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MECHANICAL DEVICES OF MECHATRONIC SYSTEMS
Comprehensive notes covering: -Mechanical part -Control system -Control subsystem -Drive -and Kinematic schemes
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cours vhdl échantillonné pour master 1 en électronique des systèmes embarqués
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It is about Introduction to Data Structures & Algorithms should be able to use it in your work or in your projects or in your courses or in your projects that you are doing in the future or in your work that you are doing right now so if you want to use something then you should be able to use it in your work or in your projects or in your courses or in your future work or in your projects that you are doing right now . So data structures and algorithms are things that help us in our work or in our projects or in our courses or in our future projects or in our work or in our projects that we are doing right now so if you want to use something then you should be able to use it in your work or in your projects or in your courses or in your future work or in your projects that you are doing right nowwater in a pot and you put the tea bag in the cup and you make the coffee Now what is the difference between data structures and algorithms? Algorithms are specific steps that need to be taken in order to solve a problem.I have been in the industry for a long time now and I have seen a lot of people learning C , C++ and when they start to learn it they get very lost & confused & they don't know what they are doing & they don't know how to use the language properly & eventually they stop learning it & they end up becoming a beginner again & that is not good for the industry & it is not good for the learners either so I would say learn C , C++ & don't get lost in the language learning process a break for a while I understand that you are very busy for the next few days and I understand that you are very busy for the next few weeks and I understand that you have a lot of questions so I 'll just stop now and I 'll come back to you in a few days and we will start the next video where we will talk about algorithms and data structures and then we will talk about interviews and then finally we will talk about the final exam and you can ask me any questions that you have in your mind so that 's all for now I 'll come back to you in a few days and we will start the next videowhat is the data structure of chrome so now we see that the data structure is called RAM so the data structure of a database is called RAM because when the program starts it will load into RAM the data structure of the database which is called RAM . And then you will get to hear this and it is asked in the interviews, "tell me, what is this?" So now let's understand database, data warehouse, and big data here we have understood the data structure that when your program starts in RAM, it will load into the random access memory.I want to keep the data in a different database so what I have to do is I have to create a new database and I have to name it `` legacyData '' I have to create a new table in that database and I have to add the following column in the table `` Birthday `` I have to add the following column in the table `` Year `` Now I have to run the program and I will get the following result in the console `` Facebook 2020 `` Facebook 2021 `` Facebook 2009 `` `` Facebook 2020 `` Facebook 2021 `` Facebook 2009 `` `` Facebook 2009 `` You retrieve and read the data from hard disk drive and update it. know these three terms because big data is what we are talking about here .memory location where the C program stores the data that is to be processed next and the heap is a memory location where the C program stores the data that is to be processed next but the data that is to be processed next is not always stored in the same location as the data that is to be processed next because the C program can keep data in different memory locations depending on the situation and this is why it is important for you to understand the stack and heap because you will be able to understand the C program better and be able to ask more questions on the C program. Alright ... I 'm talking about C program that 's why I say that you get a good picture of memory with the help of C programming therefore , data structures and algorithms is best learned from C and C++ Now here I am dividing it into segments so there is thing called code segment let 's say this is my code let 's say there is a code with the name `` harry.c '' now this code will be first loaded into my main memory I told you what is the first thing that happens ?then it will come to this line it will come to fun1 ( ) , it will execute fun1 ( ) . fun2 ( ) is calling inside fun1 ( ) now the variables that I had created inside it will be created here let 's say I have created `` k '' & `` l '' let me write here : initialize k and l and after that I 'm calling fun2 ( ) so as soon as fun2 ( ) will be called