Now the search is on for the 2024 Donegal Rose and Katie McIntyre from Ralph Mullen was our ambassador in Tralee last year and has been Donegal Rose for the past year and now she's preparing to pass on the Donegal sash and entries are already being submitted online at roseoftralee.ie and delighted that Katie actually joins us now online Katie how are you I'm good thank you are you good you're joining us from London that's your new base but not new anymore I suppose yeah well almost a year so it's still kind of new yeah yeah and and and what is what is your work in London what's your job I'm in for and some gym public relations for an agency that works with hotels in restaurants and some coffee and cupcake brands so it's a bit of everything okay yeah that is a mixed bag yeah it is a mixed bag it's it's a nice change so because you used to work with Mary Crawford I know yeah the right angle here and then just decided to spread your wings yeah and I loved the work with Mary it was great and I think the only thing I was missing was city life so yeah it was time to move over well if it's city life you want no better place than London what what is your job entailed um a lot of just writing press releases getting influencers and media to hotels and restaurants um sending out cupcakes sending out coffee all of that events different things going on so making making noise for and on behalf of your customers yeah pretty much good at making plans yes yeah all right making a you know making making noise it's getting them publicity I suppose that's what it's all about right and I think as well in London like it's completely different because there's so many brands and some it's so big and like it's a whole new it's a whole new challenge really like um and I suppose more events kind of during the week and stuff so it's a bit of everything a bit of travel involved as well so it's nice I mean stay a bit of travel that means you have to zip around London by Tube yeah I love it I absolutely love it everyone thinks it's great but I think it's just such a novelty because when you're at home and you're waiting on a bus every three hours or you're on like it's just I think it's class and then you see people running and there's like another one coming in two minutes like so yeah yeah yeah but you know as you say you know when you come from Donegal it is um it's it's it's the novelty of it um but if you're born and raised in London you know public transport is is the norm yeah but in saying that there's nothing like that and going home and getting a break from it all and being on the beach and stuff so it's nice to have it it's nice as well not being too far away so yeah absolutely London a lot closer than it than it used to be and I just going back to the the rows of trolley so you're about to hand over your sash and what sort of a year has it been as a Donegal rose it's been class well it's actually been two years because what they're doing now is they're like given some of the row centres like they're doing it every second year and the reason for that is so that everyone gets on tv so um my selection was actually in 2022 so it's been this year's been a bit better but the year before was just mad at class um between the tour the lead up to go into Chile um we were in New York we're in Philadelphia and the St. Patrick's Bay Parades we were at Christmas markets in Germany um we were everywhere and anywhere doing things here and there it was absolutely class it was so so good it was a great experience so yeah year one it was full on there was a lot of things that um that you were you were involved in year two sort of settled down a bit explain the good thing about year two was we all got to go back so I think about 40 of us or something like that went back to Chile then this year and just had the crack for the weekend so pressure was off yeah yeah explain the two-year thing to me again so they used to do it in a way that I think there was like 60 roses from all over and only 30 two or 33 would get on tv so there would be 30 that would do all the tour and do everything but then they wouldn't actually do the interview on tv and all of that but which is the part that most people know and most people see yeah but actually we were on a 10 day tour around like the south of Ireland for the lead-up to going to Chile um but I suppose the main part is like everyone does want to see their rows or whoever on tv so that's why they decided it up now so like Donny Gall's every second year and it's an alternative years to um I don't know Mayo so okay spreading it out a bit more yeah yeah exactly spreading the love and and how how was the whole experience down down in Chile because you know there's a lot of pressure attached to it you have to appear on live tv on there with Dahi and so you know that's uh you know that there's a certain amount of um preparation stuff has to go with that it's preparation I think the good thing is the tours so full on like we were in Wexford Kildare Dublin Corp obviously in Kerry we were somewhere else Waterford Wicklow I don't know somewhere we were in I think we were in seven different counties over the space of 10 days and it's all on the whole time so kind of by the time you get to Chile you've not even thought about the tv part so it's like it all happens very quickly then at the end um but you get a lot of like face-to-face time with Dahi getting to know him he gets to know you so you're kind of more relaxed then when it comes to going on stage and talking to him because and you can also the great thing about it is like when I went on stage I could see my whole family like it's not that big of a room like you can see everyone it almost feels like you like it's you just a lot more relaxed and stuff so it's not as much pressure as you think and it's a really really enjoyable experience and it looks very big on tv yeah it does it's completely deceiving actually um but yeah it's giving me see all your flags and then I kind of got a bit I was like oh god because like you can see your face obviously everywhere and you're a bit like oh not today let me just chat and and uh uh what about your um your escort how did you get on with him good yeah um yeah good yeah yeah the escorts are good crack and the Johnnyville Rose Center is actually looking for escorts as well so all right so escorts as well as potential roses yeah it's great it's such a good crack honestly they're like at the end of it all there's 60 60 people that you don't know and now you're in group chats with them and you're traveling here with them and going there with them and it's such a good way of meeting people and I think as well like one of the reasons I did it was after lockdown you're kind of like you're kind of in a rush and you're in your comfort zone and everything and it's such a good way of getting out of your comfort zone meeting the people and it's almost like a bit of a like reboot and like oh my god life is so exciting and there's so much to say and so much to do and like visiting people in Philadelphia or the New York Rose or Sydney Rose like and the Sydney Rose then when my sister moved to Sydney bumped into her a week later like it it's just there's such a sense of community everywhere you go it's fun well you know some people can embrace that whole that whole thing and you know the busier the better and time is flying in you're meeting a lot of people and you I think you're you're one of those people and so life in London and the job that you have in London you know is obviously you know certain you're down to the ground yeah but it's funny then when you go to explain the roses to people like even today I was like oh I need a bit of a few minutes to go and do an interview for this and then they were like what are you talking about but then you explain that there's a London Rose and New York Rose and it's kind of like you're just explaining that as a celebration of Irish women and that's exactly what it is it is just class it's so good yeah and and and life in London I mean you know you get obviously you know you get your time off and you get to experience you know a big city so you've been I imagine that you know since you moved there you've been doing a lot of the you know kind of the touristy bits and seeing different boroughs in London and stuff loads the touristy bit loads of the food market there's so many restaurants bars all that jazz and so much walking which I love so it's lovely and I live like in an area where there's loads of parks and stuff so you don't always feel like you're kind of in the middle of it all all the time but then also a bit of Gaelic football over here as well so yeah you still see you you got to pick up your Gaelic football career over there yeah yeah who you playing with over there there are there's like 120 girls I think signed up for the team I play for and so it's a big difference from going to training in Milford with seven girls but that's because everyone's in London and Sydney so um yeah it's good it's a it's another it's a class where I've gotten to meet people as well because I mean I've been lucky coming over here that I had friends and stuff but it's a great way of kind of getting sense because it was the time the only way that you got to meet the other Irish people who was in the pub but now you know yeah well there's that as well but yeah but you don't you don't have to rely on that you know no you don't have to rely on it yeah okay and and what about connections with home because I know you're very hands-on with the Rathmullen phone festival and used to help out is it are you able to help from remotely or yeah we're actually working on it at the minute and so there's two film challenges happening if anyone wants to go down that road and apply for that um so just check out Rathmullen film best story and um yeah so I'm able to help out remotely which is class so nice that connection and just and then also when I was home I was helping out in our family pub in the White Heart so it's a bit of is it true is it true that you've become something of a wine expert well yeah I was working in wine pure up until christmas and you know what I loved it but then it's one of those things where you know when you really enjoy a glass of wine and you don't want it to be your job as well uh-huh and so I know I would not like I mean wine expert I passed an exam that's good going well yeah in comparison to like to me you are an expert now then oh well yeah then I suppose yeah so that means that when you sit down now for um uh for about to eat and you know the subject of wine comes up you know all heads turn to you okay do you'll know you'll know what works here yeah but then it's one of those things where it's like I'm not I'm not working so you can pick I'm not a decision maker like that I hate it it's the same as you know when you're asked by music for the office or something it's just like uh-huh no way well there's some continued success and just to remind people if there's um anyone interested in going forward for the rose that they can apply online yeah so apply on the rose just truly dot i e if you have any questions just message donnie goll rose center on social media and yeah it's class you do need a sponsor a business sponsor as well but mine was from halahouse so just reach out to someone that you know and yeah it'll be a great night on the 13th of April so it'll be class okay that's that's when the that's when the donnie goll rose is selected yeah okay and you just have to be over 18 and under 30 yeah between 18 and I think it's 30 before September okay between 18 and 30 yeah but all of the details are on the rose literally website very good well listen continued success uh cary and uh maybe we'll bump into you on one of your one of your trips so maybe around about the time of the film festival but in the meantime and great to talk to you continued success over and on thank you so much thanks for having me on bye bye


