852 resultados para Automatic focus
Resumo:
Paramedics play an important role in out-of-hospital health care. They provide unscheduled care, assisting both patients with minor injuries and those experiencing life-threatening emergencies. Increasingly, paramedics are called on to manage chronic and complex health needs, including symptom relief for patients at the end of life. However, paramedics may not be well prepared to offer palliative care, as practice guidelines and education tend to focus on the management of acute medical emergencies and major trauma. Emergency medical services that employ paramedics rarely have practice guidelines or protocols that deal specifically with palliative care.
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The management and improvement of business processes are a core topic of the information systems discipline. The persistent demand in corporations within all industry sectors for increased operational efficiency and innovation, an emerging set of established and evaluated methods, tools, and techniques as well as the quickly growing body of academic and professional knowledge are indicative for the standing that Business Process Management (BPM) has nowadays. During the last decades, intensive research has been conducted with respect to the design, implementation, execution, and monitoring of business processes. Comparatively low attention, however, has been paid to questions related to organizational issues such as the adoption, usage, implications, and overall success of BPM approaches, technologies, and initiatives. This research gap motivated us to edit a corresponding special focus issue for the journal BISE/WIRTSCHAFTSINFORMATIK. We are happy that we are able to present a selection of three research papers and a state-of-the-art paper in the scientific section of the issue at hand. As these papers differ in the topics they investigate, the research method they apply, and the theoretical foundations they build on, the diversity within the BPM field becomes evident. The academic papers are complemented by an interview with Phil Gilbert, IBM’s Vice President for Business Process and Decision Management, who reflects on the relationship between business processes and the data flowing through them, the need to establish a process context for decision making, and the calibration of BPM efforts toward executives who see processes as a means to an end, rather than a first-order concept in its own right.
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In the last decade, smartphones have gained widespread usage. Since the advent of online application stores, hundreds of thousands of applications have become instantly available to millions of smart-phone users. Within the Android ecosystem, application security is governed by digital signatures and a list of coarse-grained permissions. However, this mechanism is not fine-grained enough to provide the user with a sufficient means of control of the applications' activities. Abuse of highly sensible private information such as phone numbers without users' notice is the result. We show that there is a high frequency of privacy leaks even among widely popular applications. Together with the fact that the majority of the users are not proficient in computer security, this presents a challenge to the engineers developing security solutions for the platform. Our contribution is twofold: first, we propose a service which is able to assess Android Market applications via static analysis and provide detailed, but readable reports to the user. Second, we describe a means to mitigate security and privacy threats by automated reverse-engineering and refactoring binary application packages according to the users' security preferences.
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In general optical systems, the range of distances over which the detector cannot detect any change in focus is called the depth-of-field. This may be specified by movement of the object or image planes, with the former being referred to as depth-of-field and the latter as depth-of-focus (DOF). Either term can be used in vision science, where we refer to changes in vergence which have the same value in both object and image space.
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This study explored the health needs, familial and social problems of Thai migrants in a local community in Brisbane, Australia. Five focus groups with Thai migrants were conducted. The qualitative data were examined using thematic content analysis that is specifically designed for focus group analysis. Four themes were identified: (1) positive experiences in Australia, (2) physical health problems, (3) mental health problems, and (4) familial and social health problems. This study revealed key health needs related to chronic disease and mental health, major barriers to health service use, such as language skills, and facilitating factors, such as the Thai Temple. We concluded that because the health needs, familial and social problems of Thai migrants were complex and culture bound, the development of health and community services for Thai migrants needs to take account of the ways in which Thai culture both negatively impacts health and offer positive solutions to problems.
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Eco-driving instructions could reduce fuel consumption to up to 20% (EcoMove, 2010). Participants (N=13) drove an instrumented vehicle (i.e. Toyota Camry 2007) with an automatic transmission. Fuel consumption of the participants were compared before and after they received eco-driving instructions. Participants drove the same vehicle on the same urban route under similar traffic conditions. Results show that, on free flow sections of the track, all participants drove slightly faster (on average, 0.7 Km/h faster), during the lap for which they were instructed to drive in an eco-friendly manner as compared to when they were not given the eco-driving instruction. Suprisingly, eco-driving instructions increased the RPM significantly in most cases. Fuel consumption slightly decreased (6%) after the eco-driving instructions. We have found strong evidence showing that the fuel saving observed in our experiment (urban environment, automatic transmission) fall short of the 20% reduction claimed in other international trials.
