Windowing applied to a given signal is a technique commonly used in signal processing in order to reduce spectral leakage in a signal with many data. Several windows are well known: hamming, hanning, beartlett, etc. T...Windowing applied to a given signal is a technique commonly used in signal processing in order to reduce spectral leakage in a signal with many data. Several windows are well known: hamming, hanning, beartlett, etc. The selection of a window is based on its spectral characteristics. Several papers that analyze the amplitude and width of the lobes that appear in the spectrum of various types of window have been published. This is very important because the lobes can hide information on the frequency components of the original signal, in particular when frequency components are very close to each other. In this paper it is shown that the size of the window can also have an impact in the spectral information. Until today, the size of a window has been chosen in a subjective way. As far as we know, there are no publications that show how to determine the minimum size of a window. In this work the frequency interval between two consecutive values of a Fourier Transform is considered. This interval determines if the sampling frequency and the number of samples are adequate to differentiate between two frequency components that are very close. From the analysis of this interval, a mathematical inequality is obtained, that determines in an objective way, the minimum size of a window. Two examples of the use of this criterion are presented. The results show that the hiding of information of a signal is due mainly to the wrong choice of the size of the window, but also to the relative amplitude of the frequency components and the type of window. Windowing is the main tool used in spectral analysis with nonparametric periodograms. Until now, optimization was based on the type of window. In this paper we show that the right choice of the size of a window assures on one hand that the number of data is enough to resolve the frequencies involved in the signal, and on the other, reduces the number of required data, and thus the processing time, when very long files are being analyzed.展开更多
Purpose： The main goal of this study is to provide reliable comparison of performance in higher education. In this respect, we use scientometric measures associated with faculties of medicine in the six health studie...Purpose： The main goal of this study is to provide reliable comparison of performance in higher education. In this respect, we use scientometric measures associated with faculties of medicine in the six health studies universities in Romania.Design/methodology/approach： The method to estimate the minimum necessary size, proposed in in Shen et al.（2017）, is applied in this article. We collected data from the Scopus data-base for the academics of the departments of medicine within the six health studies universities in Romania during the 2009 to 2014. And two kind of statistic treatments based on that method are implemented, pair-wise comparison and one-to-the-rest comparison. All the results of these comparisons are shown.Findings： According to the results： We deem that Cluj and Tg. Mure？ have the superior and inferior performance respectively, since their reasonably small value of the minimum representative size, in either of the kinds of comparison, whichever indexes of citations, h-index, or g-index is used. we can not reliably distinguish differences among the rest of the faculties, since the quite large value of their minimum representative size.Research limitations： There is only six faculties of medicine in health studies universities in Romania are analyzed.Practical implications： Our methods of comparison play an important role in ranking data sets associated with different collective units, such as faculties, universities, institutions, based on some aggregate scores like mean and totality. Originality/value： We applied the minimum representative size to a new emprical context- that of the departments of medicine in the health studies universities in Romania.展开更多
针对Informed-RRT(rapidly-exploring random tree)^(*)算法收敛速度慢、优化效率低和生成路径无法满足实际需求等问题,开展了基于MI-RRT^(*)(Modified Informed-RRT^(*))算法的路径规划研究,通过引入贪心采样和自适应步长的方法提高算...针对Informed-RRT(rapidly-exploring random tree)^(*)算法收敛速度慢、优化效率低和生成路径无法满足实际需求等问题,开展了基于MI-RRT^(*)(Modified Informed-RRT^(*))算法的路径规划研究,通过引入贪心采样和自适应步长的方法提高算法的收敛率,减少路径生成时间、降低内存占用;利用最小化Snap曲线优化的方法使路径平滑的同时动力也变化平缓,达到节省能量的效果,并提供实际可执行的路径。最后通过多组不同复杂度的实验环境表明,较Informed-RRT^(*)算法MI-RRT^(*)算法稳定性更高、所得规划路径平滑可执行,并且能够减少20%的迭代次数和25%的搜索时间,得出在开阔以及密集环境中MI-RRT^(*)算法较Informed-RRT^(*)和RRT^(*)算法有明显的优势。展开更多
文摘Windowing applied to a given signal is a technique commonly used in signal processing in order to reduce spectral leakage in a signal with many data. Several windows are well known: hamming, hanning, beartlett, etc. The selection of a window is based on its spectral characteristics. Several papers that analyze the amplitude and width of the lobes that appear in the spectrum of various types of window have been published. This is very important because the lobes can hide information on the frequency components of the original signal, in particular when frequency components are very close to each other. In this paper it is shown that the size of the window can also have an impact in the spectral information. Until today, the size of a window has been chosen in a subjective way. As far as we know, there are no publications that show how to determine the minimum size of a window. In this work the frequency interval between two consecutive values of a Fourier Transform is considered. This interval determines if the sampling frequency and the number of samples are adequate to differentiate between two frequency components that are very close. From the analysis of this interval, a mathematical inequality is obtained, that determines in an objective way, the minimum size of a window. Two examples of the use of this criterion are presented. The results show that the hiding of information of a signal is due mainly to the wrong choice of the size of the window, but also to the relative amplitude of the frequency components and the type of window. Windowing is the main tool used in spectral analysis with nonparametric periodograms. Until now, optimization was based on the type of window. In this paper we show that the right choice of the size of a window assures on one hand that the number of data is enough to resolve the frequencies involved in the signal, and on the other, reduces the number of required data, and thus the processing time, when very long files are being analyzed.
文摘Purpose： The main goal of this study is to provide reliable comparison of performance in higher education. In this respect, we use scientometric measures associated with faculties of medicine in the six health studies universities in Romania.Design/methodology/approach： The method to estimate the minimum necessary size, proposed in in Shen et al.（2017）, is applied in this article. We collected data from the Scopus data-base for the academics of the departments of medicine within the six health studies universities in Romania during the 2009 to 2014. And two kind of statistic treatments based on that method are implemented, pair-wise comparison and one-to-the-rest comparison. All the results of these comparisons are shown.Findings： According to the results： We deem that Cluj and Tg. Mure？ have the superior and inferior performance respectively, since their reasonably small value of the minimum representative size, in either of the kinds of comparison, whichever indexes of citations, h-index, or g-index is used. we can not reliably distinguish differences among the rest of the faculties, since the quite large value of their minimum representative size.Research limitations： There is only six faculties of medicine in health studies universities in Romania are analyzed.Practical implications： Our methods of comparison play an important role in ranking data sets associated with different collective units, such as faculties, universities, institutions, based on some aggregate scores like mean and totality. Originality/value： We applied the minimum representative size to a new emprical context- that of the departments of medicine in the health studies universities in Romania.
文摘针对Informed-RRT(rapidly-exploring random tree)^(*)算法收敛速度慢、优化效率低和生成路径无法满足实际需求等问题,开展了基于MI-RRT^(*)(Modified Informed-RRT^(*))算法的路径规划研究,通过引入贪心采样和自适应步长的方法提高算法的收敛率,减少路径生成时间、降低内存占用;利用最小化Snap曲线优化的方法使路径平滑的同时动力也变化平缓,达到节省能量的效果,并提供实际可执行的路径。最后通过多组不同复杂度的实验环境表明,较Informed-RRT^(*)算法MI-RRT^(*)算法稳定性更高、所得规划路径平滑可执行,并且能够减少20%的迭代次数和25%的搜索时间,得出在开阔以及密集环境中MI-RRT^(*)算法较Informed-RRT^(*)和RRT^(*)算法有明显的优势。
