893 resultados para data analysis: algorithms and implementation


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The Convention on Biodiversity (CBD) was created in 1992 to coordinate global governments to protect biological resources. The CBD has three goals: protection of biodiversity, achievement of sustainable use of biodiversity and facilitation of equitable sharing of the benefits of biological resources. The goal of protecting biological resources has remained both controversial and difficult to implement. This study focused more on the goal of biodiversity protection. The research was designed to examine how globally constructed environmental policies get adapted by national governments and then passed down to local levels where actual implementation takes place. Effectiveness of such policies depends on the extent of actual implementation at local levels. Therefore, compliance was divided and examined at three levels: global, national and local. The study then developed various criteria to measure compliance at these levels. Both qualitative and quantitative methods were used to analyze compliance and implementation. The study was guided by three questions broadly examining critical factors that most influence the implementation of biodiversity protection policies at the global, national and local levels. Findings show that despite an overall biodiversity deficit of 0.9 hectares per person, global compliance with the CBD goals is currently at 35%. Compliance is lowest at local levels at 14%, it is slightly better at national level at 50%, and much better at the international level 64%. Compliance appears higher at both national and international levels because compliance here is paper work based and policy formulation. If implementation at local levels continues to produce this low compliance, overall conservation outcomes can only get worse than what it is at present. There are numerous weaknesses and capacity challenges countries are yet to address in their plans. In order to increase local level compliance, the study recommends a set of robust policies that build local capacity, incentivize local resource owners, and implement biodiversity protection programs that are akin to local needs and aspirations.^

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The main focus of this thesis is to address the relative localization problem of a heterogenous team which comprises of both ground and micro aerial vehicle robots. This team configuration allows to combine the advantages of increased accessibility and better perspective provided by aerial robots with the higher computational and sensory resources provided by the ground agents, to realize a cooperative multi robotic system suitable for hostile autonomous missions. However, in such a scenario, the strict constraints in flight time, sensor pay load, and computational capability of micro aerial vehicles limits the practical applicability of popular map-based localization schemes for GPS denied navigation. Therefore, the resource limited aerial platforms of this team demand simpler localization means for autonomous navigation. Relative localization is the process of estimating the formation of a robot team using the acquired inter-robot relative measurements. This allows the team members to know their relative formation even without a global localization reference, such as GPS or a map. Thus a typical robot team would benefit from a relative localization service since it would allow the team to implement formation control, collision avoidance, and supervisory control tasks, independent of a global localization service. More importantly, a heterogenous team such as ground robots and computationally constrained aerial vehicles would benefit from a relative localization service since it provides the crucial localization information required for autonomous operation of the weaker agents. This enables less capable robots to assume supportive roles and contribute to the more powerful robots executing the mission. Hence this study proposes a relative localization-based approach for ground and micro aerial vehicle cooperation, and develops inter-robot measurement, filtering, and distributed computing modules, necessary to realize the system. The research study results in three significant contributions. First, the work designs and validates a novel inter-robot relative measurement hardware solution which has accuracy, range, and scalability characteristics, necessary for relative localization. Second, the research work performs an analysis and design of a novel nonlinear filtering method, which allows the implementation of relative localization modules and attitude reference filters on low cost devices with optimal tuning parameters. Third, this work designs and validates a novel distributed relative localization approach, which harnesses the distributed computing capability of the team to minimize communication requirements, achieve consistent estimation, and enable efficient data correspondence within the network. The work validates the complete relative localization-based system through multiple indoor experiments and numerical simulations. The relative localization based navigation concept with its sensing, filtering, and distributed computing methods introduced in this thesis complements system limitations of a ground and micro aerial vehicle team, and also targets hostile environmental conditions. Thus the work constitutes an essential step towards realizing autonomous navigation of heterogenous teams in real world applications.

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Funding Sources The NNUH Stroke and TIA Register is maintained by the NNUH NHS Foundation Trust Stroke Services and data management for this study is supported by the NNUH Research and Development Department through Research Capability Funds.

