![]() ![]() In 1948, Bell Labs scientist Claude Shannon developed similar statistical concepts of measuring microscopic uncertainty and multiplicity to the problem of random losses of information in telecommunication signals. ![]() He thereby introduced the concept of statistical disorder and probability distributions into a new field of thermodynamics, called statistical mechanics, and found the link between the microscopic interactions, which fluctuate about an average configuration, to the macroscopically observable behavior, in form of a simple logarithmic law, with a proportionality constant, the Boltzmann constant, that has become one of the defining universal constants for the modern International System of Units (SI). A consequence of the second law of thermodynamics is that certain processes are irreversible.Īustrian physicist Ludwig Boltzmann explained entropy as the measure of the number of possible microscopic arrangements or states of individual atoms and molecules of a system that comply with the macroscopic condition of the system. As a result, isolated systems evolve toward thermodynamic equilibrium, where the entropy is highest. Įntropy is central to the second law of thermodynamics, which states that the entropy of an isolated system left to spontaneous evolution cannot decrease with time. Referring to microscopic constitution and structure, in 1862, Clausius interpreted the concept as meaning disgregation. He initially described it as transformation-content, in German Verwandlungsinhalt, and later coined the term entropy from a Greek word for transformation. In 1865, German physicist Rudolf Clausius, one of the leading founders of the field of thermodynamics, defined it as the quotient of an infinitesimal amount of heat to the instantaneous temperature. The thermodynamic concept was referred to by Scottish scientist and engineer William Rankine in 1850 with the names thermodynamic function and heat-potential. It has found far-ranging applications in chemistry and physics, in biological systems and their relation to life, in cosmology, economics, sociology, weather science, climate change, and information systems including the transmission of information in telecommunication. The term and the concept are used in diverse fields, from classical thermodynamics, where it was first recognized, to the microscopic description of nature in statistical physics, and to the principles of information theory. p 2) = I( p 1) + I( p 2): the information learned from independent events is the sum of the information learned from each event.Entropy is a scientific concept, as well as a measurable physical property, that is most commonly associated with a state of disorder, randomness, or uncertainty.I(1) = 0: events that always occur do not communicate information.I( p) is monotonically decreasing in p: an increase in the probability of an event decreases the information from an observed event, and vice versa.The amount of information acquired due to the observation of event i follows from Shannon's solution of the fundamental properties of information: To understand the meaning of −Σ p i log( p i), first define an information function I in terms of an event i with probability p i. This ratio is called metric entropy and is a measure of the randomness of the information. : 14–15Įntropy can be normalized by dividing it by information length. The entropy is zero: each toss of the coin delivers no new information as the outcome of each coin toss is always certain. The extreme case is that of a double-headed coin that never comes up tails, or a double-tailed coin that never results in a head. Entropy, then, can only decrease from the value associated with uniform probability. Uniform probability yields maximum uncertainty and therefore maximum entropy. H ( X ) := − ∑ x ∈ X p ( x ) log p ( x ) = E, ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |