tag:www.mathworks.com,2005:/matlabcentral/fileexchange/feedMATLAB Central File Exchangeicon.pnglogo.pngMATLAB Central - File ExchangeUser-contributed code library2015-03-30T23:01:07-04:00235071100tag:www.mathworks.com,2005:FileInfo/503422015-03-31T01:27:33Z2015-03-31T01:27:33ZTwo Simple SmoothersFour lines of code. Good smoothing properties.<p>SMOOTH1 and SMOOTH2 each smooth a data sequence by convolving it with a filter that's well-suited to smoothing. The output and its first two derivatives will be smooth.</p>
<p>Each smoother is controlled by a user-supplied response time, in units of samples, which produces about the same agility as a moving average of that many samples. The output is much smoother though, due to far greater high-frequency attenuation.</p>
<p>SMOOTH1's impulse response is nonnegative, like a Gaussian but compact. SMOOTH2's impulse response has just enough of a negative outer lobe to flatten the low-frequency response and give perfect response to a quadratic input component. Which to prefer is hard to say, or may depend on the application.</p>
<p>Both smoothers provide strong high-frequency attenuation. Their frequency response "sidelobes" start below -60 dB and fall 18 dB per octave, making even the double-differentiated output smooth.</p>
<p>See also LSSMOOTH (ID:49789), IRLSSMOOTH (ID:49788)</p>Jimhttp://www.mathworks.com/matlabcentral/profile/authors/42338-jimMATLAB 6.5 (R13)Last line of code works in newer MATLAB versions. An equivalent last line for older versions is commented out.falsetag:www.mathworks.com,2005:FileInfo/503412015-03-30T22:10:43Z2015-03-30T22:10:43Zislatlon(lat,lon)Boolean check to see if input arrays likely represent geo coordinates.<p>Determine whether inputs may represent geographical (lat,lon) coordinates. This function does not return element-by-element determination, but rather returns true if all input lat values are between -90 and 90 inclusive and all input lon values are between -180 and 360 inclusive. If any element in either lat or lon lies outside its expected range, islatlon is false.
<br />I use this function frequently as a subfunction where users may want to enter geo lat,lon values or map x,y values. Typically, when using map coordinates, at least one value will exceed lat,lon ranges, so if values are in the thousands, it's probably map x,y coordinates. islatlon is also a nice compact way to include two function input checks with a single assertion like </p>
<p>assert(islatlon(lat,lon)==1,'Values must be geographic coordinates.')</p>Chad Greenehttp://www.mathworks.com/matlabcentral/profile/authors/1062128-chad-greeneMATLAB 8.0 (R2012b)falsetag:www.mathworks.com,2005:FileInfo/503402015-03-30T21:11:53Z2015-03-30T21:11:53ZRatioImageCreate a ratiometric image with intensity modulated display (IMD)<p>This app will compute and display an intensity modulated ratiometric image. Choose a numerator and denominator image. The pixel-by-pixel ratio image will be displayed via a hue indicated by the colormap, which is user-selectable. The color brightness, however, will be modulated by the total image intensity at that pixel location. Thus, you can make ratiometric images without having to apply thresholding. Adjust the max/min displayed ratio as well as the max/min displayed intensity via sliders below and to the right of the image. Useful for FRET, Calcium ion, or other ratiometric imaging modality.</p>Jessehttp://www.mathworks.com/matlabcentral/profile/authors/5302549-jesseMATLAB 8.3 (R2014a)Image Processing ToolboxMATLABImage Processing Toolboxfalsetag:www.mathworks.com,2005:FileInfo/503392015-03-30T20:48:06Z2015-03-30T20:48:06ZEasily Simulate a Customizable Network of Spiking Leaky Integrate and Fire NeuronsDesign and simulate your LIF neuron network in only a few lines of code.<p>In only a few lines of code you can customize and simulate a network of leaky integrate and fire neurons (LIF). This function facilitates quick testing of network architectures. The network can be simple, only specifying the weights of the connections between neurons, or complex with options ranging from offset currents, refractory periods, speed of synaptic transmission, noise, etc.
