The IBM alignment models are a sequence of increasingly complex models used in statistical machine translation to train a translation model and an alignment model, starting with lexical translation probabilities and moving to reordering and word duplication. They underpinned the majority of statistical machine translation systems for almost twenty years starting in the early 1990s, until neural machine translation began to dominate. These models offer principled probabilistic formulation and (mostly) tractable inference. The IBM alignment models were published in parts in 1988 and 1990, and the entire series is published in 1993. Every author of the 1993 paper subsequently went to the hedge fund Renaissance Technologies. The original work on statistical machine translation at IBM proposed five models, and a model 6 was proposed later. The sequence of the six models can be summarized as: Model 1: lexical translation Model 2: additional absolute alignment model Model 3: extra fertility model Model 4: added relative alignment model Model 5: fixed deficiency problem. Model 6: Model 4 combined with a HMM alignment model in a log linear way == Mathematical setup == The IBM alignment models translation as a conditional probability model. For each source-language ("foreign") sentence f {\displaystyle f} , we generate both a target-language ("English") sentence e {\displaystyle e} and an alignment a {\displaystyle a} . The problem then is to find a good statistical model for p ( e , a | f ) {\displaystyle p(e,a|f)} , the probability that we would generate English language sentence e {\displaystyle e} and an alignment a {\displaystyle a} given a foreign sentence f {\displaystyle f} . The meaning of an alignment grows increasingly complicated as the model version number grew. See Model 1 for the most simple and understandable version. == Model 1 == === Word alignment === Given any foreign-English sentence pair ( e , f ) {\displaystyle (e,f)} , an alignment for the sentence pair is a function of type { 1 , . , . . . , l e } → { 0 , 1 , . , . . . , l f } {\displaystyle \{1,.,...,l_{e}\}\to \{0,1,.,...,l_{f}\}} . That is, we assume that the English word at location i {\displaystyle i} is "explained" by the foreign word at location a ( i ) {\displaystyle a(i)} . For example, consider the following pair of sentences It will surely rain tomorrow -- 明日 は きっと 雨 だWe can align some English words to corresponding Japanese words, but not everyone:it -> ? will -> ? surely -> きっと rain -> 雨 tomorrow -> 明日This in general happens due to the different grammar and conventions of speech in different languages. English sentences require a subject, and when there is no subject available, it uses a dummy pronoun it. Japanese verbs do not have different forms for future and present tense, and the future tense is implied by the noun 明日 (tomorrow). Conversely, the topic-marker は and the grammar word だ (roughly "to be") do not correspond to any word in the English sentence. So, we can write the alignment as 1-> 0; 2 -> 0; 3 -> 3; 4 -> 4; 5 -> 1where 0 means that there is no corresponding alignment. Thus, we see that the alignment function is in general a function of type { 1 , . , . . . , l e } → { 0 , 1 , . , . . . , l f } {\displaystyle \{1,.,...,l_{e}\}\to \{0,1,.,...,l_{f}\}} . Future models will allow one English world to be aligned with multiple foreign words. === Statistical model === Given the above definition of alignment, we can define the statistical model used by Model 1: Start with a "dictionary". Its entries are of form t ( e i | f j ) {\displaystyle t(e_{i}|f_{j})} , which can be interpreted as saying "the foreign word f j {\displaystyle f_{j}} is translated to the English word e i {\displaystyle e_{i}} with probability t ( e i | f j ) {\displaystyle t(e_{i}|f_{j})} ". After being given a foreign sentence f {\displaystyle f} with length l f {\displaystyle l_{f}} , we first generate an English sentence length l e {\displaystyle l_{e}} uniformly in a range U n i f o r m [ 1 , 2 , . . . , N ] {\displaystyle Uniform[1,2,...,N]} . In particular, it does not depend on f {\displaystyle f} or l f {\displaystyle l_{f}} . Then, we generate an alignment uniformly in the set of all possible alignment functions { 1 , . , . . . , l e } → { 0 , 1 , . , . . . , l f } {\displaystyle \{1,.,...,l_{e}\}\to \{0,1,.,...,l_{f}\}} . Finally, for each English word e 1 , e 2 , . . . e l e {\displaystyle e_{1},e_{2},...e_{l_{e}}} , generate