Description of the Course
This workshop offers a comprehensive, hands-on approach to signal processing techniques designed specifically for AI applications. It emphasizes practical skills in organizing, managing, and analyzing signals, ensuring that participants are actively involved with the content from the start. The workshop further covers techniques of preprocessing signal data, including outlier handling and missing data imputation, to enhance data quality. Additionally, the workshop includes practical sessions on feature extraction, where participants will apply statistical, spectral, and wavelet analysis methods specifically designed for different types of signals. They will also engage directly in classifying signal data using a variety of classifiers and conducting performance evaluations to understand the outcomes.
Mode of Workshop : Online
Date of Workshop : 6th July, 2024
Timings: 10:00 a.m. to 01:00 p.m. (IST)
Profile of the Instructor(s)
Ms Shanthi is an experienced educator and online content developer at MathWorks, specializing in MATLAB for signal processing. She holds a master’s degree in Signal Processing. Shanthi’s professional interests span across subjects including Digital Signal Processing, Image Processing, Neural Networks, and Wireless Communication. Her scholarly contributions include publications in international journals on topics such as Video Compression Standards and their implementation in MATLAB and Background Subtraction Techniques in Image Processing. She started her career by training engineering graduates and has developed learning content for specialization courses using instructional design principles. At MathWorks, she creates online learning content related to MATLAB for signal processing and AI.
Ms Priyanka is an online content developer at MathWorks focused on learning content related to physical modeling. She holds a master’s degree in Power Systems and is pursuing her part-time Ph.D in Electrical Engineering. Her research areas are implementation of Machine Learning and Deep Learning techniques in Wind Power Systems for Power System Stability, Fault Detection, and Fault Classification. She has published papers in international journals and conferences on Deep Learning techniques for Wind Power Prediction and Fault Classification.
Intended Audience
Engineering Students, Researchers and Industry professionals working on Signal Processing and AI Applications.
Eligibility
Any Undergraduate or Masters Engineering Students, Researchers and Industry professionals working on Signal Processing and AI Applications can attend this workshop.
Prerequisites:
Basic MATLAB knowledge, Basics of Signal processing.
Certification
Certificates will be provided to all the participants who attends the workshop
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