فشرف بالشتغالي بالعلم ولا تبغي به ما عشت يا دا دا دا لا وياله من شراف عظيمي الحمد لله رب العالمين نراه الحمد الحسن والثاناء الجميل وشدوا الله إله إلا الله وحده لا شريك له يقول الحق وهو يدي السبيل وشدوا أن سيدنا ونبينا محمد صلى الله عليه وعليه وعليه وأصحابه والتابعين لهم بإحسان إلى يوم الدين أما بعد إن شاء الله تعالى this is going to be the first class or the first lesson إن شاء الله تعالى because in our last session we spoke about the chapters which Aqlida deals with but today إن شاء الله تعالى we're going to go into the first chapter and that is الإيمان بالله to believe in الله سبحانه وتعالى الإيمان بالله الإيمان means to believe إن الله تبارك وتعالى now one may ask what does it mean to believe in الله سبحانه وتعالى what it means to believe in الله سبحانه وتعالى is as follows يقوم على توحيد الله في believing in Allah it stands with you singling it stands on you singling الله in believing in Allah stands in singling الله in the three following that are here the first one is إن his ربوبية you single الله تبارك وتعالى إن his ربوبية now what does ربوبية mean ربوبية means you single الله تبارك وتعالى إفراد الله تعالى في أفعاله it means to single الله سبحانه وتعالى in his actions which Allah does you say these actions are only for Allah such as الخلق creating ورزق providing and giving رزق ورزق also والإحياء والممات taking a person's life giving a person a life these are actions which Allah does and we single him in these actions we do not associate partners with Allah in it إفراد الله to single Allah في أفعاله in Allah's actions that is what ربوبية means that you believe those actions which Allah سبحانه وتعالى does no one shares it with him he is alone on it the second one is أسماءه وصفاته it means to single Allah in his names and in his attributes you single Allah تبارك وتعالى in his names and you single Allah سبحانه وتعالى in his attributes الله تبارك وتعالى he has names 99 names that we know and yes they are more than that but those which we know are these 99 these 99 names of Allah which we know which are known as أسماء الله الحسناء الله تبارك وتعالى righteous names every name of Allah every أسماء every name of Allah it has a characteristics in it every name has a characteristics but not every characteristics is a name for Allah سبحانه وتعالى and we'll expand on that in our books that we're going to be studying إن شاء الله وتعالى after the introduction so what does it mean to single Allah in his names and attributes for example الله سبحانه وتعالى is a name for Allah it means to single Allah it means to single Allah الله سبحانه وتعالى is أسماء he is أسماء that is his name that's why you find a person is called عبد السماء سماء is Allah's name الصمد is the name of Allah الرحمن is the name of Allah these names we do not give it to anybody other than Allah سبحانه وتعالى even he is the only one who is أسماء he is the only one who is أصمد he is the only one who is الرحمن he is the only one who is Allah these are names we do not give it to anyone we single him in these names we also these characteristics that we have these characteristics of Allah which is Allah hears الله سبحانه وتعالى he sees these are characteristics that we do not give to anyone in the way that Allah hears for instance الله سبحانه وتعالى is hearing he is alone on it why? because he heard his hearing was always there whereas the hearing of the creations came to a starting point even the humans hearing is not it is not wide it can't hear every single thing it's limited whereas Allah's hearing is not limited the humans hearing it comes to an end Allah's one doesn't come to an end the hearing of the creation was given to him Allah it wasn't given to so we single him in that his names and his attributes and the third thing in which believing in Allah stands on is أولوهيته أولوهيته you single Allah in the actions you do the actions you do and the things that you are coming with you single him in it for instance you do not pray nor do you nor do you slaughter nor do you prostrate nor do you supplicate to anyone other than Allah these are actions you do الصلاة الصيام الندر الاستغاثة الاستعانة these are characteristic actions which you do seeking help seeking aid slaughtering supplicating praying fasting these actions you do them only for Allah so look at this my brothers and sisters who are watching ربوبية ربوبية means what you single Allah in the actions Allah does أولوهيته means what أولوهيته means you single Allah in the actions you do and the actions you come with now our message محمد صلى الله عليه وسلم when he was sent out to mankind not only him but the prophets that came before him the battle and the point of argument was on what was it on ربوبية was it on أسماء والصفات or was it on أولوهية it was on ألولوهية they did not want to they did not want to أفام they did not want to worship Allah alone in the actions they did not want to they knew he was the only prophet they knew he was the only creator they knew he was the only sustainer they believed in that the actions Allah did they uniquely believed it was for him they knew Allah had names and attributes they knew that that's what you find in the pre-islamic time some people were called عبد الله like the prophets father you see they also had other names of Allah that was used they had those names but what the fight and the argument was about was أولوهية worship Allah alone in your actions don't associate partners with him everything that is عبادة you do it only for Allah سبحانه وتعالى and that's when they said no and that's why every messenger was sent down as Allah said in the Quran و لقد بعثنا في كل أمة الرسولة أنعبود الله و جتريب الطاغوت الله says we have sent down all the messengers and all the prophets on what purpose واشب الله alone and don't associate partners with him واشب الله alone and do not associate partners with him every action you are doing do it for him alone and don't associate partners with him so when a person comes and says I believe in Allah I believe in Allah I believe in Allah سبحانه وتعالى it means it entails that they sing الله تبارك وتعالى in his ربوبية it means that they sing الله تبارك وتعالى in his أسماء والصفات so ربوبية means his Lordship they also single him in his names and attributes and they also single him in their worship when a person comes with these three and each and every one of them require what? it requires تحيد تحيد ربوبية تحيد الأسماء والصفات and تحيد الأروحية meaning you single Allah in the ربوبية you single Allah in his names and attributes and you single Allah in his ألو in the ألو here the actions that you come with when those three are present in you my brothers and sisters you have now come with what is known as you have come with believing in Allah correctly the way it was needed now the question that arises is if I come with these three I single Allah in his ربوبية in his أسماء والصفات and his ألو here where do I get from it what is promised for me what have I been assured and this is what I move on to on the second point that I'm going to speak about in today's class إن شاء الله that is ثمارات الإيمان بالله what are the fruits that a person or the benefits that a person gains in believing in Allah سبحانه وتعالى what are the ثمارات الإيمان بالله the fruits the outcome of believing in Allah if I believe in Allah what are the outcomes in which I will I will receive the ثمارة is the first one is سعادة you are going to receive happiness the first one is السعادة happiness believing in Allah brings you happiness the evidence for happiness is صورة النحل ayah 97 صورة النحل ayah 97 الله سبحانه وتعالى يسيس من عمل صالح من ذكر أو أنثا وهو مؤمن فلنحي أنه حيات طيبة ولنجزي أنه أجرهم بأحسن ما كانوا يعملون نقبس الله سبحانه وتعالى يسيس anyone who comes with righteous actions whether it's a male or a female من عمل صالح من ذكر أو أنثا male or female الله سيس فلنحي أنه حيات طيبة we will give you a good life ونجزي أنه أجرهم بأحسن ما كانوا يعملون and Allah will reward you for all the good which you've come with so that is happiness that you will attain by coming with أريمان بالله but in what way the way we've explained the second benefit or outcome that you will receive in believing in Allah is الله الله is allegiance you will find allegiance you will find aid and support from Allah سبحانه وتعالى as Allah says in the Quran in Surah Al-Imran ayah 68 الله says الله ولي المتقل والله ولي المؤمنين الله this ayah specifically is والله ولي المؤمنين الله is the ally of the believers the believers here is the ones who believe in and singular Allah in his البوبية his names and attributes and also in his أولوهية the third one is الجنة the people who come with الإيمان بالله believing in Allah they receive جنة as Allah سبحانه وتعالى mentions in سورة النساء آية 50 آية 57 الله سبحانه وتعالى he says والذين آمنوا وعملوا الصالحاتي those who believed in Allah and have come with righteous actions والذين آمنوا وعملوا الصالحاتي those who have believed in Allah and they have come with righteous actions الله سبحانه وتعالى he says سندخلهم جنة we will enter them into جنة تجري من تحتها الأنهار خالدين فيها أبدا وأزواج مطهرة الله سبحانه وتعالى he says those who believe in Allah سبحانه وتعالى and they come with righteous actions we will enter them into جنة and they will stay there forever the garden water beneath it they will stay there forever and Allah then says they will have their partners وأزواج مطهرة they will have partners of theirs with them who are purified who are cleansed so الجنة the fourth one is التمكين الله سبحانه وتعالى will give you تمكين he will give you strength and will give you power on this earth as Allah سبحانه وتعالى he said in surah النور آية 55 وعد الله الذين آمنوا منكم وعملوا الصالحات لا يستخلف أنهم في الأرض كما استخلف الذين من قبلهم ولا يمكن أن لهم دينهم الذي ارتضالهم ولا يبدل أنهم من بعد خوفهم الله سبحانه وتعالى يقول I made an oath وفي that oath الله سبحانه وتعالى those who believe in Allah and they come with that توحيد that is needed from them الله سبحانه وتعالى says لأنه will give them تمكين on this earth he will give them strength and power on this earth صورة النور آية 55 last but not least it is صورة أنعام آية آية 70 آية 82 آية 82 الله سبحانه وتعالى he promises the ones who come with إيمان and righteous actions the إيمان here being what believing in Allah with his ربوبية with his أسماء and his وصفاته and his ألوهية what are they or what is it that they are going to receive which is the last one الأمن والاحتداء safety and Allah will give them guidance and that is صورة أنعام آية 82 الله سبحانه وتعالى he says الذين آمنوا ولم يلبس إيمانهم بظلم أولائك لهم الأمن وهم محتدون الله سبحانه وتعالى and those who believe who then do not come with شرك they do not associate partners with Allah in his ربوبية الله سبحانه وتعالى he says those people who have come with that الله سبحانه وتعالى he says الذين آمنوا ولم يلبس إيمانهم بظلم أولائك لهم الأمن those are the ones who are going to receive safety and verily they are the ones who are and they are the ones who are going to be guided الأمن والاحتداء safety on this earth and also guidance they are the ones who are going to be guided on this earth and also guidance so today's lesson today's lesson Insha'Allah it was about الإيمان بالله believing in Allah what it means and we also took ثماراتو the fruits الإيمان بالله believing in Allah ثماراتو الإيمان بالله the fruits and that which you gain after believing in Allah and what does it mean to believe in Allah we gave evidence and we gave you also the references جزاكم الله خيرا أبارك الله فيكم والسلام عليكم ورحمة الله وبركاته


This research focuses on the analysis of laminated composite plates made from natural fibers reinforced with unsaturate polyester resin, using finite elements software ANSYS 18.1 to compute the natural frequency and mode shape of the plates under different boundary conditions, and comparing the results with literature. This article was authored by D.R.Rochkamo, K.Santhi, and K.P. Podmanobin.


