This article is about digital signals in electronics. For digital data and systems, see Digital data. For digital signals that specifically represent analog waveforms, see Digital signal (signal processing). For other uses, see Digital signal (disambiguation).For broader coverage of this topic, see Signal.A binary signal, also known as a logic signal, is a digital signal with two distinguishable levels
A digital signal is a signal that represents data as a sequence of discrete values; at any given time it can only take on, at most, one of a finite number of values.[1][2][3] This contrasts with an analog signal, which represents continuous values; at any given time it represents a real number within an infinite set of values.
Simple digital signals represent information in discrete bands of levels. All levels within a band of values represent the same information state.[1] In most digital circuits, the signal can have two possible valid values; this is called a binary signal or logic signal.[4] They are represented by two voltage bands: one near a reference value (typically termed as ground or zero volts), and the other a value near the supply voltage. These correspond to the two values zero and one (or false and true) of the Boolean domain, so at any given time a binary signal represents one binary digit (bit). Because of this discretization, relatively small changes to the signal levels do not leave the discrete envelope, and as a result are ignored by signal state sensing circuitry. As a result, digital signals have noise immunity; electronic noise, provided it is not too great, will not affect digital circuits, whereas noise always degrades the operation of analog signals to some degree.[5]
Digital signals having more than two states are occasionally used; circuitry using such signals is called multivalued logic. For example, signals that can assume three possible states are called three-valued logic.
In a digital signal, the physical quantity representing the information may be a variable electric current or voltage, the intensity, phase or polarization of an optical or other electromagnetic field, acoustic pressure, the magnetization of a magnetic storage media, etcetera. Digital signals are used in all digital electronics, notably computing equipment and data transmission.
A received digital signal may be impaired by noise and distortions without necessarily affecting the digitsDefinitions
The term digital signal has related definitions in different contexts.
In digital electronicsA five level PAM digital signal
In digital electronics, a digital signal is a pulse amplitude modulated signal, i.e. a sequence of fixed-width electrical pulses or light pulses, each occupying one of a discrete number of levels of amplitude.[6][7] A special case is a logic signal or a binary signal, which varies between a low and a high signal level.
The pulse trains in digital circuits are typically generated by metal–oxide–semiconductor field-effect transistor (MOSFET) devices, due to their rapid on–off electronic switching speed and large-scale integration (LSI) capability.[8][9] In contrast, bipolar junction transistors more slowly generate signals resembling sine waves.[8]
In signal processingIn signal processing, a digital signal is an abstraction that is discrete in time and amplitude, meaning it only exists at certain time instants.Main article: Digital signal (signal processing)
In digital signal processing, a digital signal is a representation of a physical signal that is sampled and quantized. A digital signal is an abstraction that is discrete in time and amplitude. The signal's value only exists at regular time intervals, since only the values of the corresponding physical signal at those sampled moments are significant for further digital processing. The digital signal is a sequence of codes drawn from a finite set of values.[10] The digital signal may be stored, processed or transmitted physically as a pulse-code modulation (PCM) signal.
In communicationsA frequency-shift keying (FSK) signal is alternating between two waveforms and allows passband transmission. It is considered a means of digital data transmission.An AMI coded digital signal used in baseband transmission (line coding)
In digital communications, a digital signal is a continuous-time physical signal, alternating between a discrete number of waveforms,[3] representing a bitstream. The shape of the waveform depends on the transmission scheme, which may be either a line coding scheme allowing baseband transmission; or a digital modulation scheme, allowing passband transmission over long wires or over a limited radio frequency band. Such a carrier-modulated sine wave is considered a digital signal in literature on digital communications and data transmission,[11] but considered as a bit stream converted to an analog signal in specific cases where the signal will be carried over a system meant for analog communication, such as an analog telephone line.[12]
In communications, sources of interference are usually present, and noise is frequently a significant problem. The effects of interference are typically minimized by filtering off interfering signals as much as possible and by using data redundancy. The main advantages of digital signals for communications are often considered to be noise immunity, and the ability, in many cases such as with audio and video data, to use data compression to greatly decrease the bandwidth that is required on the communication media.
Logic voltage levelsA logic signal waveform: (1) low level, (2) high level, (3) rising edge, and (4) falling edge.Main article: Logic level
A waveform that switches representing the two states of a Boolean value (0 and 1, or low and high, or false and true) is referred to as a digital signal or logic signal or binary signal when it is interpreted in terms of only two possible digits.
The two states are usually represented by some measurement of an electrical property: Voltage is the most common, but current is used in some logic families. Two ranges of voltages are typically defined for each logic family, which are frequently not directly adjacent. The signal is low when in the low range and high when in the high range, and in between the two ranges the behavior can vary between different types of gates.
The clock signal is a special digital signal that is used to synchronize many digital circuits. The image shown can be considered the waveform of a clock signal. Logic changes are triggered either by the rising edge or the falling edge. The rising edge is the transition from a low voltage (level 1 in the diagram) to a high voltage (level 2). The falling edge is the transition from a high voltage to a low one.
Although in a highly simplified and idealized model of a digital circuit, we may wish for these transitions to occur instantaneously, no real-world circuit is purely resistive and therefore no circuit can instantly change voltage levels. This means that during a short, finite transition time the output may not properly reflect the input, and will not correspond to either a logically high or low voltage.
ModulationMain article: Modulation § Digital modulation methods
To create a digital signal, a signal must be modulated with a control signal to produce it. The simplest modulation, a type of unipolar encoding, is simply to switch on and off a DC signal so that high voltages represent a '1' and low voltages are '0'.
