Deep Learning, Machine Learning, and Internet of Things

Classify Objects Using Deep Learning Algorithm on Raspberry Pi Hardware

Use the Raspberry Pi® Blockset Hardware to deploy a deep learning algorithm that classifies objects using the ResNet-50 convolutional neural network. This pretrained network is 50 layers deep and can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many more. You can experiment with different objects in your surroundings to see how accurately the network classifies images on the Raspberry Pi hardware.

Identify Objects Within Live Video Using ResNet-50 on Raspberry Pi Hardware

Predict the objects in a live video stream on Raspberry Pi® by deploying an image classification algorithm using Raspberry Pi Blockset. The algorithm uses ResNet-50 neural network to identify the objects captured by the webcam that is connected to the Raspberry Pi hardware.

Classify Static Image Using Deep Learning on Raspberry Pi

Generate and deploy code for an image classification algorithm using Raspberry Pi® Blockset. The algorithm uses the ResNet-50 neural network to identify the image that is passed as an input using the command line of Raspberry Pi.

Speech Command Recognition Code Generation on Raspberry Pi

Generate code and deploy feature extraction and speech command recognition network on Raspberry Pi hardware.

(Audio Toolbox)

Detect and Track Object Using Deep Learning on Raspberry Pi

Use the Raspberry Pi® Blockset to deploy a deep learning algorithm that detects and tracks an object in Connected IO and PIL modes. This algorithm uses the ResNet-18-based YOLOv2 neural network to identify the object captured by the camera mounted on a servo motor and connected to the Raspberry Pi hardware. You can experiment with different objects in your surroundings to see how accurately the network detects images on the Raspberry Pi hardware.

Keyword Spotting in Noise Code Generation on Raspberry Pi

Demonstrates code generation for keyword spotting using a Bidirectional Long Short-Term Memory (BiLSTM) network and mel frequency cepstral coefficient (MFCC) feature extraction on Raspberry Pi®. MATLAB® Coder™ with Deep Learning Support enables the generation of a standalone executable (.elf) file on Raspberry Pi. Communication between MATLAB (.mlx) file and the generated executable file occurs over asynchronous User Datagram Protocol (UDP). The incoming speech signal is displayed using a timescope. A mask is shown as a blue rectangle surrounding spotted instances of the keyword, YES. For more details on MFCC feature extraction and deep learning network training, visit Keyword Spotting in Noise Using MFCC and LSTM Networks.

(Audio Toolbox)

Recognize Handwritten Digits Zero to Nine Using MNIST Data Set on Raspberry Pi Hardware

Use Raspberry Pi® Blockset to recognize images of handwritten digits from zero to nine. In this example, a web camera interfaced with a Raspberry Pi hardware board is used to capture images of the handwritten numbers. The algorithm recognizes the digits and then outputs a label for the digit along with its prediction probability.