We know that it's hard to stay up to date on USIP's latest publications, so we've challenged our teams to summarize their latest research in less than a minute. This is briefly. Last year, President Trump and Kim Jong-un agreed to work toward peace and de-nuclearization on the Korean Peninsula. But any long-term agreement is unlikely without North Korea's ally and largest trading partner, China. So USIP convened experts to assess China's role in influence in the North Korean nuclear crisis. They agreed that China shares Washington's desire for de-nuclearization. But unlike Washington, China doesn't believe that immediate de-nuclearization is likely. Instead, China, like North Korea, prefers a phased process that addresses peace and de-nuclearization simultaneously. So how should the US respond? Well, it's important to remember that adopting China's two-pronged approach doesn't mean accepting their measures. Washington should work with Beijing and regional allies to develop a coordinated approach to negotiations. At the same time, it should continue to urge China to strictly enforce sanctions against North Korea until all parties can agree on a process for sanctions relief. To learn more about China's role in the North Korea conflict, visit usip.org.


So, this is lecture 29 and we have been looking at this DFE structure for the general case and the way I motivated it and where I introduced it was slightly different because the structure here requires some additional things to the minimum phase case that we saw before. So, we need to do something else which is different the final structure we derived looks something like this SK right thinking too far ahead we have a general channel head C then noise gets added to it. So, the noise PSD we take it to be some SN. So, presumably one can spectral factorize that and the structure here this one filter I wrote it as dZ times WC case you think of d of C as a filter which minimizes ISI and then W of C as the filter which whitens the noise that results. So, noise becomes non-white because you are filtering it and WC whitens the noise. And then but what happens unfortunately is when you whiten noise the signal that you somehow removed ISI from right the signal component when d of C acted on the signal component it removed the ISI and then when you wanted to whiten the noise unfortunately the signal also has to go through the W of Z right. So, signal becomes filtered by W of Z and you want to run 1 by W of Z just on the signal. So, a nice way of doing it is to run that the output of this filter through a slicer which gives you just the presumably just the signal component with some errors yes, but at least we will get the signal component alone and then you put it nicely in a feedback loop to implement 1 by WC. So, this is kind of the way in which I motivated and did the design for DFE and this filter overall filter is called a precursor equalizer and this is called a post-cursor equalizer. So, this is the general DFE structure. So, a few more remarks and comments and things to think about in these kind of suboptimal things. So, fundamentally if you want to if you want the ideal way of designing things is to minimize probability of error. So, that is the ideal way of doing it it is the optimal way of doing it there is no questions asked. But unfortunately if your channel has poles and all that and we have an infinite impulse response that thing goes out of the window you are not going to be able to implement the ideal case. So, you want to do something suboptimal. So, when things are suboptimal there is suboptimality in two ways. So, you have the ideal signal plus ISI plus noise. Noise is Gaussian. So, you know the PDF. If you have signal alone buried in noise you know what to do for detection there is no problem. But you have signal which is Gaussian plus some other signal component which is clearly non-Gaussian plus Gaussian again which is noise. So, the way this MSC criteria is dealing with the signal is kind of roughly equating that also to Gaussian and saying I will minimize the total variance of ISI plus noise. So, you see if it is just signal plus noise then you want to minimize the variance of that noise because you know probability of error in signal plus noise depends as Q of 1 by sigma. If you reduce sigma probability of error becomes better you know that clearly. But if it is signal plus some non Gaussian component plus some Gaussian component it is not clear that the optimal thing to do is to reduce the overall variance in terms of probability of error it is not clear. But that is a good approximation. So, just to generally limit the variance of anything else that is other than your signal seems like a good approximation. And if you have a lot of ISI terms adding in the central limit you might be able to argue that what you have is also Gaussian. So, roughly probability of error can be minimized. But it turns out it is a dangerous argument to make because you have other signal component contributing it is clearly not Gaussian and just minimizing the variance may not be a good idea. So, that is something to keep in mind but it is a good practical fix to minimize the mean square error even in the non-Gaussian case. In the Gaussian case it is optimal, non-Gaussian case it is not really optimal but he will live with it. So, that is the one more piece of information to think about in these designs. So, that is the DFE structure. So, now the question is what is the question now? What is the next thing to do? Given h of z and Sn you have to find d of z and w of z. So, how to find d of z and w of z is the question. In fact, the only question is how to find d of z? Why? Yeah, from d of z. So, you do spectral factorization on Sn times mod d square you are going to get w of z as 1 by the minimum phase component in the spectral factorization. So, w of z is directly derived once you know d of z. So, the question only remains only remaining question is to find d of z and for that we will take inspiration from the linear equalizer. We will say we can take d of z to be the zero forcing linear equalizer or the MMSE linear equalizer criteria that we have. So, you use those two criteria pick the d of z according to that criteria and then see what w of z it results in and then see what mean square error it results in and compare and see if it is indeed better. So, that is what we will do in this lecture predominant. It is clear right. So, that choice if you pick d of z to be the zero forcing equalizer what is the zero forcing linear equalizer? 1 by h. If you pick d of z to be 1 by h then you get what is called a zero forcing d of z. Then of course, you have to find the w of z corresponding to them. It is more work than the linear equalizer. The linear equalizer we never found any w of z we just did 1 by h and then did what? Slicing. So, you are not going to slice now you have to find the noise whitening according to the specified percentage. So, that is the that those are the two things we will see quickly go through that. So, the first choice is the zero forcing decision feedback equalizer. And here are the choices you pick d equals 1 over h. So, to calculate the error after d. So, you have to calculate the error after d and try to whiten it. So, that is important. So, what is happening to the noise? N k is flowing through d and you get e k. Am I right? That is the that is roughly well you do not get e k. Well, let me be careful here. Let me not do this. Let me not do this. I think I had this picture before. What is the picture? There are two things. S k goes through something and then n k goes through something else and then you have to subtract. So, keep that picture in mind. S k goes through h d minus 1. Am I right? So, in this case h d minus 1 becomes 0. So, there is no s k component. But in general you will have that also. And then you have n k going through d. So, let me draw the complete picture so that we can go back to this.  This is a model for e k. So, now, what is the spectral this p s d for e? Since this is 0, it is simply s n times mod d square. So, d d star. That is my s e. So, d is 1 by h. So, in fact, this is s n by mod h square. So, all you have to do is a spectral factorization on this. Given h of z assuming it is rational and s n is rational, one can always find the spectral factorization. You will know exactly what the minimum phase components are. So, suppose we do that. Suppose we write s e which is s n by mod h square as sum epsilon square times m e times m e star. This is the spectral factorization. So, remember. So, what will epsilon square be? Geometric mean of s e. So, the geometric mean of s n divided by mod h square.  