The Experts below are selected from a list of 215535 Experts worldwide ranked by ideXlab platform
Joerg Widmer - One of the best experts on this subject based on the ideXlab platform.
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Fine-grained LTE radio link estimation for Mobile Phones
Pervasive and Mobile Computing, 2018Co-Authors: Nicola Bui, Foivos Michelinakis, Joerg WidmerAbstract:Abstract Recently, spectrum optimization solutions require Mobile Phones to obtain precise, accurate and fine-grained estimates of the radio link data rate. In particular, the effectiveness of anticipatory schemes depends on the granularity of these measurements. In this paper we use a reliable LTE control channel sniffer (OWL) to extensively compare Mobile phone measurements against exact LTE radio link data rates. We also provide a detailed study of latencies measured on Mobile Phones, the sniffer, and a server to which the phone is connected. In this study, we show that Mobile Phones can accurately (if slightly biased) estimate the physical radio link data rate. We highlight the differences among measurements obtained using different Mobile Phones, communication technologies and protocols. We also provide detailed instructions on how to replicate our measurements and describe alternative measurement setups and their tradeoffs.
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WoWMoM - Fine-grained LTE radio link estimation for Mobile Phones
2017 IEEE 18th International Symposium on A World of Wireless Mobile and Multimedia Networks (WoWMoM), 2017Co-Authors: Nicola Bui, Foivos Michelinakis, Joerg WidmerAbstract:Recently, spectrum optimization solutions require Mobile Phones to obtain precise, accurate and fine-grained estimates of the radio link data rate. In particular, the effectiveness of anticipatory schemes depends on the granularity of these measurements. In this paper we use a reliable LTE control channel sniffer (OWL) to extensively compare Mobile phone measurements against exact LTE radio link data rates. We also provide a detailed study of latencies measured on Mobile Phones, the sniffer, and a server to which the phone is connected. In this study, we show that Mobile Phones can accurately (if slightly biased) estimate the physical radio link data rate. We highlight the differences among measurements obtained using different Mobile Phones, communication technologies and protocols.
Nicola Bui - One of the best experts on this subject based on the ideXlab platform.
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Fine-grained LTE radio link estimation for Mobile Phones
Pervasive and Mobile Computing, 2018Co-Authors: Nicola Bui, Foivos Michelinakis, Joerg WidmerAbstract:Abstract Recently, spectrum optimization solutions require Mobile Phones to obtain precise, accurate and fine-grained estimates of the radio link data rate. In particular, the effectiveness of anticipatory schemes depends on the granularity of these measurements. In this paper we use a reliable LTE control channel sniffer (OWL) to extensively compare Mobile phone measurements against exact LTE radio link data rates. We also provide a detailed study of latencies measured on Mobile Phones, the sniffer, and a server to which the phone is connected. In this study, we show that Mobile Phones can accurately (if slightly biased) estimate the physical radio link data rate. We highlight the differences among measurements obtained using different Mobile Phones, communication technologies and protocols. We also provide detailed instructions on how to replicate our measurements and describe alternative measurement setups and their tradeoffs.
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WoWMoM - Fine-grained LTE radio link estimation for Mobile Phones
2017 IEEE 18th International Symposium on A World of Wireless Mobile and Multimedia Networks (WoWMoM), 2017Co-Authors: Nicola Bui, Foivos Michelinakis, Joerg WidmerAbstract:Recently, spectrum optimization solutions require Mobile Phones to obtain precise, accurate and fine-grained estimates of the radio link data rate. In particular, the effectiveness of anticipatory schemes depends on the granularity of these measurements. In this paper we use a reliable LTE control channel sniffer (OWL) to extensively compare Mobile phone measurements against exact LTE radio link data rates. We also provide a detailed study of latencies measured on Mobile Phones, the sniffer, and a server to which the phone is connected. In this study, we show that Mobile Phones can accurately (if slightly biased) estimate the physical radio link data rate. We highlight the differences among measurements obtained using different Mobile Phones, communication technologies and protocols.
