First up, I'll give you a heavily unbranded slide, and that's because I actually wear two hats and I couldn't really decide what I was doing. So I didn't really want to brand heavily with either organization. So I'm doing this privately. So the two organizations I work for, the Open Geo Spatial Consortium and Surround Australia, where I work with Nick Carr and many of you know both of us. So the title was given was improving vocabulary, content and access, interoperability using profile and content negotiation by profile, which turns out to be a slightly huge subject. So I'm going to quickly go through a few things. Some of the things Nick's been working on and a few perspectives around the work I'm doing at OTC, in particular, which has been driving my involvement in this process. So introduction, well, that's me. So the first thing I'm going to presume that people are largely aware of this concept of fair, which is quite popular coming out of the European Union about interoperability, talking about things being findable, accessible, interoperable and reusable. And there's a link there to a write-up which will be updated shortly when we introduce the stuff I'm going to show you, which is the VOCPRES application, which Nick I think is previously described. And so there's a write-up there in a reference, we won't have time to go into much detail, but it talks a little bit about the nature of interoperability from those different viewpoints. And ultimately, most of those things actually are reliant on being able to understand what things mean, which means the idea is that there are pieces of information that can be semantically grounded. You can find out what they mean. You can follow somehow the metadata trail to a definition. But this actually implies that that metadata is fair too, that when you find the metadata itself, it should be findable, accessible, interoperable and reusable. And that's where sort of recovery services obviously come in heavily, preaching to the converted here. So I'm going to go into a little bit about the nature of profiling, because it may not be that obvious for my experience working with other groups, but it's actually quite a common practice, either implicitly or explicitly. Generally speaking, when you have a general purpose model, like SCOS or a DCAT or OWL or anything else, just try and get an addressing standard. When you actually try to apply it, you tend to come down to say, well, here's a simpler view of it that I need. And here's the constraints. Now, I'm not actually going to have multiple possible authors. I'm actually going to have one responsible author and it's going to be a member of my organization. So you end up with constraints when you actually implement anything. So from the fair perspective, profiles are easier to describe than a whole bunch of such constraints. If you have a whole suite of such constraints and you want to find data which follows them, how do you do that? It's actually not that easy, but if they form a well-known profile, you could potentially ask, okay, show me everything which actually conforms to the RDA vocabulary profile. Maybe more meaningful or show me anything which conforms to the Australian government geospatial data profile. Something like that is much easier to ask for than trying to detail all the constraints and try to match. Accessible means that the profile has a name. You can actually ask for the data, potentially by profile, if there's more than one option. I'll show you that in a second. Interoperable, it means that you can declare your intention to be interoperable across a suite of these constraints rather than trying to have all these constraints rather than trying to have to interpret and analyze all those detailed constraints, many of which may be written just down in words somewhere. And because they're named objects, they can be reused and they can also be inherited. One profile can constrain another profile. So I'm going to go into the deep side which probably Nick wouldn't have gone into. Which is the OGC view of the world of why we're interested in profiles is that we're aware that in the real world, there are lots of different types of information models. And we've really had a one-size-fits-all data modeling methodology for standards. But when the right-hand side, the implementations, people are doing different things with those standards with different levels of detail.  And then we want to use that. At the end of the day, we spit something out to the client at this far bottom right-hand corner who might then want to go back and find out what something actually means. So we end up with a range of different types of models on the left-hand side, this dimensional, how do we get to organize things. Then we have these general-purpose models like observations and measurements or SCOS or that. Then we have domain concepts which is where our control vocabruse tend to come in but also specific relationships. From the field, we tend to get very simplified message schemas come in which refer to those things but don't have a lot of detail about the model. And what tends to happen is we tend to bind the vocabruse to the data models by some sort of implementation profile which is our unit of interoperability which allows us to simplify the implementations which allows us to generate these sort of common schemas which include metadata. So allow us to aggregate simple data into these data warehouses. That's a really, really typical pattern but what we're starting to know and probably it sounds obvious but this middle tier of actually understanding what a profile is and the fact that it's actually a thing which people do in order to be able to use both general-purpose models and domain vocabruse consistently hasn't really been formalized in the past and that's again the work Nick and I have been working on is formalizing that. So I'm just gonna jump to a couple of examples if I can make that go away. Okay, so here's an example of a vocabulary which is a profile which is a asset description metadata schema and this is actually a profile of DCAT which it says down here for which identifies a whole series of different terms which come from other vocabruse which are recommended for use and declares just a few of its own where it says, okay, here's a particular one that we want to declare because none of the vocabruse we're profiling supplied. So this is a very, very typical pattern out there in the world and at the moment the profiling description is a document. This document is a declaration of the intent and there's a turtle vial or a vocabulary vial which generally speaking is just an aggregation of all these things come from different places. Really, really common problem. Okay, so that's just a bit of grounding the fact that profiles kind of exist in a real and what they are. The next aspect that Nick wanted to talk about was the role of profiles in particular around the publication of vocabruse. So the tool which I believe has been presented and is sort of reasonably well known vocabruse is an example of a model that, so a tool that actually uses a profile of SCOS. There's certain types of SCOS aspects that it's looking for. It depends on it recognizes and it presents those and sure enough we can walk through. And this is an example consistent with what we've just seen where this is a vocabulary extracted out of a data model. In this case, I'm a standard hydrology data model I'm familiar with. If I, but where it sort of becomes a bit more interesting is the fact that this data model itself may be available in different forms for different people. And so the idea of there being a set of alternative profiles which are available that you can ask for. So we can potentially ask for, this view of available profiles is itself a profile. It's called the alternate profile. But we can also ask for weird and wonderful things like showing me the class diagram for this particular resource. So that's a profile which is in a different representation paradigm but it's a subset of the data. We can also ask for, show me the L model. In this case, we showed me the L model as HTML or you can show me the L model as link data and JSON if I think this should be existing yet or we could show me the L model, no. So we have content negotiations, standard content negotiation plus this negotiation around what type of representation do I want. And there's an example here where this concept of punning comes up and the object is accessible as, this is actually a little thing I have to fix which is why this isn't live yet. That's actually, haven't quite got that bit right. So it's actually a visible as a vocabulary. So effectively, we take a SCOS profile view of the L object and say, right, every declaration is a vocabulary concept and we can look at it through the lens of SCOS through this tool. So that's VocPres and profiles interacting via and this concept of content negotiation by profile. And there are standards for each of these things and the definitions are available. For those via the link I had earlier the OJC definition server, which was, so this actually has links to all those standards. So it's actually reasonable, the good starting point for just understanding the concepts and the nature. There are also links built into VocPres, I think it talks about, I think it's got a bunch of, actually sorry, that's not relevant. I'll skip on. So there's another profile which Nick's been working on using the same link data API and that's a profile based on DCAT. So instead of a profile based on SCOS for documentation, it's a profile based on DCAT. And in fact, one of the things that we're in the process of setting up is a DCAT catalog of available profiles. So it's a little bit circular in eating your own dog food. As here's an example where the entry in the DCAT catalog is, so let me just go back to the root of it. There's a series of available profiles which are available and each profile itself has a series of resources or distributions which are available for themselves, which are available for this. And so that's another data profile and it's actually a catalog of profiles. So the ability to make these profiles reusable so that the content negotiation by profile is starting to use a bunch of reasonably well-known profiles is the work in progress. So at this stage we're at is that Nick's developed a whole bunch of profiles around his work Australian Government Link Data Working Group. I've developed a bunch of profiles around my work with the OGC in terms of what they need and we're currently looking at consolidating and categorizing which of those are reusable and which of them are idiosyncratic to our own organizations. So finally, this is an example of profiles being generated from data models in this case an application domain in agriculture. And we can see that this profile is a profile of a couple of different external models around agriculture and a whole bunch of resources are available, shackle constraints for the profile. So we can basically find a shackle derivation. And this is okay, that's kind of interesting. Profiles is also a way to find all the representations which is a kind of profiling of the underlying model. And there's also some tooling which is Nick probably wouldn't have gone here but I'll show you which is work I'm doing which is generating all these different versions of a vocabulary, all the different profiles automatically from the underlying information model as well as analyzing the external context and finding out what. So going back to our first pattern here, analyzing this and finding out actually what constraints does that put on decad? What constraints does it put on SCOS? And actually building the set of available profiles for that vocabulary, in this case a data model fairly automatically. So spitting out all these different flavors of profiles for a vocabulary. So that's a quick overview of a suite of interacting pieces that are held together by this concept of the profiles model which is our ability to say how does this set of constraints relates to another model or another set of constraints and what are the implementation resources which are relevant for a user to use that. So and then that profile model itself is then driving the presentation layer which basically knows how to do things with specific profiles and offer all the different profiles which may be available to the user. So it kind of links it all together. So it's been this missing piece of glue around all these different disparate practices about we have our models and we have our needs to use them in different ways. How do they all link together? And I think that's probably a good place to try to stop.


