Positives, we're trying to obviously look for today. May one must come from yourself to get 90 minutes out of three months away, must be a boost for you personally? Yeah, great boost, you know, I could have kept going for another 30 minutes, but the body's good, but I still massively hurt today with the result.  It doesn't matter how much I was on the pitch for 90 minutes, it's great, etc. It's a bad defeat, but we've got to go again. I've been in this position before, after a few results, a few losses, everything seems to be going again, it's just going loss after loss, but it does turn, and that's the great thing about football. You know, it could hit off the post, it could hit off someone's leg and go in, and it could all change again, so we need the funds to stay with us, and we need them to be behind us, because as lads, I'm going to get people going, and I'm going to try and give as much as I can to give the respect that they need on the pitch.


So I'm going to give you a reference point. My foot's on the tape. So I'm going to pick it up and I'm going to rock back. What I want you to think about is when my back foot hits, that's the hammer on a gun. I'm shooting forward. Quick step. This is our quick step. So we've got a dip step. We've got an outside step. Now this is going to be our quick step. Quick step is just a quick pressure release. It works with high crotches. It works with single legs. I can attack both sides of the body, but I'm in my stance. Pick the leg up, fire off of it. Pick the leg up, fire off of it. That's the hammer of the gun. So when I'm wrestling, do you mind if I know who Frank Milnero is? Yes. There's a guy probably, I think, good Darzi probably had the best left-handed high crotch. Brad Metcalf's got a pretty good one. But you watch their footwork. It's a quick pressure release. Here. It's a quick pressure release. So they'll create pressure. The foot will get picked up. Just fire off that back foot. Again, he's coming to me. I'm going to him. Twice as fast. So that's our quick step. Did I get your stance? Did I get your stance? Just pick your foot up. Drop back, fire off of it. Get low. Get low. It's just enough pressure for me to release and get under. It's just like my dip step. Just enough to release and get under.  Inside, outside. I want you to play on both sides of the body. Now watch what happens when I quick step and I pull the backside. Easy singles. Watch what happens when I quick step and pull the front side. Easy high crotches. One small adjustment with my feet makes all the difference in the world with my speed. One small adjustment. And if you took Brent Metcalfe's high crotch Frank Malinero's high crotch Bryce Sador's, you guys don't know who he is. He's a Navy guy that had an amazing lefty high crotch. They're all quick step guys. Fire it out of the cannon. And when this guy got the pressure, here. Get that leg, quick. Let me set it back to the backside. Now get to his body and get to his legs so quick. I want to go to the backside, pull the backside. I want to go to the frontside, pull the frontside. Set. Pressure. Quick step. Fire off that backside. Go!


Janet Jackson is an American singer, dance choreographer, actress and producer who rose to prominence alongside her world-famous family, the Jacksons, in the late 1970s. Since breaking off on her own, Janet has arguably become the second biggest member of the family next to her late brother, Michael, and certainly she's the member of the family with the greatest career longevity. As someone who's been famous since the age of 10 years old, it shouldn't come as any surprise to find out that Janet has lived in some of the nicest homes in the world over the years everywhere from Malibu, California, to a penthouse in Trump Plaza, New York, to a townhouse half the world away in London, England. These days Janet can be found living in London with her son, Issa, sticking close by her former husband, billionaire Wasam Almana whom she met back in 2010 and then divorced a few years ago. Since moving out of the townhouse, the family shared with one another, Janet is reportedly renting another place close by. And while it's unclear if Janet walked away with any of her ex-husband's substantial wealth during their divorce proceedings, she's doing alright for herself still with an estimated net worth of close to $190 million. In these videos, we don't reveal any addresses and even though I've done a house tour of my own place, please do not show up at any private residences because it's not safe for anyone. How's it going guys and girls, it's Kara here for you with a brand new house tour. This one taking a look at the series of different homes that Janet Jackson has lived in over the years. If I'm being honest with you guys, Janet has kept a pretty tight lid on the details of her homes so while we have records of almost all of them, what they're like on the inside is a little harder to view but I'll give you all the details I can. I noticed 95% of you watching aren't subscribed so be sure to hit that subscribe button because we post a new video daily. Follow me on Instagram to chat and as usual let me know who's house tour is next in those comments down below. Now let's get into this video. Take things off, we're jumping in the time machine and going way back and I do mean way back to Janet's childhood home in Gary, Indiana. This tiny house is located on Jackson Street and is reportedly a two bedroom home. Janet used to live here with her entire family including her six other siblings and she'd share a bedroom with all of them. My mother has this attachment with the house in Gary, Indiana. 2300 Jackson Street, she loves that place. She's always kept it up. She's redecorated and she loves going there. She just loves doing that. Janet actually stopped by this home only a couple of years ago to visit. She was greatly overwhelmed by her emotions and told the Northwest Indiana Times. When I saw the house I just started crying. Me and my family are so blessed. I'm so thankful. Janet moved out of this home when she was only three years old. The last time she'd visited prior to this moment was when she was about eight. From the homeless beginnings to the top of the mountain, Janet's next home will show you just how far her career was able to take her. One of the very first homes that Janet ever bought for herself after her career took off was this stunning Malibu beachfront property located on Pacific Coast Highway. She bought this 5,658 square foot home in 1991 for 4.1 million dollars which I'm pretty sure in today's dollars is like a billion gazillion but I may need someone who's good with numbers to double check that for me. Janet would hold on to this property for a number of years, selling it in the mid-2000s for 8.5 million which means she more than doubled her investment in the place. For moving on from the property, Janet gave an interview with Primetime that offered a brief glimpse inside of this property. Next up is a home that by all records Janet still owns, a penthouse located in the Trump International Hotel and Tower Condominium located at one central park west in Manhattan. Janet first purchased this unit back in 1998 for an estimated 2.8 million according to public records. Her apartment is located on the 34th floor and has some of the best to be used in the entire building overlooking Central Park. It features 2,094 square feet of living space, 3 bedrooms and 3 baths. It also houses a 28 foot long L-shaped living and dining room combo area. For the most part, Janet has gone with a semi-opulent and dark cube décor to complement the floor-to-ceiling windows and their remarkable views. The kitchen is accessible from the living room and isn't all that large, but it's well equipped with a washer and dryer combo stacked in a nearby closet and has its own tiny dining alcove in front of a fairly remarkable window with great view. Building residents can enjoy 5-star hotel services like health club access with pool and spa, maid and valet parking, a business center and 24 hour room service from high priced eatery Jean George. Back in 2014, Janet decided to lease this bad boy for 35K a month, but it only lasted for a short period of time. With her recent divorce, Janet has probably been using this home more often than she used to and she also had the locks changed once her divorce was finalized about 3 years ago. Not too long after first picking up that penthouse, Janet also bought this gorgeous home just outside of Las Vegas, Nevada. The rumor is that Janet actually bought this home for her family, either for her mother or her sister Rebbe, who are both said to reside in the state of Nevada. She purchased this property back in 2002 for around 520K. The home reportedly features 4,636 square feet. Unfortunately that's about all we really know about the place and considering it's the home to her family members that doesn't surprise me all that much. Most celebrities like to keep their family as shielded from the media as possible. In Janet's case, that's kind of ironic considering how her parents did the exact opposite to her when she was just a child. But hey, I guess it's worked out for everyone. Alright, before I bring this video to an end, we're going to quickly check in on a couple places that Janet has lived more recently. After meeting businessmen with Sam Almana in 2012, the two were married 2 years later and eventually had their son Issa in January of 2017.  I just want to keep it real with you guys for a second. Yes, I separated from my husband. We are in court and the rest is in God's hands. While still married, Janet and Issa lived in a swanky Mayfair townhouse located in the heart of London, England. Unfortunately, only pictures we really have of this place are from when Janet was moving out. Issa is worth an estimated £820 million and one has to assume that the townhouse they both shared must be in his name and owned wholly by him since Janet was the one that got the boot. After moving back stateside for a couple of years, Janet returned to England in 2019 and has reportedly found another townhouse not too far from the home she shared with Issa. Much like her previous townhouse, the only images we have of Janet and this new place are from when she was getting picked up outside of it. As far as how nice this new place looks on the inside, well I can only guess. But Janet did share a recent photo on social media that shows how she's keeping busy with her son during the worldwide quarantine, building doll houses with him. Like I said, not really all that much to go on but with a net worth of close to 200 million, I can only imagine that Janet and her baby boy are still living in the very lap of luxury. Alright guys, I'm going to end this video here. What did you guys think of the different homes that Janet Jackson has lived in over the years? Any favorite in particular, let me know in the comments down below. Follow me on Instagram to get in touch and I'll see you guys all in the next video. Bye!