Foivos Michelinakis - One of the best experts on this subject based on the ideXlab platform.
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Fine-grained LTE radio link estimation for Mobile Phones
Pervasive and Mobile Computing, 2018Co-Authors: Nicola Bui, Foivos Michelinakis, Joerg WidmerAbstract:Abstract Recently, spectrum optimization solutions require Mobile Phones to obtain precise, accurate and fine-grained estimates of the radio link data rate. In particular, the effectiveness of anticipatory schemes depends on the granularity of these measurements. In this paper we use a reliable LTE control channel sniffer (OWL) to extensively compare Mobile phone measurements against exact LTE radio link data rates. We also provide a detailed study of latencies measured on Mobile Phones, the sniffer, and a server to which the phone is connected. In this study, we show that Mobile Phones can accurately (if slightly biased) estimate the physical radio link data rate. We highlight the differences among measurements obtained using different Mobile Phones, communication technologies and protocols. We also provide detailed instructions on how to replicate our measurements and describe alternative measurement setups and their tradeoffs.
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WoWMoM - Fine-grained LTE radio link estimation for Mobile Phones
2017 IEEE 18th International Symposium on A World of Wireless Mobile and Multimedia Networks (WoWMoM), 2017Co-Authors: Nicola Bui, Foivos Michelinakis, Joerg WidmerAbstract:Recently, spectrum optimization solutions require Mobile Phones to obtain precise, accurate and fine-grained estimates of the radio link data rate. In particular, the effectiveness of anticipatory schemes depends on the granularity of these measurements. In this paper we use a reliable LTE control channel sniffer (OWL) to extensively compare Mobile phone measurements against exact LTE radio link data rates. We also provide a detailed study of latencies measured on Mobile Phones, the sniffer, and a server to which the phone is connected. In this study, we show that Mobile Phones can accurately (if slightly biased) estimate the physical radio link data rate. We highlight the differences among measurements obtained using different Mobile Phones, communication technologies and protocols.
Kari-jouko Räihä - One of the best experts on this subject based on the ideXlab platform.
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CHI - Predicting Chinese text entry speeds on Mobile Phones
Proceedings of the 28th international conference on Human factors in computing systems - CHI '10, 2010Co-Authors: Ying Liu, Kari-jouko RäihäAbstract:Chinese text entry on Mobile Phones is critical considering the large number of Chinese speakers worldwide and as a key task in many core applications. But there is still a lack of both empirical data and predictive models that explore the pattern of user behavior in the process. We propose a model to predict user performance with two types of Chinese pinyin input methods on Mobile Phones. The model integrates a language model (digraph probability) with Fitts' law for key presses, a keystroke-level model for navigation, and a linear model for visual search in pinyin marks and Chinese characters. We tested the model by comparing its predictions with the empirical measures. The predictions are satisfactory and the percentage differences are all within 4% of the empirical results, suggesting that the model can be used to evaluate user performance of Chinese pinyin text entry solutions on Mobile Phones.
Ying Liu - One of the best experts on this subject based on the ideXlab platform.
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CHI - Predicting Chinese text entry speeds on Mobile Phones
Proceedings of the 28th international conference on Human factors in computing systems - CHI '10, 2010Co-Authors: Ying Liu, Kari-jouko RäihäAbstract:Chinese text entry on Mobile Phones is critical considering the large number of Chinese speakers worldwide and as a key task in many core applications. But there is still a lack of both empirical data and predictive models that explore the pattern of user behavior in the process. We propose a model to predict user performance with two types of Chinese pinyin input methods on Mobile Phones. The model integrates a language model (digraph probability) with Fitts' law for key presses, a keystroke-level model for navigation, and a linear model for visual search in pinyin marks and Chinese characters. We tested the model by comparing its predictions with the empirical measures. The predictions are satisfactory and the percentage differences are all within 4% of the empirical results, suggesting that the model can be used to evaluate user performance of Chinese pinyin text entry solutions on Mobile Phones.