it will say to fun1 ( ) that you wait for a while , I call fun2 ( ) and be back by fetching the value whatever it will return fun1 ( ) replied that you go and get it call fun2 ( ) and get its value . me where do I give the milk to the milkman because the pointer will store the address of the malloced memory so the milkman will get the milk from the pointer and the pointer will stay there until you return the function or until you delete it.it will say to fun1 ( ) that you 're coming back and now you can start your execution again . If anyone asks you this question, that why heap is used, it can also be done from stack. So that thing is done here with the help of dynamic memory because when does the stack of a function end it ends when the function is returned. I just come back with the value you just wait here. You go and get it and then what will happen, fun function will be called. this video we will focus on linked list & binary search tree so if you want to know more about these then you should go ahead and watch the video and learn more about these things . If you still think that you want to do all of these things with Python, then I am saying that when you will go for an interview, the person in front of you will expect C, C++, or Java from you. If you don't know C properly, then I have made a 15-hour- video of C with notes. Time Complexity and Big O Notation So the input size didn't increase and the runtime of the algorithms didn't increase either .longNo , it doesn't depend on the size of the input . When we ask questions like as the input will increase, Then the runtime will change as per what? And after that Now you will go to aunty's house You will be treated. Consider there are different routes to come and go. is the algorithm that runs in constant time . K1 n to the power 0+k2+k3+k4 This time is required in algo 2 .The sentence is: Run time of it, there are some things that we will recite. Because we won't constantly use our brains again and again, as we see Big O of 1 it is constant. Now, come here and listen to another story. If we do an analysis of the first algorithm, If I do T algo1 Then what will happen here? And along with consider that game is of L3 kb. If the game is of N kb then how much time will you need? The sentence is: Run time of it, there are some things that we will recite.There are polynomial algorithms and there are exponential algorithms and there are logarithmic algorithms and there are exponential functions and there are logarithmic functions. There are also algorithms that are not linear in time. Asymptotic Notations: Big O, Big Omega and Big Theta Explained This passage discusses the complexity of an algorithm, which is measured in terms of the size of its big O graph. THe author states that the complexity of an algorithm is automatically O(n^5.), O(n^30), and O(n^100).& G ( n ) is intersecting with f ( n ). So you will get some complex function Alright so this is the solution to the problem So. What we have done is WE have taken a big function and we have made it so that it is always below the original function and that's what [UNK] means THe definition of [UNK] for a function. F(n) is the largest value of G(n) that is bigger than f(n).. Best Case, Worst Case and Average Case Analysis of an Algorithm K is an integer ) SO now I 'll write it like this K n ( K is an integer ) So now I 'll write it Like this K n ( K is an integer ) So now I 'll write it like this. K n ( K is an integer ) And now what will happen? The value of 'k ' will become very large And so 2n will be going down. The graph of ( n^2+n ) ; graph of n ; graph of ( N^2+n ) will go below 2n. THe Average Case Complexity for a given algorithm is the time it takes to run through all possible cases, divided by the total number of possibilities..?" This passage discusses Algo 2, which is a cunning person who is smart. Birbal. Algo 2 says that he will not make useless comparisons, and provides an example. An example of how he does this.. Algo 2 first takes the first and last element of an array, and then compares them. IF. They match, Algo 2 is good; if they don't match., Algo 2 will find the mid--point of the array and be okay.. The stack do at a particular time point?? SO. The stack will have a value of factorial 3 at a particular time point. SO it will go up to factorial 4 at that point in time, And then it will go back down to factorial 3. OKay? And that is how the space complexity works for a function when it calls itself recursively.. algorithm will take time X on a computer with processor Y. I can only say that the algorithm will take time y on a computer with processor X. SO. In this particular case,, my algorithm is running in 2 seconds on my computer with an i9 processor. But. It might run in 10 seconds on a computer with an i7 processor. SO that is why I say the space complexity is O ( N ). The passage. space complexity is O ( n ). The passage discusses the space complexity of the factorial function. IT states that the space complexity of the algorithm is O ( N ), where n is the