Due to its flexible and pervasive sensing ability, crowdsensing has been extensively studied recently in research communities. However, the fundamental issue of how to meet the requirement of sensing ...robustness in crowdsensing remains largely unsolved. Specifically, from the task owner's perspective, how to minimize the total payment in crowdsensing while guaranteeing the sensing data quality is a critical issue to be resolved. We elegantly model the robustness requirement over sensing data quality as chance constraints, and investigate both hard and soft chance constraints for different crowdsensing applications. For the former, we reformulate the problem through Boole's Inequality, and explore the optimal value gap between the original problem and the reformulated problem. For the latter, we study a serial of a general payment minimization problem, and propose a binary search algorithm that achieves both feasibility and low payment. The performance gap between our solution and the optimal solution is also theoretically analyzed. Extensive simulations validate our theoretical analysis.
With rapid development of GPS and wireless techniques, there accumulates a huge volume of trajectory data with long path in many applications. Thus, detecting outliers from trajectory data has become ...an attractive and interesting research topic. Like pattern matching, current researches on detecting outliers from trajectory data mainly focus on comparing trajectory's shape. This paper proposes a new framework of efficiently mining outliers from trajectory data, which were produced by the objects that move on unrestraint environment. Firstly, according to trajectory's characteristics, a distance computation method is designed, which is derived from the idea of Minimum Hausdoff Distance under Translation, which is used in pattern matching. This distance function not only considers the directory and the velocity of objects movement besides the shape, but also the costs of this distance function could be reduced sharply by R-Tree. Extensive experimental results demonstrate the efficiency and effectiveness of the proposed framework for trajectory outlier detection.
The main point of intelligent fault diagnosis theory is fault mode distinguishing principle based on data processing methods. Pointing to the problems of the traditional fault diagnosis mode, a fault ...diagnosis method based on the virtual instrument and neural networks is proposed. The signals collection and management based on virtual instrument is introduced, the basic method of the neural networks for distinguishing the faults is analyzed. For fastness and accuracy, connecting the wavelet analysis with the neural networks organically, and based on the wavelet transfer and the neural networks, the system of the speedy features extraction and identification for the faults is founded. The method of the feature extraction for the faults based on the wavelet analysis are established, the realization idea of the fault diagnosis based on the neural networks is put forward, and the hardware and software structure of the fault diagnosis based on the neural networks are discussed. The experimental and simulated results show: it is feasible that analyses for the faults with the neural networks and the wavelet analysis. The method can remarkably heighten the accuracy and credibility of the fault diagnosis results, and the results are of repeatability.
Crowdsensing has been well recognized as a promising approach to enable large scale urban data collection. In a typical crowdsensing system, the task owner usually needs to provide incentives to the ...users (say participants) to encourage their participation. Among existing incentive mechanisms, posted pricing has been widely adopted because it is easy to implement while ensuring truthfulness and fairness. One critical challenge to the task owner is to set the right posted price to recruit a crowd with small total payment and reasonable sensing quality, i.e., posted pricing problem for robust crowdsensing. However, this fundamental problem remains largely open so far. In this paper, we model the robustness requirement over sensing data quality as chance constraints in an elegant manner, and study a series of chance constrained posted pricing problems in crowdsensing systems. Although some chance-constrained optimization techniques have been applied in the literature, they cannot provide any performance guarantees for their solutions. In this work, we propose a binary search based algorithm, and show that using this algorithm allows us to establish theoretical guarantees on its performance. Extensive numerical simulations demonstrate the effectiveness of our proposed algorithm.
A modified IJF-OQPSK modulation technique Lin, F.; Song, T.; Jiang, S.
Milcom 93: Conference Record Volume 1 of 3/October 11-14, 1993 Boston, Massachusetts/93Ch32607 (Ieee Military Communications Conference),
1993, Letnik:
3
Conference Proceeding, Journal Article
The IJF-OQPSK modulation technique achieves good spectral efficiency in digital communication systems. In this paper, an improved modulation technique called Modified Intersymbol-Interference and ...Jitter Free OQPSK (MIJF-OQPSK) is presented. Comparing with IJF-OQPSK, the MIJF-OQPSK obtains more superior property: the power spectral roll-off out-of-band is lower than 15 dB; the envelope fluctuation of the modulated signal amplitude drops down to 1.3 dB; and the spectral spreading is smaller in hard-limited channels.< >
