Evidence-based library and information practice (EBLIP) or evidence-based librarianship (EBL) is the use of evidence-based practices (EBP) in the field of library and information science (LIS). This means that all practical decisions made within LIS should 1) be based on research studies and 2) that these research studies are selected and interpreted according to some specific norms characteristic for EBP. Typically such norms disregard theoretical studies and qualitative studies and consider quantitative studies according to a narrow set of criteria of what counts as evidence. If such a narrow set of methodological criteria are not applied, it is better instead to speak of research based library and information practice. == Characteristics == Evidence-based practice in general has been characterised as a positivist approach; EBLIP is therefore also a positivist approach to LIS. As such, EBLIP is an approach in contrast to other approaches to LIS. The use of statistical approaches known as meta-analysis to conclude what evidence has been reported in the literature is one among other methods which is typical for the evidence-based approach. In 2002, Booth noted the three schools of EBILP had some commonalities, including the context of day-to-day decision-making, an emphasis on improving the quality of professional practice, a pragmatic focus on the 'best available evidence', incorporation of the user perspective, the acceptance of a broad range of quantitative and qualitative research designs, and access, either first-hand or second-hand, to the (process of) evidence-based practice and its products. He added one more, that EBILP is concerned with getting the best value for money. == The role of library and information science in EBP == Evidence-based practice in general is based on a very thorough search of the scientific literature and a very thorough selection and analysis of the retrieved literature. A close familiarity with database searching is needed, and library and information professionals have important roles to play in this respect. Therefore LIS professionals should be well suited to help professionals in other disciplines doing EBP. EBLIP is the application of this approach on LIS itself. It should be mentioned, however, that EBP started in medicine as evidence-based medicine (EBM) from which it spread to other fields. Only slowly and to a limited extent has EBP moved on to LIS. The EBLIP process can be applied to a variety of scenarios in LIS, including customer service, collection development, library management and information literacy instruction. In general, quantitative methods are used in LIS research. A 2010 study revealed five categories that capture the different ways library and information professionals experience evidence-based practice: Evidence-based practice is experienced as irrelevant; Evidence-based practice is experienced as learning from published research; Evidence-based practice is experienced as service improvement; Evidence-based practice is experienced as a way of being; Evidence-based practice is experienced as a weapon.
A Logical Calculus of the Ideas Immanent in Nervous Activity
"A Logical Calculus of the Ideas Immanent in Nervous Activity" is a 1943 paper written by Warren Sturgis McCulloch and Walter Pitts, published in the journal The Bulletin of Mathematical Biophysics. The paper proposed a mathematical model of the nervous system as a network of simple logical elements, later known as artificial neurons, or McCulloch–Pitts neurons. These neurons receive inputs, perform a weighted sum, and fire an output signal based on a threshold function. By connecting these units in various configurations, McCulloch and Pitts demonstrated that their model could perform all logical functions. It is a seminal work in cognitive science, computational neuroscience, computer science, and artificial intelligence. It was a foundational result in automata theory. John von Neumann cited it as a significant result. == Mathematics == The artificial neuron used in the original paper is slightly different from the modern version. They considered neural networks that operate in discrete steps of time t = 0 , 1 , … {\displaystyle t=0,1,\dots } . The neural network contains a number of neurons. Let the state of a neuron i {\displaystyle i} at time t {\displaystyle t} be N i ( t ) {\displaystyle N_{i}(t)} . The state of a neuron can either be 0 or 1, standing for "not firing" and "firing". Each neuron also has a firing threshold θ {\displaystyle \theta } , such that it fires if the total input exceeds the threshold. Each neuron can connect to any other neuron (including itself) with positive synapses (excitatory) or negative synapses (inhibitory). That is, each neuron can connect to another neuron with a weight w {\displaystyle w} taking an integer value. A peripheral afferent is a neuron with no incoming synapses. We can regard each neural network as a directed graph, with the nodes being the neurons, and the directed edges being the synapses. A neural network has a circle