Brian, what are we going to do at the end? Like, we're going to check our word, and you see, if it makes sense, we're going to say yes, but if it doesn't, we're going to try again. Perfect. What is the title of our article, everybody? I think I grew this. Students all across the United States, the United Kingdom, and even Australia and India are joining a new trend, organic gardening in school. Their schools have their own gardens in the backyards, where students under the guidance of their teachers and parent volunteers grow and cultivate vegetables and fruits, which are used for snacks, and sometimes even lunch. So I just read the first paragraph for you. You could start right on the second paragraph, but I wanted to show you, just in this paragraph alone, I saw some words that might be difficult or unfamiliar. Could somebody give me an example of one? Alejandro? Christopher? Organic. Organic. OK, so that might be a word you're unfamiliar with.  Can you tell your partner? Yes. Yes, tell your partner, share your ideas, explain it to them, and it might not be the word you choose. If you both feel, this is a good word, because we're both unfamiliar with it, and I think with enough clues here, we could figure this out. You might choose that word.


What is the Activity Chooser? To add learning content to a Moodle course page, you use Moodle's Activity Chooser, which you can access by turning on the editing from the gear menu top right, and then in the section where you want to have your learning content, you click Add an Activity or Resource. This will bring up the Activity Chooser. If you don't see this link, but instead you see two separate drop-down menus, check with your administrator. Let's click this link to take a look at the Activity Chooser and what it does. On the left is a list of activities. If we scroll down, we see that underneath is a list of resources. A resource in Moodle is static content which students would just read or look at or listen to. So for instance, if you're uploading documents and presentations, you'd use File or Folder. To present information directly, you'd use a page, or you can use URL if you had a link to a website or an online file. Whereas, activities require students to interact with Moodle. They might submit work using an assignment, or they might contribute to a forum discussion. The list can also include resources and activities which are not standard but which your Moodle admin has added as extras. The Learn Moodle site for instance has Big Blue button for web conferencing, which isn't standard but which has been added. In order to find out about what each of these does, you click the radio button next to it. Information then appears on the right, telling you what it is, how to use it and useful teaching ideas. And if you click the More Help link, this takes you to Moodle's documentation with further information. You add an activity or resource by clicking the button and then clicking the Add button at the bottom or, if you don't need to read the information, you can simply click twice on the button and that will take you to the setup screen.


We are back with another Q&A and if you guys haven't been keeping an eye on the community page on my YouTube channel Definitely check it out around the middle of the month and you guys can post your question for the next Q&A video But let's get started if I put caviar egg yolks and ground organ meats like liver on my skin and hair Well, the vitamins minerals and fatty acids be absorbed So a few of them might be most of them will not but this does bypass your body's Natural digestion, so it's not realistic by any means and there are specific nutrients that do have a high transdermal absorption Like D3 some others like magnesium and iodine do have a lower Absorption and can be used but for the most part you want to get your nutrition through your actual diet Fluoride is coming back to my city's water plant this fall My drinking water pitcher is capable of removing fluoride But should I also get filters for my shower sink as well or just for drinking and cooking water? So I do have several videos on water if you search my channel and you want to find a natural well or spring For drinking and cooking water. That's what's kind of affordable But I do purchase glass bottled mineral or spring water for myself, which you know does add up You know hundreds of dollars if not over a thousand per year And you can buy a shower filter that does remove some of the negatives as long as you don't shower like twice a day It's not that bad how to maintain a healthy diet while on vacation Hotels restaurants no availability of high quality food Now if you don't get physically sick while eating the average standard American food It might not be too horrible to deviate for a week or two But for me I'll actually get sick insomnia be miserable So I would look for a local market or bring some food with you depending on the length of the vacation What's the deal with this paragraph stuff? Is it legit? Everyone online lately is talking about cleansing their parasites and the stuff is backordered out an entire month So it looks like a basic antiparasitic But these types of antimicrobials don't get much further than the early part of the small intestine anything is just stressing your liver and if people were putting Adequate amounts of this in capsules you know taking a bunch of them letting it go through it would basically destroy their insides diarrhea and bombing out all of the bacteria and Instead of that you need to incorporate probiotics and a proper antimicrobial regimen Which I do go over in the various videos I've done on how to heal your gut and if you did truly have a parasitic infection You would be frail you would have lost a lot of weight basically on death's door So the stages are usually you know getting sick immune system dysfunction possible liver damage And that turns into fungal infections sebo candida and if the sebo candida gets really really really bad You would have probably already lost a lot of weight it can turn into a parasite infection where you would basically be in the hospital so average person walking around on the street thinking they have parasites is just not correct and When taking these types of caustic like herbal remedies they can Damage your intestines so it'll look like your bowel movement will be different So that's why people sometimes think they have parasites you planning on moving to Pennsylvania if things go through with the slaughterhouse and other property As soon as possible. Yes, I like to relocate You know my personal life and my business to that area Hopefully a farm property in addition to the slaughterhouse, but I'm not thinking about it too much until I get the funds Which I'll give you guys an update on tomorrow.  So most of facial development is already determined in the womb and very early stages of life So it depends on your individual circumstances and current development My draw line was actually sharper before I had the draw surgery I'll try to find some pictures when I was younger, but I don't really have that many I want to drink a lot of beer as a student. Will this hinder growth? it will but Not as much as a modern diet while constantly using your cell phone You know so an alcoholic 50 years ago would be much healthier than the average sober person now And what I mean by that is the amount of oxidative stress and damage we're causing to our bodies with these new additions to our lifestyle The chemicals in the food and water the damage from the radiation from all these smart devices We're using that is far worse than drinking beer So hypothetically if you were super healthy living out in the woods in a cabin no other Modern lifestyle factors stressing your body drinking beer would be fine if anything drinking beer would actually be healthy But the additional liver stress on top of everything else It's just not good. Is dabbing every day at 16 interfering with my growth So it's without a doubt impacting your brain development by you know increasing the amount of resources your body needs And I did a video on marijuana where I go in depth on this But it's kind of the same answer as that previous question that being said By the time you're 16 17 18 You're pretty much going to be as physically developed as nutrients will allow you to be you might get a little bit taller But the majority of your facial development organ development height has already happened So if you're in your late teens, you know, it's not really comparable to What your mother was doing or what you had as a younger teenager Why do I always feel my heart beating out of my chest when I eat fat of your animal foods is this bad? Yes, that's a sign of liver damage and possibly other issues I would try a diet similar to what I'm doing and see if you feel better You can start with like lean meat and white potatoes and start incorporating more foods But definitely avoid animal fat dairy and eggs for at least a couple weeks Is only eating raw meat a healthy thing to do or are cooked foods necessary? So every group of people like tribal ancestral that were in good health consumed raw cooked and fermented foods Also from like a modern scientific analytical perspective It's not possible to get all of the vitamins and minerals naturally without eating certain cooked grains nuts seeds various plant foods Cooking makes certain nutrients more available by reducing the anti-nutrient content how to get enough K2 Do I have to supplement it or will seven ounces of the highest quality European cheese a week do it? So the reason I made organ supplements was because I believe certain nutrients cannot be obtained adequately from food Vitamin K2 being one of them. So if you go to organ supplements comm you'll see like 11 different vitamin and minerals That you should be either focusing on specific foods that are high in those or just supplementing them temporarily to gauge how you feel K2 is it's even if you're eating a food that's high in it. It's usually also Plentiful in the contrasting fat side with vitamins, which means you're not fixing a potential deficiency Because you're increasing the amounts of the other vitamins you're intaking as well thoughts on juice fasting or raw milk fasting Both can damage the body for various reasons nutrient imbalances Stress on the liver a water fast with a clean Water source that's not too high in minerals would be a much better option if you want to fast What to look for when buying water key for grains? I can't buy from you since I'm in Europe and it will cost hella extra I mean, I think that's like 15 or 20 bucks international shipping So yeah, if you go to a local farm or whoever selling it nearby you could save some money But keep in mind I'm using all organic ingredients with a high quality mineral water So if you do get someone else's water key for grains, and they do work I would go through at least a few batches to kind of like detox and cleanse the grains Just to make sure you have the highest quality key for possible tips for healthy hair growth So you have to figure out which nutrients you aren't getting, you know, what be vitamins. You're missing what minerals Balance things out and you could also be getting excesses of certain vitamins and minerals For me, it's a matter of getting plenty of sun for vitamin D supplementing magnesium supplementing K2 and You know if you're losing your hair in any capacity whatsoever or your hair just isn't growing quickly There's something wrong. It's just a matter of figuring out what it is. What age should babies start eating food? So breastfed a minimum of two years preferably three or four while the mother's on a high quality diet rich in animal foods and then Animal foods can be incorporated into the baby's diet around the two-year period You want a lot of protein a lot of omega fatty acids for all over their tissue development as well as brain development Our fragrances safe to spray directly on your skin. Also. What's your favorite fragrance? So even a natural fragrance might disrupt your skin biome and this is very subjective and certain ones can actually be harmful So try to Google research as much as possible on the ingredient No, I do have lemon grass oil in my deodorant on Frankie's naturals and a different type of oil in my scented hair pomade But you want to be careful on what you're choosing which are the fruits with the best fructose to glucose ratio So grapes are good apples pears and you can just Google this But it's not the soul-determining factor as the anti-nutrient content of the fruit might outweigh The negatives of a high fructose percentage Why do you use coconut oil versus butter ghee in your cookie recipe? So coconut has antifungal properties and and can alleviate candida to some degree and it's also a neutral flavor But just try both and see which makes you feel better What's a good fat ratio to eat on a daily basis with carbs included? So you want about 20% of your calories from protein depending on your lean body mass and the remainder from energy So 80% from carbs or fats and I just eat both to appetite Lately as you guys have seen over the past, you know months maybe even a year now. It's been mostly carbohydrates Best advice for a 20 year old college student so if I could say one thing it would be Think of something or find something that you want which makes you excited or happy and and just focus on that and Be happy and excited and enthusiastic about it Because then whatever you do in your day-to-day life You're basically acting as if you know as happy as you could be You know, don't worry about the how don't worry about what you're doing now You know if I could tell myself one thing it would be just find something that you want Think about it think about how happy and excited you would be to have it and just apply that Enthusiasm to everything you do and if you do lose enthusiasm and excitement for whatever it was You know it could change you know one week. I might want to lie on the beach with some Russian girls Another week.  