AI Image Generators

Explore the best AI Image Generators — independent reviews, comparisons, pricing and step-by-step how-to guides, curated by Aizhi.

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  YrWall

  YrWall is a Digital Graffiti Wall developed by event company Luma, where designs are created on a large wall using a modified spray paint can. The can contains no paint, instead it has an IR light which is tracked by a computer vision system and the image immediately back-projected onto the wall. The inbuilt YrWall software has much of the functionality of a typical computer paint program, with a pop-out interface which enables users to change colour, spray width, opacity, work with stencils and use animated items such as swirls, stars, drips and splats. Recent additions to YrWall include options to email a JPEG of the completed design and create personalised stickers and T-shirts. == Dragons' Den == The inventor of YrWall, Tom Hogan, and his business partner, Tim Williams, appeared on Episode 4 of Series 8 of the BBC show Dragons' Den. Seeking investment in YrWall, the entrepreneurs were successful in gaining £50,000 for 40% of the YrWall parent company Lumacoustics from Dragons Deborah Meaden and Peter Jones. == World's Largest Interactive Graffiti Wall == In September 2009 YrWall was used to create the 'World's Largest Interactive Graffiti Wall' at the Bristol Festival, UK. Artists used the standard 3.5 m2 YrWall to produce artwork which was in turn projected live onto a 26m x 10m space on the side of the iconic Lloyds amphitheatre building.

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  Plug compatibility

  Plug compatibility is a characteristic of computer hardware that performs exactly like that of another vendor. Manufacturers who made replacements for IBM peripherals were referred to as plug-compatible manufacturers (PCMs). Later plug-compatible mainframe (also PCM) referred to IBM-compatible mainframe computers. PCM can also mean plug-compatible machine or plug-compatible module. == Plug compatibility and peripherals == Before the rise of the plug-compatible peripheral industry, computing systems were either configured with peripherals designed and built by the CPU vendor or designed to use vendor-selected rebadged devices. The first examples of plug-compatible IBM subsystems were tape drives and controls offered by Telex beginning 1965. Memorex in 1968 was first to enter the IBM plug-compatible disk market, followed shortly thereafter by a number of suppliers such as CDC, Itel, and Storage Technology Corporation. This was boosted by the world's largest user of computing equipment, the US General Services Administration, buying plug-compatible equipment. Eventually there were third-party plug-compatible alternatives to most first-party peripherals and first-party system main memory. == Plug compatibility and computer systems == A plug-compatible machine is one that is backward compatible with a prior machine. In particular, a new computer system that is plug-compatible has not only the same connectors and protocol interfaces to peripherals, but also binary-code compatibility—it runs the same software as the old system. A plug compatible manufacturer, or PCM, is a company that makes such products. One recurring theme in plug-compatible systems is the ability to be bug compatible as well. That is, if the forerunner system had software or interface problems, then the successor must have (or simulate) the same problems. Otherwise, the new system may generate unpredictable results, defeating the objective of full compatibility. Thus, it is important for customers to understand the difference between a bug and a feature, where the latter is defined as an intentional modification to the previous system (e.g. higher speed, lighter weight, smaller package, better operator controls, etc.). === Plug compatibility and IBM mainframes === The original example of plug-compatible mainframes was the Amdahl 470 mainframe computer which was plug-compatible with the IBM System 360 and 370, costing millions of dollars to develop. Similar systems were available from Comparex, Fujitsu, and Hitachi. Not all were large systems. Most of these system vendors eventually left the PCM market. In late 1981, there were eight PCM companies, and collectively they had 36 IBM-compatible models. == Non-computer usage of plug compatibility == Plug compatibility may also be used to describe replacement criteria for other components available from multiple sources. For example, a plug-compatible cooling fan may need to have not only the same physical size and shape, but also similar capability, run from the same voltage, use similar power, attach with a standard electrical connector, and have similar mounting arrangements. Some non-conforming units may be re-packaged or modified to meet plug-compatible requirements, as where an adapter plate is provided for mounting, or a different tool and instructions are supplied for installation, and these modifications would be reflected in the bill of materials for such components. Similar issues arise for computer system interfaces when competitors wish to offer an easy upgrade path. In general, plug-compatible systems are designed where industry or de facto standards have rigorously defined the environment, and there is a large installed population of machines that can benefit from third-party enhancements. Plug compatible does not mean identical. However, nothing prevents a company from developing follow-on products that are backward-compatible with its own early products.

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  Nitro Zeus

  Nitro Zeus is the project name for a well funded comprehensive cyber attack plan created as a mitigation strategy after the Stuxnet malware campaign and its aftermath. Unlike Stuxnet, that was loaded onto a system after the design phase to affect its proper operation, Nitro Zeus's objectives are built into a system during the design phase unbeknownst to the system users. This built-in feature allows a more assured and effective cyber attack against the system's users. The information about its existence was raised during research and interviews carried out by Alex Gibney for his Zero Days documentary film. The proposed long term widespread infiltration of major Iranian systems would disrupt and degrade communications, power grid, and other vital systems as desired by the cyber attackers. This was to be achieved by electronic implants in Iranian computer networks. The project was seen as one pathway in alternatives to full-scale war.

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  Commercial skipping

  Commercial skipping is a feature of some digital video recorders that makes it possible to automatically skip commercials in recorded programs. This feature created controversy, with major television networks and movie studios claiming it violates copyright and should be banned. == History == After the video cassette recorder (VCR) became popular in the 1980s, the television industry began studying the impact of users fast forwarding through commercials. Advertising agencies fought the trend by making them more entertaining. For many years, video recorders manufactured for the Japanese market have been able to skip advertisements automatically, which is done by detecting when foreign language audio overdub tracks provided for many programmes go silent, as advertisements were broadcast with a single language only. The first digital video recorder (DVR) with a built-in commercial skipping feature was ReplayTV with its "4000 Series" and "5000 Series" units. In 2002, the main television networks and movie studios sued ReplayTV, claiming that skipping advertisements during replay violates copyright. Later, five owners of ReplayTV represented by Electronic Frontier Foundation and attorneys Ira Rothken and Richard Wiebe countersued, asking the federal judge to uphold consumers' rights to record TV shows and skip commercials, claiming that features like commercial skipping help parents protect their kids from excessive consumerism. ReplayTV ended up filing for bankruptcy in 2003 after fighting a copyright infringement suit over the ReplayTV's ability to skip commercials. === Commercial skipping software === In addition to the DVR devices which existed in the private market since the late 1990s, towards the mid-2000s, due to the significant advances in home computers, Home theater PCs started gaining popularity in the private market and many users began using their Home theater PCs in their living room for entertainment purposes. Following this, many DVR programs were developed, including popular programs such as Windows Media Center, which contained all of the features of the DVR devices in addition to advanced features such as HDTV and the use of Multiple TV Tuner Cards. Some independent developers began developing independent software capable of skipping the commercial segments when playing recorded videos, and permanently removing the commercial segments from recorded video files. By 2014, many DVR programs such as Windows Media Center, SageTV and MythTV had the capability to skip commercials segments in recorded TV broadcasts after installing third-party add-ons such as DVRMSToolbox, Comskip and ShowAnalyzer, which use various advanced techniques to locate the commercial segments in the video files and save their locations to text files. The text files can also be fed into programs such as MEncoder or DVRMSToolboxGUI which can delete the commercial segments from the recorded video files. A few third-party tools such as MCEBuddy automate detection and removal/marking of commercials. One of the weaknesses of commercial skippers is that, operating automatically, they may misidentify program material as a commercial. Some programs like MCEBuddy provide the ability to fine-tune commercial detection for groups of files (e.g. by channel or country) and provide tools to manually fine-tune commercial segments for individual files. In May 2012, the US Dish Network began offering a DVR with what it calls AutoHop. The device would automatically skip commercials when displaying programming that the viewer had previously recorded with the PrimeTime Anytime feature. It does not skip ads on any live programs. US broadcasters were angered at the news, and FOX embarked on legal action. Most, but not all, of Fox's claims were dismissed; ultimately an agreement was reached whereby AutoHop would only become available for Fox stations seven days after a program is transmitted; terms of the settlement were not disclosed. == The future of TV advertisements == The introduction of digital video recorders and services with skipping and fast-forward capabilities enables viewers to avoid viewing interruptive advertisements in recorded programs, either manually or automatically. While advertising separate to television shows can be skipped, advertising in TV shows themselves ("product placement") cannot be skipped. Streaming services such as Hulu show shorter advertisements with a countdown timer and tailored to the viewers interests, asking interactive questions like "Is this ad relevant to you?".