You can avoid integral in the rational case by actually doing the spectral factorization forcing m e and m e star to be monic and whatever constant you get outside will automatically be your epsilon square. So, in the rational case, you can clearly avoid integrals. You do not have huge integrals in computing epsilon square. So, this can be computed. So, if you whiten this noise. So, what is the choice for the whitening filter now? W is what? 1 by m e. So, that is how we are going to pick the whitening filter. So, inverting the minimum phase part which is eminently implementable you do that. So, once you whiten, you get an e prime of k. The variance of that e prime of k is your mean square error. So, essentially your mean square error becomes this epsilon square. So, you do spectral factorization on the noise and you are whitening the noise. So, clearly everything else goes away and the only the power square power of error simply becomes this epsilon square which is which is the mean square error, this is geometric mean of s n divided by mod h square. So, that is the story. So, it is quite simple. So, that is nothing really major going on. You just pick W equals 1 by m e, you get the answer. So, what I am going to do now is to take the standard form just an assumption for h and then show how this how these two filters work out, how the overall structure looks and make some comments on how implementable these structures are. So, let us see. Suppose I take h to be h0 times, let us say hmax times hmin. So, let us say if there is a h0, I will take some of them in hmin and some of them in hmax and adjust and do so things. So, h0 will kind of say is 1. So, we will not worry about that too much. So, suppose h is this and what is s e and I will also assume s n spectrally factorizes as gamma n squared mn mn star, the noise as power spectral density. Then s e becomes what? Just a question of substituting things there, gamma n squared mn mn star divided by h0 h0 star, you will get a mod h0 square times what? Which will come to the minimum phase, which will be the maximum phase. So, you will have hmin and hmax star as minimum phase. Do you agree? It was maximum phase outside, then you do a 1 by z star, it is going to come inside. So, that is hmax star and hmin star and hmax will go to the maximum phase component. So, once you know the way h splits and your spectrally factorized s n, the factorization for s e becomes very obvious. So, in fact, from here you find that m e becomes what? mn by assuming all these things are monic. So, you have to pull out the constants so that h0 is something so that hmin and hmax are monic. Once you do all that, you get hmax star and your epsilon square which is the m s e becomes simply gamma n squared by mod h0 squared. So, this is in fact even easier. So, you simply look at h and make sure you pull out a h0, make hmin and hmax monic. And then you do spectrally factorization on s e or in fact, find the geometric mean of s e which is gamma n squared. They do that, you divide the two, you get a mean squared error in the 0 forcing d of e. So, that is the story. All right. So, once I choose m e like this, I know my w, d also is known, d is simply 1 by h. So, I can now write the complete structure, multiply d of z by w of z and write w of z minus 1 and write the complete structure and see how it works. That is what I am going to do next. So, d times w becomes what? 1 by h which is 1 by h0 hmin hmax times w which was right here. This worked out to be or what is w? It is 1 by m e. So, hmin hmax star divided by m n. So, things will cancel and you get an interesting thing here. So, 1 by h0 which is a constant, it is okay. It does not matter. It does not make any difference. Then you have hmax star divided by hmax and then you have an m n inverse. So, this is your dw and of course, w is what I wrote here. So, if you put all these things together, the final structure I get looks like this. S k goes through my symbol rate channel model which is hc and it is going to factor as h0 hmin hmax. Remember these guys are monic. Only then I can make the simple substitutions. Then nk gets added to it. I am going to say the psd is sn which factors as gamma n square mn mn star. And then my precursor equalizer is 1 by h0 hmax star by hmax and then mn inverse. And then I have a slicer and my post-cursor equalizer is hmin hmax star divided by mn minus 1. So, this is my post-cursor equalizer. Let me do it again. s hat k. And this is my zero forcing DFE in short. And then what is the mean square error? After all this works out to be in some simple terms, gamma n squared by mod h0 square. You can also think of it as the geometric mean of sn divided by mod h square. So, it is the geometric mean of that as well. So, let me also write that because that is useful in theoretical comparisons, geometric mean. So, this is the precursor and this is the post-cursor. Anything interesting that strikes you about the precursor equalizer? So, there are lots of interesting properties about that. We will come back to it slowly. We will make some few remarks as we go along. But the first thing I want you to observe is assuming s hat of k and all is accurate, the slicer error is white. So, it is white Gaussian. So, that is a nice property of DFE. And we of course designed it like that and it is good to see that it works out that way. So, slicer error is white which is an interesting property for DFE. So, it works out very nicely and the way we designed that. The next comment is about the precursor equalizer. The precursor equalizer seems to have a constant part which is quite irrelevant. You might want to just ignore that. Then it has h max star by h max. What kind of a frequency response is that h max star by h max? Magnitude response will be flat. So, it is an all pass response. So, that is the first comment about the precursor. So, magnitude of h max star by h max is 1. So, it is definitely doing some all pass filtering. So, this is all pass. I am sorry. Yeah and then after that followed up with MN inverse which purely it does what? Whitens the noise. So, you can view the precursor as consisting of two parts. Phase filtering which is going to play around with certain things without causing too much trouble to the PSD of the noise. If you do all pass filtering the PSD of the noise is going to come through without any problem. When the PSD level the phase makes no difference. Then you remove simply. Do you do just MN inverse? This is the same as the power spectral density with the spectral factorization for Sn. So, this all pass phase filtering in terms of magnitude is nothing. Pretty much works on the signal the effect is on the signal to remove part of the ISI. So, it removes part of the ISI. That is why you call precursor removes the anti-causal part dominant. So, it is interesting that it is all pass, does not do anything else to the signal.  No, you cannot say all that. It is part of it mostly will be anti as well it will remove some parts. So, you cannot be very sure about what it removes. So, one can view it one can view certain similarities to the previous structure we had that is the only thing I am saying. Some part nothing beyond that can be assumed. So, that is the zero forcing DFE. It is interesting to the pre case of precursor works that way. So, if you have a non-minimum phase channel where h max is not 1 and h max has got zeros outside the unit circle, 1 by h max is still going to cause trouble for you. 1 by h max is going to be IAR anti-causal if you want to have a stable implementation that is going to cause trouble in the implementation. So, that is the only problem other than that is ok. So, if you want to do it, this will work out quite well. Any questions or comments on how this is working out? People are ok. So, these are. So, one thing you will notice is several times we get filters which are clearly not implementable ok. So, in practice you will have filters which are not implementable. So, what all this means is these are kind of optimal suboptimal structures ok. So, the criteria is suboptimal, but once the criteria is suboptimal you do not place any practical constraints on the kind of filter that you can implement ok. So, you are just saying ok you give me whatever filter that comes out I will see if I can implement that is the. So, in that way it is you are kind of making some optimistic assumptions ok. So, later on we will relax that we will relax that and say I want to say my filters finite tap has only so many tabs you tell me what is the best design we can. In that case the question becomes really really practical. So, we will do that also, but for now we should at least know how the general structure should look. See then if you are doing DFE and you want to do zero forcing DFE you know the structure to look for is roughly all pass and noise filtering noise whitening right. So, those kind of intuition you can get from the theory when you go to the practical side ok. So, but anyway in practice things are done slightly differently you will have to have finite tap filters ok. So, that is the only thing, but these things give you nice comparisons if you do the finite tap case you cannot do analysis analysis and you will not get any comparison ok. So, the next criteria is the MMSE DFE ok. So, here the choice for D is the MMSE choice for ISI after the filter ok. So, what is that?  