Apps for Signal Processing and Labeling

Use low-code apps to improve data quality through exploration, analysis, preprocessing, and automatic labeling of the ground truth.

AI Models

Create AI models with machine learning and deep learning algorithms or use pretrained models.

Feature Extraction

Engineer features from signals using feature extraction (pca, wavelet scattering) or time-frequency transformations (spectrogram, wavelet transform).

Deploy AI-Powered Systems

Simulate and deploy your domain-specific AI systems to embedded hardware, enterprise systems, or the cloud.

Using MATLAB, the process of preparing signals for modeling involves collecting and cleaning data from various sources. Following data cleansing, exploratory analysis identifies patterns, features are selected, and signals are transformed to suit modeling requirements. The dataset is then split for training and testing, ensuring compatibility with MATLAB’s specific model requirements. This process aims to optimize data quality to effectively train and validate models within the MATLAB environment.

Creating and deploying AI systems with MATLAB involves defining the problem, collecting and preparing data, selecting and developing models, testing, and validating them. Deployment strategies, implementation, continuous monitoring, and improvement are crucial stages. Considerations for ethics, legality, documentation, and maintenance ensure a comprehensive and ethical approach to AI system development and deployment within the MATLAB environment.

[Ebook] AI-Powered Signal Processing

Learn the basics of AI for signal processing and the tasks associated with preparing signal data and modeling a deep learning application.

[White Paper] Deep Learning for Signal Processing with MATLAB

Learn how to apply deep learning to signal processing applications. Read three examples where deep learning can be applied usefully to signal data projects using CNN, LSTM, and a fully connected neural network. Download the white paper to learn more.