The Experts below are selected from a list of 247065 Experts worldwide ranked by ideXlab platform
Letizia Lo Presti - One of the best experts on this subject based on the ideXlab platform.
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fast nearly ml estimation of doppler frequency in gnss signal Acquisition Process
Sensors, 2013Co-Authors: Xinhua Tang, Emanuela Falletti, Letizia Lo PrestiAbstract:It is known that signal Acquisition in Global Navigation Satellite System (GNSS) field provides a rough maximum-likelihood (ML) estimate based on a peak search in a two-dimensional grid. In this paper, the theoretical mathematical expression of the cross-ambiguity function (CAF) is exploited to analyze the grid and improve the accuracy of the frequency estimate. Based on the simple equation derived from this mathematical expression of the CAF, a family of novel algorithms is proposed to refine the Doppler frequency estimate with respect to that provided by a conventional Acquisition method. In an ideal scenario where there is no noise and other nuisances, the frequency estimation error can be theoretically reduced to zero. On the other hand, in the presence of noise, the new algorithm almost reaches the Cramer-Rao Lower Bound (CRLB) which is derived as benchmark. For comparison, a least-square (LS) method is proposed. It is shown that the proposed solution achieves the same performance of LS, but requires a dramatically reduced computational burden. An averaging method is proposed to mitigate the influence of noise, especially when signal-to-noise ratio (SNR) is low. Finally, the influence of the grid resolution in the search space is analyzed in both time and frequency domains.
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Fine Doppler frequency estimation in GNSS signal Acquisition Process
2012 6th ESA Workshop on Satellite Navigation Technologies (Navitec 2012) & European Workshop on GNSS Signals and Signal Processing, 2012Co-Authors: Xinhua Tang, Emanuela Falletti, Letizia Lo PrestiAbstract:It is known that signal Acquisition can be treated as a two-dimensional search and all the possible results are located in the search plane as different cells. In this paper, the theoretical expression of the cross-ambiguity function (CAF) is analyzed, and the property of the approximate expression is exploited, while it is seldom addressed in previous GNSS receiver architectures. Based on the property of the approximate CAF expression, a novel approach is proposed. In this approach, the information in the region of main peak is used to refine the Doppler frequency in the form of a simple interpolation equation. According to the results, in the absence of noise, the frequency estimation error could be theoretically decreased to zero. In the presence of noise, the novel method is equivalent to the least-square solution in terms of accuracy, but it has a significantly lower implementation complexity. Last, an averaging method is proposed to reduce the influence of noise. It shows that averaging operation can decrease the error to (-10Hz, 10Hz) in 90% of the cases when signal-to-noise ratio is C/NO=43dBHz.
Anita Prinzie - One of the best experts on this subject based on the ideXlab platform.
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Analyzing existing customers' websites to improve the customer Acquisition Process as well as the profitability prediction in B-to-B marketing
Expert Systems with Applications, 2012Co-Authors: Dirk Thorleuchter, Dirk Van Den Poel, Anita PrinzieAbstract:Research highlights? Prediction of the profitability of new customers. ? Used web mining to extract latent semantic concepts from customers' websites. ? Used clustering of latent semantic concepts to identify prevalent terms. ? Used prevalent terms to identify addresses of new profitable customers. We investigate the issue of predicting new customers as profitable based on information about existing customers in a business-to-business environment. In particular, we show how latent semantic concepts from textual information of existing customers' websites can be used to uncover characteristics of websites of companies that will turn into profitable customers. Hence, the use of predictive analytics will help to identify new potential Acquisition targets. Additionally, we show that a regression model based on these concepts is successful in the profitability prediction of new customers. In a case study, the Acquisition Process of a mail-order company is supported by creating a prioritized list of new customers generated by this approach. It is shown that the density of profitable customers in this list outperforms the density of profitable customers in traditional generated address lists (e.g. from list brokers). From a managerial point of view, this approach supports the identification of new business customers and helps to estimate the future profitability of these customers in a company. Consequently, the customer Acquisition Process can be targeted more effectively and efficiently. This leads to a competitive advantage for B2B companies and improves the Acquisition Process that is time- and cost-consuming with traditionally low conversion rates.
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analyzing existing customers websites to improve the customer Acquisition Process as well as the profitability prediction in b to b marketing
Research Papers in Economics, 2011Co-Authors: Dirk Thorleuchter, Dirk Van Den Poel, Anita PrinzieAbstract:We investigate the issue of predicting new customers as profitable based on information about existing customers in a business-to-business environment. In particular, we show how latent semantic concepts from textual information of existing customers’ websites can be used to uncover characteristics of websites of companies that will turn into profitable customers. Hence, the use of predictive analytics will help to identify new potential Acquisition targets. Additionally, we show that a regression model based on these concepts is successful in the profitability prediction of new customers. In a case study, the Acquisition Process of a mail-order company is supported by creating a prioritized list of new customers generated by this approach. It is shown that the density of profitable customers in this list outperforms the density of profitable customers in traditional generated address lists (e. g. from list brokers). From a managerial point of view, this approach supports the identification of new business customers and helps to estimate the future profitability of these customers in a company. Consequently, the customer Acquisition Process can be targeted more effectively and efficiently. This leads to a competitive advantage for B2B companies and improves the Acquisition Process that is time- and cost-consuming with traditionally low conversion rates.
Xinhua Tang - One of the best experts on this subject based on the ideXlab platform.