each one independently of every other English word. For the word e i {\displaystyle e_{i}} , generate it according to t ( e i | f a ( i ) ) {\displaystyle t(e_{i}|f_{a(i)})} . Together, we have the probability p ( e , a | f ) = 1 / N ( 1 + l f ) l e ∏ i = 1 l e t ( e i | f a ( i ) ) {\displaystyle p(e,a|f)={\frac {1/N}{(1+l_{f})^{l_{e}}}}\prod _{i=1}^{l_{e}}t(e_{i}|f_{a(i)})} IBM Model 1 uses very simplistic assumptions on the statistical model, in order to allow the following algorithm to have closed-form solution. === Learning from a corpus === If a dictionary is not provided at the start, but we have a corpus of English-foreign language pairs { ( e ( k ) , f ( k ) ) } k {\displaystyle \{(e^{(k)},f^{(k)})\}_{k}} (without alignment information), then the model can be cast into the following form: fixed parameters: the foreign sentences { f ( k ) } k {\displaystyle \{f^{(k)}\}_{k}} . learnable parameters: the entries of the dictionary t ( e i | f j ) {\displaystyle t(e_{i}|f_{j})} . observable variables: the English sentences { e ( k ) } k {\displaystyle \{e^{(k)}\}_{k}} . latent variables: the alignments { a ( k ) } k {\displaystyle \{a^{(k)}\}_{k}} In this form, this is exactly the kind of problem solved by expectation–maximization algorithm. Due to the simplistic assumptions, the algorithm has a closed-form, efficiently computable solution, which is the solution to the following equations: { max t ′ ∑ k ∑ i ∑ a ( k ) t ( a ( k ) | e ( k ) , f ( k ) ) ln t ( e i ( k ) | f a ( k ) ( i ) ( k ) ) ∑ x t ′ ( e x | f y ) = 1 ∀ y {\displaystyle {\begin{cases}\max _{t'}\sum _{k}\sum _{i}\sum _{a^{(k)}}t(a^{(k)}|e^{(k)},f^{(k)})\ln t(e_{i}^{(k)}|f_{a^{(k)}(i)}^{(k)})\\\sum _{x}t'(e_{x}|f_{y})=1\quad \forall y\end{cases}}} This can be solved by Lagrangian multipliers, then simplified. For a detailed derivation of the algorithm, see chapter 4 and. In short, the EM algorithm goes as follows:INPUT. a corpus of English-foreign sentence pairs { ( e ( k ) , f ( k ) ) } k {\displaystyle \{(e^{(k)},f^{(k)})\}_{k}} INITIALIZE. matrix of translations probabilities t ( e x | f y ) {\displaystyle t(e_{x}|f_{y})} .This could either be uniform or random. It is only required that every entry is positive, and for each y {\displaystyle y} , the probability sums to one: ∑ x t ( e x | f y ) = 1 {\displaystyle \sum _{x}t(e_{x}|f_{y})=1} . LOOP. until t ( e x | f y ) {\displaystyle t(e_{x}|f_{y})} converges: t ( e x | f y ) ← t ( e x | f y ) λ y ∑ k , i , j δ ( e x , e i ( k ) ) δ ( f y , f j ( k ) ) ∑ j ′ t ( e i ( k ) | f j ′ ( k ) ) {\displaystyle t(e_{x}|f_{y})\leftarrow {\frac {t(e_{x}|f_{y})}{\lambda _{y}}}\sum _{k,i,j}{\frac {\delta (e_{x},e_{i}^{(k)})\delta (f_{y},f_{j}^{(k)})}{\sum _{j'}t(e_{i}^{(k)}|f_{j'}^{(k)})}}} where each λ y {\displaystyle \lambda _{y}} is a normalization constant that makes sure each ∑ x t ( e x | f y ) = 1 {\displaystyle \sum _{x}t(e_{x}|f_{y})=1} .RETURN. t ( e x | f y ) {\displaystyle t(e_{x}|f_{y})} .In the above formula, δ {\displaystyle \delta } is the Dirac delta function -- it equals 1 if the two entries are equal, and 0 otherwise. The index notation is as follows: k {\displaystyle k} ranges over English-foreign sentence pairs in corpus; i {\displaystyle i} ranges over words in English sentences; j {\displaystyle j} ranges over words in foreign language sentences; x {\displaystyle x} ranges over the entire vocabulary of English words in the corpus; y {\displaystyle y} ranges over the entire vocabulary of foreign words in the corpus. === Limitations === There are several limitations to the IBM model 1. No fluency: Given any sentence pair ( e , f ) {\displaystyle (e,f)} , any permutation of the English sentence is equally likely: p ( e | f ) = p ( e ′ | f ) {\displaystyle p(e|f)=p(e'|f)} for any permutation of the English sentence e {\displaystyle e} into e ′ {\displaystyle e'} . No length preference: The probability of each length of translation is equal: ∑ e has length l p ( e | f ) = 1 N {\displaystyle \sum _{e{\text{ has length }}l}p(e|f)={\frac {1}{N}}} for any l ∈ { 1 , 2 , . . . , N } {\displaystyle l\in \{1,2,...,N\}} . Does not explicitly model fertility: some foreign words tend to produce a fixed number of English words. For example, for German-to-English translation, ja is usually omitted, and zum is usually translated to one of to the, for the, to a, for a. == Model 2 == Model 2 allows alignment to be conditional on sentence lengths. That is, we have a probability distribution p a ( j | i , l e , l f ) {\displaystyle