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The Convention on Biodiversity (CBD) was created in 1992 to coordinate global governments to protect biological resources. The CBD has three goals: protection of biodiversity, achievement of sustainable use of biodiversity and facilitation of equitable sharing of the benefits of biological resources. The goal of protecting biological resources has remained both controversial and difficult to implement. This study focused more on the goal of biodiversity protection. The research was designed to examine how globally constructed environmental policies get adapted by national governments and then passed down to local levels where actual implementation takes place. Effectiveness of such policies depends on the extent of actual implementation at local levels. Therefore, compliance was divided and examined at three levels: global, national and local. The study then developed various criteria to measure compliance at these levels. Both qualitative and quantitative methods were used to analyze compliance and implementation. The study was guided by three questions broadly examining critical factors that most influence the implementation of biodiversity protection policies at the global, national and local levels. Findings show that despite an overall biodiversity deficit of 0.9 hectares per person, global compliance with the CBD goals is currently at 35%. Compliance is lowest at local levels at 14%, it is slightly better at national level at 50%, and much better at the international level 64%. Compliance appears higher at both national and international levels because compliance here is paper work based and policy formulation. If implementation at local levels continues to produce this low compliance, overall conservation outcomes can only get worse than what it is at present. There are numerous weaknesses and capacity challenges countries are yet to address in their plans. In order to increase local level compliance, the study recommends a set of robust policies that build local capacity, incentivize local resource owners, and implement biodiversity protection programs that are akin to local needs and aspirations.

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Objectives This paper describes the methods used in the International Cancer Benchmarking Partnership Module 4 Survey (ICBPM4) which examines time intervals and routes to cancer diagnosis in 10 jurisdictions. We present the study design with defining and measuring time intervals, identifying patients with cancer, questionnaire development, data management and analyses.
Design and setting Recruitment of participants to the ICBPM4 survey is based on cancer registries in each jurisdiction. Questionnaires draw on previous instruments and have been through a process of cognitive testing and piloting in three jurisdictions followed by standardised translation and adaptation. Data analysis focuses on comparing differences in time intervals and routes to diagnosis in the jurisdictions.
Participants Our target is 200 patients with symptomatic breast, lung, colorectal and ovarian cancer in each jurisdiction. Patients are approached directly or via their primary care physician (PCP). Patients’ PCPs and cancer treatment specialists (CTSs) are surveyed, anddata rules’ are applied to combine and reconcile conflicting information. Where CTS information is unavailable, audit information is sought from treatment records and databases.
Main outcomes Reliability testing of the patient questionnaire showed that agreement was complete (κ=1) in four items and substantial (κ=0.8, 95% CI 0.333 to 1) in one item. The identification of eligible patients is sufficient to meet the targets for breast, lung and colorectal cancer. Initial patient and PCP survey response rates from the UK and Sweden are comparable with similar published surveys. Data collection was completed in early 2016 for all cancer types.
Conclusion An international questionnaire-based survey of patients with cancer, PCPs and CTSs has been developed and launched in 10 jurisdictions. ICBPM4 will help to further understand international differences in cancer survival by comparing time intervals and routes to cancer diagnosis.

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Thesis (Ph.D.)--University of Washington, 2016-08

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Americans are accustomed to a wide range of data collection in their lives: census, polls, surveys, user registrations, and disclosure forms. When logging onto the Internet, users’ actions are being tracked everywhere: clicking, typing, tapping, swiping, searching, and placing orders. All of this data is stored to create data-driven profiles of each user. Social network sites, furthermore, set the voluntarily sharing of personal data as the default mode of engagement. But people’s time and energy devoted to creating this massive amount of data, on paper and online, are taken for granted. Few people would consider their time and energy spent on data production as labor. Even if some people do acknowledge their labor for data, they believe it is accessory to the activities at hand. In the face of pervasive data collection and the rising time spent on screens, why do people keep ignoring their labor for data? How has labor for data been become invisible, as something that is disregarded by many users? What does invisible labor for data imply for everyday cultural practices in the United States? Invisible Labor for Data addresses these questions. I argue that three intertwined forces contribute to framing data production as being void of labor: data production institutions throughout history, the Internet’s technological infrastructure (especially with the implementation of algorithms), and the multiplication of virtual spaces. There is a common tendency in the framework of human interactions with computers to deprive data and bodies of their materiality. My Introduction and Chapter 1 offer theoretical interventions by reinstating embodied materiality and redefining labor for data as an ongoing process. The middle Chapters present case studies explaining how labor for data is pushed to the margin of the narratives about data production. I focus on a nationwide debate in the 1960s on whether the U.S. should build a databank, contemporary Big Data practices in the data broker and the Internet industries, and the group of people who are hired to produce data for other people’s avatars in the virtual games. I conclude with a discussion on how the new development of crowdsourcing projects may usher in the new chapter in exploiting invisible and discounted labor for data.