So thank you guys for joining me today If I missed your question or if you have one just leave it down in the comments below and I'll get to it By the end of today. So if you could also drop a like on the video Subscribe so that YouTube can unsubscribe you next week and check that notification bell So YouTube doesn't notify you of my videos Therefore you can go to frank-to-found out calm check out all of my businesses including organ supplements where you can support me further Thanks again guys, and I'll see you for tomorrow


Alright, good Tuesday morning everyone. It is time to talk about the markets with Jim Cramer on the floor of the New York Stock Exchange and Jim Let's start with earnings from Pepsi and actual earth's plus name. Yeah, okay It's really important. Listen up. PepsiCo's gonna report ended up being the best Organic grower. It's got the free-to-late business is not Capable being crushed by the wars among Amazon Walmart target. It's a convenience store plate, too There's a lot of innovation here that drives a lot of the growth But it doesn't matter because you see you don't have any compares until later on And so what the pattern has been with PepsiCo is it reports people don't realize unless it just comes out You know raise forecast and that's not what they're doing right now What what'll happen is people sell it down and we expected that that's why we trim some You know, we made a very good sale and explained to people in the club that look you know It's kind of run but then what happens is it goes down because people say hey you know what they didn't raise the forecast they didn't boost it even though they did better and That's your opportunity and that's saying things playing out right now You're gonna have more on this for the call for club members tomorrow 11 a.m. Absolutely, okay. Let's also talk about Amazon Prime Day I know you tried to buy a Breone suit on Amazon. They had one they had one 48 and I'm a 42 Okay Amazon Prime Is it's something it does attract people who have never been on Amazon? Those who are on Amazon all the time are not particularly blown away by it and I'm wondering whether there won't be a backlash Beginning today where when we hear that it's about 20% year-over-year maybe even more that maybe people come back to some of the retailers They've been driven down by a lot of short selling Watch Costco watch Costco. It's still expensive Costco 23 times earnings But I think the Costco is the one that was hit from 181 down to 150 when we know things are good So maybe perhaps this kind of for a moment There's a lead up in the selling in these best in the best of breed retailers, okay? Meanwhile snap falling below its IPO price Yeah, there's a devastating piece by Morgan Stanley really just kind of upfront first analysts to just say listen Instagram it's going after the bread and butter and Instagram is powerful enough obviously on by Facebook to be able to cut its Rates for whatever directly competes with snap in order to make trouble for snap And it also says that the return on investment for advertisers is not clear for snap Now when I spoke to you Johnson, who was the the CFO of PepsiCo this morning on squawk on the street? He's he's also the on the board of Twitter. He says he sees more ad dollars still going to online So it's not like that that snaps a disaster But what it says is now it's of course below the price where the deal came What it says is that if advertisers can't get a good return and Instagram is going after them It's become a quizzical holding. It's quizzical to own this guy Okay, meanwhile Tesla expanding its service network. Yeah, I mean I was talking to my friend Phil LaBeau and it's very interesting Tesla's been going down down down the stock and it's only the expansion of the service network well timed obviously by Elon Musk, but then he said well look at you well time. He's got this on the agenda It's always been on but whatever it comes as an interesting time where the dialogue has been controlled by the bears And this is a dramatic expansion. You wouldn't be doing this unless you saw a lot of demand All right, Western digital matching some bids for Toshiba's the club members own Western digital and the fact that they're matching the bid is very bullish because what's really Made it so Western digital has not been Made it so that it's so ranker says that Western digital has been trying to get away with buying a company for less than other bidders If they are got the same bid as everybody else Then there's no reason why it shouldn't win because Western digital's Sandist division is indeed the principal partner of Toshiba for their flash business And then on stop trading on swark on the street you talked about Exxon mobile. Yeah, I mean it look oil line the sand yesterday The club members know we had a patchy. We've been very frustrated with oil very frustrated with a couple of our retail positions TJX and And Walgreens very frustrated with snap one It's nice to see oil get a little bit of lift here by a Barclays call But don't get too excited in the end you need oil inventories down dramatically You need to bake your rig countdown and you need some event that either takes Libya offline or Nigeria offline That is a series of things that have to go right for oil to rally so be patient Don't pull the trigger fascinating interview on mad money last night with on course Bob Shepherd Yeah, I mean what what's going on course very interesting This is a battle of two brilliant billionaires on the one hand you've got Warren Buffett wants to own this Okay, because it's terrific asset on the other hand the guy who owns the principal amount of debt is a Paul singer from Elliot and Elliot's basically saying listen you've got to pay us more. I think the bankruptcy court is going to say Buffett's got nine billion Elliot's group that he's putting together is going to once it willing to pay nine point three billion I think that the it's incumbent upon the bankruptcy court to say listen We've got to take the nine point three billion dollar for it because we need to have the most that you can get So this is a tricky situation Will Buffett top that bid well you have to see whether the bid is firm if the bid is firm I think that they're gonna get it because I think the Buffett has said listen. It's nine billion dollars That's what I'll pay and no more, but you got to watch this. This is Elliot versus Buffett one two of the smartest Greatest investors of our time. We'll be watching that one and then tonight. You have the constellation brands that you are Yeah, Rob Sands has just done a remarkable job. We've been behind constellation the whole way now This is one of those examples where because I own a bar bar Saint Miguel I saw early on how powerful bedello and corona are I mean that is what people drink also now the drink Pacifico Since then he's bought high-wesk, which is just a fantastic whiskey brand And he's bought ballast point, which is a great craft beer and he's bought Casin noble Now if you saw Diageo pay for Costa Migos, you're got to be laughing if you're Rob Sands We'll ask that because cost of noble is a better property and and he paid about 20 million for it instead of a billion Rob Sands buys right. He has some tremendous. You know, he's Raffini. I was over in Tuscany. We were drinking Raffini That's his to his opus one. He has up some amazing wine brands. Me only prisoner and I I'm always in awe of what he's done. He's a spirits guy, and he's a tough guy. I really like him That's tonight at 6 p.m. On mad money and then meanwhile President Trump to nominate reyna quarrels as the top-fit bank You know, it's interesting We didn't go over that this morning on squawk on the street And I think one of the reasons why we didn't is because well, who did we think that they would get this is a guy? Dan when Dan to Rulo Left Dan to Rulo was the guy who really ruled banks. He was the toughest taskmaster banks He's gone really didn't matter. You didn't think there was no way that Trump was gonna put on someone who was Who was tough on the banks this guy'll be easy on the banks and maybe the banks need this now You know, there's always people say listen, we haven't solved too big to fail and we got to bring up the banks I find it tiresome dialogue the banks have too much capital Okay, same with the banks Jenny Yellen testifying on Capitol Hill Wednesday and Thursday. Yeah I mean Jenny Yellen has historically when she goes on the hill not giving you any real Insight about number of rate hikes that are needed Usually just says listen. We're going to be data dependent and we're going to be patient and careful But we will raise if we have to raise that's kind of been her dialogue and I think she's gonna stick with her dialogue All right, we just want to reiterate the club member call tomorrow at 11 a.m. Yes, it should be a lot of fun I've got some interesting anecdotes from the old days. Yes, and you always get great feedback on those calls All right Jim Cramer. Thank you so much and for more information on the stocks Jim mentioned Please head back to the street comm


Northern Eye Center in Pikeville, your local vision experts experience doctors using state-of-the-art technology. Choose from top designer frames from Oakley, Ray Ban, Nike and more. We accept Medicaid and Medicare, plus most insurances are accepted. Northern Eye Center in Pikeville, next to Tractor Supply. The fifth annual October Fest will be held at the Appalachian Wireless Arena on Saturday, October 7th. We'll have beers from all over Kentucky, including here locally, over 50 different styles, ciders and beers. And we're post-UK kickoff, so the UK game should be over, be a nice day out on the plaza here. Breweries from all over Kentucky will be present and have many different flavors of beer and cider to try, and food trucks will be lined up for those in attendance. Every ticket for October Fest comes with 15 tasting tickets. We get a lot of people regionally come in. We got people from Lexton, all the surrounding areas come in for this one. A lot of people enjoy different beers, and this is their chance to get to enjoy it, get to hang out with some familiar faces from the area, and seriously, just enjoy the taste. Tickets for October Fest are $35. Designated drivers get in free. For more information, you can visit the Appalachian Wireless Arena Facebook page. For Mountain Top News, I'm Kelcey Dean.