or a circuit if there exists a directed circle in the graph. Let w i j ( t ) {\displaystyle w_{ij}(t)} be the connection weight from neuron j {\displaystyle j} to neuron i {\displaystyle i} at time t {\displaystyle t} , then its next state is N i ( t + 1 ) = H ( ∑ j = 1 n w i j ( t ) N j ( t ) − θ i ( t ) ) , {\displaystyle N_{i}(t+1)=H\left(\sum _{j=1}^{n}w_{ij}(t)N_{j}(t)-\theta _{i}(t)\right),} where H {\displaystyle H} is the Heaviside step function (outputting 1 if the input is greater than or equal to 0, and 0 otherwise). === Symbolic logic === The paper used, as a logical language for describing neural networks, "Language II" from The Logical Syntax of Language by Rudolf Carnap with some notations taken from Principia Mathematica by Alfred North Whitehead and Bertrand Russell. Language II covers substantial parts of classical mathematics, including real analysis and portions of set theory. To describe a neural network with peripheral afferents N 1 , N 2 , … , N p {\displaystyle N_{1},N_{2},\dots ,N_{p}} and non-peripheral afferents N p + 1 , N p + 2 , … , N n {\displaystyle N_{p+1},N_{p+2},\dots ,N_{n}} they considered logical predicate of form P r ( N 1 , N 2 , … , N p , t ) {\displaystyle Pr(N_{1},N_{2},\dots ,N_{p},t)} where P r {\displaystyle Pr} is a first-order logic predicate function (a function that outputs a boolean), N 1 , … , N p {\displaystyle N_{1},\dots ,N_{p}} are predicates that take t {\displaystyle t} as an argument, and t {\displaystyle t} is the only free variable in the predicate. Intuitively speaking, N 1 , … , N p {\displaystyle N_{1},\dots ,N_{p}} specifies the binary input patterns going into the neural network over all time, and P r ( N 1 , N 2 , … , N n , t ) {\displaystyle Pr(N_{1},N_{2},\dots ,N_{n},t)} is a function that takes some binary input patterns, and constructs an output binary pattern P r ( N 1 , N 2 , … , N n , 0 ) , P r ( N 1 , N 2 , … , N n , 1 ) , … {\displaystyle Pr(N_{1},N_{2},\dots ,N_{n},0),Pr(N_{1},N_{2},\dots ,N_{n},1),\dots } . A logical sentence P r ( N 1 , N 2 , … , N n , t ) {\displaystyle Pr(N_{1},N_{2},\dots ,N_{n},t)} is realized by a neural network iff there exists a time-delay T ≥ 0 {\displaystyle T\geq 0} , a neuron i {\displaystyle i} in the network, and an initial state for the non-peripheral neurons N p + 1 ( 0 ) , … , N n ( 0 ) {\displaystyle N_{p+1}(0),\dots ,N_{n}(0)} , such that for any time t {\displaystyle t} , the truth-value of the logical sentence is equal to the state of the neuron i {\displaystyle i} at time t + T {\displaystyle t+T} . That is, ∀ t = 0 , 1 , 2 , … , P r ( N 1 , N 2 , … , N p , t ) = N i ( t + T ) {\displaystyle \forall t=0,1,2,\dots ,\quad Pr(N_{1},N_{2},\dots ,N_{p},t)=N_{i}(t+T)} === Equivalence === In the paper, they considered some alternative definitions of artificial neural networks, and have shown them to be equivalent, that is, neural networks under one definition realizes precisely the same logical sentences as neural networks under another definition. They considered three forms of inhibition: relative inhibition, absolute inhibition, and extinction. The definition above is relative inhibition. By "absolute inhibition" they meant that if any negative synapse fires, then the neuron will not fire. By "extinction" they meant that if at time t {\displaystyle t} , any inhibitory synapse fires on a neuron i {\displaystyle i} , then θ i ( t + j ) = θ i ( 0 ) + b j {\displaystyle \theta _{i}(t+j)=\theta _{i}(0)+b_{j}} for j = 1 , 2 , 3 , … {\displaystyle j=1,2,3,\dots } , until the next time an inhibitory synapse fires on i {\displaystyle i} . It is required that b j = 0 {\displaystyle b_{j}=0} for all large j {\displaystyle j} . Theorem 4 and 5 state that these are equivalent. They considered three forms of excitation: spatial summation, temporal summation, and facilitation. The definition above is spatial summation (which they pictured as having multiple synapses placed close together, so that the effect of their firing sums up). By "temporal summation" they meant that the total incoming signal is ∑ τ = 0 T ∑ j = 1 n w i j ( t ) N j ( t − τ ) {\displaystyle \sum _{\tau =0}^{T}\sum _{j=1}^{n}w_{ij}(t)N_{j}(t-\tau )} for some T ≥ 1 {\displaystyle T\geq 1} . By "facilitation" they meant the same as extinction, except that b j ≤ 0 {\displaystyle b_{j}\leq 0} . Theorem 6 states that these are equivalent. They considered neural networks that do not change, and those that change by Hebbian learning. That is, they assume that at t = 0 {\displaystyle t=0} , some excitatory synaptic connections are not active. If at any t {\displaystyle t} , both N i ( t ) = 1 , N j ( t ) = 1 {\displaystyle N_{i}(t)=1,N_{j}(t)=1} , then any latent excitatory synapse between i , j {\displaystyle i,j} becomes active. Theorem 7 states that these are equivalent. === Logical expressivity === They considered "temporal propositional expressions" (TPE), which are propositional formulas with one free variable t {\displaystyle t} . For example, N 1 ( t ) ∨ N 2 ( t ) ∧ ¬ N 3 ( t ) {\displaystyle N_{1}(t)\vee N_{2}(t)\wedge \neg N_{3}(t)} is such an expression. Theorem 1 and 2 together showed that neural nets without circles are equivalent to TPE. For neural nets with loops, they noted that "realizable P r {\displaystyle Pr} may involve reference to past events of an indefinite degree of remoteness". These then encodes for sentences like "There was some x such that x was a ψ" or ( ∃ x ) ( ψ x ) {\displaystyle (\exists x)(\psi x)} . Theorems 8 to 10 showed that neural nets with loops can encode all first-order logic with equality and conversely, any looped neural networks is equivalent to a sentence in first-order logic with equality, thus showing that they are equivalent in logical expressiveness. As a remark, they noted that a neural network, if furnished with a tape, scanners, and write-heads, is equivalent to a Turing machine, and conversely, every Turing machine is equivalent to some such neural network. Thus, these neural networks are equivalent to Turing computability and Church's lambda-definability. == Context == === Previous work === The paper built upon several previous strands of work. In the symbolic logic side, it built on the previous work by Carnap, Whitehead, and Russell. This was contributed by Walter Pitts, who had a strong proficiency with symbolic logic. Pitts provided mathematical and logical rigor to McCulloch’s vague ideas on psychons (atoms of psychological events) and circular causality. In the neuroscience side, it built on previous work by the mathematical biology research group centered around Nicolas Rashevsky, of which McCulloch was a member. The paper was published in the Bulletin of Mathematical Biophysics, which was founded by Rashevsky in 1939. During the late 1930s, Rashevsky's research group was producing papers that had difficulty publishing in other journals at the time, so Rashevsky decided to found a new journal exclusively devoted to mathematical biophysics. Also in the Rashevsky's group was Alston Scott Householder, who in 1941 published an abstract model
Over-the-top media services in India
As per Govt of India, there are currently about 57 providers of over-the-top media services (OTT) in India, which distribute streaming media or video on demand over the Internet. == History and growth == The first dependent Indian OTT platform was BIGFlix, launched by Reliance Entertainment in 2008. In 2010 Digivive launched India's first OTT mobile app called nexGTv, which provides access to both live TV and on–demand content. nexGTV was the first app to live–stream Indian Premier League matches on smart phones and did so during 2013 and 2014. The livestream of the IPL since 2015, when rights were won, played an important role in the growth of another OTT platform, Hotstar (now JioHotstar) in India. OTT Platforms gained significant momentum in India when both DittoTV (Zee) and Sony Liv were launched in the Indian market around 2013. Following the initial push of Regional OTT platforms like Aha, Hoichoi, Sun NXT, Planet Marathi, Chaupal & MX Player. The Indian OTT industry saw rapid transformation with the entry of global OTT companies such as Netflix and Amazon Prime Video into the Indian market in 2016. Replacement of this competition with global enterprises caused local rivals to innovate in both region and hyper-regional content. === Hotstar === Hotstar (now JioHotstar) is the most subscribed–to OTT platform in India, owned by JioStar as of February 2025, with around 500 million active users and over 650 million downloads. According to Hotstar's India Watch Report 2018, 96% of watch time on Hotstar comes from videos longer than 20 minutes, while one–third of Hotstar subscribers watch television shows. In 2019, Hotstar began investing ₹120 crore in generating original content such as "Hotstar Specials." 80% of the viewership on Hotstar comes from drama, movies and sports programs. Hotstar has the exclusive streaming rights of IPL in India. === Netflix === American streaming service Netflix entered India in January 2016. In April 2017, it was registered as a limited liability partnership (LLP) and started commissioning content. It earned a net profit of ₹2020,000 (₹2.02 million) for fiscal year 2017. In fiscal year 2018, Netflix earned revenues of ₹580 million. According to Morgan Stanley Research, Netflix had the highest average watch time of more than 120 minutes but viewer counts of around 20 million in July 2018. As of 2018, Netflix has six million subscribers, of which 5–6% are paid members. India was not affected by Netflix's July 2018 increase in subscription rates for the US and Latin America. Netflix has stated its intent to invest ₹600 crore in the production of Indian original programming. In late 2018, Netflix bought 150,000 square feet (14,000 m2) of office space in Bandra–Kurla Complex (BKC) in Mumbai as their head office. As of December 2018, Netflix has more than 40 employees in India. === Other OTT providers === Sun NXT is an Indian video on demand service run by Sun TV Network. It was launched in June 2017, streaming in the Tamil language and six other