Accurate identification of functionally relevant variants against the ubiquitous background genetic variations is a significant challenge facing bioinformatics researchers and the challenge becomes more severe for non-coding variants. In this study, a novel computational method to identify candidate disease-associated non-coding single nucleotide polymorphisms (SNPs) of human genome is presented. To characterize SNPs, an extensive range of features, such as sequence context, DNA structure, evolutionary conservation and histone modification signals etc. are extracted. Then random forest is adopted to build the classifier model together with an ensemble method to deal with unbalanced data. 10-fold cross-validation result shows that the proposed method can achieve accuracy with the area under ROC curve (AUC) of 0.74. All the original data and the source matlab codes involved are available at https://sourceforge.net/projects/dissnp-predict/.

Unmanned aerial vehicles (UAVs) are versatile in maneuverability for both civilian and military applications. To facilitate the long-term tasks of UAVs, autonomous rendezvous and docking (ARaD) will be a need in the emerging field of UAV research. In this paper, we proposed a vision-based auxiliary system (VAS) for multirotor UAVs to implement autonomous rendezvous and docking. The VAS consists of image acquisition and processing unit, wireless communication unit, and tracking and docking control unit. Continuously adaptive mean shift (CamShift) algorithm was applied for tracking the target and obtaining its 3D coordinates. A specific Zigbee protocol was designed to ensure the steady and rapid transmission of status data between UAVs and the ground station. A straight-forward tracking and docking control algorithm was proposed to assist the rendezvous and docking between the two UAVs. Physical simulation experiments were performed by two six-rotor rotorcrafts, which demonstrate the feasibility and practicability of our proposed vision-based auxiliary system for the future application.

Sparse representation (or sparse coding) has been applied to deal with frontal face recognition. Two representative methods are the sparse representation‐based classification (SRC) and the collaborative representation‐based classification (CRC), in which the query face image is represented by a sparse linear combination of all the training samples. The difference between SRC and CRC is that the L 1 ‐norm constraint of coding is employed in the former to guarantee the sparse property, while the L 2 ‐norm constraint is utilised in the latter. In this paper, we propose a novel loose L 1/2 regularised sparse representation (SR) for face recognition, named L 1/2 classification (LHC), which is inspired by L 1/2 regularisation. Additionally, an iterative Tikhonov regularisation (ITR) is proposed to solve LHC efficiently compared with the original algorithm. Using ITR, the balance between the collaborative representation (CR) and the SR can be tuned by the iterations. Attributed to the sparser L 1/2 regularisation and the iterative solution mechanism, a better performance can be achieved by LHC. Extensive experiments on three benchmark face databases demonstrated that LHC is more effective than the state‐of‐the‐art SR‐based methods in dealing with frontal face recognition.

Range‐free, distributed localisation method is a challenging issue in wireless sensor networks (WSN). Typical hop count‐based localisation methods assume that the Hopsize for different hop count is the same. However, many observations in WSN indicate that this assumption does not always correspond to the practical situation. In this study, the authors propose an approximate distribution of k ‐hop node distance and derive the expectation of k ‐hop node distance under the above distribution. Furthermore, the authors propose a novel range‐free localisation method based on the multi‐hop distance unbiased estimation (MDUE), and the simulation results demonstrate that more accurate distance and localisation can be obtained by MDUE than the state‐of‐the‐art methods.

Range-free, distributed localization method is an important and challenging issue in wireless sensor networks. Typical hop-count-based method always assumes that the average hop size for different hop count is the same. In this paper, we analyze the hop progress for different hop counts, and modify the assumption as the hop progress is the same for different hop counts except the 1-hop. We propose two improved DV-Hop estimation distance methods based on the new assumption. simulation result shows that the proposed methods obtain more accurate estimation distance and get better performance of localization.

In the existing methods for solving matrix completion, such as singular value thresholding (SVT), soft-impute and fixed point continuation (FPCA) algorithms, it is typically required to repeatedly implement singular value decompositions (SVD) of matrices.When the size of the matrix in question is large, the computational complexity of finding a solution is costly. To reduce this expensive computational complexity, we apply Kronecker products to handle the matrix completion problem. In particular, we propose using Kronecker factorization, which approximates a matrix by the Kronecker product of several matrices of smaller sizes. Weintroduce Kronecker factorization into the soft-impute framework and devise an effective matrix completion algorithm.Especially when the factorized matrices have about the samesizes, the computational complexity of our algorithm is improved substantially.

Until recently, most oil pumping units (OPUs) have been using manual control in the oilfield. In this paper, network based automatic control is proposed for OPU management. This proposed network can sequentially realize automatic data sensing, automatic malfunction detection, remote data transmission, intelligent data organization and management, automatic malfunction warning, automatic stroke adjustment and state report via GSM short message service (SMS). Specifically, the proposed network based automatic control system contains four parts: 1) the first level sensors (FLS) for data sensing; 2) the developed intelligent sensors (ISs), which are the second level sensors, for data storage and elementary processing; 3) a communication protocol for data transmission; and 4) a software-based network center for data processing, data management, malfunction detection, remote stroke adjustment, GSM SMS and so on. In this paper, we focus on describing the IS design and reporting the elementary experiment results of the proposed system at the oil well HUACHI-38-122.