995 resultados para back-tracking deployment (BTD)


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This paper presents a review of back-tracking geometry not only for single axis but also for two-axis tracking and analyses the corresponding energy gains. It compares the different back-tracking strategies with the ideal tracking in terms of energy yield concluding, on the one hand, that back-tracking is more useful for single horizontal axis than for the single vertical one, and on the other hand, that back-tracking is more efficient when applied in the primary axis of a two-axis tracker

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This paper addresses the tradeoff 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 in order to prolong node lifetime. We use empirical GPS and radio contact data from a largescale animal tracking deployment to model node mobility, GPS and radio performance. These models are 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 a versatile contact logging strategy that relies on RSSI ranging and GPS lock back-offs for reducing the node energy consumption relative to GPS duty cycling. Results show that our strategy can cut the node energy consumption by half while meeting application specific positioning criteria.

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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.

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We have conducted a program of trigonometric distance measurements to 13 members of the TW Hydrae Association (TWA), which will enable us (through back-tracking methods) to derive a convincing estimate of the age of the association, independent of stellar evolutionary models. With age, distance, and luminosity known for an ensemble of TWA stars and brown dwarfs, models of early stellar evolution (which are still uncertain for young ages and substellar masses) will then be constrained by observations over a wide range of masses (0.025 to 0.7 M⊙).

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While microbial communities of aerosols have been examined, little is known about their sources. Nutrient composition and microbial communities of potential dust sources, saline lake sediments (SLS) and adjacent biological soil crusts (BSC), from Southern Australia were determined and compared with a previously analyzed dust sample. Multivariate analyses of fingerprinting profiles indicated that the bacterial communities of SLS and BSC were different, and these differences were mainly explained by salinity. Nutrient concentrations varied among the sites but could not explain the differences in microbial diversity patterns. Comparison of microbial communities with dust samples showed that deflation selects against filamentous cyanobacteria, such as the Nostocales group. This could be attributed to the firm attachment of cyanobacterial filaments to soil particles and/or because deflation occurs mainly in disturbed BSC, where cyanobacterial diversity is often low. Other bacterial groups, such as Actinobacteria and the spore-forming Firmicutes, were found in both dust and its sources. While Firmicutes-related sequences were mostly detected in the SLS bacterial communities (10% of total sequences), the actinobacterial sequences were retrieved from both (11-13%). In conclusion, the potential dust sources examined here show highly diverse bacterial communities and contain nutrients that can be transported with aerosols. The obtained fingerprinting and sequencing data may enable back tracking of dust plumes and their microorganisms.

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Any new hospital communication architecture has to support existing services, but at the same time new added features should not affect normal tasks. This article deals with issues regarding old and new systems’ interoperability, as well as the effect the human factor has in a deployed architecture. It also presents valuable information, which is a product of a real scenario. Tracking services are also tested in order to monitor and administer several medical resources.

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In recent years, ocean scientists have started to employ many new forms of technology as integral pieces in oceanographic data collection for the study and prediction of complex and dynamic ocean phenomena. One area of technological advancement in ocean sampling if the use of Autonomous Underwater Vehicles (AUVs) as mobile sensor plat- forms. Currently, most AUV deployments execute a lawnmower- type pattern or repeated transects for surveys and sampling missions. An advantage of these missions is that the regularity of the trajectory design generally makes it easier to extract the exact path of the vehicle via post-processing. However, if the deployment region for the pattern is poorly selected, the AUV can entirely miss collecting data during an event of specific interest. Here, we consider an innovative technology toolchain to assist in determining the deployment location and executed paths for AUVs to maximize scientific information gain about dynamically evolving ocean phenomena. In particular, we provide an assessment of computed paths based on ocean model predictions designed to put AUVs in the right place at the right time to gather data related to the understanding of algal and phytoplankton blooms.

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Trajectory design for Autonomous Underwater Vehicles (AUVs) is of great importance to the oceanographic research community. Intelligent planning is required to maneuver a vehicle to high-valued locations for data collection. We consider the use of ocean model predictions to determine the locations to be visited by an AUV, which then provides near-real time, in situ measurements back to the model to increase the skill of future predictions. The motion planning problem of steering the vehicle between the computed waypoints is not considered here. Our focus is on the algorithm to determine relevant points of interest for a chosen oceanographic feature. This represents a first approach to an end to end autonomous prediction and tasking system for aquatic, mobile sensor networks. We design a sampling plan and present experimental results with AUV retasking in the Southern California Bight (SCB) off the coast of Los Angeles.

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The availability of new information and communication technologies creates opportunities for new, mobile tele-health services. While many promising tele-health projects deliver working R&D prototypes, they often do not result in actual deployment. We aim to identify critical issues than can increase our understanding and enhance the viability of the mobile tele-health services beyond the R&D phase by developing a business model. The present study describes the systematic development and evaluation of a service-oriented business model for tele-monitoring and -treatment of chronic lower back pain patients based on a mobile technology prototype. We address challenges of multi-sector collaboration and disruptive innovation.

