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SHOW Prints the full contents of a multi-element or array structure
  Description: Prints the full contents of a structure variable to the Command
               Window or to a file. Ideal for showing contents of a nested
               structure or a structure array.
      Often it takes multiple commands to explore the contents of a structure,
      especially if it is a structure array or has nested structure elements.
      For example, take this relatively simple case:
        >> s = struct('data',{struct('value',{0,'a',pi}),'str',magic(3),1-2i})
      This structure is displayed by MATLAB as:
        s =
        1x4 struct array with fields:
      Not much information there, other than the top level size and field(s).
      To explore the actual contents, you would need to type:
        >> s(1)
        >> s(1).data(1)
        >> s(1).data(2)
        >> s(1).data(3)
        >> s(2)
        >> s(3)
        >> s(4)
      With show, all that effort is done for you in one simple command:
        >> show(s)
      Also, the printed output is more readable and concise, with field names
      tab-indented to represent nested structure elements.
      VAR - (MxN struct) variable to be shown
      FID - (optional) file identifier to print contents to a file
  Outputs: None.
      fid = fopen('file.txt','w'); show(var,fid); fclose(fid);
      % Show full contents of the image info (needs Image Processing Toolbox)
      structInfo = imfinfo('cloudCombined.jpg');
      myStruct = struct('nestedStruct',struct('version','1.0','size',[800 600], ...
          'path','/home/user/matlab','file','data.mat', 'matrixData',magic(3), ...
          'img',uint8(rand(600,800,3)),'cellField',{{'a','b','c'}}, ...
          'shortStringField','A string with 100 characters or less is printed', ...
          'longStringField',repmat('Only size/type shown for long strings. ',1,3), ...
          'rowVectorData',rand(1,3),'columnVectorData',sort(rand(5,1))), ...
      fprintf('nCompare default MATLAB "disp" above versus "show" below:nn');
      % Print contents to a file
      [uv,sv] = memory;
      memoryInfo = struct('UserView',uv,'SystemView',sv);
      fid = fopen('memory.txt','w');
      % Print contents to the screen
      [uv,sv] = memory;
      memoryInfo = struct('UserView',uv,'SystemView',sv);
  Author: Joseph Kirk
  Email: jdkirk630@gmail.com
  Release: 1.0
  Release Date: 10/5/15

Code for Webinar "Signal Processing and Machine Learning Techniques for Sensor Data Analytics

These files contain all the code necessary to run the example in the Webinar "Signal Processing and Machine Learning Techniques for sensor Data Analytics". They also include code to automate the download and preparation of the dataset used. 
In that webinar we presented an example of a classification system able to identify the physical activity that a human subject is engaged in, based on the accelerometer signals generated by his or her smartphone. �We discussed signal processing methods to extract highly-descriptive features, and we gave an overview of a number of techniques to choose and train a classification algorithm. Along the way we demonstrated the use of Parallel Computing to accelerated the extraction of features from a large dataset.�We also presented a workflow to transition signal processing and predictive algorithms to embeddable software implementations - first using DSP system modelling, and then automatically generating C/C++ source code directly from MATLAB.

Gradient-Enhanced Sparse Grid

In higher-dimensional interpolation, we face the 'curse of dimensionality': When we increase the number of dimensions, the number of samples increases exponentially. One approach to reduce this effect is to use sparse grids. When gradient information is available, for example from an adjoint solver, gradient-enhanced sparse grids offer the possibility to reduce the number of samples even further.

RC Receive Driver Block for Arduino

With this block, a user can use Simulink to generate code for an Arduino which receives signals from an RC Transmitter.
When the output of an RC Receiver is connected to an Arduino, this custom driver block outputs the length of the RC signal's pulse in microseconds, which corresponds directly to the signal's value.
This material is meant to support the MakerZone article "How to use an RC Controller with Arduino and Simulink".

For examples and documentation showing how to use block, see the "Supplemental Software" section of the main doc page. There will be link to the "Arduino RC Receive Toolbox".

Legacy Code:
To access the old version, type the following command after installation:

This driver block was tested with the Cirrus-DPR-4FM Transmitter and Receiver. All Receivers output servo protocol so the block will work with all RC Transmitter/Receivers.
The block works with the Arduino Uno, Mega 2560, and Due. The maximum channels supported is 6.

Essential Statistics for the Pharmaceutical Sciences, 2nd Edition


Essential Statistics for the Pharmaceutical Sciences is targeted at all those involved in research in pharmacology, pharmacy or other areas of pharmaceutical science; everybody from undergraduate project students to experienced researchers should find the material they need.

This book will guide all those who are not specialist statisticians in using sound statistical principles throughout the whole journey of a research project - designing the work

Aerial Recovery Concept Demo (Gauss's Principle)

The towing vehicle (mothership) is commanded to follow a circular orbit that results a circular orbit of the towed body (drogue) with smaller radius and lower velocity relative to the mothership. The miniature aerial vehicle (MAV), which is to be retrieved, is regulated to follow the drogue orbit and to approach the drogue with a relatively low airspeed. After the MAV dock on the drogue, the towed cable and bodies (both drogue and MAV) are reeled into the mothership to complete the aerial recovery.
In this demo, Gauss’s Principle is used to derive the dynamic model of the cable-drogue system. The MAV uses missile guidance strategies to intercept the drogue.
For more information see:

Sun, L., Beard, R. W., and Colton, M. B., McLain, T. W., “Dynamics and Control of Cable-drogue System in Aerial Recovery of Micro Air Vehicles Based on Gauss’s Principle.” St. Louis, MO, USA: American Control Conference, June 2009, pp. 4729–4734.


Colton, M. B., Sun, L., Carlson, D. C., and Beard, R. W., "�Multi-vehicle Dynamics and Control for Aerial Recovery of Micro Air Vehicles",� Int. J. Vehicle Autonomous Systems, Vol. 9, 2011, pp. 78�-107.


MATLAB and Simulink Racing Lounge: CFD Simulation Data Processing

This is a collection of all files used in the MATLAB and Simulink Racing Lounge episode "Processing CFD Simulation Data".

Automated Simulink Model Creator from Ordinary Differential Equation

Please refer the "Notes" in the bottom-most section of the user interface of this app to get help on how to provide the equation of an ODE and numerically solve it using an auto-generated Simulink model.

Convolution Implementation

This code is about implementing convolution using Linear buffer (FIFO), double buffer, circular buffer and double circular buffer


See the Google-Code web page:
for more info.
The following utilities are included:
- 3-D mesh generator for iso-surfaces.
- Closest point searching on meshes, including surface triangulations in 3-D.

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