Search

Additional file 6. Table S1. GO and KEGG pathways of interest in Jurkat cells. Table S2. GO and KEGG pathways of interest in NK-92 cells. Table S3. GO and KEGG pathways of interest in THP-1 cells.

The GPS/SINS/BARO integrated system can provide high dynamic and accuracy navigation for UAV flight. In this paper, a GPS/SINS/BARO integrated system for UAV is presented. The system consists of a GPS receiver, an MEMS-based inertial measurement unit (IMU), a Barometer and a navigation processing unit. The GPS receiver provides three-dimensional velocity and position, the IMU provides three-dimensional linear accelerations and angular rates, the barometer provides the altitude of the vehicle indirectly, and the navigation processing unit performs real-time navigation computation with these measurements. The performance of the proposed GPS/SINS/BARO integrated system has been tested with a quad-rotor named ‘X500-D’. The results show that the integrated system can provide high accuracy navigation for the guidance and control of the UAV.

Global positioning system has been extremely matured in the navigation and positioning field. However, there are still some limits in some aspects and some special application areas, the performance should be improved by other techniques. The pseudolite as a GPS-like ground transmitter provides a new research direction to get a high positioning accuracy and reliability. In this paper, we have described the core technologies of design and simulation realization about the C/A codes of the pseudolite system construction. It is shown that the implementation in Matlab contributes to analyzing of the signal structure and designing the C/A codes and both the simulation results and VHDL realization results are consistent.

This paper presents a Multi-Classification Schema (MCS) which combines Weighted SVMs (WSVM) and Spectrum-based kNN (SkNN). Basic SVM is equipped with belief coefficients to reveal its capacity in identifying classes. And basic SVM is built in individual feature space to bring adaptation to diverse training data context. Coupled with a weighted voting strategy and a local informative metric, SkNN is used to address the case rejected by all basic classifiers. The local metric is derived from most discriminant directions carried by data spectrum information. Two strategies of MCS benefit computational cost: training dataset reduction, and pre-specification of SkNN working set. Experiments on real datasets show MCS improves classification accuracy with moderate cost compared with the state of the art.

A novel two-phase support vector clustering (TPSVC) algorithm is proposed in this paper, which is implemented in multi-relational data mining (MRDM). Based on the designed kernel which is incorporated with MRDM environment, TPSVC provides an appreciate description of cluster contours using support vectors at the first step and then a support vector machine (SVM) classification procedure is employed to further extract the information of cluster central zones. The algorithm does the cluster assignment according to desired definition of affinity without suffering the expensive operations of adjacent matrix computation used in traditional support vector clustering (SVC). Experimental results indicate that the designed kernel can capture the features of relational schema and TPSVC is of fine clustering performance

The effects of defocus and astigmatism aberration of the turbulent atmosphere on the intensity distribution of a focused vortex carrying Gaussian beam are investigated. The integral expression of the intensity distribution on the focal plane in slant path is derived from the extended Fresnel-Kirchhoff diffraction integral and the quadratic approximation of phase structure function. And the influences of the three factors, turbulent strength, the propagation distance and the topological charge of the initial beam, are investigated by numerical calculation. Our results show that in weak turbulent region, the effects of defocus and astigmatism aberration in turbulent atmosphere on the intensity distribution of a vortex carrying Gaussian beam are very small and can be ignored. However, in the middle turbulent region, the effects of defocus and astigmatism are pronounced. As the propagation distance and the turbulent strength increase, both aberrations cause the decrease of the intensity peak value, the spreading of the beam, and the stretching of the dark core. For the beams with single topological charge, under the influence of astigmatism, the peak value of the intensity decreases more seriously, the intensity of the secondary maximum stripe increases much faster, and the focal spot expands more than the effect of defocus aberration. Compared with single topological charge, the effects of defocus and astigmatism of vortex beam with double topological charge are more obvious. The focal spots are expanded, and the secondary maximum stripes are more obvious. However, under the effect of astigmatism, because of the reduction of the coherence and the deflection effect of the beam, the dark core on the focal plane tends to break into two dark regions separated by a bright region.

A novel method of dynamic object modeling for visual tracking is presented. The Haar transformation is first applied on the incoming image of the video to get features, which are over-complete description of the image. Then, the Fisher criteria are employed for ranking features based on their contributions to the discrimination between the tracked objects and the background. After that, the objects are modeled by the subset of top-ranked features. During tracking, a Kalman filter is used to predict the upcoming destinations of the tracked objects and the features are re-ranked by the discrimination between the objects and predicted locations. Thereafter, objects models will be updated and only discriminative features are kept in it. This proposed strategy aims to maximally maintain the basic discrimination and reduce computational cost simultaneously. To evaluate the performance of the proposed method, several experiments have been conducted on long video sequences. The experimental results show that the proposed method can handle various uncertain factors under the real world conditions and successfully track the