AI Generator Detector

AI Generator Detector — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

• 

  Data annotation

  Data annotation is the process of labeling or tagging relevant metadata within a dataset to enable machines to interpret the data accurately. The dataset can take various forms, including images, audio files, video footage, or text. == Applications == Data is a fundamental component in the development of artificial intelligence (AI). Training AI models, particularly in computer vision and natural language processing, requires large volumes of annotated data. Proper annotation ensures that machine learning algorithms can recognize patterns and make accurate predictions. Common types of data annotation include classification, bounding boxes, semantic segmentation, and keypoint annotation. Data annotation is used in AI-driven fields, including healthcare, autonomous vehicles, retail, security, and entertainment. By accurately labeling data, machine learning models can perform complex tasks such as object detection, sentiment analysis, and speech recognition with greater precision. This growing demand has led to the emergence of specialized sectors and platforms dedicated to AI training and human-in-the-loop workflows, which often utilize Reinforcement Learning from Human Feedback (RLHF) to refine model behavior. == In computer vision == === Image classification === Image classification, also known as image categorization, involves assigning predefined labels to images. Machine learning algorithms trained on classified images can later recognize objects and differentiate between categories. For instance, an AI model trained to recognize furniture styles can distinguish between Georgian and Rococo armchairs. === Semantic segmentation === Semantic segmentation assigns each pixel in an image to a specific class, such as trees, vehicles, humans, or buildings. This type of annotation enables machine learning models to differentiate objects by grouping similar pixels, allowing for a detailed understanding of an image. === Bounding boxes === Bounding box annotation involves drawing rectangular boxes around objects in an image. This technique is commonly used in autonomous driving, security surveillance, and retail analytics to detect and classify objects such as pedestrians, vehicles, and products on store shelves. === 3D cuboids === 3D cuboid annotation enhances traditional bounding boxes by adding depth, enabling models to predict an object's spatial orientation, movement, and size. This method is particularly useful for autonomous vehicles and robotics, where understanding object dimensions and depth is critical. === Polygonal annotation === For objects with irregular shapes, such as curved or multi-sided items, polygonal annotation provides more precise labeling than bounding boxes. This technique is often used in applications that require detailed object recognition, such as medical imaging or aerial mapping. === Keypoint annotation === Keypoint annotation marks specific points on an object, such as facial landmarks or body joints, to enable tracking and motion analysis. This method is widely used in facial recognition, emotion detection, sports analytics, and augmented reality applications.

  Read more →
• 

  Giditraffic

  GidiTraffic (or GIDITRAFFIC) is an online social service started on 23 September 2011. Based primarily on social media, the service employs crowdsourcing as its primary means of providing real-time traffic updates to subscribers on its platform. The service, delivered free of charge, affords its users access to various types of information. Though its broadest category of users is road users and motorists, GIDITRAFFIC lends itself as a platform for answering inquiries from anyone who requires information on any subject of interest. GIDITRAFFIC's core competence is in vehicular traffic reports, however, the service also handles all other forms of traffic (going by the fact that the word traffic also means "the mutual exchange of information"). == Operation == Users of the service log on to its Twitter feed to get up-to-date traffic information or to post a general inquiry, which GIDITRAFFIC then publishes to all subscribers. Through crowdsourced replies, a requester receives numerous responses from other subscribers who have seen the question and can provide a relevant answer. In addition, updates are provided by subscribers to the platform via their mobile devices, thereby making the service effective in delivering traffic updates as they occur, and providing timely answers to other user inquiries. This informs GIDITRAFFIC's motto of "Lending each other an eye", alluding to the collaboration and cooperation between the platform's users in making the service indispensable to its users. == Reception == On Twitter, which is its primary platform, the service caters to over 1,800,000 subscribers, with the number increasing daily. The popularity of the platform stems from the fact that it not only keeps its subscribers abreast of the traffic situation in Lagos, the commercial capital city of Nigeria (well known for its many traffic jams), but users in other parts of the world. For a regular user of the platform, knowing where to avoid getting to a set destination in good time is well worth the two or three minutes it takes to access and scroll through the GIDITRAFFIC feed for updates. Another interesting aspect of this platform is the identity of the person behind it. The sustained anonymity of this individual has sparked many discussions centering on his or her possible identity. Online, GIDITRAFFIC continuously publishes traffic updates and user questions, while keeping up witty interactions with the platform's followers round the clock – adding to the mystery and persona of the GIDITRAFFIC owner. == Awards and recognition == In early 2012, GIDITRAFFIC received a nomination for a Shorty Award in the Life-Saving Hero category. Although this did not translate into a win, it brought recognition and wider exposure for the service from international news outlets such as the BBC, Washington Post. and New York Times. Back home in Nigeria, also in 2012, GIDITRAFFIC was honored with a Future Award for Best Use of New Media in recognition of the huge impact the service has had in terms of helping Lagos residents better manage time spent in traffic. == Mobile Applications == In 2012, GIDITRAFFIC partnered with telecommunications company Nokia to produce a downloadable mobile traffic application (the GIDITRAFFIC application, available for Nokia Asha phones on Nokia's online store). There are plans to extend the application to a wider range of mobile phone platforms. On 4 September 2013, the GIDITRAFFIC application for Nokia Lumia phones using Windows Phone 8 was launched on the Windows App Store.