Human Race Machine
The Human Race Machine (HRM) is a computerized console composed of four different programs. The Human Race Machine program allows participants to see themselves with the facial characteristics of six different races: Asian, White, African, Middle Eastern, and Indian, mapped onto their own face. The Age Machine allows viewers see an aged version of his or her face. A version of this methodology has been used for over twenty years by the FBI and the National Center for Missing and Exploited Children to help locate kidnap victims and missing children. The Couples Machine combines photographs of two people in different percentages to show the appearance of their child. The Anomaly Machine lets viewers see themselves with facial anomalies. The HRM was created by artist Nancy Burson and David Kramlich; it uses morphing technology. It was shown on Oprah on 2006-02-16.
Honeywell JetWave
Honeywell's JetWave is a piece of satellite communications hardware produced by Honeywell that enables global in-flight internet connectivity. Its connectivity is provided using Inmarsat’s GX Aviation network. The JetWave platform is used in business and general aviation, as well as defense and commercial airline users. == History == In 2012, Honeywell announced it would provide Inmarsat with the hardware for its GX Ka-band in-flight connectivity network. The Ka-band (pronounced either "kay-ay band" or "ka band") is a portion of the microwave part of the electromagnetic spectrum defined as frequencies in the range 27.5 to 31 gigahertz (GHz). In satellite communications, the Ka-band allows higher bandwidth communication. In 2017, after five years and more than 180 flight hours and testing, JetWave was launched as part of GX Aviation with Lufthansa Group. Honeywell’s JetWave was the exclusive terminal hardware option for the Inmarsat GX Aviation network; however, the exclusivity clause in that contract has expired. In July 2019, the United States Air Force selected Honeywell’s JetWave satcom system for 70 of its C-17 Globemaster III cargo planes. In December 2019, it was reported that six AirAsia aircraft had been fitted with Inmarsat’s GX Aviation Ka-band connectivity system and is slated to be implemented fleetwide across AirAsia’s Airbus A320 and A330 models in 2020, requiring installation of JetWave atop AirAsia’s fuselages. Today, Honeywell’s JetWave hardware is installed on over 1,000 aircraft worldwide. In August 2021, the Civil Aviation Administration of China approved a validation of Honeywell’s MCS-8420 JetWave satellite connectivity system for Airbus 320 aircraft. In December 2021, Honeywell, SES, and Hughes Network Systems demonstrated multi-orbit high-speed airborne connectivity for military customers using Honeywell’s JetWave MCX terminal with a Hughes HM-series modem, and SES satellites in both medium Earth orbit (MEO) and geostationary orbit (GEO). The tests achieved full duplex data rates of more than 40 megabits per second via a number of SES' (GEO) satellites including GovSat-1, and the high-throughput, low-latency O3b MEO satellite constellation, with connections moving between GEO/MEO links in under 30 sec. == Uses == === Commercial aviation === Honeywell’s JetWave enables air transport and regional aircraft to connect to Inmarsat’s GX Aviation network. The multichannel satellite (MSC) JetWave terminals share the same antenna controller, modem and router hardware with the business market, but have an MCS-8200 fuselage-mounted antenna. === Business aviation === Honeywell’s JetWave hardware allows users to connect to Inmarsat’s Jet ConneX, a business aviation broadband connectivity offering to provide Wi-Fi for connected devices. JetWave offers a tail-mount antenna for business jets. === Defense === Honeywell’s JetWave satellite communications system for defense allows users to connect to the Inmarsat GX network, offering global coverage for military airborne operators, including over water, over nontraditional flight paths and in remote areas. JetWave and the Inmarsat GX network enable mission-critical applications like real-time weather; videoconferencing; large file transfers; encryption capabilities; in-flight briefings; intelligence, surveillance, and reconnaissance video; and secure communications. JetWave is configurable for a variety of military platforms and offers antennas for large and small airframes.