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This study examined the tracking of selected measures of physical activity, inactivity, and fitness in a cohort of rural youth. Students (N = 181, 54.7% female, 63.5% African American) completed test batteries during their fifth-(age = 10.7 +/- 0.7 years), sixth-, and seventh-grade years. The Previous Day Physical Activity Recall (PDPAR) was used to assess 30-min blocks of vigorous physical activity (VPA), moderate-to-vigorous physical activity (MVPA), TV watching and other sedentary activities, and estimated energy expenditure (EE). Fitness measures included the PWC 170 cycle ergometer test, strength tests, triceps skinfold thickness, and BMI. Intraclass correlation coefficients (ICCs) for VPA, MVPA, and after-school EE ranged from 0.63 to 0.78. ICCs ranged from 0.49 to 0.71 for measures of inactivity and from 0.78 to 0.82 for the fitness measures. These results indicate that measures of physical activity, inactivity, and physical fitness tend to track during the transition from elementary to middle school.

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Many research and development projects that are carried out by firms and research institutes are technology-oriented. There is a large gap between research results, for instance in the form of prototypes, and the actual service offerings to customers. This becomes problematic when an organization wants to bring the results from such a project to the market, which will be particularly troublesome when the research results do not readily fit traditional offerings, roles and capabilities in the industry, nor the financial arrangements. In this chapter, we discuss the design of a business model for a mobile health service, starting with a research prototype that was developed for patients with chronic lower back pain, using the STOF model and method. In a number of design sessions, an initial business model was developed that identifies critical design issues that play a role in moving from prototype toward market deployment. The business model serves as a starting-point to identify and commit relevant stakeholders, and to draw up a business plan and case. This chapter is structured as follows. We begin by discussing the need for mobile health business models. Next, the research and development project on mobile health and the prototype for chronic lower back pain patients are introduced, after which the approach used to develop the business model is described, followed by a discussion of the developed mobile health business model for each of the STOF domains. We conclude with a discussion regarding the lessons that were learned with respect to the development of a business model on the basis of a prototype.

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This paper investigates communication protocols for relaying sensor data from animal tracking applications back to base stations. While Delay Tolerant Networks (DTNs) are well suited to such challenging environments, most existing protocols do not consider the available energy that is particularly important when tracking devices can harvest energy. This limits both the network lifetime and delivery probability in energy-constrained applications to the point when routing performance becomes worse than using no routing at all. Our work shows that substantial improvement in data yields can be achieved through simple yet efficient energy-aware strategies. Conceptually, there is need for balancing the energy spent on sensing, data mulling, and delivery of direct packets to destination. We use empirical traces collected in a flying fox (fruit bat) tracking project and show that simple threshold-based energy-aware strategies yield up to 20% higher delivery rates. Furthermore, these results generalize well for a wide range of operating conditions.

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In this paper, the trajectory tracking control of an autonomous underwater vehicle (AUVs) in six-degrees-of-freedom (6-DOFs) is addressed. It is assumed that the system parameters are unknown and the vehicle is underactuated. An adaptive controller is proposed, based on Lyapunov׳s direct method and the back-stepping technique, which interestingly guarantees robustness against parameter uncertainties. The desired trajectory can be any sufficiently smooth bounded curve parameterized by time even if consist of straight line. In contrast with the majority of research in this field, the likelihood of actuators׳ saturation is considered and another adaptive controller is designed to overcome this problem, in which control signals are bounded using saturation functions. The nonlinear adaptive control scheme yields asymptotic convergence of the vehicle to the reference trajectory, in the presence of parametric uncertainties. The stability of the presented control laws is proved in the sense of Lyapunov theory and Barbalat׳s lemma. Efficiency of presented controller using saturation functions is verified through comparing numerical simulations of both controllers.

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In this paper, a method of tracking the peak power in a wind energy conversion system (WECS) is proposed, which is independent of the turbine parameters and air density. The algorithm searches for the peak power by varying the speed in the desired direction. The generator is operated in the speed control mode with the speed reference being dynamically modified in accordance with the magnitude and direction of change of active power. The peak power points in the P-omega curve correspond to dP/domega = 0. This fact is made use of in the optimum point search algorithm. The generator considered is a wound rotor induction machine whose stator is connected directly to the grid and the rotor is fed through back-to-back pulse-width-modulation (PWM) converters. Stator flux-oriented vector control is applied to control the active and reactive current loops independently. The turbine characteristics are generated by a dc motor fed from a commercial dc drive. All of the control loops are executed by a single-chip digital signal processor (DSP) controller TMS320F240. Experimental results show that the performance of the control algorithm compares well with the conventional torque control method.

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The Brushless Doubly-Fed Machine (BDFM) is a brushless electrical generator which allows variable speed operation with a power converter rated at only a fraction of the machine rating. This paper details an example implementation of the BDFM in a medium-scale wind turbine. Details of a simplified design procedure based on electrical and magnetic loadings are given along with the results of tests on the manufactured machine. These show that a BDFM of the scale works as expected but that the 4/8 BDFM chosen was slower and thus larger than the turbine's original induction machine. The implementation of the turbine system is discussed, including the vector-based control scheme that ensures the BDFM operates at a demanded speed and the Maximum Power Point Tracking (MPPT) scheme that selects the rotor speed that extracts the most power from the incident wind conditions.