Welcome to the audio renaissance unabridged presentation of full tilt Written by John Artavanovich and Charlotte Hughes final production supervised by Tony Hutz and read by Laura Lai King Chapter one Our relationship has grown stale Give me a break max. You're shallow muffin Shallow? She gave a snort of disgust This coming from a man who was married to a gold digger named bunny for three years now. There's a relationship with depth He grinned There's something to be said for gold diggers. A man always knows where he stands in the end both parties get what they want And we both know you always get what you want, but let me remind you Maximilian Holt I'm the best thing that has ever happened to you. You need me. I listened to your problems I feed that enormous ego of yours and I can match wits with you any day with both hands tied behind my back I might add Don't forget. I made you what you are today sweetheart without me.   You're running on fumes How far to the nearest gas station a good 10 miles. You could have told me sooner. Yes I could have I've created a monster Max guided the radically customized car down the narrow mountain road Taking each twist and turn with the precision of a professional driver a Pink Floyd CD played from a cutting-edge sound system that would not be available to consumers for at least another year Max took a great deal of pride in his automobile. The same one is friends laughingly referred to as his Maxmobile The car had been designed from the chassis up by former NASA scientists The body and frame were composed of titanium and a newly identified polymer that offered the lightness of fiberglass and the durability of the strongest steel The end result had resembled a Porsche But Max's version was bigger better and could do things that car manufacturers would not find on their drawing boards for years to come Nothing was indestructible, but the Maxmobile Came close The dashboard was more complicated than the cockpit of a leered jet a team of first-rate computer whizzes Hired away from top government contractors had created the car's instrumentation using state-of-the-art equipment Spread out among luxury automotive goodies like a tachometer an altimeter and a global positioning satellite system Were a highly enhanced PDA keyboard a digital speech recognition module a photo quality printer a fax a satellite phone an HDTV display screen and a full video conferencing suite all Operated by a high powered computer that was smaller than an ashtray Thanks to all these modifications Max if he wanted to could run his fast business empire without ever getting out of his car Only a man like Max Holt would have laid out the kind of money it had taken to build such a machine And only a man like Max would have created computer intelligence with voice recognition technology and a sassy personality to match Just for the fun of it He had named her muffin and programmed her with a sexy voice that one employee claimed gave him a stiffy every time he heard it There were those who said it couldn't be done Max had proved them wrong He insisted on the best. He drove himself and his employees hard If he exuded confidence it was because he always succeeded in what he set out to do always Not a difficult task for a man with an off-the-charts IQ and a business acumen that put fear in the hearts of his toughest competitors He created two companies simply to put a scare into AOL and Microsoft The television network he purchased ten years ago had grown far beyond even his own imagination He had recently sold all three companies for a King's ransom Simply because they no longer offered the challenges he craved The New York Times Newsweek and Money magazine were clamoring for interviews But Maximilian Holt did not give interviews. He maintained a low profile at all costs Sure photographers had grainy pictures of him slipping into buildings wearing expensive Italian suits or ducking into stretch limos with a Gorgeous model or actress on his own Sample complete ready to continue


Hey folks welcome bookshelf mounting a grid of Linux boxes. That's the thumbnail for this update You know I'm trying to reach out to new folks and to do that to be more accessible by being as Specific as I can about what the content is in each particular video. So that's what this one's mostly gonna be about I mean, you know the t2 tile project is about Reinventing computing and it has you know consequences from hardware to software to artificial life to humans in society But that's a little bit of a longer story. So I'm gonna try to stick to this. We're gonna have a video, you know, I'm mostly a software person not a Electrical engineer not a mechanical engineer not a furniture designer interior decorator or any of these things I'm a total amateur at all of that. So be prepared So we're gonna start by watching a little 90-second demo of the kind of things that we could do with this grid of Linux boxes And then we'll circle back around to introduce the main video And as always we'll start with the computing team creed First be robust then as correct as possible and as a Okay, this is the demo a Eugene time-lapse Standard laws of physics that come with the tiles at the moment that weird shape was the two lotuses 38 t2 tiles each one running Linux It's the same that you can see in this thumbnail for the video. So again, let me just warn you This what you're gonna see here, maybe isn't pretty, you know trigger warnings for incompetence, but you know, I Do software I do this other stuff because that's what it seems to be necessary to advance the T2 tile project, which is about actually building a real thing not not just in simulation But to explore the issues from physics to Philosophy so let's take a look at the the video Gonna have to raise this whole thing up basically from here To the wall is six feet and that would be enough if we could continue The support pipe as long as we could go up some extensions that would Screw into the bookshelf One small one and one big one. Oh great What do you say, huh? Do by 25 This piece away too bad. It's good at seven and three-eighths Exactly how we're gonna cut that. I Don't know. We got to figure out how to make this Diagular cut use the Makita instead of the for a penny exceptionally terrible. Oh Man, I want to fire up the jointer even this out all right, well, I Cleaned it up a Little bit For not showing you me cleaning up when the jointer was I'm actually short on Electrical outlets and I've got the Fuji on the battery replacement thing because that's its main lot in life At least I thought far enough ahead to make these non critical. These are the non critical measurements ranked down to 1100 rpm Assuming this plywood counts as soft wood It's not deep enough made a super mess of it. Now. We've got Piece of wood clean on the back Clean enough from the front at the two shelves the two risers I'm calling them more or less lined up by the thing that's going to be against the wall and the most important thing is that the holes line up And I have no idea how I did that, but I am skilled in the art of screwing up measurements Said he was a sigh. I'm a very bad whittler I whittled the ship till it's small as a boat now. I will hold it how it float So he threw it away and cut a throat When he saw his head was gone. He whittled another and put that on John shorty just for the record There's the joint there. It's going to go a little bit deep So we messed up we always mess, but I don't think it's fatal at least not yet Extra crappy looking stuff is going to be against the wall on the left side So that we can Lift the pipe out in position For the rod as close to the bookcase for the shells will support when we push on it This is the maintenance position when we want to get in behind. All right, but why don't we just Buzz off Big ranch and cut this in place You think I've got to be able to just wail on it hard enough it could go at it with With the saber saw and I'm gonna unplug the camera. It's quite a bit slower going Then I thought with this blade, but it did get through and then complete this mission by raising the bar It doesn't seem Lens Fixed focal length so we'd have to get lucky so we might be able to Get the whole grid in this shot We'll see Laura has been raised is the picture of what it looks like, you know from further back We see the the Fuji X-T4 There as well, you know, there's a ton of problems with this It doesn't actually lay that well against the bookshelves part of that I think is due to the bookshelves themselves Not exactly being lined up with each other these things could be tweaked in various ways in addition You know gonna have to pull at least a few more shelves worth of books out to clear Get some clearance behind there.  So step by step That is the development process that we have for this update Last time it was, you know, here's where we were at. We're still at the same number of tiles All the stuff is still the same except the next step was rethink locations I mean, it was looking like maybe we were gonna need to go to a whole, you know It'll rent an office space or something like that. But now we raised the bar I think the camera will be able to get a half decent shot on it That's good Coming forward, I still want to actually build Lotus 3 and I have some kind of confidence that it might actually be able to stay there And not have the whole thing get disassembled again. All right, that's the development story. So very quickly The 2022 project over T2 the T2 tile project is to redo the event engine In a much more robust fashion and part of that idea was to redo the mechanism But whereby adjacent tiles compete to see who can have the next event and to instead of Doing a long complicated protocol with packets being sent back and forth and resolving races between them That maybe we could use this ring oscillator idea that I've talked about in the last couple of Updates now I want to show something really quick here Okay, this is the time. This is what it's about. It's just two weeks. So Let's take a look at it and see I mean there's not much to see it's because it's all still very low-level stuff, but you'll see Okay, so here we've got three tiles that are Are running? We've seen this sort of thing before but in this particular case these tiles are not actually connected at the moment We have this one has a power line jet power injector of his own These two over here are sharing a power injector even though in fact, you know This whole thing is really just coming from a from a Y splitter over here But the point is that allows these things to be connected or And so now let's let's connect This is the key master the white one Let's hook it up to its Northwest Okay, and we start seeing update G-drow in e2 1 and e2 0 and e2 1 What that is is they The key master and it's Northwest Northeast, which is the Southwest to the back Have started to do a ring oscillator between them And so they're passing a zero around and then when it gets back It's getting flipped to a ones that's going around and so forth now It's going incredibly slow because all of this stuff is slowed down for debugging purposes The important point for me is that it's it's very robust You know, so if we pull the thing out, you know So now we pulled it out There's a bunch of other messages that come and go through there as well because still the higher the old event engine is still running at The moment as I'm working on this stuff But the G-drow ones are the ones that were things Overheating it does a lot of that with all the lights on but let's plug it in again and see it pick right back up All right and boom right there It's it's right back to passing the shared clock around But now the cool thing is the thing that's really is new for me is let's hook up another one There we go So now we get east But east 2 and east 3 if you see that So