Hello everybody at the Cloud Native Security Conference. Welcome. Today we're going to discuss from theory to practice with Sean Anderson, myself, Anna McTigert, and Michael Hackett. Here we are. And Michael Hackett and I are at Red Hat. I'm a security analyst and he is a principal product experience engineer. Sean Anderson is a PhD student at Portland State University with a focus on formal methods. So here's a little thing. This is actually from practice to theory. The title was created prior to this decision. We're going to be starting off for clarity of this talk, the practical security used in SAF, the flaws with those methods, and a better way with theory. We're going to start off by discussing Rook for SAF, moving into the shortfalls of this current practice and other security features with SAF. We're going to show how theory and in particular formal methods will help us fix these flaws. And I'm going to hand it over to Michael Hackett now. Thanks Anna. So before we get into the practical security side of Rook SAF, I'd like to just give a quick overview on why we're talking about Rook SAF today, right? We all know from a development standpoint that looking at deploying cloud native applications can be extremely complex and challenging when we're looking at it from the outside in, right? Large clouds, large number of compute can lead to a very overwhelming situation. So why we're looking at Rook SAF is because we're simplifying the underlying storage aspect and allowing our developers or our end customers to focus on writing applications and testing code. What we've done with Rook SAF is we've basically designed a way to automate packaging, deployment, cluster management, upgrading and scaling of storage, all underneath the stateful applications as well as providing infrastructure services such as an area to provide logging and metrics and registry to Kubernetes clusters. We do this by augmenting Kubernetes by giving them access to storage services including block, file system and object storage. And you can go to the next slide. It's cool. So why are we focusing on Rook SAF, right? Working at Red Hat, we can see the container adoption is just exploding throughout our customers. More and more businesses are looking at using Kubernetes buzzwords as ways to automate and drive automation, just improve overall efficiency with their teams and just be able to scale at need be without the requirements of going out and purchasing these large storage arrays. The trouble that we have with cloud native environments, which we're looking to cover here, is how do we actually secure and protect all this data that's going in here. We don't want to leave this to the development engineer in order to figure this out. So we need to have these features in place that are able to provide this type of security and protection for the data that resides in our Kubernetes clusters. Go ahead. Go to the next slide, please. So where are we currently at with Rook? Rook gives us the ability to auto scale itself and also auto heal itself when we're facing any type of underlying hardware failure or something that may impact the underlying cluster situation or data access for our end users. There's no requirement to go in there and recover a field object storage data yourself. The Rook operator automatically goes out there, restarts the pod for you. So we're dealing with a self-healing cluster where the developer doesn't have to take his time away from moving forward on any of this application work to troubleshoot an issue and auto recover the cluster. This is auto done all from the Rooksef operator. We're also allowing Ceph, which can be a complex storage product to run on our Kubernetes platform with these via the Rooksef operator. This is enabling the benefits of containerization in the ease of using containers and push that down to our developers. The ease of using containers gives us the auto packaging, the auto upgrade and those types of features that I discussed in the previous slide. Looking at security from a practical standpoint, we can apply these from OpenShift to our Rooksef clusters and we can implement these based upon a individual YAML file or in your CRDs specified for each one of your resources. A CRD is a custom resource definition. In Ceph standalone outside of Kubernetes, when you're dealing with these types of changes to any of the security policies or features inside Ceph, it's usually a very manual process where you update configuration and then push out to a set of nodes. That's a manual configuration. When we're dealing with Ceph and the Rooksef operator, we're still dealing with somewhat of a manual configuration here, but it's a lot less overhead than it would be if we're dealing with a bare metal Ceph cluster outside of Kubernetes. You can go to the next slide please. Addressing a little bit more where we are from the practical side of security with Ceph, I want to go over a little bit of what we're currently offering. We can deploy our object storage daemons, which are your underlying data storage devices in Ceph. We can deploy these as being encrypted using DmCrick during the OSDs creation. These can be done in multiple different ways using Lux. This can be done on the actual device layer, or we can do it on the LVM abstraction layer. As of our Nautilus release, we're actually supporting external KMS via HashiCorp being the only KMS server that we're supporting currently, and that was made available with the 1.5 release. The way that we set encryption on these OSDs does need to be applied manually, so this isn't an automated concentrate that can be done, or something that can be done to an OSD that's already online, unfortunately. This is done by updating a respected storage class device, YAML, or template, and setting your encryption value to true. By default, this is stored in Kubernetes Secret, which from a security standpoint isn't as secure as that would be fit for a certain requirements for users, which is why we introduced support for the external KMS, HashiCorp Vault, when a requirement is for the key to live outside of Kubernetes and on an external vault device. We also, in Nautilus, released something called the V2 Messenger Protocol. What the V2 Messenger Protocol is, it actually introduces encryption on the wire. When we're talking the encrypted level of OSDs, we're talking data at rest encryption. What we're doing with the V2 Messenger is actually encrypting data that is running through the network, and this is done utilizing our own authentication system called Cephex. What we do there is there is a default method called CRCCheck, which basically sets a valid CRC on traffic flowing through the network and validates it when we reach an additional endpoint. CRCCheck is the default method of a V2 Messenger that we use, but there is also a secure setting, which is a more in-depth full encryption of all the traffic going through. We're looking at a full cryptographic integrity check of the traffic running through the network. This requires also a manual editing setting of the cluster CRD during the cluster creation. We can't just turn on secure mode during the cluster being up and running. This is a setting that is required to be set while the cluster is in creation. This is done by defining your cluster CRD. Another area that we look at from a security standpoint is actually the ability to modify user permissions per pools. This is giving certain users read, write, or executive access to any of the pools. This can be configured by setting the client custom resource definition for a specific client. Looking at this primarily for LibRBD use cases, for example, OpenStack running on OpenShift, where we may have a registry or something like that where a user may be required permissions to access specific pools. There are also other use cases. For example, your radio gateway or object store user CRD can also be set to specify specific users to access different sets of object store pools on the underlying cluster as well. That's currently from the tactical standpoint. What we're offering right now from the CEPH Rook standpoint, we understand we are limiting in some areas. In particular, we're looking at a, there is no automation when we're looking at setting these types of security principles on Rook CEPH. We're looking at manual additions and changes to anything required to enable these types of features. You want to cover the next slide, please? Yeah. Thank you, Michael. So this is really state of the art stuff and it's really important. It's amazing. CEPH is continually improving our security as are many other places in industry. It's really good. It's not enough. I still have a job analyzing CVEs day to day. CEPH on Rook is really good. It enables all the benefits of containerization. It enables a lot of security benefits, but it doesn't formally guarantee a whole lot. It's not bullet proof. We still get CVEs. What causes our CVEs and how could we reduce the number of them? Going from a whole lot of CVEs to just a few. So let's think about some of our sources of flaws and CVEs. Many of our flaws occur with access control and with buffer overflow attacks. Going to discuss these categories of flaws and then how to prevent them in theory and in practice. So access control. You know, we deal with this. We're dealing with a lot of stuff by applying permissions, checking user authorization, making sure our key management is set up. It hasn't eliminated the flaws. It has decreased the number of flaws down to about 1% under CWE 284, aka improper access control in 2020. And it's a decrease from prior years, but these flaws still exist. We could eliminate these flaws if we had formal guarantees that state only certain users are authorized to access particular things. We don't have a formal bullet proof guarantee. We just have software that enables that and that roughly provides a general way to generally prevent these flaws. Another, the most common and overwhelming source of CVEs are related to buffer overflow and memory buffer attacks. This is 21% of our CVEs in 2019. These are entirely preventable with guardrails from overflowing languages, from programming languages. There is no need to have a buffer overflow in 2021. It happens due to the use of languages and of course we fix some when they occur, but there are languages that prevent buffer overflow attacks. For example, Go and Rust. Go and Rust are both memory safe, meaning that you can't write to sections of memory that you don't mean to write to. They eliminate buffer overflow attacks. Just by having these guardrails from a programming language and taking a little bit of a technique that says that you cannot have this, you can design a system so that these attacks are impossible. Go and Rust have different benefits. Go obviously has garbage collection, Rust does not, and it all depends, but Rust can be a little bit faster, so it all depends on your goal as to which language you use, but we can eliminate buffer overflow attacks. Even with older languages, with C, you can have verified compilers that do not allow security flaws. The biggest downside is efficiency, but as we're seeing it's starting to be practical with languages such as Go and Rust being widely adopted in industry. These techniques are not just in the realm of theory. They're very practical. So how are we even enforcing security nowadays? On RHEL, we're using SeLinux and SecComp, and this helps, but it relies on user configuration, which as we touched on, can be a huge pain doing all this manual user configuration. Containerization really helps to contain flaws and decrease their security severity, but alone it doesn't eliminate the flaws. It reduces it, it provides a stop gap, but we still get CVEs. Again, we want to eliminate these security flaws eventually so that there are no more CVEs of these types. So how would we do that? Well, we really need to make it so that security is impossible to mess up. Again, we are very optimistic. We're making progress in reality. We want to make this impossible to mess up, and we want to make it so that programmer errors don't lead to exploits. A programmer error should not just be a personal flaw, but it should be a flaw within your design. How do we make it so it's idiot-proof? You can't mess up, you can't create a major security flaw. So we can do that using formal methods. Currently, we're the deniades. We're bringing our buckets of water, our patches, to a CVE that pours it out day in, day out for all of eternity. It's annoying. It's honest work to be fixing all of these CVEs, but it can feel repetitive. So what's our way to the future? I'm going to let Sean here speak about formalizing security and formal methods and ways to actually stop these at the source. We're no longer the deniades. We're no longer having it flow out the bottom, but we're slapping on a CVE. We're going a little bit more than