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  Kernel density estimation

  In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights. KDE answers a fundamental data smoothing problem where inferences about the population are made based on a finite data sample. In some fields such as signal processing and econometrics it is also termed the Parzen–Rosenblatt window method, after Emanuel Parzen and Murray Rosenblatt, who are usually credited with independently creating it in its current form. One of the famous applications of kernel density estimation is in estimating the class-conditional marginal densities of data when using a naive Bayes classifier, which can improve its prediction accuracy. == Definition == Let x = ( x 1 , x 2 , x 3 , . . . ) {\displaystyle \mathbf {x} =\left(x_{1},x_{2},x_{3},...\right)} be independent and identically distributed samples drawn from some univariate distribution with an unknown density f at any given point x. We are interested in estimating the shape of this function f. Its kernel density estimator is f ^ h ( x ) = 1 n ∑ i = 1 n K h ( x − x i ) = 1 n h ∑ i = 1 n K ( x − x i h ) , {\displaystyle {\hat {f}}_{h}(x)={\frac {1}{n}}\sum _{i=1}^{n}K_{h}(x-x_{i})={\frac {1}{nh}}\sum _{i=1}^{n}K{\left({\frac {x-x_{i}}{h}}\right)},} where K is the kernel — a non-negative function — and h > 0 is a smoothing parameter called the bandwidth or simply width. A kernel with subscript h is called the scaled kernel and defined as Kh(x) = ⁠1/h⁠ K(⁠x/h⁠). Intuitively one wants to choose h as small as the data will allow; however, there is always a trade-off between the bias of the estimator and its variance. The choice of bandwidth is discussed in more detail below. A range of kernel functions are commonly used: uniform, triangular, biweight, triweight, Epanechnikov (parabolic), normal, and others. The Epanechnikov kernel is optimal in a mean square error sense, though the loss of efficiency is small for the kernels listed previously. Due to its convenient mathematical properties, the normal kernel is often used, which means K(x) = ϕ(x), where ϕ is the standard normal density function. The kernel density estimator then becomes f ^ h ( x ) = 1 n ∑ i = 1 n 1 h 2 π exp ⁡ ( − ( x − x i ) 2 2 h 2 ) , {\displaystyle {\hat {f}}_{h}(x)={\frac {1}{n}}\sum _{i=1}^{n}{\frac {1}{h{\sqrt {2\pi }}}}\exp \left({\frac {-(x-x_{i})^{2}}{2h^{2}}}\right),} where h {\displaystyle h} is the standard deviation of the sample x {\displaystyle \mathbf {x} } . The construction of a kernel density estimate finds interpretations in fields outside of density estimation. For example, in thermodynamics, this is equivalent to the amount of heat generated when heat kernels (the fundamental solution to the heat equation) are placed at each data point locations xi. Similar methods are used to construct discrete Laplace operators on point clouds for manifold learning (e.g. diffusion map). == Example == Kernel density estimates are closely related to histograms, but can be endowed with properties such as smoothness or continuity by using a suitable kernel. The diagram below based on these 6 data points illustrates this relationship: For the histogram, first, the horizontal axis is divided into sub-intervals or bins which cover the range of the data: In this case, six bins each of width 2. Whenever a data point falls inside this interval, a box of height 1/12 is placed there. If more than one data point falls inside the same bin, the boxes are stacked on top of each other. For the kernel density estimate, normal kernels with a standard deviation of 1.5 (indicated by the red dashed lines) are placed on each of the data points xi. The kernels are summed to make the kernel density estimate (solid blue curve). The smoothness of the kernel density estimate (compared to the discreteness of the histogram) illustrates how kernel density estimates converge faster to the true underlying density for continuous random variables. == Bandwidth selection == The bandwidth of the kernel is a free parameter which exhibits a strong influence on the resulting estimate. To illustrate its effect, we take a simulated random sample from the standard normal distribution (plotted at the blue spikes in the rug plot on the horizontal axis). The grey curve is the true density (a normal density with mean 0 and variance 1). In comparison, the red curve is undersmoothed since it contains too many spurious data artifacts arising from using a bandwidth h = 0.05, which is too small. The green curve is oversmoothed since using the bandwidth h = 2 obscures much of the underlying structure. The black curve with a bandwidth of h = 0.337 is considered to be optimally smoothed since its density estimate is close to the true density. An extreme situation is encountered in the limit h → 0 {\displaystyle h\to 0} (no smoothing), where the estimate is a sum of n delta functions centered at the coordinates of analyzed samples. In the other extreme limit h → ∞ {\displaystyle h\to \infty } the estimate retains the shape of the used kernel, centered on the mean of the samples (completely smooth). The most common optimality criterion used to select this parameter is the expected L2 risk function, also termed the mean integrated squared error: MISE ⁡ ( h ) = E [ ∫ ( f ^ h ( x ) − f ( x ) ) 2 d x ] {\displaystyle \operatorname {MISE} (h)=\operatorname {E} \!\left[\int \!{\left({\hat {f}}\!_{h}(x)-f(x)\right)}^{2}dx\right]} Under weak assumptions on f and K, (f is the, generally unknown, real density function), MISE ⁡ ( h ) = AMISE ⁡ ( h ) + o ( ( n h ) − 1 + h 4 ) {\displaystyle \operatorname {MISE} (h)=\operatorname {AMISE} (h)+{\mathcal {o}}{\left((nh)^{-1}+h^{4}\right)}} where o is the little o notation, and n the sample size (as above). The AMISE is the asymptotic MISE, i. e. the two leading terms, AMISE ⁡ ( h ) = R ( K ) n h + 1 4 m 2 ( K ) 2 h 4 R ( f ″ ) {\displaystyle \operatorname {AMISE} (h)={\frac {R(K)}{nh}}+{\frac {1}{4}}m_{2}(K)^{2}h^{4}R(f'')} where R ( g ) = ∫ g ( x ) 2 d x {\textstyle R(g)=\int g(x)^{2}\,dx} for a function g, m 2 ( K ) = ∫ x 2 K ( x ) d x {\textstyle m_{2}(K)=\int x^{2}K(x)\,dx} and f ″ {\displaystyle f''} is the second derivative of f {\displaystyle f} and K {\displaystyle K} is the kernel. The minimum of this AMISE is the solution to this differential equation ∂ ∂ h AMISE ⁡ ( h ) = − R ( K ) n h 2 + m 2 ( K ) 2 h 3 R ( f ″ ) = 0 {\displaystyle {\frac {\partial }{\partial h}}\operatorname {AMISE} (h)=-{\frac {R(K)}{nh^{2}}}+m_{2}(K)^{2}h^{3}R(f'')=0} or h AMISE = R ( K ) 1 / 5 m 2 ( K ) 2 / 5 R ( f ″ ) 1 / 5 n − 1 / 5 = C n − 1 / 5 {\displaystyle h_{\operatorname {AMISE} }={\frac {R(K)^{1/5}}{m_{2}(K)^{2/5}R(f'')^{1/5}}}n^{-1/5}=Cn^{-1/5}} Neither the AMISE nor the hAMISE formulas can be used directly since they involve the unknown density function f {\displaystyle f} or its second derivative f ″ {\displaystyle f''} . To overcome that difficulty, a variety of automatic, data-based methods have been developed to select the bandwidth. Several review studies have been undertaken to compare their efficacies, with the general consensus that the plug-in selectors and cross validation selectors are the most useful over a wide range of data sets. Substituting any bandwidth h which has the same asymptotic order n−1/5 as hAMISE into the AMISE gives that AMISE(h) = O(n−4/5), where O is the big O notation. It can be shown that, under weak assumptions, there cannot exist a non-parametric estimator that converges at a faster rate than the kernel estimator. Note that the n−4/5 rate is slower than the typical n−1 convergence rate of parametric methods. If the bandwidth is not held fixed, but is varied depending upon the location of either the estimate (balloon estimator) or the samples (pointwise estimator), this produces a particularly powerful method termed adaptive or variable bandwidth kernel density estimation. Bandwidth selection for kernel density estimation of heavy-tailed distributions is relatively difficult. === A rule-of-thumb bandwidth estimator === If Gaussian basis functions are used to approximate univariate data, and the underlying density being estimated is Gaussian, the optimal choice for h (that is, the bandwidth that minimises the mean integrated squared error) is: h = ( 4 σ ^ 5 3 n ) 1 / 5 ≈ 1.06 σ ^ n − 1 / 5 , {\displaystyle h={\left({\frac {4{\hat {\sigma }}^{5}}{3n}}\right)}^{1/5}\approx 1.06\,{\hat {\sigma }}\,n^{-1/5},} An h {\displaystyle h} value is considered more robust when it improves the fit for long-tailed and skewed distributions or for bimodal mixture distributions. This is often done empirically by replacing the standard deviation σ ^ {\displaystyle {\hat {\sigma }}} by the parameter A {\displaystyle A} below: A = min ( σ ^ , I Q R 1.34 ) {\displaystyle A=\min \left({\hat {\sigma }},{\frac {\mathrm {IQR} }{1.34}}\right)} where IQR is the

