882 resultados para Methods : Data Analysis


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In this work, we further extend the recently developed adaptive data analysis method, the Sparse Time-Frequency Representation (STFR) method. This method is based on the assumption that many physical signals inherently contain AM-FM representations. We propose a sparse optimization method to extract the AM-FM representations of such signals. We prove the convergence of the method for periodic signals under certain assumptions and provide practical algorithms specifically for the non-periodic STFR, which extends the method to tackle problems that former STFR methods could not handle, including stability to noise and non-periodic data analysis. This is a significant improvement since many adaptive and non-adaptive signal processing methods are not fully capable of handling non-periodic signals. Moreover, we propose a new STFR algorithm to study intrawave signals with strong frequency modulation and analyze the convergence of this new algorithm for periodic signals. Such signals have previously remained a bottleneck for all signal processing methods. Furthermore, we propose a modified version of STFR that facilitates the extraction of intrawaves that have overlaping frequency content. We show that the STFR methods can be applied to the realm of dynamical systems and cardiovascular signals. In particular, we present a simplified and modified version of the STFR algorithm that is potentially useful for the diagnosis of some cardiovascular diseases. We further explain some preliminary work on the nature of Intrinsic Mode Functions (IMFs) and how they can have different representations in different phase coordinates. This analysis shows that the uncertainty principle is fundamental to all oscillating signals.

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This project, as part of a broader Sustainable Sub-divisions research agenda, addresses the role of natural ventilation in reducing the use of energy required to cool dwellings

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This report provides an introduction to our analyses of secondary data with respect to violent acts and incidents relating to males living in rural settings in Australia. It clarifies important aspects of our overall approach primarily by concentrating on three elements that required early scoping and resolution. Firstly, a wide and inclusive view of violence which encompasses measures of violent acts and incidents and also data identifying risk taking behaviour and the consequences of violence is outlined and justified. Secondly, the classification used to make comparisons between the city and the bush together with associated caveats is outlined. The third element discussed is in relation to national injury data. Additional commentary resulting from exploration, examination and analyses of secondary data is published online in five subsequent reports in this series.

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Acoustic emission (AE) technique is one of the popular diagnostic techniques used for structural health monitoring of mechanical, aerospace and civil structures. But several challenges still exist in successful application of AE technique. This paper explores various tools for analysis of recorded AE data to address two primary challenges: discriminating spurious signals from genuine signals and devising ways to quantify damage levels.

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Monitoring and assessing environmental health is becoming increasingly important as human activity and climate change place greater pressure on global biodiversity. Acoustic sensors provide the ability to collect data passively, objectively and continuously across large areas for extended periods of time. While these factors make acoustic sensors attractive as autonomous data collectors, there are significant issues associated with large-scale data manipulation and analysis. We present our current research into techniques for analysing large volumes of acoustic data effectively and efficiently. We provide an overview of a novel online acoustic environmental workbench and discuss a number of approaches to scaling analysis of acoustic data; collaboration, manual, automatic and human-in-the loop analysis.

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Data analysis sessions are a common feature of discourse analytic communities, often involving participants with varying levels of expertise to those with significant expertise. Learning how to do data analysis and working with transcripts, however, are often new experiences for doctoral candidates within the social sciences. While many guides to doctoral education focus on procedures associated with data analysis (Heath, Hindmarsh, & Luff, 2010; McHoul & Rapley, 2001; Silverman, 2011; Wetherall, Taylor, & Yates, 2001), the in situ practices of doing data analysis are relatively undocumented. This chapter has been collaboratively written by members of a special interest research group, the Transcript Analysis Group (TAG), who meet regularly to examine transcripts representing audio- and video-recorded interactional data. Here, we investigate our own actual interactional practices and participation in this group where each member is both analyst and participant. We particularly focus on the pedagogic practices enacted in the group through investigating how members engage in the scholarly practice of data analysis. A key feature of talk within the data sessions is that members work collaboratively to identify and discuss ‘noticings’ from the audio-recorded and transcribed talk being examined, produce candidate analytic observations based on these discussions, and evaluate these observations. Our investigation of how talk constructs social practices in these sessions shows that participants move fluidly between actions that demonstrate pedagogic practices and expertise. Within any one session, members can display their expertise as analysts and, at the same time, display that they have gained an understanding that they did not have before. We take an ethnomethodological position that asks, ‘what’s going on here?’ in the data analysis session. By observing the in situ practices in fine-grained detail, we show how members participate in the data analysis sessions and make sense of a transcript.

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Acoustic sensors play an important role in augmenting the traditional biodiversity monitoring activities carried out by ecologists and conservation biologists. With this ability however comes the burden of analysing large volumes of complex acoustic data. Given the complexity of acoustic sensor data, fully automated analysis for a wide range of species is still a significant challenge. This research investigates the use of citizen scientists to analyse large volumes of environmental acoustic data in order to identify bird species. Specifically, it investigates ways in which the efficiency of a user can be improved through the use of species identification tools and the use of reputation models to predict the accuracy of users with unidentified skill levels. Initial experimental results are reported.

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This report is an update of an earlier version produced in January 2010 (see Carrington et al. 2010) which remains as an ePrint through the project’s home page. The report provides an introduction to our analyses of extant secondary data with respect to violent acts and incidents relating to males living in rural settings in Australia using data which were available in public data bases at the time of production. It clarifies important aspects of our overall approach primarily by concentrating on three elements that required early scoping and resolution.

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This report is an update of an earlier one produced in January 2010 (see Carrington et al. 2010) which remains as an ePrint through the project’s home page. This report focuses on our examination of extant data which have been sourced with respect to intentional violence perpetrated or experienced by males living in regional and remote Australia . and which were available in public data bases at production. The nature of intentional violent acts can be physical, sexual or psychological or involve deprivation or neglect.