Redshift (theory)
Redshift is a techno-economic theory suggesting hypersegmentation of information technology markets based on whether individual computing needs are over or under-served by Moore's law, which predicts the doubling of computing transistors (and therefore roughly computing power) every two years. The theory, proposed and named by New Enterprise Associates partner and former Sun Microsystems CTO Greg Papadopoulos, categorized a series of high growth markets (redshifting) while predicting slower GDP-driven growth in traditional computing markets (blueshifting). Papadopoulos predicted the result will be a fundamental redesign of components comprising computing systems. == Hypergrowth market segments (redshifting) == According to the Redshift theory, applications "redshift" when they grow dramatically faster than Moore's Law allows, growing quickly in their absolute number of systems. In these markets, customers are running out of datacenter real-estate, power and cooling infrastructure. According to Dell Senior Vice President Brad Anderson, “Businesses requiring hyperscale computing environments – where infrastructure deployments are measured by up to millions of servers, storage and networking equipment – are changing the way they approach IT.” While various Redshift proponents offer minor alterations on the original presentation, “Redshifting” generally includes: === ΣBW (Sum-of-Bandwidth) === These are companies that drive heavy Internet traffic. This includes popular web-portals like Google, Yahoo, AOL and MSN. It also includes telecoms, multimedia, television over IP, online games like World of Warcraft and others. This segment has been enabled by widespread availability of high-bandwidth Internet connections to consumers through a DSL or cable modem. A simple way to understand this market is that for every byte of content served to a PC, mobile phone or other device over a network, there must exist computing systems to send it over the network. === High performance computing (HPC) === These are companies that do complex simulations that involve (for example) weather, stock markets or drug-design simulations. This is a generally elastic market because businesses frequently spend every "available" dollar budgeted for IT. A common anecdote claims that cutting the cost of computing by half causes customers in this segment to buy at least twice as much, because each marginal IT dollar spent contributes to business advantage. === prise (or "Star-prise") === These are companies that aggregate traditional computing applications and offer them as services, typically in the form of Software as a Service (SaaS). For example, companies that deploy CRM are over-served by Moore's Law, but companies that aggregate CRM functions and offer them as a service, such as Salesforce.com, grow faster than Moore's Law. === The eBay crisis === A prime example of redshift was a crisis at eBay. In 1999 eBay suffered a database crisis when a single Oracle Database running on the fastest Sun machine available (these tracking Moore's law in this period) was not enough to cope with eBay's growth. The solution was to massively parallelise their system architecture. == Traditional computing markets (blueshifting) == Redshift theory suggests that traditional computing markets, such as those serving enterprise resource planning or customer relationship management applications, have reached relative saturation in industrialized nations. Thereafter, proponents argued further market growth will closely follow gross domestic product growth, which typically remains under 10% for most countries annually. Given that Moore's Law continues to predict accurately the rate of computing transistor growth, which roughly translates into computing power doubling every two years, the Redshift theory suggests that traditional computing markets will ultimately contract as a percentage of computing expenditures over time. Functionally, this means “Blueshifting” customers can satisfy computing requirement growth by swapping in faster processors without increasing the absolute number of computing systems. == Consequences and industry commentary == Papadopoulos argued that while traditional computing markets remain the dominant source of revenue through the late 2000s, a shift to hypergrowth markets will inevitably occur. When that shift occurs, he argued computing (but not computers) will become a utility, and differentiation in the IT market will be based upon a company's ability to deliver computing at massive scale, efficiently and with predictable service levels, much like electricity at that time. If computing is to be delivered