  Read more →
• 

  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

  Read more →
• 

  DiscoVision

  DiscoVision is the name of several things related to the video LaserDisc format. It was the original name of the "Reflective Optical Videodisc System" format later known as "LaserVision" or LaserDisc. == Description == MCA DiscoVision, Inc. was a division of entertainment giant MCA (Music Corporation of America), established in 1969 to develop and sell an optical videodisc system. MCA released discs pressed in Carson and Costa Mesa, California on the DiscoVision label from the format's Atlanta, Georgia launch in 1978 to 1982 and the release of the film The Four Seasons. DiscoVision titles included films from Universal Pictures, Paramount Pictures, Warner Bros. Pictures, and Disney content. Agreements were made with Columbia Pictures and United Artists, though no discs were released on the DiscoVision label from either studio. Most of these companies later established their own labels for the format, the first being Paramount with a dozen movies released on the Paramount Home Video label in the summer of 1981. The successor to MCA DiscoVision, DiscoVision Associates (DVA), was the result of a partnership between IBM and MCA. It was hoped that the merger would provide the basis for improvement of the quality of DiscoVision pressings, but no appreciable improvement ever took hold. In 1981, responsibility for the laser videodisc was sold to Pioneer Electronic Corporation, after MCA Discovision had previously started a partnership in 1977 with Pioneer, Universal Pioneer, to produce the Pioneer PR-7820 player (the first industrial model of DiscoVision player from 1978), as well as establishing disc pressing plants in Japan. As part of the partnership, Pioneer, in association with MCA, had a disc replication facility in Kofu, Japan that produced discs. Some of the last DiscoVision label discs were manufactured by Pioneer in Japan. In the same year, MCA discontinued their DiscoVision branding, due to the sale of the technology to Pioneer (who then rebranded the format as LaserDisc) and in turn rebranded their laserdisc releases, now fabricated by Pioneer, under the MCA Videodisc banner; this was changed to the "MCA Home Video" name for both its VHS and videodisc releases. Some of DiscoVision's technical staff went on to form MCA Video Games, in an effort to produce video game cartridges. DiscoVision Associates later evolved into a patent holding company which manages and licenses intellectual property related to LaserDisc, Compact Disc, and optical disc technologies, as well as other non-disc related fields. In 1989, Pioneer acquired DiscoVision Associates where it continues to license its technologies independently. As the portfolio of patent expired, the presence of DiscoVision became less visible. However, it established the success of a patent holding company, which other companies are stimulated to generate royalty income from their own patent portfolio.