In digital radio schemes one or more carrier waves are amplitude, frequency or phase modulated by the control signal to produce a digital signal suitable for transmission.
Asymmetric Digital Subscriber Line (ADSL) over telephone wires, does not primarily use binary logic; the digital signals for individual carriers are modulated with different valued logics, depending on the Shannon capacity of the individual channel.
ClockingClocking digital signals through a clocked flip-flop
Digital signals may be sampled by a clock signal at regular intervals by passing the signal through a flip-flop. When this is done, the input is measured at the clock edge and the signal from that time. The signal is then held steady until the next clock. This process is the basis of synchronous logic.
Asynchronous logic also exists, which uses no single clock, and generally operates more quickly, and may use less power, but is significantly harder to design.
See alsoIntersymbol interferenceReferences Robert K. Dueck (2005). Digital Design with CPLD Applications and VHDL. Thomson/Delmar Learning. ISBN 1401840302. Archived from the original on 2017-12-17. Retrieved 2017-08-30. A digital representation can have only specific discrete values Proakis, John G.; Manolakis, Dimitris G. (2007-01-01). Digital Signal Processing. Pearson Prentice Hall. ISBN 9780131873742. Archived from the original on 2016-05-20. Retrieved 2015-09-22. Analogue and Digital Communication Techniques Archived 2017-12-17 at the Wayback Machine: "A digital signal is a complex waveform and can be defined as a discrete waveform having a finite set of levels" "Digital Signal". Archived from the original on 2016-03-04. Retrieved 2016-08-13. Horowitz, Paul; Hill, Winfield (1989). The Art Of Electronics, 2nd Ed. Cambridge University Press. pp. 471–473. ISBN 0521370957. B. SOMANATHAN NAIR (2002). Digital electronics and logic design. PHI Learning Pvt. Ltd. p. 289. ISBN 9788120319561. Digital signals are fixed-width pulses, which occupy only one of two levels of amplitude. Joseph Migga Kizza (2005). Computer Network Security. Springer Science & Business Media. ISBN 9780387204734. "Applying MOSFETs to Today's Power-Switching Designs". Electronic Design. 23 May 2016. Archived from the original on 10 August 2019. Retrieved 10 August 2019. 2000 Solved Problems in Digital Electronics. Tata McGraw-Hill Education. 2005. p. 151. ISBN 978-0-07-058831-8. Vinod Kumar Khanna (2009). Digital Signal Processing. S. Chand. p. 3. ISBN 9788121930956. A digital signal is a special form of discrete-time signal which is discrete in both time and amplitude, obtained by permitting each value (sample) of a discrete-time signal to acquire a finite set of values (quantization), assigning it a numerical symbol according to a code ... A digital signal is a sequence or list of numbers drawn from a finite set. J.S.Chitode, Communication Systems, 2008: "When a digital signal is transmitted over a long distance, it needs CW modulation." Fred Halsall, Computer Networking and the Internet: "In order to transmit a digital signal over an analog subscriber line, modulated transmission must be used; that is the electrical signal that represents the binary bit stream of the source (digital) output must first be converted to an analog signal that is compatible with a (telephony) speech signal."
m Wikipedia, the free encyclopediaFor other uses, see Information (disambiguation).CommunicationPortalHistoryGeneral aspectsCommunication theoryInformationSemioticsLanguageLogicSociologyFieldsDiscourse analysisLinguisticsMass communicationOrganizational communicationPragmaticsSemioticsSociolinguisticsDisciplinesPublic speakingDiscourseCultureArgumentationPersuasionResearchRhetoricMediaCategoriesOutlinevte
Information is an abstract concept that refers to something which has the power to inform. At the most fundamental level, it pertains to the interpretation (perhaps formally) of that which may be sensed, or their abstractions. Any natural process that is not completely random and any observable pattern in any medium can be said to convey some amount of information. Whereas digital signals and other data use discrete signs to convey information, other phenomena and artifacts such as analogue signals, poems, pictures, music or other sounds, and currents convey information in a more continuous form.[1] Information is not knowledge itself, but the meaning that may be derived from a representation through interpretation.[2]
The concept of information is relevant or connected to various concepts,[3] including constraint, communication, control, data, form, education, knowledge, meaning, understanding, mental stimuli, pattern, perception, proposition, representation, and entropy.
Information is often processed iteratively: Data available at one step are processed into information to be interpreted and processed at the next step. For example, in written text each symbol or letter conveys information relevant to the word it is part of, each word conveys information relevant to the phrase it is part of, each phrase conveys information relevant to the sentence it is part of, and so on until at the final step information is interpreted and becomes knowledge in a given domain. In a digital signal, bits may be interpreted into the symbols, letters, numbers, or structures that convey the information available at the next level up. The key characteristic of information is that it is subject to interpretation and processing.
The derivation of information from a signal or message may be thought of as the resolution of ambiguity or uncertainty that arises during the interpretation of patterns within the signal or message.[4]
Information may be structured as data. Redundant data can be compressed up to an optimal size, which is the theoretical limit of compression.
The information available through a collection of data may be derived by analysis. For example, a restaurant collects data from every customer order. That information may be analyzed to produce knowledge that is put to use when the business subsequently wants to identify the most popular or least popular dish.[citation needed]
Information can be transmitted in time, via data storage, and space, via communication and telecommunication.[5] Information is expressed either as the content of a message or through direct or indirect observation. That which is perceived can be construed as a message in its own right, and in that sense, all information is always conveyed as the content of a message.
Information can be encoded into various forms for transmission and interpretation (for example, information may be encoded into a sequence of signs, or transmitted via a signal). It can also be encrypted for safe storage and communication.
