Image Processing Webinar

(23/7/2015)

So this is my second series of the Mathworks webinar. This time, we have had Brett Shoelson (a PhD and the Principal Application Engineer for Mathworks, specialized in image processing) to conduct the seminar on image processing by using an example form a research work in Finding Parasitic Infections with Matlab. 

Somehow the example from this seminar is more relevant in biological science (Parasitic Infections), however, the algorithm and techniques involved are absolutely relevant to computing and IT. We were introduced to some typical real-time challenges in the discipline, and some discussions about the possible approaches in dealing with them.

The part that I am really benefiting from this seminar is the techniques from computer vision and machine learning, which were used to automatically recognize (quantify as per said by the speaker) the target in the image set. In my own project, I will be focusing on computer vision to acquire real-time ground data, and this data will be then processed by some means of image processing probably involving machine learning. I downloaded the example codes and hopefully they will be pointing me to some relevant solutions for my project.

A Webinar on Data Analytics with MATLAB

(17/7/2015)

I am an experienced user of Matlab - a high-level language and interactive environment for rapid scripting and algorithm testing (at least that was how I have used Matlab all the while for the past 10 years).

A few weeks ago, I was notified about a webinar from Mathworks (the company that developed Matlab) on the topic of Data Analysis. The advertisement caught my attention with the following short description: "Using Data Analytics to turn large volumes of complex data into actionable information can help you improve engineering design and decision-making processes... and so on"

So I signed-up the seminar, and wow! I have actually learned some new and interesting techniques from it.  It was a short 1-hour session, but the pain is the time difference between my local zone and the organizer's zone in the United State (for me that was some time in the midnight).

Let's get back to the topic, so I have got a chance to learn techniques for exploring, visualizing and combining complex multivariate data sets, which I don't normally perform using Matlab. Another technique which could be closer to my current research project is prototyping and testing predictive models using machine learning methods. This is something advance and new to me, and to pick up the skill is luckily not that hard. To conclude, this was a really useful online seminar, at least technically.

PS: Following this, I have signed-up for more webinars from Mathworks. More to come in my next post.

Communicating Research (Workshop 3)

Following the previous workshop, this workshop 3 of the series highlighted the communication of our research to the non-academia (outside academia). Since we knew already what, when and where to publish our works, now we are encouraged to communicate to some other audience.

The focus of communication should consider the following two things:

1. How much details does your audience want?
2. Which part of your story would your audience want to hear most?

The first impression is also critical. As such, there are a few things that we have to make prominent in order to invite or retain the readers. The order follows as the main title, section headings, first text box and finally the rest of the information.

In this workshop, we were having some fun time by firstly criticizing some past poster, and next, revising our abstract for the industry audience. One of the techniques to draw the attention of the industry audience is to highlight the outcome or the potential product of the research. The workshop has reminded me again and again on the importance of knowing our audience before the writing to achieve the best readership.