Robust Inter-Vehicle Distance Measurement Using Cooperative Vehicle Localization.

Precise localization is critical to safety for connected and automated vehicles (CAV). The global navigation satellite system is the most common vehicle positioning method and has been widely studied to improve localization accuracy. In addition to single-vehicle localization, some recently developed CAV applications require accurate measurement of the inter-vehicle distance (IVD). Thus, this paper proposes a cooperative localization framework that shares the absolute position or pseudorange by using V2X communication devices to estimate the IVD. Four IVD estimation methods are presented: Absolute Position Differencing (APD), Pseudorange Differencing (PD), Single Differencing (SD) and Double Differencing (DD). Several static and dynamic experiments are conducted to evaluate and compare their measurement accuracy. The results show that the proposed methods may have different performances under different conditions. The DD shows the superior performance among the four methods if the uncorrelated errors are small or negligible (static experiment or dynamic experiment with open-sky conditions). When multi-path errors emerge due to the blocked GPS signal, the PD method using the original pseudorange is more effective because the uncorrelated errors cannot be eliminated by the differential technique.

Prediction of DNase I hypersensitive sites in plant genome using multiple modes of pseudo components.

DNase I hypersensitive sites (DHSs) are accessible chromatin zones hypersensitive to DNase I endonucleases in plant genome. DHSs have been used as markers for the presence of transcriptional regulatory elements. It is an important complement to develop computational methods to identify DHSs for discovering potential regulatory elements. To the best of our knowledge, several machine learning approaches have been proposed for the DHSs prediction, but there is still room for improvements. In this work, a new predictor called pDHS-WE was proposed for prediction of DHSs in plant genome by using weighted ensemble learning framework. Here, five classes of heterogeneous features were used to represent the sequences. Five random forest (RF) operators were constructed based on these five classes of features. The proposed pDHS-WE was formed by fusing the five individual RF classifiers into an ensemble predictor. Genetic algorithm was employed to obtain the weights of different classes of features. In the experiments, pDHS-WE obtained accuracy of 88.5%, sensitivity of 89.1%, specificity of 88.0%, and AUC of 0.958, which was more than 2.7%, 2%, 3.5% and 2.6% higher than state-of-the-art methods, respectively. The results suggested that pDHS-WE may become a useful tool for transcriptional regulatory elements analysis in plant genome.