Self-alignment of full skewed RSINS: observability analysis and full-observable Kalman filter

Traditional orthogonal strapdown inertial navigation sys-tem （SINS） cannot achieve satisfactory self-alignment accuracy in the stationary base： taking more than 5 minutes and al the iner-tial sensors biases cannot get ful observability except the up-axis accelerometer. However, the ful skewed redundant SINS （RSINS） can not only enhance the reliability of the system, but also improve the accuracy of the system, such as the initial alignment. Firstly, the observability of the system state includes attitude errors and al the inertial sensors biases are analyzed with the global perspective method： any three gyroscopes and three accelerometers can be assembled into an independent subordinate SINS （sub-SINS）;the system state can be uniquely confirmed by the coupling connec-tions of al the sub-SINSs;the attitude errors and random constant biases of al the inertial sensors are observable. However, the ran-dom noises of the inertial sensors are not taken into account in the above analyzing process. Secondly, the ful-observable Kalman filter which can be applied to the actual RSINS containing random noises is established; the system state includes the position, ve-locity, attitude errors of al the sub-SINSs and the random constant biases of the redundant inertial sensors. At last, the initial self-alignment process of a typical four-redundancy ful skewed RSINS is simulated： the horizontal attitudes （pitch, rol ） errors and yaw error can be exactly evaluated within 80 s and 100 s respectively, while the random constant biases of gyroscopes and accelero-meters can be precisely evaluated within 120 s. For the ful skewed RSINS, the self-alignment accuracy is greatly improved, mean-while the self-alignment time is widely shortened.

Synthesis and biological evaluation of a lipopeptide-based methamphetamine vaccine

We describe an application of carrier protein-free strategy in constructing a fully synthetic methamphetamine(METH)vaccine that contains three components:Toll-like receptor 2 ligand,Th2 epitope,and METH hapten.The immunological evaluation in mice revealed high titers of METH-specific antibodies induced by the construct and the activation of humoral immunity that would be beneficial for neutralization and clearance of the METH molecule.Behavio ral experiments indicated that the synthetic vaccine attenuated the acquisition of METH-induced conditioned place preference and inhibited the initiation and expression of METH-induced locomotor sensitization.These results demonstrate that the lipopeptide-based vaccine has invo ked an immune response and showed the potential of preventing the rewarding and psychoactive effects of METH.

A novel dense descriptor based on structure tensor voting for multi-modal image matching

Automatic and robust matching of multi-modal images can be challenging owing to the nonlinear intensity differences caused by radiometric variations among these images.To address this problem,a novel dense feature descriptor and improved similarity measure are proposed for enhancing the matching performance.The proposed descriptor is built on a voting scheme of structure tensor that can effectively capture the geometric structural properties of images.It is not only illumination and contrast invariant but also robust against the degradation caused by significant noise.Further,the similarity measure is improved to adapt to the reversal of orientation caused by the intensity inversion between multi-modal images.The proposed dense feature descriptor and improved similarity measure enable the development of a robust and practical templatematching algorithm for multi-modal images.We verify the proposed algorithm with a broad range of multi-modal images including optical,infrared,Synthetic Aperture Radar(SAR),digital surface model,and map data.The experimental results confirm its superiority to the state-of-the-art methods.

Real-time mosaicking for infrared videos from an oblique sweeping camera

Image mosaicking is widely used in Geographic Information Systems(GISs)for largescale ground surface analysis.However,most existing mosaicking methods can only be used in offline processing due to the enormous amounts of computation.In this paper,we propose a novel and practical algorithm for real-time infrared video mosaicking.To achieve this,a novel fast template matching algorithm based on Sum of Cosine Differences(SCD)is proposed to coarsely match the sequential images.The high speed of the proposed template matching algorithm is obtained by computing correlation with Fast Fourier Transform(FFT).We also propose a novel fast Least Squares Matching(LSM)algorithm for inter-frame fine registration,which can significantly reduce the computation without degrading the matching accuracy.In addition,the proposed fast LSM can effectively adapt for noise degradation and geometric distortion.Based on the proposed fast template matching algorithm and fine registration algorithm,we develop a practical real-time mosaicking approach which can produce seamless mosaic image highly efficiently.Experiments on synthetic and real-world datasets demonstrate that the proposed algorithm is not just computationally efficient but also robust against various noise distortions.