of discuss the methods for solution method different methods of the first method is iterative method. So, you have a given the what is called algebraic Riccati equation that algebraic Riccati equation will arise in case of infinite time regulator problem agree. So, this is the algebraic Riccati equation. So, this term and this term we club together again p into a this one p into b r inverse b transpose p there is a another one additional term is given added with a negative sign. So, with positive sign we have used here p b r inverse b transpose p. So, this and this cancelled. So, this will remain same as the previous equation. So, this we have written it like this way now we introduce some p we have introduced p of suffix k means value of the p matrix at kth iteration and outside this bracket is this one p k plus 1 value of p at k plus 1th iteration knowing the value of p at kth iteration we can compute the value of p at k plus 1th iteration. So, we have written into like this way. So, in order to solve this equation it is a something like Lyapunov type equation that we come across in the solution of stability analysis of a autonomous system means suppose you have x dot is equal to a x and we want to study the stability of the system based on Lyapunov function. Then we consider a energy function b in quadratic form x transpose p x then we this energy function is a positive definite function positive semi definite function. And we have to show for the system a to be stable provided b dot means rate of change of energy should decrease with time that we have to show it and that condition is that one a transpose p plus p a is equal to minus q. So, this is called a Lyapunov type equation while we will study the stability of a autonomous systems. How to solve this one that you assign what is called q is a positive definite matrix then solve a transpose p a for p you solve it if p is a positive definite matrix it indicates a matrix is a stable one based on the derivative of the Lyapunov function we arrive this condition. So, here also we initial value let us call k is equal to 0.  So, it is b k and you have I told you you have to select p of 0 k is equal to 0 in such a way this matrix is stable. So, it is a transpose a c transpose or whole matrix I can write a k is nothing but a closed loop system matrix at k th iteration.  So, I know p of 0 I have assigned initially in such a way so that a minus b r inverse b transpose p k is 0 sorry stable. So, since this is a positive definite matrix q is a positive semi definite matrix result will be positive definite matrix and if the result is positive definite matrix we know that a minus b r inverse b transpose p k is the stable one. So, if you solve this one then we will get p k plus 1 k plus 1 th iteration the p value will be positive definite and next is once I know from the knowledge of p 0 if I can compute p 1 then use the p 1 in this expression a minus b r inverse b transpose p p is p 1 now you find out p of 2 by solving the Liapunap equation and in this process you go on solving recursively until and unless the p converges to a some constant value.   So, it is stable initial choice is such that it is stable subsequently you can write it that subsequently it can be shown it can be shown that all matrices this for k is equal to 1 2 3 a k are stable and next observation we can see it rather p k plus 1 is less than equal to p k that means this p k minus in other words you can say p k plus minus p of k this matrix is less than equal to 0 means this is a negative definite matrix. That means in other words I can say the p k plus 1 is less positive definite than the p k then I can write third one is limit k tends to infinity the iteration if you go on increasing in the like this way recursively if you solve it the Lyapunov equation then you will see p of k will equal to p which is a constant it converts to a constant matrix. So, only things is when you will start the iteration you have to select p of 0 in such a way a of 0 is stable and then start recursively solving this one until unless that p k plus 1 and p minus p of k is less than equal to norm of this one is less than equal to some p specific values a small values of there or when p converts to a some constant value. So, this is the this method in this way we can solve the algebraic equation that is called iterative method. But one disadvantage of this method is that if your initial guess is not appropriate then it will take longer time to converge the solution of the equation p to a constant value this is the one of the disadvantage of this method. So, next method is is a eigenvalue eigenvector method that means based on the concept of eigenvalue and eigenvector one can find out the solution of solution of algebraic equations. So, method 2 method 2 is based on the eigenvalue and eigenvector method that means solution of the algebraic equation based on eigenvalue eigenvector method of the Hamiltonian matrix. So, first step step one so first you all the when you are designing a l q r problem first check first check it is there you have to see whether the system is controllable or not. So, the controllability test we have already mention it here. So, just mention first check the controllability of the of the system agree that means if you rank of b a b dot dot a n minus 1 b must be equal to n n is the number of states order of the systems and b is the input matrix a is the system matrix the rank of this matrix must be n if it is n then we will call system is completely controllable means all the states we will be able to from initial state we will we will be able to drive the state a desired state with a finite control actions agree in a finite time. So, this is the thing if this fails then this program will show the error message that means we will not be able to design the l q r problems if the system is not controllable return with an error message the error this message will be there that will not be able to design a l q r controller for this systems step 2. So, from the Hamiltonian matrix matrix and to form the Hamiltonian matrix if you recollect we need the information a matrix b matrix q matrix and r matrix. So, a minus b r inverse b transpose then q minus a transpose and this is the Hamiltonian matrix and this Hamiltonian with how we have formed it if you recollect that x dot of t and lambda dot of t this is the cost state vector it is the base state vector this equal to h m into x of t lambda of t. So, this is the augmented state vector with state vector and the cost state vector and each dimension this dimension is 2 n cross 1 this dimension is 2 n cross 1 2 n cross 1 if you see this one. Now this we form the Hamiltonian matrix this Hamiltonian matrix dimension 2 n cross 2 n so this is the m. So, thus this matrix have a how many Eigen values are there there are 2 n Eigen values are there we have shown it analytically that this matrix having a set of Eigen values that mean n Eigen values are in the left top of this plane and or you can say n Eigen values are with negative real parts and remaining n Eigen values are the write up with a positive sign with a positive sign again values with a real positive sign. That means n Eigen values are stable Eigen values and n Eigen values are unstable Eigen values and they are symmetric about the imaginary axis. So, this are the Hamiltonian matrix and all its Eigen values let us call we have define the Eigen values are mu 1 mu 2 mu 3 dot dot mu n and another set of Eigen values will be minus mu 1 minus mu 2 dot dot mu n minus this one. We told we have seen it that Hamiltonian matrix there are 2 sets of Eigen values they are symmetric about the imaginary axis of the complex plane. So, that is let us call this set of Eigen values are the stable Eigen values of H m Hamiltonian matrix and this are the since this is if this is the stable since it is a preceded with minus that will be unstable. Because this stable means the Eigen values the real term with a negative sign if it is a stable one and negative sign and there is a preceded with negative means positive sign. So, the Eigen values real term real these Eigen values are with a positive sign that means unstable Eigen values of H m Hamiltonian matrix. So, this positive real parts this is the negative real parts positive real parts of Eigen values real parts of Eigen values negative real parts one then this will be positive real parts of Eigen values of n this is the Eigen values of that. So, this we have just defined this one then if you that will just see this one that Eigen values are symmetric about the imaginary axis of the symmetry about the imaginary axis to show this one we have already discussed this one earlier at this stage we just briefly discussed how we are making comments like this way there are Eigen values of H m Hamiltonian matrix are symmetric about the real X imaginary axis. So, do the similarity transmission similarity T transmissions. So, let us call X of T lambda of T this costate vector argument vector of this one we have used the transformation like this way. So, let us call this is the equation number we have just this equation let us call equation number one again this is the equation number one from equation one or you can say equation one can be written equation one can be rewritten as rewritten as that X dot. So, it is a I is a constant matrix P I then your X bar dot of T agree then lambda bar of T lambda bar X dot lambda dot is equal to this into X dot X bar dot lambda bar dot this equal to we are writing we have a Hamiltonian matrix H m then we are writing for this one I then 0 P I this into H bar of T lambda bar of T agree. So, this we can write it for this one that what is H m we know the H m expression then we can write it left hand side lambda bar of T is equal to lambda X bar of T augmented with a lambda bar of T this equal to I 0 P I whole inverse then our Hamiltonian matrix. If you write the expression for Hamiltonian matrix this is nothing but a B R inverse B transpose that is minus Q minus A transpose multiplied by then your I just I have simplified that one X bar top lambda bar of T. So, if you use the matrix inversion lambda of this one as we discussed earlier and if you do it after simplification we will get the final results is like this way A minus B R inverse B transpose P and this is nothing but our K if you see this equal to minus B R inverse B transpose that equal to that side is equal to A transpose P plus P A minus P B R inverse B transpose P plus Q and that will get minus of that matrix A minus B R inverse B transpose P this equal to X bar T lambda bar of T. So, if you just multiply from here to here you will get this matrix. So, now see this one this is the algebraic equation this quantity is equal to 0 for this one and this one is 0.  