995 resultados para mobile sensors


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Preparing valuations is a time consuming process involving site inspections, research and report formulation. The ease of access to the internet has changed how and where valuations may be undertaken. No longer is it necessary to return to the office to finalise reports, or leave your desk in order to undertake research. This enables more streamlined service delivery and is viewed as a positive. However, it is not without negative impacts. This paper seeks to inform practitioners of the work environment changes flowing from increased access to the internet. It identifies how increased accessibility to, and use of, technology and the internet has, and will continue to, impact upon valuation service provision into the future.

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We conducted a randomized controlled trial to test whether a Brief Mobile Treatment (BMT) intervention could improve outcomes relative to usual care among suicide attempters. The intervention included training in problem solving therapy, meditation, a brief intervention to increase social support as well as advice on alcohol and other drugs, and mobile phone follow-up. The effect of the intervention was measured in terms of a reduction in suicidal ideation, depression and self-harm at Baseline, six and 12 months. A wait-list control group received usual care. A total of 68 participants was recruited from a Sri Lankan hospital following a suicide attempt. Participants who received the intervention were found to achieve significant improvements in reducing suicidal ideation and depression than those receiving usual care. The BMT group also experienced a significant improvement of social support when compared to the control group. However, the BMT group did not demonstrate a significant effect in reducing actual self-harm and most substance use, and differential effects on alcohol use were restricted to men. Although the present study was limited in revealing which component of the intervention was more effective in preventing suicide, it showed its efficacy in reducing suicide as a whole.

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Vertical displacements are one of the most relevant parameters for structural health monitoring of bridges in both the short and long terms. Bridge managers around the globe are always looking for a simple way to measure vertical displacements of bridges. However, it is difficult to carry out such measurements. On the other hand, in recent years, with the advancement of fiber-optic technologies, fiber Bragg grating (FBG) sensors are more commonly used in structural health monitoring due to their outstanding advantages including multiplexing capability, immunity of electromagnetic interference as well as high resolution and accuracy. For these reasons, using FBG sensors is proposed to develop a simple, inexpensive and practical method to measure vertical displacements of bridges. A curvature approach for vertical displacement measurements using curvature measurements is proposed. In addition, with the successful development of FBG tilt sensors, an inclination approach is also proposed using inclination measurements. A series of simulation tests of a full- scale bridge was conducted. It shows that both of the approaches can be implemented to determine vertical displacements for bridges with various support conditions, varying stiffness (EI) along the spans and without any prior known loading. These approaches can thus measure vertical displacements for most of slab-on-girder and box-girder bridges. Besides, the approaches are feasible to implement for bridges under various loading. Moreover, with the advantages of FBG sensors, they can be implemented to monitor bridge behavior remotely and in real time. A beam loading test was conducted to determine vertical displacements using FBG strain sensors and tilt sensors. The discrepancies as compared with dial gauges reading using the curvature and inclination approaches are 0.14mm (1.1%) and 0.41mm (3.2%), respectively. Further recommendations of these approaches for developments will also be discussed at the end of the paper.

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“Our students have changed radically. Today’s students are no longer the people our educational system was designed to teach” (Prensky, 2001, p. 1). The influx of available new technology has helped to democratise knowledge, transforming when, where and how learning takes place, and changing perceptions of traditional learning landscapes (JISC, 2006; Neary et al., 2010). Mobile computers combined with wireless technology, have completely transformed the educational world; students have turned nomad[ic], engaging in conversations and thinking across traditional campus spaces (Alexander, 2004; Fisher, 2005). In this workshop we will be attempting to de-mystify a facet of mobile learning, by working in small groups to set up and kick start a number of social media sites, which can be used for collaboration and information exchange, in the design studio.

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Climate change and land use pressures are making environmental monitoring increasingly important. As environmental health is degrading at an alarming rate, ecologists have tried to tackle the problem by monitoring the composition and condition of environment. However, traditional monitoring methods using experts are manual and expensive; to address this issue government organisations designed a simpler and faster surrogate-based assessment technique for consultants, landholders and ordinary citizens. However, it remains complex, subjective and error prone. This makes collected data difficult to interpret and compare. In this paper we describe a work-in-progress mobile application designed to address these shortcomings through the use of augmented reality and multimedia smartphone technology.

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Our everyday environment is full of text but this rich source of information remains largely inaccessible to mobile robots. In this paper we describe an active text spotting system that uses a small number of wide angle views to locate putative text in the environment and then foveates and zooms onto that text in order to improve the reliability of text recognition. We present extensive experimental results obtained with a pan/tilt/zoom camera and a ROS-based mobile robot operating in an indoor environment.

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Smartphones are steadily gaining popularity, creating new application areas as their capabilities increase in terms of computational power, sensors and communication. Emerging new features of mobile devices give opportunity to new threats. Android is one of the newer operating systems targeting smartphones. While being based on a Linux kernel, Android has unique properties and specific limitations due to its mobile nature. This makes it harder to detect and react upon malware attacks if using conventional techniques. In this paper, we propose an Android Application Sandbox (AASandbox) which is able to perform both static and dynamic analysis on Android programs to automatically detect suspicious applications. Static analysis scans the software for malicious patterns without installing it. Dynamic analysis executes the application in a fully isolated environment, i.e. sandbox, which intervenes and logs low-level interactions with the system for further analysis. Both the sandbox and the detection algorithms can be deployed in the cloud, providing a fast and distributed detection of suspicious software in a mobile software store akin to Google's Android Market. Additionally, AASandbox might be used to improve the efficiency of classical anti-virus applications available for the Android operating system.

