 The project objective is to promote the use of open source software in research and academia. So, how many over here actually use open source software? Out of your free will or because your professors force you to use it? Free will? Good, okay. So, how many over here have actually used any one software that's been listed over here? Mostly Python, any Sylab? Anyone's ever heard of OSDAQ or ESM? Okay, no, okay. Good. So, these are the different open source software that we promote. And Sylab is the open source equivalent of Matlab. I hope you've at least heard of Sylab. And most of you have heard of Python. Openform is for CFD simulations. OSDAQ is for steel structure simulations. ESM is for circuit simulations, I believe. And then DWSM is for chemical process simulations. OR tools is mostly used for optimization problems. There are a set of tools that are used for optimizing problems and Blaster's X-Cos based on the which is again based on Sylab. So, in order to promote the various software that you just saw, we have different activities like textbook companion project and a couple of other activities like migration or not. Has anyone ever taken part in textbook companion over here? That is probably the most popular project that we have within the FOSSI group. No one's ever heard of textbook companion? Okay. I encourage you to at least go and look at textbook companion. It's something that's really popular and I hope you will be interested in participating in it also. And apart from this, we also have conferences and workshops throughout the year in various open source software that we promote. Okay. So, the first thing that I'll be talking about is the Sylab Toolboxes. How many over here have used Sylab? Oh my God. That's really low. That's really bad. Okay. So, Sylab is the open source equivalent of Matlab. Have you used Matlab over here? All of you? Yeah. Okay. This is the first thing I want to talk about. The various toolboxes that we are developing to match the toolboxes that are available in Matlab. So, you may ask why you use Sylab? So, first thing is it is free and open source. You may not go for something that is as expensive as Matlab. I'm sure if you have a Matlab on your desktop or on your system, it's most probably a pirated version. You will not be able to afford an original license. So, rather than going for something pirated, why don't you try something that is open and free? So, that is Sylab. And you may ask, is it easy to use? If you're comfortable using Matlab, then you will be quite comfortable using Sylab also. And then there is a simulink equivalent in Sylab. It's called XCOS. And our objective of this internship is to develop toolboxes, develop various toolboxes that will match the requirements for most UG students. So, most of you would have used Matlab for maybe image processing, signal processing. Is there anything else you used Matlab for? Sorry. Computational analysis, that would be mostly core functions. I don't think you used any toolbox as such. Any other toolbox you used in Matlab? Sorry. Machine learning, okay. That is a kind of a niche topic. I don't think we'll be going to machine learning right now. So, these are the various toolboxes that we are developing in Sylab. So, we hope that with these toolboxes, we'll be able to meet requirements of most UG students. And among these toolboxes, we require interns for these three toolboxes. So, have you guys seen the requirements that were posted online for these three toolboxes? So, I hope you know that we require students with some image processing and some computer vision experience. If it's not there, I don't know what to do. But please, I do encourage you to participate in this internship. Now, has anyone any experience in optimization? No. No, okay. At least any C++ coding experience? Yes. Okay. Good. So, we need C++ coding experience for all the three toolboxes. I did mention Python for, I think, computer vision. But we don't require Python. We need C++ coding experience for computer vision. So, in image processing, we are close to completing the toolbox. What's left right now are mostly GUI-based functions and functions related to, you know, geometric transformation, spatial referencing and all. Even if you don't have a really extensive experience in image processing, that's fine. You would be able to work on the GUI-based functions. So, apart from that, we also have a couple of import-export functions which are related to special file formats. And these are the tools that we're using. It's okay if you don't know how to use Octave, that's fine. If you don't know how to use OpenCV, you would want you to know OpenCV, but even if you don't know it, it's okay. But C++ is a must. Now, in computer vision, anyone is experiencing computer vision here? Only one. We have different categories within computer vision. So, these are the different categories under which we'll be developing functions. Again, C++ is a must. Hopefully, you would have some experience in OpenCV. Even if you don't, I would want interns to at least have an interest in learning new things. And after this, we have optimization. Again, it might look really difficult right now, but it isn't because the libraries that you'll be calling have already been written. So, you don't have to actually write anything new, but you'll need to know how to work with C++ and pointers and how to call different libraries using Salabepia. So, I do encourage you to participate in optimization because you will get to know a lot about the underlying mathematical principles for all these optimization problems. Okay. All right. I just have a very short demo of what we have developed in image processing just to entice you guys to join the toolbox internship. So, we have two functions here. Have you at least heard if I am read? Yeah. Image processing. Yes. Okay. So, we have something similar. We have developed, we are calling it raw underscore I am read. We can always change the name. That's not a problem. So, we are reading an image called tape.png. And first thing is I am show. So, I'll just execute that and I'll show you the image that has been read. So, this is the image that has been read. Okay. So, if you can see this, it is actually a tape and you can see something circular. Now, I am find circle is a function that the interns have written where given an image matrix, it detects circles within that image for the given radius range. All right. So, it has, I think it has already calculated the center and the radius. That is the center, the X and Y coordinates and this is the radius. Now, you may ask, how do we know it is correct? How do we know if the circle is, the circle that has been detected is correct? Let's do one thing. Let's draw a circle with the same center and the radius. And if you see, there is now a circle around the tape. So, it shows it has detected a proper circle within that image and it has drawn a circle with that same center and the radius. All right. So, these are the functions that have been developed across the past one year by different interns and we would want your help in developing further more functions of such sort. I want to end with this note. One of the interns who worked with us last year during the summer, she's interning right now at Microsoft. Another intern who, I think, she was also here last summer. She's now placed in BPCF. So, if you choose this project and you work really, really hard, you'd probably be able to use this internship as a stepping stone for something bigger. All right.