as a utility, Nicholas Carr suggested Papadopoulos' vision compares with Microsoft researcher Jim Hamilton, who both agree that computing is most efficiently generated in shipping containers. Industry analysts are also beginning to quantify Redshifting and Blueshifting markets. According to International Data Corporation vice president Matthew Eastwood, "IDC believes that the IT market is in a period of hyper segmentation... This a class of customers that is Moore's law driven and as price performance gains continue, IDC believes that these organizations will accelerate their consumption of IT infrastructure.” == History and nomenclature == Key portions of Papadopoulos' theory were first presented by Sun Microsystems CEO Jonathan Schwartz in late 2006. Papadopoulos later gave a full presentation on Redshift to Sun's annual Analyst Summit in February 2007. The term Redshift refers to what happens when electromagnetic radiation, usually visible light, moves away from an observer. Papadopoulos chose this term to reflect growth markets because redshift helped cosmologists explain the expansion of the universe. Papadopoulos originally depicted traditional IT markets as green to represent their revenue base, but later changed them to “blueshift,” which occurs when a light source moves toward an observer, similar to what would happen during a contraction of the universe.
KKday
KKday is an online travel e-commerce platform focused on connecting independent travelers with authentic, curated local experiences, tours, activities, and attraction tickets. == History == KKday was founded in 2014 in Taipei, Taiwan, by CEO Ming Chen, who previously started and led both Star Travel and Ezfly to IPO. In March of 2016, the company raised US$4.5 million in a Series A round led by AppWorks Ventures with participation by 91Capital. The raise allowed KKday to open offices and expand into Hong Kong, Japan, South Korea and Singapore by 2016. By the end of 2016, KKday offered over 6,000 travel experiences across 53 countries and 174 cities, marking early international expansion with its official launch in Singapore in October 2016, accompanied by promotional campaigns to attract regional users. Expansion into Malaysia, Thailand, Vietnam and the Philippines continued throughout 2017 and into 2018, with the company opening offices in Indonesia and mainland China. KKday rapidly expanded its inventory, reaching over 10,000 experiences in more than 500 cities across 80 countries by 2018, with key markets in Taiwan, Hong Kong, and South Korea. In February 2018, KKday raised $10.5 million in a funding round led by Japanese travel giant H.I.S., allowing integration with larger travel networks and further global growth. Forbes reports that by the end of 2018, the company operated in 11 countries and regions, employed around 400 staff, and recorded over 4 million weekly website views with more than 1 million app downloads. A combination of a Japanese and South Korean trade dispute, along with the Covid-19 pandemic in 2020, lead KKday to pivot quickly toward domestic staycations and local experiences while initially raising $70m in their Series C which, was later extended to $95m. The Series C funds were partially used to accelerate and expand Rezio. Launched in 2019, Rezio is KKday's B2B SaaS booking management platform for travel providers, allowing them to track inventory, manage reservations and sell tickets. FineDayClub was launched in 2020 by KKday as a personalized luxury subscription travel service to cater to high end clients. KKday’s CFO, Jenny Tsai pivoted to lead KKday’s new venture. KKday was able to successfully navigate and adapt to travel patterns during the Covid-19 pandemic by reducing user acquisition costs by two thirds and focusing on domestic travel experiences to drive bookings and revenue. KKday was particularly successful in Vietnam, with bookings increased by 2,000% through 2022 and the company's travel operator platform Rezio, onboarding over 1,200 operators inside the country. In 2021, KKday acquired Activity Japan, a domestic focused travel company, founded by Kimiharu Obuchi in 2014. The successful acquisition, a key factor in KKday’s rapid expansion in the Japanese market, was facilitated by H.I.S., a common early investor in both platforms. In 2023 KKday inked a partnership with Rail Europe to create an all-in-one platform for 150 rail lines over 33 European countries with the intent of increasing ridership across Europe. In late 2024, KKday completed its Series D at $70M, bringing the total amount of capital raised to over $250M. The funds are to be earmarked for continued global expansion, artificial