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Modern mobile computing devices are versatile, but bring the burden of constant settings adjustment according to the current conditions of the environment. While until today, this task has to be accomplished by the human user, the variety of sensors usually deployed in such a handset provides enough data for autonomous self-configuration by a learning, adaptive system. However, this data is not fully available at certain points in time, or can contain false values. Handling potentially incomplete sensor data to detect context changes without a semantic layer represents a scientific challenge which we address with our approach. A novel machine learning technique is presented - the Missing-Values-SOM - which solves this problem by predicting setting adjustments based on context information. Our method is centered around a self-organizing map, extending it to provide a means of handling missing values. We demonstrate the performance of our approach on mobile context snapshots, as well as on classical machine learning datasets.

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As a novel sensitive element and due to its advantages of immunity to electrical interference, distributed measurement, etc., fiber Bragg grating (FBG) has been researched widely. To realize the substitution of high accurate electronic temperature sensors, high sensitive FBG temperature sensors can be made by taking advantage of its characters of being sensitive to both temperature and strain. Although there are reports about high sensitive FBG temperature sensors, however, few about their stability have been done. We manufactured a high sensitive FBG temperature sensor, and put it together with an average FBG temperature sensor and an electronic crystal temperature sensor into a stainless steel container filled by water to observe the room temperature change. By comparing their results in two weeks, we have found out that: although the high sensitive FBG temperature sensor is in much better agreement with the electronic crystal sensor than the average FBG sensor is, it has occurred some small drifts. Because the drifts appeared in the process of further pulling the FBG, it might be a result of the slip of the FBG fixing points. This contributes some good experiences to the application of FBG in high accuracy temperature measurement.

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Substantial growth has occurred in the telecommunication sector in Papua New Guinea (PNG) since 2007. Mobile telephony has spread to rural and remote localities, following decades of inadequate telephone services. This paper examines the introduction of mobile telephones into a rural village in PNG, and focuses on information access during emergency situations. It considers three tsunami alerts: one immediately prior to the introduction of mobile phone services in the area, and two which occurred after mobile phone reception became available. The research shows that for people with limited access to information, responses to threats such as tsunamis can be inappropriate and driven by fear and panic. By contrast, when there is reliable, timely information available, measured responses can be adopted. This research demonstrates how the use of newly-introduced communication technologies for handling emergencies may work in practice, benefitting people in poorer, rural communities.

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Could mobile telephony be harnessed for development in Papua New Guinea (PNG)? Could mobile phones be utilised to enhance the security and prosperity of rural communities? Could mobile phones be a useful tool in the achievement of the PNG 2050 Vision targets? This paper is based on literature review around use of mobile phones in development in Asia, Africa, and the Caribbean. It also draws on discussions with key players in PNG, such as NGOs, UN agencies, donor partners, telecommunication companies and the government of PNG. Anticipated benefits of mobile phone availability have not been fully realised in rural areas of PNG to date due to pricing, difficulties with recharging handset batteries in communities which do not have mains electricity supply, and also concerns about negative social changes related to mobile telephony, for example parental stress over youth forming unsuitable relationships. Nonetheless, there are manifest possible ways for mobile phone technology to change user communication patterns positively regarding economic output. In sectors as diverse as health, education and law and justice, discussions are currently underway to establish how mobile phones could be used to increase service delivery, particularly to rural and marginal communities.

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Compared to conventional metal-foil strain gauges, nanocomposite piezoresistive strain sensors have demonstrated high strain sensitivity and have been attracting increasing attention in recent years. To fulfil their ultimate success, the performance of vapor growth carbon fiber (VGCF)/epoxy nanocomposite strain sensors subjected to static cyclic loads was evaluated in this work. A strain-equivalent quantity (resistance change ratio) in cantilever beams with intentionally induced notches in bending was evaluated using the conventional metal-foil strain gauges and the VGCF/epoxy nanocomposite sensors. Compared to the metal-foil strain gauges, the nanocomposite sensors are much more sensitive to even slight structural damage. Therefore, it was confirmed that the signal stability, reproducibility, and durability of these nanocomposite sensors are very promising, leading to the present endeavor to apply them for static structural health monitoring.

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GPS is a commonly used and convenient technology for determining absolute position in outdoor environments, but its high power consumption leads to rapid battery depletion in mobile devices. An obvious solution is to duty cycle the GPS module, which prolongs the device lifetime at the cost of increased position uncertainty while the GPS is off. This article addresses the trade-off between energy consumption and localization performance in a mobile sensor network application. The focus is on augmenting GPS location with more energy-efficient location sensors to bound position estimate uncertainty while GPS is off. Empirical GPS and radio contact data from a large-scale animal tracking deployment is used to model node mobility, radio performance, and GPS. Because GPS takes a considerable, and variable, time after powering up before it delivers a good position measurement, we model the GPS behaviour through empirical measurements of two GPS modules. These models are then used to explore duty cycling strategies for maintaining position uncertainty within specified bounds. We then explore the benefits of using short-range radio contact logging alongside GPS as an energy-inexpensive means of lowering uncertainty while the GPS is off, and we propose strategies that use RSSI ranging and GPS back-offs to further reduce energy consumption. Results show that our combined strategies can cut node energy consumption by one third while still meeting application-specific positioning criteria.