slapping on a CVE fix here and actually fixing that entire bucket so that it's not just all together with the duct tape, but there's a nice solid glass beaker up there. Over to Sean. Thanks, Anna. All right, so we've been hearing this term formal methods a bunch in this talk, and one might reasonably wonder what is that? What do I mean by formalizing and why is this helpful? And why do we have these visions that one day this will take us beyond the era of flex tape security? So the essence of formal methods, it's a kind of broad class of research connected to programming language theory and formal logic. The essence is that we're going to represent programs and systems in some kind of logical system that is amenable to mathematical proof. So we're going to rigorously model our systems and programs. And what that means, for instance, in the area of my research is that we can define logical propositions representing the essence of some concept of security. And so we can think of this as just a very rigorous way of specifying what our system is supposed to do. And then ideally, we then take that specification and the fact that it's embedded in this formal logic, and we actually build a logical proof that guarantees that for a given system, whatever property we've claimed we want, actually does apply to that system. And then the very nice thing is that with recent technologies, we can we make these formal logics machine checkable. So it's not just that I wrote down in my notebook a proof that you take UI ball and say it looks right. But actually we can feed the proof into a machine that will say, yes, absolutely this proof holds. Now that is a pretty high bar. And as I'll get to in a couple of slides, it's not always it's not currently viewed as super practical to do this for large scale projects. So for my part of this talk, I also kind of want to focus on the secondary benefit of formal methods that which is that when you start thinking in terms of these logical systems and properties, you kind of change the way that you think about security in a way that even absent the formal proofs can be helpful in understanding the nature of security in a system. So we want to get beyond thinking about just individual exploits and examples of bad behavior. And we want to think at a higher level a more abstract level. Hit the slide. So we want to think in some sense in terms of abstractions. And this is something that as computer scientists we I think most programmers have some natural ability to do this just by the nature of programming. Certainly we have many, many layers of abstractions in all of our systems. And it's useful to just think more explicitly about what those abstractions are and what they do for us. Certainly programmers, perhaps our end user or perhaps the programmers working on a system like Rooksef, rely on abstractions that are given to them by the programming language. We rely on the idea that if I call Alec I'm going to get a block of a particular size that is sort of separate from everything else in the system. That's memory safety. I expect that if I write through a pointer to this block I don't accidentally write into this other block. That's an abstraction and that makes it easier to write a program. But often those abstractions don't actually hold. You click twice, Anna. Often the programmer's mental model of what's going on in the system has some holes in it. And there are all sorts of different places that these holes can exist. And a lot of CVEs come from the fact that someone was thinking in terms of some kind of higher level abstraction and missed the fact that underneath that abstraction there's actually a much lower level more concrete system that's doing something a little unexpected. And classic example that Anna referenced is the memory, the buffer overflow. The fact is that our buffers are not separate from one another. And in languages like C you can do a simple pointer or arithmetic and jump from one to another and nothing will actually stop you even though it's not legal. And so these assumptions we can almost think of as being in our trusted computing base. And that's sometimes a problem because we aren't actually, we shouldn't actually trust them. So if you took the next slide. What the formal methods community will sometimes get into when we're talking about solutions to this is, can be thought, some people find it a bit utopian. It's the idea that we can actually specify at all the layers of abstraction in a system what things do and how they work. And that gives the programmers working up near the top the ability to prove and then verify. They're starting to specify and then verify exactly what their program is supposed to do. And then because the specifications for the lower levels should also be verified, they are actually able to trust that the abstractions they're relying on are going to hold. And that's very important. There's been plenty of work done in specific programs being verified. A flagship example is Compser, a C compiler that's verified. And the verification there is, well, it does exactly what it's supposed to do. It's not that it doesn't have security vulnerabilities. Luckily with a compiler, you're not running it actively on a production system. So maybe that's not as big a deal. But we sort of have the, like you can verify a program and there's also a lot of work being done in like mitigation of these abstraction breaking things in the lower levels of our system. And that includes things like hardware and software security mechanisms. There's a whole movement toward hardware that provides security enforcement primitives. There's also when Anna is talking about programming language features that exclude certain kinds of bugs such as memory safety and Rust and Go, these help us be more confident in the abstractions that we're giving the final programmer even without actually doing all the work of specifying and verifying every level, which is a lot of work. You have to specify everything and then do a formal proof and that involves, we have good tools for writing those proofs now. We have things like Coq and Isabel Hall that are proof assistants and let us do that in an interactive environment. But it's still, it's a lot of person months to verify even fairly simple programs. And then you have to build up this whole system or you have started the top and you make assumptions. So often this is viewed as impractical. Maybe we'll be doing something. In fact, there are some programs that are getting close to making realistic attempts at this and there are many things that prove a core set of functionality. One thing I want to call attention to is Micro V, a recent work that does this with a fairly realistic hypervisor. But given that this is not something that we're going to just start being able to do overnight, what are some takeaways that we can do right now? If you hit the slide. I think one of the big ones is just kind of maybe a development philosophy level starting to think in terms of these abstractions, in terms of the properties that we are depending on and that we are offering further users of our software. And this is where, you know, if I want to connect this to Seth, I might think, okay, so we have all these different security features that are being offered to the end user. But what are the, what is the end user actually using these for? What, you know, can I write down a formal description of how the system should behave so that the end user can rely on that? You know, if the end user were writing their own code that they were going to verify in this very rigorous way, and I were providing them with some axioms that they'll use in their proof, what would they want to assume? Can you hit the slide? And there is a whole area of research around just writing these properties. I won't spend a huge amount of time on them now, but like, for example, if we're talking about containers, the sort of official way in the security literature to describe a compartmentalization system where containers don't talk to each other, is something called non-interference. Actually, we use non-interference for a lot of different purposes, but in this case, non-interference means, you know, if I'm a container living in some larger system, well, maybe that larger system could be two different systems, and I can't tell the difference between them because the parts of the system that I can see, my own memory, my own storage, between these two systems, it's identical. But other containers in the system might be doing who knows what, right? They might be different from one another. So if I'm living in either of these environments, and I can't tell which one, and then we execute for a while, I should still not be able to tell the difference between the rest of the system, you know, if I'm in system A or system B. And that means that that's just kind of an abstract notion, but we can think about what that means. That means that if out there in that system there is an attacker in system A, but no attacker in system B, and I can't tell the difference, then clearly I didn't get attacked, right? And on the flip side, if I'm an attacker, and out there in system A, there's some secret that I'm trying to uncover, and if there's a different secret in system B, well, if I still can't tell which system I'm in after executing for a while, then I must not have discovered that secret, right? And by phrasing things in this way, it may seem kind of convoluted or, you know, just like too abstract, but it means that we don't have to talk about specific actions like memory accesses or, you know, things like that. And so we can actually capture a wider range of behaviors that lead to the leaking of data or the interference with programs. So you can get the slide. The other thing to think about with, let's pretend that you sat down for a bit and said, well, how would I show my system obeys this non-interference property? And you start thinking about, like, okay, in C semantics, if we step and step and step. These are the different steps we can take. And at each of these steps, I want to say, well, I'm only reading from the things that I'm allowed to see, and I'm only writing to things that I'm allowed to change. We'll pretty rapidly hit some circumstance where actually this doesn't hold. This property, as I've stated it here, is too strong. And that's kind of intentional. This is like the utmost level of safety. So if we want to think about applying it in practice, we need maybe to make it probabilistic because the underlying mechanisms that Michael talked about rely on encryption. And encryption gives us probabilistic guarantees and not absolute ones. And of course, many systems will have ways that containers are going to talk to each other and so we'll need to model that. And it's not going to be this nice straightforward isolation thing. But the really powerful thing about a model like this is it gives us the starting point to say this is the extreme of what security is. And now here are the ways that it doesn't apply. And we can build our model from there and hit the slide. And then we can talk about what kind of security guarantees we do and don't offer. And so if I'm going to give any guidance as to how to use formal methods without really formalizing everything and just informally getting value from these ideas, it's that you think about these abstractions and the way that you're relying on them. And you think about the abstractions that you offer. And you kind of do in your head an informal kind of reasoning about what those are and why you believe that your security mechanisms are sufficient to offer them. And then you can tell your customers that and you can have some new buzzwords. We all love a good security buzzword and I'm frankly a little surprised that non-interference isn't one of them yet. I'm sure the marketing people would love to slap that on your software if you have a reasonable belief that you can provide it. But just by going through that process of thinking about security in this way, you have a kind of higher level view of the sort of security that you're trying to offer and the reasons that you might fail to offer it. And that's even before you've started doing any real formal methods work. And of course as a formal methods person, I advocate for you to also do the formal methods work. But I'm a little biased there. And then of course the other thing is, as this talk is an example of, it's good for developers and product people and security people and formal methods people to all be talking about our own different perspectives on how to make systems more secure. And I think that cross pollination is going to be very valuable because at the moment in industry at large and academia, these are kind of very siloed disciplines and we really could use more cross pollination. So I think that's it for this talk and we'll now take questions.