Did you ever dream when it all started you'd be playing for no a little bit all started in the house by the way That's just sort of playing you for a lot. You're right But I mean I played a little in college, but once I got to the NBA obviously You know everybody was six nine six ten so I was like oh good I'm back to the wing position can put together and put on for all of these young men We talked about that quite a bit but that's why you see so much more talent different types of players and bodies and How will they can perform because they're starting to take care of themselves early and understand the things you need to do To put yourself in position to be a successful player, but that's on and off the court as well Well, I think for them now coming up. They have the luxury of the cryo and range may have missed Head of the other way Allie Marshall. She doesn't miss some point blank right now. They've gone back in front 17 16 Long two-pointer off-target Spassowski, though right place right time for the putback and sometimes those long This sells a chance to win and same goes and for on the other side with Europe There's rules that you can't play a zone until you're 16 years old So they all play man-to-man, but coach was saying to me less of it sometimes is about defensively Experience in this situation and understands how important big shots are in Time and score and drive through the defense though speaking of the big shot time and score and with that it's all over And the first of the six championships That could be one over the course of the next 24 hours is the Europe girls in the international bracket 45 42 final over Asia Pacific


open world with you guys. So we're here with Satish Lakshmanan, who's at QLogic, a very knowledgeable Satish. Great to have you back on the queue. Thanks Dave, great to be here. Very knowledgeable individual about a lot of things. We're going to talk about convergence. You're in the host solutions group, which is a real core of QLogic's business. A lot of people might not know. QLogic essentially builds the glue between the servers and the storage and the network and makes all that data flow and makes it such that we don't lose the data, which is very important, and it gets there fast. So QLogic makes IP that essentially sits in between those key components and has a lot of secret sauce to make sure that data's reliable and secure and as I say, fast. But so we're going to talk about convergence, a favorite topic of ours. What do we mean by convergence? Bringing together storage and networking and servers. How that fits into virtualization and cloud. Well I mean QLogic last year Dave, I mean at least from my perspective, really you guys moved the needle on your brand. I mean I think QLogic last year really emerged out of the card business and established your brand as a data center brand with the switches and the technology. So congratulations. You guys are always in a horse race with the OEM deals. You guys had great success last year and I thought it was a great year. You guys did great and so how do you top that? What's next? So tell us. If you think about QLogic's pedigree in history, we've been more of a fiber channel storage infrastructure company. And with convergence happening, our opportunity to grow, whether it's 10 gigabit ethernet, whether it's InfiniBand, whether it's FCOE, which essentially is block based storage over 10 gigabit ethernet, these are all net new growth areas for QLogic. So I think the opportunity's out there. If you follow some of the industry analyst reports, if you look at the growth opportunity for just pure 10 gig, out in 2014, if you look at Delaro, for example, or Creehen Research, we have the opportunity to pretty much double our TAM, this total available market out there through the products that QLogic brings to the enterprise customer base. So I think the opportunity is there for us. The challenge will be to produce the right products that end users want, the right products that OEMs are willing to qualify and embed in their portfolio. And it's for us to capitalize on that growth market. So we talk about convergence. We talk about the bringing together of servers and storage and networking into a single technology. So are people doing that? Why are they doing that? What are the reasons for doing that? Sure. A great question. I think, yeah, convergence is definitely happening. If you turn the clock back to three, four years ago when Cisco and QLogic and EMC and all the other partners in the ecosystem introduced FCOE, people were skeptical about it. People understood, at least on paper, the value proposition of FCOE and the benefits that it brought from a total cost of ownership perspective, reducing the cabling and complexity at the edge. And people now are starting to embrace that technology and actually deploy it, not just in their development environment but in actual production data centers. In fact, a few months ago, they will be published a case study along with a healthcare customer for the healthcare vertical, Payformance. They're key to billing type of applications when it comes to healthcare facilities. So Payformance actually adopted QLogic's CNA technology, tried to basically test and evaluate the value proposition of FCOE initially in their development environment and I've gone around and deployed it and actually running traffic today over FCOE. So it's happening, albeit maybe a little slower than expected, but I think the position of QLogic right now is a very good one and I've used this metaphor earlier. We kind of like the arms dealer. We want people to embrace convergence if that's the problem that they wanted to resolve in the data center but at the end of the day, if FCOE works for them and continues to work for them, they should go ahead and deploy a fiber channel, native fiber channel. What about the cloud? Well, let's talk about the cloud or at EMC, cloud meets big data. What's your cloud strategy? What's the unique value proposition that you bring to cloud computing? So great question, Dave. So if you think about QLogic as a technology provider, so are we provide our controller chips to EMC as a matter of fact that power their platforms and allow native fiber channel or FCOE connectivity to the fabric and to the backend? So we are part and parcel of EMC's big data and cloud strategy. But if I were to use a metaphor for cloud and I parse it down to the three layers, which is infrastructure as a service, platform as a service or application as a service and I think about the adaptive convergence, technology and capabilities we introduced into your product in October last year. So what we allow, so if you look at an infrastructure as a service customer for example, today in the cloud model and if they want to be able to support multiple protocols simultaneously in the same hardware, they can do so through QLogic. So different customers, whether they want native fiber channel connectivity for backend, if they want pure ethernet connectivity for the front and mid tier of the data center, if they want infinite band for high performance computing, they can get all of that through QLogic technology. So we provide enabling technologies to infrastructure as a service customers. Let's talk about platform as a service. So we support multiple VMware, Microsoft operating systems, we're tested and validated across all those operating systems. So we're in a great position to be able to provide platform as a service vendors, that abstraction layer so to speak from underlying infrastructure and we're tested and validated and today we ship more than 10 million fiber channel ports. So really proven implementations across multiple technologies. Let's talk application as a service. So for applications as a service providers, virtualization puts a lot more burden on them when it comes to pure IO performance. They don't want IO to become a bottleneck when it comes to serving applications and as you virtualize and put more applications in the same physical server or physical storage, you basically want to make sure IO doesn't become the bottleneck. So our technology which allows for IO virtualization whether it's Nick partitioning or whether it's industry standard virtualization like SRIOV, through the deployment of that, through the deployment of full offload. And IO is hot too because I mean look at flash, right? I mean I think the flash movement points to what your value proposition has been for years got to make that IO work. And I think that data center focus is really key and what I'm wondering is how does that extend out into the chip side? Because you guys have that chip vision, the ASIC vision, how does that all come together? Can you give us an update on that? Because that integration point is the number one thing people want to know about. So what our vision right now is to support multiple protocols, simultaneously a one piece of hardware. There are certainly development efficiencies involved in making that decision from Qlogic's perspective, right? We don't want to have different product lines, different architectures supporting fundamentally the IO's. So we believe in having a single chip that supports multiple protocols whether it's native fiber channel, FCOE, iSCSI block based storage or file based storage over TCPIP. We want to be able to power that through one chip. So it's convergence, true convergence from a hardware and software perspective and Qlogic completely embraces it. You talked about 10 million fiber channel ports out there. I thought fiber channel was dead, Satish. Is fiber channel alive and well? Oh, it's a great question. I don't think fiber channel is dead at all. Recently you saw a spate of announcements about 16 gig fiber channel. Qlogic has continued to invest in 16 gig fiber channel. We have several OEM design wins and products under development right now that I unfortunately can't publicly talk about. So it's definitely not dead. We're investing in 32 gig fiber channel. So I think it's going to be healthy and it will continue for a long time. Yeah, I hosted, as you know, a panel out at SNW and the fiber channel industry association was very active in that panel. It's got a strong roadmap. I joke because a lot of people say, oh, fiber channel is dead. But if you want to save mission critical data or store mission critical data, you're absolutely more likely going to store it on the fiber channel. We've been having a lot of conversations. Dave and I have been having a lot of conversations about specifically IO in context to the IPO of Fusion IO and everyone's talking about SSD flash as the innovation in storage. Multi-cores. And it brings up a lot of philosophical conversations around the future of the data center. And my vision is a little bit different than some of the old school analysts that are out there. No offense, I'm not Dave's one of them, but a lot of Dave's friends. Like who? Like Steve Duclessy, our old school. And my philosophy is that people are looking at Facebook as a one-off. I