So, that is the minimum main square choice for the for D we saw this right ES times SZ inverse head star. So, this is SZ ok. So, SZ is ES times mod head squared plus SN ok. So, that is SZ alright. So, now for the error term you cannot ignore the two parts, but you know I have minimized my SZ ok. See if from the expression the MSE also is very easy to derive here ok. So, you know SC is what in this case right you completed squares and you set one term to 0 and whatever was remaining will be your SE and what remains was what ES times SZ inverse SN right that was my previous case ok. This was the power spectral density for the noise that remains after your MMSE. So, now I have to widen this noise which means I have to do a spectral factorization on SE of on this PSD and figure out what the minimum phase component is that is what I am going to do ok. So, clearly SZ is a part of this. So, I might want to do spectral factorization on SZ itself ok and of course, SN also has a spectral factorization. So, eventually this works out to gamma N squared by gamma Z squared MN by MZ times MN star by MZ star ok. So, this will be the spectral factorization ok. So, you notice this is your mean square error. So, ME works out to MN by MZ and your mean square error after whitening is going to work out to ES times gamma N squared by gamma Z squared ok right. Another way of writing the MSC is what it is the geometric mean of what the SE right. So, geometric mean is going to be ES times SN divided by ES mod H squared plus SN and for comparisons with the previous case you might want to push the ES down to the denominator ok. If you push the ES to the denominator you get this ok. So, if you go ahead and compare with the previous case in the previous case what was the mean square error 0 forcing DFE it was SN by mod H squared ok and the MMSC DFE the minimum the mean square error is going down ok SN by mod H squared plus SN by ES ok. But remember the mean square error going down may not mean anything in many cases ok. So, it just goes down you have picked a weight of filter it goes down ok maybe this is the minimum mean square error you can get just fine it is great ok, but that may not mean much why because the error distribution is not Gaussian right DFE is going to allow some symbol ISI to come through as well ok. So, it is not Gaussian. So, it does not directly translate into probability of errors there might be some adjustments you can make to improve on probability of error ok. So, those are interesting things to study, but in general this is the minimum mean square error possible you cannot possibly go below this ok. So, that is something we have used ok. So, that is the MMSC alright. So, this is ME and anything else I should do ok. So, let me again once again do that final picture which has does W and everything together ok. So, D is this and maybe I should do W here. So, W becomes what 1 by ME which is MZ by MN ok this becomes the whitening filter W. So, now, I am going to do the entire structure which means I will need D times W. D times W is going to be ES times head star divided by SZ right SZ we factored as gamma Z square MZ times MZ star then W which was MZ by MN ok.   So, I am not going to do H min H max ok it is not too important. D times W becomes this interesting filter which is ES by gamma Z square times head star by MZ star MN inverse ok. So, where did where did all this gamma Z and all come from came from the power spectral density of Z which factored as ok. So, let me write the first expression power spectral density of Z is going to be ES times mod H squared plus SN clearly and that is going to factor as gamma Z square MZ MZ star ok. So, that gave me all these things and then I do my post cursor which has a slicer which gives me the symbols back and then you filter through to get MZ MN inverse minus 1 that gives you your 1 by 8 ok. So, that is your that is your MMSC DFE. So, one can show that within this structure within the DFE structure the mean square error obtained using the MMSC DFE is the minimum. You can never go below that and what is that mean square error we add two different expressions for it one expression is ES gamma N square by gamma C square and also it is the geometric mean of this point ok. So, that is the structure. So, you see there are lots of striking similarities with the with the previous case the zero forcing DFE case ok. You have a head star by MZ star which is not all pass that is the first interesting thing. So, it is not it is going to do something strange to the way things are going to behave and then you do an MN inverse which is not which just finally ends up whitening anyway. So, you see that is the that is the way it works. So, you visualize it slightly differently to see the whitening and then you do a 1 by MZ MN inverse minus 1. So, in fact, the W of Z is MZ MN inverse it is a slightly different thing. So, W of Z looks eminently implementable it is a nice filter, but here once again the head star might cause problems for you. So, it is a match filter right match filters with if they have the original thing has maximum components zeros then you are going to have trouble. So, it just may not work very easily. So, so that is one thing to pay attention to I am sorry though. If the original filter has poles inside then head star is going to have poles outside and that is going to cause IR anti-causal problems. Ok. So, poles if the if your channel response has poles then match filtering is tough. Ok. So, by poles inside match filtering is tough. So, that becomes a problem. So, so that is something to watch out for and again MZ star is showing up in the denominator that is also one more thing to watch out for. So, all these things might cause trouble for you. Ok. So, one thing I want to do before we proceed is to take this zero forcing DFE and MMSE DFE specialized to the case when H of Z is M of Z which is minimum phase. So, derive write down those two structures and see what we get. So, the approximation. So, this is a special case where we are going to take two we make two assumption the first assumption is H of Z is M of Z which is minimum phase. Ok. So, what happens in this case and SN will take to be white. Ok. So, this is the assumptions that we are going to make and if you do that what happens to the zero forcing DFE what is the precursor? You can go back and look at the expressions and try to derive it. So, H naught is 1 and the minimum phase is M of Z. It is 1. So, the overall finally, the precursor will become 1. Precursor will not do anything and what will be W of Z? Equal to M of Z. It has to work out this way. So, you see the zero forcing DFE in the special case became the same zero forcing DFE we had before in the M of Z. Noise was already white so you do not have to make it white by doing anything else and then you do slicing and then you do just 1 by M of Z for the signals. So, that is M of Z minus 1. What is the MMSE DFE for that case? What is the precursor? Precursor will have some does not it have an M star? It should be an M star ESM star by ES mod M squared plus N naught. Am I right? So, it is a nasty filter. It is not a very easy filter. Am I right? Is it okay? So, it will be ESM star by ES mod M squared plus SN. So, and what is W of Z? It is not easy to figure out this. This thing is a little bit more difficult. It is the minimum phase component of the denominator that you have here. Okay. So, if this you are able to write as ESM star by gamma squared MZ MZ star. Okay. So, this will work out too. Is that fine? Okay. So, I am sorry. It is not M of Z. It is MZ of Z. Okay. So, be careful. Okay. So, that was a problem with the way I chose my. Oh, I am sorry. So, it is D times. Okay. You are right. You are right. So, what is the final expression? What does it work out to? Oh my God. I am getting totally confused now. Okay. So, what is the precursor? So, you are saying this is not correct. You are right. So, that is for D. Right. So, I do not want to write D. I want to write D times W. Okay. So, this will work out to ES by gamma Z squared M star divided by MZ star. Right. Is that fine? And W is fine. Right. W is fine. So, I wrote the expression for D in terms of instead of DW. So, precursor is this and MZ will be a 1 by N naught. Oh yeah, 1 by N naught. So, N naught will come here. Okay. So, the MSE in this case is what? For the zero forcing TFE. So, it will be the geometric mean of N naught by mod M squared. Right. Am I right? And the MSE here will be geometric mean of N naught by mod M squared plus N naught by ES. Okay. So, you can also write it in so many other ways. This will work out to the same point. Okay. So, I know we have some time left, but I think this is a good place to stop. And the next class, we will look at some, if we take a simple example and work it through and study it. Okay.


