MARCH EVENTS (2)

Overview

Deep Learning and Machine Learning are powerful tools for to build applications for signals and time-series data across a broad range of industries. These applications range from predictive maintenance and health monitoring to financial portfolio forecasting and advanced driver assistance systems.

In this session, through detailed examples we will showcase several techniques and apps in MATLAB to build predictive models for real-life applications. We will cover how to build your signal datasets, label your signals using apps, and preprocess the data. We will explore various feature extraction techniques that help to create robust and accurate AI models. We will also examine what are the key types of networks used for deep learning and how they are applied and how the trained models can be deployed on embedded hardware

deep-learning-and-machine-learning-for-signal-processing-applications

Key Highlights

  • Easily manage signal datasets using datastores
  • Using Signal Labeler and Signal Analyzer App for AI workflows
  • Feature extraction techniques including AutoML techniques such as wavelet scattering and time-frequency representations
  • Acceleration of training using GPUs and deployment on embedded hardware like Raspberry Pi

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Speakers

Siti Safwana

Siti Safwana is an Application Engineer at TechSource Systems. She is specialized in the field of image processing, computer vision, machine learning, and deep learning with MATLAB. She helps various customers across different industries on projects such as real-time object recognition, manufacturing defect objects detection using Deep Learning and Computer Vision, data forecasting analytics, and multiple image processing project-based.

She additionally holds an HRDF Train-The-Trainer (TTT) certificate and trained in official MathWorks training programs like MATLAB Fundamental, Image Processing, and Machine Learning. She teaches and covers ASEAN region such as Malaysia and Philippines.

Siti Safwana holds an M.Eng in Science in Electronics from Universiti Teknikal Malaysia Melaka (UTeM) and currently pursuing her Ph.D. in Science in Electronic researching computer vision field in the domain of depth/disparity map and 3D reconstruction using Artificial Intelligence at UTeM under the department of Faculty of Electronic and Computer Engineering.

Engr. Nor Aziah Binti Mohd Azubir

Nor Aziah is a Senior Application Engineer at TechSource Systems. Her main field of specialization is in control systems, especially for Electric Vehicle applications. Previously, she worked as a Control System Engineer for electric vehicle prototype development in Perusahaan Otomobil Nasional (Proton). She also had joined MRCB-George Kent as an Electronic Access Control (EAC) Project Engineer for the LRT3 project.

 Then, she had the opportunity to work with simulation on torque vectoring for the electric vehicle's prototype development in Systems Consultation and Services (SCS), using the MATLAB/Simulink. Now, she is undergoing projects related to electric vehicles, automated driving systems, and microgrids.

Aziah holds a Master of Philosophy (MPhil) in Vehicle System Engineering from Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia. Previously, she holds a bachelor's degree in Electrical-Mechatronics Engineering in Universiti Teknologi Malaysia.

 

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