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The observer design pattern is a behavioural pattern listed among the 23 well-known "Gang of Four" design patterns that address recurring design challenges in order to design flexible and reusable object-oriented software, yielding objects that are easier to implement, change, test and reuse.
In computing, reactive programming is a declarative programming paradigm concerned with data streams and the propagation of change. With this paradigm, it is possible to express static (e.g., arrays) or dynamic (e.g., event emitters) data streams with ease, and also communicate that an inferred dependency within the associated execution model exists, which facilitates the automatic propagation ...
A lower data-centric publish-subscribe (DCPS) level that is targeted towards the efficient delivery of the proper information to the proper recipients. An optional higher data local reconstruction layer (DLRL), which allows for a simple integration of DDS into the application layer. Other related standards followed the initial core document.
Publish–subscribe is a sibling of the message queue paradigm, and is typically one part of a larger message-oriented middleware system. Most messaging systems support both the pub/sub and message queue models in their API ; e.g., Java Message Service (JMS).
A system is said to be observable if, for every possible evolution of state and control vectors, the current state can be estimated using only the information from outputs (physically, this generally corresponds to information obtained by sensors). In other words, one can determine the behavior of the entire system from the system's outputs.
Example side effects include modifying a non-local variable, a static local variable or a mutable argument passed by reference; raising errors or exceptions; performing I/O; or calling other functions with side-effects. [1] In the presence of side effects, a program's behaviour may depend on history; that is, the order of evaluation matters.
is observable, where , , and are, respectively, , , and matrices. One of the many ways one can achieve such goal is by the use of the Observability Gramian. Observability in LTI Systems
Figure 1. Probabilistic parameters of a hidden Markov model (example) X — states y — possible observations a — state transition probabilities b — output probabilities. In its discrete form, a hidden Markov process can be visualized as a generalization of the urn problem with replacement (where each item from the urn is returned to the original urn before the next step). [7]