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  Access-independent services

  Access-independent service (AIS) is a service concept in which a service does not depend on guaranteed access network cooperation for service delivery. Telecommunications industry analyst Dean Bubley first used the term in a report on Telco-OTT in February 2012. Traditionally, most telecom company or internet service provider services are access-dependent, because they rely heavily on guaranteed access cooperation on the network the service is delivered over. For instance, traditional IP-based TV service (IPTV) delivered by a telecom company is generally a managed service. This means that IPTV service assumes the IPTV service provider has control over the access network that the IPTV service is delivered over, and network quality of service (QoS) guarantees are available for IPTV service delivery. As a result, the reach of a telecom company's IPTV service is generally restricted by the reach of the telecom company's access network. In contrast, services offered by non-traditional video content delivery service providers such as Netflix, Hulu, and Amazon Video are considered access-independent services. Netflix's video content streaming service, for example, dynamically adapts to network conditions in real-time to strive for the best overall quality of experience (QoE) and does not assume guaranteed cooperation from the underlying IP network, such as QoS. As a result, without considering content rights and different countries' government restrictions, the reach of Netflix's video content streaming service is, in theory, the reach of the Internet. Skype is another example of AIS, because Skype offers an IP-based telephony service over the Internet without depending on IP network cooperation guarantees other than basic IP network connectivity. In the context of telecom service delivery, the concept of access independent services is also commonly described by the term "over-the-top" (OTT) services. OTT service providers such as but not limited to Facebook, WeChat, and Netflix generally do not own or directly manage any wide-area access network to begin with, so they design their services for overall quality of experience, with no assumptions on guaranteed access network cooperation.

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  Scalable Video Coding

  Scalable Video Coding (SVC) is a video compression standard developed jointly by the ITU-T and the ISO/IEC. The two organizations formed the Joint Video Team (JVT) to create the H.264/MPEG-4 AVC standard (ITU-T Rec. H.264 | ISO/IEC 14496-10 AVC). SVC aims to provide adaptable or scalable content, allowing a single encoded video stream to be decoded at various bitrates, resolutions, and quality levels, thus catering to diverse devices and network conditions. == History == In October 2003, the Moving Picture Experts Group (MPEG) issued a Call for Proposals on SVC Technology. Fourteen proposals were submitted, twelve of which utilized wavelet compression, while the remaining two were extensions of H.264/MPEG-4 AVC. The proposal from the Heinrich-Hertz-Institut (HHI) was selected by MPEG as the foundation for the SVC standardization project. In January 2005, MPEG and the Video Coding Experts Group (VCEG) agreed to finalize SVC as an amendment to the H.264/MPEG-4 AVC standard. In November 2008, Google launched Gmail Video Chat, which employed an H.264/SVC codec, marking the first consumer application of the standard. This service was succeeded by Google+ Hangouts in 2012. In 2011, Google Code highlighted SVC as the successor to the open-source RVC video chat engine, noting its prominence in 2010. == Principles of scalability == === Overview === Scalability refers to the ability to represent a video signal at multiple levels of detail within a single encoded bitstream. This enables decoding of a base layer for basic quality and additional enhancement layers for progressively higher quality. SVC defines three types of scalability: Spatial scalability: Supports multiple resolution levels. Temporal scalability: Enables varying frame rates. Quality scalability: Provides different image quality levels. === Spatial scalability === Spatial scalability allows the reconstruction of video at different resolutions, such as QCIF, CIF, or SD. This is achieved through a pyramidal decomposition into multiple spatial layers. === Temporal scalability === Temporal scalability adjusts the frame rate of the decoded video stream. Various frame rates are supported using a hierarchical structure of video frames. === Quality scalability === Quality scalability, or Signal-to-Noise Ratio (SNR) scalability, improves the signal-to-noise ratio of a layer, reducing quantization distortion between the original and reconstructed images. SVC supports two approaches: Fine Grain Scalability (FGS) and Coarse Grain Scalability (CGS). ==== Coarse Grain Scalability (CGS) ==== CGS incorporates quality scalability across spatial resolutions. Each spatial resolution is encoded as a separate layer, refining texture and motion data. For a given resolution, quality scalability is achieved by encoding multiple quality layers with progressively finer quantization steps, starting from a base layer with minimal quality. ==== Fine Grain Scalability (FGS) ==== FGS enables progressive refinement of transformed coefficients within a single spatial layer. The base quality layer is encoded using the AVC standard with an initial quantization parameter (QP) ensuring minimal acceptable quality. Subsequent refinement layers reduce the QP by six, halving the quantization step. The refinement data stream can be truncated at any point, allowing fine-grained quality scalability.