Hey Finny, are you ready to free fly? Are you excited about free flying? Won't that be fun? Yeah. Sniffers, my name is Marlene McCohen and you will never believe what we're going to do today. But first, let me introduce you to my best friend Leo. Now if you love birds or just wanna know more about really cool exotic animals, such as my parrots, then you definitely wanna hit that subscribe button and join me on this ride. Because today we're gonna do something crazy that you guys might have been waiting for for a while now. Now before I go on, I wanna let you know who I am. I'm here making you guys love and learn about parrots. One person at a time. There are so many birds locked in people's cages all over this world. And that's why I created my hashtag engage not cage. So I'm trying to show you guys how to not only not cage your birds, but also how to engage with them, which is extremely important. I can't save the rainforest yet, although God knows I'd love to try and save their natural habitat. Obviously wish that birds could be free flying and never be in people's homes, but it's already a crisis. So I'm showing you how to make their lives better one video at a time. All right, I love you guys. Guess what's happening today? Vinnie is going to Vegas. Yes, we're going to Vegas and when I get in the car I'll tell you a little bit more about why. This isn't Vinnie, by the way. Vinnie is my little gangsta talking subtitle bird. This is going to be good. But I can't leave without getting kisses from this one. Look at this one. Are you cute? No, yeah, that's right, you're not. Yes, you are. No, you are the sweetest one. Vinnie is ready to go. Look at my baby. He went right in there guys. He's like, no, he's like, no, he's going to do something exciting. All right guys, before you leave always make sure all of your birds are locked up if you have more than one bird. There's already two loose birds hiding because they don't want me to find them. That would be Jersey and Cody. If you are on my Patreon, you might have seen my exclusive footage where I come home and Jersey was left out. Rocky. Rocky. That's for Vinnie. He sneaked out, he's good. He snuck out, look. He snuck out. You like this box? How about I just take this box over there? This is a famous box. Oh, the box. The famous box, that's right. Woo hoo hoo. Look at him, look, he went inside. He's super excited now. Whoa. Shh. Not anymore. Okay guys, ready? One time for bed. Two times for bed. Three times for bed. Okay guys. What's up Vinnie? You ready to go? Vinnie, you ready? That's it, right? Let's go, man. Let's make this happen. Let's go. We're going out. Look Vinnie. We're going to a big city. So yeah guys, we're going to meet Jamie and Bondi. Are you excited to meet Bondi? I'm really excited to meet them. I think this is gonna be so much fun, right George? Yeah, this is gonna be a cool experience. Do you remember once you said, hey, this girl's doing what you're doing and I think you guys would really get along? Yeah. Yeah. Cause she has the same attitude. Yeah, so I'm excited. I think it's gonna be a lot of fun, a lot of positive energy. Vinnie's going to Vegas. Now Vinnie is in Vegas. Vinnie's in Vegas, yep. He's so ready for bed. Look, he went in there himself. Okay, Vinnie's going to sleep soon. We'll turn the lights off. I hear him eating, so he's gonna keep eating. Okay, this, I can't get over. This is like if you want to do a photo shoot.  Vinnie, you ready to wake up?  Still tired, still got his clothes on.  I love you. You want to help me put my makeup on? This is Vinnie, he's helping me put my makeup on right now and by helping, kind of offers advice, but most of all, he's my company. I haven't been alone without a bird since I was seven years old, so everything I do, I do with them. And Vinnie doesn't really like chew anything.  I wonder if he understands about the water. The water? Makeup by Vinnie. Yeah, you like it? Tell them what, this water looks good. Guys, look at your boo-boos, mommy's boo-boos. Okay guys, we're going to see bird tricks.  He's doing pretty well and he's got the best view that he's ever seen, so maybe some birds will fly by and excite him. So what I'm gonna do is I'm gonna put do not disturb on on the door, hopefully, so that nobody comes in. I'm gonna cover him a tiny bit just from one side, so essentially, he won't feel like he's covered. He gets this whole view, but just from the back here, so that when you walk in, he has his privacy. Vinnie, we're gonna be back in tomorrow. We're doing something exciting I think it'll be interesting for him to have such a nice view. This will be the first time I ever met them, so kind of excited to have a bird friend. Another one. We're here. What's up? Hi. Do you wanna follow me out there? Yeah, we'll follow you. Okay, cool. She's so cute.  Yeah, look out there, you hear her cries right now. I know, seriously. That's how mine's gonna look today. That's all we just got our car cleaned. Yeah.  Every time I get my mini Cooper cleaned, I'm on some sort of mountain two days later. Fly, they already did. It was fantastic. We missed it. We missed it. Yeah, like usual. We went to the wrong, nevermind. Anyway, well look. Straight up, hold the Vinnie. Wow. Such a show stealer.  Hey, cutie. Standable too, you see? She's a different color than Vinnie. Than Vinnie? Yeah, she's got a little bit of a brownish though. She's so beautiful. So the winds were kind of turning. But how gorgeous. Maybe tomorrow, if the weather looks good. Yes. Beautiful. Oh my God, beautiful, beautiful, first. Hey, cutie.   Really? Cause I don't know if he says that kind of nice stuff. How was your first flight? Amazing, both of them have been great. Yes, yesterday, today was the second. You came all the way out here for Jamie and Dave to be with you on your first free flight. Yes. Was it worth it? They came all the way down. They're amazing, huh? Yes. They're amazing. This place is cool. This is really cool. This is a cool location. It feels like I can do anything. Oh, look at all the shots. George, he thinks it's cool cause he wants to get on some sort of like four wheeler, one of those things. We took a hot air balloon from here the other day.   Well, it's like she's got that certain type of attitude that like is perfect for her. She's cute. Like, and then she got those special colors and their eyes are red. She's so cute. And no, but I think he's gonna like her because she opened her wings and like did her head thing just like him. And then what did she say to you? She said, hi cutie. Hi cutie. And she says that to Vinny.  Good morning, Vinny. Oh, mommy's baby. Yeah. Okay guys, I have complete anxiety. We're supposed to meet Jamie and say for a free flight, Vinny's throwing a tantrum and they be getting fly boos. Literally like the valet had our car and didn't come out for 20 minutes, which has made us late. Like we were really gonna be on time. And then we're driving and the car has no gas. Like it's on zero. So we had to stop. Hey Vinny. Do you want a free flight? Why don't we just throw you out there and see if you can free fly? What do you think? Vinny, are you ready for your free flight? What do you think George, should we free fly Vinny? I mean, we have the professionals. Yeah, I think it's worth it. Here we go, we're gonna do this. We're gonna do this. You ready for it? Yeah. Vinny, you ready for it? Vinny, we're gonna take you free flying, okay? This is your moment. You ready buddy, let's go. Come on.  All right Vinny, you excited? Yeah, right, you gotta be holding her. Hey Vinny, what's up buddy? How was your free flight? We're gonna go hang out now. You wanna come hang out with us? What's going on Vinny? Did you enjoy your free flight? What's going on, let's go. All right, what's going on? I saw a beautiful girl today. Okay guys, that was our free flight. We had such a good time, did you? Let us know in the comments if you would free fly your bird. If you do want to do something like that, make sure you get good training from people like Birdtricks. Yeah. They know what they're doing. 100%. That was so much fun guys. I love that. Don't forget to subscribe. Let me know what other videos you guys would like to see. I have a video coming up with Birdtricks on the myths and rumors and truths about free flight. So, yeah, stay tuned.


Hello and welcome to CMC Markets on Tuesday the 8th of September and the weekly market update and we're coming off the back of a rather mixed non-farm payrolls report, a report that actually saw US markets trade lower into the end of last week, a G20 meeting where there was some broad agreement about the various measures that China had been implementing to stabilise its economy and its stock market, but nonetheless I don't think we're really any the wiser with respect to what the Fed may well do at its policy meeting next week and I think that going forward is going to have significant effects I think on potential stock market volatility over the course of the next few days and possibly weeks. Chinese data once again for August came in particularly disappointing and I think with respect to the narrative on that I have to take you back to events in July in early August where that is when we first started to see the really big volatility that we've been seeing in equity markets over the course of the past few weeks it was those July trade numbers that saw significant declines in Chinese exports to Europe and Japan and prompted to the revaluation on the one and the current volatility in equity markets and currency markets so this morning's disappointing trade numbers I think are going to continue to feed into that narrative continue to feed into further expectations of Chinese one weakness further easing from the Chinese central bank and could actually impact on Fed policy next week because actually when you go through the guts of last week's payrolls report or last month's payrolls report while the headline number was disappointing at 173,000 there was upward revisions to June and July the unemployment rate fell and average earnings edged slightly higher so the argument sort of comes around to whether or not the Fed will raise rates can they afford to just based on the economic data out of the US potentially yes but and it's and it's quite a big but in this case it's concerns about deflation or disinflation those concerns haven't gone away the Fed has an inflation mandate it also has a financial stability mandate it's missing its inflation mandate and financial stability at the moment is a little bit you know it's it's it's under reference shall we say so in that context I think we're going to continue to find equity markets particularly volatile and I'm going to look at the UK 100 and the Germany 30 and the key chart points there because both those indices look very vulnerable to a little bit of a sell off at the moment we could get a rebound we'll look at the key resistances there if you remember my video last week where I talked about oil prices I still think there's potential for a rebound there so if you want to go back and look at that particular video please feel free to do so it's in the library also going to look at the euro against the Chinese and whether or not we can expect to see further gains there and also look ahead to the Bank of England policy meeting on Thursday and the prospects for a significant rebound in the pound after nine consecutive days of losses so I'm going to start the chart section of this video with the Germany 30 this is a four-hour chart that we're currently looking at and as you can see we're pretty much in a bit of a trading range at the moment the top of that trading range is just above 10,400 where I've drawn the horizontal line and the bottom of the range is just below 10,000 around about 9,900 a word of warning we just we have just posted on the daily charts a death cross now death cross is where the 50 day moving average crosses below the 200 day so potentially that is that is