proposition for the development of indoor positioning systems. In this paper, we propose a passive indoor positioning system to provide navigational aid and discuss findings from our pilot experiment using NFC technology

Development of A “PharmaComm” Serious Game for Teaching Pharmacist Communication and Drug Administration in a Virtual Hospital Setting

Practical experience is crucial in pharmacy education, but it can be difficult to provide pharmacy students with a sufficient level of experience during their education due to a number of challenges. Video games might provide a platform where students can gain positive learning experience in a virtual environment. Serious games have been used successfully across many industries, which suggest that a well-designed serious game can have positive learning outcomes. Increased engagement and motivation are mentioned by experts as some of the main benefits of serious games. Being able to practise skills before utilising them in real-world scenarios is another advantage identified. In this paper, we present a virtual patient simulator which is designed specifically teaching pharmacy students patient communication and administration of drugs. A pilot study and an expert review were carried out to evaluate the effectiveness of the application and its findings are presented

Using Serious Games to Create Awareness on Visual Impairments

Visual impairments define a wide spectrum of disabilities that vary in severity, from the need to wear glasses, to permanent loss of vision or blindness. This paper discusses the process undertaken in creating two simulators, one which emulates partially-sighted visual impairment and another focused on full -blindness. In order to create the simulators, extensive research was conducted surrounding the effects of partially-sightedness and blindness, highlighting existing software and games that promote awareness for visual impairments. This paper underlines the necessity of raising awareness for visual impairments and the effectiveness of applying serious games for this very goal. After developing the simulators, experiments were conducted to evaluate the effectiveness of it. Findings from the experiments were analysed and documented

VICTour 1.1: Introducing virtual learning environments and gamification

Game engines are one of the few solutions to providing a true virtual campus tour experience. In this paper, we explore the evolution of the virtual campus tour and thereby the current and future work of game technology within virtual touring. An investigation is conducted into the application of objective-based gamification and its ability to encourage exploration of a virtual world. We also examine virtual campus tours as an alternate form of taught content via virtual learning environments (VLEs). An investigation into the use of head mounted displays to improve immersion is also explored

Multiple Density Maps Information Fusion for Effectively Assessing Intensity Pattern of Lifelogging Physical Activity

Physical activity (PA) measurement is a crucial task in healthcare technology aimed at monitoring the progression and treatment of many chronic diseases. Traditional lifelogging PA measures require relatively high cost and can only be conducted in controlled or semi-controlled environments, though they exhibit remarkable precision of PA monitoring outcomes. Recent advancement of commercial wearable devices and smartphones for recording one’s lifelogging PA has popularized data capture in uncontrolled environments. However, due to diverse life patterns and heterogeneity of connected devices as well as the PA recognition accuracy, lifelogging PA data measured by wearable devices and mobile phones contains much uncertainty thereby limiting their adoption for healthcare studies. To improve the feasibility of PA tracking datasets from commercial wearable/mobile devices, this paper proposes a lifelogging PA intensity pattern decision making approach for lifelong PA measures. The method is to firstly remove some irregular uncertainties (IU) via an Ellipse fitting model, and then construct a series of monthly based hour-day density map images for representing PA intensity patterns with regular uncertainties (RU) on each month. Finally it explores Dempster-Shafer theory of evidence fusing information from these density map images for generating a decision making model of a final personal lifelogging PA intensity pattern. The approach has significantly reduced the uncertainties and incompleteness of datasets from third party devices. Two case studies on a mobile personalized healthcare platform MHA [1] connecting the mobile app Moves are carried out. The results indicate that the proposed approach can improve effectiveness of PA tracking devices or apps for various types of people who frequently use them as a healthcare indicator

Uncertainty Investigation for Personalised Lifelogging Physical Activity Intensity Pattern Assessment with Mobile Devices

Lifelogging physical activity (PA) assessment is crucial to healthcare technologies and studies for the purpose of treatments and interventions of chronic diseases. Traditional lifelogging PA monitoring is conducted in non-naturalistic settings by means of wearable devices or mobile phones such as fixed placements, controlled durations or dedicated sensors. Although they achieved satisfactory outcomes for healthcare studies, the practicability become the key issues. Recent advance of mobile devices make lifelogging PA tracking for healthy or unhealthy individuals possible. However, owning to diverse physical characteristics, immaturity of PA recognition techniques, different settings from manufactories and a majority of uncertainties in real life, the results of PA measurement is leading to be inapplicable for PA pattern detection in a long range, especially hardly exploited in the wellbeing monitoring or behaviour changes. This paper investigates and compares uncertainties of existing mobile devices for individual’s PA tracking. Irregular uncertainties (IU) are firstly removed by exploiting Ellipse fitting model, and then monthly density maps that contain regular uncertainties (RU) are constructed based on metabolic equivalents (METs) of different activity types. Five months of four subjects PA intensity changes using the mobile app tracker Moves [1] and Google Fit app on wearable device Samsung wear S2 are carried out from a mobile personalised healthcare platform MHA [2]. The result indicates that uncertainty of PA intensity monitored by mobile phone is 90% lower than wearable device, where the datasets tend to be further explored by healthcare/fitness studies. Whilst PA activity monitoring by mobile phone is still a challenging issue by far due to much more uncertainties than wearable devices