So, this will be a unstable this matrix Eigen values are unstable. So, this matrix whose dimension is n cross n this dimension n rows this is n columns similarly this is n columns and this is the n rows. So, n Eigen values of this stable and this block Eigen values are unstable. So, whole system Eigen values after transformation we know the Eigen values does not change it only Eigen vector with similarly transformation Eigen vector will change. So, since it is a block diagonal matrix the Eigen values of the whole matrix is nothing but Eigen values of this block which is the stable Eigen values and Eigen values of this block which is the unstable Eigen values because of that minus sign is preceded with that matrix. So, that we have shown it earlier also here also now see that this is the second step if you just see the first step is you will check it the controllability test if the system is controllable then go the second step in second step you just form Hamiltonian matrix and other things what we have discussed just to show the Hamiltonian matrix Eigen values there are 2 n Eigen values are there n Eigen values are lying to the left of the screen in the sense the l n Eigen values with a negative real parts and remaining n Eigen values are positive real parts they are symmetric about the imaginary axis. So, once you find out this h m matrix then find out the step 3 step 3 the find out the Eigen vectors of the Hamiltonian matrix find the Eigen vectors of the Hamiltonian matrix corresponding to the stable Eigen values I repeat I once again find out the Eigen values of the Hamiltonian matrix whose dimension is 2 n cross 2 n you find out the Eigen vector corresponding to the stable Eigen values of h m that is our next step. So, step f let that v i u i whose dimension is v i dimension is n row 1 column this is the 1 column this is the n row that means this dimension twice n into 1 let atho is the Eigen vector or generalized Eigen vector of the matrix h m corresponding to negative real parts corresponding to in other words corresponding to mu i and what is mu i the stable Eigen values what is stable Eigen values the Eigen values with negative real parts Eigen values with negative real parts means this corresponds to stable Eigen values Eigen value of h m that is. So, you find out the Eigen vector of that 1 corresponding to mu i. So, if you find out this mu 1 and you can find out we know the hour for i is equal to 1 2 dot dot n you see this one i i how many stable Eigen values are there we have a n stable Eigen values are there n unstable Eigen values are there we are we are concentrated or we are we are attention to find out the Eigen vector corresponding to n stable Eigen values of h m. So, for i is equal to 1 2 n. So, the solution of the proof we will give it later the solution of algebraic Riccati equation then one can find out that means this solution A transpose P A minus P B R inverse B transpose P plus Q is equal to 0 the solution of this one is obtained as P is equal to that you form a U 1 U 2 dot dot U n then this matrix B 1 B 2 dot dot V n whole inverse. Now, see this one which one that now see this one here this is the Eigen vector corresponding to Eigen value mu i which is stable Eigen values and when you are getting the first n rows of this column vector you partition as a V i remaining n per n rows of this vector you partition as a V i. So, since we have a what is called n stable Eigen values the first partition n first n rows I am writing V 1 V 2 V 3 dot dot V n and remaining last n rows of the Eigen vector corresponding to stable Eigen values I am writing U 1 U 2 U 3 dot dot U n. So, the solution of the Riccati equation of this one. So, this is the stable Eigen vectors of h m corresponding to U 1 corresponding to mu 1 again last n rows of this Eigen vectors is U 1 the last n rows of the Eigen vector corresponding to mu is equal to mu 2 is the U 2 and so on and this is the first n rows of the Eigen vector corresponding to stable Eigen values mu 1 this is stable Eigen values mu 2 first n rows of the Eigen vectors Eigen vectors in this way. So, if you compute this that will give you the solution of the Riccati equation. So, this dimension if you see how many rows are there n rows this is column 1 column 1 in this way we have a n column 1. So, this dimension is if you see n cross n this dimension is n cross n and this inversion is exist because Eigen vectors of that one comprises that one V 1 V 1 all these things. So, let us see the proof of this one. So, these are the Eigen vectors what you can write it for this one if you see that one this what is called this is the closed loop system Eigen values for closed loop system matrix A minus B A minus B R inverse B transpose P is the closed loop system matrix. So, this way you can solve it this one. So, let us prove that how we are getting this expression.  Let us call this is equation number 1 this equation we are writing into different form look mu i is the Eigen values of the matrix Hamiltonian matrix corresponding to a stable Eigen values mu i this is I have added with a A transpose multiplied by P A transpose of this is more one term is added that is subtracted like this way mu i I agree then A minus plus B R inverse B transpose P plus Q let us call this is equation number 2 now see mu i mu mu i P minus P mu i agree that if you multiply by this one mu is a scalar quantity agree.  So, it is same as equation number 1. So, now next is the Eigen values let us call now if the Eigen values of A minus B R inverse B transpose P R mu 1 mu 2 dot mu n and that corresponds to stable Eigen values. If the Eigen values of this matrix are mu 1 mu 2 dot this agree stable Eigen values then we can write by definition of Eigen value and Eigen vector the definition A B R inverse B transpose P this into that vector that vector is V i let us call corresponding to this vector this is the Eigen values corresponding to this vectors are V i V i i is equal to 1 to n then V 1 V 2 is the Eigen vectors corresponding to this matrix. So, this plus is equal to that mu i into V i this is the definition of Eigen values Eigen vector this is the matrix and closed loop system it is that Eigen values are mu 1 mu 2 which are the stable Eigen values we are considered. So, by definition of Eigen values Eigen vector I can write it closed loop system matrix into corresponding Eigen vector if it is V i must be equal to mu i into V i. So, this I can write it after bring it in this side B R inverse B transpose P this minus you bring it mu i i whole V i is equal to 0 push it P V i in the inside. So, it will be V i minus P B R inverse B transpose P V i again minus mu i mu i V i. So, if I take it right inside it will be mu i V i.  So, it will be a vector which I am denoted by U i is equal to mu i V i. So, let us call this equation is equation number 3 and this is valid for n number of equations. So, this is the one equation 3 get it. Now, if you multiply by this equation 2 by V i post multiplied by V i both sides then what will get it this equal to that is this equal to 0 null matrix from equation 1 you see we have adjusted this equal to right inside null matrix. So, here I missed it is a null matrix here.  Now, look at this expression this expression you see this expression what you can write it A V i if you multiply by A V i you see this one A V i V R inverse V transpose U i minus mu i V i is equal to 0 if you take this one this side it will be 0. So, if you see is here then A V i R inverse B transpose P V i then mu i but it is a minus with this one. So, this quantity is 0 this quantity is 0. So, I can write it now mu i i plus A transpose P V i then I am taking the minus sign common it will pass plus A minus B R inverse B transpose P V i this is the minus sign if I take it common this is the plus. So, it will be minus mu i V i plus Q V i is equal to 0. So, this quantity from equation 0 from 3.   So, let us call this is equation number 4 now you just write from equation 3 and 4 from 3 and this from equation 4 we can write from 3 and 4 note V i is the what if you recollect V i is the eigenvector of the matrix closed loop system matrix corresponding to a eigenvalue mu i corresponding to eigenvalue mu i this is the eigenvector and mu i is the what is mu i mu i is nothing but a the stable eigenvalues stable eigenvalues of Hamiltonian matrix that is. So, from 3 and 4 we can write it A minus B R inverse B transpose minus Q minus A transpose this is the Hamiltonian matrix and this is equal to V i U i then is equal to mu i and V i U i and that is for i is equal to 1 2 dot dot n. So, let us call equation number 5 and these are the what this is if you see this is the eigenvector this dimension is what if you see this dimension is this matrix dimension 2 cos 2 n and this dimension is 1 cross twice n this is the twice n. So, 1 column twice n. So, these are the eigenvectors corresponding to Hamiltonian matrix again this is the that what I told you earlier also you find out the eigenvectors of the Hamiltonian matrix corresponding to the stable eigenvalues of H m means Hamiltonian matrix and then you can find out the what is called solution of algebraic equation this and this is equal and write it eigenvector eigenvector eigenvector of H m corresponding to by definition of eigenvalues eigenvector you see A x is equal to lambda into x corresponding to mu i which is a stable eigenvalue eigenvalue of H m this is or you can say stable eigenvalues of A minus B R inverse B transpose P this is the stable eigenvalues of this is the stable eigenvalues of this matrix also agree this matrix also so now this I can write it now from so we know we know or we have defined you see P v is equal to u i and i is equal to 1 2 dot dot n. So, I can write it P v 1 v 2 dot dot v n is equal to u 1 u 2 dot dot u n augmenting the i is equal to 1 2 n this matrices are these vectors are augmented like this way. So, this is nothing but a this dimension is n cross n because this this is the row is n rows this is the column 1 this is the column 1. So, we have this this is also n cross n.  This are the eigenvector eigenvector of this matrix eigenvector of this matrix corresponding to the eigenvalues mu i corresponding to eigenvalue mu i.  