languages. The platform has more than 4,000 Tamil movies and 200 Tamil shows, as well as regional movies and shows. Sun NXT also streams a large library of its own Sun TV shows and movies. Amazon Prime Video was launched in 2016. The platform has 2,300 titles available including 2,000 movies and about 400 shows. It has announced that it will invest ₹20 billion in creating original content in India. Besides English, Prime Video is available in six Indian languages as of December 2018. Amazon India launched Amazon Prime Music in February 2018. Eros Now, an OTT platform launched by Eros International, has the most content among the OTT providers in India, including over 12,000 films, 100,000 music tracks and albums, and 100 TV shows. Eros Now was named the Best OTT Platform of the Year 2019 at the British Asian Media Awards. It has 211.5 million registered users and 36.2 million paying subscribers as of September 2020. In February 2020, Aha OTT platform was launched, broadcasting exclusively Telugu content. In 2021, Planet Marathi became the first OTT platform dedicated to Marathi content in India, including web-series, films, music, theater, fiction and non-fiction reality shows. It is available for both Android and iOS mobile devices along with Android TV and Amazon Fire TV devices. Bollywood actress Madhuri Dixit helped launch the platform. With rising interest for Korean dramas, Rakuten Viki saw its biggest jump of web traffic from India in 2020 due to the COVID-19 lockdown, which led to ad localization on the platform. The OTT market in fiscal year 2020 was estimated to be worth $1.7 billion. === SonyLIV and ZEE5 === In December 2021, Sony and Zee announced their merger, and announced plans to merge their OTT platforms. The merger was called off. === OTT services launched as Amazon Prime video channels === The list is by alphabetical order, not by rank or popularity. == Content regulation == Due to the absence of any rules and regulation regarding OTT content, many OTT providers were accused of showing nudity, vulgarity and obscenity and hurting Hindu religious sentiments in their shows. Series which were the focus of controversy include Four More Shots Please!, Tandav, Paatal Lok, Sacred Games, Mirzapur Lust stories franchise, Rana Naidu. Thank You for Coming, and Annapoorani (2023). According to media reports, between 2018 and 2024, some OTT platforms emerged which started showing porn in the form of web series. Both the Supreme Court and Delhi High Court say that OTT regulation is necessary. === OTT regulation === On 25 Feb 2021, Indian govt introduced self-regulation rules for OTT platforms to stop obscene content and abusive language. On 19 March 2023, I&B minister Anurag Thakur said that self regulation does not mean that OTT should show obscenity and nudity. On 15 April 2023, I&B Secretary Apurva Chandra has said because of the government's soft-touch regulations on OTT industry have led to the creation of content that is undesirable and vulgar. On 26 April 2023, MIB India said that if nudity and obscenity is seen on any OTT platform, strict action will be taken against it. On 16 May 2023, Don't show obscene content, parliamentary panel told to Netflix and Amazon Prime Video. On 20 June 2023, the government told Netflix, Disney+ Hotstar and all other streaming services that their content should be independently reviewed for obscenity and violence before being shown online. On 27 June 2023, DPCGC took punitive action against Ullu for streaming obscene content and asked them to remove all their explicit shows or remove all adult scenes within 15 days. On 18 July 2023, Anarug Thakur said in a meeting with all OTT stakeholders that demeaning Indian culture will not be tolerated. OTT can't show vulgarity and nudity in the garb of 'creative expression'.The cited sources do not mention vulgarity - they say this was about demeaning Indian culture/society. On 22 August 2023, Indian government assured that it will bring rules and regulation to regulate vulgar and obscene content on social media and OTT platforms. On 10 November 2023, MIB India introduces the 'Broadcasting Service Regulation Bill', which included Programme code with Content Evaluation Committee(CEC) for every OTT platforms. Currently public consultation is ongoing till 15 January 2024. The draft bill mandates that all OTT streaming platforms can only broadcast those web series or content, which will be duly certified by Content Evaluation Committee(CEC). On 14 March 2024, the Ministry of Information and Broadcasting banned over 18 OTT apps from Google play store and suspended all of their 57 social media accounts, as well as closed nineteen streaming websites. The banned platforms were MoodX, Prime Play, Hunters, Besharams, Rabbit movies, Voovi, Fugi, Mojflix, Chikooflix, Nuefliks, Xtramood, NeonX VIP, X Prime, Tri Flicks, Uncut Adda, Dreams Films, Hot Shots VIP, and Yessma. On 25 July 2025, the Ministry of Information and Broadcasting banned from 25 OTT apps from Google play store and suspended all of their 40 social media