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  Web syndication

  Web syndication is making content available from one website to other sites. Most commonly, websites are made available to provide either summaries or full renditions of a website's recently added content. The term may also describe other kinds of content licensing for reuse. Contemporary web syndicates include: MSN, Excite, and Yahoo! News. == Motivation == For the subscribing sites, syndication is an effective way of adding greater depth and immediacy of information to their pages, making them more attractive to users. For the provider site, syndication increases exposure. This generates new traffic for the provider site—making syndication an easy and relatively cheap, or even free, form of advertisement. Content syndication has become an effective strategy for link building, as search engine optimization has become an increasingly important topic among website owners and online marketers. Links embedded within the syndicated content are typically optimized around anchor terms that will point an optimized link back to the website that the content author is trying to promote. These links tell the algorithms of the search engines that the website being linked to is an authority for the keyword that is being used as the anchor text. However the rollout of Google Panda's algorithm may not reflect this authority in its SERP rankings based on quality scores generated by the sites linking to the authority. The prevalence of web syndication is also of note to online marketers, since web surfers are becoming increasingly wary of providing personal information for marketing materials (such as signing up for a newsletter) and expect the ability to subscribe to a feed instead. Although the format could be anything transported over HTTP, such as HTML or JavaScript, it is more commonly XML. Web syndication formats include RSS, Atom, and JSON Feed. == History == Syndication first arose in earlier media such as print, radio, and television, allowing content creators to reach a wider audience. In the case of radio, the United States Federal government proposed a syndicate in 1924 so that the country's executives could quickly and efficiently reach the entire population. In the case of television, it is often said that "Syndication is where the real money is." Additionally, syndication accounts for the bulk of TV programming. One predecessor of web syndication is the Meta Content Framework (MCF), developed in 1996 by Ramanathan V. Guha and others in Apple Computer's Advanced Technology Group. Today, millions of online publishers, including newspapers, commercial websites, and blogs, distribute their news headlines, product offers, and blog postings in the news feed. == As a commercial model == Conventional syndication businesses such as Reuters and Associated Press thrive on the internet by offering their content to media partners on a subscription basis, using business models established in earlier media forms. Commercial web syndication can be categorized in three ways: by business models by types of content by methods for selecting distribution partners Commercial web syndication involves partnerships between content producers and distribution outlets. There are different structures of partnership agreements. One such structure is licensing content, in which distribution partners pay a fee to the content creators for the right to publish the content. Another structure is ad-supported content, in which publishers share revenues derived from advertising on syndicated content with that content's producer. A third structure is free, or barter syndication, in which no currency changes hands between publishers and content producers. This requires the content producers to generate revenue from another source, such as embedded advertising or subscriptions. Alternatively, they could distribute content without remuneration. Typically, those who create and distribute content free are promotional entities, vanity publishers, or government entities. Types of content syndicated include RSS or Atom Feeds and full content. With RSS feeds, headlines, summaries, and sometimes a modified version of the original full content is displayed on users' feed readers. With full content, the entire content—which might be text, audio, video, applications/widgets, or user-generated content—appears unaltered on the publisher's site. There are two methods for selecting distribution partners. The content creator can hand-pick syndication partners based on specific criteria, such as the size or quality of their audiences. Alternatively, the content creator can allow publisher sites or users to opt into carrying the content through an automated system. Some of these automated "content marketplace" systems involve careful screening of potential publishers by the content creator to ensure that the material does not end up in an inappropriate environment. Just as syndication is a source of profit for TV producers and radio producers, it also functions to maximize profit for Internet content producers. As the Internet has increased in size it has become increasingly difficult for content producers to aggregate a sufficiently large audience to support the creation of high-quality content. Syndication enables content creators to amortize the cost of producing content by licensing it across multiple publishers or by maximizing the distribution of advertising-supported content. A potential drawback for content creators, however, is that they can lose control over the presentation of their content when they syndicate it to other parties. Distribution partners benefit by receiving content either at a discounted price, or free. One potential drawback for publishers, however, is that because the content is duplicated at other publisher sites, they cannot have an "exclusive" on the content. For users, the fact that syndication enables the production and maintenance of content allows them to find and consume content on the Internet. One potential drawback for them is that they may run into duplicate content, which could be an annoyance. == E-commerce == Web syndication has been used to distribute product content such as feature descriptions, images, and specifications. As manufacturers are regarded as authorities and most sales are not achieved on manufacturer websites, manufacturers allow retailers or dealers to publish the information on their sites. Through syndication, manufacturers may pass relevant information to channel partners. Such web syndication has been shown to increase sales. Web syndication has also been found effective as a search engine optimization technique.

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  Singularity studies

  Singularity studies is an interdisciplinary academic field which examines the idea of technological singularity — the hypothesised point at which artificial intelligence may surpass human intelligence, might be attained by artificial intelligence (AI), robotics, and other technologies and sciences, and its social impacts. In this academic field, the study and research are conducted across a broad array of terrains such as information science, robotics, social informatics, economics, philosophy, and ethics. The primary aim of singularity studies is to gain an integrative understanding of the transformation of social systems occurring in tandem with the explosive evolution of AI and also the changes to be effected by such transformation in the view of humans, ethics, and legal systems. == History == An academic work on technological singurality has appeared in computer science, philosophy, sociology, and law since the early 1990s. Early discussions of an intelligence explosion were popularised by science-fiction writer Vernor Vinge in 1993 and later systematised by futurist Ray Kurzweil. Since the 2010s, universities such as Oxford, Stanford, and Keio have established dedicated programmes, while peer-reviewed journals have begun to publish scenario analyses and policy studies. Ongoing debates question the predictive value of singularity scenarios and warn against a deterministic view of technology. == Characteristics of research == Singularity studies extends beyond mere future predictions and offer an intellectual foundation for proactively designing and creating a desirable future. Principal research themes in this realm include: Ethics of AI; Social implications of technologies; Possibility of harmonious coexistence of humans and AI; Communication with AI; and Redesign of social systems. == Technologists and academics == Vernor Vinge: Propounded the concept of singularity in 1993, making a massive impact on the academic and science-fiction spheres. Ray Kurzweil: Predicted the advent around 2045 of the technological singularity in his 2005 book The Singularity Is Near. Nick Bostrom: Offered philosophical reflections on superintelligence and the risks posed by AI. He is the founding director of the now-dissolved Future of Humanity Institute at the University of Oxford. === Japan === Kento Sasano: A social informatician, AI educator, and inventor. He is the president of the Japan Society of Singularity Studies. == Challenges and outlook == Singularity studies is still evolving as an academic field, and quite a few challenges remain unresolved in regard to the systematization of their theories, research methods, and educational curricula. That said, in this day and age of accelerating technological and societal shifts, interdisciplinary approaches have gained in importance and are drawing much attention in the arenas of scholarly research, intercorporate collaboration, and policy planning.