intelligence integration and enhanced partnerships, similar to the partnership with Tablelog, which now allows users to book restaurant reservations at 42,000 restaurants in Japan through the platform. == Platform == KKDay is an e-commerce online travel agency operating in 92 countries with over 350,000 travel experiences available for booking. The company started with focus on authentic local travel experiences in the Asian Pacific market and has expanded to a more global focus. KKday connects travelers with travel services and experiences such as attraction tickets, theme parks, cultural experiences, and seasonal events. KKday has positioned itself as an all-in-one travel super app with booking for hotels, rental cars, flights, sim cards, rail passes, dining and tickets. === Rezio === Rezio is a cloud-based SaaS booking management platform developed by KKday specifically for tour operators, activity providers, and attractions in the travel industry. It serves as an all-in-one system designed to help these businesses digitize their operations, particularly those previously relying on offline processes. Features include a mobile app for on-the-go order management, customer information checks, and voucher scanning, as well as channel management, analytics for customer data, and integrations with multiple OTAs and payment providers. Unlike KKday, which is an OTA marketplace for consumer exposure (with commissions), Rezio focuses on backend operations for suppliers, allowing brand independence, operational efficiency, and direct customer relationships while optionally connecting to OTAs like KKday. Rezio supports over 5,000 merchants, 30,000 experiences, and 10 million travelers worldwide, with a strong presence in Asia. One of the brands successful implementations was at the Nikko Toshogu Shrine where Rezio was implemented to help with long lines and wait times due to over-tourism. The shrine was able to implement the inventory management features to allow online booking and cashless payments onsite. === FineDayClub === FineDayClub is a membership-based travel concierge service launched in late 2020 by KKday. It is aimed at families, and organizations seeking customized travel experiences. It offers one-on-one advisory services. === ActivityJapan === ActivityJapan is a Japanese comprehensive online travel site that specializes in authentic Japanese travel experiences. It was purchased by KKday in 2021 but continues to operate independently.
LMArena
Arena (formerly LMArena and Chatbot Arena) is a public, web-based platform that evaluates large language models (LLMs). Users enter prompts for two anonymous models to respond to and vote on the model that gave the better response, after which the models' identities are revealed. Users can also choose models to test themselves via the "Direct" selection. Companies which have supplied the company with their large language models include OpenAI, Google DeepMind, and Anthropic. The website has been used for preview releases of upcoming models. Chinese company DeepSeek tested its prototype models in the Arena months before its R1 model gained attention in Western media. Other notable pre-release models include OpenAI's GPT-5 under the codename "summit" and Google DeepMind's Gemini 2.5 Flash Image (an image-generation and editing model) under the codename "Nano Banana". Research has identified specific limitations in Arena's methodology. == History == Chatbot Arena was released on April 24, 2023. In June 2024, Chatbot Arena added image support. In September 2024, Chatbot Arena moved to its own dedicated domain name, lmarena.ai (or LMArena). In April 2025, Meta released Llama 4. Llama 4 Maverick beat GPT-4o and Gemini 2.0 Flash on LMArena, but the version of Maverick on LMArena unfairly differed from the publicly available version. LMArena updated their policies in response. In April 2025, LMArena incorporated as an independent company. That May, LMArena raised $100 million in a seed funding round, valuing the company at $600 million. Participants in the seed funding round included Andreessen Horowitz, UC Investments, Lightspeed Venture Partners, Felicis Ventures, and Kleiner Perkins. On January 6, 2026, LMArena announced the closing of a $150 million Series A funding round, bringing the company’s post-money valuation to approximately $1.7 billion. The round was led by Felicis and UC Investments (University of California), with participation from Andreessen Horowitz, The House Fund, LDVP, Kleiner Perkins, Lightspeed Venture Partners, and Laude Ventures. In January 2026, LMArena added video support. On January 28, 2026, LMArena rebranded to "Arena".