So, the inversion exist for this one agree. So, this way you can one can get the solution of this one. So, if you summarize this one then what is the step to solve the algebraic equation first you check first step is you check whether the system is controllable or not. If the system is controllable then proceed second step. If it is not controllable send a message that this we cannot design a controller based on LQR. That means solution of lickety equation does not exist or we are not be able to stabilize the systems. Second step is here that you form a Hamiltonian matrix and once you form a Hamiltonian matrix then you find out you can immediately find out because you know a Hamiltonian matrix is known to you completely because you know a you know b you know q and you know r. So, Hamiltonian matrix is known to you completely then you find out the eigenvalues of this matrix and consider only these stable eigenvalues of the Hamiltonian matrix and find out the corresponding eigenvector of the Hamiltonian matrix corresponding to the stable eigenvalues agree and once you got the eigenvectors of the Hamiltonian matrix corresponding stable eigenvalues mu1 mu2 dot dot mu n. then first n rows of this vector you picked up that is denoted by v i and remaining n rows or last n rows you denoted by u i then you form a matrix like this way take the inverse then this will be the solution of the recalculation that is the proof of that one but this this is our method what we have considered this is the eigenvalue and eigenvector method this method is numerically is not much sound in the sense when you are finding out the eigenvalues eigenvalues or eigenvectors and when the eigenvalues are the repeated eigenvalues then we have to look for what is called generalized eigenvectors and competition of generalized eigenvectors may create what is called numerically some problems can be not it is not reliable much to when we will compute the eigenvectors by this method ordinary method so the next method is numerically stable methods method 3 is a numerically more stable method while we will computing the eigenvectors of the Hamiltonian matrix corresponding to stable eigenvalues of h m in semi-numerically matrix so this method is called sure sure method to solve a re algebraic equation and the sure method is based on q r decomposition method decomposition method and that is numerically stable method is numerically stable or more reliable when you will compute the eigenvalues of eigenvectors of this matrix when the eigenvalues having a repeated eigenvalues are there so one can see the reference of this one good paper you can see a sure method for solving algebraic equation that is appeared in IEEE transaction AC volume 24 volume 24 number 6 June issue and 1971 79 even though it is a very old paper but it is a basic things is given here only and page 119 292 101 author is l n j lam so we will just briefly discuss the solution of this Riccati equation algebraic equation based on what is called the sure complement my sure method so step algorithmic steps so step 1 but in this method what will do it will just find out the eigenvectors of the matrix h m corresponding to stable eigenvalues me 1 me 2 me 1 that is the method only once you know the eigenvectors of this one and we know how to find out the solution of the Riccati equation p that means first n rows we picked up and will form v 1 v 2 v 3 v n and this is nothing but a the eigenvalue eigenvector corresponding to closed loop system eigenvalues and the remaining n eigenvalues that of this Hamiltonian matrix eigenvectors that eigenvectors such that 2 n cross 1 eigen dimension the first n rows is denoted by v 1 and the remaining n rows that means last n rows is denoted by u 1 so from that information we can find out the solution of p and in this method sure method is giving the competition of eigenvector corresponding to eigenvectors of h m corresponding to stable eigenvalues me 1 me 2 me 1 so first step is form Hamiltonian matrix h m is equal to a b r inverse b transpose minus q minus a transpose this this is the equation number 1 whose dimension is n cross twice n into twice n this step 2 with the help of with the help of q r algorithm the matrix h m is transform into upper sure form first strike structure is the s is equal to w transpose h m of w and w is the orthogonal matrix orthogonal matrix and orthogonal matrix means if you if we inverse of this matrix is nothing but a transpose of that same matrix this is orthogonal matrix and orthogonal w w transpose is equal to identity matrix that the properties of orthogonal matrix so this is the orthogonal matrix and the structure of a if you look at this expression this dimension is h n dimension twice n cross twice n and this orthogonal matrix also dimension is twice n into twice n this because this dimension is twice n into twice n agree so the structure of s the structure of s you will get it s 1 1 s 1 2 dot dot s 1 k s 2 2 s 2 3 dot dot s 2 k and in this way if you just go on s k k so this whole matrix dimension twice cross twice n just like here this dimension of this one is also twice n into twice n this orthogonal matrix now what will do it you do the sequence of orthogonal transformation on h m such that the structure of s matrix when you are premultivate by w transpose and w the structure of p matrix will be this form upper triangular structure this will be a upper triangular structure agree this form and look at this expression each block either s 1 1 s 2 2 s 2 3 3 4 4 s k k 4 k k this block will be either 1 by 1 block or 2 by 2 block this will be s i i where s i i i is equal to 1 2 dot dot k is either 1 cross 1 mean scalar or 2 cross 2 matrix again this will be the this matrix this matrix so this is the a sequence of orthogonal transformation we can make it on a h m matrix such that this matrix will be converted into this form either 1 by 1 block or 2 by 4 if the 2 by 2 block is coming in there then this Eigen values of that one s you see Eigen values one can conclude the Eigen values of a is same as the Eigen values of h m because you are doing the some similarity transformation w transpose is nothing but a w inverse of this one you can think of it like this way so Eigen values are remain same so this Eigen values are either it will be 1 1 this book or it will be 2 2 block that means this Eigen values will be either complex or real this if it is a 2 by 2 blocks are there so now see how it is done it so this is just to get some idea that what we do it in this equation that suppose given given a our problem is convert into a decompose a is equal to a into q r again so let us call the matrix a is decompose into q into r q is a orthogonal matrix an r is the upper triangular matrix upper triangular matrix again now this is a is equal to this this is the a matrix can be decompose into this q into r form this can be done by using a sequence of orthogonal transformation or in other words one can do a sequence of householder transformation which will decompose a matrix into this q into r let us call we know how we assume that this technique we know how to decompose a matrix q into r so if it is so then how will you get in sure form into this form a is equal to I am writing is the given matrix is written assigned with a 0 which is equal to our q 0 r 0 now I have divided a 1 if you reverse the order of this one because both are a dimension if it is n cross n this each each is n cross n this is also n cross n so if you reverse this order the dimension this matrix will be different from a 0 or a so this matrix I am now considering is a 1 is a r 0 q 0 so what is r 0 if you from here you take the inverse of both side so it will be a q 0 inverse means transpose because q is orthogonal matrix q 0 is orthogonal matrix so this will be this one into a 0 into q 0 so what is r 1 r 1 is nothing but a r 0 q 0 and whatever the decomposition we do rate q 0 r 0 reverse order you write it r 0 q 0 is a 1 similarly I can write it now a 2 a 2 that that a 1 is just like if you decompose this matrix decompose into a q 1 r 1 I decompose in write the reverse order which is denoted by a 2 is equal to r 1 q 1 so what is our r 1 r 1 is nothing but a q 1 transpose a 1 r 1 is nothing but a q 1 transpose a 1 into you see r 1 is nothing but a q 1 from here r 1 below I am writing r 1 below I am writing q transpose a 1 multiplied by q 1 again so what is a 1 just now you got it a 1 is this one so it is a q 1 transpose q 0 transpose a 0 q 0 q 0 q 0 q 1 again so if you write it like this way then I can write it is nothing but a 2 is equal to q 1 transpose q 0 transpose a 0 q 0 q 1 this so which is I can write it if you see q 0 q 1 whole transpose a 0 q 0 q 1 so next is what I will write it I will write it a 2 a 3 is equal to because this I can this matrix a 2 matrix I can decompose a 2 is what I am written into r 1 into q 1 so I can decompose this a 2 is equal to q 2 r 2 agree q 2 r 2 I can I decompose this matrix and how I will write it this simply in this way that q 2 is this and what you can write it for this one after simplification in this way then I am writing a 3 a 3 is q 2 a 3 what will can write it a 3 similarly I can write finally q 0 q 1 q 2 whole transpose a 0 q 0 q 1 q 2 and ultimately if you see if you do the kth iteration a k what you can write it a k that q 0 q 1 q 2 dot dot q minus 1 whole transpose a 0 q 0 q 1 q k minus 1 in this agree so ultimately it is nothing but a q transpose a 0 and q the 2 orthogonal matrix multiplication results is orthogonal matrix agree so it is something like this way if you do this sequence like this way ultimately I will get a is in the sure form into sure form means into this form just like into this form into this structure this is all r 0 that means either a 1 s 1 1 may be 2 by 2 block or 1 by 1 block this may be 1 by 1 block or 2 by 2 block but it cannot be more than 2 by 2 block it is may 1 by 1 block or 2 by 2 block so ultimately if you proceed like this way you will get it a k into sure form so I just leave it this is an exercise please do it a 2 this one agree this you just proceed that one and next class I will just complete the algorithm how to solve that algebraic equation using the sure complement is sure form and sure form is nothing but a numerically you are finding out the eigenvectors of a of Hamiltonian matrix corresponding to a stable eigenvalues that is all only agree so we will stop it here


I'm Major General Andy Minera, the Commanding General of U.S. Army Connect Command. It's a national junior ROTC drill and fitness championships where we have over 5,000 kids from probably about 100 schools that are here competing and giving us the best of the best. So they're all great events, but my absolute favorite event is the unarmed drill competition. These kids are absolutely amazing. They come up with these routines all on their own and the hard work, the dedication, the attention to the detail, the amount of time they have to spend practicing to get it right. It's absolutely amazing and it just showcases the talent of these cadets. Events like these are just a chance for our kids to showcase their talent. I mean it's the best of junior ROTC and it shows the power of junior ROTC. I mean if I was a recruiter for any organization, I would be down here watching these kids because what it takes to be successful in life is all here in the competition. You know, kids that are leaders of character, they understand hard work, they understand teamwork, they understand the importance of repetition and practice, all things that are going to make them successful and whatever they decide to do in life. Hello, my name is First Lieutenant Delaney Maxwell and I'm the overall unarmed commander. JRTC means a lot to me. It's my family. I've built friendships and lifelong relationships through this program and it's just been such an eye-opener for me. It's been a program that has gotten me out of my shell. It's really bloomed me as a person and going on in my future career of what I want to do, it has really prepared me for what reality will be and what I will have to do with my future career. It is important to JRTC because it shows what cadets go through throughout the whole year, the time, the dedication that they put in to such an important event and it really shows which teams are giving it their all. So the Calvary Angels, the unarmed team at Leavenworth, we wake up around five every morning, we get to the school by six and we practice till seven thirty, Monday through Friday, every single week. The only time we get off is if it's a national holiday. We even practice during breaks sometimes. It means everything. This is what we give our mornings to, this is what we give our dedication to, this is what we give, this is what we want, this is what we prepare for and this is pretty much our end goal. Winning nationals is the end goal for us. Leavenworth Battalion, JRTC, is a family. We are a small town but we have about 300 cadets and all of us are like brother and sister and that it may be hard at times, committing to a team is hard but the end result is always worth it.


