High-speed, locational, phone-to-phone (HLPP) games and apps constitute a provocative class of mobile apps that are currently unsupported on commodity mobile devices. This work looks at a key problem for enabling HLPP: a specific variant of the localization problem in which two phones estimate each other's relative positions in 3D space without infrastructure support. Moreover, position estimates should reflect changes due to the phones' possible mobility.We present a solution for achieving high speed 3D continuous localization for phone-to-phone scenarios. Our basic approach uses acoustic cues based on time-of-arrival and power level. It assumes at least two microphones and one speaker per phone, which is common on new smartphones. Accelerometers and digital compasses assist in resolving ambiguous acoustic-only localization. Continuous localization is achieved with the aid of a loose time synchronization protocol and an extended Kalman filter. Experimental results across a range of motion paths show localization resolution to within 13.9 centimeters for 90% of estimates, and to within 4.9 centimeters for 50% of estimates when the phones are several meters apart.
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We make an approach to the logic optimization algorithm including converting multi-valued logic into two-valued logic and converting two-valued logic into multi-valued logic. We discuss the algorithm converting two-valued logic into multi-valued logic on the basis of building an assignment graph and present multi-valued logic optimization algorithm. In this paper, we analyze and study the logic optimization algorithms based on mini-terms and design a software system on logic optimization. It overpasses testing of Benchmark and right validate and it shows that the function of the software system on logic optimization is good and the optimization efficiency is high by testing.
This paper analyses the Min-min algorithm and its improved algorithms through the performances of load balance, time span, quality of service and economic principle. Based on the analysis of the merits of these algorithms, we propose an improved algorithm as PMTS (Priority-based maximum time-span algorithm) by integrating. In the instance of the application, we analyse and compare the performances of these algorithms, and experimental results show that, PMTS algorithm is better than other algorithms in the comprehensive performance of load-balance, time-span, quality of service and other aspects.
Decision tree classification is one of the most widely-used methods in data mining which can provide useful decision-making analysis for users. But most of the decision tree methods have some efficiency bottle-necks and can only applied to small-scale datasets. In this paper, we present an new improved synthesized decision tree algorithm named CA which includes three important parts like dimension reduction, pre-clustering and decision tree method, and also give out its formalized specification. Through dimension reduction and synthesized pre-clustering methods, we can optimize the initial dataset and considerably reduce the decision tree’s input computation costs. We also improve the decision tree method by introducing parallel processing concept which can enhance its calculation precision and decision efficiency. This paper applies CA into maize seed breeding and analyzes its efficiency in every part comparing with original methods, and the results shows that CA algorithm is better.
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