Outline of telecommunication
The following outline is provided as an overview of and topical guide to telecommunication: Telecommunication – the transmission of signals over a distance for the purpose of communication. In modern times, this process almost always involves the use of electromagnetic waves by transmitters and receivers, but in earlier years it also involved the use of drums and visual signals such as smoke, fire, beacons, semaphore lines and other optical communications. == Modes of telecommunication == E-mail Fax Instant messaging Radio Satellite SMS Telegraphy Telephony Television Television broadcasting mobile telephony Videoconferencing VoIP Voicemail == Types of telecommunication networks == Telecommunications network Computer networks ARPANET Ethernet Internet Wireless networks Public switched telephone networks (PSTN) Packet switched networks Radio network Broadband Wireless Broadband == Aspects of telecommunication transmission == Telecommunication Analog Digital Functional profile Optics === Telecommunication technology === Modulation Amplitude modulation Frequency modulation Quadrature amplitude modulation Nyquist rate Nyquist ISI criterion Pulse shaping Intersymbol interference === Communications media types === Physical media for Telecommunication Twisted pair Coaxial cable Optical fiber Telecommunication through Free Space Broadcast radio frequency including television and radio Line-of-sight Communications satellite Terrestrial Microwave Wireless LAN === Relationship between media and transmitters === Physical access to media Simplex Duplex (telecommunications) Logical relationships Return channel Two-way alternating Two-way simultaneous === Multiple access to media === Multiplexing Analog Frequency division multiplexing Space division multiplexing Digital Time-division multiplexing Statistical multiplexing and Packet switching Media Access Control Contention Token-based Centralized token control Distributed token control == History of telecommunication == History of telecommunication History of telegraphy History of the telephone Invention of the telephone Timeline of the telephone History of radio History of television History of videophones History of mobile phones History of computing hardware History of the Internet == Major telecommunications equipment manufacturers == Alcatel-Lucent – French global telecommunications equipment company Aricent – Former company AT&T – American telecommunications company Avaya – American technology company Ciena – American telecommunications company Cisco Systems – American multinational technology companyPages displaying short descriptions of redirect targets Ericsson – Swedish multinational networking and telecommunications company Fujitsu – Japanese multinational technology company HCL Technologies – Indian multinational technology companyPages displaying short descriptions of redirect targets Huawei – Chinese multinational technology company NEC – Japanese technology corporation Nokia – Multinational data networking and telecommunications equipment company ShoreTel – US telecommunications company Verizon – American telecommunications company ZTE – Chinese telecommunications company == Major telecommunications service providers == List of mobile network operators List of telephone operating companies == Telecommunication organizations == Alliance for Telecommunications Industry Solutions Telecommunications Industry Association == Telecommunication publications == Magazines Billing and OSS World Cabling Installation & Maintenance Call Center Communications News Communications System Design Lightwave Mobile Radio Technology (MRT) New Telephony Phone+ RCR Wireless News Telecom Asia Telecommunications Magazine Telephony WhatSatphone Magazine Wireless Systems Design Wireless Week Xchange == Persons influential in telecommunication == Edwin Howard Armstrong – American radio-frequency engineer and inventor (1890–1954) John Logie Baird – Scottish inventor (1888–1946) Paul Baran – American-Jewish engineer (1926–2011) Alexander Graham Bell – Inventor of the telephone (1847–1922) Tim Berners-Lee – English computer scientist (born 1955) Jagadish Chandra Bose – Physicist, biologist and botanist (1857–1937) Vint Cerf – American computer scientist and Internet pioneer (born 1943) Claude Chappe – Late 18th-century French inventor Donald Davies – British computer scientist (1924–2000) Louis Pouzin – French computer scientist and Internet pioneer (born 1931) Lee de Forest – American inventor (1873–1961) Philo Farnsworth – American inventor (1906–1971) Reginald Fessenden – Canadian-American electrical engineer and inventor (1866–1932) Elisha Gray – American electrical engineer (1835–1901) Innocenzo Manzetti – Italian inventor (1826–1877) Guglielmo Marconi – Italian radio-frequency engineer and inventor (1874–1937) Antonio Meucci – Italian inventor (1808–1889) Alexander Stepanovich Popov – Russian physicist (1859–1906)Pages displaying short descriptions of redirect targets Johann Philipp Reis – German scientist and inventor Almon Brown Strowger – American inventor of the telephone exchange (1839–1902) Nikola Tesla – Serbian-American engineer and inventor (1856–1943) Camille Tissot – French physicist (1868–1917) Alfred Vail – 19th-century American machinist and inventor Charles Wheatstone – English physicist and inventor (1802–1875) Vladimir K. Zworykin – Russian-American engineer (1888–1982)