Now let's get on with the show. Our first speakers today, we're gonna have a twofer. Twofer Monday. It's my pleasure to introduce Nydia Ruff and Brian Bellendorf. Nydia serves as the at-large director and chairperson of the Linux Foundation Board of Directors. Nydia has a long history of service and open source, having championed the adoption of enterprise, adoption of open source and the enterprise, influencing organizations to invest in and give back to open source communities. She actively works to advance the mission of the Linux Foundation in building a sustainable ecosystem that's built upon open collaboration, which is pretty amazing. Nydia joins, she joined Amazon as the head of the open source program office where she'll continue to drive investment and compliance in open source. She's a passionate advocate and a speaker for opening doors to new and diverse people in technology. So I'm gonna invite her up to the stage to help get plugged in. And after Nydia speaks, we're gonna have Brian Bellendorf, who's the general manager of the open SSF. Brian founded and helped lead open source communities and initiatives for more than 30 years. First as the founder of Apache Software Foundation and then later as a founding member of both the open source initiative and the Mozilla Foundation. Brian is co-founded or was a CTO of a series of startups, including Wired Magazine, Organic Online, and CollabNet before pivoting towards public service in the White House as a CTO office under President Obama and then served as a CTO for the World Economic Forum. So please welcome me in joining Nydia and let's give her a nice round of applause. Thank you. Thank you everyone. I know it's common to say gray beards in open source. There's also others with gray hair like me. And I must admit that one of the privileges of having been in open source for so long is a chance to talk about history and what we've learned from history and why we are at the moment we are today with open SSF and what we need to do to move the needle from an open SSF perspective. As indicated, I sit on the board and I'm very excited to also be part of the Amazon open source program office and really acknowledging Juneteenth, acknowledging Pride Month and I love, love, love the Linux Foundation logo that's really reflecting what we're celebrating this month. So let me start with the 80s and 90s when I actually was around and I was involved in open source around 1998. So I think we've got to acknowledge that open source came from very, very fringe elements, quote, if you will. It wasn't an enterprise thing. It wasn't invented by some company. It wasn't created in some company. It was really folks in the MIT research lab saying how can we give people the freedom to actually examine software, modify software, change software, et cetera. And so therein was Bon Ganu and the Free Software Foundation and the license called GPL, which gave people a number of freedoms, freedom to modify freedom, to share source code, freedom to distribute, freedom to use it for any purpose that they want, which was a dramatic departure from how software was typically shared. I think this is important because during these days in the 80s and 90s, companies really hadn't fully discovered open source yet and they feared open source and it was kind of on the side. And then comes the 90s and you start seeing people like Lina Storval's releasing the kernel that he had created and he used GPL to share this with others which some people say is the reason why Linux became so widely adopted and shared and the innovation was so fast and so aggressive and there was just so much collaboration, open collaboration going on. And then comes the open source initiative which was founded to protect the freedoms, the open source definition and the freedoms of open source and the term open source really came into being. Before that it was really free and open source software. A lot of companies thought, oh my gosh, free sounds economically free and I don't wanna associate myself with something that people have an expectation that I give away for free as from an economic perspective. So the word open source was coined actually by a woman, a consultant, Christine and I forget Christine's last name. And then comes open source development labs and here's an interesting thing because companies actually came together to create open source DL and also free standards group because they felt that they needed to collaborate together to take this new thing called Linux and open source forward and make it enterprise ready. And so they felt that they needed to come together in a neutral way, in some sort of a foundation way to really work on this together and that no one company could do it by themselves. Then comes the foundation called Apache which Brian knows very well and he may cover some of that in his talk. Apache is a 501c3 so it was started as a nonprofit started to protect the emerging development of Tomcat and other Apache web servers and projects and the Linux foundation also came into being around the same time and it was started more as a trade association with the merger of the free standards group and the OSDL. And again, it was to protect this nascent thing called open source and Linux and to grow this and develop this and move this forward and make it available for all of us and also to provide a neutral home for the creator Alina Storbaals so that everyone could benefit from the work that he did and then to start creating the constructs like open governance and how do we deal with trademarks and legal constructs around open source and how do we build community and so on and so forth. And then you start seeing some young new companies, tech companies like Sun and SGI and HP who start saying, how can I start using Linux in my servers? How can I ship product based on Linux? How can I support Linux and IBM here hit it out of the park by doing a one billion dollar, I think, support? Jeff will correct me if I'm wrong, one billion I think is the number and started the Eclipse Foundation and started saying, we believe in Linux. And that then became, I guess, a call to action if you will for other companies to say, I think Linux is something that companies should use and adopt and it's become mainstream and it's something that we can work with. Then you start seeing a number of companies that were born in the age of open source like Google and Facebook and Amazon and Netflix and they built from ground up using open source. They built these hyper scale, web scale companies using open source to fuel their search engines or social media or dot com or streaming and they also did something interesting. They started kind of contributing back projects that they had used in production. So you started seeing things like Hadoop and other big, big projects being contributed back and so you now start seeing a huge body of work that's scalable and usable by companies and becomes safe to start really building on open source. The cloud start building on open source so if you pivot a lot of companies who really created their own infrastructures for say in the case of Amazon for dot com, they start saying, I wonder if other companies could use the same infrastructure and could benefit from this infrastructure and start creating cloud services and so also you can see Google doing the same, Microsoft doing the same and other cloud companies and then people started saying, I want to see use open source but can you deliver it as a service? So you start seeing cloud companies deliver Kubernetes as a service and other things as a service. The other adoption that started happening, I would say in the 2000s and beyond and I actually worked for an enterprise company called Comcast is that enterprises which were not at all in the business of systems, right? Were in banking or in media and entertainment or were in retail started saying, you know what? I need to build my business on software. I need to digitally transform. I need to become a software company because I am competing with new upstarts in the technology side and my customers want a transformed experience and in order to be agile and digitally forward, I need to start using software. So you start seeing enterprises using open source software very, very prevalently. I ran the open source program office at Comcast so you can imagine capital one has one, target has an open source program office, fidelity and so on and so forth. So what I'm trying to say is you're seeing industry after industry, organization after organization starts using open source, including open data, open hardware and new industries like energy and you start seeing agriculture, healthcare and we are building so much on open source. Our digital infrastructure is built on open source. So there's so much writing on open source today and so the protection of all this is so, so critical today and open source in governments is such a thing because governments are realizing they need to transform digitally but they also need to be transparent and working much more closely with their citizens and also develop industry in their country and encourage innovation, et cetera. So all this comes up to say success and ubiquity of open source comes with responsibilities because now so much of the world depends upon us and is built upon open source. It is so incumbent upon us to make sure that that trust is not betrayed and that faith is supported in what we do. It means that we need to grow up. It means that we need to find a way to secure open source and especially in the last decade or so there's been so many, many issues with Heartbleed and Log4j, et cetera, that have really kind of created an alarm for us to say, I think we need to work better at this. You know, unlike working with one monolithic maybe blob of code from a proprietary company where you can hold them accountable, it's a whole different ballgame in open source. And so what happens with open source suppliers now we have suppliers who are diverse. There's like, I don't know, millions of projects that we consume, big and smalls. Some have maybe great security posture, some don't. Some are well maintained, some don't. So you really don't have a standard in terms of how you work with them, how you maintain them, et cetera. And most of the open source projects were started with solving a technical problem in mind. So most of the maintainers tend to be innovators and problem solvers, but they're not security folks, they're not documenters, they're not community leaders and they need the help. They need to know how to do it right. And many lack security training. Frankly, security and open source groups used to be separate in companies and in life, so they were different disciplines and so they often never talked. And maintainer burnout is also real. A lot of maintainers are saying I can do this much anymore. And so I need help. I need help to maintain the software, I need help to do it right and I can do it alone. And then you see the supply side which is our users like us. You start seeing us using open source more and more for mission critical things. I mean, company, businesses and infrastructure like energy grids are built on open source. These are pretty mission critical things. They've gone other days when people would say, I think I'll do something light on open source and for production I'll use proprietary. And developers, I feel a lot of new developers take open source for granted because it's just there. It's used by everybody. You see it as a de facto standard. You say, oh, it's already vetted by so many people. I don't need to do my due diligence. I can just download it. I don't need to know the license. I don't need to know the health. I'll just use it. And a lot of dependency management tools are also pretty lacking and need a lot more development. You can see your direct dependencies, but you may not be able to see all of your transitive dependencies and then how to deal with it, which ones to deal with. And I know there's a lot of work going on with our sponsors and others to improve this, but it's still a challenge. And a lot of companies still struggle with how do we work with upstream? How much should we invest in upstream? Do we really need to put some people directly involved in upstream? And do we develop a policy around open source security? And upstream especially is one hard thing to solve inside a company because we're really created to serve customers and customer problems always come first and you think, you don't see the long-term positive benefits of contributing to open source because it's an indirect benefit. And so you kind of don't see why you need to have a seat at the table or give back to community and make sure that it's sustained and make sure that you are improving it. And that needs to change. So you can see a mismatch between open source, how open source suppliers work and how open source users work. And this needs to come together. So I want to end by saying that's why history has taught us, as you can see, throughout the adoption of open source in various industries and various phases of open source, collaboration has always come to the rescue, collaboration of industries coming together, companies coming together, foundations kind of acting as a neutral home, if you will, for people to come together and solve big, big problems. And what's also interesting with the open source security issue is that the government is now getting involved as well. And they're saying, we need this to change because it is a nation at risk and it is infrastructure at risk and it's citizens at risk. And so I think collaboration is coming to the rescue again, open collaboration to help us all come together to solve this big initiative that we have in front of us of securing open source. So I know I have five minutes left, but I am done and I'm gonna hand it off to Brian so that Brian can really take you into the practicalities of how we do this. Thank you. Thank you.


What's up everybody, Jason here for JasperJasperH.com. We just sold out 19-20 cup hockey six box case break picker teams number 19, R&B for the Devils, Hawks, Maple Leafs, and Knights. And again, you get a piece of these teams for a fraction of the price. And of course, everyone gets a number from 0-3-9. First number on the left side of the zero number dash. So we go by. Good luck, here's a dash. Oh, there's the customer names and the numbers. Let's roll it. And we got ourselves a five and a three eight times. One, two, three, four, five, six, seven, eight. Eight times. David at the top down a PJ. One, four, five, six, seven, eight times. Three down a six. All right, David with three as well as one. Any one of ones for these teams will be yours. PJ with five, two, my tower with eight. PJ with nine, four, seven, zero, and six. Any and all reductions for these teams as well. So I appreciate you guys. One more R&B to do, which is for the 11 teams in the break. And then we'll start the cup hockey break, guys. Appreciate it, guys. And good luck.