The uncertainty of an event is measured by its probability of occurrence. Uncertainty is proportional to the negative logarithm of the probability of occurrence. Information theory takes advantage of this by concluding that more uncertain events require more information to resolve their uncertainty. The bit is a typical unit of information. It is 'that which reduces uncertainty by half'.[6] Other units such as the nat may be used. For example, the information encoded in one "fair" coin flip is log2(2/1) = 1 bit, and in two fair coin flips is log2(4/1) = 2 bits. A 2011 Science article estimates that 97% of technologically stored information was already in digital bits in 2007 and that the year 2002 was the beginning of the digital age for information storage (with digital storage capacity bypassing analogue for the first time).[7]
Etymology and history of the conceptThis section has multiple issues. Please help improve it or discuss these issues on the talk page. (Learn how and when to remove these messages)This article contains too many or overly lengthy quotations. (August 2025)You can help expand this section with text translated from the corresponding article in Russian. (August 2025) Click [show] for important translation instructions.
The English word "information" comes from Middle French enformacion/informacion/information 'a criminal investigation' and its etymon, Latin informatiō(n) 'conception, teaching, creation'.[8]
In English, "information" is an uncountable mass noun.
References on "formation or molding of the mind or character, training, instruction, teaching" date from the 14th century in both English (according to Oxford English Dictionary) and other European languages. In the transition from Middle Ages to Modernity the use of the concept of information reflected a fundamental turn in epistemological basis – from "giving a (substantial) form to matter" to "communicating something to someone". Peters (1988, pp. 12–13) concludes:
Information was readily deployed in empiricist psychology (though it played a less important role than other words such as impression or idea) because it seemed to describe the mechanics of sensation: objects in the world inform the senses. But sensation is entirely different from "form" – the one is sensual, the other intellectual; the one is subjective, the other objective. My sensation of things is fleeting, elusive, and idiosyncratic. For Hume, especially, sensory experience is a swirl of impressions cut off from any sure link to the real world... In any case, the empiricist problematic was how the mind is informed by sensations of the world. At first informed meant shaped by; later it came to mean received reports from. As its site of action drifted from cosmos to consciousness, the term's sense shifted from unities (Aristotle's forms) to units (of sensation). Information came less and less to refer to internal ordering or formation, since empiricism allowed for no preexisting intellectual forms outside of sensation itself. Instead, information came to refer to the fragmentary, fluctuating, haphazard stuff of sense. Information, like the early modern worldview in general, shifted from a divinely ordered cosmos to a system governed by the motion of corpuscles. Under the tutelage of empiricism, information gradually moved from structure to stuff, from form to substance, from intellectual order to sensory impulses.[9]
In the modern era, the most important influence on the concept of information is derived from the Information theory developed by Claude Shannon and others. This theory, however, reflects a fundamental contradiction. Northrup (1993)[10] wrote:
Thus, actually two conflicting metaphors are being used: The well-known metaphor of information as a quantity, like water in the water-pipe, is at work, but so is a second metaphor, that of information as a choice, a choice made by :an information provider, and a forced choice made by an :information receiver. Actually, the second metaphor implies that the information sent isn't necessarily equal to the information received, because any choice implies a comparison with a list of possibilities, i.e., a list of possible meanings. Here, meaning is involved, thus spoiling the idea of information as a pure "Ding an sich." Thus, much of the confusion regarding the concept of information seems to be related to the basic confusion of metaphors in Shannon's theory: is information an autonomous quantity, or is information always per SE information to an observer? Actually, I don't think that Shannon himself chose one of the two definitions. Logically speaking, his theory implied information as a subjective phenomenon. But this had so wide-ranging epistemological impacts that Shannon didn't seem to fully realize this logical fact. Consequently, he continued to use metaphors about information as if it were an objective substance. This is the basic, inherent contradiction in Shannon's information theory." (Northrup, 1993, p. 5)
In their seminal book The Study of Information: Interdisciplinary Messages,[11] Almach and Mansfield (1983) collected key views on the interdisciplinary controversy in computer science, artificial intelligence, library and information science, linguistics, psychology, and physics, as well as in the social sciences. Almach (1983,[12] p. 660) himself disagrees with the use of the concept of information in the context of signal transmission, the basic senses of information in his view all referring "to telling something or to the something that is being told. Information is addressed to human minds and is received by human minds." All other senses, including its use with regard to nonhuman organisms as well to society as a whole, are, according to Machlup, metaphoric and, as in the case of cybernetics, anthropomorphic.
Hjørland (2007) [13] describes the fundamental difference between objective and subjective views of information and argues that the subjective view has been supported by, among others, Bateson,[14] Yovits,[15][16] Span-Hansen,[17] Brier,[18] Buckland,[19] Goguen,[20] and Hjørland.[21] Hjørland provided the following example:
A stone on a field could contain different information for different people (or from one situation to another). It is not possible for information systems to map all the stone's possible information for every individual. Nor is any one mapping the one "true" mapping. But people have different educational backgrounds and play different roles in the division of labor in society. A stone in a field represents typical one kind of information for the geologist, another for the archaeologist. The information from the stone can be mapped into different collective knowledge structures produced by e.g. geology and archaeology. Information can be identified, described, represented in information systems for different domains of knowledge. Of course, there are much uncertainty and many and difficult problems in determining whether a thing is informative or not for a domain. Some domains have high degree of consensus and rather explicit criteria of relevance. Other domains have different, conflicting paradigms, each containing its own more or less implicate view of the informativeness of different kinds of information sources. (Hjørland, 1997, p. 111, emphasis in original).Information theoryMain article: Information theory
Information theory is the scientific study of the quantification, storage, and communication of information. The field itself was fundamentally established by the work of Claude Shannon in the 1940s, with earlier contributions by Harry Nyquist and Ralph Hartley in the 1920s.[22][23] The field is at the intersection of probability theory, statistics, computer science, statistical mechanics, information engineering, and electrical engineering.
