Method 1: directly find the point that is smaller than its neighbours Cons : seems to be hard cut-off and not very flexible 1. use matlab function BW = imregionalmax (I,conn), where conn defines the connected neighbours around a local minimum point, conn can be 4 or 8 in 2D situation http://au.mathworks.com/help/images/ref/imregionalmax.html#inputarg_conn Comment: I think it is a little bit lack of flexibility, for example, what if the size of local minimum changes in a wide range and thus even 8 connected neighbours are not enough. Or what if the noisy pixel is in the neighbours that the center point is bigger than the nosie? 2 Raphael's method: based on a paper, energy function optimization method 2. Raphael's idea: first find the local minimum point (similar to using imregionalmax ), then delet the noisy points Method 2 Boundary-based method/ image segmentation method Idea : find the boundary of a local cluster, which contains a local minimum. 1. bw = activecontour (A,mask,method) Comment: snake contour and active contour are similar Can we try using watershed algorithm , which is also a dilation algorith,? 2. errosion and expasion Method 3 signal processing methods: for example, detect valleys in 1-D/2D signals Idea : The common way is to first smooth the whole picture, so as to get rid of noise, and then find local minimum. Cons : But after smoothing, the location of local minimum in the smoothed map will probably be differnt from the locations in the original map 1. http://www.mathworks.com/matlabcentral/fileexchange/37388-fast-2d-peak-finder Cons : This algorithm only consider 1-pixel noise, which is not true 2. http://blogs.mathworks.com/pick/2008/05/09/finding-local-extrema/ find valleys in a 1D signal P.S. refer to http://au.mathworks.com/matlabcentral/newsreader/view_thread/102944 for more answers
Int J Adv Manuf Technol (2013) 69:1219–1232 DOI 10.1007/s00170-013-5109-4 Definition and determination of the minimum uncut chip thickness of microcutting Liu Zhanqiang*, Shi Zhenyu, Wan Yi Abstract: Uncut chip thickness is comparable to cutting edge radius in micromachining. If the uncut chip thickness is less than a critical value, there will be no chip formation. This critical value is termed as minimum uncut chip thickness (MUCT). Although minimum uncut chip thickness has been well defined in orthogonal cutting, it is often poorly understood in practical complex turning and milling processes. In this paper, a set of definitions of minimum uncut chip thickness for three - dimensional turning and milling processes are presented. This paper presents an analysis of the state-of- the- art research on minimum uncut chip thickness in precision micromachining. Numerical and experimental methods for determination of MUCT values and their effects on process mechanics and surface integrity in micro-cutting will be critically assessed in this paper. In addition, a detailed discussion on the characteristics of different methods to determine minimum uncut chip thickness and several unsolved problems are proposed for the future work. Keywords Minimumuncut chip thickness . Microcutting . Cutting mechanics 2013-Definition and determination of the minimum uncut chip.pdf
aLRT (parametric bootstrap)和 standard bootstrap(nonparametric bootstrap)的区别，aLRT 是phyML计算支持率的另外一种方法，其中Chi2-based aLRT (approximate Likelihood-Ratio Test) for branches 得到的支持率比较松散，SH-like 得到的比较相近 -b (or --bootstrap) int int = -1 : approximate likelihood ratio test returning aLRT statistics. int = -2 : approximate likelihood ratio test returning Chi2-based parametric branch supports. int = -3 : minimum of Chi2-based parametric and SH-like branch supports. int = -4 : SH-like branch supports alone. aLRT is a statistical test to compute branch supports. It applies to every (internal) branch and is computed along PhyML run on the original data set. Thus, aLRT is much faster than standard bootstrap which requires running PhyML 100-1,000 times with resampled data sets. As with any test, the aLRT branch support is significant when it is larger than 0.90-0.99. With good quality data (enough signal and sites), the sets of branches with bootstrap proportion 0.75 and aLRT0 aLRT (approximate Likelihood-Ratio Test) for branches -b (or --bootstrap) int int = -1 : approximate likelihood ratio test returning aLRT statistics. int = -2 : approximate likelihood ratio test returning Chi2-based parametric branch supports. int = -3 : minimum of Chi2-based parametric and SH-like branch supports. int = -4 : SH-like branch supports alone. aLRT is a statistical test to compute branch supports. It applies to every (internal) branch and is computed along PhyML run on the original data set. Thus, aLRT is much faster than standard bootstrap which requires running PhyML 100-1,000 times with resampled data sets. As with any test, the aLRT branch support is significant when it is larger than 0.90-0.99. With good quality data (enough signal and sites), the sets of branches with bootstrap proportion 0.75 and aLRT0.9 (SH-like option) tend to be similar. Perform bootstrap and number of resampled data sets -b (or --bootstrap) int int 0 : int is the number of bootstrap replicates. int = 0 : neither approximate likelihood ratio test nor bootstrap values are computed. When there is only one data set you can ask PhyML to generate resampled bootstrap data sets from this original data set. PhyML then returns the bootstrap tree with branch lengths and bootstrap values, using standard NEWICK format. The "Print pseudo trees" option gives the pseudo trees in a *_boot_trees.txt file. option) tend to be similar. Perform bootstrap and number of resampled data sets -b (or --bootstrap) int int 0 : int is the number of bootstrap replicates. int = 0 : neither approximate likelihood ratio test nor bootstrap values are computed. When there is only one data set you can ask PhyML to generate resampled bootstrap data sets from this original data set. PhyML then returns the bootstrap tree with branch lengths and bootstrap values, using standard NEWICK format. The "Print pseudo trees" option gives the pseudo trees in a *_boot_trees.txt file. reference linking: http://www.atgc-montpellier.fr/phyml/usersguide.php?type=command http://www.atgc-montpellier.fr/phyml/alrt/
I will be giving a seminar at Xia Da, and Bei Da. I will share my experience in the past four year, from a new comer (ecosystem modeling) to an expert (sort of). Oxygen Minimum Zones in the Northern Indian Ocean Time: 10:00 am on April 25th Place: Room B206 State Key Laboratory of Marine Environmental Science (MEL), Xiamen University Time: 10:00 am on May 3rd Place: Room 576 Department of Atmospheric and Oceanic Sciences Peking University
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