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  Digital data

  Digital data or digital information, in information theory and information systems, is data or information represented as a string of discrete symbols, each of which can take on one of only a finite number of values from some alphabet, such as letters or digits. An example is a text document, which consists of a string of alphanumeric characters. The most common form of digital data in modern information systems is binary data, which is represented by a string of binary digits (bits) each of which can have one of two values, either 0 or 1. Digital data can be contrasted with analog data, which is represented by a value from a continuous range of real numbers. Analog data is transmitted by an analog signal, which not only takes on continuous values but can vary continuously with time, a continuous real-valued function of time. An example is the air pressure variation in a sound wave. Data requires interpretation to become information. In modern (post-1960) computer systems, all data is digital. The word digital comes from the same source as the words digit and digitus (the Latin word for finger), as fingers are often used for counting. Mathematician George Stibitz of Bell Telephone Laboratories used the word digital in reference to the fast electric pulses emitted by a device designed to aim and fire anti-aircraft guns in 1942. The term is most commonly used in computing and electronics, especially where real-world information is converted to binary numeric form as in digital audio and digital photography. == Symbol to digital conversion == Since symbols (for example, alphanumeric characters) are not continuous, representing symbols digitally is rather simpler than conversion of continuous or analog information to digital. Instead of sampling and quantization as in analog-to-digital conversion, such techniques as polling and encoding are used. A symbol input device usually consists of a group of switches that are polled at regular intervals to see which switches are switched. Data will be lost if, within a single polling interval, two switches are pressed, or a switch is pressed, released, and pressed again. This polling can be done by a specialized processor in the device to prevent burdening the main CPU. When a new symbol has been entered, the device typically sends an interrupt, in a specialized format, so that the CPU can read it. For devices with only a few switches (such as the buttons on a joystick), the status of each can be encoded as bits (usually 0 for released and 1 for pressed) in a single word. This is useful when combinations of key presses are meaningful, and is sometimes used for passing the status of modifier keys on a keyboard (such as shift and control). But it does not scale to support more keys than the number of bits in a single byte or word. Devices with many switches (such as a computer keyboard) usually arrange these switches in a scan matrix, with the individual switches on the intersections of x and y lines. When a switch is pressed, it connects the corresponding x and y lines together. Polling (often called scanning in this case) is done by activating each x line in sequence and detecting which y lines then have a signal, thus which keys are pressed. When the keyboard processor detects that a key has changed state, it sends a signal to the CPU indicating the scan code of the key and its new state. The symbol is then encoded or converted into a number based on the status of modifier keys and the desired character encoding. A custom encoding can be used for a specific application with no loss of data. However, using a standard encoding such as ASCII is problematic if a symbol such as 'ß' needs to be converted but is not in the standard. It is estimated that in the year 1986, less than 1% of the world's technological capacity to store information was digital and in 2007 it was already 94%. The year 2002 is assumed to be the year when humankind was able to store more information in digital than in analog format (the "beginning of the digital age"). == States == Digital data come in these three states: data at rest, data in transit, and data in use. The confidentiality, integrity, and availability have to be managed during the entire lifecycle from 'birth' to the destruction of the data. === Data at rest === Data at rest in information technology means data that is housed physically on computer data storage in any digital form (e.g. cloud storage, file hosting services, databases, data warehouses, spreadsheets, archives, tapes, off-site or cloud backups, mobile devices etc.). Data at rest includes both structured and unstructured data. This type of data is subject to threats from hackers and other malicious threats to gain access to the data digitally or physical theft of the data storage media. To prevent this data from being accessed, modified or stolen, organizations will often employ security protection measures such as password protection, data encryption, or a combination of both. The security options used for this type of data are broadly referred to as data-at-rest protection (DARP). Definitions include: "...all data in computer storage while excluding data that is traversing a network or temporarily residing in computer memory to be read or updated." "...all data in storage but excludes any data that frequently traverses the network or that which resides in temporary memory. Data at rest includes but is not limited to archived data, data which is not accessed or changed frequently, files stored on hard drives, USB thumb drives, files stored on backup tape and disks, and also files stored off-site or on a storage area network (SAN)." While it is generally accepted that archive data (i.e. which never changes), regardless of its storage medium, is data at rest and active data subject to constant or frequent change is data in use. “Inactive data” could be taken to mean data which may change, but infrequently. The imprecise nature of terms such as “constant” and “frequent” means that some stored data cannot be comprehensively defined as either data at rest or in use. These definitions could be taken to assume that Data at Rest is a superset of data in use; however, data in use, subject to frequent change, has distinct processing requirements from data at rest, whether completely static or subject to occasional change. ==== Security ==== Because of its nature data at rest is of increasing concern to businesses, government agencies and other institutions. Mobile devices are often subject to specific security protocols to protect data at rest from unauthorized access when lost or stolen and there is an increasing recognition that database management systems and file servers should also be considered as at risk; the longer data is left unused in storage, the more likely it might be retrieved by unauthorized individuals outside the network. Data encryption, which prevents data visibility in the event of its unauthorized access or theft, is commonly used to protect data in motion and increasingly promoted for protecting data at rest. The encryption of data at rest should only include strong encryption methods such as AES or RSA. Encrypted data should remain encrypted when access controls such as usernames and password fail. Increasing encryption on multiple levels is recommended. Cryptography can be implemented on the database housing the data and on the physical storage where the databases are stored. Data encryption keys should be updated on a regular basis. Encryption keys should be stored separately from the data. Encryption also enables crypto-shredding at the end of the data or hardware lifecycle. Periodic auditing of sensitive data should be part of policy and should occur on scheduled occurrences. Finally, only store the minimum possible amount of sensitive data. Tokenization is a non-mathematical approach to protecting data at rest that replaces sensitive data with non-sensitive substitutes, referred to as tokens, which have no extrinsic or exploitable meaning or value. This process does not alter the type or length of data, which means it can be processed by legacy systems such as databases that may be sensitive to data length and type. Tokens require significantly less computational resources to process and less storage space in databases than traditionally encrypted data. This is achieved by keeping specific data fully or partially visible for processing and analytics while sensitive information is kept hidden. Lower processing and storage requirements makes tokenization an ideal method of securing data at rest in systems that manage large volumes of data. A further method of preventing unwanted access to data at rest is the use of data federation especially when data is distributed globally (e.g. in off-shore archives). An example of this would be a European organisation which stores its archived data off-site in the US. Under the terms of the USA PATRIOT Act the American authorities can demand

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  Rider Spoke

  Rider Spoke developed by Blast Theory in collaboration with the Mixed Reality Lab was first staged at the Barbican, London in October 2007. Created for cyclists, it combines elements of theatre, performance, game play and state of the art technology. Rider Spoke was built in the IPerG project on the EQUIP architecture. Rider Spoke has since been presented in Athens (2008), Brighton (2008), Budapest (2008), Sydney (2009, Adelaide (2009) and Liverpool (2010).