A key measure in information theory is entropy. Entropy quantifies the amount of uncertainty involved in the value of a random variable or the outcome of a random process. For example, identifying the outcome of a fair coin flip (with two equally likely outcomes) provides less information (lower entropy) than specifying the outcome from a roll of a die (with six equally likely outcomes). Some other important measures in information theory are mutual information, channel capacity, error exponents, and relative entropy. Important sub-fields of information theory include source coding, algorithmic complexity theory, algorithmic information theory, and information-theoretic security.[citation needed]
Applications of fundamental topics of information theory include source coding/data compression (e.g. for ZIP files), and channel coding/error detection and correction (e.g. for DSL). Its impact has been crucial to the success of the Voyager missions to deep space, the invention of the compact disc, the feasibility of mobile phones and the development of the Internet. The theory has also found applications in other areas, including statistical inference,[24] cryptography, neurobiology,[25] perception,[26] linguistics, the evolution[27] and function[28] of molecular codes (bioinformatics), thermal physics,[29] quantum computing, black holes, information retrieval, intelligence gathering, plagiarism detection,[30] pattern recognition, anomaly detection[31] and even art creation.
As sensory input
Often information can be viewed as a type of input to an organism or system. Inputs are of two kinds. Some inputs are important to the function of the organism (for example, food) or system (energy) by themselves. In his book Sensory Ecology[32] biophysicist David B. Dusenbery called these causal inputs. Other inputs (information) are important only because they are associated with causal inputs and can be used to predict the occurrence of a causal input at a later time (and perhaps another place). Some information is important because of association with other information but eventually there must be a connection to a causal input.
In practice, information is usually carried by weak stimuli that must be detected by specialized sensory systems and amplified by energy inputs before they can be functional to the organism or system. For example, light is mainly (but not only, e.g. plants can grow in the direction of the light source) a causal input to plants but for animals it only provides information. The colored light reflected from a flower is too weak for photosynthesis but the visual system of the bee detects it and the bee's nervous system uses the information to guide the bee to the flower, where the bee often finds nectar or pollen, which are causal inputs, a nutritional function.
As an influence that leads to transformation
Information is any type of pattern that influences the formation or transformation of other patterns.[33][34] In this sense, there is no need for a conscious mind to perceive, much less appreciate, the pattern. Consider, for example, DNA. The sequence of nucleotides is a pattern that influences the formation and development of an organism without any need for a conscious mind. One might argue though that for a human to consciously define a pattern, for example a nucleotide, naturally involves conscious information processing. However, the existence of unicellular and multicellular organisms, with the complex biochemistry that leads, among other events, to the existence of enzymes and polynucleotides that interact maintaining the biological order and participating in the development of multicellular organisms, precedes by millions of years the emergence of human consciousness and the creation of the scientific culture that produced the chemical nomenclature.
Systems theory at times seems to refer to information in this sense, assuming information does not necessarily involve any conscious mind, and patterns circulating (due to feedback) in the system can be called information. In other words, it can be said that information in this sense is something potentially perceived as representation, though not created or presented for that purpose. For example, Gregory Bateson defines "information" as a "difference that makes a difference".[35]
If, however, the premise of "influence" implies that information has been perceived by a conscious mind and also interpreted by it, the specific context associated with this interpretation may cause the transformation of the information into knowledge. Complex definitions of both "information" and "knowledge" make such semantic and logical analysis difficult, but the condition of "transformation" is an important point in the study of information as it relates to knowledge, especially in the business discipline of knowledge management. In this practice, tools and processes are used to assist a knowledge worker in performing research and making decisions, including steps such as:
Review information to effectively derive value and meaningReference metadata if availableEstablish relevant context, often from many possible contextsDerive new knowledge from the informationMake decisions or recommendations from the resulting knowledge
Stewart (2001) argues that transformation of information into knowledge is critical, lying at the core of value creation and competitive advantage for the modern enterprise.
In a biological framework, Mizraji [36] has described information as an entity emerging from the interaction of patterns with receptor systems (e.g.: in molecular or neural receptors capable of interacting with specific patterns, information emerges from those interactions). In addition, he has incorporated the idea of "information catalysts", structures where emerging information promotes the transition from pattern recognition to goal-directed action (for example, the specific transformation of a substrate into a product by an enzyme, or auditory reception of words and the production of an oral response)
The Danish Dictionary of Information Terms[37] argues that information only provides an answer to a posed question. Whether the answer provides knowledge depends on the informed person. So a generalized definition of the concept should be: "Information" = An answer to a specific question".
When Marshall McLuhan speaks of media and their effects on human cultures, he refers to the structure of artifacts that in turn shape our behaviors and mindsets. Also, pheromones are often said to be "information" in this sense.
Technologically mediated informationFurther information: Information Age
These sections are using measurements of data rather than information, as information cannot be directly measured.
