The problem of blind adaptive equalization of underwater single-input multiple-output (SIMO) acoustic channels was analyzed by using the linear prediction method.Minimum mean square error (MMSE) blind equalizers with ...The problem of blind adaptive equalization of underwater single-input multiple-output (SIMO) acoustic channels was analyzed by using the linear prediction method.Minimum mean square error (MMSE) blind equalizers with arbitrary delay were described on a basis of channel identification.Two methods for calculating linear MMSE equalizers were proposed.One was based on full channel identification and realized using RLS adaptive algorithms,and the other was based on the zero-delay MMSE equalizer and realized using LMS and RLS adaptive algorithms,respectively.Performance of the three proposed algorithms and comparison with two existing zero-forcing (ZF) equalization algorithms were investigated by simulations utilizing two underwater acoustic channels.The results show that the proposed algorithms are robust enough to channel order mismatch.They have almost the same performance as the corresponding ZF algorithms under a high signal-to-noise (SNR) ratio and better performance under a low SNR.展开更多
The rapid pace of development of GIS （geographical information system） has assisted in identification of conservation priority sites by delineating species distribution using models on habitat suitability. Gaur, Bos...The rapid pace of development of GIS （geographical information system） has assisted in identification of conservation priority sites by delineating species distribution using models on habitat suitability. Gaur, Bos gaurus, is categorized as ＂Vulnerable＂ in the IUCN Red List of Threatened Species, 2009. The study has used ENFA （ecological niche factor analysis） to understand the distribution status of Gaur in TATR （Tadoba-Andhari Tiger Reserve）, Central India. TATR was sampled using stratified random sampling strategy. A total of 21 continuous variables were used, categorised under 4 environmental descriptors categories viz. habitat, anthropogenic, topographic and hydrological variables. All the variables were tested for the correlation and one of the variable among strongly correlated （r 〉 0.7） variables was discarded to avoid redundancy. A total of 14 variables were retained. The model resulted in marginality of 0.56 and specialization of 2.608. Presence of Gaur showed the positive association with canopy density classes （〈 30% ＆ 40-60%） and open forest. However, it was negatively associated with elevation, non-forest, riparian forest, scrub and teak forest. The study has delineated the areas where appropriate habitat conditions exist to sustain Gaur populations vital for planning strategies for conservation of this megaherbivore species in tropical forests.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No.60372086the Foundation for the Author of National Excellent Doctoral Dissertation of China under Grant No.200753
文摘The problem of blind adaptive equalization of underwater single-input multiple-output (SIMO) acoustic channels was analyzed by using the linear prediction method.Minimum mean square error (MMSE) blind equalizers with arbitrary delay were described on a basis of channel identification.Two methods for calculating linear MMSE equalizers were proposed.One was based on full channel identification and realized using RLS adaptive algorithms,and the other was based on the zero-delay MMSE equalizer and realized using LMS and RLS adaptive algorithms,respectively.Performance of the three proposed algorithms and comparison with two existing zero-forcing (ZF) equalization algorithms were investigated by simulations utilizing two underwater acoustic channels.The results show that the proposed algorithms are robust enough to channel order mismatch.They have almost the same performance as the corresponding ZF algorithms under a high signal-to-noise (SNR) ratio and better performance under a low SNR.
文摘The rapid pace of development of GIS （geographical information system） has assisted in identification of conservation priority sites by delineating species distribution using models on habitat suitability. Gaur, Bos gaurus, is categorized as ＂Vulnerable＂ in the IUCN Red List of Threatened Species, 2009. The study has used ENFA （ecological niche factor analysis） to understand the distribution status of Gaur in TATR （Tadoba-Andhari Tiger Reserve）, Central India. TATR was sampled using stratified random sampling strategy. A total of 21 continuous variables were used, categorised under 4 environmental descriptors categories viz. habitat, anthropogenic, topographic and hydrological variables. All the variables were tested for the correlation and one of the variable among strongly correlated （r 〉 0.7） variables was discarded to avoid redundancy. A total of 14 variables were retained. The model resulted in marginality of 0.56 and specialization of 2.608. Presence of Gaur showed the positive association with canopy density classes （〈 30% ＆ 40-60%） and open forest. However, it was negatively associated with elevation, non-forest, riparian forest, scrub and teak forest. The study has delineated the areas where appropriate habitat conditions exist to sustain Gaur populations vital for planning strategies for conservation of this megaherbivore species in tropical forests.