what's going on is each of these pair-wise tile pairs is has a shared ring oscillator going just between them That's why it's two because there's two tiles involved But now in addition there is a three-way shared clock going around this whole corner going You know from from the to the key master to the guy to his northeast the guy to his southeast To the guy who who's his west and back around and that's the east three One that's also happening simultaneously and and again, you know, these are all very robust We can pull them out we can plug them back in it's happening at a lower level Then the whole packet exchange that the stuff is built on and and so all right So that's that's really mostly what I wanted to see You know we could start up a start up a huge gene here as well cuz wine But it's not our point for the moment all right, so there's the demo and You know don't forget about this this this is the circuit Schematic diagram for the circuit board that is on the back of each t2 tile that that ties the whole thing together But what I want to call your attention to is this is one of the intertile connectors the ITC's and you know, there are 16 pins, you know, so VDD that's power. There's three power GND. That's ground. There's three grounds that's six pins we've got a bunch more and You know, so what are they? Well the The are the RX and TX RX and TX ready those four pins are the things that are actually Sending the packet signals back and forth that the whole thing is built on but there's also these Lock pins that if you dig way back in the t2 trial project archives You can find discussions of what these were being used for but I never really got it to work and I never really got it To work reliably and I never really got it to work in a way that would play along with the packet transfers Which have all of these delays due to buffering so I have repurposed these pins To do the generalized distributed ring oscillator, so the the Eiger lock and the I rec lock, you know, they are actually doing the Four stroke cycling while in four stroke in the case of going across the pair And then four stroke all the way around the ring Using the other ones like that. So there's a chance and again I don't know that any of this is really going to end up working out in the end But it is in the spirit of you know be you know UDP ish don't assume that there's perfect transit Down below but you have build up a ring now One of the problems with the ring oscillators in fact is that it doesn't guarantee that there's only one Edge between a one and a zero going around if you have a ring oscillator with lots and lots of stages You could have an island of one that's so cycling around with an island of zeros behind it And then another island one another island of zeros and they would just keep going around and around as two separate things And if you were counting on the edges between zero and one and one and zero as being a mark of uniqueness Something in the grid that wouldn't be true. And so that's a real problem in a distributed systems in general We're going to avoid that we're going to mitigate it anyway because in each step We're deliberately randomizing the slightly randomizing the amount of delay time through the stage How long you get to hold the turd the the token when it's your turn and in that case I think you could do some theory probably pretty easy to show that the islands will collapse into each other rather quickly And once we've gotten to a single Single ring a single island of what all ones to all zeros to all ones It'll stay that way unless there's further, you know errors and disruption in the system in which case You know that's what self-stabilizing systems is for so that's the current status of that I just wanted to You know get it marked down. Okay, and finally, you know outreach So in the last episode BJ Manon Submitted the science fiction story to as a mobs science fiction magazine. It's under review You know my my feeling at this point, you know, we're waiting at least probably another three weeks And maybe even a couple of months beyond that, you know My feeling is most likely they're gonna pass and the reason that they're probably gonna pass is it's not accessible enough, you know, this is like Back to the Thumbnails and making everything more accessible. It's a problem. I have So we'll see I mean the story From the first few readers that I got feedback from the story has gotten tons more accessible A lot easier to understand what's going on But still it's packed pretty dense with computer stuff and all kinds of crazy things So I'm thinking they'll probably pass We'll find out. All right, and the living computation foundation for folks that don't know They is backing the entire t2 project the t2 tile project and A bunch of other stuff that's in the broader mission of living computation. So, uh, the second half 2021 second half summary is is now out. Uh, uh, you know, there it is under the the news item and you know I get depressed.  Um, and We've got some new living computation foundation nerds if you don't know for for a five dollar donation to the foundation Which is a nonprofit You can get your own living computation foundation nerd number Um, and so we've we've we have a bunch of them including a Core of you know, folks who are actually recurring donors, which I didn't expect at all and it's like I don't thank you guys enough But well, I don't thank y'all enough. So thank you Uh, uh, it really it makes a huge difference. And we've got a couple of new ones Uh, uh, one that I've owed a thank you mail for a while and another one more recently in the most recent one It was actually pretty substantial contribution like three digits Uh, uh, specifically calling out computing up the computing up podcast, which I super appreciated Uh, uh, so there that's the computing up homepage. In fact, we have another we're due out to have another one Uh, uh, well yesterday, but it's probably gonna come out today or tomorrow depending on when I get some sleep So, uh, uh, that is it. Um My goals for next time is to let let let's let's welcome our new nerds do it or die Uh, and then to try to try to take the generalized distributed ring oscillator the ringo idea that I just took a brief little look at Uh, uh, and take it up to, uh To user space so that we can at least see it working. I'd like to build Lotus 3 Uh, uh on the grid and once again always remind myself have some fun You do too. Uh, uh, thanks so much for coming. I hope to see you next time


Hi everyone, Joe for jaskiescasewrakes.com knocking out another case of 2022 tops clearly authentic baseball 20 box picker team at number seven Big thanks to everyone here for making this happen If you have a little rooftop next year, and that means you won that spot in the filler that we did So big thanks everybody for getting on the action Appreciate it Travis you ended up with last spot mojo before we pulled out the remaining of teams Yeah, Riffer there. He is in the chat looking for a jeter a judge All right. Good luck everyone To pop this case open. There you go clearly authentic. He didn't find the right box Could be a supply chain issue That's why they have to fill it with all these paper too Yeah, clear. I think just flying all over the place like a Like like a like a football referees flag. There's 20 boxes here everyone everyone make sure you keep count We're gonna start off with Logan Gilbert. We were talking a little Mariners baseball before we started this video Logan Gilbert for Nestor and the Mariners got the nest got the Nestor got the Seattle in the In the filler break Still learning still learning. We got Chas McCormick Eight out of ten for the Stroves.  There's Reed Detmer's rookie auto for the halos That's gonna be for Joe He's who else is in the mix He's got to right. Oh Kirby that's right. Hmm next up is will the thrill will Clark 75 For my rivals the Giants mark the Giants Yeah, I mean I think Courier Kirby might have pitched his way into a rotation spot Here's Jaren Duran Duran save a prayer for me now Or save it to the morning after Diego with the Red Sox Jaren Duran Duran Not it's not too bad having Logan Gilbert come out of the the pen though Maybe for long relief duty or something like that There's Casey Meyers get well soon Casey Meyers 72 s 75 Generation now autograph going to Going to Kevin M and the Tigers Tiger uppercut and we've got yord on Alvarez. We were just talking about him before this video talking about You know just some bad baseball trades in recent memory Andrew Freeman the Dodgers GM said that he doesn't regret most trades, but that one he does I guess he's like that. That one was obviously mistake Next up is mmm burgers Jake burger I'll say I've said it before I'll say it again. I'm looking forward to a future Jake burger and Seth beer Duel auto white socks Kyle Harper With the shy socks, you know, I've not seen a single episode of that of Cobra Kai I've been told that it's actually quite good There's a captain Jack Flaherty Cardinals that's going to go to Richard and the Redbirds Next up is Jake McCarthy 11 out of 99 for the snakes the X line the Diamondbacks Dodgers and Diamondbacks are tied at two in the top of the ninth. I'll wreck thinking a a Burger beer and noot bar triple auto That'd be pretty cool, and we've got Austin Meadows the meadow What movie is that from the meadow That's a deep. That's a deep cut. I think Tampa Bay rays. That'll be for Adam and the rays It's gonna walk good eye Cody. Good eye. And next up is Hoy Park rookie auto for the Pirates Oh, I see there because there's another logo right there. I was losing my mind for a second And for a second, I thought he assigned the back of the car Matthew with the Pirates Yeah, I mean, well, I'm sure there'll be plenty for me to do next week There should there should be plenty of cases to do for both. What up doc? Do I good and for James and the Metropolitan's? Come on Cody deep breath Focus you got the average to 200 you're above the Mendoza line Nice. Good. I that's a walk. All right. We got Robin yout 55 out of 99 for the brew crew It's for Patrick and the Brewers Paul O'Neill Yankees, I don't know if this this it's not the Yankee that Riffer was looking for. Do you think Paul O'Neill should have gotten his number retired? Am I am I under rating Paul O'Neill? Right his number got retired, right? There's Max Muncie 85 out of 99 Max Muncie that funky Muncie That goes to Matthew and the Dodgers got the Dodgers in the the filler here Two homes for me. Paul O'Neill this shipper. It was amazing on Seinfeld's this time Was a part of a lot of World Series champs, but I mean O'Neill's the 23rd Yankees player or manager according to MLB comm To have his number retired the first and in the first since Derek Jeter There's Alejo Lopez six out of 99 That is for the red legs that's going to go to John and the Reds Next up is Jake Myers Jake Jake Jake Jake Jake 16 out of 99 That's for Nathan and the Astros Jakey Yo, Jake. All right next up is Fernando datis jr. 64 out of 99. That's for the Padres. That's for the X line the motorcyclist The ring warmer. It's gonna take a little time for him to work his way back into the good graces of the people but I Don't know an MVP Calvary season probably does it right then all's forgiven Colton Welker That's for the X line and the Rockies There you go, ladies and gentlemen another case of clearly authentic in the books. We did load up another case Let's run it back. I'm Joe for jassies case breaks calm and I'll see you next time for the next one. Bye. Bye