As of 2007
It is estimated that the world's technological capacity to store information grew from 2.6 (optimally compressed) exabytes in 1986 – which is the informational equivalent to less than one 730-MB CD-ROM per person (539 MB per person) – to 295 (optimally compressed) exabytes in 2007.[7] This is the informational equivalent of almost 61 CD-ROM per person in 2007.[5]
The world's combined technological capacity to receive information through one-way broadcast networks was the informational equivalent of 174 newspapers per person per day in 2007.[7]
The world's combined effective capacity to exchange information through two-way telecommunication networks was the informational equivalent of 6 newspapers per person per day in 2007.[5]
As of 2007, an estimated 90% of all new information is digital, mostly stored on hard drives.[38]
As of 2020
The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 64.2 zettabytes in 2020. Over the next five years up to 2025, global data creation is projected to grow to more than 180 zettabytes.[39]
As recordsPart of a series onLibrary and information scienceOutlineGlossaryHistoriesFocusCurationInterdisciplinary fieldsAreasWikiProject Categoryvte
Records are specialized forms of information. Essentially, records are information produced consciously or as by-products of business activities or transactions and retained because of their value. Primarily, their value is as evidence of the activities of the organization but they may also be retained for their informational value. Sound records management ensures that the integrity of records is preserved for as long as they are required.[citation needed]
The international standard on records management, ISO 15489, defines records as "information created, received, and maintained as evidence and information by an organization or person, in pursuance of legal obligations or in the transaction of business".[40] The International Committee on Archives (ICA) Committee on electronic records defined a record as, "recorded information produced or received in the initiation, conduct or completion of an institutional or individual activity and that comprises content, context and structure sufficient to provide evidence of the activity".[41]
Records may be maintained to retain corporate memory of the organization or to meet legal, fiscal or accountability requirements imposed on the organization. Willis expressed the view that sound management of business records and information delivered "...six key requirements for good corporate governance...transparency; accountability; due process; compliance; meeting statutory and common law requirements; and security of personal and corporate information."[42]
Semiotics
Michael Buckland has classified "information" in terms of its uses: "information as process", "information as knowledge", and "information as thing".[43]
Beynon-Davies[44][45] explains the multi-faceted concept of information in terms of signs and signal-sign systems. Signs themselves can be considered in terms of four inter-dependent levels, layers or branches of semiotics: pragmatics, semantics, syntax, and empirics. These four layers serve to connect the social world on the one hand with the physical or technical world on the other.
Pragmatics is concerned with the purpose of communication. Pragmatics links the issue of signs with the context within which signs are used. The focus of pragmatics is on the intentions of living agents underlying communicative behaviour. In other words, pragmatics link language to action.
Semantics is concerned with the meaning of a message conveyed in a communicative act. Semantics considers the content of communication. Semantics is the study of the meaning of signs – the association between signs and behaviour. Semantics can be considered as the study of the link between symbols and their referents or concepts – particularly the way that signs relate to human behavior.
Syntax is concerned with the formalism used to represent a message. Syntax as an area studies the form of communication in terms of the logic and grammar of sign systems. Syntax is devoted to the study of the form rather than the content of signs and sign systems.
Nielsen (2008) discusses the relationship between semiotics and information in relation to dictionaries. He introduces the concept of lexicographic information costs and refers to the effort a user of a dictionary must make to first find, and then understand data so that they can generate information.[46]
Communication normally exists within the context of some social situation. The social situation sets the context for the intentions conveyed (pragmatics) and the form of communication. In a communicative situation intentions are expressed through messages that comprise collections of inter-related signs taken from a language mutually understood by the agents involved in the communication. Mutual understanding implies that agents involved understand the chosen language in terms of its agreed syntax and semantics. The sender codes the message in the language and sends the message as signals along some communication channel (empirics). The chosen communication channel has inherent properties that determine outcomes such as the speed at which communication can take place, and over what distance.
Physics and determinacy
The existence of information about a closed system is a major concept in both classical physics and quantum mechanics, encompassing the ability, real or theoretical, of an agent to predict the future state of a system based on knowledge gathered during its past and present. Determinism is a philosophical theory holding that causal determination can predict all future events,[47] positing a fully predictable universe described by classical physicist Pierre-Simon Laplace as "the effect of its past and the cause of its future".[48]
Quantum physics instead encodes information as a wave function, a mathematical description of a system from which the probabilities of measurement outcomes can be computed. A fundamental feature of quantum theory is that the predictions it makes are probabilistic. Prior to the publication of Bell's theorem, determinists reconciled with this behavior using hidden variable theories, which argued that the information necessary to predict the future of a function must exist, even if it is not accessible for humans, a view expressed by Albert Einstein with the assertion that "God does not play dice".[49]
Modern astronomy cites the mechanical sense of information in the black hole information paradox, positing that, because the complete evaporation of a black hole into Hawking radiation leaves nothing except an expanding cloud of homogeneous particles, this results in the irrecoverability of any information about the matter to have originally crossed the event horizon, violating both classical and quantum assertions against the ability to destroy information.[50][51]
The application of information study
The information cycle (addressed as a whole or in its distinct components) is of great concern to information technology, information systems, as well as information science. These fields deal with those processes and techniques pertaining to information capture (through sensors) and generation (through computation, formulation or composition), processing (including encoding, encryption, compression, packaging), transmission (including all telecommunication methods), presentation (including visualization / display methods), storage (such as magnetic or optical, including holographic methods), etc.
Information visualization (shortened as InfoVis) depends on the computation and digital representation of data, and assists users in pattern recognition and anomaly detection.
Partial map of the Internet, with nodes representing IP addresses Galactic (including dark) matter distribution in a cubic section of the Universe Visual representation of a strange attractor, with converted data of its fractal structure
Information security (shortened as InfoSec) is the ongoing process of exercising due diligence to protect information, and information systems, from unauthorized access, use, disclosure, destruction, modification, disruption or distribution, through algorithms and procedures focused on monitoring and detection, as well as incident response and repair.
