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The purpose of this paper is to retrieve and study the highly cited papers as well as the correlation between the citation frequency and the download frequency of the 20 traditional Chinese medicine journals in China, in order to provide the guidance for improving the influence and academic quality of these journals. Bibliometric analyses were conducted on 1103 papers of 20 traditional Chinese medicine journals from 2011 to 2020 by retrieving for the China Academic Journal Network Publishing Database (CAJD) in China National Knowledge Infrastructure (CNKI). SPSS 17.0 software was used to analyze the correlation between the citation frequency and the download frequency via conducting regression fitting and establishing the mathematical models. The results showed that the total citations of the 1103 papers were 93051 times and the average citations were 84.36 times per paper. The total downloads of the 1103 papers were 2058442 times, and the average downloads were 1866.22 times per paper. China Journal of Chinese Materia Medica ranked first according to the number of papers, total citations and total downloads. The citations of Journal of Chinese Medicinal Materials ranked first based on the number of citations per paper. One of Li's paper had been cited the most (983 times). There were 629 (57.03%) papers whose first author was from universities. The scopes of the first authors were distributed in 29 regions and 2 special administrative regions (Macao, Hong Kong) in China. The authors from Beijing published 283 (25.66%) papers, ranking number one. The number of papers supported by funds was 882 (79.96%). The research results of correlation showed that the citation frequency and the download frequency of the highly cited papers had a highly positive correlation from both journal and paper level for whether the sample data of journals was normally distributed or nonnormally distributed. The correlation coefficients of the 20 journals at journal level and that at paper level were 0.9765 and 0.6677, respectively. The correlation was better at journal level than that at paper level, while the optimal regression fitting was all cubic polynomial. Among the 1103 papers, there were 684 (62.01%) research papers and 419 (37.99%) review papers. The main citation period of the top 15 papers was from the 2nd year to the 6th year after publication, accounting for 78.39%. Papers on clinical therapeutics research, papers on the pharmacological effects and its mechanism of traditional Chinese medicine, and papers on traditional Chinese medicine and natural medicine were the main source of the highly cited papers of the traditional Chinese medicine journals. Editors of the journals should focus on the above-mentioned research areas to select manuscripts for exploiting the excellent sources extensively, while paying attention to review papers, focusing on national major or key projects, paying attention to network spreading, stabilizing authors with quality services, in order to improve the influence and the academic quality of journals. [ABSTRACT FROM AUTHOR]

In the image recognition field, there are several techniques that allow identifying patterns in digital images, correlation being one of them. In a correlation, you have to obtain an output plane that is as clean as possible. To measure the sharpness of the correlation peak and the cleanliness of the output plane, a performance metric called Peak to Correlation Energy (PCE) is used. In this paper, the fractional correlation is applied to recognize real phytoplankton images. This fractional correlation guarantees a higher PCE compared to the conventional correlation. The results of PCE are two-orders of magnitude higher than those obtained with the conventional correlation and manage to identify 91.23% of the images, while the conventional correlation only manages to identify 87.42% of them. This methodology was tested using images in salt and pepper or Gaussian noise, and the fractional correlation output plane always is cleaner and generates a better-defined correlation peak when compared with the classical correlation. [ABSTRACT FROM AUTHOR]

Correspondence is one of the major problems that must be solved in stereo vision. Correlation has been commonly used in the past for this problem. However, most classical linear correlation methods fail near depth discontinuities and in the presence of occlusions. Many robust methods have been proposed that claim to effectively deal with some or all of these issues. Many of these robust methods are transformation-based, however, other robust methods are non-transformation based. This paper gives five requirements that should be met by a transformation-based robust correlation method. We compare some of the robust correspondence methods and demonstrate their utility on different data sets. Based on these results, we propose a solution to the correspondence problem which represents a compromise between the speed of classical correlation and the improved results obtained from a more robust correspondence method. Also, we propose a median filtering technique that removes noise from the disparity maps while preserving certain image features usually removed by ordinary median filtering. [ABSTRACT FROM AUTHOR]