accounts, as well as 26 closed streaming websites. The banned platforms were include ALTT, Ullu, Big Shots App, Desiflix, Boomex, NeonX VIP, Navarasa Lite, Gulab App, Kangan App, Bull App, ShowHit, Jalva App, Wow Entertainment, Look Entertainment, Hitprime, Fugi, Feneo, ShowX, Sol Talkies, Adda TV, HotX VIP, Hulchul App, MoodX, Triflicks, and Mojflix. On 24 February 2026, the Ministry of Information and Broadcasting banned from 5 OTT apps from Google play store and suspended all of their 5 social media accounts, as well as 5 closed streaming websites. The banned platforms were include Feel App, Digi Movieplex, Jugnu App, MoodX VIP, and Koyal Playpro. === Legal action === Currently OTT is regulated under the IT Rules 2021, which clearly stated that 'No content that is prohibited by law at the time being force can be Publishing or transmitted'. MIB has continuously taking action
Key & See
Key & See is a variation of the TV Key service that forms part of the open, standards-based interactive TV services platform provided by Miniweb Interactive. Key & See allows viewers to access the interactive TV content made available by broadcasters and channel owners while leaving quarter of their screen tuned to the programme they are already watching Like TV Key, Key & See can be used with interactive TV services on UK satellite TV provider Sky Digital (BSkyB) Key & See works in the same way as a TV Key but the numeric shortcut code is associated with a broadcaster and a particular TV channel or programme. Miniweb Interactive offers commercial brands and broadcasters the chance to utilise TV Key and Key & See technology as part of its interactive TV services platform
Glossary of operating systems terms
This page is a glossary of Operating systems terminology. == A == access token: In Microsoft Windows operating systems, an access token contains the security credentials for a login session and identifies the user, the user's groups, the user's privileges, and, in some cases, a particular application. == B == binary semaphore: See semaphore. booting: In computing, booting (also known as booting up) is the initial set of operations that a computer performs after electrical power is switched on or when the computer is reset. This can take tens of seconds and typically involves performing a power-on self-test, locating and initializing peripheral devices, and then finding, loading and starting the operating system. == C == cache: In computer science, a cache is a component that transparently stores data so that future requests for that data can be served faster. The data that is stored within a cache might be values that have been computed earlier or duplicates of original values that are stored elsewhere. cloud: Cloud computing operating systems are recent, and were not mentioned in Gagne's 8th Edition (2009). In contrast, by Gagne's 9th (2012), cloud o/s received 3 pages of coverage (41, 42, 716). Doeppner (2011) mentions them (p. 3), but only to prove that operating systems "are not a solved problem" and that even if the day of the dedicated PC is waning, cloud computing has created an entirely new opportunity for o/s development ala sharing, networks, memory, parallelism, etc. Gagne (2012) adds that in addition to numerous traditional o/s's at cloud warehouses, Virtual machine o/s (VMMs), Eucalyptus, Vware, vCloud Director and others are being developed specifically for cloud management with numerous traditional o/s features (security, threads, file and memory management, guis, etc.) (p. 42). Microsoft's investment in cloud aspects of o/s tend to support that argument. concurrency == D == daemon: Operating systems often start daemons at boot time and serve the function of responding to network requests, hardware activity, or other programs by performing some task. Daemons can also configure hardware (like udevd on some Linux systems), run scheduled tasks (like cron), and perform a variety of other tasks. == E == == F == == G == == H == == I == == J == == K == kernel: In computing, the kernel is a computer program that manages input/output requests from software and translates them into data processing instructions for the central processing unit and other electronic components of a computer. The kernel is a fundamental part of a modern computer's operating system. == L == lock: In computer science, a lock or mutex (from mutual exclusion) is a synchronization mechanism for enforcing limits on access to a resource in an environment where there are many threads of execution. A lock is designed to enforce a mutual exclusion concurrency control policy. == M == mutual exclusion: Mutual exclusion is to allow only one process at a time to access the same critical section (a part of code which accesses the critical resource). This helps prevent race conditions. mutex: See lock. == N == == O == == P == paging daemon: See daemon. process == Q == == R == == S == semaphore: In computer science, particularly in operating systems, a semaphore is a variable or abstract data type that is used for controlling access, by