Point-of-care testing(POCT),as a portable and user-friendly technology,can obtain accurate test results immediately at the sampling point.Nowadays,microfluidic paper-based analysis devices(μPads)have attracted the ey...Point-of-care testing(POCT),as a portable and user-friendly technology,can obtain accurate test results immediately at the sampling point.Nowadays,microfluidic paper-based analysis devices(μPads)have attracted the eye of the public and accelerated the development of POCT.A variety of detection methods are combined withμPads to realize precise,rapid and sensitive POCT.This article mainly introduced the development of electrochemistry and optical detection methods onμPads for POCT and their applications on disease analysis,environmental monitoring and food control in the past 5 years.Finally,the challenges and future development prospects ofμPads for POCT were discussed.展开更多
The feature extraction algorithm plays an important role in face recognition. However, the extracted features also have overlapping discriminant information. A property of the statistical uncorrelated criterion is tha...The feature extraction algorithm plays an important role in face recognition. However, the extracted features also have overlapping discriminant information. A property of the statistical uncorrelated criterion is that it eliminates the redundancy among the extracted discriminant features, while many algorithms generally ignore this property. In this paper, we introduce a novel feature extraction method called local uncorrelated local discriminant embedding(LULDE). The proposed approach can be seen as an extension of a local discriminant embedding(LDE)framework in three ways. First, a new local statistical uncorrelated criterion is proposed, which effectively captures the local information of interclass and intraclass. Second, we reconstruct the affinity matrices of an intrinsic graph and a penalty graph, which are mentioned in LDE to enhance the discriminant property. Finally, it overcomes the small-sample-size problem without using principal component analysis to preprocess the original data, which avoids losing some discriminant information. Experimental results on Yale, ORL, Extended Yale B, and FERET databases demonstrate that LULDE outperforms LDE and other representative uncorrelated feature extraction methods.展开更多
To overcome the high computational complexity in real-time classifier design, we propose a fast classification scheme. A new measure called 'reconstruction proportion' is exploited to reflect the discriminant ...To overcome the high computational complexity in real-time classifier design, we propose a fast classification scheme. A new measure called 'reconstruction proportion' is exploited to reflect the discriminant information. A novel space called the 'reconstruction space' is constructed according to the reconstruction proportions. A point in the reconstruction space denotes the case of a sample reconstructed using training samples. This is used to search for an optimal mapping from the conventional sample space to the reconstruction space. When the projection from the sample space to the reconstruction space is obtained, a new sample after mapping to the new discriminant space would be classified quickly according to the reconstruction proportions in the reconstruction space. This projection technique results in a diversion of time-consuming calculations from the classification stage to the training stage. Though training time is prolonged, it is advantageous in that classification problems such as identification can be solved in real time. Experimental results on the ORL, Yale, YaleB, and CMU PIE face databases showed that the proposed fast classification scheme greatly outperforms conventional classifiers in classification accuracy and efficiency.展开更多
基金Shaanxi Province Science Foundation(2021JM-193)for funding this workthe Fundamental Research Funds for the Central Universities(GK201902009,GK201701002)Program for Innovative Research Team in Shaanxi Province(2014KCT-28)for supporting this work
文摘Point-of-care testing(POCT),as a portable and user-friendly technology,can obtain accurate test results immediately at the sampling point.Nowadays,microfluidic paper-based analysis devices(μPads)have attracted the eye of the public and accelerated the development of POCT.A variety of detection methods are combined withμPads to realize precise,rapid and sensitive POCT.This article mainly introduced the development of electrochemistry and optical detection methods onμPads for POCT and their applications on disease analysis,environmental monitoring and food control in the past 5 years.Finally,the challenges and future development prospects ofμPads for POCT were discussed.
基金Project supported by the National Natural Science Foundation of China(No.61402310)the Natural Science Foundation of Jiangsu Province,China(No.BK20141195)the State Key Laboratory for Novel Software Technology Foundation of Nanjing University,China(No.KFKT2014B11)
文摘The feature extraction algorithm plays an important role in face recognition. However, the extracted features also have overlapping discriminant information. A property of the statistical uncorrelated criterion is that it eliminates the redundancy among the extracted discriminant features, while many algorithms generally ignore this property. In this paper, we introduce a novel feature extraction method called local uncorrelated local discriminant embedding(LULDE). The proposed approach can be seen as an extension of a local discriminant embedding(LDE)framework in three ways. First, a new local statistical uncorrelated criterion is proposed, which effectively captures the local information of interclass and intraclass. Second, we reconstruct the affinity matrices of an intrinsic graph and a penalty graph, which are mentioned in LDE to enhance the discriminant property. Finally, it overcomes the small-sample-size problem without using principal component analysis to preprocess the original data, which avoids losing some discriminant information. Experimental results on Yale, ORL, Extended Yale B, and FERET databases demonstrate that LULDE outperforms LDE and other representative uncorrelated feature extraction methods.
文摘To overcome the high computational complexity in real-time classifier design, we propose a fast classification scheme. A new measure called 'reconstruction proportion' is exploited to reflect the discriminant information. A novel space called the 'reconstruction space' is constructed according to the reconstruction proportions. A point in the reconstruction space denotes the case of a sample reconstructed using training samples. This is used to search for an optimal mapping from the conventional sample space to the reconstruction space. When the projection from the sample space to the reconstruction space is obtained, a new sample after mapping to the new discriminant space would be classified quickly according to the reconstruction proportions in the reconstruction space. This projection technique results in a diversion of time-consuming calculations from the classification stage to the training stage. Though training time is prolonged, it is advantageous in that classification problems such as identification can be solved in real time. Experimental results on the ORL, Yale, YaleB, and CMU PIE face databases showed that the proposed fast classification scheme greatly outperforms conventional classifiers in classification accuracy and efficiency.