So, I read a lot of articles, I read a lot of articles every day. And one of the things I can do is sometimes create some ice-train and just the difficulty in pouring through all the articles and scrolling back and forth. There's actually an easier way and this is the thing I've been using for a long time. There's been a few different companies that have created software like this and this one has been one I've liked for a while called Sprint Reader Speed Reading Extension. And it kind of helps you narrow down and focus. So, right here, here's an article that says there's a subtle difference between a businessman and an entrepreneur. And all they did is I usually start at the bottom and I highlighted the article. And then you right-click on the highlighted text and the plugin's already installed, so we're going to say Sprint Read Selected Text. Now you can actually read this things out of the clipboard, you can read other files, it's pretty straightforward. The control's really simple, play, reset, step forward, and you control it from the keyboard. Now I always start it off at a slower rate. So it starts out at 300 words per minute, which is below most people's with the average person probably reads. I think people read closer to about 600 words a minute or so. Kind of depends on how much reading you do. But watch how this makes you focus because one, I don't have a bunch of distractions or ads and things around me. I'm now focused directly on the article and it's going to pick a highlight. And this highlight is where you focus your eyes. So now I don't have to scroll and watch it play. So you can see you've become more focused on it and you're just reading.  Well, this is where the keyboard controls, Z and X speed it up and you can speed it up until I get where I want it for depending on the technical details of the article. So right here, say a thousand words a minute. And you can see this is still pretty readable. I read like this a lot and as you get used to it because it's obviously new if you haven't done this before, it takes a little bit of time. But once you've done this for a little while, you get very like focused on it. And this lets you read stories so much better. I've been trying to find a good one for the phone too and I got to do some playing around with them. I like reading on the phone, but scrolling on the phone becomes a problem. So this concept works really well for that. But you can choose the background colors, you can choose the screen, you can pause it and you can look down at the bottom and see where you're at as highlighting the word. It also has the option so you can step forward and backwards. So we can step backwards and forwards through some of the words. This is actually nice because I'll jump backwards a little bit when I'm reading something overly technical and it'll have something in it. I need to understand these statistics better. For example, it doesn't do well with large number sets, but for doing just the reading of the article, it's such a great way to push words into my head as I put it. You know, I can just go through and read it. If I want to reread the same article again to get a better understanding, I can just hit R and play it back over again and watch it. The plug-in's free. It's really handy for reading articles. I think it helps me focus better on the article and the content of the article without all the distractions of all the graphics and flashing ads that are around it. So definitely a great tool. It's free. I'll put a link. It's available for Google Chrome. I think they have it available for Firefox as well. So free browser plug-in and helps you read and focus better and push more information in your head at maybe even a faster rate. Try the time. Let me know how fast you get with this, but like I said, the 1,000 is pretty comfortable for me on it, reading a lot of articles sometimes a little bit faster. Thanks. Oh, if you like the content here, like and subscribe.


Section 13 of Persian Self-Tought in Roman Characters with English Phonetic Pronunciation by Sheikh Hassan This is a LibriVox recording. All LibriVox recordings are in the public domain. For more information or to volunteer, please visit LibriVox.org Recording by Nicholas James Bridgewater Persian Self-Tought in Roman Characters with English Phonetic Pronunciation by Sheikh Hassan Section 13 The Verb In the Persian verb, there is only one conjugation. All verbs are formed from two principal parts, the infinitive and imperative. To these, the same terminations are added for all verbs. The auxiliary verb, to be, has infinitive, imperative, but some tenses are derived from the radical, and in this respect are slightly irregular. To be, to be, indicative, present. I am Thou art He, she, it is, asked We are You are They are Present, stronger form I am Thou art He, she, it is, asked We are You are They are Present, negative I am not Thou art not He, she, it is not We are not You are not Preterate I was Thou wast He, she, it was We were You were They were Imperfect I used to be Thou did used to be He, she, it used to be We used to be You used to be They used to be Perfect I have been I have been Thou hast been He has been We have been You have been They have been Perfect I had been Thou had been Thou had been He had been We had been You had been They had been Future Future formed with the auxiliary verb And the shortened infinitive I shall be Thou wilt be He will be We shall be You will be They will be To be Past participle Be Thou Be Thou For the other persons subjunctive present is used So for all verbs Subjunctive present I may be Thou mayst be He may be We may be You may be They may be Imperfect I might be Thou mightst be He might be We might be You might be They might be For the pluperfect Boud Or Or May be used Boud May he, she or it be Is used as obstative to express a wish Example May your life be long Or Or May your life be long Or The auxiliary verb To become is regular And therefore forms its present indicative and subjunctive from the imperative show But the other tenses from shortened infinitive Should The final verb in shore is pronounced as V before a vowel Example Shavam Indicative present I become I become Thou mayst Thou mayst He, she, it becomes Mishavad He, she, it becomes Mishavad We become You become Mishavad Preterate I became I became Thou didst become He became We became You became They became Imperfect I became That it's become He became We became You became They became Perfect I have become Thou hast become He has become We have become You have become They have become Perfect I had become Thou hast become He had become We had become You had become They had become Future I shall become Thou shall become He shall become We shall become You shall become They shall become Infinitive to become Present participle becoming Very seldom used Past participle become Gerund, what ought to become Subjunctive present I may become Thou hast become He may become We may become You may become They may become Imperfect and pluperfect the same as in the indicative, and so for all verbs. The regular verb. All infinitives end in Dan or Tan. To conjugate a verb, we must know it's infinitive and imperative. The imperative of verb, whose infinitives end in Dan, are formed by cutting off this termination. Example To present imperative Present. From the imperative are formed By prefixing me and adding the termination E, add, etc. Example To the present subjunctive by prefixing All other tenses are formed from the shortened infinitive Paradigm of the regular verb In colloquial Persian it is usual to prefix bear before the imperative as Begir, etc. In colloquial Persian it is usual to prefix bear before the imperative as Begir, etc. In indicative present, I take Migiram, migiram, I take Migiram, thou takest Migiri, migiri, thou takest Migiri, he takes Migirad, migirad, he takes Migirad, we take Migirim, migirim, we take Migirim, you take Migirid, migirid, you take Migirid, they take Migirand, migirand, they take Migirand Preterite, I took Gerefdam, gerefdam, I took Gerefdam, thou didst Gerefde, Gerefde, thou didst Gerefde, he took Gerefdad, gerefdad, he took Gerefdad, we took Gerefdaim, gerefdaim, we took Gerefdaim, you took Gerefdead, gerefdead Gerefdead, They took They took. Getф standalone. anesthepl GetFdam imperfect aitook or i used to take Why get if dам I get EF誰 I took or I use to take me get f famprobes take me get vest e maybe giv d It's take me get FDP He took meet G tribe miniاف다 he took migreth dad we took migreth deem migreth deem we took migreth deem you took migreth deed migreth deed you took migreth deed they took migreth dand migreth dand they took me Gerefdand. Perfect. I have taken. Gerefde am, Gerefde am. I have taken. Gerefde am. Thou hast taken. Gerefde i, Gerefde i. Thou hast taken. Gerefde i he has taken. Gerefde haste, Gerefde haste. He has taken. Gerefde haste. We have taken. Gerefde aim, Gerefde aim. We have taken. Gerefde aim. You have taken. get if they eat, you have taken, get if they eat they have taken, get if they end, get if they end they have taken, get if they end Plough perfect I had taken, get if they put down He had taken. He had taken. He had taken. He had taken. He had taken. He had taken. He had taken. He had taken. He had taken. He had taken. He had taken. He had taken. He had taken. He had taken. had taken, gerefte buddid, they had taken, gerefte buddand, gerefte buddand, they had taken, gerefte buddand, future, I shall take, khaw ham gereft, khaw ham gereft, I shall take, khaw ham gereft, thou shalt take, khaw hi gereft, khaw hi gereft, thou shalt take, khaw hi gereft, he shall take, khaw had gereft, khaw had gereft, he shall take, khaw had gereft, we shall take, khaw him gereft, khaw him gereft, we shall take, khaw him You shall take. Chalhid Gereft, Chalhid Gereft. You shall take. Chalhid Gereft. They shall take. Chalhand Gereft, Chalhand Gereft. They shall take. Chalhand Gereft. Infinitive to take. Gereftan, Gereftan. To take, Gereftan. Present participle. One who is talking. talking. Girande. Girande. One who is talking. Girande. Past participle. Taken. Gerefte. Gerefte. Taken. Gerefte. Gerund. What must be taken? Gereftani. Gereftani. What must be taken? Gereftani. End