950 resultados para location-based services


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This study intends to explore the impact of customer experience on customer satisfaction and loyalty by trying to understand how location-based mobile marketing might enhance the customer experience. Primary data was collected from 201 smartphone users in 24 countries. Results have indicated that targeted location-based marketing positively influences customers’ experiences. Besides, the analysis has also shown a favorable impact on customers’ satisfaction and self-perceived loyalty. This suggests that location-based mobile marketing has the potential to positively add value to a customer’s experience and should therefore be considered an important tool in marketing communications.

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This project attempts to provide an in-depth competitive assessment of the Portuguese indoor location-based analytics market, and to elaborate an entry-pricing strategy for Business Intelligence Positioning System (BIPS) implementation in Portuguese shopping centre stores. The role of industry forces and company’s organizational resources platform to sustain company’s competitive advantage was explored. A customer value-based pricing approach was adopted to assess BIPS value to retailers and maximize Sonae Sierra profitability. The exploratory quantitative research found that there is a market opportunity to explore every store area types with tailored proposals, and to set higher-than-tested membership fees to allow a rapid ROI, concluding there are propitious conditions for Sierra to succeed in BIPS store’s business model in Portugal.

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BACKGROUND: Shared decision-making (SDM) is an emergent research topic in the field of mental health care and is considered to be a central component of a recovery-oriented system. Despite the evidence suggesting the benefits of this change in the power relationship between users and practitioners, the method has not been widely implemented in clinical practice. OBJECTIVE: The objective of this study was to investigate decisional and information needs among users with mental illness as a prerequisite for the development of a decision support tool aimed at supporting SDM in community-based mental health services in Sweden. METHODS: Three semi-structured focus group interviews were conducted with 22 adult users with mental illness. The transcribed interviews were analyzed using a directed content analysis. This method was used to develop an in-depth understanding of the decisional process as well as to validate and conceptually extend Elwyn et al.'s model of SDM. RESULTS: The model Elwyn et al. have created for SDM in somatic care fits well for mental health services, both in terms of process and content. However, the results also suggest an extension of the model because decisions related to mental illness are often complex and involve a number of life domains. Issues related to social context and individual recovery point to the need for a preparation phase focused on establishing cooperation and mutual understanding as well as a clear follow-up phase that allows for feedback and adjustments to the decision-making process. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: The current study contributes to a deeper understanding of decisional and information needs among users of community-based mental health services that may reduce barriers to participation in decision-making. The results also shed light on attitudinal, relationship-based, and cognitive factors that are important to consider in adapting SDM in the mental health system.

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This paper provides location estimation based power control strategy for cellular radio systems via a location based interference management scheme. Our approach considers the carrier-to-interference as dependent on the transmitter and receiver separation distance and therefore an accurate estimation of the precise locations can provide the power critical mobile user to control the transition power accordingly. In this fully
distributed algorithms, we propose using a Robust Extended Kalman Filter (REKF) to derive an estimate of the mobile user’s closest mobile base station from the user’s location, heading and altitude. Our analysis demonstrates that this algorithm can successfully track the mobile users with less system complexity, as it requires measurements from only one or two closest mobile base stations and hence enable the user to transmit at the rate that is sufficient for the interference management. Our power control
algorithms based on this estimation converges to the desired power trajectory. Further, the technique is robust against system uncertainties caused by the inherent deterministic nature of the mobility model. Through simulation, we show the accuracy of our prediction algorithm and the simplicity of its implementation.

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This paper provides a location based power control strategy for disconnected sensory nodes deployed for long term service. Power conservation is of importance particularly when sensors communicate with a mobile robot used for data collection. The proposed algorithm uses estimations from a Robust Extended Kalman Filter (REKF) with RSSI measurements, in implementing a sigmoid function based power control algorithm which essentially approaches a desired power emission trajectory based on carrier-to-interference ratios(CIR) to ensure interferenceless reception. The more realistic modelling we use incorporates physical dynamics between the mobile robot and the sensors together with the wireless propagation parameters between the transmitter and receiver to formulate a sophisticated and effective power control strategy for the exclusive usage of energy critical disconnected nodes in a sensory network increasing their life span.