A warm welcome to the 38th lecture on the subject of wavelets and multirate digital signal processing. As promised in the previous lecture, we shall use this session to discuss two applications of wavelets and time frequency methods in great depth. In fact, the two students based on whose application assignments this lecture is constructed are going to discuss what they only introduced very briefly in the previous lecture. They had kind of given a trailer to their presentations in the previous session in which they had just explained the essence of the application that they had done. In this lecture, they shall be explaining the details of the application and also pointing to some of the results that they have obtained. I must mention that these are two students who have actually used these two lectures to learn the subject, use the lectures over the semester to learn the subject and have undertaken to explore two applications of wavelets all over the semester out of a batch of about 15 to 18 students. The assignments that are going to be presented today have been found to be some of the best in the class and therefore, in a sense it is also an appreciation of the excellent work that these two groups have done that they have been invited to record their presentations today. In a broader sense, this is also to encourage whenever people use these lectures outside that students should be involved in exploring applications and we hope that the hard work and the very intelligent efforts put in by these two students, these two groups actually of students would inspire many other students who listen to this lecture to explore several other applications of wavelets many as they are. Anyway, with that little introduction, let me put before you the two applications that are going to be presented in depth today. You have had a trailer of them, but I would like to put them in the broader perspective of the subject. So, in the lectures today, we are first going to look at the application on data mining. Kunal Kunalsha is going to present the application on data mining on behalf of his group of two students namely, he himself Kunalsha and Orko Choudhury. Now, data mining is a generalization of representation. So, in data mining Kunal is going to show us how one can use the properties of wavelets in efficient representation to advantage in retrieval and other such applications from a database. The second application which is going to be discussed today is face recognition. Now, you know there are also going to be certain differences between detection and recognition and so on and the issues I do not want to take away the thunder as I said from the person making the presentation. But face recognition is an important and increasingly important application in many security systems and other image processing or vision systems. So, both of these applications are of great importance in the modern world and we shall now without much ado invite these two speakers, young students speakers to present the work that they have done over the semester and to put before you both the concept and the result of what they have done. I shall first invite Kunalsha to make the presentation based on the work done by his group of two students Kunalsha and Orko Choudhury. Thank you. Hello friends, today I would like to talk on the various applications of wavelets in data mining as Sir said data mining is used for efficient representation of data. So, today I would like to speak on that so, the problem statement is given a time series data the time series data could be huge our aim is to improve the efficiency of multilevel surprise and trend queries from the time series data. Now, first of all what is the meaning of surprise and trend queries? Now, when we have a long time series data generally we do not encounter point queries. For example, if we record the temperature of a particular city for a year say we never ask what was the temperature at this date of this month. We always ask the trend how was the change in the temperature during the month such queries are called as trend queries. One more type of query which we encounter is surprise query was there any sudden change in the temperature in a particular city of this month. So, such type of queries we have to handle and wavelets efficiently handle such type of queries that we have to look. Now, what is the meaning of multilevel? Yes, such queries are generally encountered at various level of abstractions. It could be a month, it could be a year, it could be a decade. So, if someone asks what is the change in the temperature during a particular month we should be able to answer that easily and if we ask what is the particular temperature during a decade sudden change or average in a decade that also we should answer. So, decade, year, month shows various levels of abstractions. So, we have to improve the efficiency of such type of queries. So, first of all how such huge data is represented. So, suppose x till day is my whole data. So, I represent that data in the form of a matrix say of size m cross n. Suppose I store the stock prices of a particular company or say many companies. So, let n be the total number of stock prices that I store for a particular company. Say if I store for one year then n will be 365. Let m be the number of companies of which I am storing the data. So, each row of this matrix represents the stock prices of that particular company. This is how my whole data is represented. Now, what I need to do? I need to do three things. First of all I need to efficiently store this data. How can I efficiently store this huge amount of data? Say for example, if I have data of say one decade, so n will be equivalent to one decade and m will be say 100 companies or 200 companies. So, efficient storage is very important. Secondly, how can I retrieve the data efficiently? And thirdly, if I want to add or modify certain things in the data, how can I easily modify it? These are the three things. If these three things could be done efficiently then we can using wavelets, then it will be a very good tool to represent this data. So, first of all let us look at one of the methods which are already used in data mining, namely singular value decomposition method. What is done in this method is I have a data say x tilde. I represent it in matrix form u, which is a column orthogonal matrix of size m cross r. This is a diagonal matrix of size r cross r and this is again a row orthogonal matrix of size r cross n, where r is the rank of matrix x tilde. So, I am representing this data in terms of three matrices. Now, instead of storing the whole data, I am storing these three matrices. So, if r is small, I am already saving under storage capacity of my data, which I need to store m cross n matrix. Now, I am storing just three matrices m cross r, r cross r and r cross n of which one of them is a diagonal matrix. Now, I have u diagonal matrix and v. Suppose I want to extract the data of a particular company, say a particular row of this matrix x tilde, let that be represented by x. So, to extract a particular row that means to extract the data of a particular company, the complexity required is of the order of size of v because multiplying these two, since this is a diagonal matrix, multiplying these two, I will be able to extract a row of a particular matrix. So, to extract a particular row, the complexity required is of the size of v, but the size of v is r cross n and n is huge in our application, say a data of a decade. So, still the complexity is huge. So, the complexity of reconstruction in singular value decomposition is huge. Now, one can argue here, let us reverse the order instead of storing it as m cross n, why not store n cross m? Because m is not that huge, but the problem here is, now if you want to extract a particular company's data, you need to extract the whole column rather than a row. The complexity of extraction of a row is of the order of size of v not of the column. So, again if you want to extract column, your size is equivalent to the complexity of v. So, you are not improving your efficiency by reversing the order. Further, one more disadvantage in this method is, if I want to modify the data, I need to recompute all the three matrices again, which is not the case with wavelets as we will see. I would just like to warn the audience here that this does not imply that singular value decomposition method is not a good method. In fact, it is used extensively, but in this case, since our n is huge and since we require frequent updation, wavelet has a upper hand over this method. Now, what is wavelet? How are we going to store the data using wavelets? Subspace is one row of that whole matrix extended. I am decomposing it into two subspaces, approximate subspace and a detailed subspace. The approximate subspace is also decomposed into two, one more a second level of decomposition, approximate subspace and a detailed subspace. This is called as a TSA tree. This is trend surprise abstraction tree. This stores all the trend data and this stores all the surprise data. So, wavelets naturally store decompose the data into two forms trend and surprise. You do not need to extract something, because wavelets naturally can decompose any data into trend and surprise form. This is the biggest catch here in this type of application. Now, how does this splitting and if I want to merge this merging take place? This split operation is very easy. If I have a data AX, AX, I, I just pass through a low pass filter and a high pass filter down sample by 2 and I get the next level of subspace. So, this is a split operation. This is generally done in the in the course. So, I did not emphasize on this. Similarly, the merge operation is just the reverse of this. Up sample by 2 pass through the same filters and then add. So, you go one level higher. So, this is the merge operation. The split and merge operations are easy, but the properties that this tree hold are very important. First of all, we can get perfect reconstruction. So, now what I am doing here is I am not storing this. Rather than this, I am storing this approximation subspace and detailed subspace. So, I am storing this data rather than storing the signal and now I am saying I can perfectly reconstruct this original signal from this data and this is very important. Perfect reconstruction is very important. If it is not so, then if in case I have a point query, say what is the temperature during this day, how will I get using that approximation and detailed subspace? So, perfect reconstruction is very important, which is the case with wavelets. This we have already studied. Again, the power complementary property is also very important because on decomposition, we are still preserving the power and this is also done. The third is very important. As we move down the tree, the size of each node, these are called as nodes is decreasing. So, if the size of this signal is say n, this will be n by 2, n by 2 and so on. This will be n by 4, n by 4, n by 8, n by 8. So, size is continuously decreasing. So, that is very important that we will see how. Secondly, these nodes are called as leaf nodes and we will see there that instead of storing all the nodes, it is appropriate to store only the leaf nodes. Now, why this since the size is decreasing? Why this point is very important? Because here, we are assuming that the split and merge operations do not incur any cost. They incur negligible cost. Whatever cost is incurred is to extract the data or cost is due to the disk IO operation. So, the amount of data that you are extracting is actually incurring cost. So, cost is directly proportional to the size of the data and bingo, our size is decreasing. If I want a 8 level decomposition, what I need is just the approximation and detail subspace of the 8 level and the size of that is very small as compared to n. So, the cost incurred is very less. Just require a small post processing and you can reach x. For example, if I want a third level decomposition, I just need to extract a data equal to n by 8 and n by 8. I can perfectly reconstruct here, I can reach here and by that I can reach here