From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. Predictions are all the rage at this time of year. Now on December 29th, 2020, in collaboration with Eric Porter Bradley of Enterprise Technology Research, ETR, we put forth our predictions for 2021 and the focus of our prognostications included tech spending, remote work, productivity apps, cybersecurity, IPOs, SPACs, M&A, data architecture, cloud, hybrid cloud, multi-cloud, AI, containers, automation and semiconductors. We covered a lot of ground. Now over the past several weeks, we've been inundated with literally thousands of inbound emails pitching us on various predictions and trends in these and other areas. Here's my predictions folder. And this is only a portion of the documents that I've received by email, obviously printed them out, killed a few trees, sorry. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis, we're going to review briefly each of our predictions for this past year, 2021, and suggest a grade as to how we did. We're going to do this as a little warm up for our 2022 predictions, which we'll be doing over the next couple of weeks. Now, before we dig in, I want to make an observation. Many of the predictions that we received, they were observations of trends and sometimes not really predictions, or not surprising, we got a lot of self-serving marketing statements. Predictions in our view, they should be measurable. So you can look back and say, okay, did they get it right? Now granted, there are gray areas, so that's why we'll use a grading system today. Now, there are also many really well-done and thought-provoking predictions. This is an example of one that we received that is strong. It's from Equinex CIO, Millen Wagley, who said within the decade, data centers will be powered by 100% renewable energy. Okay, so that's clear and we can measure that. But anyway, thanks to all the PR folks who sent along, like I said, literally thousands of predictions. We tried to read them all, but the volume over the past week or so was just so overwhelming, and we'll try to scan them before we do our 2022 predictions. But today, we want to do that warm-up by evaluating how we did in 2021, so let's get started. Our first prediction was that tech spending would increase by 4% this year, coming off of what we had thought was a contraction in 2020. And depending on which data you look at, best case maybe was flat. We definitely correctly called the continuation into 2022 of the remote work trend and the positive impact it would have on PCs and the like, but we underestimated the shape of that rebound, that spendback curve. IDC has tech spending growth this year at 5.5%, so we feel like while we called the bounce back, it was more pronounced than we had thought. In fact, we think that IDC number is probably going to go up even higher and we'll address that in our 2022 predictions. So we'll give ourselves a B minus here. Okay, next prediction was remote worker trends become fossilized, settling in at an average of 34% by year end 2021. So on average, 34% of the workers would be remote by the end of this year. Now, you know, we made the call, but we missed Delta, you know, we missed Omicron. We said 34% remote, which would be 2x the historical norms. Now the ETR data suggests it was 52% in September and it's probably going to be somewhere in the 40 to 45% range by the end of this month and in December. And the thing is 75% of the workforce is probably still working either fully remote or in a hybrid model. And hybrid work is probably going to be the dominant trend and we're going to have to revisit that framework or how we think about this whole structure. And we'll do that again in our 2022 predictions. So we'll give ourselves a C on that one. We'll take some credit for the permanence of the trend, but the percentage was well off the mark, you know, thanks to the variance, as well as some cultural shifts, that whole hybrid notion. Okay, so hey, not really a great start for Eric and me, but we rebound with the next one. The productivity increases we said seen in 2020 will lead organizations to double down on the successes and certain productivity apps will benefit. So to measure this, we said, let's take a look at the most recent quarterly earnings, engage the revenue growth year on year as an indicator. DocuSign was up 42%. Smartsheet we also called up was up 46% in revenue. Twilio up 65%. Zoom growth was 35% down from 325%, confirming our layup call that Zoom growth would moderate it had nowhere to go but down. And Microsoft Teams has never been more ubiquitous, has never seen greater adoption with hundreds of companies having 100,000 or more users and thousands of companies with 10,000 users or more. So we really feel like we nailed this one. So we're going to give ourselves an A plus. Okay, so now on to cyber. It's an area that we've been making calls in for a couple of years now. And we're really pleased looking back here. We said permanent shifts in CISO strategies are going to lead to share shifts in network security. Now we said, to give you more detail, maybe that sounds like an easy one, but we said specifically identity, cloud security and endpoint security would continue to benefit. And we specifically named CrowdStrike, Okta, Zscaler and a few others sort of targeting their growth rates. Now Gartner has the security market growing at 11%. Okta and Zscaler revenues last quarter grew at 62% year over year, CrowdStrike 63%. Illumio, we also called out, they raised $225 million on a $2.75 billion valuation on the strength of its growth. That was in September. Now Akamai acquired Guardiocore for $600 million, another company we called out that they would do it, they did that as a ransomware protection play and they paid a huge revenue multiple for the company. And it seems the guys listed on the last line are all talking about subscriptions, SAS, ARR, remaining performance obligations or RPO. So we feel very good about this look back. We'll take an A on this one. No, it's not an A plus because we were too conservative on the growth of Okta, CrowdStrike and Zscaler topping at 50%, they blew that away by another 10 points or so, 10 to 15. But look, pretty good call nonetheless. Okay, again, the next one you might feel like is a layup, but not really. So we said the increased tech spend would drive even more IPOs, SPACs and M&A. According to SPAC analytics, IPOs were up 109% this year. The SPAC attack continued up 109% in 2021 on top of a record 2020. And according to KPMG, M&A dollar volume was up 19%. Okay, you might say, that was easy call, but there was much more underneath this prediction. We called out UiPASS IPO, which was a lock, but also said automation anywhere would go public. UiPASS did, AA didn't. We did correctly call the HashiCorp IPO. We said they'd either go IPO or get acquired. And Cloudflare grew revenue 219% last quarter, but Akamai was not acquired. So the degree of difficulty on the overall prediction wasn't high, but the automation anywhere and Akamai events, we made those calls that didn't happen and those were obviously tougher calls. So we think this still deserves a B grade. All right, as you know, data is one of our favorite subjects and we've reported extensively on the successes and failures of so-called big data. We said in the next prediction that in the 2020s, 75% of large organizations will re-architect their big data platforms. And we said this would occur in earnest over the next four to five years. Now again, you may say duh, Dave, but you have to evaluate the prediction based on the underlying comments here. The jury is still out on things like Snowflake's data cloud, but we absolutely believe that it's the right direction. But then you have Databricks coming in taking a different approach. They're coming at the problem from a data science angle, trying to take on traditional BI and then you've got Snowflake coming from the analytic space and moving into AI and data science. And you know, we asked at AWS re-invent, we asked Ben Wadjaville on theCUBE if there needs to be a semantic layer to bring these two worlds together. And he said yes, and that's what he claims Snowflake is building. Meanwhile, you got the big whales like Oracle, they continue to invest in their capabilities to try to eliminate data movement. And then there's AWS taking a totally different approach to data where it gives customers maximum optionality of offerings and database and other services. And you can't forget Microsoft and Google. So many customers might not take the steps that we predicted because they're comfortable where they are. And specifically we're talking about here a shift toward domain ownership and data product thinking and the reorganization of hyper specialized technical teams. Many of the principles put forth by DataMesh. And we've said this change is going to take a number of years to play out four to five years. So we start noticing in 2021 that that's clearly been the case as we reported on parts of JP Morgan Chase rethinking its data architecture, HelloFresh and many