But what would happen if the start bit could not be initiated? The PIC has a habit in some scenarios where the I2C bus can lock up and control cannot be restored. This means that your code will forever sit in that while loop. What's worse is that, even if you attempt to re-initiate the start bit, there is no chance that the bus will unlock itself. This is where the finite state machine really rocks! Let's take a look at a graphical layout of an I2C finite state machine that is to be included in our main program. Program as an FSM Firstly, there are two finite state machines! The first FSM is the main code that loops around, calling the three main functions. The second finite state machine is the I2C handler that can be in a number of possible states, the most important state being the idle state. The idle state is important because it will tell us that the I2C system is not doing anything—it is at that point we can ask it to send start bits, send a byte, receive a byte, or any other I2C related operation.
Incorrect combinations land us back in the initial locked state Knowing what we know, let's make a simple and basic programming example. Let's say we have our friend Mario in our favourite video game. So, Mario can do the following actions: Stand still Run Jump Duck We can see that Mario can do a lot of things and all of those things should be a specific state. Before writing any single line of code, we should ask ourselves how all of our different states fit together. We need to know exactly what Mario can do and how and when he can do these actions. For example, Mario can stand still then run when we push the corresponding button, but cannot run while he is jumping. So, an input that doesn't cause a change of state is ignored.
Traffic lights A simple traffic light system can be modeled with a Finite State Machine. Let's look at each core component and identify what it would be for traffics lights: States: a traffic light generally has three states: Red, Green and Yellow. Initial State: Green Accepting States: in the real world traffic lights run indefinitely, so there would be no accepting states for this model. Alphabet: positive integers representing seconds. Transitions: If we're in state Green and wait for 360 seconds (6 minutes), then we can go to state Yellow. If we're in state Yellow and wait 10 seconds, then we can go to state Red. If we're in state Red and wait 120 seconds, then we can go to state Green. This Finite State Machine can be drawn as follows: Enemy AI Finite State Machines allows us to map the flow of actions in a game's computer-controlled players. Let's say we were making an action game where guards patrol an area of the map. We can have a Finite State Machine with the following properties: States: For our simplistic shooter we can have: Patrol, Attack, Reload, Take Cover, and Deceased.
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Return a new transducer which can simultaneously process an input with the machines self and other where the output labels are \(d\) -tuples of the original output labels. INPUT: other - a finite state machine (if \(d=2\)) or a list (or other iterable) of \(d-1\) finite state machines only_accessible_components – If True (default), then the result is piped through accessible_components(). If no new_input_alphabet is given, it is determined by determine_alphabets(). OUTPUT: A transducer which can simultaneously process an input with self and the machine(s) in other. The set of states of the new transducer is the Cartesian product of the set of states of self and other. Let \((A_j, B_j, a_j, b_j)\) for \(j\in\{1, \ldots, d\}\) be transitions in the machines self and in other. Then there is a transition \(((A_1, \ldots, A_d), (B_1, \ldots, B_d), a, (b_1, \ldots, b_d))\) in the new transducer if \(a_1 = \cdots = a_d =: a\). EXAMPLES: sage: transducer1 = Transducer ([( 'A', 'A', 0, 0),.... : ( 'A', 'A', 1, 1)],.... : initial_states = [ 'A'],.... : final_states = [ 'A'],.... : determine_alphabets = True) sage: transducer2 = Transducer ([( 0, 1, 0, [ 'b', 'c']),.... : ( 0, 0, 1, 'b'),.... : ( 1, 1, 0, 'a')],.... : initial_states = [ 0],.... : final_states = [ 1],.... : determine_alphabets = True) sage: result = transducer1.
Now for your reward: a complete I2C finite state machine! All this needs is I2C_INIT() called at the start and then just call i2c_update() inside your main loop
handleInput(this, input);} void update() { (this);}} We could go a little further. We could use a Stack-Based FSM. With our previous solution, we have no concept of history. We know our current state, but we can't go back to the previous state. To solve this problem, we could use a Stack, which stores elements in LIFO style ( Last In, First Out), to save our different states. That means the current state is the one on the top of the Stack. Then, when we want to transition to a new state, we push that new state onto the Stack and this state becomes the current state. When we are done, we pop this state and the previous state becomes the current state. Of course, it is our responsibility to manage which state we want in the Stack and which we want to discard. Through this article, we saw what a Finite State Machine is. We saw that a Finite State Machine is a model of computation based on a hypothetical machine made of one or more states and only one single state of this machine can be active at the same time.
This is done manually in this case. deposit >= cost of coffee The FSM evaluates the amount of deposited cash either in a loop or when the amount changes (recommended in this case) If you deposit enough cash into the coffee machine, the FSM will go from 'Open' to 'ReadyToBuy'. ShutdownMachine The machine will automatically go from Open to PoweredOff through the ShutDownMachine method if the condition 'no more coffees left' is met. DispenseCoffee In the ReadyToBuy state the user can buy a coffee whereafter it will be brewed and dispensed. The condition is when the BuyCoffee event (! Link to observer pattern! ) fires. (not shown in diagram) CancelCoffee If the user opts to cancel, the machine will go from ReadyToBuy to Open. ShutDownMachine The machine will go to PoweredOff state States In every state there is defined behavior which will only be executed when the object is in that state. For instance, during PoweredOff the coffee machine won't brew coffee before it's powered on, during the Open state it will wait either until there's enough cash inserted, until the power down command is given, or until it runs out of coffee.
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The user experience in such cases is really bad Scalability: if some server is overwhelmed we can't easily scale out, as some users are tied to that particular server until the end of the session. Replicating sessions between servers is pretty complex A different approach is keeping the session in cookies on the client and/or in some datastore like Redis. Thanks to that we keep our servers stateless, facilitating load balancers to distribute requests efficiently. This example takes us through state between requests, but we could have state inside a single request. Let's see how OOP handles state. State and Behaviour in OOP Objects and Actors are responsible for keeping their own state. That encapsulation forces clients to interact with that state through exposed interfaces. State affects the object's behaviour as we can see in this example: class Account(var balance: Int, var overdraft: Int = 0) { def deposit(value: Int) = { balance = balance + value} def withdrawal(value: Int) = { val remaining = balance - value if (remaining < 0) { balance = 0 overdraft = overdraft + notifyAccountHolder(overdraft)} else { balance = remaining}}} We're swapping the behaviour of withdrawal depending on the state contained in balance.