x. So, I just have a cost equal to one-eighth, the cost which I require by extracting the whole signal. Now, as I pointed out, the leaf nodes are sufficient to give us the trend as well as the surprise details. How? Suppose, I require a trend at the third level. So, what I do is I just extract this data and without using this, I do not perform perfect reconstruction. I reach this data by upsampling and passing through a low pass filter. Again without using this, I upsample and pass through a low pass filter and reach at this level and in this way I reach at this level. So, using a data equal to size n by 8, I am reaching at this level without perfect reconstruction and I am getting the trend at the AX3 level of decomposition. If I want the surprise data, I go from here, go to AX2, go to AX1 and AX. Just here, I need to pass through a high pass filter instead of a low pass filter. This is the synthesis branch, merging operation. Now, if I want at this level, then first I need to extract these two both and I need to perform perfect reconstruction at this level and then I can go do approximation. So, this is how I with small amount of post-processing of split and merge operation, I can find out the trend and surprise queries. Now, the optimal TSA tree is that tree which stores only the leaf nodes. Now, when I store only the leaf nodes, it is very easy to see that the total size of the leaf nodes is equal to the size of the original signal. So, I am not increasing my size by storing only the leaf nodes. I am not storing the whole tree. I am still getting all the information by using only the leaf nodes. So, my optimal TSA tree is that tree which incurs a minimum cost and a minimum storage and by storing the leaf nodes, I am incurring the minimum cost as well as the minimum storage. Further, if I introduce one more node which is not a leaf node, I will improve my performance, but that is also not difficult to prove that the performance increase is just marginal. So, it is no point to store any other node except for the leaf node. Once we have seen that even retrieval is cost efficient and even storage is cost efficient, can we improve more on the storage by reducing further on the leaf nodes.  Yes, wavelets are very good at compression. You compress your leaf nodes and still you can efficiently store that data and improve your accuracy. I would not say improve your accuracy, but you are not compromising more on the accuracy. So, one of the methods is node dropping and in node dropping, we are exploiting a very important property of wavelets which is the orthogonality property. What does that say? Suppose, I drop one of the leaf nodes, say d x 3 and I reconstruct the whole signal using the other leaf nodes and let that reconstructed signal be x cap. Now, if I find the norm, norm square in the error where x is the original signal and x cap is the reconstructed signal by removing one of the nodes, then because of the orthogonality property, norm square is equal to the norm of the node which we have removed. So, the error is actually the error in the coefficients which we have already removed. So, s is the number of nodes which we have removed. This is true only because of the orthogonality property of wavelets which is not the case always. So, if this is true, then we can use a greedy algorithm to determine which are the nodes significant in the data. So, what I calculate for each node, each leaf node, I calculate the norm square of that node and divide by the size of the node and if I calculate the maximum of all and this is the most significant node which I find. So, I store that node, then again I store the second best node, again I store the third best node and I reach a particular stage and if I find that I have already completed my disk space, then I remove the remaining nodes. So, this method is called as node dropping, but here there is a slight disadvantage in this method is that if the node which I have removed contains some outliers or some important information, then that is lost which is not the case with coefficient dropping which we shall see. In coefficient dropping, I store all the leaf nodes as a sequence and each coefficient whichever are the significant coefficients, I store the whole significant coefficients rather than the nodes and since I am storing coefficients, I need to store the index of that coefficient also as to this belongs to which node. Since now for every coefficient I am using double the memory, storing the coefficient as well as storing the index, I have to just check how many coefficients I have used of a particular node. If I am using a memory greater than that node size, then it is better to store the whole node rather than storing the coefficients. If I am not storing any of the coefficients of that node, I will remove the whole node and if this size is less than the size of that node, then I will store thus these coefficients and this is called as a coefficient dropping method or hybrid coefficient dropping method. Now, let us see the results what we have got here. So, I will just switch over to the slides to see the results. Here these are the group members Kunal Shah, myself and Orko Chaudhary. These are the details of the trend analysis. This is the first graph is the original data. It is taken from Yahoo stock market and it is the data of SBI, State Bank of India of the two years. So, it has around 700 coefficients. These are the trends or the decomposition at various levels. The second graph is a two day decomposition, the third graph the four day, fourth graph the eighth day and so on. And as you see as you are going down, the averaging is increased. So, these are the trend analysis. If I see the last level and if you ask me what is the average over a particular eight level data, then this is the eight level data average which you are seeing. It would be more appropriate to show to interpret it on surprise analysis. If you see on the surprise analysis, here the first graph shows the there is a small outlier in the original data which is more prominent in the second day average in the second day surprise as you can see. But as you go on increasing your decomposition level, you can interpret the data this way. The surface which was looking prominent in two day is not looking as prominent as you increase the number of days. It is averaging out. In fact, you might see that by inspection from the original data you could very well observe that surprise. But as you go down the level that surface is actually averaged out. So, if you look at the last level, there are many surprises which you can see in the previous data which by inspection you cannot encounter only by decomposition you can encounter that here also there was a surprise component. Now, this is the note dropping method. The above signal is the original signal and this is the recovered signal after note dropping and we can see that it is almost the same. But the disadvantage here is that we have missed that outlier which is seen that impulse sort of which is seen in we have missed in the note dropping and we have removed 300 coefficients in this out of the 700 coefficients and this is the result. So, we have removed almost 40 percent of the coefficients retained only 60 percent. But in coefficient dropping method as you can see that outlier is retained even though we have removed 300 coefficients. In the trend analysis, we have used Dobash wavelet and we see that as the as we move above the family the only thing that is changing is the averaging is more as you increase the filter length. So, as you increase the improve go on increasing from dot 2 to dot 4 to dot 8 the averaging is increased. This was all about application of wavelets in data mining. The reference paper is the following is as seen on this slide. I have just read this paper and interpreted or as a student what I can interpret from this paper. So, my observations my interpretations from this paper I have just shown in this application of assignment and this is the reference which I have taken for stock prices of SBI from Yahoo stock. I hope you enjoyed the application assignment. So, that was a very interesting presentation from Kunal Shah on behalf of Kunal Shah and Orko Choudhury. The group which worked on the application of wavelets specifically they have looked at the application of the Dobash family in representing databases efficiently. You will notice that the idea of course representation and incremental information was given a different meaning in this context. So, the idea of course representation gave what is called the approximation information which he called AX and the so called incremental information gave you the surprises the novel information at every scale. Now, one must also take note of the difference between the node dropping approach and the coefficient dropping approach. In the node dropping approach one noticed that with the same level of compression some important information was lost a sudden spike was not seen so clearly. When coefficient dropping approaches were used then one could see that that was retained rather well. So, in addition to the transform that is used how one uses the data obtained from the transform is equally important in wavelet based representation that is what Kunal's presentation has amply demonstrated. Now, taking further the presentations of students on what they have done for their applications we have the second presentation which was very briefly introduced in the previous lecture namely that on face recognition. Without taking away from his presentation I shall now invite Ronak, Ronak Shah to make his presentation here based on the application of wavelets in face recognition. So, Ronak for you now. As professor Gadre mentioned the second application that we are going to look into today is on face recognition through wave packet analysis. So, Kunal mentioned an application in data mining and it was in one dimension. So, the data that he has was in one dimension. When we move from data mining to face recognition the data that we have is in two dimension. As I mentioned in the last lecture there are two keywords here one is face recognition and another is wave packet analysis. Before going into details I would like to briefly summarize what these keywords mean. Start with wave packet analysis, why we need wave packet analysis for the task of face recognition. As I mentioned in the last lecture we need some kind of decorrelation in spatial as well as in frequency domain for the task of classification. As we know the task of classification can be better accomplished if we can do some kind of decorrelation in spatial as well as in frequency domain. So, in that way wavelet analysis naturally feeds into the scheme of things. But then why we go for wave packet analysis. Again as I mentioned in the last lecture in wave packet analysis we decompose not only approximation subspaces, but we also decompose detailed subspaces as we have seen in lectures. Now when we do the task of classification we need richer representation of the underlying signal that is we should not miss anything out from the signal from the underlying signal and the underlying signal here is face that we have seen. So, face images will be our signals. So, the task of face recognition can be can be accomplished by wavelets as well as wave packet analysis. But as I mentioned we use wave packet analysis just for the richer representation and wave packet analysis also provides decorrelation in spatial as well as in frequency domain. So, that is better suited. Now before going into nitty gritties and details I would like to first provide references. So, the work that I am going to present here is based on the work done by Ghashriya, Zikos and Ziritas. It was a research paper in European conference on computer vision and the work nicely by utilizing wavelet based