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  Webedia

  Webedia S.A. is a company specializing in online media, a subsidiary of the Fimalac group based in Levallois-Perret, France. Webedia is active in more than twenty countries including France (AlloCiné, Jeuxvideo.com, MGG, Puremédias, Ode, Pureshopping, Volum, Terrafemina, 750g, easyVoyage, l’Automobile Magazine, Le 10 Sport), Brazil (AdoroCinema, Tudo Gostoso, Minhavida), Germany (Filmstarts, Moviepilot, GameStar), Spain and Latin America (Xataka, SensaCine, Raiser Games), Poland (Gry-Online and GetHero) and the United States (Boxoffice Pro). == History == === Early years (2007-2013) === Webedia was created in France in 2007, following the successive launches of the websites Purepeople, Puretrend and Purefans. Webedia bought the comparison shopping website Shopoon in 2008 and renamed it Pureshopping, and the website Ozap (media news) from M6 group in 2011 and renamed it Puremédias. Webedia was acquired by Fimalac in May 2013 and became its Internet media subsidiary. === Growth (2013-2016) === In 2013, Fimalac acquired AlloCiné, the websites Newsring and Youmag, the cooking website 750g and the cultural platform Exponaute. In 2014, Webedia acquired OverBlog, Jeuxvideo.com (through L'Odyssée Interactive and moved to Paris in 2015), Moviepilot (Germany), and Gameo Consulting (owner of Millenium, electronic sports), In December 2014, Webedia announced a license agreement with Ziff Davis to launch sites under the IGN franchise in Brazil and France at the beginning of 2015. The French version of IGN was launched on 2, it targets the general public and casual gamers. In 2015, Webedia acquired Côté Ciné Group (technological solutions for movie theaters and specialized press magazines: BoxOffice Pro in the United States and Côté Ciné in France), 57% of Easyvoyage group (online travel comparators Easyvol and Alibabuy, Mixicom (website JeuxActu and multi-channel network), 50% of the Brazilian network Paramaker, and West World Media (digital marketing company for the film industry). In 2016, Webedia bought Scimob (mobile video game studio), Surprizemi (home-delivered surprise boxes), Eklablog (blogging platform) Oxent (eSports World Convention), and Bang Bang Management (sports PR agency). In addition, an agreement is made with Paris Saint-Germain for Webedia to recruit and manage e-sports players on behalf of Paris Saint-Germain eSports. On November 15, 2016, the LFP announced that it had reached an agreement with beIN Sports and Webedia for the broadcasting of the first edition of the e-League 1. The competition is renewed for two additional seasons on July 26, 2017, the broadcasting agreements are renewed. On December 8, 2016, Webedia joined forces with Chronopost to launch Pourdebon, a home delivery service that connects Internet users and labeled producers (AOC, organic AB, etc.). Webedia has a slight majority (53%) in this new platform. === 2017 === On January 19, 2017, Webedia announced the acquisition of the English company Peach Digital, specializing in web development and digital marketing for movie theaters. In February 2017, Le Figaro announced that Webedia had invested 10 million euros in Illico Fresco, a home delivery service for baskets of recipes. The same month, FDJ and Webedia announced a partnership for the creation of eSports competitions: a professional one (FDJ Masters League) and another one for amateur gamers (FDJ Open Series) starting in March 2017. They are broadcast on Webedia's Web TV. At the end of February 2017, the media group finalized the acquisition of MyPoseo, a SaaS publisher specialized on SEO analytics. On March 8, 2017, Webedia launched LeStream, a Twitch Web TV dedicated to video games, the result of two years of development, in the company of several YouTubers including Cyprien and Squeezie,. On March 29, 2017, Webedia bought the Brazilian web publisher Minha Vida, a website devoted to health, nutrition, beauty and fitness, which attracts 14.3 million unique monthly visitors. Webedia reaches 44 million unique visitors in Brazil, and thus becomes the leading publisher on entertainment themes. In June 2017, the company made its largest international acquisition, with the American agency 3BlackDot, a media and marketing agency focused on videogamers. The agency, based in Los Angeles, manages 36 YouTubers followed by millions of subscribers on their channels which total 700 million videos viewed per month. In July 2017, Webedia bought IDZ, an audiovisual production company, and thus strengthened its production activities and its leadership on the YouTube channel networks in France. That year, Webedia was the first French media group to use the measurement of their global audiences by Comscore. It represents deduplicated coverage on desktops, laptops, smartphones and tablets, and includes audiences for websites, mobile applications and videos. This new measure allows Webedia to establish a deduplicated global audience of 177 million unique visitors in April 2017. In October 2017, Webedia announced its intention to launch a TV channel dedicated to electronic sports, called ES1. The channel was officially launched on January 10, 2018, on Orange TV and on February 6, 2018, on Free and Bouygues Telecom. In November 2017, Webedia, with the support of CDC International Capital, entered into exclusive negotiations with the Saudi company Uturn Entertainment, specializing in online entertainment, particularly on YouTube, and the production of digital content for the region's youth, with a view to merging it with Diwanee, a Webedia subsidiary in the Middle East, for an amount close to $100 million. In December 2017, Webedia acquired a majority stake in the United States–based company called Creators Media, which brings together social and video production platforms specializing in popular culture and entertainment. That same month, Webedia joined forces with Elephant, Emmanuel Chain's audiovisual production company, to create a new content production label aimed at Millennials. === 2018-2019 === In January 2018, Webedia launched a sports marketing agency: Only Sports & Passions. That same month, Illico Fresco, specialist in the delivery of kit meals belonging to Webedia, joined forces with Weight Watchers, the world leader in slimming products. In April 2018, Webedia published new audience figures in partnership with Comscore, 188 million unique monthly visitors in December 2017, an increase of 6.2% compared to the previous measure dating from April 2017. The same month, Webedia unveils its ambitions concerning content production, as a partnership with the video game studio Focus Home Interactive is signed with a title "Fear the Wolves" already planned for 2018, co-production projects of films, cartoons or series are announced. In July 2018, Webedia bought the American authors company Full Fathom Five, a company that helps authors produce books, TV series, films and video games. In October 2018, Webedia announced that it was focusing on both esports clubs PSG Esports and LeStream Esport. The first one being geared towards international competitions and the second devoted mainly to the French esports scene. The "Millenium" brand is thus refocusing around its media activities and esports merchandising products, and the "Millenium esport club" being gradually closed. The same month, the company announced the acquisition of Weblogs, a Spanish-speaking website publisher, thereby strengthening its activity in Spain and Latin America. On October 22, 2018, Webedia announced the merger of BoxOffice magazine with Film Journal International. On November 13, 2018, Groupe SEB announced the acquisition from Webedia of 750g International, the international branch of the French recipe site 750g (the original French website 750g.com being retained by Webedia). The group is thus separating from Gourmandize (United States and United Kingdom), HeimGourmet (Germany), Rebañando (Spain), Receitas Sem Fronteiras (Brazil / Portugal) and Tribù Golosa (Italy). The same month, Webedia joined forces with Riot Games to launch the French League of League of Legends (LFL), the first French professional league on the League of Legends game, which will bring together the 8 best teams on the French scene. In March 2019, Webedia bought 51% of the audiovisual production company Elephant. The new set will weigh 500 million euros, a quarter of which will be made outside France. The same month, Webedia purchased a majority stake in the company Partoo, which publishes a SaaS platform specializing in local marketing for brands and merchants. On March 14, 2019, a new measurement of the international audience of Webedia sites was produced by Comscore, posting 250 million unique visitors in December 2018, up 9.2% compared to December 2017. In June 2019, the group joined forces with Michel Cymes, a famous doctor and French TV host by taking a majority stake in his company Club Santé Débat, in order to develop a health platform around the Dr. Good! Brand. In Sep