multiple processes, to a common resource in a parallel programming or a multi user environment. == T == thread: In computer science, a thread of execution is the smallest sequence of programmed instructions that can be managed independently by an operating system scheduler. The scheduler itself is a light-weight process. The implementation of threads and processes differs from one operating system to another, but in most cases, a thread is contained inside a process. templating: In an o/s context, templating refers to creating a single virtual machine image as a guest operating system, then saving it as a tool for multiple running virtual machines (Gagne, 2012, p. 716). The technique is used both in virtualization and cloud computing management, and is common in large server warehouses. == U == == V == == W == == Z ==
Haskins Laboratories
Haskins Laboratories, Inc. is an independent research laboratory, founded in 1935 and located in New Haven, Connecticut since 1970. Many current Haskins researchers are affiliated with Yale University's Child Study Center and/or the University of Connecticut. Haskins is a multidisciplinary and international community of researchers who conduct basic research on spoken and written language and global literacy. A guiding perspective of their research has been to view speech and language as emerging from biological processes, including those of adaptation, response to stimuli, and conspecific interaction. Haskins Laboratories has a long history of technological and theoretical innovation, from creating systems of rules for speech synthesis and development of an early working prototype of a reading machine for the blind to developing the landmark concept of phonemic awareness as the critical preparation for learning to read an alphabetic writing system. == Research tools and facilities == Haskins Laboratories is equipped, in-house, with a comprehensive suite of tools and capabilities to advance its mission of research into language and literacy. As of 2014, these included: Anechoic chamber Electroencephalography BioSemi 264 electrode, 24 bit Active Two System EGI 128 electrode, Geodesic EEG System 300 Electromagnetic articulography (EMMA) Carstens AG501 NDI WAVE Eye tracking: HL is equipped with 3 SR Research eye-trackers. 2 Model Eyelink 1000 systems. 1 Model Eyelink 1000plus system. Magnetic resonance imaging: Haskins has access to MRI scanners through agreements with the University of Connecticut and the Yale School of Medicine. On-site, HL has a Linux computer cluster dedicated to analysis of MRI data. Motion capture: HL is equipped with a Vicon motion capture system with one Basler high-speed digital camera, six Vicon MX T-20 cameras and a Vicon MX Giganet for synching camera data and connecting cameras to the data capture computer. Near infrared spectroscopy: HL has a TechEn CW6 8x8 system (four emitters; eight detectors). Ultrasound sonogram == History == Many researchers have contributed to scientific breakthroughs at Haskins Laboratories since its founding. All of them are indebted to the pioneering work and leadership of Caryl Parker Haskins, Franklin S. Cooper, Alvin Liberman, Seymour Hutner and Luigi Provasoli. The history presented here focuses on the research program of the division of Haskins Laboratories that, since the 1940s, has been most well known for its work in the areas of speech, language, and reading. === 1930s === Caryl Haskins and Franklin S. Cooper established Haskins Laboratories in 1935. It was originally affiliated with Harvard University, MIT, and Union College in Schenectady, NY. Caryl Haskins conducted research in microbiology, radiation physics, and other fields in Cambridge, MA and Schenectady. In 1939 Haskins Laboratories moved its center to New York City. Seymour Hutner joined the staff to set up a research program in microbiology, genetics, and nutrition. The descendant of the division led by Hutner program eventually became a department of Pace University in New York. The two identically named organizations are no longer formally affiliated. === 1940s === The U. S. Office of Scientific Research and Development, under Vannevar Bush asked Haskins Laboratories to evaluate and develop technologies for assisting blinded World War II veterans. Experimental psychologist Alvin Liberman joined Haskins Laboratories to assist in developing a "sound alphabet" to represent the letters in a text for use in a reading machine for the blind. Luigi Provasoli joined Haskins Laboratories to set up a research program in marine biology. The program in marine biology moved to Yale University in 1970 and disbanded with Provasoli's retirement in 1978. === 1950s === Franklin S. Cooper invented the pattern playback, a machine that converts pictures of the acoustic patterns of speech back into sound. With this device, Alvin Liberman, Cooper, and Pierre Delattre (and later joined by Katherine Safford Harris, Leigh Lisker, Arthur Abramson, and others), discovered the acoustic cues for the perception of phonetic segments (consonants and vowels). Liberman