potentially a very negative sign now that's not to suggest that we're going to continue to go lower we could well squeeze all the way back to 11,000 but while the price action and the moving averages the faster moving averages stays below the slow moving average then the bias will still remain to the downside even if we get a move through 10,400 towards that upper moving average and those mid-august highs around 11,000 moving on to the UK 100 the footsie 100 again it's a similar sort of story we've got what I would call a sideways trading range starting to play out at the moment the top of that trading range is around about 6,270 which is the highs at the end of August but we've also got a little bit of resistance just above 6,200 while on the downside we have a decent area of support through the 6,000 level so what I'll be looking for there is continued range trading and a potential break higher if we get through 6,200 is certainly potential to go all the way back to 6,400 but once again here the bias remains for a move lower but we could squeeze higher first now we've heard an awful lot in the past few days about potential further weakness in the Chinese currency and I to a certain extent I think that is inevitable and I think to really reinforce that point I think we need to look at the Euro-Chinese Remnambi chart because I think in this context we've seen a significant break out on this daily chart from a sideways consolidation from the April lows to the peaks that we saw in May now we've broken higher we've broken above the 200 day moving average we've peaked at around about 7.64 and we've come all the way back to just below 7.2 if we hold above the support which is currently around about 709 which also coincides with the peaks that we saw in May then the bias for this move remains for further Euro gains and further one losses back to the highs that we saw in the middle of August now how will that play out I think it's more likely to play out in terms of a move higher in Euro dollar than a gradual weakening of the one against the US dollar so certainly keep an eye on the Euro dollar in the context of this particular cross because I think Euro dollar could be the primary driver of this particular move let's finish up with cable pound against the dollar come off the back of nine consecutive down moves and we saw a very nice rebound on Monday and I'm paying I'm paying an awful lot of attention to this rebound simply because it's a bullish engulfing day or a key reversal day that suggests to me we've seen the bottom we've seen the bottom the tweezer bottom around about 151 70 and the potential is that we could we'll see further gains through 154 and back towards 155 now what's the catalyst for that going to be to be quite honest I'm not really interested in what the catalyst could be we've certainly got the Bank of England policy meeting at on Thursday at the end of this week it's unlikely that we're going to get a change from the eight to one voting pattern that we saw at the last meeting simply because the economic data doesn't warrant another hawk coming on board and asking for a rate rise the policy maker I'm particularly interested in is Martin wheel the data that we've seen since August hasn't been particularly great so I don't think it's likely that we will see any further descent from the NPC and in that context I think we could well see the pound start to wedge a little bit higher against the dollar okay so that's pretty much it for this week once again thanks very much for listening this is Michael Houston talking to you from CMC markets


SCP-009 is approximately one of the leaders of a substance which superficially resembles distilled water, except with a distinct bright red hue. The most hazardous property of SCP-009, however, is its ability to contaminate normal H2O. SCP-009 will, through unknown mechanisms, transfer its anomalous properties to other objects and creatures. Exponential increase in temperature of SCP-009 causes runaway reaction throughout subjects' body. Actual blood loss is minimal due to ice crystals, allowing subjects to remain alive and conscious for up to 100 hours.


Welcome to JSA TV, the Newsroom for Tech and Telecom Professionals. I'm Jamie Scott-Ogutay of JSA here at ITW 2019 in Atlanta. And I'm here with Kyle Hildebrand. He is the Vice President of Project Development for EX Square Technology. Kyle, welcome to JSA TV. Thank you. Glad to be here. We are honored to have you in here in Atlanta, a key city for you. I know there's some news about EX Square and Atlanta Belt Line. Can you tell us a little bit more about that? Absolutely. So one of the things we're doing is we're helping the Atlanta Belt Line finish out their fiber network. And in part of doing so, we'll enable them, you know, to work on all of the important things that they want to do and future-proof their city, put in smart city applications and be able to leverage the things that helps the user experience for all the constituents. And you guys, of course, the leaders in smart city technology, another huge headline for you that just came across this week here at ITW. You head up about 34 million dollar intelligent transportation system project for Indiana toll roads. Can you tell us more about that? Absolutely. So this project is really something that's transformational for the state of Indiana and all along Northern Indiana quarter along the Indiana toll road between Illinois and Ohio. And it bridges the gap and it puts together all of their fiber network to create a better user experience and help enable toll road operations. It's a combination of fiber infrastructure and all of the applications that sit on top of it to help make the user experience as they travel through the state safer and more efficient. That includes dynamic message signs to alert them of upcoming, you know, traffic situations, variable speed limit signs to adjust the speed limit to appropriate conditions, traffic management and monitoring systems and surveillance systems so they can keep, you know, in touch with the traffic flows and the first ever wrong way detection system along the Indiana toll road. That's huge. I mean, you're literally saving lives. Absolutely. So Indiana toll road has been very forthcoming and forward thinking in what they want to do and they want to make that user experience better in every way. And so we've been able to help them do that and safety is always at the top of their priority list. And we think the systems that we're putting in are really essential to that. I mean, just the technology alone, it's mind-blowing. Can you explain a little bit more about the fiber technology that you're using as part of the scope of this project? Absolutely. The fiber technology that we're putting in is a end-to-end fiber solution. They had a small piece of fiber in the middle, but we're connecting the entire 157 miles of the toll road corridor. Part of doing so is all of the infrastructure upgrades and the electronics within the toll plazas and all of the tolling facilities to help them more efficiently run their operations. So there's a lot of electronic components behind the fiber. It's not just regular fiber. You know, it's not just the, you know, what you think of putting the cable on the ground. It's all the stuff that goes with it. Unbelievable. Unbelievable. And certainly here at ITW, the audience predominantly in the telecom ecosystem, service providers, what are the benefits potentially for service providers? You know, if the situation arises itself in the future and the toll road determines that there's excess capacity and more fiber, it will really allow providers and anyone along the corridor to maybe utilize that fiber in an open access basis to help enable all of the different things that fiber creates, especially economic development for the state of Indiana. But they need to get through everything that they're doing right now and make sure that, you know, there actually is excess fiber before they get to that point and right now that, you know, they're not sure where that is. Yes. Absolutely amazing. And what you do, the smart city technology, really making a difference to the city of Atlanta, our previous news, as well as the state of Indiana and so many other projects that are well underway over at EX squared. So definitely a company to watch. Where can our viewers go to learn more? Then go to our website, www.ex2technology.com. EX2technology.com. We're putting it up right on the screen here for those to go ahead and click over. Thank you so much, Kyle, for joining us on JSA TV. Thank you. And thank you viewers for tuning in to JSA TV here at ITW in Atlanta. Happy networking.


What's up everybody? Jason here from JASV's. These are little hit recap videos guys. We're going to do a separate one for each break. We did a couple breaks here on the Fanatics live app today on this Tuesday, 7.25. So again, this is a hit recap video for 2022 Tops Triple Dreads baseball nine box case break. Pick your team number two to the second half of the case. So again, this is broken exclusively on Fanatics Live. And a quick little hit recap video just to kind of show you guys what we got out of there for the customers that weren't maybe able to watch live. But like I said, a couple little base guys here like Julio, Bobby Witt, Wunder, Franklin, of course. Everything does ships. You're going to get all of these cards here. So again, just a little quick walk through through the base. I'll go a little slower right now for the hits and stuff. So don't worry. So these are all base cards right here that you guys will be receiving. All right, thought right there. More base, base, base. All about that base, about that base. All right, sweet, right? Perfect. Now these were the numbered cards in the Tops Triple Dreads break. So again, you got a nice little number card of breggy there, O'Neill Cruz, Mel Ott. And of course, I think that is a New York Giants which go to San Francisco. Trevor Story, Little Hunter Green Base, Ted Williams, Mike Piazza, Ted Williams color, Mike Piazza color there, Ernie Banks, Ozzy, Trey Turner, Jose Altuve, Garrett Cole, Honus Wagner, Stan Musil, Tony Glenn. Let's grab another little stack right here. Boba Shat, Christian Gellich, Mike Piazza again, Vladdy, or sorry, Yachty, Mariana Rivera, Carlos Correa, Ernie Banks, Derek Jeter, Chris Bride, Vladdy Guerrero, Giancarlo, Shane Boz, Bryson Stott, Sandberg. And then last little stack right here, Bryson Stott, Aaron Judge, Derek Jeter, and Manny Machado. So there is that. Now here are all the relics. So Donaldson to nine. We have a Austin Raleigh right there for the Atlanta Braves. Pete Alonzo, Braggie. Each of the main. Jordan Alvarez. We got a Josh Lowe, Jess Chisholm, Fernando Tautis, Ozzy Aldi's, Chris Salem, Glibertura, a little triple relic there for the Phillies, Ryan Howard, Chase Elliott, and Bryce Harper. We have a Joe Ryan, Manny Machado, and maybe Severino. Now you're the autographs guys. Nico Horner tonight and a Tarix Hubeau, Eugenio Suarez, Jared Walsh, a little three out of three, Steven Strasberg, patch autograph, McGinsey Gore, Dylan Carlson, we had a White Whale, Andy Pettiforthy Yankees, one on one, Kukuchi, patch autograph, Alfonso Rivas, patch autograph, Nick Fortes, we have a Dennis Eckersley, Jared Kellanick, Kenzie Gore again, a one on one Roger Bueller patch.  And then our triple autograph, triple relic, was a White Sox hit. Luis Robert, Tim Reigns, Eli Heavenes.  There's the Nationals owner there. It was a ten-way Deca, relic book, had featured, Devers, Julia Rodriguez, Juan Soto, Ronald Acuña, Boba Chet, and Vladdy Jr., Otani Tatis, and Wander Franco, Luis Robert. So that was a really, really nice decade. We did a randomizer for that. And the winner was Juan Soto and the Nationals. So there you go, guys. That was the hit recap video for the Nine Box Intercase Break at Triple Threads, Pikachu number two on Fanatics Live App. Appreciate it, guys. Jason here from Jaspies.