don't see Facebook as a one-off. I think Facebook will look like a lot of the data centers of the future. A lot of open source, a lot of big data, a lot of IO, a lot of applications. So that's your wheelhouse. You guys are playing and I know you can't talk about it, but from what I've heard, a lot of the big data centers like Facebook and Google. What's your, how do you talk about that new environment? How does Q-Logic see that? So I think you're exactly right. It's different from the traditional view of the data center. Our participation in it, we're an infrastructure company as you mentioned when we first talked about Q-Logic. So our desire is to sell infrastructure to whoever, however they deploy it, right? So that's why I talked about our participation in the cloud. It's whether it's private cloud, public cloud, hybrid model at the end of the day as long as they use Q-Logic technology. Whatever protocols that they want to support, we are willing to support anything, common piece of hardware. So for us, it's providing all those- You're not getting in the way. We're not getting in the way. We want to enable, provide all those enabling technologies that lets them deploy and use applications, how they see fit in a virtualized or a non-virtualized environment. So how about innovation? You know, where is Q-Logic's roadmap taking it from an innovation standpoint? Where are you putting your efforts and you've done a good job of differentiating? You know, you made a really big push to own your IP, to invest in Silicon. You've made some acquisitions to get into the Lanham motherboard business. What's next in terms of innovation? So in terms of innovation, for us, we believe in having a single architecture for all our products.  We can support multiple protocols whether it's a native fiber channel, 10-gubed Ethernet, which is TCP, IP, FCO, and iSCSI. So that's the first piece of it. From an innovation perspective, which is the question you asked, I think there are basically four pillars where we are investing. We want to make sure that you can support multiple protocols in one piece of hardware. We want to make sure that we have full offload capability, so it's not you chewing up CPU resources, but basically we're offloading all of that IO protocol to the hardware, so CPU and memory resources available for applications. We want to make sure that we provide the right security for cloud environment. There's so much discussion these days at EMC World about cloud, but security is definitely a big concern for the end customer. So we believe in providing IP sec-based security for TCP, IP, or iSCSI traffic. Future products will support FCSP, both encryption and authentication. So we want to make sure we embrace security technologies and of course virtualization. IO virtualization is something that we're focusing on and we want to make sure that we are able to provide either single root IO virtualization or N-PAR, which is Q-Logic developed technology that provides a lot of differentiation to OEMs because it doesn't depend on a switch for VM to VM communication. And also, we support OEM proprietary IO virtualization technologies like Flex10. Yeah, so you've always had a culture of R&D innovation. You guys have been very proud of that. Your former CEO is really well known as a visionary, right? HK is really widely respected. John mentioned up front that you guys had a good year in terms of just transitioning the brand. You know, you basically announced the deal where you were second sourcing switches to HP, kind of popping Cisco out. Of course, HP and Cisco are involved in a urinary Olympics and everybody knows that well, but so that was for two years for you. You're not viewed as a competitor to HP as Cisco is. How's that FC switch business going and what's the future hold there? Oh, a great question. I think it's done very well. So if you follow some of Delaro's market share reports, you know, the last two, three years Australia gained market share, where it's 16% of market for fixed switch ports. So overall, our switch business, I think, is doing really well. Our EMC Select actually carries our switch portfolio as well so they can fulfill that to the end user. So there's a lot of opportunity for us through the different channels that we have, the OEM channel, where OEMs like HP have embraced our ad switch technology. We'll continue to fulfill that. EMC Select will continue to fulfill it. And Q-Logic brand through Q-Logic's go-to-market. So I think there's tremendous opportunity for not just fiber channel switching, but also for infinite band products, post and switch products. Excellent. So Q-Logic transitioning the brand, leading in CNAs, you know, leading in FCOE, innovating, talking about, you know, the pillars of innovation. Satish, great as always to have you on theCUBE. Thanks very much for coming on. It's great to meet you guys and thanks for the time. Great to see you again.


All right guys welcome to the one percenter podcast. It's Andy Elliott. I'm here today with my man Kenny Kenny's killing it in the space He's a badass business guy. He's a killer entrepreneur. He's a coach He does all kinds of cool stuff But listen to me if you want to hear one of the coolest comeback stories in the world and talk about like Overcomers which you guys know I'm really attracted to Overcomers a lot of you right now that you're watching you've had things happen The reason why you're not going to the next level is because you're holding on to your past It's time to forget that once you hear his story You're gonna want to go to the next level.  It's like the Elliott group language is like reinvent yourself like we just say it every day when I see this happen I try to find these stories so I can share them with you guys because I really think there's a lot of haters out there that anytime You try to go to another level in life or you did something, you know You don't feel like you're worthy of it like everybody wants to just pin you down to your old life But that's the past. That's not who you are failure is an event. It's not a person You know failures the greatest teacher.  And that's what Kenny did so Kenny I'm here with you today number one. We love you. We appreciate it. He's a part of our family. He's in our trainings He's always here with coaching and by the way, he coaches to notice the greatest coaches the greatest, you know Players they're always training, you know, I'm saying like like let's just find the common and on a Dominary with successful people is that the successful people the most ones are always Consuming more knowledge, you know, they want to they want to self-develop faster There's they're always learning what you know successful people learn like they're always, you know learning So like what I'm around Kenny like he's ultra successful But he's always digging in because he's like man, I know there's more, you know If you're doing something like I want to know what you're doing and I love that and I'm obsessed with that And so is he so as I introduce Kenny Kenny tell us about yourself.  Look at Kenny. So tell us who you are what's going on and let's crush it. Yeah My name is Ken Baden. I'm the owner of PCR roofing, which is an acronym stands for Potomac customer modeling We dialed back on anything else other than roofing man We wanted to just really Lades our focus on what we do best, which is roofing, but I've been in that space for 16 years, man I've been in the door-to-door You know, I started selling windows, you know, and and you're right by the way, I do have a past Where are you out of Maryland? Okay, you're out of Maryland. Okay.  We just got started. Yeah, I was gonna say man It's hard for me, you know, that's one of you know So I just started working with you as a coach Ryan Stuman is one of my coaches and he would say that I'm brutally honest in that I don't I have these super high goals like way up here I go with by December 31st 2026 to be at a hundred million fifty million Net ten million dollars at EBITDA, right? That should get us a 10x Do the math, right? So right and that's the goal and we will do it to your point. We will do it Yeah, I have all the proof in the pudding. Hey guys, what's going on? It's Andy a lot of you leave comments telling me that you need help do me a favor I'm gonna say the best way to get a hold of me. Shoot me a text message right now 918 210 254 918 210 254 I'll help you with whatever you need.  Let's get back to the video PE is talking to us now And if you are in the blue collar spaces, which is why we created blue collar ballers the coaching group I don't think people really understand man. The opportunities that are out there like prior people are the rich folks that are in Private equity are looking to invest and they found the blue collar in the home service spaces So there's opportunities. We want to make sure that folks not only know about them, but you know Know how to exit know how to scale know how to sell yeah So if you're talking here, and he's like hey guys in two years my goal is to exit on a hundred million dollar company Let's let's let's unpack this. Let's rewind. Okay. Let's go to like You graduated from Maryland or what? school I went to Maryland for one semester and I followed my girl had the time To Frostburg, which is a you know a little D3 school and the mountains a little nowhere school But it was a blast. It was a lot of fun and right around then my freshman year of college I got into a car accident car accident led to Ever seen that show or is it pain kill you kind of watch much TV, man? You're always no, but I get it I understand she got in a bad car and then I'm guessing they prescribed killers and then you know the story writes itself but look man, I went from college graduate and I don't know man homecoming gives normal guy, right and To convicted felon absolutely Just a total liability of a human being to where I was You know, I started going from pills to heroin and that didn't happen overnight But like you wouldn't think I certainly didn't think that my family certainly didn't think that that's not who they raised My father's former military none of them knew what the hell any of this was, you know We didn't know what it was and back then they were much more liberal prescribing this stuff But that's not an excuse man. That's what happened. I did I did it and I went homeless two different instances Again, I you know, I try to rob a store that got me into Jail for they don't they frown upon that so that got me some time Got me a felony and by the way anybody watching this right now I guess he's talking about it like this is one of those things that I want you to know that it doesn't matter what anybody goes through What matters is who you're gonna become now, sir And so as Kenny's talking to you right like I mean clearly we just start I could just talk about everything cool that he's doing right now But I think the most important thing is hey when you were homeless like how did you get out of that? Okay, when you know when you were struggling with drugs, how did you get out of that? You know when the car wreck happened right that wasn't planned no like do like deaths happen in families. I got a good friend of mine Their best friend. He's 37 years old. He was at their house two nights ago. He just died Like he didn't wake up like he was perfectly healthy. He's got two kids and a wife like like their whole family now is in turmoil and Just last week they were talking about taking over the world like like like my point is is that you don't ever know right? Like what happened so the car wreck happens you get prescription you get in getting hooked on that Which I know a lot of people that that happens to you start hanging out with the wrong people and then talk to us So how did how did you get out of this stuff? How did you say what you just said by the way because it's a total family, you know The families are really the ones that suffer the most because brother They just get completely ripped apart But they don't get any of the relief or experience or any of the things that come with using these substances, man And I completely shattered mine. I mean I destroyed my grandma's boy my grandma You couldn't tell her I could do anything wrong even when I stole from her.  