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fast nearly ml estimation of doppler frequency in gnss signal Acquisition Process
Sensors, 2013Co-Authors: Xinhua Tang, Emanuela Falletti, Letizia Lo PrestiAbstract:It is known that signal Acquisition in Global Navigation Satellite System (GNSS) field provides a rough maximum-likelihood (ML) estimate based on a peak search in a two-dimensional grid. In this paper, the theoretical mathematical expression of the cross-ambiguity function (CAF) is exploited to analyze the grid and improve the accuracy of the frequency estimate. Based on the simple equation derived from this mathematical expression of the CAF, a family of novel algorithms is proposed to refine the Doppler frequency estimate with respect to that provided by a conventional Acquisition method. In an ideal scenario where there is no noise and other nuisances, the frequency estimation error can be theoretically reduced to zero. On the other hand, in the presence of noise, the new algorithm almost reaches the Cramer-Rao Lower Bound (CRLB) which is derived as benchmark. For comparison, a least-square (LS) method is proposed. It is shown that the proposed solution achieves the same performance of LS, but requires a dramatically reduced computational burden. An averaging method is proposed to mitigate the influence of noise, especially when signal-to-noise ratio (SNR) is low. Finally, the influence of the grid resolution in the search space is analyzed in both time and frequency domains.
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Fine Doppler frequency estimation in GNSS signal Acquisition Process
2012 6th ESA Workshop on Satellite Navigation Technologies (Navitec 2012) & European Workshop on GNSS Signals and Signal Processing, 2012Co-Authors: Xinhua Tang, Emanuela Falletti, Letizia Lo PrestiAbstract:It is known that signal Acquisition can be treated as a two-dimensional search and all the possible results are located in the search plane as different cells. In this paper, the theoretical expression of the cross-ambiguity function (CAF) is analyzed, and the property of the approximate expression is exploited, while it is seldom addressed in previous GNSS receiver architectures. Based on the property of the approximate CAF expression, a novel approach is proposed. In this approach, the information in the region of main peak is used to refine the Doppler frequency in the form of a simple interpolation equation. According to the results, in the absence of noise, the frequency estimation error could be theoretically decreased to zero. In the presence of noise, the novel method is equivalent to the least-square solution in terms of accuracy, but it has a significantly lower implementation complexity. Last, an averaging method is proposed to reduce the influence of noise. It shows that averaging operation can decrease the error to (-10Hz, 10Hz) in 90% of the cases when signal-to-noise ratio is C/NO=43dBHz.
Diane Larsenfreeman - One of the best experts on this subject based on the ideXlab platform.
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on the complementarity of chaos complexity theory and dynamic systems theory in understanding the second language Acquisition Process
Bilingualism: Language and Cognition, 2007Co-Authors: Diane LarsenfreemanAbstract:In the late 1980s, I reviewed what was known about the field of second language Acquisition (SLA), first with a co-author in a book (Larsen-Freeman and Long, 1991) and then in a highly condensed form for a 25th anniversary issue of the TESOL Quarterly (Larsen-Freeman, 1991). Stock-taking activities can be very useful, and I was grateful for these opportunities.
Dirk Thorleuchter - One of the best experts on this subject based on the ideXlab platform.
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Analyzing existing customers' websites to improve the customer Acquisition Process as well as the profitability prediction in B-to-B marketing
Expert Systems with Applications, 2012Co-Authors: Dirk Thorleuchter, Dirk Van Den Poel, Anita PrinzieAbstract:Research highlights? Prediction of the profitability of new customers. ? Used web mining to extract latent semantic concepts from customers' websites. ? Used clustering of latent semantic concepts to identify prevalent terms. ? Used prevalent terms to identify addresses of new profitable customers. We investigate the issue of predicting new customers as profitable based on information about existing customers in a business-to-business environment. In particular, we show how latent semantic concepts from textual information of existing customers' websites can be used to uncover characteristics of websites of companies that will turn into profitable customers. Hence, the use of predictive analytics will help to identify new potential Acquisition targets. Additionally, we show that a regression model based on these concepts is successful in the profitability prediction of new customers. In a case study, the Acquisition Process of a mail-order company is supported by creating a prioritized list of new customers generated by this approach. It is shown that the density of profitable customers in this list outperforms the density of profitable customers in traditional generated address lists (e.g. from list brokers). From a managerial point of view, this approach supports the identification of new business customers and helps to estimate the future profitability of these customers in a company. Consequently, the customer Acquisition Process can be targeted more effectively and efficiently. This leads to a competitive advantage for B2B companies and improves the Acquisition Process that is time- and cost-consuming with traditionally low conversion rates.
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analyzing existing customers websites to improve the customer Acquisition Process as well as the profitability prediction in b to b marketing
Research Papers in Economics, 2011Co-Authors: Dirk Thorleuchter, Dirk Van Den Poel, Anita PrinzieAbstract:We investigate the issue of predicting new customers as profitable based on information about existing customers in a business-to-business environment. In particular, we show how latent semantic concepts from textual information of existing customers’ websites can be used to uncover characteristics of websites of companies that will turn into profitable customers. Hence, the use of predictive analytics will help to identify new potential Acquisition targets. Additionally, we show that a regression model based on these concepts is successful in the profitability prediction of new customers. In a case study, the Acquisition Process of a mail-order company is supported by creating a prioritized list of new customers generated by this approach. It is shown that the density of profitable customers in this list outperforms the density of profitable customers in traditional generated address lists (e. g. from list brokers). From a managerial point of view, this approach supports the identification of new business customers and helps to estimate the future profitability of these customers in a company. Consequently, the customer Acquisition Process can be targeted more effectively and efficiently. This leads to a competitive advantage for B2B companies and improves the Acquisition Process that is time- and cost-consuming with traditionally low conversion rates.