By the way, I check the markets every day. I get up at 4.30, 5 o'clock the latest in the morning, go through a morning ritual, check all financials, check all financial news coming in, political news coming in, stuff like this. It's an amazing experience gang. These are amazing times. Our world is going through major changes. There are legacy companies, legacy systems that are completely collapsing. There's disruptive innovation that is taking over and it is amazing watching the symphony take place. If you are interested in history, politics, economics, society, current events, I don't care what your interests are that are centered around humanity, these are interesting times to be alive. So much to consume.


Hello. Thank you for joining us for a presentation on using machine learning to expand access to World War two in cursory data. This work is part of a project funded by the National Park Service Japanese American Confinement sites grant program, and it's being run through the bankrupt library UC Berkeley. Next. My name is Mary Aylings I am the interim deputy director at the bankrupt library, and I'm the principal investigator on the grant. I'm joined today by Marissa Friedman who is the project manager and digital project archivist at bankrupt library on the project and Cameron Ford who's the co founder of Doxy AI, which is a company formed by former UC Berkeley school data science students. And he's here to talk about the machine learning work that we're doing with them. So our project was conceived in 2018 to be different from our previous for Jack's program grants, in which we were basically digitizing and publishing archival records related to the internment. So for this project, we are going a step further in trying to extract data held in these records, so it can be used for computational research. Next, we've been exploring this kind of work as part of our engagement in the collections is data work, which encourages computational use of digital and born digital collections, and supports ethical access to collections, and data that we steward. We were codified by Thomas Padilla at all in 2017 in the Santa Barbara statement on collections is data. So our project is in alignment with these principles, and we hope we will also develop these as future models for our digital initiatives here at bankrupt. So the goals of our project are four fold. First, of course we are creating preservation images of the unique resources in our collections and preserving those for the long term. We're also creating a data set to build in this case a more accurate data set and more complete data set to represent the Japanese American incarceration. We're required to engage with community partners to guide our ethical data curation plans, and this is part of a sort of a co curation model with community members to talk about ethical access or responsible access workflows to our materials. And then finally of course to iterate and implement tools and workflows to expand computational access to our digital special collections again in line with the collections as data workflows. The transition of our project really is about finding a way to efficiently and effectively extract data held within our digital collections, and to increase use of that data through computational research methods. We'll be talking about the material we're working with in this specific collection which it lends itself to the machine learning process and at this point I'll turn it over to Marissa to tell us more. I'll start with a little background on the records that we're working with the WRA or war relocation authority used a census type two page form known as form WRA 26 or individual record to collect demographic educational occupational and biographical data about every American who is incarcerated in one of 10 WRA relocation camps as they were euphemistically referred to. From 1942 to April 1943 in cursory is interviewed a rivals in the camp to collect information about individuals ages birth dates birthplaces skills and hobbies health and physical defects. The dates and heights language proficiencies education backgrounds occupations and even religions. Next please. The WRA 26 forms are primarily type written, although several thousand forms from the bankrupts holdings have entirely handwritten responses. Many forms contain stamps handwritten corrections strike through notes and other marginalia which you can see some examples of this on the slide. And the existing set of form 26 records at the bank cross library numbering over 110,000 are believed to be the only remaining complete set of form 26 records organized by camp in existence. Next please. Data from the forms were coded by incarceraries and other WRA office staffers to early computer punch cards during World War two. At the conclusion of the war, a copy of the punch cards and the original forms from which the punch cards were coded were deposited at the bank cross library. In the 1960s the library worked with what was at that time the nascent UC Berkeley computer science department to transfer form 26 data on the computer punch cards onto magnetic tape. The office of redress administration or ORA acquired a copy of this data in 1998 or 1988 to aid with identifying and dispersing reparations to former Japanese American incarceraries. When this work was finished the file was transferred to the National Archives, and a copy of the data file was also acquired by the Japanese American National Museum in LA. It quickly became a popular information resource for those formerly incarcerated and their families. The National Archives published the data file it acquired from the ORA in 2003 as part of its access to archival databases project referred to as the Japanese American internal data file. It currently serves as an authoritative resource for genealogical and statistical information for former inmates and their family members, as well as social science researchers. Next please. There are however problems with the existing data file that we are hoping to start to address with our project. The first gaps truncations and errors were introduced over the course of the many data migrations that I previously outlined. There are also a number of handwritten annotations or corrections on the original forms that may or may not have been integrated into the data file at NARA. There are also many fields that are missing are not fully represented. The NARA data site currently has 36 available fields, while we have identified over twice that many distinct data points in the original forms. Information present in the original forms that missing from the NARA database include things like significant activities, skills, hobbies, educational and employment history and more. If you compare the top image on the slide, which is a snapshot of an individual's educational history from their form 26 to the bottom which is a screenshot of what appears in the NARA data file. You'll see that a lot of granular detail of educational institutions, dates, etc. are missing and the educational history has been coded to represent only the highest grade level completed. Next please. Some information was coded to or collapsed into a predetermined set of classifications. This is the case with things like occupational categories, which you can compare in the bottom two images of the slide. The left is a snapshot of what appears in the NARA data file and the right is what is represented for that individual on her original form. As illustrated on the top image of the slide, the employment history section found in the original form contains salary information and other crucial details that are missing in their entirety from the existing data sets. But we believe that they can provide valuable historical data for researchers. So what's important to note here is that there's a tremendous loss of detail and missing information in the existing data file. And we believe that the digitization of these forms provides a new opportunity to bring this data to light, particularly by leveraging the potential efficiency of machine learning. Our goal is to create a new, more granular data set that improves upon the existing data file. Next please. So to give you a sense of the scope of the project, I will I'll just quickly walk you through the major stages of the project from my vantage point as project manager. The first step is digitization where you know that the digital or the digital archivist prepares the original forms for shipment to an offsite vendor for digitization. So once we have the digitalized files, we can begin to apply a trained model to read them. Initially in the document discovery phase, the library work to doc CAI to establish a working data model, which identified the data model. As the project has progressed, however, we've also made quite a few discoveries about the content and structure of the data in the original records. And so library staff collaborate with doc CAI to integrate new data into the data model. And so as the project has progressed, however, we've also made quite a few discoveries about the content and structure of the data in the original records. So library staff collaborate with doc CAI to integrate new observations about the records into the pipeline as we go. The next stage is testing as Cameron will address we've adopted an iterative approach to developing the pipeline, which handles sets of form 26 records one camp at a time, do in large part to the variability in form content and other characteristics of the original documents. So set of forms from a particular camp, we walk through any anticipated problems, and the doxy team performs initial testing to make any needed adjustments. Once initial exploratory results are inspected and improved by both doxy and library staff, the pipeline is frozen in its current state, and all the files associated with one camp or run through a customized OCR pipeline, which has targeted pre and post processing interventions to improve the quality of results. Then there's data storage doxy uploads the extracted data to a shared private get her repository with some personally identifiable information such as social social security numbers already redacted library staff provide feedback on results and note areas for improvement and preparation for the next camp. Then there's data cleaning bank rough staff uses tools like open refine to start to clean and normalize data. We also may later anonymize the sensitive fields based on input from our community advisory group. As Mary has mentioned we'll be working with this advisory group to help us to think through any ethical issues pertaining to providing digital access to the data and to the digitized forms themselves. Then there's data publication, the data and accompanying documentation will eventually be published on a public get her repository, and the access copies of the digitized forms will, if deemed appropriate, be available for public view on UC Berkeley's digital collections platform. Now I'll turn it over to Cameron to discuss the implementation of the pipeline and our results in greater detail. Thank you very much, Marissa. And on behalf of Doxy, and we feel very humbled to be part of this project with bankrupt. It's a vital project for the community and we're very grateful that this has been our sort of founding