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The actual proportion of eligible people who participate in clinical trials is low. Consequently, a qualitative study of the willingness of women who are postmenopausal to participate in a long-term randomized control trial of hormone replacement therapy (HRT) designed to investigate the prevention of degenerative diseases was conducted. Focus group methodology was employed to explore the personal and social aspects of decision making about trial participation. Participants were randomly selected from the patient age-sex registers of four University of Adelaide general practices. Twenty-one women participated in four focus groups. The reasons for and against trial participation were examined using qualitative content analysis; ( n = 18) women were unwilling to participate in the trial. The lack of perceived individual benefit, minimal altruism, the risk of breast cancer and side effects, not wanting to take unnecessary medication, a ten-year commitment, and negative experiences of HRT use, were the main reasons given for not entering the trial. Of the few women ( n = 3) who clearly would enter the trial, free prescriptions and a positive history of using HRT were the main reasons for participation. The perceived disadvantages of clinical trials of HRT deter women from participating in a long-term clinical trial of HRT. An investment in education and information to eligible participants about both the risks and potential benefits of HRT may improve trial recruitment.
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This research makes a major contribution which enables efficient searching and indexing of large archives of spoken audio based on speaker identity. It introduces a novel technique dubbed as “speaker attribution” which is the task of automatically determining ‘who spoke when?’ in recordings and then automatically linking the unique speaker identities within each recording across multiple recordings. The outcome of the research will also have significant impact in improving the performance of automatic speech recognition systems through the extracted speaker identities.
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A user’s query is considered to be an imprecise description of their information need. Automatic query expansion is the process of reformulating the original query with the goal of improving retrieval effectiveness. Many successful query expansion techniques ignore information about the dependencies that exist between words in natural language. However, more recent approaches have demonstrated that by explicitly modeling associations between terms significant improvements in retrieval effectiveness can be achieved over those that ignore these dependencies. State-of-the-art dependency-based approaches have been shown to primarily model syntagmatic associations. Syntagmatic associations infer a likelihood that two terms co-occur more often than by chance. However, structural linguistics relies on both syntagmatic and paradigmatic associations to deduce the meaning of a word. Given the success of dependency-based approaches and the reliance on word meanings in the query formulation process, we argue that modeling both syntagmatic and paradigmatic information in the query expansion process will improve retrieval effectiveness. This article develops and evaluates a new query expansion technique that is based on a formal, corpus-based model of word meaning that models syntagmatic and paradigmatic associations. We demonstrate that when sufficient statistical information exists, as in the case of longer queries, including paradigmatic information alone provides significant improvements in retrieval effectiveness across a wide variety of data sets. More generally, when our new query expansion approach is applied to large-scale web retrieval it demonstrates significant improvements in retrieval effectiveness over a strong baseline system, based on a commercial search engine.
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This study presents a segmentation pipeline that fuses colour and depth information to automatically separate objects of interest in video sequences captured from a quadcopter. Many approaches assume that cameras are static with known position, a condition which cannot be preserved in most outdoor robotic applications. In this study, the authors compute depth information and camera positions from a monocular video sequence using structure from motion and use this information as an additional cue to colour for accurate segmentation. The authors model the problem similarly to standard segmentation routines as a Markov random field and perform the segmentation using graph cuts optimisation. Manual intervention is minimised and is only required to determine pixel seeds in the first frame which are then automatically reprojected into the remaining frames of the sequence. The authors also describe an automated method to adjust the relative weights for colour and depth according to their discriminative properties in each frame. Experimental results are presented for two video sequences captured using a quadcopter. The quality of the segmentation is compared to a ground truth and other state-of-the-art methods with consistently accurate results.
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This study presents a disturbance attenuation controller for horizontal position stabilisation for hover and automatic landings of a rotary-wing unmanned aerial vehicle (RUAV) operating close to the landing deck in rough seas. Based on a helicopter model representing aerodynamics during the landing phase, a non-linear state feedback H∞ controller is designed to achieve rapid horizontal position tracking in a gusty environment. Practical constraints including flapping dynamics, servo dynamics and time lag effect are considered. A high-fidelity closed-loop simulation using parameters of the Vario XLC gas-turbine helicopter verifies performance of the proposed horizontal position controller. The proposed controller not only increases the disturbance attenuation capability of the RUAV, but also enables rapid position response when gusts occur. Comparative studies show that the H∞ controller exhibits performance improvement and can be applied to ship/RUAV landing systems.