Information analysis is the process of inspecting, transforming, and modeling information, by converting raw data into actionable knowledge, in support of the decision-making process.
Information quality (shortened as InfoQ) is the potential of a dataset to achieve a specific (scientific or practical) goal using a given empirical analysis method.
Information communication represents the convergence of informatics, telecommunication and audio-visual media & content.
See alsoAccuracy and precisionComplex adaptive systemComplex systemData storageEngramFree Information InfrastructureFreedom of informationInformaticsInformation and communication technologiesInformation architectureInformation brokerInformation continuumInformation ecologyInformation engineeringInformation geometryInformation inequityInformation infrastructureInformation managementInformation metabolismInformation overloadInformation quality (InfoQ)Information scienceInformation sensitivityInformation technologyInformation theoryInformation warfareInfosphereLexicographic information costLibrary scienceMemePhilosophy of informationQuantum informationReceiver operating characteristicSatisficingReferences John B. Anderson; Rolf Johnnesson (1996). Understanding Information Transmission. Ieee Press. ISBN 978-0-471-71120-9. Hubert P. Yockey (2005). Information Theory, Evolution, and the Origin of Life. Cambridge University Press. p. 7. ISBN 978-0-511-54643-3. Luciano Floridi (2010). Information – A Very Short Introduction. Oxford University Press. ISBN 978-0-19-160954-1. Webler, Forrest (25 February 2022). "Measurement in the Age of Information". Information. 13 (3): 111. doi:10.3390/info13030111. "World_info_capacity_animation". YouTube. 11 June 2011. Archived from the original on 21 December 2021. Retrieved 1 May 2017. "DT&SC 4-5: Information Theory Primer, Online Course". YouTube. University of California. 2015. Hilbert, Martin; López, Priscila (2011). "The World's Technological Capacity to Store, Communicate, and Compute Information". Science. 332 (6025): 60–65. Bibcode:2011Sci...332...60H. doi:10.1126/science.1200970. PMID 21310967. S2CID 206531385. Free access to the article at martinhilbert.net/WorldInfoCapacity.html Oxford English Dictionary, Third Edition, 2009, full text Peters, J. D. (1988). Information: Notes Toward a Critical History. Journal of Communication Inquiry, 12, 10-24. Qvortrup, L. (1993). The controversy over the concept of information. An overview and a selected and annotated bibliography. Cybernetics & Human Knowing 1(4), 3-24. Machlup, Fritz & Una Mansfield (eds.). 1983. The Study of Information: Interdisciplinary Messages. New York: Wiley. Machlup, Fritz. 1983. "Semantic Quirks in Studies of Information," pp. 641-71 in Fritz Machlup & Una Mansfield, The Study of Information: Interdisciplinary Messages. New York: Wiley. Hjørland, B. (2007). Information: Objective or Subjective/Situational?. Journal of the American Society for Information Science and Technology, 58(10), 1448-1456. Bateson, G. (1972). Steps to an ecology of mind. New York: Ballantine. Yovits, M.C. (1969). Information science: Toward the development of a true scientific discipline. American Documentation (Vol. 20, pp. 369–376). Yovits, M. C. (1975). A theoretical framework for the development of information science. In International Federation for Documentation. Study Committee Research on the Theoretical Basis of Information. Meeting (1974: Moscow) Information science, its scope, objects of research and problems: Collection of papers [presented at the meeting of the FID Study Committee "Research on the Theoretical Basis of Information"] 24–26 April 1974, Moscow (pp. 90–114). FID 530. Moscow: VINITI Spang-Hanssen, H. (2001). How to teach about information as related to documentation. Human IT, (1), 125–143. Retrieved May 14, 2007, from http://www.hb.se/bhs/ith/1-01/hsh.htm Archived 2008-02-19 at the Wayback Machine Brier, S. (1996). Cybersemiotics: A new interdisciplinary development applied to the problems of knowledge organisation and document retrieval in information science. Journal of Documentation, 52(3), 296–344. Buckland, M. (1991). Information and information systems. New York: Greenwood Press. Goguen, J. A. (1997). Towards a social, ethical theory of information. In G. Bowker, L. Gasser, L. Star, & W. Turner, Erlbaum (Eds.), Social science research, technical systems and cooperative work: Beyond the great divide (pp. 27–56). Hillsdale, NJ: Erlbaum. Retrieved May 14, 2007, from http://cseweb.ucsd.edu/~goguen/ps/sti.pdf Hjørland, B. (1997). Information seeking and subject representation. An activity-theoretical approach to information science. Westport: Greenwood Press. Pérez-Montoro Gutiérrez, Mario; Edelstein, Dick (2007). The Phenomenon of Information: A Conceptual Approach to Information Flow. Lanham (Md.): Scarecrow Press. pp. 21–22. ISBN 978-0-8108-5942-5. Wesołowski, Krzysztof (2009). Introduction to Digital Communication Systems (PDF) (1. publ ed.). Chichester: Wiley. p. 2. ISBN 978-0-470-98629-5. Burnham, K. P. and Anderson D. R. (2002) Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, Second Edition (Springer Science, New York) ISBN 978-0-387-95364-9. F. Rieke; D. Warland; R Ruyter van Steveninck; W Bialek (1997). Spikes: Exploring the Neural Code. The MIT press. ISBN 978-0-262-68108-7. Delgado-Bonal, Alfonso; Martín-Torres, Javier (3 November 2016). "Human vision is determined based on information theory". Scientific Reports. 