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By Nicholas Brown. I was recently in the same boat. This automotive microcontroller went from intimidating me with its seemingly complex interface, to amazing me with its simplicity. This can be done using pwmStartpwmStoppwmSetDutyamong other functions. This tutorial will introduce you to those functions so you can turn your PWM signal on or off with only one line of code. This enables you to spend less time learning how PWM works, and more time completing your project. Next, go to the HET tab and click the Pwm subtab. You can use whichever you like, but for this example I used PWM 0. Enable PWM 0 as I did in the screenshot below, and select pin 8. This just means it will supply the PWM signal to pin 8. The next step is to launch Code Composer Studio. Build it by clicking the hammer iconand run it by clicking the green bug icon, which uploads your code to the TMS The following lines of code set the PWM duty cycle and stop the PWM signal respectively place them in the main function after hetInitas you did with the code above. The third parameter 5U is the duty cycle of the PWM signal. Increasing it will increase the brightness of the LED, and vice versa. This causes the LED to blink every microseconds or 0. You could use it to control the speed of a fan, the brightness of a light bulb, among many other things. PWM is desirable and widely used because it is an energy-efficient way to control the speed of motors. Example applications include hybrid and electric vehicle motors, inverter air conditioners, inverter refrigerators, computer fans, among others. The possibilities they provide are endless! To get started, two helpful tools would be a multimeter and an oscilloscope to ensure voltages and currents are what they should be, and to view oscillating signals for example: PWM signals and alternating current respectively. You must log in to post a comment. Sign in Join. Sign in. Log into your account.
Running HALCoGen on Mac or Linux With WINE
You may wish to make customizations the way that this package is configured. For example, you may wish to change the RM48 family part number to the part that you plan to target; or change to a TMS series part. Or, you may wish to change the core's clock rate, communication baud rate or some other device setting that affects performance. We will provide guidance in this section to help make this package better suite your needs. We will start with a description of key files in this package and what their roles are in the build, download, and simulation phases are. All filenames are relative to the target pacakges root installation directory. Assuming that you have already gone through the setup steps, the target root directory can be found by the command:. This file creates instances for each host OS platform of an coder. This file primarily configures the Compiler, Assembler, and Linker options for three build profiles: Faster Builds - Optimization Level 0 Faster Runs Debug - Optimization Level 0 and Debug Symbols For each of these build profiles, the basic compiler options needed to target an RM48xx device are also defined in this file. This script saves the coder. MAT files, with one. MAT file for each host OS platform. The toolchain registration methods that make the RM48 appear in the simulink model configuration dialog read these. MAT files. Therefore, this script must be run each time changes are made to the toolchain script or else the changes will not be reflected in the makefiles that are generated during the build process. This replacement enables the code execution profiling capabilities available during a PIL simulation. The constructor method gets a handle to the object's RTW. BuildInfo object using the getBuildInfo method. So, for example, all of the HalCoGen sources and includes are added in this constructor. If you decide to target a differnet device, you may wish to generate a set of HalCoGen sources in a separate folder. Then you would change the path to the HalCoGen sources and includes inside this file to point to your new source folder. This class ties the correct builder, launcher and communicator subclasses together to create a ConnectivityConfig object that is used to communicate with the target during a PIL simulation. Note that the application framework is included through the builder.
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Disclaimer About BCLC Who We Are What We Do Social Responsibility Careers Media Centre Latest News Connect With Us Join Our Community Panel Social Media Directory Share Your Feedback PlayNow. When a binary outcome variable is modeled using logistic regression, it is assumed that the logit transformation of the outcome variable has a linear relationship with the predictor variables. This makes the interpretation of the regression coefficients somewhat tricky.
In this page, we will walk through the concept of odds ratio and try to interpret the logistic regression results using the concept of odds ratio in a couple of examples. Everything starts with the concept of probability. Then the probability of failure is 1. The odds of success are defined as the ratio of the probability of success over the probability of failure. In our example, the odds of success are. That is to say that the odds of success are 4 to 1. If the probability of success is.
The transformation from probability to odds is a monotonic transformation, meaning the odds increase as the probability increases or vice versa. Probability ranges from 0 and 1. Odds range from 0 and positive infinity. Below is a table of the transformation from probability to odds and we have also plotted for the range of p less than or equal to. Again this is a monotonic transformation. That is to say, the greater the odds, the greater the log of odds and vice versa.
The table below shows the relationship among the probability, odds and log of odds. We have also shown the plot of log odds against odds. One reason is that it is usually difficult to model a variable which has restricted range, such as probability. This transformation is an attempt to get around the restricted range problem. It maps probability ranging between 0 and 1 to log odds ranging from negative infinity to positive infinity.
You can also paginate, filter, and order your samples. You can also list all of your correlations. Example: "This is a description of my new correlation" discretization Object Global numeric field transformation parameters.
See the discretization table below. None of the fields in the dataset is excluded. Specifies the fields that won't be included in the correlation.
That is, no names or preferred statuses are changed. This can be used to change the names of the fields in the correlation with respect to the original names in the dataset or to tell BigML that certain fields should be preferred. All the fields in the dataset Specifies the fields to be considered to create the correlation. It is to be applied globally with all input fields. A Discretization object is composed of any combination of the following properties.
For example, let's say type is set to "width", size is 7, trim is 0. Field Discretizations is also used to transform numeric input fields to categoricals before further processing. However, it allows the user to specify parameters on a per field basis, taking precedence over the global discretization. It is a map whose keys are field ids and whose values are maps with the same format as discretization. It also accepts edges, which is a numeric array manually specifying edge boundary locations.
If this parameter is present, the corresponding field will be discretized according to those defined bins, and the remaining discretization parameters will be ignored. The maximum value of the field's distribution is automatically set as the last value in the edges array. A value object of a Field Discretizations object is composed of any combination of the following properties.
You can also use curl to customize a new correlation. If you do not specify a range of instances, BigML. If you do not specify any input fields, BigML. Read the Section on Sampling Your Dataset to lean how to sample your dataset. Once a correlation has been successfully created it will have the following properties. The Correlations Object of test has the following properties.
If p-value is greater than the accepted significance level, then then it fails to reject the null hypothesis, meaning there is no statistically significant difference between the treatment groups.
It has the following properties: The Chi-Square Object contains the chi-square statistic used to investigate whether distributions of categorical variables differ from one another. This test is used to compare a collection of categorical data with some theoretical expected distribution. The object has the following properties.
ANOVA is used to compare the means of numerical data samples. The ANOVA tests the null hypothesis that samples in two or more groups are drawn from populations with the same mean values.