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New morpho-bathymetric and tectono-stratigraphic data on Naples and Salerno Gulfs, derived from bathymetric and seismic data analysis and integrated geologic interpretation are here presented. The CUBE(Combined Uncertainty Bathymetric Estimator) method has been applied to complex morphologies, such as the Capri continental slope and the related geological structures occurring in the Salerno Gulf.The bathymetric data analysis has been carried out for marine geological maps of the whole Campania continental margin at scales ranging from 1:25.000 to 1:10.000, including focused examples in Naples and Salerno Gulfs, Naples harbour, Capri and Ischia Islands and Salerno Valley. Seismic data analysis has allowed for the correlation of main morpho-structural lineaments recognized at a regional scale through multichannel profiles with morphological features cropping out at the sea bottom, evident from bathymetry.Main fault systems in the area have been represented on a tectonic sketch map, including the master fault located northwards to the Salerno Valley half graben. Some normal faults parallel to the master fault have been interpreted from the slope map derived from bathymetric data. A complex system of antithetic faults bound two morpho-structural highs located 20km to the south of the Capri Island. Some hints of compressional reactivation of normal faults in an extensional setting involving the whole Campania continental margin have been shown from seismic interpretation.

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Scientific applications rely heavily on floating point data types. Floating point operations are complex and require complicated hardware that is both area and power intensive. The emergence of massively parallel architectures like Rigel creates new challenges and poses new questions with respect to floating point support. The massively parallel aspect of Rigel places great emphasis on area efficient, low power designs. At the same time, Rigel is a general purpose accelerator and must provide high performance for a wide class of applications. This thesis presents an analysis of various floating point unit (FPU) components with respect to Rigel, and attempts to present a candidate design of an FPU that balances performance, area, and power and is suitable for massively parallel architectures like Rigel.

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Wind-generated waves in the Kara, Laptev, and East-Siberian Seas are investigated using altimeter data from Envisat RA-2 and SARAL-AltiKa. Only isolated ice-free zones had been selected for analysis. Wind seas can be treated as pure wind-generated waves without any contamination by ambient swell. Such zones were identified using ice concentration data from microwave radiometers. Altimeter data, both significant wave height (SWH) and wind speed, for these areas were further obtained for the period 2002-2012 using Envisat RA-2 measurements, and for 2013 using SARAL-AltiKa. Dependencies of dimensionless SWH and wavelength on dimensionless wave generation spatial scale are compared to known empirical dependencies for fetch-limited wind wave development. We further check sensitivity of Ka- and Ku-band and discuss new possibilities that AltiKa's higher resolution can open.

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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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The purpose of this study was to explore the relationship between faculty perceptions, selected demographics, implementation of elements of transactional distance theory and online web-based course completion rates. This theory posits that the high transactional distance of online courses makes it difficult for students to complete these courses successfully; too often this is associated with low completion rates. Faculty members play an indispensable role in course design, whether online or face-to-face. They also influence course delivery format from design through implementation and ultimately to how students will experience the course. This study used transactional distance theory as the conceptual framework to examine the relationship between teaching and learning strategies used by faculty members to help students complete online courses. Faculty members’ sex, number of years teaching online at the college, and their online course completion rates were considered. A researcher-developed survey was used to collect data from 348 faculty members who teach online at two prominent colleges in the southeastern part of United States. An exploratory factor analysis resulted in six factors related to transactional distance theory. The factors accounted for slightly over 65% of the variance of transactional distance scores as measured by the survey instrument. Results provided support for Moore’s (1993) theory of transactional distance. Female faculty members scored higher in all the factors of transactional distance theory when compared to men. Faculty number of years teaching online at the college level correlated significantly with all the elements of transactional distance theory. Regression analysis was used to determine that two of the factors, instructor interface and instructor-learner interaction, accounted for 12% of the variance in student online course completion rates. In conclusion, of the six factors found, the two with the highest percentage scores were instructor interface and instructor-learner interaction. This finding, while in alignment with the literature concerning the dialogue element of transactional distance theory, brings a special interest to the importance of instructor interface as a factor. Surprisingly, based on the reviewed literature on transactional distance theory, faculty perceptions concerning learner-learner interaction was not an important factor and there was no learner-content interaction factor.

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Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.