framework for face recognition. Now the work that I am going to present has been motivated by their work. This is an extension as well as a novel description of the work done by them. So, here we utilize a different database namely the Yale database and the work provided by Ghashriya, Zikos and Ziritas was based on different database like ferret database as well as face is database. So, here we do not only provide extension to different database, but here the understanding is purely from a point of view of a student. How a student can look into this application and how this particular framework based on wavelet, it fits into the whole framework of face recognition. Like what are the approaches that have been there in the literature and how that fit into how this approach fits into a particular framework. There are some other references like the second reference here is based on Eigen phases and this is quite a popular approach. Also, there are some other references like the approach based on PCA principle component analysis. This framework has also been popular. The reference that I mentioned the first reference compares this PCA based approach and Eigen phases based approach to wavelet based approach. The fifth approach here that is mentioned like by Zao, Chalapa, Philips and Rosenfeld. It provides a nice framework or a nice literature survey on the overall work that is done in the task of face recognition. So, this is for references. Now, we shall move into like why face recognition is required. As I mentioned in the last class, face recognition can be required for biometric authentication, but as I mentioned the face like the signal that is obtained from face namely the image a 2D image. It can easily be morphed like someone can grow moustache or someone can grow beard in a given period of time and that can easily be morphed or someone can come up with sunglasses even. There are some other signals which are available like retina or let us say for that case fingerprints. They cannot easily be morphed and they can easily be utilized for biometric authentication. So, the task of face recognition for biometric authentication framework is limited. However, we need face recognition for the task of surveillance. In surveillance, one needs some area or region to be manned and some generally in this kind of region only a limited number of persons are allowed. So, the access is allowed. Also, one might be interested in what that subject is doing inside that particular region. Also, we have some learned prototypes like which kind of activities are allowed. Someone is stealing inside that particular region or someone is doing some kind of abnormal activity inside that region. We want to detect that as well and generally the queue for this kind of framework starts with face recognition. So, under the task of surveillance, we may do activity tracking as well as recognition and if we can do this faithfully, then we can also provide abnormalities. Not only in this task, but also in tasks such as automatic character recognition in let us say movie clips. If there are some broken movie clips or let us say some parts of the clips are available on let us say for example YouTube, then we can also perform automatic actor recognition which actors are present in which scenes and we can also do effective archival based on actors.  So, we can classify some of the SOPs like soaps and serials based on what are the characters that are present inside those scenes. So, this is for the task of face recognition, why we require face recognition and we see the task of face recognition is crucial. So, accuracy should also be crucial for that case. Now, the approach that I am going to mention falls under the falls under the approach of feature based approaches. So, there are two approaches that are basically used for the task of face recognition. One is geometric approach, another one is feature based approach. In geometric approach, one goes on detecting the basic features of let us say nose and eyes, cheeks, chins, etcetera and generates features based on this extracted features of nose, eyes and cheeks and chins. But the problem here is these features are difficult to extract because what we have in effect is just a 2D image. So, why not to look at face image as just a 2D image and we can represent in some other features rather than extracting eyes, nose and other features etcetera. So, the other feature that I am the other approach that I am going to talk about is a feature based approach and the approach that I am going to mention falls under the category of feature based approach. The block diagram if you look at of the approach that I am going to mention looks similar to this. This is for prototype learning. So, given an image or video, one may detect faces. So, this is a face detection algorithm, but this is not part of this discussion because as I mentioned in the last lecture, these two things can be decoupled easily because in face detection one looks for the features which are very similar across all the faces. So, that we can faithfully detect what are the faces that are available in the image or let us say for example, video. Whereas in case of face recognition, we look at features which are distinctive across faces from faces. So, this can be decoupled and hence we are not going to talk about face detection here. What we are going to talk about is face recognition. So, after extracting a region of interest in which faces are available, we can perform subband decomposition. And here we are going to perform subband decomposition by using wave packet analysis. After performing subband decomposition, we go for feature extraction. Now, this feature extraction is a crucial job because we cannot involve each and everything to be inside a feature vector that I am going to discuss next. We can also perform feature normalization because we do not want one feature vector for each image rather we want feature vector per class. So, if we have let us say 6 to 8 images per one subject, then we want a compact feature vector for each subject and not for each image. So, feature normalization can also be performed. This will be basically give us learned prototypes. And this learned prototypes can be stored in memory and can be accessed in future when a query image comes to us. This is the block diagram of matching. So, if we have learned prototypes with us in which feature vectors of different classes are stored, then we can perform matching. So, given an image like this, a query image, we can first do face detection and then we can basically apply the same algorithm. We will end up getting a feature vector and we have learned prototypes that we can access from memory and we can utilize some distance metric or some similarity to come up with different matches. So, based on application, we can give output as one image or multiple images. Now, there can be two kind of applications for face recognition. One may be based on content based image retrieval. When one image is given as input in content based image retrieval framework, we need all the images that are there in the database to be extracted which match to the query image. And in another application, we can perform a simple traditional classification in which an image is given and we just want to know to which class it belongs to. In that case, only one match is required. Now, how do we perform decomposition? Like in case of wavelet analysis like this application, like so this slide provides how we can perform decomposition of a 2D image. This differs slightly from a 1D signal because now the image is 2D here. So, this is a 2D signal. So, we need to perform filtering in rows as well as in columns and down sampling will also be performed in rows as well as in columns. So, first we perform filtering across rows and we do down sampling across rows. After getting images here by performing filtering as well as down sampling across rows, we can move to columns. We can perform high pass filtering and low pass filtering and down sampling across columns and we can get 4 sub images. So, this LL is the approximation subspace here because it is passed through by 2 low pass filters and other filters like other sub images required to be passed from high pass filters. So, they contain high frequencies in effect, but this differs from wave packet analysis. In wavelet analysis, we move from first level to second level of decomposition by only decomposing approximation subspaces. Whereas, in case of wave packet decomposition, we decompose detailed subspaces as well. So, if this is the first level of decomposition, this is the approximation subspace and these are detailed subspaces, then we do not only decompose approximation subspace, but we also decompose detailed subspaces. So, we will end up getting 16 subspaces here in which one is the approximation subspace and other 15 are detailed subspaces. So, this approximation subspace will contain low frequencies as desired and this detailed subspaces will only contain high frequencies in this order. So, this last detailed subspace will contain the most high frequencies. So, we get some kind of decomposition in spatial as well as in frequency domain and this also provides decorrelation. So, now we can generate feature vectors, but before going into that, we need to look into which kind of filters we have utilized for this purpose. Now, the important advantage of using this wave packet application is we can utilize any filters here. When we go for phase recognition, we do not want synthesis or perfect reconstruction to be done. We only want our analysis filters to be good, so that they can provide decorrelation in spatial as well as in frequency domain. So, here without worrying about orthogonality property or any other property, we can generate those filters which provide very good feature vectors for our classification task. So, in practice one can go for empirically searching filters which are base suited for this application of phase recognition and here are the features which were found to be empirically suited to the task of phase recognition and these are the impulse responses of low pass filter and high pass filters that are used for the task of phase recognition. These filters are discrete in nature, but for the ease of visualization they are connected like the two intermediate samples are connected by straight lines. So, here we can see the impulse responses of low pass and high pass filters. One might also worry about how they look in frequency domain and this slide basically shows their magnitude responses. So, this corresponds to crudely a low pass filter and this corresponds to a high pass filter. So, the filters that we have used, they do not, they are not so much different from what we have seen in the course. They are basically low pass filters and high pass filters, but these magnitude responses were found to be better suited to phase recognition task. So, when we talk about first level of decomposition, this high pass filter and low pass filters were found to be better, but we are decomposing this to the second level because after second level generally the images that are found for phase recognition, they are quite small in nature. So, as we go on decomposing these images, they become smaller and smaller. So, it becomes really tedious after second level of decomposition to handle such small images and also the localization that we obtained in time domain becomes really crude. So, they are not of much importance, they do not yield much information. So, we go up to a second level of decomposition. When we look at this view packet analysis, in second level of decomposition we obtain four filters and I am going to present the magnitude responses of these four filters. So, when we decompose an approximation subspace, we obtain magnitude responses of this low pass filter and