Based on my previous positive experience with Mathworks online seminar series, I joined other 3 seminars from 13th August to 1st September. I summarized them in the following sections.
Integrating MATLAB into your C/C++ Product Development Workflow (13th August 2015)
As I will be coding my prototype mainly using C/C++ in Robot Operating System and Linux (specifically Ubuntu), I found the highlights of this seminar are definitely helpful. At the early stage of my research, I prefer to use Matlab for prototyping. However, due to the needs of implementing the actual working application on an embedded platform for my wearable technology, I have to eventually switch from Matlab code to C/C++. So this seminar has shed some lights on the integration of my Matlab code into the development workflow. Technically, I can tackle the issue from both ways - either by generating C code from Matlab code to seed new designs; or integrating my existing C code into Matlab for simulation or prototyping. These could have made things much more faster and easier for me.
PID Control Made Easy (18th August 2015)
A proportional–integral–derivative (PID) controller is a control feedback mechanism commonly used in industrial control processes or systems. I have enrolled into this seminar mainly due to the belief that I might eventually be using some sort of control mechanism in the prototype to provide feedback to the blind and low vision users. This is something I am going to implement at the very end of the prototype development. The seminar introduced Matlab Simulink Control Design toolbox as a straightforward and partly automated process to tune PID controllers. The environment also allows automatic code generation to deploy PID controllers. I am seeing that the techniques learned here might be relevant in designing my HCI feedback control.
Signal Processing and Machine Learning Techniques for Sensor Data Analytics (1st September 2015)
This is the most relevant topic for me among all the series from Mathworks seminars. My project application needs the joint use of signal (image) processing and machine learning techniques on real-time and sensor data. Based on the speaker, MATLAB can speedup the development of data analysis and sensor processing systems. This is achieved by having a full range of modelling and design capabilities within a single environment in Matlab. In the seminar, an example form a classification system for human activity is shared and discussed.
Having acquired some skills from these seminars, I am looking forward to putting them into good use of my research project.
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