<br />Usage notes and sources are given in the help.</p>Zachary Danzigerhttp://www.mathworks.com/matlabcentral/profile/authors/1044524-zachary-danzigerMATLAB 7.13 (R2011b)falsetag:www.mathworks.com,2005:FileInfo/503382015-03-30T20:36:54Z2015-03-30T20:36:54ZPCA-CSIFT featureimage feature based on PCA-CSIFT<p>This image feature is extracted based on Y. Ke and R. Sukthankar, Computer Vision and Pattern Recognition, 2004. However, before that, the image is replace by the color invariance image form CSIFT: A SIFT Descriptor with Color Invariant Characteristics, Abdel-Hakim, A.E. ; Farag, A.A. , Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference</p>Xing Dihttp://www.mathworks.com/matlabcentral/profile/authors/5677986-xing-diMATLAB 8.4 (R2014b)falsetag:www.mathworks.com,2005:FileInfo/503372015-03-30T20:04:13Z2015-03-30T20:04:13ZPCA-SIFT featurejust download and run<p>the code is inspired by Y. Ke and R. Sukthankar, Computer Vision and Pattern Recognition, 2004. and their MATLAB code. This code is only for study and research.</p>Xing Dihttp://www.mathworks.com/matlabcentral/profile/authors/5677986-xing-diMATLAB 8.4 (R2014b)falsetag:www.mathworks.com,2005:FileInfo/502322015-03-27T21:21:09Z2015-03-30T19:03:17ZMachine Learning Made EasyMATLAB files from the webinar<p>These files accompany the 'Machine Learning Made Easy' webinar which can be viewed here:
<br /><a href="http://www.mathworks.com/videos/machine-learning-with-matlab-81984.html">http://www.mathworks.com/videos/machine-learning-with-matlab-81984.html</a>
<br />About the webinar:
<br />Machine learning is ubiquitous. From medical diagnosis, speech, and handwriting recognition to automated trading and movie recommendations, machine learning techniques are being used to make critical business and life decisions every moment of the day. Each machine learning problem is unique, so it can be challenging to manage raw data, identify key features that impact your model, train multiple models, and perform model assessments.
<br />In this session we explore the fundamentals of machine learning using MATLAB®.
<br />Highlights include:
<br />• Accessing, exploring, analyzing, and visualizing data in MATLAB
<br />• Using the Classification Learner app and functions in the Statistics and Machine Learning Toolbox® to perform common machine learning tasks such as:
<br /> o Feature selection and feature transformation
<br /> o Specifying cross-validation schemes
<br /> o Training a range of classification models, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor, and discriminant analysis
<br /> o Performing model assessment and model comparisons using confusion matrices and ROC curves to help choose the best model for your data
<br />• Integrating trained models into applications such as computer vision, signal processing, and data analytics.</p>Shashank Prasannahttp://www.mathworks.com/matlabcentral/profile/authors/2963954-shashank-prasannaMATLAB 8.5 (R2015a)Statistics ToolboxComputer Vision System Toolboxfalsetag:www.mathworks.com,2005:FileInfo/503362015-03-30T17:25:46Z2015-03-30T17:25:46ZLLLL<p>LL</p>wenli lianghttp://www.mathworks.com/matlabcentral/profile/authors/6349136-wenli-liangMATLAB 7.7 (R2008b)MATLABfalsetag:www.mathworks.com,2005:FileInfo/502822015-03-30T16:58:45Z2015-03-30T16:58:45ZblinkBlinking text in graphics windows<p>Are you nostalgic for the beautifully designed web pages of the 1990s, wishing that you could relive just a little bit of the magic? These pages had everything - animated GIFs, dynamic backgrounds, scrolling text, and best of all - blinking text.
<br />You can relive just a portion of those glory years with blink - a simple function that makes any text in a figure window blink.</p>Michelle Hirschhttp://www.mathworks.com/matlabcentral/profile/authors/869375-michelle-hirschMATLAB 8.5 (R2015a)MATLABA sense of humor is a definite must!falsetag:www.mathworks.com,2005:FileInfo/503352015-03-30T15:14:12Z2015-03-30T15:14:12ZD-STEMA software for the analysis and mapping of environmental space-time variables<p>The software D-STEM is as a statistical tool for the analysis and mapping of environmental space-time variables. The software is based on a flexible hierarchical space-time model which is able to deal with multiple variables, heterogeneous spatial supports, heterogeneous sampling networks and missing data.
<br />Model estimation is based on the expectation maximization algorithm and it can be performed using a distributed computing environment to reduce computing time when dealing with large data sets.
<br />The estimated model is eventually used to dynamically map the variables over the geographic region of interest.
<br />Examples of increasing complexity illustrate usage and capabilities of D-STEM, both in terms of modelling and implementation, starting from a univariate model and arriving to a multivariate data fusion with tapering.</p>Francesco Finazzihttp://www.mathworks.com/matlabcentral/profile/authors/1832201-francesco-finazziMATLAB 8.2 (R2013b)Mapping ToolboxOptimization ToolboxStatistics Toolboxfalse