and colleagues proposed a motor theory of speech perception to resolve the acoustic complexity: they hypothesized that we perceive speech by tapping into a biological specialization, a speech module, that contains knowledge of the acoustic consequences of articulation. Liberman, aided by Frances Ingemann and others, organized the results of the work on speech cues into a groundbreaking set of rules for speech synthesis by the Pattern Playback. === 1960s === Franklin S. Cooper and Katherine Safford Harris, working with Peter MacNeilage, were the first researchers in the U.S. to use electromyographic techniques, pioneered at the University of Tokyo, to study the neuromuscular organization of speech. Leigh Lisker and Arthur Abramson looked for simplification at the level of articulatory action in the voicing of certain contrasting consonants. They showed that many acoustic properties of voicing contrasts arise from variations in voice onset time, the relative phasing of the onset of vocal cord vibration and the end of a consonant. Their work has been widely replicated and elaborated, here and abroad, over the following decades. Donald Shankweiler and Michael Studdert-Kennedy used a dichotic listening technique (presenting different nonsense syllables simultaneously to opposite ears) to demonstrate the dissociation of phonetic (speech) and auditory (nonspeech) perception by finding that phonetic structure devoid of meaning is an integral part of language, typically processed in the left cerebral hemisphere. Liberman, Cooper, Shankweiler, and Studdert-Kennedy summarized and interpreted fifteen years of research in "Perception of the Speech Code", still among the most cited papers in the speech literature. It set the agenda for many years of research at Haskins and elsewhere by describing speech as a code in which speakers overlap (or coarticulate) segments to form syllables. Researchers at Haskins connected their first computer to a speech synthesizer designed by Haskins Laboratories' engineers. Ignatius Mattingly, with British collaborators, John N. Holmes and J.N. Shearme, adapted the Pattern playback rules to write the first computer program for synthesizing continuous speech from a phonetically spelled input. A further step toward a reading machine for the blind combined Mattingly's program with an automatic look-up procedure for converting alphabetic text into strings of phonetic symbols. === 1970s === In 1970, Haskins Laboratories moved to New Haven, Connecticut, and entered into affiliation agreements with Yale University and the University of Connecticut; Haskins remains fully independent of both Yale and UConn, administratively and financially. The lab's original location in New Haven, at 270 Crown Street (from 1970 to 2005), was leased from Yale University. Isabelle Liberman, Donald Shankweiler, and Alvin Liberman teamed up with Ignatius Mattingly to study the relationship between speech perception and reading, a topic implicit in Haskins Laboratories' research program since its inception. They developed the concept of phonemic awareness, the knowledge that would-be readers must be aware of the phonemic structure of their language in order to be able to read. Leonard Katz related the work to contemporary cognitive theory and provided expertise in experimental design and data analysis. Under the broad rubric of the "alphabetic principle", this is the core of the lab's present program of reading pedagogy. Patrick Nye joined Haskins Laboratories to lead a team working on the reading machine for the blind. The project culminated when the addition of an optical character recognizer allowed investigators to assemble the first automatic text-to-speech reading machine. By the end of the decade this technology had advanced to the point where commercial concerns assumed the task of designing and manufacturing reading machines for the blind. In 1973, Franklin S. Cooper was selected to form a panel of six experts charged with investigating the famous 18-minute gap in the White House office tapes of President Richard Nixon related to the Watergate scandal. Building on earlier work, Philip Rubin developed the sinewave synthesis program, which was then used by Robert Remez, Rubin, and colleagues to show that listeners can perceive continuous speech without traditional speech cues from a pattern of sinewaves that track the changing resonances of the vocal tract. This paved the way for a view of speech as a dynamic pattern of trajectories through articulatory-acoustic space. Philip Rubin and colleagues developed Paul Mermelstein's anatomically simplified vocal tract model, originally worked on at Bell Laboratories, into the first articulatory synthesizer that can be controlled in a phy
Signal-to-interference-plus-noise ratio