of section 13. Recording by Nicholas James Bridgewater. Recorded in Amman, Jordan. Section 14 of Persian Self-Tort in Roman characters with English phonetic pronunciation by Sheikh Hassan. This is a LibriVox recording. All LibriVox recordings are in the public domain. For more information or to volunteer, please visit LibriVox.org. Recording by Nicholas James Bridgewater. Persian Self-Tort in Roman characters with English phonetic pronunciation by Sheikh Hassan. Section 14. Prepositions. Space will not allow us to give in detail this part of Persian grammar, but the following list gives a few of the most important. At. Dar. Dar. At. Dar. At the church. Dar keli saw. Dar keli saw. At the church. Dar keli saw. In. To. To. In. To. In or into the room. To ye o talk. To ye o talk. In or into the room. To ye o talk. To. Be. Be. To. Be. To. Everybody. Behame kass. Behame kass. To. Everybody. Behame kass. On. Ruh. Ruh. On. Ruh. On or at the table. Ruhye mees. Ruhye mees. On or at the table. Ruhye mees. On. Sar, sar, on, sar. On the fire, sar-e-o-tash, sar-e-o-tash, on the fire, sar-e-o-tash, with, bo, bo, with, bo. Come with me, bo-man-b-o, bo-man-b-o. Come with me, bo-man-b-o, bo-man-b-o, bo-man-b-o. Without b, b, without b, without trouble, b-zah-mat, b-zah-mat, without trouble, b-zah-mat, from, az, az, from, az. I came from Tabriz, az Tabriz-a-madam, az Tabriz-a-madam. I came from Tabriz, az Tabriz-a-madam. Tu, ta, ta, tu, ta. It is far from Tehran to Rasht, az Tehran ta Rasht dur ast. Az Tehran ta Rasht dur ast. It is far from Tehran to Rasht, az Tehran ta Rasht dur ast. Up to, ta-be, ta-be, up to, ta-be. Until we may see, ta-bebinim, ta-bebinim. Until we may see, ta-bebinim. Bottom, po-in, po-in, bottom, po-in, at the bottom of the street, po-ine-kuche, po-ine-kuche, at the bottom of the street, po-ine-kuche. Upon, bo-lo, bo-lo, upon, bo-lo, upon the tree, bo-lo'e de-raht, bo-lo'e de-raht. Outside, bi-roon, bi-roon, outside, bi-roon, outside the town, bi-roon e-shaar, bi-roon e-shaar, outside the town, bi-roon e-shaar. 4. Baroye, Baroye, 4. Baroye, I sent for you. Baroye to feris dodam, Baroye to feris dodam, I sent for you. Baroye to feris dodam. Conjunctions. For these, see vocabulary pages 50 to 52. Adverbs. There are no adverbs properly so called in Persian. They're places taken by nouns with or without prepositions or by adjectives. Examples. Without reason. Be sabab. Be sabab. Without reason. Be sabab. Well. Be chubi. Be chubi. Well. Be chubi. Safely. Or sudagi. Or sudagi. Safely. Or sudagi. Arabic adverbs with terminations and are also used. Examples. Approximately. Tachminan. Approximately. Certainly. Mostly. End of section 14. Recording by Nicholas James Bridgewater. Recorded in Amman Jordan. Section 15. Of Persian self-taught in Roman characters with English phonetic pronunciation by Sheikh Hassan. This is a LibriVox recording. All LibriVox recordings are in the public domain. For more information or to volunteer, please visit LibriVox.org. Recording by Nicholas James Bridgewater. Persian self-taught in Roman characters with English phonetic pronunciation by Sheikh Hassan. Section 15. Conversational phrases and sentences. Useful and necessary idiomatic expressions. Kalemat vajomlehoyemostaleh mofidvalazem. Good morning. Good afternoon. Good day. Good evening. Salamun alaykum. Salamun alaykum. Good morning. Good afternoon. Good day. Good evening. Salamun alaykum. i.e. peace be upon you. This salutation is used on all occasions. The answer is. alaykumossalam. alaykumossalam. i.e. upon you be peace. alaykumossalam. How do you do? ahvaleshomachetorast. ahvaleshomachetorast. How do you do? ahvaleshomachetorast. i.e. how is your condition to inferior? How are you? ahvaleshomachetorast. ahvaleshomachetorast. How are you? ahvaleshomachetorast. i.e. how is your august or sacred health to superior? Good night to one going to bed. shabeshomabikhayr. shabeshomabikhayr. Good night to one going to bed. shabeshomabikhayr. i.e. your night be blessed. Thank you. Answer to. How are you? Alhamdulillah. Alhamdulillah. Thank you. Answer to. How are you? Alhamdulillah. i.e. thanks to God. Thank you. Thank you very much. Lotfeshomaziyad. Lotfeshomaziyad. Thank you. Thank you very much. Lotfeshomaziyad. No. Thank you. Na. Lotfeshomaziyad. Na. Lotfeshomaziyad. No. Thank you. Na. Lotfeshomaziyad. Don't mention it. Zahmatinist. Zahmatinist. Don't mention it. Zahmatinist. i.e. it is no trouble. Thanks for your trouble. Besyar zahmat keshidid. Thanks for your trouble. Besyar zahmat keshidid. It does not matter. Zarari nadorad. Zarari nadorad. It does not matter. Zarari nadorad. I can scarcely ask. Asking a favor. Agar che hejolat mikesham. Agar che hejolat mikesham. I can scarcely ask. Asking a favor. Agar che hejolat mikesham. Please do. Befarmaid. Befarmaid. Please do. Befarmaid. You are quite indeed welcome. Cheili khosha madid. Cheili khosha madid. You are quite indeed welcome. Cheili khosha madid. Do just as you please. Haar tor mail dorid. Haar tor mail dorid. Do just as you please. Haar tor mail dorid. You are quite right. Rost mi farmaid. Rost mi farmaid. You are quite right. Rost mi farmaid. Or haq dorid. Haq dorid. You are quite right. Haq dorid. Take as much as you please. Befarmaid. Haar qad mail dorid. Befarmaid. Haar qad mail dorid. Take as much as you please. Befarmaid. Haar qad mail dorid. It is lucky that. Asbabe khosbaqtist ke. Asbabe khosbaqtist ke. It is lucky that. Asbabe khosbaqtist ke. As you know. Che nan che mi donid. Che nan che mi donid. As you know. Che nan che mi donid. It must be done. Boyad beshavad. Boyad beshavad. It must be done. Boyad beshavad. What are you doing? Che mi koni. Che mi koni. What are you doing? Che mi koni. To inferior. What are you doing? Che mi konid. Che mi konid. What are you doing? Che mi koni superior. Please come back soon. Lotfanzud bargad. Lotfanzud bargad. Please come back soon. Lotfanzud bargad. Come here. Biayinja. Biayinja. Come here. Biayinja. I have come on business. Man baraye kar amadam. Man baraye kar amadam. I have come on business. Man baraye kar amadam. When will you come? Kei khahid amad. Kei khahid amad. When will you come? Kei khahid amad. Will you come? Kei khahid amad. Will you come? Kei khahid amad. Where are you going? Koja miravid. Koja miravid. Where are you going? Koja miravid. Go and look. Bero negah kon. Bero negah kon. Go and look. Bero negah kon. Bero negah kon. What is the matter? Che khabarast. Che khabarast. What is the matter? Che khabarast. Or, che shod. Che shod. What is the matter? Che shod. As soon as possible. Har qard zood ke momken ast. Har qard zood ke momken ast. As soon as possible. Har qard zood ke momken ast. Oh no, that is not so. Nah, che ni niest. Nah, che ni niest. Oh no, that is not so. Nah, che ni niest. If possible. Agar momken ast. Agar momken ast. If possible. Agar momken ast. What does this mean? In janice. In janice. What does this mean? In janice. I don't know. Man nemidonam. Man nemidonam. I don't know. Man nemidonam. I don't quite understand. Man dorost nemifahmam. Man dorost nemifahmam. I don't quite understand. Man dorost nemifahmam. I haven't the slightest idea. Man heech nemifahmam. Man heech nemifahmam. I haven't the slightest idea. Man heech nemifahmam. I have never seen it. Man uro heech nadidam. Man uro heech nadidam. I have never seen it. Man uro heech nadidam. I haven't seen you for a long time. Mordatis chomara nadidam. Mordatis chomara nadidam. I haven't seen you for a long time. Mordatis chomara nadidam. Who said so? Qui goft? Who said so? Qui goft? I will speak to him about it. I will speak to him about it. He won't hear of it. He won't hear of it. He cannot be relied on. Beu khatere jam nemitavan shod. Beu khatere jam nemitavan shod. He cannot be relied on. Beu khatere jam nemitavan shod. He is honest and hardworking. He is honest and hard-working. He is honest and hard-working. You must not do that. You must not do that. Do not tell anyone. I will do my best. I can do without it. It will do afterwards. It will do at your leisure. Be careful. I will bring it for you. I will bring it for you. Do you want this? I don't want that. I want an answer. What is your answer? Wait a minute. Do you like this? I like it very much. How do you like it? Kindly let me know. You are very fortunate. You need not be anxious. It is too difficult. It is well done. What is your address? My address is... Give me his address. He lives in... Give me his address. Give me his address. Do you speak English? I am English. I don't understand. I can speak a little Persian. I had not time. I really cannot wait. Where have you been? Is it convenient tomorrow? I will return it tomorrow. Please let me know immediately. He has already left. He probably does not know. This article wears well, clothes. This article wears well, things. Are there any bookshops in? Are there any bookshops in? I inquired but there was none. I see you for the first time. Where are you going? Come here. I have something for you to do. Come here. I have something for you to do. May I ask who you are? When do you return to Europe? Is anything the matter with you? Or, are you ill? Who said so? Please wait. You come as well. I am told he returned yesterday. That is the important point. In that case, it is alright. There is no help for it. It is extremely inconvenient. You must be careful. I did not break it. Under what circumstances? Under what circumstances? What is the reason? There is no mistake about it. You were certainly to blame. What is your opinion? Please make a note of it. I say, Mr. Without ceremony, be taklif. Speak louder, please. Look. Is it not beautiful? It is indeed grand. Congratulations. End of section 15, recording by Nicholas James Bridgewater, recorded in Manchester, England. Section 16 of Persian self-taught in Roman characters with English phonetic pronunciation by Sheikh Hassan. This is a LibriVox recording. All LibriVox recordings are in the public domain. For more information or to volunteer, please visit LibriVox.org, recording by Nicholas James Bridgewater. Persian self-taught in Roman characters with English phonetic pronunciation by Sheikh Hassan. Expressions of emotion, etc. What? Indeed, haqiqatan. Stuff, nonsense, bihude. Stuff, nonsense, bihude. What a statement. What a statement. That is impossible. That cannot be. You surprise me. Take care. Or, bepal. My fault, taqsireman. Oh, it is nothing. I am sorry. I am sorry, asking pardon. What a pity. Or, afsus. It is a sad thing. It is a sad thing.  