6 (1) 36038. Bibcode:2016NatSR...636038D. doi:10.1038/srep36038. ISSN 2045-2322. PMC 5093619. PMID 27808236. cf; Huelsenbeck, J. P.; Ronquist, F.; Nielsen, R.; Bollback, J. P. (2001). "Bayesian inference of phylogeny and its impact on evolutionary biology". Science. 294 (5550): 2310–2314. Bibcode:2001Sci...294.2310H. doi:10.1126/science.1065889. PMID 11743192. S2CID 2138288. Allikmets, Rando; Wasserman, Wyeth W.; Hutchinson, Amy; Smallwood, Philip; Nathans, Jeremy; Rogan, Peter K. (1998). "Thomas D. Schneider], Michael Dean (1998) Organization of the ABCR gene: analysis of promoter and splice junction sequences". Gene. 215 (1): 111–122. doi:10.1016/s0378-1119(98)00269-8. PMID 9666097. Jaynes, E. T. (1957). "Information Theory and Statistical Mechanics". Phys. Rev. 106 (4): 620. Bibcode:1957PhRv..106..620J. doi:10.1103/physrev.106.620. S2CID 17870175. Bennett, Charles H.; Li, Ming; Ma, Bin (2003). "Chain Letters and Evolutionary Histories". Scientific American. 288 (6): 76–81. Bibcode:2003SciAm.288f..76B. doi:10.1038/scientificamerican0603-76. PMID 12764940. Archived from the original on 7 October 2007. Retrieved 11 March 2008. David R. Anderson (1 November 2003). "Some background on why people in the empirical sciences may want to better understand the information-theoretic methods" (PDF). Archived from the original (PDF) on 23 July 2011. Retrieved 23 June 2010. Dusenbery, David B. (1992). Sensory Ecology. New York: W.H. Freeman. ISBN 978-0-7167-2333-2. Shannon, Claude E. (1949). The Mathematical Theory of Communication. Bibcode:1949mtc..book.....S. Casagrande, David (1999). "Information as verb: Re-conceptualizing information for cognitive and ecological models" (PDF). Journal of Ecological Anthropology. 3 (1): 4–13. doi:10.5038/2162-4593.3.1.1. Bateson, Gregory (1972). Form, Substance, and Difference, in Steps to an Ecology of Mind. University of Chicago Press. pp. 448–466. Mizraji, E. (2021). "The biological Maxwell's demons: exploring ideas about the information processing in biological systems". Theory in Biosciences. 140 (3): 307–318. doi:10.1007/s12064-021-00354-6. PMC 8568868. PMID 34449033. Simonsen, Bo Krantz. "Informationsordbogen – vis begreb". Informationsordbogen.dk. Retrieved 1 May 2017. Failure Trends in a Large Disk Drive Population. Eduardo Pinheiro, Wolf-Dietrich Weber and Luiz Andre Barroso "Total data volume worldwide 2010–2025". Statista. Retrieved 6 August 2021. ISO 15489 Committee on Electronic Records (February 1997). "Guide For Managing Electronic Records From An Archival Perspective" (PDF). www.ica.org. International Committee on Archives. p. 22. Retrieved 9 February 2019. Willis, Anthony (1 August 2005). "Corporate governance and management of information and records". Records Management Journal. 15 (2): 86–97. doi:10.1108/09565690510614238. Buckland, Michael K. (June 1991). "Information as thing". Journal of the American Society for Information Science. 42 (5): 351–360. doi:10.1002/(SICI)1097-4571(199106)42:5<351::AID-ASI5>3.0.CO;2-3. Beynon-Davies, P. (2002). Information Systems: an introduction to informatics in Organisations. Basingstoke, UK: Palgrave. ISBN 978-0-333-96390-6. Beynon-Davies, P. (2009). Business Information Systems. Basingstoke: Palgrave. ISBN 978-0-230-20368-6. Nielsen, Sandro (2008). "The Effect of Lexicographical Information Costs on Dictionary Making and Use". Lexikos. 18. Stellenbosch: Bureau of the WAT: 170–189. doi:10.5788/18-0-483. Ernest Nagel (1999). "§V: Alternative descriptions of physical state". The Structure of Science: Problems in the Logic of Scientific Explanation (2nd ed.). Hackett. pp. 285–292. ISBN 978-0-915144-71-6. A theory is deterministic if, and only if, given its state variables for some initial period, the theory logically determines a unique set of values for those variables for any other period. Laplace, Pierre Simon, A Philosophical Essay on Probabilities, translated into English from the original French 6th ed. by Truscott, F.W. and Emory, F.L., Dover Publications (New York, 1951) p.4. The Collected Papers of Albert Einstein, Volume 15: The Berlin Years: Writings & Correspondence, June 1925-May 1927 (English Translation Supplement), p. 403 Hawking, Stephen (2006). The Hawking Paradox. Discovery Channel. Archived from the original on 2 August 2013. Retrieved 13 August 2013. Overbye, Dennis (12 August 2013). "A Black Hole Mystery Wrapped in a Firewall Paradox". The New York Times. Retrieved 12 August 2013.Further readingLiu, Alan (2004). The Laws of Cool: Knowledge Work and the Culture of Information. University of Chicago Press. ISBN 9780226486987.Bekenstein, Jacob D. (August 2003). "Information in the holographic universe". Scientific American. 289 (2): 58–65. Bibcode:2003SciAm.289b..58B. doi:10.1038/scientificamerican0803-58. PMID 12884539.Gleick, James (2011). The Information: A History, a Theory, a Flood. New York, NY: Pantheon. ISBN 9780375423727.Lin, Shu-Kun (2008). "Gibbs Paradox and the Concepts of Information, Symmetry, Similarity and Their Relationship". Entropy. 10 (1): 1–5. arXiv:0803.2571. Bibcode:2008Entrp..10....1L. doi:10.3390/entropy-e10010001. S2CID 41159530.Floridi, Luciano (2005). "Is Information Meaningful Data?" (PDF). Philosophy and Phenomenological Research. 70 (2): 351–370. doi:10.1111/j.1933-1592.2005.tb00531.x. hdl:2299/1825. S2CID 5593220.Floridi, Luciano (2005). "Semantic Conceptions of Information". In Zalta, Edward N. (ed.). The Stanford Encyclopedia of Philosophy (Winter 2005 e