size of the input.. THe space complexity is measured in stack frames, and it is observed that no matter how large an input's factorial, there will be a corresponding number of activation records.. This means that at any given time, the algorithm will be able to fit in a maximum of three stack frames.. This passage provides insights into the algorithm's computation complexity.. The algorithm calculates in 10 seconds, and as input grows, so does the time it takes to calculate. This is why the algorithm measures growth in terms of asymptotic analysis.. How to Calculate Time Complexity of an Algorithm + Solved Questions Before Solving Some Questions of Time Complexity I will tell you some tricks to get rid of time complexity. After that we will do some set of questions. which will make you a very good grasp in such questions. due to the time complexity of any algorithm when you have to find it so what is the first step that you do and at the same time how to approach this problem. In this way, whatever instructions are going on here , it is taking almost ( k ) time. We believe that these operations are all (k ) time consuming This for loop , that is , how much time is being taken for this fragment It seems ( kn ) , okay So before this ( int i ) would have been written here, ( int k=0 ) would be written here. The third technique that I want to tell you is this : That break the code into fragments. The first fragment turned out to be this one , with a little bit of initialization. It took constant time because it is not such that if the value of ( n ) increases, then its time will increases. I will go for ( n = 100 ) to determine whether i will be going for n = 1000. I will accept it in ( k4) and ( n * k4 ) I will do it in k4 and (n* k4), and it will happen O (n²) If you do it ( k=0 ) , ( k < n ) and if you look at it , it will come out O ( n²) Okay, it will not (N²) ok remember you this thing. There will be some code on it which will take ( k1 ) Now I have become so smart, by doing questions , and you will be done too That (k1) it is will going to be non-dominant , if constant is being added then we will remove it. So once the value of ( i ) will be zero (0) and then the value of ( j ) will run for. Then ( j=1) will become Then ( 0,2) Then (i=0 , and j=0 ) will then run for Okay. The value of ( i ) will be zero ( 0 ) for ( n ) times running then the value of (i ) will become ( 1 ) , it will run again (n ) times then it will go on till n. When ( n) is running out, watch carefully , watch very closely. Then later I will ask the question, then I am telling if it is not done. value of i will be ( 1) , ( n ), it will be n-1 because I am taking the index ( i=0 ) then (i=n-1) will be and here is ( n- 1 ) I told you guys If it 's not clear to you why it will work ( n² ) times So I 'd say let 's go look at it for 3 and 3 and print here (i , j ) and make a count variable and count it , how many times it is running You write ( c ) program , write in Python, write in Java, write in Python and write in the Java. But when there are 2 loops inside one , then that will run for n² times. And if another loop is given inside it , then it will run ( n³ ) times. If there is a double for loop, then it becomes straight (n² ) I have handpicked some questions which I am going to give to you guys here. And I have also given their programs to you. So you see here I have opened this folder in visual studio code. So it 's saying that Find the time complexity ( Func1 ) function in the program shown in program1. c as follows. Even if you come from another programming language nothing is going to be change. The time of (F1+F2+F3) will be that I will take as the overall time of the whole function. The time is not depending on Array 's length so i 'll accept it ( k1 ) and I can not accept ( F2 ) as ( k ) , i will accept it as k2 * n. So now if I find a total time complexity that is , if I T ( n ) come out , then what will it be ? T (n ) will be done as T (N) =F1 +F2 +F3. The answer is O (length) The algorithm it is talking in terms of length over here. The time complexity of the Func function in the program from program2. c as follows. Find the , find the , I have written fine here , let me correct it I wrote fine above also , find this man , this is find Never mind , these small mistakes are made. If you look carefully above it is written ( n ) times, if you write 1 for ( n times as ( 1+1+1 +1 + n) So what will happen if I write (n²) so it is done O ( n²) So anytime you see the double for loop , and it 's going to [ 0, n ] , [ 0 , n ] and [ 0 n ] i. e. for 0 , ( n) times it will run OK , so this will run 0 for (n) times, then 1 for n times then it will again 2 for n. times. And there is some constant work going on inside it so it means The average processing time is T ( n ) so T ( 6 ) is a small number. If I directly solve for T (6 ) I will try to solve the problem directly for T. only. And if this is a simple problem, then the expression of T (n ) can also come out.
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