We're living a world right now where people aren't honest anymore Yeah, no, they're not okay. Listen to me people won't tell that stuff because they're ashamed of it You know what? I love I love who you are today. Yeah, and I love that when you tell this stuff I'm sure that every person they could be like, oh man, but slow down look at your life Do listen to me.  I did this I did that only these people reach the highest level of success Because this is how you you do it all wrong before you learn to do it. All right in most cases Okay, and I'm very descriptive and very so I didn't used to be Ryan actually encouraged that right so I have a private Facebook group I hit all that right and I thought if I could just do enough like I'm gonna go out and do 10 million I'm gonna do all these things.  I know it's a lot I've built a couple seven figure companies Soon to be eight coaching group multiple podcasts, you know, I've got somebody who I followed like Ryan That's now a good friend of mine that we're doing things together.   I came back because I got dropped off in Calvert County, Maryland as opposed to Charles County, Maryland If I had been dropped off in Charles County, Maryland, I would have stayed homeless and I'd be dead by now And by God's grace explain that So one area is very rural and there's absolutely nothing there and it's just so the girl I was dating this was after many many attempts man in and out in and out families gone Rightfully, so they can't they can't deal with it. All right. Love me, but they can't deal with most people They don't change so they hit rock bottom and this is like an even deeper, right? Well, I thought my rock bottom was robbing the store mind you, right? So I'm like, I'll never go back to that. Well, nope This was this was worse I won't get into the gory details man But I tried to take stuff from the people that had it and it didn't go so well for me So I'm completely beat to shit And I had done stuff like that many times and the girl that I'm dating at the time who was the last person Who would talk to me is just like, dude, I can't and drops me off in the middle of the woods really and that's what saved My life man because had she taken me where I wanted to go Which was where all the other homeless folks were to go do homeless people shit and beg for money And this is a college graduate somebody comes from a good family and I have been reduced to that And I was okay with it, dude I was ready to accept that for the rest of my life but because of God's grace Which is not you know, that's not earned as God's grace.   Yeah, so it's so simple. You just ask for help That is a theme in my life brother that That was the first time that I asked for help Because before that I can't tell you how many times I over complicated like I got this I got a will why can't I will myself? I'm a strong guy Can I will that is a huge lesson right now a lot of people they need to get help and I mean it and by the way like listen Me that guy that guy that got dropped off in the woods a guy That's totally lost ready to give up completely Just done ask for help and now you are here you are today and looking at you man You're a good-looking guy. You take care of yourself. You're well dressed.  Well, like dude Like no one could put that that person is you today So you go ask for help. What happens? And it's a slow burn man from there Yeah good to rehab And it's you know, I'm again, I'm gonna spare you I didn't really want to go but I was out of options So I went and again man like I asked now I asked and this is a process and for the sake of anonymity and some of those back things in these 12-step programs They like their anonymity.  So like who cares? I don't care Personally, but I asked somebody in one of those communities to sponsor me mentor me, right? And he did and Next thing, you know, I catch myself wanting to stay longer in places like rehab Where I couldn't before I'd just be itching to get out and fix things right away And I'd always been in sales mind you like throughout this whole time Even after the pain because I'd get some glimpses of sobriety and I could sell so they'd always hire me back Well your door-to-door which is the greatest salespeople that ever exist and they'd always hire me back But I couldn't put any time together man So I'd make a lot of money and burn it all down make a lot of money burn it all down get a management job Murden all down VP sales that wasn't until my sobriety stand this time So anyhow, I get back in and I asked someone for help to show me how he did it and there's a theme here because I believe There's a real theme in the parallels between the recovery world and business I hear Bradley I hear you guys talk about things like gratitude and a lot of these things are that are prerequisites and the things that we need To be successful or the strongly suggested things if you want to those are things that are life Like musts and sobriety or your ass is dead, you know, so like here's how we did it you can take these suggestions or good luck, right and The gratitude thing like if I don't stay in gratitude, I'm dead, right? If I don't give back. I'm dead All right, I mean I can put it differently in my only but that's ultimately what it means, right? And truly there's fates that are worse than death and one would be going back and living like that No, but it's put a bullet in my head and it would never happen. Yeah, you know for me No, but it's not an option. No, it's not an option So so how do you keep yourself on your a game now like how did like what were the things you said? You found somebody they taught you kind of how to get to that next way. So obviously it sounds like you found a mentor so that's a mentor right and then They call him a sponsor, but again and so next thing, you know, it's a couple years ago But I start to get gained some confidence because at this time, brother. I had I mean nothing nothing I'm in a halfway house. I'm 32 years old. I'm taking my life server. I'm on food stamps and government assistance I don't have a driver's license. I don't have a car can't get anywhere Some guy and I asked for help. I said I need a job. Hey, I need a job at a meeting next morning Somebody wakes me up at 4 a.m. It says hey man, can you paint? I can paint today. You know what I mean?  He didn't need my help like that. He was helping people He picked me up 30 minutes to and from my halfway house We worked on the house next door to him. He just was doing that to help dude Yeah, that was such a great thing for me to see like this dude doesn't yeah Now you're paying it forward. That's the reason why you care so much and yeah Just and he died recently man But like that guy was at my first year anniversary and like it just meant it just was such a good foundational piece Like you mentioned something about building that foundation for me these examples of gratitude and like this is what you do This guy has 30 plus years and he's picking my ass up doesn't need to doesn't need my help He's finding work for me all because he thinks I got a chance to make it. There was two of us originally He didn't pick the other guy up the next day that guy's attitude sucked that guy was late. He said you'd be here at this time I'm driving to come pick you all up if you're not out here He come out with his little pajamas on trying to pull his shorts up He said he just said get in and just took me and that's how I got the job But he liked my attitude he liked that I was I was serious about staying in getting some help and then from there It was just one little step at a time man.  That just makes me want to do it more We were just talking about yeah, so it fuels you but With this man, it's like my dad was like can't how do you eat an elephant one bite at a time man? Because for me, I wasn't so much debt Andy again. I didn't plan on coming back.  I can't do anything about it anyhow I'm a player bankruptcy. My dad thought that was the idea the woman. I'm married to now I just got out of law school and I got a message in my phone because I got caught trying to rob a friend of mine's house And this is just one of the many things I she said if you come into my graduation, I will call the police We're married now. You know what I mean? She's an attorney, you know, she met with me. This is after I got sober I said I need some help again. I need some help. I need some help with my finances I think I got a declare bankruptcy. Would you help me? She met me at Starbucks At this point I had saved up enough money to get an s10 a 1998 s10 with 300,000 miles that leaked clutch fluid, but I bought it for $900 It was like a thousand he won like 13. I'll like give you nine dude. He was like alright So I gave him the 900 bucks cash had a little dot three with me I would get me at this point, too I had gotten a job with another guy that helped me in recovery. That's a hey I'm in retrofitting lights. It pays more. It's a real job.  This is a truck Yeah, so I got the job got the raise got the 22 bucks an hour Which I really could use and I needed that man that company ended up folding, but it was like It got me to the next phase and all of these things are like little steps I had to make and asked for help help from Becca who is now my wife who said you're not declaring bankruptcy, bro Like this is like 60 70k not minus your your don't count your student loans because they don't count them like that So it's not as much as you think you can get out of this And I trusted she was a hell of a lot smarter than me like you feel about your missus Okay, if you say it cuz my dad was like yep sounds right go go declare bankruptcy. I'm like cool pops Let's do it. I would not have my business. I would not have my homes This gets me emotional thinking about it because now she gets to reap those benefits of that decision that day And if you don't think God was involved in that, I don't know what to tell you Hey guys, what's going on? It's Andy a lot of you leave comments Tell me that you need help do me a favor I'm gonna take the best way to get a hold of me Shoot me a text message right now nine one eight two one oh zero two five four nine one eight Two one oh zero two five four. I'll help you with whatever you need. I got your back for life Let's get back to the video. You know what I mean? Well, and you had to make a decision to trust her.  