others. So this is still in incomplete. The professor will give ourselves an incomplete on this one but we think it's trending in the right direction. Okay, the next one is always a fun discussion. That's the battle to define hybrid and multicloud. We said that's going to escalate in 2021 and we'll create bifurcated CIO strategies. Now, here we go. AWS sees the world as bringing its APIs and primitives and model to the edge. And the data center to AWS is just another edge node. And the company says that it still believes in the fullness of time that all data will be in the cloud. However, that's defined. And AWS say all this talk about hybrid of connecting on-prem to a cloud they would flat out say Adam Salipsky told us this that's not cloud is what he said. Then on the other side of the table at the likes of Cisco, Dell, HPE, et cetera, saying, hold on, cloud is an operating model. It's not a place. And AWS might say, yeah, and AWS along with its customers is defining that operating model. And these other guys would say, no, actually, you're not. We are with our customers. And this battle 100% escalated in 2021 with the launch of Apex by Dell, HPE doubled down on GreenLake. Cisco's as a service models. And then of course, Oracle, which actually announced a true same, same public to on-prem hybrid capability two years before AWS announced outposts. And of course, Oracle's executing on that strategy in earnest in 2021. And the other nuance here is a concept that we introduced called super cloud, which refers to the notion that look something like for example, multi-cloud is not about running within a respective cloud. It's not about cloud compatibility. Rather it's about abstracting the complexity of the underlying cloud primitives and building value on top of those cloud services, on top of the investments in CAPEX that the hyperscalers have made. Now, some people didn't like the term super cloud. Maybe Uber cloud would be a better term. We're going to continue to use it to describe this capability. We think it has meaning. And we're seeing new examples like Goldman Sachs's financial cloud running on top of AWS. So a super cloud is not an application or a suite of applications running on a single cloud. Now, if those applications span multiple clouds like Snowflake is trying to do, okay, that's a service that could span multiple clouds or in the case of Goldman Sachs, it's a portfolio of data, tools and software that's made accessible as a service that floats on top of a single or even multiple clouds regardless. We feel that this was a correct call given the evidence and we'll give ourselves an A minus taking points off for the somewhat anecdotal and observational measurement system that we apply to look back at this prediction. Okay, the next prediction we made was cloud containers, AI and ML automation are going to power that those big four are going to power 2021 spending. Here's a graphic we use to predict that it plots survey data for the various technologies within the ETR taxonomy, net score or spending momentum on the vertical axis and market share or presence in the data set. It's a pervasive measurement on the horizontal axis. The one that matters here is the vertical, that dotted line of 40%. Anything above that is considered highly elevated and these four areas have held serve this year based on recent ETR survey data that we're not showing here. We'll bring that into our 2022 prediction. So this prediction came in correctly for the most recent survey data and that's our measurement system on this one. So we're going to take an A for this one too. Now on the penultimate prediction, here we came back to automation saying that the automation mandate accelerates in 2021. UI path and automation anywhere we said it would go public but Microsoft remains a threat to these pure play RPA vendors. Well, we gave ourselves a B on this one doubling down on automation anywhere going public. You know, that was wrong but we definitely saw this year companies leaning hard into automation and Microsoft despite the fact that it doesn't have as feature rich a product and offering as UI path and automation anywhere Microsoft remains a very large presence. You know, we spoke to a lot of customers at the UI path forward for event in October in Las Vegas, physical event and they confirmed, you know, this is true but at the same time, so they're using power automate from Microsoft but also using in this case UI path. So they've kind of confirmed that, yeah, it's not the same. We use that for some of our productivity we're an Azure customer, it's easy for us but they're still leaning heavily and investing heavily into UI path and I think the same can be said for automation anywhere but power automate shows up as a big time leader in the magic garden of magic quadrant so it can't be ignored but clearly the two leaders in RPA have a sizable product advantage relative to the legacy software players. Now if you look at the comment on PEGA systems they cold off a bit as measured by their stock price their revenue grew 13% last quarter on a year on a year basis but perhaps we overestimated the tailwind effect and the company's momentum so we'll take a B on this prediction correct call on the automation trend and the big software vendors piling in IBM, et cetera but the chance we took on automation anywhere again was a miss. So we'll ding ourselves on that. And our last prediction for 2021 was 5G rollouts push new edge IoT workloads and necessitate new system architectures. Now much of this prediction you can see in the underlying bullets here really related to the observation that ARM was dominating at the edge it would find its way into the mainstream enterprise workloads and we've been asking a lot of the mainstream companies, the OEMs what do you see with ARM in the enterprise is that we don't see it yet but very clearly this came into focus in 2021 is AWS announced Graviton 3 now and new inference and new training silicon these are different types of workloads that are emerging in the enterprise these are all based on ARM Microsoft, Google, Alibaba, Oracle and others are now shipping or readying ARM based systems for the enterprise when you look at new storage network and security appliances and other systems they're very offering often including ARM based processors to assist with the offloads and look Intel is definitely under under pressure as we've predicted many times not just in our predictions posts even Pat Gelsinger has admitted this is a turnaround it's going to take at least five years that's kind of new and recent data that he's made public. So we're going to take an A minus on this one we're going to take off some points of the fact that five G rollouts and edge are evolving and this is a longer term trend but the underlying points that we made on this slide are still pretty solid. Now, if we use the following scale where A plus is 100 out of 100 A minus is a 90 a B is an 85 a B minus is an 80 and a C is a 75 out of 100 and we exclude that incomplete prediction on data architectures we average out to an 87.8 so that's a solid B plus so the professor in us said hey, yellow sticky good effort as most of the predictions could be quantified and or, you know, we tried to objectively score them there were some layups in there so yeah, maybe we'll try to take more risks you know, or not, we'll see we like winning and so, you know, you always have to couch some of these things with some obvious ones but really try to give some detail underneath that's maybe non-obvious and we'll try to keep it down in the legs we did this year to one or two multi-year predictions so what's next? Well, Eric Bradley and I we're working on our 2022 predictions we're going to release those in the next couple of weeks so stay tuned for that you know, what do you think? How did we do? You know, we're grading ourselves here love to know you know, if we're off base, on base we're too hard on ourselves, too easy give us your feedback don't forget these episodes they're all available as podcasts wherever you listen all you got to do is search breaking analysis podcasts check out ETR's website at ETR.plus remember we also publish a full report every week on wikibon.com and siliconangle.com you can always get in touch with email david.volante at siliconangle.com you can DM me at dvolante or comment on our LinkedIn posts this is Dave Vellante for theCUBE Insights powered by ETR have a great week everybody stay safe, be well and see you next time


I don't know, I'm just guessing. Why should the outcome be the same for us? And I think it goes to one of the points you made, which is arbitrary distribution of wealth, or it's not arbitrary at all. The distribution of wealth is determined by what you produce. You produce, you have stuff. You don't produce, you don't have stuff. Seems just to me, right? It seems just that I get my stuff. You get your stuff. I don't have a right to take your stuff. You don't have the right to take my stuff. You'll produce a little, maybe. I'll produce a lot or I'll produce a little.


