![]() ![]() ![]() Then on cloudy days, we use it to supplement the lower power generation. To smooth out the generated power, on sunny days we can store some of the power in a battery. Predicting and managing the variable production and demand is an important part of renewable energy generation. Of course, the power depends on the amount of sunshine, which depends on the time of day. We’ll use measurements of the power produced by the array every 15 minutes. We’ll assess the performance of our algorithm and, once it’s ready, convert our model into C code that can be embedded into real-time hardware. So let’s get started…Īt the MathWorks headquarters in Natick, Massachusetts, there are solar panels that generate electrical power. Based on that, we’ll design and build digital filters as part of a signal processing algorithm. We’ll perform spectral analysis to explore the signal. In this video we’ll use Simulink to process a signal from a sensor. They’re in robots in factories, in our cars, on our wrists, even in our refrigerators making sure our food stays fresh. You can use Simulink Coder™, Embedded Coder ® or GPU Coder to generate C++ or CUDA code and deploy deep learning networks on Intel ®, ARM ®, or NVIDIA platforms.We live in a world of sensors. Once the system design is verified in simulation, you can generate code from the Simulink model for deployment. To configure the model for GPU Acceleration, check the ‘GPU Acceleration’ box under Simulation Target in the Model Settings. Finally, you can also use GPU Coder with NVIDIA ® GPUs to accelerate simulation of deep learning models in Simulink. The output of the simulation displays the annotated traffic video as expected. Clicking the Run button will under the hood generate code from the Predict and MATLAB Function blocks and compile it for CPU-accelerated simulation. The last thing we need to do before we run the simulation, is to set the simulation target language to C++ in the model settings. The MATLAB Function block can also be used with other types of networks like LSTMs, and you can also use it to output activations from specific layers of the network. Next, we will specify the dimensions of the block outputs. Inside the MATLAB Function block we will load the pretrained network, and call the detect method to get the bounding boxes and associated confidence scores. For vehicle detection, we will use a MATLAB Function block to perform inference on the pretrained yolov2 vehicle detector. The predict block will output two lane boundaries represented by a parabolic equation with 3 parameters that are then transformed into lanes in image coordinates. Another option here would be to use a MATLAB function. To link the block to the lane detector object we are providing the path to the appropriate MATLAB file. Next we will use the ‘Predict’ block from the deep learning block library to perform inference on the trained network. For lane detection, first we are resizing the video frames to match the input expected by the trained lane detection network. In the Simulink model, we are reading from a traffic video file, and after the vehicle and lane detection parts we are displaying the traffic video again with lane and vehicle annotations. Finally, we assume that we have a pretrained lane detection network as well as a pretrained yolov2 vehicle detector stored in MATLAB files. We will also need the support packages that provide interfaces from MATLAB Coder™ and GPU coder™ to target-specific deep learning libraries. The first thing we will need is a C++ compiler. Let’s see how we can create a subsystem that performs vehicle and lane detection in Simulink. For example, to design a highway lane following system, you can use the deep learning blocks to create a Simulink subsystem that performs lane and vehicle detection, integrate this subsystem with a larger Simulink model that includes additional components such as the vehicle dynamics model, the lane following controller, sensor fusion and 3D visualization, and verify performance of the overall design through system-level simulation before deployment. As of R2020b release of MATLAB ®, you can use the MATLAB Function block as well as the Deep Learning Toolbox™ block library to simulate and generate code from trained deep learning models in Simulink ®. ![]()
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