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  Color quantization

  In computer graphics, color quantization or color image quantization is quantization applied to color spaces; it is a process that reduces the number of distinct colors used in an image, usually with the intention that the new image should be as visually similar as possible to the original image. Computer algorithms to perform color quantization on bitmaps have been studied since the 1970s. Color quantization is critical for displaying images with many colors on devices that can only display a limited number of colors, usually due to memory limitations, and enables efficient compression of certain types of images. The name "color quantization" is primarily used in computer graphics research literature; in applications, terms such as optimized palette generation, optimal palette generation, or decreasing color depth are used. Some of these are misleading, as the palettes generated by standard algorithms are not necessarily the best possible. == Algorithms == Most standard techniques treat color quantization as a problem of clustering points in three-dimensional space, where the points represent colors found in the original image and the three axes represent the three color channels. Almost any three-dimensional clustering algorithm can be applied to color quantization, and vice versa. After the clusters are located, typically the points in each cluster are averaged to obtain the representative color that all colors in that cluster are mapped to. The three color channels are usually red, green, and blue, but another popular choice is the Lab color space, in which Euclidean distance is more consistent with perceptual difference. The most popular algorithm by far for color quantization, invented by Paul Heckbert in 1979, is the median cut algorithm. Many variations on this scheme are in use. Before this time, most color quantization was done using the population algorithm or population method, which essentially constructs a histogram of equal-sized ranges and assigns colors to the ranges containing the most points. A more modern popular method is clustering using octrees, first conceived by Gervautz and Purgathofer and improved by Xerox PARC researcher Dan Bloomberg. If the palette is fixed, as is often the case in real-time color quantization systems such as those used in operating systems, color quantization is usually done using the "straight-line distance" or "nearest color" algorithm, which simply takes each color in the original image and finds the closest palette entry, where distance is determined by the distance between the two corresponding points in three-dimensional space. In other words, if the colors are ( r 1 , g 1 , b 1 ) {\displaystyle (r_{1},g_{1},b_{1})} and ( r 2 , g 2 , b 2 ) {\displaystyle (r_{2},g_{2},b_{2})} , we want to minimize the Euclidean distance: ( r 1 − r 2 ) 2 + ( g 1 − g 2 ) 2 + ( b 1 − b 2 ) 2 . {\displaystyle {\sqrt {(r_{1}-r_{2})^{2}+(g_{1}-g_{2})^{2}+(b_{1}-b_{2})^{2}}}.} This effectively decomposes the color cube into a Voronoi diagram, where the palette entries are the points and a cell contains all colors mapping to a single palette entry. There are efficient algorithms from computational geometry for computing Voronoi diagrams and determining which region a given point falls in; in practice, indexed palettes are so small that these are usually overkill. Color quantization is frequently combined with dithering, which can eliminate unpleasant artifacts such as banding that appear when quantizing smooth gradients and give the appearance of a larger number of colors. Some modern schemes for color quantization attempt to combine palette selection with dithering in one stage, rather than perform them independently. A number of other much less frequently used methods have been invented that use entirely different approaches. The Local K-means algorithm, conceived by Oleg Verevka in 1995, is designed for use in windowing systems where a core set of "reserved colors" is fixed for use by the system and many images with different color schemes might be displayed simultaneously. It is a post-clustering scheme that makes an initial guess at the palette and then iteratively refines it. In the early days of color quantization, the k-means clustering algorithm was deemed unsuitable because of its high computational requirements and sensitivity to initialization. In 2011, M. Emre Celebi reinvestigated the performance of k-means as a color quantizer. He demonstrated that an efficient implementation of k-means outperforms a large number of color quantization methods. The high-quality but slow NeuQuant algorithm reduces images to 256 colors by training a Kohonen neural network "which self-organises through learning to match the distribution of colours in an input image. Taking the position in RGB-space of each neuron gives a high-quality colour map in which adjacent colours are similar." It is particularly advantageous for images with gradients. Finally, one of the newer methods is spatial color quantization, conceived by Puzicha, Held, Ketterer, Buhmann, and Fellner of the University of Bonn, which combines dithering with palette generation and a simplified model of human perception to produce visually impressive results even for very small numbers of colors. It does not treat palette selection strictly as a clustering problem, in that the colors of nearby pixels in the original image also affect the color of a pixel. See sample images. == History and applications == In the early days of PCs, it was common for video adapters to support only 2, 4, 16, or (eventually) 256 colors due to video memory limitations; they preferred to dedicate the video memory to having more pixels (higher resolution) rather than more colors. Color quantization helped to justify this tradeoff by making it possible to display many high color images in 16- and 256-color modes with limited visual degradation. Many operating systems automatically perform quantization and dithering when viewing high color images in a 256 color video mode, which was important when video devices limited to 256 color modes were dominant. Modern computers can now display millions of colors at once, far more than can be distinguished by the human eye, limiting this application primarily to mobile devices and legacy hardware. Nowadays, color quantization is mainly used in GIF and PNG images. GIF, for a long time the most popular lossless and animated bitmap format on the World Wide Web, only supports up to 256 colors, necessitating quantization for many images. Some early web browsers constrained images to use a specific palette known as the web colors, leading to severe degradation in quality compared to optimized palettes. PNG images support 24-bit color, but can often be made much smaller in filesize without much visual degradation by application of color quantization, since PNG files use fewer bits per pixel for palettized images. The infinite number of colors available through the lens of a camera is impossible to display on a computer screen; thus converting any photograph to a digital representation necessarily involves some quantization. Practically speaking, 24-bit color is sufficiently rich to represent almost all colors perceivable by humans with sufficiently small error as to be visually identical (if presented faithfully), within the available color space. However, the digitization of color, either in a camera detector or on a screen, necessarily limits the available color space. Consequently there are many colors that may be impossible to reproduce, regardless of how many bits are used to represent the color. For example, it is impossible in typical RGB color spaces (common on computer monitors) to reproduce the full range of green colors that the human eye is capable of perceiving. With the few colors available on early computers, different quantization algorithms produced very different-looking output images. As a result, a lot of time was spent on writing sophisticated algorithms to be more lifelike. === Quantization for image compression === Many image file formats support indexed color. A whole-image palette typically selects 256 "representative" colors for the entire image, where each pixel references any one of the colors in the palette, as in the GIF and PNG file formats. A block palette typically selects 2 or 4 colors for each block of 4x4 pixels, used in BTC, CCC, S2TC, and S3TC. === Editor support === Many bitmap graphics editors contain built-in support for color quantization, and will automatically perform it when converting an image with many colors to an image format with fewer colors. Most of these implementations allow the user to set exactly the number of desired colors. Examples of such support include: Photoshop's Mode→Indexed Color function supplies a number of quantization algorithms ranging from the fixed Windows system and Web palettes to the proprietary Local and Global algorithms for generating palettes suited to a particu