Hello, welcome to the lecture on Microwave Measurements. In this lecture, we are going to study how to measure the circuit characteristics using test instruments. We will study this in two parts. In first part, I will explain some fundamental theoretical aspects of microwave measurements and in the second part, we will study how to do the measurements practically. Let us begin. So, unlike low frequency circuits where voltage and currents are measured against time, in microwave circuits we usually make power and phase measurements in frequency domain. So, the basic quantities which are measured in RF and microwave circuits are frequency, power, impedance, port parameters which are S parameters and noise and these measurements are primarily done by using two main instruments which is spectrum analyzer and network analyzer. The spectrum analyzer is capable of doing signal frequency as well as power measurements fundamentally. It can also measure the phase noise of a signal. It is also used to measure the harmonic distortion as well as inter modulation distortion. Network analyzers are used for S parameter measurements primarily and it can be also used to measure the impedance of a network which is input or output impedance and it is also used to measure the gain compression point which is P1 dB of a network. Let us see how frequency, power and phase noise can be measured using a spectrum analyzer instrument. So, some basics. We have a sinusoidally varying signal V of t equal to A cos 2 pi f1 of t and ideally the spectrum looks like this. So, we have an impulse at frequency f1 and height of this impulse should be A, but practically the impulse is flared at the bottom mainly because of phase noise present in the signal and we can see that this phase noise is measured in dbc per hertz at some delta f offset from the center frequency which is f1. The setup is quite simple, the dut which is typically a VCO or signal generator or a PLL is connected to the input of a spectrum analyzer and the measurements can be done accordingly. Now, for phase noise measurements the dut which is VCO or PLL has to be noisier than the spectrum analyzers reference source, then only the spectrum analyzer can measure the phase noise of the dut. The power and frequency measurements are straightforward and we will see this in the practical measurement of these quantities. Next is harmonic distortion measurement. So, you have an RF excitation source for a non-linear dut. The output of the dut is connected to the input of the spectrum analyzer. Here are some settings of the spectrum analyzer and we have to make sure that the output of the dut let us say in case of amplifier should not hamper the spectrum analyzer circuitry. In that case you may want to put an attenuator in between the dut output and the input of the spectrum analyzer. This is the precaution we have to take. Next is IP3 measurement. So, there are two tones applied at the input of the dut through a power combiner and the third order inter modulation product which is nothing but twice F1 minus F2 or twice F2 minus F1 is present at the dut output. These are some spectrum analyzer settings. One thing to be noted here the power combiner should give a very high isolation between these two ports and also it should have a very high written loss for F1 and F2. Now comes network measurements which are done using network analyzers or vector network analyzer VNA and the dut to be measured is placed across the two ports of the VNA in case of two port dut. In case of one port dut you simply connected to one of the two ports available at the vector network analyzer. So, before starting the measurements of the dut calibration has to be done for the VNA. Calibration is a process to remove the measurement errors arising due to the imperfections within the VNA system which are called as systematic errors and during the process the systematic errors are actually quantified by measuring characteristics of known devices which are called as absolute impedance standards and once these quantified systematic errors are known they can be adjusted in the actual dut measurements to give more accurate results. We will see how to do the calibration process and how to actually measure the S parameters in the practical aspect of this lecture. Hello, welcome to the second part of the lecture on Microwave Measurements. In this lecture we are going to demonstrate how to use spectrum analyzer and network analyzer to test or measure various characteristics of microwave circuits such as frequency, power levels, gain, phase noise, S parameters and so on. So, the instruments that we are going to use are mainly the spectrum analyzer and the network analyzer along with that we are going to use a signal generator, a DC power supply and here is another signal generator and here you can see the spectrum analyzer. We will start with the spectrum analyzer first and then we will move to network analyzer measurements. Let us begin. So, we have a Rode and Schwartz spectrum analyzer of FSL type. So, the spectrum analyzer's frequency range is from 9 kilohertz to 3 gigahertz and as you can see the spectrum analyzer has an embedded display and various controls to enter different settings and measure different parameters. So, the display typically has an x axis and the y axis. On the x axis we have a frequency and on the y axis the amplitude level or the power level of the frequency components are displayed. This is a VCO come PLL board. So, the IC that you can see here is LTC 6946, it is a frequency synthesizer along with PLL and VCO. So, we have studied this particular circuit or we have studied this IC based VCO design in the oscillator lecture and microcontroller PIC 18A 4 double 5 0 is used to program the PLL IC. The output of the PLL is taken over here and what you see here is an SMA type connector. This is a USB connector and the entire board is powered using this USB power cable. Let us power on the board. This PLL has been programmed at a frequency of 1 gigahertz and now we wish to measure whether the output frequency is 1 gigahertz or not. So, for that you have to go to the frequency button, press the frequency button and you can enter the desired frequency value which will be the center frequency value for the spectrum analysis. So, I will enter 1 gigahertz, enter. So, now you can see that the display has been centered to 1 gigahertz. The span currently is 2 gigahertz, I will reduce the span so that I can view this particular thing more clearly. So, I will set the span to let us say 200 megahertz. Now you see that you can view the frequency component in more details, but still the amplitude level is not correctly shown. For that if you read the reference level is minus 20 dBm and we need to increase the reference level for that go to amplitude and reference level set the reference level as 0 dBm. Now I can see the peak of the frequency component. To measure the frequency go to markers, set the marker you see that marker M1 has been placed and the marker reading can be read from these two lines. So, M1 has a frequency reading of 1.00040000 gigahertz which is very very close to the 1 gigahertz value which has been programmed in this particular PLL. So, at the output of this PLL we have a frequency component at 1 gigahertz and the level of this frequency component or the power level of this frequency component is minus 1.85 dBm. The program value or the program amplitude level was 0 dBm and the minus 1.85 dBm accounts for the cable losses and other losses in the circuit. So, this is how we can measure a frequency and the corresponding power level of a signal using spectrum analyzer. For phase noise measurements you have to keep the span to the lowest value possible. So, let us reduce the span, click on span and span manual and let us keep the span to 50 kilohertz value. Now, you see that the signal can be observed in more detail and you see that the noise level is quite visible now. To smoothen this let us go to trace, click on trace then click on trace mode and choose the averaging function. So, click on the average and now you see that the things are more stable. Now, let us go for measurement of the phase noise. So, go to marker. Now, the marker M1 has been set to the desired center frequency which is approximately 1 gigahertz. Now, go to marker 2, marker 2 is of delta type D2 and the delta is displayed over here. Now, for phase noise measurement all you have to do is click on this function which is phase noise reference fixed function. So, I will hit on this and I see that the offset is currently minus 100 hertz. So, the marker phase noise is measured at a particular offset frequency which is typically 10 kilohertz, 100 kilohertz or 1 megahertz. Let us keep the offset frequency to 10 kilohertz. So, I will enter 10 and kilohertz and all you have to do to know the phase noise is to read the D2 marker measurement which is shown over here. So, if you observe phase noise 2 which corresponds to marker 2 is around 67 dbc per hertz at an offset of 10 kilohertz. Now, the rated phase noise of this PLL IC according to datasheet is around minus 85 dbc per hertz, but due to the overlapping of AM noise and the system limitations of this particular board we get the phase noise measurement of around minus 67 dbc per hertz which is close to the desired value. This is how the phase noise measurements can be done using a spectrum analyzer. Next, I will demonstrate how to measure the harmonic distortion in a nonlinear circuit. This is an amplifier circuit developed using MMG 3001 IC and we are going to measure the total harmonic distortion using spectrum analyzer. Let us have a look at the setup. So, we have a signal generator which is generating a input signal of 500 megahertz frequency and the amplitude is 0 dBm. The signal is given at the input of the amplifier circuit over here and the output of the amplifier will be checked on to the spectrum analyzer. Now, this amplifier circuit will be powered using a DC power supply. The voltage is 5.6 volt and the current will be drawn accordingly by the amplifier operation. Let us turn on the amplifier. So, as you can see the voltage is 5.6 and the amplifier is drawing around 100 milliamperes of current which is the correct operating point. And now, we will observe the output of this amplifier on the spectrum analyzer. As you can observe on the spectrum analyzer display, we have the fundamental frequency output which corresponds to 500 megahertz and we have different harmonics of the fundamental frequency appearing into the output because of the nonlinearity present in the amplifier. Now, we will see how to measure the total harmonic distortion. For that you have to go to measure, click on it, go to more. You can see harmonic distortion function, select it. You can see that the upper graph demonstrates the fundamental and various harmonics of the fundamental against time and amplitude levels. One below table, you see that the first harmonic which is the fundamental frequency is 500 megahertz and this table has a frequency resolution bandwidth and power level. So, the fundamental frequency which is 500 megahertz has been checked or analyzed with a resolution bandwidth of 3 megahertz and the power level is 17 dBm. The second harmonic which is at 1 gigahertz has been analyzed or measured with a resolution bandwidth of 10 megahertz and the power level is minus 23.2 dBc. Now, dBc is the power level with respect to the fundamental power level, third harmonic, fourth harmonic, fifth harmonic and so on. So, the total harmonic distortion is 7.9 percent, in dB it is minus 21.9 dB. So, this is how you can measure the total harmonic distortion present at the output of