Plants are exposed to many stress factors such as disease, temperature changes, herbivory, injury and more.[1] Therefore, in order to respond or be ready for any kind of physiological state, they need to develop some sort of system for their survival in the moment and/or for the future. Plant communication encompasses communication using volatile organic compounds, electrical signaling, and common mycorrhizal networks between plants and a host of other organisms such as soil microbes,[2] other plants[3] (of the same or other species), animals,[4] insects,[5] and fungi.[6]
Plants communicate through a host of volatile organic compounds (VOCs) that can be separated into four broad categories, each the product of distinct chemical pathways: fatty acid derivatives, phenylpropanoids/benzenoids, amino acid derivatives, and terpenoids.[7] Due to the physical/chemical constraints most VOCs are of low molecular mass (< 300 Da), are hydrophobic, and have high vapor pressures.[8] The responses of organisms to plant emitted VOCs varies from attracting the predator of a specific herbivore to reduce mechanical damage inflicted on the plant [5] to the induction of chemical defenses of a neighboring plant before it is being attacked.[9] In addition, the host of VOCs emitted varies from plant to plant, where for example, the Venus Fly Trap can emit VOCs to specifically target and attract starved prey.[10]
While these VOCs typically lead to increased resistance to herbivory in neighboring plants, there is no clear benefit to the emitting plant in helping nearby plants. As such, whether neighboring plants have evolved the capability to "eavesdrop" or whether there is an unknown tradeoff occurring is subject to much scientific debate.[11] As related to the aspect of meaning-making, the field is also identified as phytosemiotics.[12]
Volatile communication
In Runyon et al. 2006, the researchers demonstrate how the parasitic plant, Cuscuta pentagona (field dodder), uses VOCs to interact with various hosts and determine locations. Dodder seedlings show direct growth toward tomato plants (Lycopersicon esculentum) and, specifically, tomato plant volatile organic compounds. This was tested by growing a dodder weed seedling in a contained environment, connected to two different chambers. One chamber contained tomato VOCs while the other had artificial tomato plants. After 4 days of growth, the dodder weed seedling showed a significant growth towards the direction of the chamber with tomato VOC's. Their experiments also showed that the dodder weed seedlings could distinguish between wheat (Triticum aestivum) VOCs and tomato plant volatiles. As when one chamber was filled with each of the two different VOCs, dodder weeds grew towards tomato plants as one of the wheat VOC's is repellent. These findings show evidence that volatile organic compounds determine ecological interactions between plant species and show statistical significance that the dodder weed can distinguish between different plant species by sensing their VOCs.[13]
Tomato plant to plant communication is further examined in Zebelo et al. 2012, which studies tomato plant response to herbivory. Upon herbivory by Spodoptera littoralis, tomato plants emit VOCs that are released into the atmosphere and induce responses in neighboring tomato plants. When the herbivory-induced VOCs bind to receptors on other nearby tomato plants, responses occur within seconds. The neighboring plants experience a rapid depolarization in cell potential and increase in cytosolic calcium. Plant receptors are most commonly found on plasma membranes as well as within the cytosol, endoplasmic reticulum, nucleus, and other cellular compartments. VOCs that bind to plant receptors often induce signal amplification by action of secondary messengers including calcium influx as seen in response to neighboring herbivory. These emitted volatiles were measured by GC-MS and the most notable were 2-hexenal and 3-hexenal acetate. It was found that depolarization increased with increasing green leaf volatile concentrations. These results indicate that tomato plants communicate with one another via airborne volatile cues, and when these VOC's are perceived by receptor plants, responses such as depolarization and calcium influx occur within seconds.[14]
TerpenoidsThe terpenoid verbenone is a plant pheromone, signalling to insects that a tree is already infested by beetles.[15]Further information: Terpenoid
Terpenoids facilitate communication between plants and insects, mammals, fungi, microorganisms, and other plants.[16] Terpenoids may act as both attractants and repellants for various insects. For example, pine shoot beetles (Tomicus piniperda) are attracted to certain monoterpenes ( (+/-)-a-pinene, (+)-3-carene and terpinolene) produced by Scots pines (Pinus sylvestris), while being repelled by others (such as verbenone).[17]
Terpenoids are a large family of biological molecules with over 22,000 compounds.[18] Terpenoids are similar to terpenes in their carbon skeleton but unlike terpenes contain functional groups. The structure of terpenoids is described by the biogenetic isoprene rule which states that terpenoids can be thought of being made of isoprenoid subunits, arranged either regularly or irregularly.[19] The biosynthesis of terpenoids occurs via the methylerythritol phosphate (MEP) and mevalonic acid(MVA) pathways[7] both of which include isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP) as key components.[20] The MEP pathway produces hemiterpenes, monoterpenes, diterpenes, and volatile carotenoid derivatives while the MVA pathway produces sesquiterpenes.[7]