I was like I was unfinished business here.  Yes, man. That's the theme and then not being Before this happened.  I'm gonna be honest.  I had no Goals no real aspirations.  This is the best thing that ever happened to me So if you think that I'm gonna hide details first of all, you know, many people hit me up in their DMs like brother I need your help. I need I've hired people in recovery I mean like and that's worked out and has it worked out But like I'm gonna tell all the gory details because I know that that I know where I'm going Yeah, it's up up up. Well, I think I think that everybody needs to understand that when reason why I like these podcasts The reason I like social media, especially from real people is because it's refreshing It's refreshing to hear that people can lose It's a refreshing to hear that real people that have real problems That can really make a decision to either crash or rise and to look at the situation Like you can watch this and you can be like damn man Like I'm listening to you and I'm like dude a lot of people think they got problems Dude, you don't have any problems like you can't overcome what you're going through and look at what he overcame Like this is crazy.  How'd that guy get so lucky?  I just got a bank account three years will change everything. So so how did that? So how'd you start your own company?    I really didn't so I know I don't have another way Like this is completely God's grace completely so and how hard it was to get back But it really also got back so much faster than I would have ever thought man.  That's my first place. I got to live on my own She's like old school taking cash I'm like, cool. No background like really like me.  It was awesome. Like everything I did everywhere. I went every next step was just like Yes, and and then I got into sales again, right and now I'm all right. I'm back in this and I'm on fire, dude I'm in roofing sales now back, you know, I started years ago selling windows and I've been you know, I'm old I was selling windows Not door-to-door that was an appointment and then it was door-to-door then it was door-to-door solar Then it was door-to-door roofing and roofing is the the sexy thing now, right? So I go back to what I know only I get recruited as a VP. They're like, no, we don't want you to sell We want you to run the whole department to start up and I've been a part of a couple of these now I've been a manager before so I'm like, all right, cool Once you get in the industry so long, you know a bunch of people just like you do and it's like all right I'll come build this with you. Yeah, they did a little a little they were a little dishonest at the people I had but that's okay.  I had no excuses. You're all right Cool, we're gonna do it like I grinded I made calls. I built that department We had a lot of success for retail and we made it happen and that gave me the confidence to your point now I'm working. I'm the intrapreneur and I would have stayed that intrapreneur. I thought this was it I'm in I'm with it. I'm gonna build this company up.   I'm watching leaders and companies all around the world Okay, who have these intrapreneurs that are working for them who want to help them build their dreams who want all they want is trust and Loyalty and to make sure they're never going to get betrayed and they want to be able to just have a good life They want to give everything they got to build that business But you want to make sure that there's like a circle of safety in which you know that you don't have anything to worry about And then once you get betrayed and you've given all you've got Then you're like dude. All right. I'm gonna start my own business now like dude I don't want to do this but like I I'll do this versus going and giving all I got for someone else to get lied to again so if you want to build a company that is just like Insulated from the rest of the world that no one can mess with you must build a culture of trust And that's all you wanted my your situation is the same as mine. All right. Keep going. So yeah So you and this other guy who currently own your own companies and who are killing it wanted to work for this guy He messed that up now. What happens? We'd still be there now at this point I had just gotten a bank account. So it wasn't that long ago, but I knew my credit was No, it wasn't gonna happen, right? I think I might have been able to just get a car financed But I wasn't gonna get it I didn't think I was gonna get the things I needed for a business in Maryland Slowly coming back right at that point, but it was I ready. I had fear, you know, there's two themes there's asking for help and there's Realizing an overcoming fear and starting to see it in the different forms that it manifests You know, either the fear of loss or fear you're not gonna get something that you that you want, right?  No way now with my record now with my credit So I had the brilliant idea of calling a buddy of mine I'm gonna be partners, you know, he has nothing to know. There was nothing about any of this But I thought he had the MHIC license, which is what we needed He didn't but he was willing to go get it and he was like, I know you know everything there is to know about this And that didn't work out very good, man I mean, there's the whole story about like we did great the partnership didn't do so good We didn't sign paperwork We didn't do the things the right way that they suggest that you do now like an operating agreement have everything clearly Even your separation so many people make this big mistake that they believe and they want something so much That they don't think they need an agreement in place and you even said even from a separation agreement, right? Because they don't think that far ahead.  Oh my god. Yeah, but this hopefully some of you right now in the future or today Like this will save you millions. Oh my god Million you watch this and you're going into a partnership from someone who has experienced this I don't care for your brother your father doesn't matter who it is doesn't matter You need an operating agreement clearly outline whose roles are what who's gets what what's expected from who and what happens If you guys decide to split it should not be weird My wife was an attorney. She signed and did and put together a whole what do you call it prenup? Solely to protect and insulate the business Nothing else is protected That's the best advice and I've never had anybody say that but I mean I hear people say but I'm in our podcast So that's huge. So Okay, so make sure you protect yourself and then that deal didn't work So then what happened the business worked, but we just didn't get along man. I mean, well, there's no point in going Yeah, so the business well I let him I said, hey man, you know what?   The reality is is hey, okay Cool way now we have these two businesses and we had a plan to make them work But the reality is I think we both knew that there was always a chance that Given how things were going. I might go here. He might go there and never the two should you know meet I walked away from a quarter million bucks that I could really use at that time Even when we started that business, I walked away because that guy lied to me I could no longer trust that guy that owner at the last company Yeah, man, and I'm like and it was always the theme is my backs against the wall my backs against the wall I didn't have savings when I left that last company But to me it was irrelevant because I couldn't trust you to pay me So what difference does it make if I have any savings like you might not pay me So what you know, where's the security outside of betting on me?     That was it now. It's splitting these companies That's like a divorce by the way.   It's heartbreaking.  We did six seven by Year and nine months in we're approached by Owens Corning, which ours is our distributor. That's when I get that's our second Yeah, I don't know what to do with the money.  I I I've been a part of four of these Remodeling businesses that have gone to five to seven. I know how to do that. I know I can do that Scaling past that I've seen a lot fail And I've been a part of a couple that did not that I had anything but still I've seen that like that trajectory from to 20 It gets a little more dicey what you know, right? So I'm like, all right I'm gonna take some business classes because we're already at six seven shit's popping off Owens Corning Which is our part? We're platinum with them now. They're approaching us.  I'm doing a bazillion dollars this year okay, right so but We were doing it the proof is that it's real right like we're platinum You can't take that away from us and technically you're supposed to that's not relevant It means what it means and if you're in the industry, you know what it means. So yeah, we're rocking man We're from but all I can see is what we're not doing I'm comparing myself to guys like these hundred million like damn man because I've been in doing it 16 years And I'm not even appreciating the fact that in the two years that we've been rocking Comparatively the timeline shows like brother just be patient man like in a couple years You'll be like look at our goals now, right? So even this year we you know We've learned lessons. We've grown We've I've continued to learn in leadership Ryan stewman post about looking for another coaching client decide to spend 150k Find out it's taxable Again, you didn't know what you didn't know now. I find out I can invest in myself in like coaching and then I learn What you do which is you would have spent money at night school and you want to learn and like he said at the beginning of this I'm always constantly I spent a half a million dollars last year in personal and professional development.   I'm getting into his networks Stu Man's one thing. He's amazing.  You did what I want to do brother come here Please will you tell me absolutely dude? Like you just get access to all these people and if you're in roofing if you're in oh my god Give me the playbook. Here's the playbook. Here's the EBITDA. Here's what we did. Here's how we do it Anything else you want to know hop on a phone with me and my COO. Oh, I'm like Jesus Well, I take my money anybody else. There's this Andy Elliott guy Now I'm like sitting here with you like holy shit This guy is I can learn from him how to be a better leader how to be a better husband How to be a better all around just I watch how you operate and I see how important that is in the day to day The business and everything is great But if I'm not functioning at one optimally and I say I want to be the best version of myself Spiritually mentally it's funny you corrected yourself because that's the order that I do it spiritually mentally physically and professionally right and so like I See you operating. I'm like dude. I want this whatever he's got.  