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Permutation modeling is challenging because of the combinatorial nature of the problem. However, such modeling is often required in many real-world applications, including activity recognition where subactivities are often permuted and partially ordered. This paper introduces a novel Hidden Permutation Model (HPM) that can learn the partial ordering constraints in permuted state sequences. The HPM is parameterized as an exponential family distribution and is flexible so that it can encode constraints via different feature functions. A chain-flipping Metropolis-Hastings Markov chain Monte Carlo (MCMC) is employed for inference to overcome the O(n!) complexity. Gradient-based maximum likelihood parameter learning is presented for two cases when the permutation is known and when it is hidden. The HPM is evaluated using both simulated and real data from a location-based activity recognition domain. Experimental results indicate that the HPM performs far better than other baseline models, including the naive Bayes classifier, the HMM classifier, and Kirshner's multinomial permutation model. Our presented HPM is generic and can potentially be utilized in any problem where the modeling of permuted states from noisy data is needed.

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Power loss of a distribution system can be reduced significantly by using optimum size and location of distributed generation (DG). Proper allocation of DG with appropriate size maximizes overall system efficiency. Moreover it improves the reliability and voltage profile of the distribution system. In this paper, IEEE 123 node test feeder has been considered to determine the optimum size and location of a synchronous machine based DG for loss reduction of the system. This paper also investigates the steady-state and dynamic voltage profile of that three phase unbalance distribution network in presence of DG with optimum size. This analysis shows that optimum size of DG at proper location minimizes the power loss as well as improves the dynamic voltage profile of the distribution system.

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With the increase use of location-based services, location privacy has recently raised serious concerns. To protect a user from being identified, a cloaked spatial region that contains other k-1 nearest neighbors of the user is used to replace the accurate position. In this paper, we consider location-aware applications that services are different among regions. To search nearest neighbors, we define a novel distance measurement that combines the semantic distance and the Euclidean distance to address the privacy preserving issue in the above-mentioned applications. We also propose an algorithm kNNH to implement our proposed method. The experimental results further suggest that the proposed distance metric and the algorithm can successfully retain the utility of the location services while preserving users’ privacy.

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The constrained battery power of mobile devices poses a serious impact on user experience. As an increasingly prevalent type of applications in mobile cloud environments, location-based applications (LBAs) present some inherent limitations concerning energy. For example, the Global Positioning System based positioning mechanism is well-known for its extremely power-hungry attribute. Due to the severity of the issue, considerable researches have focused on energy-efficient locating sensing mechanism in the last a few years. In this paper, we provide a comprehensive survey of recent work on low-power design of LBAs. An overview of LBAs and different locating sensing technologies used today are introduced. Methods for energy saving with existing locating technologies are investigated. Reductions of location updating queries and simplifications of trajectory data are also mentioned. Moreover, we discuss cloud-based schemes in detail which try to develop new energy-efficient locating technologies by leveraging the cloud capabilities of storage, computation and sharing. Finally, we conclude the survey and discuss the future research directions. © 2013 Springer-Verlag Wien.

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Location aware content-based experiences have a substantial tradition in HCI, several projects over the last two decades have explored the association of digital media to specific locations or objects. However, a large portion of the literature has little focus on the creative side of designing of the experience and on the iterative process of user evaluations. In this thesis we present two iterations in the design and evaluation of a location based story delivery system (LBSDS), inspired by local folklore and oral storytelling in Madeira. We started by testing an already existing location based story platform, PlaceWear, with short multimedia clips that recounted local traditions and folktales, to this experience we called iLand. An initial evaluation of iLand, was conducted; we shadowed users during the experience and then they responded to a questionnaire. By analyzing the evaluation results we uncovered several issues that informed the redesign of the system itself as well as part of the story content. The outcome of this re design was the 7Stories experience. In the new experience we performed the integration of visual markers in the interface and the framing of the fragmented story content through the literary technique of the narrator. This was done aiming to improving the connection of the audience to the physical context where the experience is delivered. The 7Stories experience was evaluated following a similar methodology to the iLand evaluation but the user’s experience resulted considerably different; because of the same setting for the experience in both versions and the constancy of the most of the content across the two versions we were able to assess the specific effect of the new design and discuss its strengths and shortcomings. Although we did not run a formal and strict comparative test between the two evaluations, it is evident from the collected data how the specific design changes to our LBSDS influenced the user experience.

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Trattasi di un sistema basato sulla mappatura degli access point utilizzabile su un'applicazione per smartphone con piattaforma Android. Sono stati usati ambienti di lavoro come Eclipse, affiancato da Lamp e API, utili per visualizzare le mappe da Google Maps. E' stato creato un database online, in modo tale che gli utenti possano condividere le proprie registrazioni (solo quelle che non richiedono una password e quindi utilizzabili da tutti). Il tutto testato tramite un emulatore Android

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Viene proposta l'implementazione di un oracolo che utilizza la posizione geografica e informazioni sugli AP WiFi per determinare quali interfacce di un dispositivo mobile mantenere attive.