节点定位是无线传感器在安全监测应用中的重要环节。利用接收信号强度指示(Received Signal Strength Indication)进行定位存在较大的误差,定位精度不高。为提高定位精度,提出了一种基于粒子群算法的定位算法。该算法无需额外增加硬件,...节点定位是无线传感器在安全监测应用中的重要环节。利用接收信号强度指示(Received Signal Strength Indication)进行定位存在较大的误差,定位精度不高。为提高定位精度,提出了一种基于粒子群算法的定位算法。该算法无需额外增加硬件,费用低,易实现。仿真实验结果表明,该算法的定位误差比极大使然法减小了0.4798%。展开更多
Collaborative representation-based classification(CRC) is a distance based method, and it obtains the original contributions from all samples to solve the sparse representation coefficient. We find out that it helps t...Collaborative representation-based classification(CRC) is a distance based method, and it obtains the original contributions from all samples to solve the sparse representation coefficient. We find out that it helps to enhance the discrimination in classification by integrating other distance based features and/or adding signal preprocessing to the original samples. In this paper, we propose an improved version of the CRC method which uses the Gabor wavelet transformation to preprocess the samples and also adapts the nearest neighbor(NN)features, and hence we call it GNN-CRC. Firstly, Gabor wavelet transformation is applied to minimize the effects from the background in face images and build Gabor features into the input data. Secondly, the distances solved by NN and CRC are fused together to obtain a more discriminative classification. Extensive experiments are conducted to evaluate the proposed method for face recognition with different instantiations. The experimental results illustrate that our method outperforms the naive CRC as well as some other state-of-the-art algorithms.展开更多
文摘节点定位是无线传感器在安全监测应用中的重要环节。利用接收信号强度指示(Received Signal Strength Indication)进行定位存在较大的误差,定位精度不高。为提高定位精度,提出了一种基于粒子群算法的定位算法。该算法无需额外增加硬件,费用低,易实现。仿真实验结果表明,该算法的定位误差比极大使然法减小了0.4798%。
基金the National Natural Science Foundation of China(No.61502208)the Natural Science Foundation of Jiangsu Province of China(No.BK20150522)+1 种基金the Scientific and Technical Program of City of Huizhou(Nos.2016X0422037 and 2017C0405021)the Natural Science Foundation of Huizhou University(Nos.hzux1201606 and hzu201701)
文摘Collaborative representation-based classification(CRC) is a distance based method, and it obtains the original contributions from all samples to solve the sparse representation coefficient. We find out that it helps to enhance the discrimination in classification by integrating other distance based features and/or adding signal preprocessing to the original samples. In this paper, we propose an improved version of the CRC method which uses the Gabor wavelet transformation to preprocess the samples and also adapts the nearest neighbor(NN)features, and hence we call it GNN-CRC. Firstly, Gabor wavelet transformation is applied to minimize the effects from the background in face images and build Gabor features into the input data. Secondly, the distances solved by NN and CRC are fused together to obtain a more discriminative classification. Extensive experiments are conducted to evaluate the proposed method for face recognition with different instantiations. The experimental results illustrate that our method outperforms the naive CRC as well as some other state-of-the-art algorithms.
