An idea of estimating the direct sequence spread spectrum（DSSS） signal pseudo-noise（PN） sequence is presented. Without the apriority knowledge about the DSSS signal in the non-cooperation condition, we propose a s...An idea of estimating the direct sequence spread spectrum（DSSS） signal pseudo-noise（PN） sequence is presented. Without the apriority knowledge about the DSSS signal in the non-cooperation condition, we propose a self-organizing feature map（SOFM） neural network algorithm to detect and identify the PN sequence. A non-supervised learning algorithm is proposed according the Kohonen rule in SOFM. The blind algorithm can also estimate the PN sequence in a low signal-to-noise（SNR） and computer simulation demonstrates that the algorithm is effective. Compared with the traditional correlation algorithm based on slip-correlation, the proposed algorithm＇s bit error rate（BER） and complexity are lower.展开更多
Selection of effective agronomic and industrial parameters of oat cultivars is a decisive step in oat breeding programs fordevelopment of new oat and elite cultivars. In this study, a new approach was utilized to dist...Selection of effective agronomic and industrial parameters of oat cultivars is a decisive step in oat breeding programs fordevelopment of new oat and elite cultivars. In this study, a new approach was utilized to distinguish the most informative agronomicand industrial parameters that are most affected with fungicide application in oat cultivars. Four subsequent field experiments from2007 to 2010 were conducted in completely randomized block design （CRBD） with split plots. Total nine oat cultivars with orwithout fungicide application were evaluated for plant height, sieve yield, grain yield, lodging index, weight of hectoliter andde-hulling index. Soft independent modeling of class analogy （SIMCA） was conducted as one-class and multi-classes models toidentify important variables that can be used to discriminate samples. Results showed that SIMCA was effective, and lodging index,de-hulling index, sieve yield, plant height and grain yield were most affected oat parameters. Therefore, SIMCA algorithm can beused to easily discriminate some agronomic and quality parameters of oats.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.61271168
文摘An idea of estimating the direct sequence spread spectrum（DSSS） signal pseudo-noise（PN） sequence is presented. Without the apriority knowledge about the DSSS signal in the non-cooperation condition, we propose a self-organizing feature map（SOFM） neural network algorithm to detect and identify the PN sequence. A non-supervised learning algorithm is proposed according the Kohonen rule in SOFM. The blind algorithm can also estimate the PN sequence in a low signal-to-noise（SNR） and computer simulation demonstrates that the algorithm is effective. Compared with the traditional correlation algorithm based on slip-correlation, the proposed algorithm＇s bit error rate（BER） and complexity are lower.
文摘Selection of effective agronomic and industrial parameters of oat cultivars is a decisive step in oat breeding programs fordevelopment of new oat and elite cultivars. In this study, a new approach was utilized to distinguish the most informative agronomicand industrial parameters that are most affected with fungicide application in oat cultivars. Four subsequent field experiments from2007 to 2010 were conducted in completely randomized block design （CRBD） with split plots. Total nine oat cultivars with orwithout fungicide application were evaluated for plant height, sieve yield, grain yield, lodging index, weight of hectoliter andde-hulling index. Soft independent modeling of class analogy （SIMCA） was conducted as one-class and multi-classes models toidentify important variables that can be used to discriminate samples. Results showed that SIMCA was effective, and lodging index,de-hulling index, sieve yield, plant height and grain yield were most affected oat parameters. Therefore, SIMCA algorithm can beused to easily discriminate some agronomic and quality parameters of oats.