Or, abnist. I am very glad. Bravo. Well done. How fortunate. How excellent. I am very angry. Hold your tongue. Don't answer. All right. Oh dear, oh dear. Be off, gumsho. Or, jahanamsho. It is no good. Get out, borogumsho. My dear, azizeman. Help, emdod. Mercy, aman. End of section 16. Recorded by Nicholas James Bridgewater. Recorded in Manchester, England. Section 17 of Persian self-taught in Roman characters with English phonetic pronunciation by Sheikh Hassan. This is a LibriVox recording. All LibriVox recordings are in the public domain. For more information or to volunteer, please visit LibriVox.org. Recorded by Nicholas James Bridgewater. Persian self-taught in Roman characters with English phonetic pronunciation by Sheikh Hassan. Section 17. End queries. So a lot. What do you say to superior? Ceh mi far moid. What do you say to superior? Ceh mi far moid. What do you say to inferior? Ceh mi gui. What do you say to inferior? Ceh mi gui. Do you hear me? Oya mi senavid. Do you hear me? Oya mi senavid. Will you kindly? Will you kindly? Do you understand me? Oya mi fahmid. Do you understand me? What does that mean? In yani ceh. What does that mean? In yani ceh. What is that? In ceh ciiz ast. What is that? In ceh ciiz ast. Can you speak English? Englisi mi tavonid harf bezhanid. Can you speak English? Englisi mi tavonid harf bezhanid. Do you know, mister? Oga ra mi senavid. Do you know, mister? Oga ra mi senavid. Where is the kojost? What do they call that in Persian? Inro dar farsi ceh mi guyand. What do they call that in Persian? What is that good for? In baraye ceh chub ast. Who is that? O kist. Who are you? Shoma kistid. Who is there? Anja kist. Or kist anja. What do you want? Ceh mi khahid. What have you got? Ceh gereftid. What is the matter? Ceh khabarast. Or ce shod. Can you tell me mi tavonid beman beguid? Why is that? Who sent you? What is your name? No mi shoma kist. Why did you go? Ceh reftid. End of section 17, recording by Nicholas James Bridgewater, recorded in Manchester, England. Section 18 of Persian self-taught in Roman characters with English phonetic pronunciation by Sheikh Hassan. This is a LibriVox recording. All LibriVox recordings are in the public domain. For more information or to volunteer, please visit LibriVox.org. Recording by Nicholas James Bridgewater. Persian self-taught in Roman characters with English phonetic pronunciation by Sheikh Hassan. Section 18. The Railway. Or shaman da fer. For vocabulary, see page 31. To the station. To the station. To the station. Here is my luggage. Here is my luggage. Take it to the cloakroom. Take it to the cloakroom. Take it to the cloakroom. I wish to register my luggage for. I wish to register my luggage for. I wish to register my luggage for. The luggage is overweight. The luggage is overweight. Get my luggage. Get my luggage. Here is the ticket. Here is the ticket. How many packages are there? How many packages are there? How many packages are there? Where is the waiting room? Where is the waiting room? Where is the booking office? Where is the refreshment room? Where is the refreshment room? Where is the lavatory? Where is the lavatory? Where is the lavatory? Where is the train for? Where is the train for? Are you going by the express? Are you going by the express? Show me a timetable. Show me a timetable. Show me a timetable. When does the train start? When does the train start? Can I book through two? Can I book through two? A first-class single ticket to A first-class single ticket to A second-class single ticket to A second-class single ticket to Return ticket. Return ticket. How much is it? We want a sleeping carriage. We want a sleeping carriage. A corridor carriage. A corridor carriage for ladies. A corridor carriage for ladies. A non-smoking compartment. A non-smoking compartment. Is this the train for? Do I change anywhere? Do I change anywhere? Do I change anywhere? Where must I change for? Where must I change for? Is this seat engaged? Is this seat engaged? There is no room. There is no room. Call the God. Call the God. The train is just going to start. The train is just going to start. Open the door. Open the door. Open the door. Open the window. Here is the station. Here is the station. Here is the station. Do we stop here? Do we stop here? Do we light here? Do we light here? Do we change carriages here? Do we change carriages here? How long do we stop here? How long do we stop here? Five minutes. Five minutes. My luggage is lost. My luggage is lost. When it arrives, forward it onto. When it arrives, forward it onto. Give me your ticket to Superior. Give me your ticket to Inferior. Give me your ticket to Inferior. Here it is to Superior. Here it is to Superior. Here it is to Inferior. Here it is to Inferior. End of section 18, recording by Nicholas James Bridgewater, recorded in Manchester, England. Section 19 of Persian self-taught in Roman characters with English phonetic pronunciation by Sheikh Hassan. This is a LibriVox recording. All LibriVox recordings are in the public domain. For more information or to volunteer, please visit LibriVox.org. This is a LibriVox recording by Nicholas James Bridgewater. Persian self-taught in Roman characters with English phonetic pronunciation by Sheikh Hassan. Section 19. I am going by boat. I am going by boat. I am going by boat. Man bokeshti miravam. I'm going by boat. Man bokeshti miravam. When do we start? Kei khahim raft. Send a steward to me. Man bokeshti miravam. Send a steward to me. Man bokeshti miravam. What is the first cabin fare? Kei khahim raft. What is the first cabin fare? Kei khahim raft. Please show me my birth. Man bokeshti miravam. Please show me my birth. Man bokeshti miravam. Is this birth taken? Oya injaroh gerefteand. Is this birth taken? Oya injaroh gerefteand. Where is the captain? Na khodoh kojost. Where is the captain? Na khodoh kojost. I will engage the whole cabin. Man tamame inotagra keraye mikhaham bekonam. I will engage the whole cabin. Man tamame inotagra keraye mikhaham bekonam. Put my bag in the cabin. Kei fe mara bokzar beotag. Put my bag in the cabin. Kei fe mara bokzar beotag. Have you your passport? Oya tazkere darid. Have you your passport? Oya tazkere darid. What is the number of your cabin? Nomre yeotageshoma cheest. What is the number of your cabin? Nomre yeotageshoma cheest. I am in number three. Man dar nomre sehastam. I am in number three. Man dar nomre sehastam. Will it be rough? Oya kulok hohad shod. The sea is rough. Dar yeo kulok ast. The sea is rough. Dar yeo kulok ast. Is she a good boat? Oya keshdeye chubist. Shall you stay on deck? Oya shoma balay satheye keshdi khahidistad. No, I shall go below. No, I shall go below. I feel very sick. Ahvolam besior badast. I am going below to my cabin. Man mirevam payin beotage chodam. I am going below to my cabin. Man mirevam payin beotage chodam. Can we have dinner on board? Oya dar keshdey shom midehand. Can we have dinner on board? Oya dar keshdey shom midehand.  Pangerero beband. Pangerero beband.  Pangerero beband. Open the pothole. Pangerero boscon. Open the pothole. Pangerero boscon. Here, boatman. In goyek chiest. How much to take me ashore? How much to take me ashore? How much to take me ashore? How much to take me ashore? Please let me see your passport. Lotvan tazkereye chodro bedehid. Please let me see your passport. Lotvan tazkereye chodro bedehid. End of section 19. Recorded by Nicholas James Bridgewater. Recorded in Manchester, England. Section 20 of Persian self-taught in Roman characters with English phonetic pronunciation by Sheikh Hassan. This is a LibriVox recording. All LibriVox recordings are in the public domain. For more information or to volunteer, please visit LibriVox.org. Recording by Nicholas James Bridgewater. Persian self-taught in Roman characters with English phonetic pronunciation by Sheikh Hassan. Section 20. Arrival. Recid. Vorud. Here is my luggage. In ashore manast. In ashore manast. Here is my luggage. In ashore manast. Where is the custom house? Gomrok kojost. Gomrok kojost. Where is the custom house? Gomrok kojost. Bring that trunk to the custom house. In kifra birbe Gomrok. In kifra birbe Gomrok. Bring that trunk to the custom house. In kifra birbe Gomrok. Or in sandukra birbe Gomrok. In sandukra birbe Gomrok. Bring that trunk to the custom house. In sandukra birbe Gomrok. Have you anything to declare? Cheesy Gomrok ki dorid. Cheesy Gomrok ki dorid. Have you anything to declare? Cheesy Gomrok ki dorid. I have nothing dutiable. Cheesy Gomrok shodani nadaram. Cheesy Gomrok shodani nadaram. I have nothing dutiable. Cheesy Gomrok shodani nadaram. Please give me your keys. Lotfan ke lidra bedehid. Lotfan ke lidra bedehid. Please give me your keys. Lotfan ke lidra bedehid. Here are the keys. Inast ke lid. Inast ke lid. Here are the keys. Inast ke lid. Is there a bus? Oyo injo gorihast. Oyo injo gorihast. Is there a bus? Oyo injo gorihast. Call a carriage. Yek kaleske sedakon. Yek kaleske sedakon. Call a carriage. Yek kaleske sedakon. Or yek doroshke sedakon. Yek doroshke sedakon. Call a carriage. Yek doroshke sedakon. There is no carriage. Injo kaleske niest. Injo kaleske niest. What is the fare to the hotel? Ker o ye to hotel chandast. Ker o ye to hotel chandast. Tell the driver where to take me. Be kaleske chi begumaro koja boyad bebarad? Be kaleske chi begumaro koja boyad bebarad? Tell the driver where to take me.  Bring me a cushion What do you require sir? Give me some hot water Give me some hot water Clean my shoes I want to shave I want to shave Is there a barber about here? Send my boots to be repaired Send my boots to be repaired Let this linen go to the laundry My clothes are wet Please dry them Brush my clothes Are you going to change your clothes sir? Let us have the bill Let us have the bill Is the luggage ready? Is the luggage ready? Send the luggage to the station Take me to the station Send the luggage to the station Send the luggage to the station Send the luggage to the station End of section 21 Recorded in Manchester, England