any non-linear circuit. Next, we will see how to measure IP 3 point of a non-linear duty using spectrum analyzer. Let me explain the setup to you. The setup contains the duty under test which is the MMG amplifier which we have just seen. At the input of the amplifier for IP 3 measurements, we have to give two tone input which means two frequency input. This two frequency input is given to the input of the amplifier using a power combiner such that there is a isolation between these two input frequency sources. This power combiner has been designed using the techniques that has been taught in this course and the isolation between these two ports is around 25 dB. The first tone which is at a frequency of 500 megahertz and an amplitude level of minus 5 dBm is given to one of the input of the power combiner. Now, make sure that the amplitude levels of these two frequency tones has to be very close to each other or nearly equal. The MMG is powered using a DC power supply with a 5.6 volt input and the current drawn is around 90 milliampere which shows correct operation. Now, with the two tones at the input of the amplifier, we will observe the inter modulation distortion at the output of the amplifier and the IP 3 point is measured by measuring the level of the third order modulation products which is twice F 1 minus F 2 or twice F 2 minus F 1 frequency components relative to the fundamental frequency amplitude. Let us see the response of this circuit on the spectrum analyzer display. So, this is how the output of the amplifier on the spectrum analyzer looks like. So, as you can observe there are two fundamental outputs which correspond to the two tone input signals and along with those you can see various spurious signals generated and we are interested in the third order spurious signals or third order distortion signals which are at 200 megahertz and 1.1 gigahertz according to the input signals of 500 megahertz and 800 megahertz.  Let us verify that we get some signals at these frequencies for that let us go to markers and we have marker M 1 you can move the marker like this and you observe that at 200 megahertz we do have some signal which is nothing, but twice F 1 minus F 2 where F 1 is 500 megahertz F 2 is 800 megahertz and at 1.1 let us see if we have any signal. So, even at 1.1 gigahertz we do have some signal at an amplitude level of minus 35 dBm and the TOI which is third order intercept point can be measured by using inbuilt functionality of the spectrum analyzer. For that again go to the measure tab. So, click on this button measure and you see that you have a TOI function here just select that function and you observe that there are two markers M 1 and M 2 at the fundamental frequencies. So, M 1 is at 505 which is close to 500 megahertz, M 2 is at 800 megahertz approximately and M 3 and M 4 should be at the desired third order products. So, let us move the markers slightly so that they are at the exact level so M 2 or M 3 I will move it to 200 megahertz and M 3 I will slightly move it to 1.1 gigahertz and the third order intercept point as you can read from this line is 29.6 dBm. The data sheet mentions the TOI to be 32 dBm which is the output IP 3 point and the measured value by using this spectrum analyzer and this setup is 29 dBm which is very close to the specified value. So, vector network analyzer to measure input impedance S parameter and VSWR end phase so you have been taught many microstrip circuits like power combiner, filter, coupler, etc. In this case vector network analyzer that we are using is a two port vector network analyzer. So here we will be using this power combiner to measure the S parameters, input impedance and VSWR. So, the vector network analyzer that we are using is E 5071C the frequency range for this vector network analyzer is from 100 kilohertz to 8.5 gigahertz. So, to start the measurement firstly you should be knowing about your DUT what is the frequency range in which you want to do the measurement. So, this device that I showed you power combiner is designed for GSM 900 band the bandwidth that it provides is from 400 to 1.4 gigahertz. So, we will select the frequency range accordingly. In order to select the frequency range use this option start click here then give the start frequency range that is 400 megahertz then define stop frequency range use this option stop give the stop frequency range that is 1.4 gigahertz. So, you have selected the frequency range next you need to do the calibration. So, select KEL then you should select KEL kit that you are using for the calibration. So, the calibration kit that we are using is 85033E. So, the manufacturer provides you open load and short terminals. So, firstly I will tell you what is the calibration. So, the calibration accounts for the extreme condition like when the cables are connected to open load and the short and they account for the losses in these terminals. And then accordingly it adjusts the measurement results. So, select the calibration kit you can use this option KEL kit then select the KEL kit that you are using we are using the 85033E. So, we have selected this number then press calibrate. Now we know this is a 2 port network analyzer and we are doing measurement for power combiner or power divider. So, we need to do the calibration for 2 port. So, press 2 port calibration then press reflection then you should connect the cables with open load and short. So, first connect with open and then press this option then click on this option port 1 open.  And similarly you replicate the procedure for port 2. Now after doing the calibration for reflection you should check the calibration for how much power is going from this cable to this cable. So, use this transmission option and connect these cables using the female adapter. Now connect this adapter to connect these cables you can see here these are the cables which have SMA male on both the ends. So, connect these cables using SMA female adapter. After connecting this just click on this option port 1 to port 2 through we have just connected these cables. So, S21 and S12 ideally it should be 0, but it is showing some value which is because of the losses in the cable. However, if you see S11 and S22 they are well below 20 dB throughout the range. After doing this just press done and disconnect the adapter. So, after doing calibration this vector network analyzer accommodate the losses in the cable. So, here you can see this S11 is 0. You can also see S21 so it is less than minus 50 dB so it is calibrated. Now you should connect your device to these cables to do the measurement. You can also save this calibration state using this option save recall then select save type state and kill then select state only and save state. And you can define whatever name you want to define so I will just maybe the name is 1, 2, 3, 4, 5 and then enter. So, at the later stage if you do not want to repeat the calibration you can recall this calibration state in order to recall select save and recall then recall state and then from file dialog box you can use the state that you have saved ok. Now you connect these cables with the DUT since we have only two port vector network analyzer so we will have to terminate one port with load. So, here I have connected these cables at the two ports and I will terminate the third port with load. Then you can see S21 that is the transmission from port 1 to port 2 so this is S21. If you want to see S11, S21 all the transmission and reflection coefficient in one window then just select this display and select the number of traces S4 and then you define these traces in order to define select measure then let us say for the first trace we will give the value S11 for the next trace we will define S21 and for the next trace again we will define S12 and for last trace we will define S22 ok. Now after allocating all the traces that is S11, S21, S12 and S22 you can see here all the transmission coefficient and reflection coefficient in one window. Now just to locate the value of these transmission coefficient or reflection coefficient select marker press marker 1 then you can move the position as per your desired frequency point ok. So, in our case just maybe select 900 megahertz so here you can see the value of green curve that is S22 at 900 megahertz. Now if you want to see S21 all you need to do is move to next trace ok. Now here you can see the value of S21 is minus 4.4 dB so ideally this power divider should provide minus 3 dB, but this power divider is designed on FR4 substrate which is lossy so that is why you are getting minus 4.4 dB and S11 you are getting minus 17 dB. You can see here it is well below 10 dB throughout the frequency range. So this is how you calculate the transmission coefficient and reflection coefficient. Now I want to show you that when you connect the output ports of these power divider how much will be the isolation. Now if I connect these two cables at these two output ports and terminate the other ports with 50 ohm like this then I should get the isolated power that is how much power is going from this port to this port and that will be the measure of S21. Now you come to window here you can see blue line or the pink line. It shows you the value of S21. So if you just try to locate one more marker go to marker then select one more marker and then just try to locate it just give the value 700 megahertz and for marker 1 maybe give value 1000 megahertz. So here you can see that the isolation between these ports is less than 20 dB throughout this particular frequency range. So we can say that the output ports are isolated so this is how you can measure the various parameter of your device whether it is a one port device or two port device. Now we would like to do the measurement for active device. So the active device that we are using is low noise amplifier here. This is the IC of low noise amplifier the IC number is SGL0622 the voltage requirement for this IC is 3 volt and it withdraws 10 milliampere current. So here we have given 3 volt as voltage and the current withdrawn by this IC is 10 milliampere. So I have connected first port to the RF-IN of this IC and the second port to the RF output. Now this vector network analyzer can receive the power up to 17 dBm. So we should reduce the RF-IN power so the gain for this IC is around 33 dBm. So by default the network analyzer power is 0 dBm if we give 0 dBm power then it will show you warding because it will saturate. So we will change the power level from this option sweep then go to power then give may be minus 40 dBm ok then go to measure and you see here. It shows you the variation of S21 and this is the variation of S12 and this is the variation of S11 and S22. So that is the reflection coefficient at input and output port and this is the transmission when the power is given to port 1. So here you can see how much is the gain of this particular low noise amplifier by just locating the marker. So here you can see from S21 that the gain provided by this particular low noise amplifier is around 33 dB at 570 megahertz. So this is how you can check the gain of this active device in the frequency range of your interest. To summarize at microwave frequencies instead of measuring voltage and currents against time or in time domain we measure the signal power and phase in frequency domain and for different microwave circuits that we have been studying in this course we can measure the S parameters, the gains, the noise figure, the linearity using spectrum and network analyzers. And we have studied how frequency, power, port parameters, impedance values can be measured simply using these test instruments. We will stop at this point. Thank you.