I owe it to my wife think about where I came from six years I didn't believe in me. I got young men and women with families Whole family the whole office full of people that are all counting on me to do the right thing They don't see me like that. They see me as an educated smart trustworthy Total leader dude.   I'm gonna change some lives, dude You know, I love it. Well, dude, listen, you probably have one of the craziest stories, right? And I hear a lot of them but like what I love is and what I what I learned let me tell you what I've learned number one Never quit. No, okay, because you probably wanted to quit. I noticed a million times. You're like God. I thought I was done Yeah, most of you at some point you're gonna say I thought I was done. Okay, so number one You're never done right number two.  Okay.  You said you were grandma's boy. Yeah, right? 27 not eight. I wish it was yeah, so it's like it's like so I still grandma's boy though. Yeah. Yeah Well, that's what I'm saying like like but it's like it's like this can happen to anybody and everybody's qualified and Maybe you know, maybe you've been perfect and maybe you've ran into some struggles. What are you doing? Are you beating the victim drum?  Are you complaining?  I think also you need to understand that he said that he found a coach Right and that he found that when he got help as a coach, which he said talked about he asked for help a lots of times The network inside of coaching companies the network inside of the people that are like-minded That are in those coaching companies any coaching company in the world There's people in there that are like-minded just like you that paid to get in there and so many people get in there just for him But you actually use those people as a resource to say hey, what are you doing? What are you doing? What are you doing? What are you doing and by the way? Also, we said he only spent a half a million dollars this last year on self-development Now I want you to ask yourself this question when I look at your bank statement, you know, what's your education budget look like?    So the idea of this entire Like the last 45 minutes has been about truly one thing proximity You being in proximity of the right people those good people that let you live in their barn that spent time with you Those people gave you a different perspective. Hell you like dude I wish I could live there how many people want to live in the loft of them of a barn But that was to him heaven. It was amazing and by the way, it probably was kick-ass and cool But the idea of it is I want you to understand this being around good people You know, that's like that's the key being around people that are positive That's the key being around people that are smarter than you like that's the key A lot of people don't be like being around people that are smarter than them because they don't they want They want to be around people who aren't as smart of them so they can build themselves up and feel more powerful Do listen the higher the proximity of being around people that are better than you the more you're gonna rise And by the way the more that you learn from other people the more that you can teach other people and now and now you're a teacher So he teaches now he coaches now. He's also still a student. He's thinking about his family He's doing all the guys imagine this all the things that he once ruined Now he's doing all those things at a really high level Like in that beautiful in that cool. So like that's the idea So where are you at if you made it to this part in the video right now? Where are you at? What are your excuses? What kind of life do you want? Okay? Who are you getting around to push you? Like who is it? Who is it that's gonna who are the three people or the five people that are gonna get you to Where you want to go and then lastly, you know, I'm gonna I'm gonna be close to Kenny for a long time You know, where is Kenny gonna be in two years from now? Okay, like what's his projectory by what he's doing now going to be in two years from now, okay? What's your projectory? What do you want to do?  Oh, I know but in two years when we watch it and people like We'll be videotaping that for sure Yeah, well everything that I said was gonna happen sometimes my team will go back and they're like Andy You said you were gonna do this Yeah, and then they show it and I'm like damn I told you fools I wasn't playing and you know, it's really you know because the future is ahead But I think right now the key is you're living in your in your current presence You're taking care of your wife. You're trying to be a better husband to her which is so cool and to your kids That's what I want to learn a lot more from you know what I mean like I do worry, but that's why I'm here Yeah, I want to learn from you man. Well wife and and all that. I know I could be better But yeah, and it isn't a cool listen a lot of you right now You're listening to him and you're like damn dude.   I want to be a better man. Dude. I want to be better to my family like this right here This is the secret to killing it in this world Well in this market will reward men like you that are taking care of their families that are doing a good job Taking care of themselves that are continually self educating themselves because if they're a leader and they call themselves the leader They want to be a great example to everybody You don't want to let anybody down You might have been a stumbling block for a lot of people for a long time just like I was but now you want to Be the light for a lot of people, you know, and it's super cool. So I just want to say Kenny We love you, bro. I appreciate you if somebody wants to join Okay, be a part of your team because I because I love this part where people like dude I want to roll with Kenny right. I'm Kenny. How do they reach out to you? They hit you up on social media. They text you want to apply to the if you're in the DMV Because we're in Maryland, Virginia, Delaware and technically Pennsylvania as well. So if you're in the DMV go to info at Pcr Paul Charlie Roger roofing calm info at PCI roofing calm You can also check out the coaching group, which is blue collar ballers and that website is Blue collar ballers group calm And we have the Facebook group look all our ballers that Sean's in Danny's in we got to get you in there big dog Yeah, how do you know that you couldn't pay?  Hey How do they find you on social media to Kenny Baden on Facebook and blue collar closer on Insta blue collar closer You guys got it Kenny's the man, dude. He's killing it guys. He's doing big stuff Question is we're going into 2024 2025 2026. What are you gonna do in these next 90 days? You know these next, you know 120 days. We're gonna do this next six months.   He became and really he became the person he needed to be Okay, so the sooner that you become the person you need to be I think the sooner the success is gonna come Okay, because now he's proud of himself and look at the accomplishments coming out of it So love you, bro. Appreciate man. Thank you guys. Make sure you hit Kenny up.  We got daily sales training videos dropping I'll see you soon


to today's class. So we are in module 3 and we are trying to understand about image classification. And the tagline of this module is, if I cannot see the features, how do I classify them? This is with respect to microwave imagery. So the contents of module 3 have been discussed as part of previous lecture, but just to reiterate, we are trying to learn classification of SAR intensity images, as well as we will try to understand about different types of classification, supervised as well as unsupervised classification and we will also get introduced to fuzzy classification, okay. So as part of previous lectures, we have a basic understanding about what exactly is classification isn't it? Because it consists of two steps, number one, recognizing or identifying real world objects. Now by real world objects, I mean water body, it can be vegetation, it can be building that is built up area and so on. And the second step in classification is labeling of pixels to be classified. Remember the example I showed you in the previous lecture where we discussed that labeling in literature can be conducted by two main processes that are supervised and unsupervised methods. We will be discussing more details about that shortly. Now just a quick recap, that is we covered till now the geometrical basis of classification, okay, geometrical basis of classification and we also learned what is meant by minimum distance to mean classification and the parallel pipette classification. We discussed that in minimum distance to mean classification, each time the distance in Euclidean space shall be calculated between the pixel which is to be classified and the center or mean of a cluster or group of pixels which are close together in Euclidean space and which form part of a cluster, okay. Now we also discussed that when we use the distance formulae, the advantage is that you know it is simple, it is easy to understand, easy to compute and it is computationally less taxing, is not it? Because after all we are using the distance formulae. Here mu Ck and mu Cl refers to the mean, mean of what? Mean of a cluster of points where C refers to the class, okay and K and L refer to the band. We discussed this, okay and Dn refers to the digital number, pixel values, okay, minimum distance to mean classifier. Now towards the right hand side, you see a summary of what we discussed as part of previous lecture that is parallel pipette classification. Now this is based on decision rules. Here the difference is that we not only use the mean but we also use the variance of each class or each cluster and the decision rule is defined as highlighted here wherein you see the lower boundary as well as the higher boundary, mu plus sigma and mu minus sigma and this is using a one standard deviation threshold, okay. Now we also discussed one more thing that is a disadvantage of parallel pipette classification method. Now just you know instead of hearing me verbally speak, let me try to show you a diagram, okay. Now this diagram has three axis, the catchment area on the x-axis. I have the other two axis showing the river discharge as well as elevation above mean sea level. Now if you correlate with my similar diagram which you may have seen in the earlier lecture, you can point out the difference that here I have increased the number of data points that is the green circles are more in number here, isn't it? And I have done this just to highlight the