project as a group of data scientists recently entering the field after graduating from the high school at Berkeley. This has been a wonderful project for us to engage on. And part of what I want to talk about today is Marissa has walked you through kind of the journey of the data from incorporation together of bringing the data to Doxy and then the processing passing it back and then what will happen afterwards. And the collaboration piece between bankrupt and Doxy is a critical part to the success of this, this venture and so I want to really talk about the value of that partnership and, and why we at Doxy believe that this, this model will be very useful going forward with similar projects. One of the things that we're really talking about is automation, and it's about these, these forms contain very rich data, and it's challenging to capture all of that data through manual efforts as seen by the previous attempts where it simplified the data quite drastically to be able to likely move, move through it in an efficient manner. But in an attempt to capture as much of the data as possible, that requires a lot of automation because doing everything by hand is untenable. And so that really is where machine learning comes in. And standard machine learning is the combination of these top two circles here, where it's taken into account coding as well as statistics, and that's where you'd see a lot of standard models in the marketplace. The challenge with these standard models is it doesn't incorporate anything about the domain that you're approaching. So much of the development in today's world is around how to automatically read forms that are present today such as tax forms, maybe receipts, different papers that we would encounter on a day to day basis, not these incarcerary records from 70 years ago. And so there's what this presents is issues with the models not being prepared to handle the uniqueness of the data, which results in vast amounts of inaccuracy, whether it's reading parts of dotted lines as symbols or vertical breaks in the page as letters. And part that what that returns is a big blob of text and symbols, which can be searchable potentially, but it will be really hard to then parse that apart, and that leads to potentially mishandling sensitive data. So if you have sensitive data like a social security number that that's mixed in with these other artifacts that appear due to the inaccuracies, it can you can potentially be leaving sensitive data present. And so that's that's really vital for us to be able to handle appropriately due to ethical concerns. And then lastly, there's a lot of unique fields unique ways that this was developed due to being type written or handwritten. And so having a field by field approach allows us to handle those appropriately. An example this are the checkboxes that you saw, for example around languages spoken and understood. The benefits with the doxy partnership with bankrupt is that we together the two organizations are leaning in to share on doxy side machine learning knowledge and domain expertise from bankrupt side. So there's a deep understanding of this field and of these forums that are vital to the development of a pipeline that handles the data in a sensitive manner. And then do is go field by field so we develop a pipeline where each individual piece of data we ensure that we're capturing that as accurately as possible. Then we go through the, we also have the process of handling sensitive data scrubbing things out as as appropriate. And then what's really great in the field by field approaches we can put in place specific qc processes such as setting a dictionary of a potential terms we would expect out of a particular field. Or if we compare age to the year of the form, we can see or sorry the date of birth, we can flag something as hey this, if we read it incorrectly, you probably need to review this and so we can add aspects into this data and look at it uniquely. And this really then again at the end of the day what it really comes down to is saving time and cost, because we know that funding is challenging and time is challenging in this field and so it's, it's really vital that we partner together to find the most efficient ways to make this data accessible. And the process as Marissa mentioned is really a collaborative one that has that. If we break it down into the simplest form is we start with bringing the data in designing around how we want that to the outputs to look and what we what we need to be doing to handle the data as accurately as possible, and then implementing a model there. The next step is then to measure and reflect on that so that's where the qc process goes in and every time that we run a new model, we are looking at how our results compared to the last time we ran and putting in place checks on the certain number of fields to ensure that we're, we're improving. And then dirt after that measurement we can assess those and that's when we come back together as a greater team and we start to see okay what is what is happening here, what are the things that are standing out to us. And an example of this was that we found in one of the camps that they actually use two types of forms, and so then we are able to create a model that identified which form was being used and then based off of the results of that we put in place a different pipeline. So it's that sort of collaborative process that you would miss if you just use a standard model but with a custom pipeline you're able to iteratively develop and capture those. The results in is a high, we feel is a very high quality of outputs, and so at the base, baseline is we hand back to Baincroft structure data that's ready for research and we've set a threshold that we expect the field accuracy to be at so each field we're measuring how accurate are we and 90% of the fields are above the expected results there, and quite a number are actually much higher than that bar so there's just a few few of those fields that present unique challenges and we're constantly iterating to improve. And a quick back of the map napkin math as well in terms of the time savings with 100 over 100,000 of these forms. 15 minutes per form you start to add that up to over 13 years of people going through and of manual effort to transcribe these but in six roughly six months of work we're able to extract a large amount of that data out of there and so. This is saving a massive amount of not just manual labor but of time to get this to the community in an appropriate manner. And then of course through this we're doing things like adding custom flags in there for expected errors to help with the next step of that process which process which is to iterate on this data further and clean it and ensure that it's again up to standards. So I think at this point I will turn it back over to to Marissa to talk about the final conclusion here. And next slide please.  And, you know with the pipeline in place with the pipeline in place, we get structured data back as Cameron mentioned, and prior to this we really only had very basic and very messy OCR text files for this, which as Cameron mentioned are searchable somewhat but don't provide the same sort of research ready high quality data for for users so again just sort of reiterating the benefits of this process. Next. Okay. Yeah, so I think you know we've learned a lot so far. The project is is not over yet. I think, you know the successes really have been built on leveraging our partnerships. You know, all the work has been iterative we've been working closely with doc CAI in developing again, you know, the work together and, you know, they bring that technical heavy lifting, and we bring that you know the domain expertise and it's been a really great partnership, but we learned a lot along the way. We also have partnerships that we're looking forward to in terms of data cleanup and working with other data sets that are either out there or being developed, you know for things like name correction and other kinds of data cleanup. Another part that we're really interested and working on at this at this moment is our our community co curation model so this is our community our advisory group meeting that we're holding next spring with members of the community who are going to come together and we're going to talk about responsible access and and you know you see Berkeley's developed some responsible access workflows as part of our digital lifecycle program and our Office of Scholar Communications is going to be part of that program to walk through what community members through what we have come up with as responsible access workflows. So we really want to look at engaging community involving community in this work as we do it so that we can be thoughtful and responsive to those whose data we steward and and how we make it available to others. There's a constant scalability of machine learning versus, you know our other former ways of extracting data. You know, we need to look at what we've learned from this project. You know the size and quantity of a resource can tell you whether or not machine learning is going to make sense. You know, is it a big enough set of records for it to learn from, you know, looking at things like content and structure. We thought we had forms that were all the same shape and size and it turns out that's really not the case because they modified form in the middle of World War two and things changed and things replaced it when they added a question and, you know, while they look very similar. Initially, when you're doing something with a machine it really does change and I think working with doxyclose lead to help, you know, us both understand that and then understand that I'm working with, you know, making their pipeline more dynamic has been a really great experience but you know again we're thinking about how much consistency matters when we're looking at using machine learning to extract data from an archival or historic resource. And then finally thinking about the collection is data side of this you know we want to extract data from the resources we're digitizing or the born digital collections we're bringing in. So collections as data principles really don't tell us how far to go and cleaning up that data, how, how clean does it need to be, you know, do we do entity extraction do we do other sorts of correction how far do we go and how do we document each change. So, you know, the question of what is research ready data look like has always been a big part of my, my research interest and I think as we do this project. That's another question we hopefully will answer at some level but I'm not sure if we will. So we're going to do to look at that as we look at different sorts of collections to apply this kind of a process to. So, yeah, we have a ways to go yet we hope we'll talk again in the future about our community advisor group meeting after that's taken place and then you know some follow up on the final project. But it's been a great learning experience so far. And, you know, we're really happy to answer any questions if, if anyone wants to reach out to us. Next slide. You have our contact information. So, thank you for joining us. We appreciate your time and please do let us know if you have any questions we'd be happy to answer them. Thank you.