Machine learning and Deep Learning are powerful tools for solving complex modeling problems across a broad range of industries. The benefits of machine learning are being realized in applications everywhere, including predictive maintenance, health monitoring, financial portfolio forecasting, and advanced driver assistance.
However, developing predictive models for signals obtained from sensors is not a trivial task. Moreover, there is an increasing need for developing smart sensor signal processing algorithms which can be either deployed on edge nodes / embedded devices or on the cloud depending on the application.
In this session we will explore how you can use Signal Processing Toolbox and Wavelet Toolbox for analyzing real world signals. We will also explore how other addon tools like Statistics and Machine Learning Toolbox and Neural Network Toolbox can help for performing machine learning and deep learning.
Using real-data we will explore the following topics:
Feature detection and extraction techniques for machine learning workflows
Developing predictive models for signals using Deep Learning workflows
Leverage high-performance computing resources, such as multicore computers, GPUs, computer to scale up the performance
Please allow approximately 45 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand.
About the Presenter
Ian M. Alferez
Lead Application Engineer, TechSource Systems
Ian M. Alferez is the Lead Application Engineer at Techsource Systems. He specializes in the field of embedded system (embedded coder configuration), data analytics (Machine Learning) and technical computing with MATLAB/Simulink. He holds a Bachelor of Science in Electronics and Communication Engineering from the University of San Carlos in Cebu, Philippines. Before joining Techsource Asia, he was working as a Software Development Engineer at Lear Corporation where he refined his skill in Model Based Design with regards to the Verification and Validation Workflow and Embedded Software / Hardware.
As he progressed in his career with Techsource, he has built his forte in:
Production Code Customization, Optimization and Generation with Simulink
MATLAB / Simulink Algorithm for Auto Code Generation and Hardware Target Deployment
https://www.speedgoat.com (Real Time Hardware) for Rapid Prototyping / Hardware in the loop
Customizing the Auto Test Generation / Property Proving with SLDV (Formal Methods)
Data Analytics / Machine Learning -> Deployment with Embedded Systems (MCU and FPGA)
Deep Learning -> Deployment with OpenCV and GPU Coder