disadvantage of the classification algorithm, okay. In particular you can see two pixels that have been specially highlighted in red, isn't it? Because through parallel pipette classifier instead of defining or enclosing the points in a circle or ellipse I am trying to enclose the points that are clustered together using a parallel pipette, okay. But then you know the points in red I am slightly confused because I do not know which class do I assign these pixels to. Say the first cluster of points if I call them 1, say cluster 1 and say the second group of points I call them as cluster 2, okay. Now I can say that all the points that lie inside the parallel pipette belong to that particular cluster which means all these points they lie inside the first parallel pipette we have already named it as cluster 1 and all these points belong to cluster 2. But then I am confused because these red points they overlap in both cluster 1 as well as cluster 2. The parallel pipette themselves are overlapping, okay. Because in the presence of covariance the rectangular decision regions using a parallel pipette classifier they tend to fit the training data very poorly, okay. Which means that now we should look for a classification method that considers the covariance also, okay. A method a classification method that considers covariance as well will be more efficient to classify pixels, isn't it? Now before we proceed to the next classification technique let us try to understand what is covariance. Now covariance between two random variables is a measurement of the nature of association between the two. To understand say I am going to give you a pair of values, one set of values representing x and the other set of values representing y. I want to find out the covariance between these two random variables x and y. Assume that large values of x if it results in large values of y or small values of x result in small values of y then positive x minus mu will result in positive y minus mu and negative x minus mu x shall result in negative y minus mu of y. Let me try to repeat once again x and y are nothing but two random variables. Say I am giving you a column matrix containing x, a column matrix containing y and the number of data points in x and y are kept same. Say you have 100 values in both x as well as y and then I am asking you to find covariance between x and y. Now say the mean of x can be written as mu of x, x of x and the mean of y can be written as mu of y. Now if large values of x often result in large values of y or smaller values of x result in smaller values of y then positive x minus mu of x will often result in positive y minus mu of y and negative x minus mu of x will often result in negative y minus mu of y. Remember this is we can call it as case one. Case one that is both are moving together. By covariance we want to estimate how two variables are moving together. You can visualize using Cartesian coordinates system, the Euclidean space. All right. Now let me give you a reverse scenario. Say case two, say large values of x often result in small values of y. And then let me pose a question to you. If large values of x result in smaller values of y, what will be the sign of x minus mu of x into y minus mu of y? Think about it for one second. Large values of x, if it is resulting in small values of y, the product that is x minus mu of x multiplied by y minus mu of y will be negative, isn't it? Which means the sign of covariance indicates whether the relationship between two dependent random variables is positive or negative. Let me repeat. The sign of covariance, okay, how two variables co-vari, move together. So the sign of covariance indicates whether the relationship between two dependent random variables is positive or negative. And when x and y are statistically independent, it can be shown that the covariance is 0 but then the converse however is not generally true. I won't get much into the details but I hope you understand the concept that if I give you the digital numbers that is the pixel values of two bands of satellite imagery, both of which has been captured over the same geographical extent at the same time by the same satellite, which means they are going to have the same resolution, then I can easily find out co-variance between both the pixel values of both the bands, you know, covariance of two random variables x and y, okay. So given here is the expression to compute the covariance. During your tutorials, you may have covered a tutorial on image statistics using Python. So please try to recollect the explanations. Once again, the co-variance between two sets of random variables, here k and j refers to the bands, okay, can be written as sigma summation of digital number ij minus mu j multiplied by dn ik minus mu k whole divided by n minus 1, okay, to make it unbiased. That is why we are subtracting by 1 in the denominator n minus 1, where n is the total number of samples in the image, alright. So again, why are we learning about all this, you know, because we understood that Parallopiped classifier has a disadvantage, it would not be able to perform well when the set of data points have high co-variance, okay. And then we understood how to compute co-variance between two random variables. Now one important point I have highlighted here is that the sign of co-variance, whether it is positive or negative, it will indicate whether the relationship between two random variables is positive or negative, okay. Sign is important, okay, sign of co-variance. Now just to reiterate, we learnt about co-variance because we need to understand about one supervised classification method which is using mean variants as well as co-variance to classify pixels to clusters to groups, okay. To be more specific, we shall now begin to understand the third supervised classification method known as maximum likelihood classifier, okay. Maximum likelihood classification method. Again, before we actually go into the definition of maximum likelihood classification, let us first try to clear few basics and probability. Consider an example of a discrete random variable, okay, discrete random variable and say the variable X represents the number of times an event occurs, okay. How many times does it rain in Mumbai? The number of times an event occurs. Let variable X represent that and the table that you see, it gives the possible values of their probabilities, okay, possible values of their probabilities. So, I have the value of X, 0, the probability of X being 0 is 1 by 3. The next value of X taken is 1, the probability of X to be 1 is half. Again, X to be 3, the probability is 1 by 6, okay. Let it be any event, the probability that is the number of times an event occurs, that is what the variable X represents and in the table, I have given you the different values that X can take and the probability of X happening. Then, the set of ordered pairs that is X, f of X, set of ordered pairs, it is called as the probability function or probability distribution of the discrete random variable X, okay. Once again, the probability distribution of the discrete random variable X for each possible outcome small x is given like this, okay, alright. Now, for continuous random variable, you know, f of X is usually called as a probability density function and a probability density function is constructed so that the area under its curve is bounded by the x-axis and it is equal to 1 when computed over the range of x for which fx is defined. I am assuming that some background in probability is available with all of us, okay. But just in case, you know, if you have forgotten the basics, this is to help you brush up the basics, okay. For continuous random variable, f of X is usually called as the probability density function and a probability density function can be constructed so that the area under its curve bounded by the x-axis is equal to 1 when computed over the range of x for which f of X is defined, alright. And the function f of X is the probability density function for the continuous random variable X defined over the set of real numbers if these three conditions are satisfied, okay. I will give it some time to sink in, alright. So, coming on to why we are learning all this. So, I will leave you with one more concept that is the Gaussian distribution. Now, the density function of the normal random variable X with mean of mu and variance of sigma square is given by this relationship Gaussian distribution, okay. And now coming on to maximum likelihood classification algorithm. You know, the maximum likelihood decision rule assigns the pixel to the class that has the largest or maximum value. And the maximum likelihood classification of remotely sensed data involves considerable computational effort because as you can see in this relationship, now this method is considered to be accurate, most accurate in fact. And for a pixel, its likelihood of belonging to a particular class is computed using the probability density function of the normal curve which is parametrized using mean and the covariance metrics. Now, instead of listening to me speak, let us try to understand about maximum likelihood classification through a numerical, okay.  One is class sand and second is class water, okay. And the information is given in three bands. I am going to call it B1, B2 and B3 to signify band 1, band 2 and band 3, okay. So, the training data for two classes are given here. Towards your right side what is highlighted in yellow are the values for two pixels whose pixel values are given B1, B2, B3 values in three bands are given here and these pixels are having the name data ID 1 and data ID 2, okay. Now the question is if you have the training data information which class do these unknown pixels belong to? That is data ID 1, does it belong to sand, does it belong to water? Similarly, for data ID 2, does it belong to sand or water? That is the first question. Again, the next question reads, can you estimate this using minimum distance to mean classifier and maximum likelihood classifier, okay. So, as before, let us try to first understand what is given in the question, okay. So, given are the training data values in three bands B1, B2, B3 for two classes that is sand and water, okay. And we have already seen as part of an earlier lecture how to compute the minimum distance to means, okay, using the Euclidean space, the distance formula, isn't it? Which means I need to compute the mean of each class. In this case, the mean will have three values, one for each band, isn't it? The mean for class water is also computed and in minimum distance to mean classifier, the distance from the mean to the unknown pixel is computed, which means I need to calculate the distance of data ID 1 from mu of sand. I need to compute the distance of data ID 1 from mu of water and data ID 1 that that particular pixel is going to be assigned to the closest cluster, okay. Which means if the value the distance of the pixel from mu s is least, that pixel is going to be assigned