I'm naturally a right-footed player, but I have two strong feet. In this video I'm going to teach you how to improve your weak foot in soccer. So you want to improve your weak foot. That is fantastic. But why do you even have a weak foot in the beginning? I think it's become so acceptable in the game for players to have one foot that's really strong and the other one is useless. Just because you see the majority of people, majority of players, that's how they play the game. If you're watching this video right now, I'm assuming you don't want to play like that. You want to be a two-footed player and you want to have the ability to go both ways, to dribble both ways, to take shots on both feet, powerful shots, to score your chances when the ball comes onto your weaker side, to play passes, to have a good first touch, to have confidence wherever the ball is on either side with both feet. That's how I want you to play. That's how I play. I'm naturally a right-footed player, but my left is just as strong if not stronger. Sometimes I hit better shots, I hit better pings, better passes, better skill on the left, and a lot of people would ask me, which foot are you? So if you want to play like that, this video is for you. I'm going to talk about how to improve your weak foot faster. So number one is, without even touching the ball, it's the ability to flex the foot and move the foot in different ways.  It's probably easy for you to flex that foot, to point the toes down, to lock the ankle, and if you did a full circle to flex, to have a really strong range of motion, really big strong, a big range of motion, but also the strength in which you can flex your foot. Hey, what's up? I hope you're finding this content helpful. Watch this all the way to the end if you want to find out which subscriber got this video's special shout out. Different ways. If you go on your weaker side, it's probably very hard and feels awkward to flex your foot like that. So that's the first thing I would say you need to work on, is actually building strength and flexibility in your weaker foot.  It feels so weak. Just because I haven't done it enough times in my life, then how am I supposed to hit a hard shot? Okay, so number one is the flexion, the technique of the foot, the technique of the toes, the flexion of the leg, the strength in the leg, and the mobility, the range of motion. So you might just want to start with building up your range of motion and your ability to flex and tense your foot in different positions. Maybe it's a pass, maybe it's a ping, maybe it's laces, maybe it's outside of the foot. But if you can't flex your foot in those ways and make it strong, how are you supposed to produce good technique on your weaker side? So that's the first thing I would have you think about.  So I'll give you examples of dribbling and ball control and passing and shooting, but I just want you to think about these topics first. So number one was the flexion, the mobility, the actual technique of the foot. Next is comparison. So if I'm a right-footed player and on my right side I strike the ball very clean, I'm happy with how I hit it on my right side, but then I go to my left and I'm saying, why can't I do it on my weaker side? Well, if you look at what you're doing, if you analyze what you're doing, it's totally different. So how is it supposed to produce the same result, the same product? I want a hard straight shot on my left, but on my right I have a nice big backswing full through the ball, locking the ankles, the technique is beautiful, but then when I come on my left, I have no backswing. I'm hitting off the inside of the foot, I'm swinging across my body, the technique is not the same, so you need to learn how to analyze your technique on your stronger foot. So compare whatever it is, if I'm doing a pass, if I'm doing a ping, if I'm doing a strike, if I'm just dribbling and cutting the ball in different ways, how come when I do it on my strong foot it does what I want? When I do it on my weaker foot it does not. I have to learn how to compare the techniques of the two. And if I can make the technique on my left the same as the technique on my right, then I will produce the same result. Okay so number one, the flexion, the emphasis on the technique of the foot and how you control it, the range of motion, the strength, the flexibility to comparison and analysis. If I want to improve my weak foot, but I'm just doing drills and I'm not actually thinking about okay that time I hit it here, this time I hit it there, I follow through there, so you have to actually analyze what you're doing with every technique, with every repetition.  Okay you're watching this video and you're asking yourself how can I improve my weak foot, but you already know the answer to that because you already improved your strong foot and what did you do? You just practiced with it all the time. So in order to get this one as much as this one you just have to give it the attention and love that it deserves. So to improve your weak foot, get those repetitions. Every time you do a drill on your right, do it on your left. And because this one is so far behind right now, you might just have to give this guy a break and just do everything with your weaker foot. So I'm assuming you want a few drills to help with technique on your weaker foot, but the truth is you already have the drills. It's all the drills you're doing on your other foot, just not using this one enough. But some good places to start. Just basic footwork drills inside, outside cuts. Really focus on the weaker side. You're probably having trouble. Like I said, just control the foot. You're trying to cut it, but it's going forward. You're not turning the foot enough. You're not turning the hips enough. Okay, so just little touches like this. Even coming here, just get comfortable, comfortable moving the ball, rolling the ball in different ways on the weaker foot, dragging it. Okay, and it's the touch. It's also the movement of the hips. You probably move your body completely different on one side compared to the other. So analyze again, compare, but different footwork drills. Just getting those touches, getting those repetitions, getting the rhythm, giving that foot the love. Okay, juggling. So maybe you're just starting there with a bounce. Backspin on the ball. You're trying to set a record on your weaker foot. What is it today? 20? Make it 25 tomorrow. Okay, so little touches. Holy, I need some balance. Little touches. These are really good. And one ball control exercise that's awesome for weaker foot, strengthening it up is a foot stall. So maybe start with a bounce. But if you have a really poor weak foot right now, it's probably difficult to flex your toes like that. So if you're doing any of these techniques, shooting, dribbling, control like this, and you're trying to flex your foot in one way or the other and it's crapping, that just means you don't have the strength in that foot yet. You don't have the muscle memory. It's not used to doing that. So that will go away over time, but you have to. You have to fight through that. You have to flex through that.  Then ball striking, whether you have a wall. Okay, just practicing passing technique against a wall. First touch, back off that foot. Just keep it all on your weaker foot. Get a whole weak foot session. Gain those repetitions, striking the ball, pinging the ball with your weak foot. Shooting the ball against a wall, I always say is best because it'll come back to you. However, even something like this, if you had a rebounder, you don't have to shoot from outside the 18-yard box. All you need right now is repetitions. You don't have to blast it hard. All you have to focus on is technique. That's the reason you can't hit it hard on your weaker foot. It's not because your leg is necessarily weaker or smaller. It's because you don't have proper technique. Technique creates power. So if you want a stronger shot, you need stronger technique. Okay, so practice these things. Think about those concepts I talked about and you're going to improve your weaker foot a lot faster. This is the featured subscriber for this video. If you'd like to be featured in future videos, hit that like button, drop a comment below and let me know how my videos have helped you improve and achieve more in this sport.


NSA Naples Fleet and Family Center toured a local World War II battle site. Petty Officer Sanders takes us to San Pietro with more. NSA Naples Fleet and Family Support Center sponsored a group tour to the small town of San Pietro, Infine, a battleground at which more than 7,000 American, German and Italian soldiers perished during World War II. The battle, following an armistice with Italy, was the first in which Italian troops fought alongside Allied troops. It's important because especially for the Americans living over here, sometimes they miss how many ties really exist between the Americans and the Italians. Among the American troops that came over here, some of them were actually Italian Americans where their fathers were probably from the same town. It's really important that the people have memory of places like this so that they can make better choices in the future. Petty Officer Kelby Sanders, San Pietro, Italy.