In information theory and telecommunication engineering, the signal-to-interference-plus-noise ratio (SINR) (also known as the signal-to-noise-plus-interference ratio (SNIR)) is a quantity used to give theoretical upper bounds on channel capacity (or the rate of information transfer) in wireless communication systems such as networks. Analogous to the signal-to-noise ratio (SNR) used often in wired communications systems, the SINR is defined as the power of a certain signal of interest divided by the sum of the interference power (from all the other interfering signals) and the power of some background noise. If the power of noise term is zero, then the SINR reduces to the signal-to-interference ratio (SIR). Conversely, zero interference reduces the SINR to the SNR, which is used less often when developing mathematical models of wireless networks such as cellular networks. The complexity and randomness of certain types of wireless networks and signal propagation has motivated the use of stochastic geometry models in order to model the SINR, particularly for cellular or mobile phone networks. == Description == SINR is commonly used in wireless communication as a way to measure the quality of wireless connections. Typically, the energy of a signal fades with distance, which is referred to as a path loss in wireless networks. Conversely, in wired networks the existence of a wired path between the sender or transmitter and the receiver determines the correct reception of data. In a wireless network one has to take other factors into account (e.g. the background noise, interfering strength of other simultaneous transmission). The concept of SINR attempts to create a representation of this aspect. == Mathematical definition == The definition of SINR is usually defined for a particular receiver (or user). In particular, for a receiver located at some point x in space (usually, on the plane), then its corresponding SINR given by S I N R ( x ) = P I + N {\displaystyle \mathrm {SINR} (x){=}{\frac {P}{I+N}}} where P is the power of the incoming signal of interest, I is the interference power of the other (interfering) signals in the network, and N is some noise term, which may be a constant or random. Like other ratios in electronic engineering and related fields, the SINR is often expressed in decibels or dB. == Propagation model == To develop a mathematical model for estimating the SINR, a suitable mathematical model is needed to represent the propagation of the incoming signal and the interfering signals. A common model approach is to assume the propagation model consists of a random component and non-random (or deterministic) component. The deterministic component seeks to capture how a signal decays or attenuates as it travels a medium such as air, which is done by introducing a path-loss or attenuation function. A common choice for the path-loss function is a simple power-law. For example, if a signal travels from point x to point y, then it decays by a factor given by the path-loss function ℓ ( | x − y | ) = | x − y | α {\displaystyle \ell (|x-y|)=|x-y|^{\alpha }} , where the path-loss exponent α>2, and |x-y| denotes the distance between point y of the user and the signal source at point x. Although this model suffers from a singularity (when x=y), its simple nature results in it often being used due to the relatively tractable models it gives. Exponential functions are sometimes used to model fast decaying signals. The random component of the model entails representing multipath fading of the signal, which is caused by signals colliding with and reflecting off various obstacles such as buildings. This is incorporated into the model by introducing a random variable with some probability distribution. The probability distribution is chosen depending on the type of fading model and include Rayleigh, Rician, log-normal shadow (or shadowing), and Nakagami. == SINR model == The propagation model leads to a model for the SINR. Consider a collection of n {\displaystyle n} base stations located at points x 1 {\displaystyle x_{1}} to x n {\displaystyle x_{n}} in the plane or 3D space. Then for a user located at, say x = 0 {\displaystyle x=0} , then the SINR for a signal coming from base station, say, x i {\displaystyle x_{i}} , is given by S I N R ( x i ) = F i ℓ ( | x i | ) ∑ j ≠ i [ F j ℓ ( | x j | ) ] + N {\displaystyle \mathrm {SINR} (x_{i}){=}{\frac {\frac {F_{i}}{\ell (|x_{i}|)}}{\sum _{j\neq i}\left[{\frac {F_{j}}{\ell (|x_{j}|)}}\right]+N}}} , where F i {\displaystyle F_{i}} are fading random variables of some distribution. Under the simple power-law path-loss model becomes S I N R ( x i ) = F i | x i | α ∑ j ≠ i F j | x j | α + N {\displaystyle \mathrm {SINR} (x_{i}){=}{\frac {\frac {F_{i}}{|x_{i}|^{\alpha }}}{\sum _{j\neq i}{\frac {F_{j}}{|x_{j}|^{\alpha }}}+N}}} . == Stochastic geometry models == In wireless networks, the factors that contribute to the SINR are often random (or appear random) including the signal propagation and the positioning of network transmitters and receivers. Consequently, in recent years this has motivated research in developing tractable stochastic geometry models in order to estimate the SINR in wireless networks. The related field of continuum percolation theory has also been used to derive bounds on the SINR in wireless networks.