Fast acquisition of L2C CL codes based on combination of hyper codes and averaging correlation

This paper provides a direct and fast acquisition algorithm of civilian long length（CL） codes in the L2 civil（L2C） signal. The proposed algorithm simultaneously reduces the number of fast Fourier transformation（FFT） correlation through hyper code technique and the amount of points in every FFT correlation by using an averaging correlation method. To validate the proposed acquisition performance, the paper applies this algorithm to the real L2C signal collected by the global positioning system（GPS） L2C intermediate frequency（IF） signal sampler—SIS100L2C. The acquisition results show that the proposed modified algorithm can acquire the code phase accurately with less calculation and its acquisition performance is better than the single hyper code method.

A Practical Approach for Missing Wireless Sensor Networks Data Recovery

In wireless sensor networks(WSNs),the performance of related applications is highly dependent on the quality of data collected.Unfortunately,missing data is almost inevitable in the process of data acquisition and transmission.Existing methods often rely on prior information such as low-rank characteristics or spatiotemporal correlation when recovering missing WSNs data.However,in realistic application scenarios,it is very difficult to obtain these prior information from incomplete data sets.Therefore,we aim to recover the missing WSNs data effectively while getting rid of the perplexity of prior information.By designing the corresponding measurement matrix that can capture the position of missing data and sparse representation matrix,a compressive sensing(CS)based missing data recovery model is established.Then,we design a comparison standard to select the best sparse representation basis and introduce average cross-correlation to examine the rationality of the established model.Furthermore,an improved fast matching pursuit algorithm is proposed to solve the model.Simulation results show that the proposed method can effectively recover the missing WSNs data.

Quality of correlation in quantum information

A strong and stable correlation in quantum information is of high quality for quantum information processing.We define two quantities,selective average correlation and ripple coefficient,to evaluate the quality of correlation in quantum information in a time interval.As a new communication channel,Heisenberg spin chains are widely investigated.We select a two-qubit Heisenberg XXZs pin chain with Dzyaloshinskii-Moriya interaction in an inhomogeneous magnetic field as an example,and use the two quantities to evaluate the qualities of the correlation in quantum information with different measures.The result shows that,if the time evolutions are similar,there needs only evaluating one of them to know when the correlation has high quality for quantum information processing.