to the class of sand and likewise. As we have already covered the computations for minimum distance to means classification, I won't repeat it, but then I have the values for you to compute and then compare, okay. So, every time I am going to compute the distance between data ID 1 as seen in your screen to the mean of class sand and class water. So, here the distance between data ID 1 and mean of class sand is 9.75 and the distance between data ID 1 and mean of class water comes out to be 70.16. So, the lowest being 9.75, hence data ID 1 is going to be assigned to class sand. Similarly, we can compute the distance between data ID 2 and mean of class sand which comes out to be 16.81 as seen in your screen and the distance between data ID 2 and mean of class water comes out to be 76.77. Again, the lowest is 16.81. So, data ID 2 is going to be assigned to class sand, okay. So, what we will do is we will try to solve now using the next classifier. Let us try to understand about maximum likelihood classification now. We already know the relationship that is the maximum likelihood how to compute it using the mean of each class, the digital numbers, variance covariance matrix and is the number of data samples, okay. We have all the information with us except that we need to compute the variance covariance matrix now. The mean of each class has already been computed as part of an earlier solution, is not it? So, let us try to estimate the variance covariance matrix, okay. So, the variance covariance matrix of 3 bands of a class shall have a size 3 cross 3. The first element is variance of band 1 with band 1, sigma 1 or sigma 11, band 1 with band 2, I can call it as sigma 12, band 1 with band 3, I can call it as sigma 13 and so on, okay. Now, how do we compute variance covariance matrix of class sand? You know, given the details that is training data information is given, mean of class sand is given. So, to compute the first value of the variance covariance matrix, say I want to compute sigma 1 square, okay. So, what do we do? Every time we need to subtract the value with the mean, okay, 132 minus 138.87, okay. So, what I will do is I will do the computations for just one band and then assume that you are able to follow the rest. So, coming on to the third value, it is going to be 137 minus 138.87. Similarly, 139 minus 138.7, 140 minus, okay. So, what am I doing? Every time I am subtracting the value with the mean, okay, because it is sigma 1 square, the first value in the variance covariance matrix, isn't it, okay. And then I can take the summation to get a value. So, I have pre-computed, you can check if the values are correct. And then to calculate the first value that is sigma 11 or sigma 1 square, it is going to be 288.81 by n minus 1. Why minus 1? To make it unbiased, n is the total number of samples. So, you will get a value of 41.25. Remember, this is the variance covariance matrix of class sand, which has a matrix size of 3 cross 3. And what we just did is we computed the first value that is sigma 1 square, which comes out to be 41.25. Now similarly, you can compute the variance covariance matrix for class water also, okay. And then before we compute the likelihood function, we need to estimate the determinant of variance covariance matrix, okay, determinant. And then we need to take the inverse of variance covariance matrix, that is this term. After you have computed both determinant as well as the inverse, we can start with the computation of maximum likelihood function. Completed the calculations and once you completed, you can get the negative log of probabilities for data ID when class is sand, these are the values I was getting. When class is water, these are the values I was getting. The least belongs to class sand, which means data ID 1 is assigned to class of sand. Similarly, I can compute the negative log of probability for data ID 2 when class is sand, data ID 2 when class is water, least belongs to the sand class and therefore, data ID 2 is assigned to the class of sand, all right. So, we have already computed or we already know how to solve a problem using minimum distance to means classification, okay. So, that was not covered here. Right now, we saw how to manually compute and classify a pixel using maximum likelihood classification approach. Now, let me try to confuse you a bit, okay. So, the same question I have expanded now by adding two more classes. So, I am going to call it question 2. So, given here are the training data for four classes namely sand, water, urban and vegetation. So, the same question I have just added two more classes here and the values of pixels are given in three bands that is band 1, band 2 and band 3 respectively which are denoted by B1, B2 and B3. Again, the two pixels which I will call as unknown pixels their values are given against data ID 1 and data ID 2. So, the question here is which class do these unknown pixels belong to that is the first question and this time remember there are four options to choose from. It can be either sand, it can be water, it can be urban or it can be vegetation and the second question is can you estimate using minimum distance to mean classifier and maximum likelihood classifier. Now, just imagine the amount of computations you have to carry out to estimate the probabilities, the negative log probabilities for each and every pixel to belong to each class which means I need to calculate the probabilities of data ID 1 to belong to all the four classes of sand, water, urban and vegetation. Similarly, I need to also compute the maximum likelihood of data ID 2 to belong to class sand, water, urban and vegetation. So, let us see how to solve this. What I will do is I will try playing a video wherein the solutions are made available in an Excel sheet. So, the computations have been done in Excel. You can see that the training data exists for all the four classes, the unknown data for classification is shown here, data ID 1 and 2 and its values in three bands are shown and now coming on to the calculations, as before you can compute the band mean. You will have four times the computations here because we have four classes. Using minimum distance to means classifier, you estimate the distance, distance of data ID 1 to sand and similarly to water, urban vegetation. Similarly, distance of data ID 2 to all the four classes. Again, we can compute the variance covariance matrix which is going to be a 3 cross 3 matrix for all the four classes because there are three bands. I can estimate the determinant of variance covariance matrix and take its inverse because maximum likelihood classification requires variance covariance matrix, its determinant as well as its inverse. So, once I compute the inverse, I can directly go for the calculations of x minus mean. I am following the steps here and ultimately I get to compute the negative log of probabilities. So, you can see pixel ID 1 and 2 for both the pixel IDs I have eight values, the negative log of probabilities, probability of it to belong to four classes. Similarly, for the pixel ID 2, data ID 2 also I have four probabilities and finally I am going to assign the unknown pixel to the class to which negative log of probability is low. Remember, it is not probability, it is negative log of probability that is why I am picking the lowest and also when you are doing computations using an excel sheet, please be mindful of the relationship it takes to compute covariance, whether the division is by n that is total number of values or it is division by n minus 1 to make it unbiased. So, as part of this lecture, we tried to understand about maximum likelihood classification algorithm and we also tried to manually compute the values for a small sample of data having eight values. Remember, typically the thumb rule is that you need to have more than 30 values to make it large sample. But then just for representation purposes, we have tried to manually compute and solve the question using eight set of values. So, let me hope that you understood the concept and followed the calculations. I will see you in the next class. Thank you so much.


Welcome to Toffy TV, it is a start in 11 for Everton versus Chelsea 12-30 at Gwreson Park. Duncan Ferguson's managing the team. This is the team I would like to see and put out tomorrow. Do you open pick for the goal? I thought about Jonas Laisle, but I just think let's try and keep as much steady as possible. I'm going to stay with pick for this one, see how he does tomorrow before I decide to bring Laisle in maybe next week. Right back, Jibril Sidderby survived for what? 24 minutes at Amfield the other night, but James Coleman is still injured, but Sidderby are getting anyway to be honest, he's playing much better than Coleman at the moment. Left back Lluke Dean and the two centre backs went back to a flat back four of Mason Hallgate and Yeri Mina for me. Let's get back to try and be solid. We tried the five at the back, done alright against Leicester, it didn't work at all against Liverpool, so back to a flat back four. In midfield Morgan Snidelin, we haven't really got anyone else to play that defensive midfield role with Fabian Delph still being out injured. So Snidelin in there and then I'd have Tom Davis and I know he wasn't great the other night but Tom Davis and Alex O'Wobee either side of him get some legs back in that midfield because Chelsea are a young mobile side so we need people who can compete with them in the middle of the park. I'd have Bernard off the left in front of Lluke Dean trying to get that partnership going again down that left hand side. I think Lluke Dean always looks better when he's got Bernard in front of him. On the right hand side I'd go with Richard Larson and I'll be moving him about a bit but you know he's proved and scored from the right hand side against Chelsea. I'll be moving him back in April and then through the middle I'd go with Moise Keane. Thoughts about Tom but I just think Keane done enough in that half an hour cameo where I'm feeling other night to have a go, try something different. I think playing with Alex O'Wobee behind him who can thread balls through to him as we see him and he missed a good chance against Liverpool the other night but you know we got in that position so that's who I'd go with. Obviously you've got the likes of Dominic Calvert-Lewin, you've got Theo Walcott on the bench if you feel as though you want to change something up there. I think I'd have even Anthony Gordon in that squad as well to be honest just on the bench to give it another little edge but that is the start and 11 I would go for against Chelsea. Let me know what you think in the comments section below. I'm sure a few of you would want Pickford out and I'm quite sure a few of you judging by previous weeks will want Morgan Snidel and I'll decide as well. But let me know in the comments section what you think. Subscribe, give the video a thumbs up and get over it and join us on Patreon as well. Thanks for watching. See you later.