993 resultados para mobile surveillance


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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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The purpose of this paper is to review the incidence of upper-body morbidity (arm and breast symptoms, impairments, and lymphedema), methods for diagnosis, and prevention and treatment strategies. It was also the purpose to highlight the evidence base for integration of prospective surveillance for upper-body morbidity within standard clinical care of women with breast cancer. Between 10% and 64% of women report upper-body symptoms between 6 months and 3 years after breast cancer, and approximately 20% develop lymphedema. Symptoms remain common into longer-term survivorship, and although lymphedema may be transient for some, those who present with mild lymphedema are at increased risk of developing moderate to severe lymphedema. The etiology of morbidity seems to be multifactorial, with the most consistent risk factors being those associated with extent of treatment. However, known risk factors cannot reliably distinguish between those who will and will not develop upper-body morbidity. Upper-body morbidity may be treatable with physical therapy. There is also evidence in support of integrating regular surveillance for upper-body morbidity into the routine care provided to women with breast cancer, with early diagnosis potentially contributing to more effective management and prevention of progression of these conditions.

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The Implementation Guide for the Hospital Surveillance of SAB has been produced by the Healthcare Associated Infection (HAI) Technical Working Group of the Australian Commission on Safety and Quality in Health Care (ACSQHC), and endorsed by the HAI Advisory Group. The Technical Working Group is made up of representatives invited from surveillance units and the ACSQHC, who have had input into the preparation of this Guide. The Guide has been developed to ensure consistency in reporting of SAB across public and private hospitals to enable accurate national reporting and benchmarking. It is intended to be used by Australian hospitals and organisations to support the implementation of healthcare associated Staphylococcus aureus bacteraemia(SAB) surveillance using the endorsed case definition1 in the box below and further detail in the Data Set Specification.

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The Implementation Guide for hospital surveillance of Clostridium difficile infection (CDI) has been produced by the Healthcare Associated Infection (HAI) Technical Working Group of the Australian Commission on Safety and Quality in Health Care (ACSQHC), and endorsed by the HAI Advisory Group. State jurisdictions and the ACSQHC have representatives on the Technical Working Group, and have had input into this document. (See acknowledgements on inside front cover)...

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The implementation guide for the surveillance of CLABSI in intensive care units (ICU) was produced by the Healthcare Associated Infection (HAI) Technical Working Group of the Australian Commission on Safety and Quality in Health Care(ACSQHC), and endorsed by the ACSQHC HAI Advisory Committee. State surveillance units, the ACSQHC and the Australian and New Zealand Intensive Care Society (ANZICS) have representatives on the Technical Working Group, and have provided input into this document.

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This paper presents an efficient face detection method suitable for real-time surveillance applications. Improved efficiency is achieved by constraining the search window of an AdaBoost face detector to pre-selected regions. Firstly, the proposed method takes a sparse grid of sample pixels from the image to reduce whole image scan time. A fusion of foreground segmentation and skin colour segmentation is then used to select candidate face regions. Finally, a classifier-based face detector is applied only to selected regions to verify the presence of a face (the Viola-Jones detector is used in this paper). The proposed system is evaluated using 640 x 480 pixels test images and compared with other relevant methods. Experimental results show that the proposed method reduces the detection time to 42 ms, where the Viola-Jones detector alone requires 565 ms (on a desktop processor). This improvement makes the face detector suitable for real-time applications. Furthermore, the proposed method requires 50% of the computation time of the best competing method, while reducing the false positive rate by 3.2% and maintaining the same hit rate.

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This paper summarises the achievements of the Smart Skies Project, a three-year, multi-award winning international project that researched, developed and extensively flight tested four enabling aviation technologies: an electrooptical mid-air collision avoidance system, a static obstacle avoidance system, a mobile ground-based air traffic surveillance system, and a global automated airspace separation management system. The project included the development of manned and unmanned flight test aircraft, which were used to characterise the performance of the prototype systems for a range of realistic scenarios under a variety of environmental conditions. In addition to the collection of invaluable flight data, the project achieved world-firsts in the demonstration of future automated collision avoidance and separation management concepts. This paper summarises these outcomes, the overall objectives of the project, the research and the development of the prototype systems, the engineering of the flight test systems, and the results obtained from flight-testing.

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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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The selection of optimal camera configurations (camera locations, orientations etc.) for multi-camera networks remains an unsolved problem. Previous approaches largely focus on proposing various objective functions to achieve different tasks. Most of them, however, do not generalize well to large scale networks. To tackle this, we introduce a statistical formulation of the optimal selection of camera configurations as well as propose a Trans-Dimensional Simulated Annealing (TDSA) algorithm to effectively solve the problem. We compare our approach with a state-of-the-art method based on Binary Integer Programming (BIP) and show that our approach offers similar performance on small scale problems. However, we also demonstrate the capability of our approach in dealing with large scale problems and show that our approach produces better results than 2 alternative heuristics designed to deal with the scalability issue of BIP.

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In this paper, we propose an approach which attempts to solve the problem of surveillance event detection, assuming that we know the definition of the events. To facilitate the discussion, we first define two concepts. The event of interest refers to the event that the user requests the system to detect; and the background activities are any other events in the video corpus. This is an unsolved problem due to many factors as listed below: 1) Occlusions and clustering: The surveillance scenes which are of significant interest at locations such as airports, railway stations, shopping centers are often crowded, where occlusions and clustering of people are frequently encountered. This significantly affects the feature extraction step, and for instance, trajectories generated by object tracking algorithms are usually not robust under such a situation. 2) The requirement for real time detection: The system should process the video fast enough in both of the feature extraction and the detection step to facilitate real time operation. 3) Massive size of the training data set: Suppose there is an event that lasts for 1 minute in a video with a frame rate of 25fps, the number of frames for this events is 60X25 = 1500. If we want to have a training data set with many positive instances of the event, the video is likely to be very large in size (i.e. hundreds of thousands of frames or more). How to handle such a large data set is a problem frequently encountered in this application. 4) Difficulty in separating the event of interest from background activities: The events of interest often co-exist with a set of background activities. Temporal groundtruth typically very ambiguous, as it does not distinguish the event of interest from a wide range of co-existing background activities. However, it is not practical to annotate the locations of the events in large amounts of video data. This problem becomes more serious in the detection of multi-agent interactions, since the location of these events can often not be constrained to within a bounding box. 5) Challenges in determining the temporal boundaries of the events: An event can occur at any arbitrary time with an arbitrary duration. The temporal segmentation of events is difficult and ambiguous, and also affected by other factors such as occlusions.

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Internet chatrooms are common means of interaction and communications, and they carry valuable information about formal or ad-hoc formation of groups with diverse objectives. This work presents a fully automated surveillance system for data collection and analysis in Internet chatrooms. The system has two components: First, it has an eavesdropping tool which collects statistics on individual (chatter) and chatroom behavior. This data can be used to profile a chatroom and its chatters. Second, it has a computational discovery algorithm based on Singular Value Decomposition (SVD) to locate hidden communities and communication patterns within a chatroom. The eavesdropping tool is used for fine tuning the SVD-based discovery algorithm which can be deployed in real-time and requires no semantic information processing. The evaluation of the system on real data shows that (i) statistical properties of different chatrooms vary significantly, thus profiling is possible, (ii) SVD-based algorithm has up to 70-80% accuracy to discover groups of chatters.

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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.