this band pass filter. Here we can see that this is crudely a low pass filter again, but this cutoff is quite stringent now and here this is a band pass filter, but this mostly emphasizes the low frequencies which are available because we are decomposing approximation subspace. Also when we decompose a detailed subspace as is required in wave packet analysis, we end up getting these two filters. Again one is a high pass filter and one is a band pass filter, but this band pass filter emphasizes high frequencies rather than low frequencies as we saw in the previous slide. Now this filters are okay when we talk about one dimensional signal, but here we are talking about two dimensional signal, but the important characteristics of this filters are they are separable in nature. So, as you apply in one dimension, you can apply in second dimension and we end up getting 16 filters rather than four filters as in one dimension case. But the ease of visualization in one dimension helps us to talk about one dimension and I will not talk about two dimension impulse responses and magnitude responses because they are just natural extensions as well as they are difficult to visualize in nature rather than one D signal. So, given a Lena image, let us say for example, we can end up getting this kind of subspaces where this is the approximation subspace. We can easily visualize this because this contain low frequencies and the information here is more compared to other detailed subspaces. These are seven other detailed subspaces important thing to note here is this detailed subspaces and approximation subspaces are quite small in nature. But just for ease of visualization, they have been zoomed into also like this detailed subspaces are bipolar in nature like they contain negative as well as positive values. So, just for visualization, they have been taken as absolute the values are absolute here. The other subspaces are also detailed subspaces and we can visualize here. So, those were it and these are again eight other detailed subspaces. So, these are all 16 subspaces that we are going to obtain. Here we can see that some of the features like eyes are very clear in the high frequency subspaces and other subspaces contain the overall shape of the face image. Now, after getting this subspaces 16 subspaces namely we can go on to do feature vector extraction. Now, the trouble with feature extraction see here the important thing is to extract those features which are relevant to face images.  But the problem doing this is this feature vector will be quite long and if you want to do a classification task this becomes increasingly difficult because if you have a long feature vector we better have long number of images large number of images then and then we can go on to do a better classification. So, rather than going into this we can provide a compact representation of the faces by providing one way of doing this is to go for moments. So, first order moment or second order moment we can go for mean and variance is basically. So, we have 16 sub images we can go for mean and variance in each some image. So, we can extract a 32 dimensional feature vector but here if we look at a detail subspaces then they have 0 mean. So, we do not need to capture that. So, we can basically go on to represent 17 dimensional feature vector for each face image. But if we can see at approximation and detail subspaces they are different in nature in approximation subspace we have more information. So, it caters for handling approximation subspace differently for rather than detail subspaces. So, if you look at this approximation subspace what we can do is we can bound it by two boxes. In one box we can basically go for a high dimensional features like what is the exact shape of the face or what are other fringe information that we have in that bounding box and we can go on to cater for mean and variance for that bounding box. We can take another bounding box in which other information related to shape like face of eyes and nose are related. So, we can extract mean and variance in that bounding box. So, we can extract four features from approximation subspace and we can extract variance is in each detail sub image and we can extract 15 dimensional feature vector from here. So, four dimensional feature vector from approximation subspace and 15 dimensional feature vector from a detail subspaces. We can extract 19 dimensional feature vector for each image. Now, given a task of classification we can go on to extract 19 dimensional feature vector for each face image and in effect we can do some feature normalization. So, that we can end up getting one feature vector for each class and after that we can compare the feature vectors that we have in database with the feature vector of the query image and we can do some kind of we can utilize some kind of distance metric. But the important thing here to note that is we have utilized mean as well as variance. So, we have information related to probability density functions of the underlying feature vectors. Now, if we utilize Euclidean distance as a feature vector as a distance metric, then the trouble is Euclidean distance assumes all the dimensions to be independent of each other. So, it is it does not cater for the probability density functions that we have. Rather than we use Bhattacharya distance that takes care of probability density functions that we have. It basically goes on for mean as well as variance. So, we have two different entities here one for mean as well as one for variance. So, if you have the same probability density function mean will be similar as well as variances will be similar. So, both these terms will vanish and we will get a zero distance at the exact match. Also, Bhattacharya distance caters for all the requirements needed for it to be a proper distance metric. So, Bhattacharya distance can be utilized, but there is nothing magical about Bhattacharya distance. Any other distance metric which caters for PDFs underlying PDFs for feature vectors can be utilized for this purpose. Now, I will quickly move towards the results that we that I have obtained by utilizing the yield database. Yield database is basically available for free download for non-commercial use from this link. And these are the typical phases that are available in yield database for one subject. Yield database basically contains 165 images for 15 classes and 11 images per class. This is just the typical images that are available in yield database. I will just provide the typical archival result that I have obtained from the CBR interface for phase recognition task. So, given an input like this a query image with a surprise expression over the phase of the subject, we could retrieve images similar to the subject like the subject is similar in all the retrieved images, but expressions are different like one is winking for example, the other is with a happy look on the phase etc. So, this is the CBR interface, but in order to quantitatively evaluate the algorithm, we need to provide quantitative results. And this can be provided by traditional classification task. So, these are the experimental results. Three experiments were performed based on different number of images utilized per class for learning. In first experiment, four images per class were utilized for learning. In second experiment, six images were utilized. And in third experiment, eight images were utilized per class. So, in total in first experiment, we had 40 images for learning. In second experiment, we had 60 images for learning. And third experiment, we had 80 images per learning. All the 80 images were utilized as query images. And the fifth column shows the accuracy that we have obtained. In first experiment, 66 images match to the native class. In second experiment, 64 images and third experiment, 64 images again match to the native class. So, the accuracy is around 82, 82.5 percent to 80 percent within the accuracy of 1.25 percent. Now, before going before dwelling too much on results, I would like to remark on some of the things like this experimental results can be enhanced like this 80 to 82 percent accuracy can be enhanced to 90 to 95 percent of accuracy if one goes for machine learning techniques like for example, support vector machines. Or one can probably utilize more number of moments like kurtosis or third order moments etcetera. And this accuracy can be enhanced. With this remark, I would like to conclude this application. Thank you very much. That was a very beautiful presentation on the application of wavelets specifically wave packets in face recognition. I would like to emphasize a few of the points that were made in this presentation.  In fact, in a sense, wavelets for the matter wave packets are suited both to the detection and the recognition problem. In detection, one looks for commonality. What constitutes a general face? In recognition, one looks for specificity. What is the incremental information in that face? So, the separation into common and incremental information is again critical in the context of both face detection and face recognition. We looked at the beautiful decompositions which Roanuck showed using wave packet analysis. We noticed that different kinds of features came out in different subbands. Now, in fact, one can study that in greater depth. What has been presented here is an indicative study. What different subbands show? You know, Roanuck also presented the frequency responses of the filters at the second iteration in wave packet analysis. And you notice again a confirmation of the theoretical discussion that we had when we carried out wave packet analysis in one of the previous lectures. We saw that when we decomposed the high frequency band, that is when we followed a high pass filter and a down sampler by the analysis filter bank again, there was a band inversion. So, high followed by low actually gives you the higher frequencies and high followed by high gives you the lower frequencies. This should be noted. As you notice, as expected, there were two band pass filters aspiring to become band pass filters between pi by 4 and pi by 2 on the normalized omega axis and pi by 2 and 3 pi by 4 on the normalized omega axis. And of course, there was a low pass filter with cut off pi by 4 and there was a high pass filter with cut off 3 pi by 4. These were all aspiring to become these kinds of filters. Now, we have seen two very interesting applications of wavelets and filter banks today. There are several others. I shall just mention a couple before we conclude the lecture today. One of the other important areas of application of wavelets and time frequency methods is biomedical signals. They could involve evoke potentials. They could be magnetic resonance imaging signals, MRI signals. They could be signals obtained from cat scanning, computerized axial tomography scanning and many other imaging modalities that are useful in medical image processing today. That is one area in which wavelets and time frequency methods, filter banks have been heavily employed. We hope to have another presentation of an application of this kind in a subsequent lecture. Wavelets have been used also or proposed for use in digital communication. People have talked about building modulation and demodulation systems on wavelets because they are nice time frequency atoms. Another area for the mathematician in which wavelets have found great applications is the use of wavelets in solving differential equations. At this point, I shall not say more, save to mention that these are just some of the myriad areas in which wavelets are used. We shall hopefully be able to see some of these applications in greater depth in subsequent lectures. With that then, we come to the end of this lecture and we shall proceed to discuss something different in the next one. Thank you.