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  Vinyl cutter

  A vinyl cutter is an entry-level machine for making signs. Computer-designed vector files with patterns and letters are directly cut on the roll of vinyl which is mounted and fed into the vinyl cutter through USB or serial cable. Vinyl cutters are mainly used to make signs, banners and advertisements. Advertisements seen on automobiles and vans are often made with vinyl cut letters. While these machines were designed for cutting vinyl, they can also cut through computer and specialty papers, as well as thicker items like thin sheets of magnet. In addition to sign business, vinyl cutters are commonly used for apparel decoration. To decorate apparel, a vector design needs to be cut in mirror image, weeded, and then heat applied using a commercial heat press or a hand iron for home use. Some businesses use their vinyl cutter to produce both signs and custom apparel. Many crafters also have vinyl cutters for home use. These require little maintenance, and the vinyl can be bought in bulk relatively cheaply. Vinyl cutters are also often used by stencil artists to create single use or reusable stencil art and lettering == How it works == A vinyl cutter is a type of computer-controlled machine tool. The computer controls the movement of a sharp blade over the surface of the material as it would the nozzles of an ink-jet printer. This blade is used to cut out shapes and letters from sheets of thin self-adhesive plastic (vinyl). The vinyl can then be stuck to a variety of surfaces depending on the adhesive and type of material. To cut out a design, a vector-based image must be created using vector drawing software. Some vinyl cutters are marketed to small in-home businesses and require download and use of a proprietary editing software. The design is then sent to the cutter where it cuts along the vector paths laid out in the design. The cutter is capable of moving the blade on an X and Y axis over the material, cutting it into the required shapes. The vinyl material comes in long rolls allowing projects with significant length like banners or billboards to be easily cut. A major limitation with vinyl cutters is that they can only cut shapes from solid colours of vinyl, paper, card or thin plastic sheets such as Mylar. The type and thickness of material will vary for each cutter and how much downforce the cutter is capable of. If the material has no backing, a backing sheet, material or cutting mat and a temporary adhesive are needed to allow the cutter to cut through the material. A design with multiple colours must have each colour cut separately and then layered on top of each other as it is applied to the substrate. This is a process that is often applied in stencil art. Also, since the shapes are cut out of solid colours, photographs and gradients cannot be reproduced with a stand-alone cutter. === Design creation === Designs are created using vector-based software like Adobe Illustrator, FlexiSign, EasyCutPro, or other software. Vector artwork is either drawn with lines, shapes and text or images are vectorized thus create vector shapes. Most cutters (also called plotters) require special software to load/edit the artwork and communicate with the cutter. Computer designed images are loaded onto the vinyl cutter via a wired connection or over a wireless protocol. Then the vinyl is loaded into the machine where it is automatically fed through and cut to follow the set design. The vinyl can be placed on an adhesive mat to stabilize the vinyl when cutting smaller designs. === Types of vinyl === Adhesive vinyl is the type of vinyl used for store windows, car decals, signage, and more. Adhesive vinyl is applied with a transfer medium often called "transfer tape" or "carrier sheet". Heat transfer vinyl is the type of vinyl used to apply a design to fabric including t-shirts, tea towels, canvas bags, and more. Heat Transfer vinyl can be applied using a heat press or an iron, though the constant pressure and heat from a heat press is recommended by experts. === Using other materials === In addition to vinyl some cutters are capable of cutting other materials such as paper, card, plastic sheets and even thin wood. The thickness and type of material that can be cut will depend on the model of the cutter and heavily depends on the downforce. Cricut is a popular home cutter used by arts and craft enthusiasts since it allows for a wide use of different materials and is similar in size to a household printer and has strong downforce for its size. === Backing and cutting mat === If you cut material that doesn't have an adhesive backing you will require a cutting mat that you need to attach your material to. Some cutting mats are sticky, others will require you to use a temporary adhesive and/or masking tape to keep the material in place when cutting. === Cutting === The vinyl cutter uses a small knife or blade to precisely cut the outline of figures into a sheet or piece of vinyl, but not the release liner. The process of cutting vinyl material without penetrating it completely is referred to as "kiss cutting". The knife moves side to side and turns, while the vinyl is moved beneath the knife. The results from the cut process is an image cut into the material. === Weeding === The material is then 'weeded' where the excess parts of the figures are removed from the release liner. It is possible to remove the positive parts, which would give a negative decal, or remove the negative parts, giving a positive decal. Removing the figure would be like removing the positive, giving a negative image of the figures. === Transfer tape === A sheet of transfer tape with an adhesive backing is laid on the weeded vinyl when necessary. Heat Transfer vinyl often does not require use of a separate transfer tape. A roller is applied to the tape, causing it to adhere to the vinyl. The transfer tape and the weeded vinyl is pulled off the release liner, and applied to a substrate, such as a sheet of aluminium. This results in an aluminium sign with vinyl figures. == Uses == In addition to the capabilities of the cutter itself, adhesive vinyl comes in a wide variety of colors and materials including gold and silver foil, vinyl that simulates frosted glass, holographic vinyl, reflective vinyl, thermal transfer material, and even clear vinyl embedded with gold leaf. (Often used in the lettering on fire trucks and rescue vehicles.) As the vinyl film is supplied by the manufacturer, it comes attached to a release liner. == Challenges when cutting on a vinyl cutter == Cutting on a vinyl cutter requires careful calibration to achieve clean and accurate results, especially when the goal is to cut through only the top layer of material while leaving the backing intact. One of the most common challenges is setting the correct cutting depth. If the blade is not lowered enough, the vinyl material may not separate properly; if it goes too deep, it can cut through the backing layer and potentially damage the cutting mat. The cutting depth on the vinyl cutter machines typically does not exceed 1 mm. Another frequent issue is the mismatch between the blade and the type of material being processed. Using an inappropriate blade can lead to uneven cuts, premature dulling of the edge, and torn or frayed material. The overall quality of the output also depends on factors such as the cutting speed, blade sharpening and cutting angle, and the material the knife is made of.

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  Optical recording

  The history of optical recording can be divided into a few number of distinct major contributions. The pioneers of optical recording worked mostly independently, and their solutions to the many technical challenges have very distinctive features, such as reflective disc (Compaan and Kramer) transparent disc (Gregg) floppy disc (Russell) rigid disc (Compaan and Kramer) focused laser beam for read-out through transparent substrate (Compaan and Kramer). == Gregg 1958 == Laserdisc technology, using a transparent disc, was invented by David Paul Gregg in 1958 (and patented in 1970 and 1990). By 1969 Philips had developed a videodisc in reflective mode, which has great advantages over the transparent mode. MCA and Philips decided to join their efforts. They first publicly demonstrated the videodisc in 1972. Laserdisc was first available on the market, in Atlanta, on December 15, 1978, two years after the VHS VCR and four years before the CD, which is based on Laserdisc technology. Philips produced the players and MCA produced the discs. The Philips/MCA cooperation was not successful, and discontinued after a few years. Several of the scientists responsible for the early research (John Winslow, Richard Wilkinson and Ray Dakin) founded Optical Disc Corporation (now ODC Nimbus). == Russell 1965 == While working at Pacific Northwest National Laboratory, James Russell invented an optical storage system for digital audio and video, patenting the concept in 1970. The earliest patents by Russell, US 3,501,586, and 3,795,902 were filed in 1966, and 1969. respectively. He built prototypes, and the first was operating in 1973. Russell had found a way to record digital information onto a photosensitive plate in tiny dark spots, each spot one micrometre from centre to centre, with a laser that wrote the binary patterns. Russell's first optical disc was distinctly different from the eventual compact disc product: the disc in the player was not read by laser light. A key characteristic of Russell's invention is that a laser is not used for the reading the disc, instead the entire disc or oblong sheet to be read is illuminated by a large playback light source at the back of the transparent foil. As a result, the information density is relatively low. By 1985, Russell held over 25 patents to various technologies related to optical recording and playback. Russell's intellectual property was purchased by Optical Recording Corporation (ORC) in Toronto in 1985, and this firm notified a number of CD manufacturers that their CD technology was based on patents held by ORC. In 1987, ORC signed an agreement with Sony whereby Sony paid for licensing of the technology. Further licenses followed from Philips and others. Warner Communications did not sign, and was sued by ORC. In 1992, the large CD manufacturer, now called Time Warner, was ordered to pay ORC US$30 million in patent violations. In the 1970 patent, the spot diameter was around 10 micrometres. Thus, the areal information density was around a factor hundred less than that of the CD as later developed. Russell continued to refine the concept throughout the 1970s. Philips and Sony, however, were able to put far greater resources into the parallel development of the concept, arriving at a smaller and more sophisticated product in just a few years. Russell's various partners and ventures failed to produce a single consumer product. == Korpel 1968 == Adrianus Korpel worked for the Zenith Electronics Corporation, when he developed very early optical videodisc systems, including holographic storage. == Kramer and Compaan 1969 == The Philips development of the videodisc technology began in 1969 with efforts by Dutch physicists Klaas Compaan and Piet Kramer to record video images in holographic form on disc. Their prototype Laserdisc shown in 1972 used a laser beam in reflective mode to read a track of pits using an FM video signal. Together with MCA, Philips brought the optical videodisk to market in 1978. The cooperation between Philips and MCA did not last long, and discontinued after a few years. == Immink and Doi 1979 == The Compact Disc (CD), which is based on MCA/Philips Laserdisc technology, was developed by a taskforce of Sony and Philips in 1979–1980. Toshi Doi and Kees Schouhamer Immink created the digital technologies that turned the analog Laserdisc into a high-density low-cost digital audio disc. The CD, available on the market since October 1982, remains the standard physical medium for sale of commercial audio recordings Standard CDs have a diameter of 120 mm and can hold up to 80 minutes of audio (700 MB of data). The Mini CD has various diameters ranging from 60 to 80 mm; they are sometimes used for CD singles or device drivers, storing up to 24 minutes of audio. The technology was later adapted and expanded to include data storage CD-ROM, write-once audio and data storage CD-R, rewritable media CD-RW, Super Audio CD (SACD), Video Compact Discs (VCD), Super Video Compact Discs (SVCD), PhotoCD, PictureCD, CD-i, and Enhanced CD. CD-ROMs and CD-Rs remain widely used technologies in the computer industry. The CD and its extensions have been extremely successful: in 2004, worldwide sales of CD audio, CD-ROM, and CD-R reached about 30 billion discs. By 2007, 200 billion CDs had been sold worldwide.

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