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This paper presents a novel and practical procedure for estimating the mean deck height to assist in automatic landing operations of a Rotorcraft Unmanned Aerial Vehicle (RUAV) in harsh sea environments. A modified Prony Analysis (PA) procedure is outlined to deal with real-time observations of deck displacement, which involves developing an appropriate dynamic model to approach real deck motion with parameters identified through implementing the Forgetting Factor Recursive Least Square (FFRLS) method. The model order is specified using a proper order-selection criterion based on minimizing the summation of accumulated estimation errors. In addition, a feasible threshold criterion is proposed to separate the dominant components of deck displacement, which results in an accurate instantaneous estimation of the mean deck position. Simulation results demonstrate that the proposed recursive procedure exhibits satisfactory estimation performance when applied to real-time deck displacement measurements, making it well suited for integration into ship-RUAV approach and landing guidance systems.
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Following eco-driving instructions can reduce fuel consumption between 5 to 20% on urban roads with manual cars. The majority of Australian cars have an automatic transmission gear-box. It is therefore of interest to verify whether current eco-driving instructions are e cient for such vehicles. In this pilot study, participants (N=13) drove an instrumented vehicle (Toyota Camry 2007) with an automatic transmission. Fuel consumption of the participants was compared before and after they received simple eco-driving instructions. Participants drove the same vehicle on the same urban route under similar tra c conditions. We found that participants drove at similar speeds during their baseline and eco-friendly drives, and reduced the level of their accelerations and decelerations during eco-driving. Fuel consumption decreased for the complete drive by 7%, but not on the motorway and inclined sections of the study. Gas emissions were estimated with the VT-micro model, and emissions of the studied pollutants (CO2, CO, NOX and HC) were reduced, but no di erence was observed for CO2 on the motorway and inclined sections. The di erence for the complete lap is 3% for CO2. We have found evidence showing that simple eco-driving instructions are e cient in the case of automatic transmission in an urban environment, but towards the lowest values of the spectrum of fuel consumption reduction from the di erent eco-driving studies.
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Raven and Song Scope are two automated sound anal-ysis tools based on machine learning technique for en-vironmental monitoring. Many research works have been conducted upon them, however, no or rare explo-ration mentions about the performance and comparison between them. This paper investigates the comparisons from six aspects: theory, software interface, ease of use, detection targets, detection accuracy, and potential application. Through deep exploration one critical gap is identified that there is a lack of approach to detect both syllables and call structures, since Raven only aims to detect syllables while Song Scope targets call structures. Therefore, a Timed Probabilistic Automata (TPA) system is proposed which separates syllables first and clusters them into complex structures after.
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The assessment of choroidal thickness from optical coherence tomography (OCT) images of the human choroid is an important clinical and research task, since it provides valuable information regarding the eye’s normal anatomy and physiology, and changes associated with various eye diseases and the development of refractive error. Due to the time consuming and subjective nature of manual image analysis, there is a need for the development of reliable objective automated methods of image segmentation to derive choroidal thickness measures. However, the detection of the two boundaries which delineate the choroid is a complicated and challenging task, in particular the detection of the outer choroidal boundary, due to a number of issues including: (i) the vascular ocular tissue is non-uniform and rich in non-homogeneous features, and (ii) the boundary can have a low contrast. In this paper, an automatic segmentation technique based on graph-search theory is presented to segment the inner choroidal boundary (ICB) and the outer choroidal boundary (OCB) to obtain the choroid thickness profile from OCT images. Before the segmentation, the B-scan is pre-processed to enhance the two boundaries of interest and to minimize the artifacts produced by surrounding features. The algorithm to detect the ICB is based on a simple edge filter and a directional weighted map penalty, while the algorithm to detect the OCB is based on OCT image enhancement and a dual brightness probability gradient. The method was tested on a large data set of images from a pediatric (1083 B-scans) and an adult (90 B-scans) population, which were previously manually segmented by an experienced observer. The results demonstrate the proposed method provides robust detection of the boundaries of interest and is a useful tool to extract clinical data.