  Read more →
• 

  TikTok

  TikTok is a social media and short-form online video platform. It hosts user-submitted videos, which range in duration from three seconds to 60 minutes. It can be accessed through a mobile app or through its website. Since its launch, TikTok has become one of the world's most popular social media platforms, using recommendation algorithms to connect content creators and influencers with new audiences. In April 2020, TikTok surpassed two billion mobile downloads worldwide. The popularity of TikTok has allowed viral trends in food, fashion, and music to take off and increase the platform's cultural impact worldwide. TikTok has come under scrutiny due to data privacy violations, mental health concerns, misinformation, offensive content, addictive algorithm, its role during the Gaza war, and, following its 2026 divestiture in the U.S., alleged censorship of criticism of Donald Trump and discussions of Jeffrey Epstein. While TikTok remains accessible to users in most countries, a minority of countries (including India and Afghanistan) have implemented full or partial bans. Many other countries limit TikTok's use on government-issued devices for security or privacy reasons. == Corporate structure == TikTok Ltd was incorporated in the Cayman Islands in the Caribbean and is based in both Singapore and Los Angeles. It owns entities which are based respectively in Australia (which also runs the New Zealand business), United Kingdom (also owns subsidiaries in the European Union), and Singapore (owns operations in Southeast Asia and India). A spin-off company, TikTok USDS Joint Venture LLC was formed on 22 January 2026 to handle TikTok and other ByteDance properties in the United States, Oracle Corporation, MGX Fund Management Limited, Silver Lake each holding a 15% stake, ByteDance holds a 19.9% stake and the remaining 35.1% is shared between Dell Technologies founder Michael Dell and Vastmere Strategic Investments. Its parent company, Beijing-based ByteDance, is owned by founders and Chinese investors, other global investors, and employees. One of ByteDance's main domestic subsidiaries is owned by Chinese state funds and entities through a 1% golden share. Employees have reported that multiple overlaps exist between TikTok and ByteDance in terms of personnel management and product development. TikTok says that since 2020, its US-based CEO is responsible for making important decisions, and has downplayed its China connection. == History == === Douyin === Douyin (Chinese: 抖音; pinyin: Dǒuyīn; lit. 'Shaking Sound') was launched on 20 September 2016, by ByteDance, originally under the name A.me, before changing its name to Douyin in December 2016. Douyin was developed in nearly 7 months and within a year had 100 million users, with more than one billion videos viewed every day. While TikTok and Douyin share a similar user interface, the platforms operate separately. Douyin includes an in-video search feature that can search by people's faces for more videos of them, along with other features such as buying, booking hotels, and making geo-tagged reviews. === TikTok === ByteDance planned on Douyin expanding overseas. The founder of ByteDance, Zhang Yiming, stated that "China is home to only one-fifth of Internet users globally. If we don't expand on a global scale, we are bound to lose to peers eyeing the four-fifths. So, going global is a must." ByteDance created TikTok as an overseas version of Douyin. TikTok was launched in the international market in September 2017. On 9 November 2017, ByteDance spent nearly $1 billion to purchase Musical.ly, a startup headquartered in Shanghai with an overseas office in Santa Monica, California. Musical.ly was a social media video platform that allowed users to create short lip-sync and comedy videos, initially released in August 2014. TikTok merged with Musical.ly on 2 August 2018 with existing accounts and data consolidated into one app, keeping the title TikTok. On 23 January 2018, the TikTok app ranked first among free application downloads on app stores in Thailand and other countries. TikTok has been downloaded more than 130 million times in the United States and has reached 2 billion downloads worldwide, according to data from mobile research firm Sensor Tower (those numbers exclude Android users in China). In the United States, Jimmy Fallon, Tony Hawk, and other celebrities began using the app in 2018. Other celebrities like Jennifer Lopez, Jessica Alba, Will Smith, and Justin Bieber joined TikTok. In January 2019, TikTok allowed creators to embed merchandise sale links into their videos. On 3 September 2019, TikTok and the US National Football League (NFL) announced a multi-year partnership. The agreement came just two days before the NFL's 100th season kick-off at Soldier Field in Chicago where TikTok hosted activities for fans in honor of the deal. The partnership entails the launch of an official NFL TikTok account, which is to bring about new marketing opportunities such as sponsored videos and hashtag challenges. In July 2020, TikTok, excluding Douyin, reported close to 800 million monthly active users worldwide after less than four years of existence. In May 2021, TikTok appointed Shou Zi Chew as their new CEO who assumed the position from interim CEO Vanessa Pappas, following the resignation of Kevin A. Mayer on 27 August 2020. In September 2021, TikTok reported that it had reached 1 billion users. In 2021, TikTok earned $4 billion in advertising revenue. In October 2022, TikTok was reported to be planning an expansion into the e-commerce market in the US, following the launch of TikTok Shop in the United Kingdom. The company posted job listings for staff for a series of order fulfillment centers in the US and was reportedly planning to start the new live shopping business before the end of the year. The Financial Times reported that TikTok will launch a video gaming channel, but the report was denied in a statement to Digiday, with TikTok instead aiming to be a social hub for the gaming community. According to data from app analytics group Sensor Tower, advertising on TikTok in the US grew by 11% in March 2023, with companies including Pepsi, DoorDash, Amazon, and Apple among the top spenders. According to estimates from research group Insider Intelligence, TikTok is projected to generate $14.15 billion in revenue in 2023, up from $9.89 billion in 2022. In March 2024, The Wall Street Journal reported that TikTok's growth in the US had stagnated. ==== Plans to sell TikTok's US operations ==== Since at least 2020, following calls to ban TikTok in the country, the Committee on Foreign Investment in the United States (CFIUS) has been investigating the company's 2017 merger with Musical.ly but has not finalized any of its negotiations with TikTok, such as the Project Texas proposal, waiting instead for Congress to act. In January 2025, Chinese officials began preliminary talks about potentially selling TikTok's US operations to Elon Musk if the app faced an impending ban due to national security concerns. While Beijing preferred TikTok remain under ByteDance's control, the sale could happen through a competitive process or with US government involvement. One possibility involved Musk's platform, X, taking over TikTok's US business. The move came ahead of a Supreme Court case that upheld the constitutionality of a law that would force a sale or ban of TikTok in the US by 19 January 2025, due to national security concerns regarding its ties to China. Other potential buyers included Project Liberty's "The People's Bid For TikTok" consortium of Frank McCourt with Kevin O'Leary, Steven Mnuchin, MrBeast and Bobby Kotick, the seriousness of these potential buyers was unclear. The day before the impending ban, California-based conversational search engine company Perplexity AI submitted a bid for a merger with TikTok US. On 14 September 2025, the Wall Street Journal reported the US and China have reached the "framework of a deal" for the US operations of TikTok to be sold to a consortium of investors in the US including close Trump ally Larry Ellison of Oracle. The deal was completed by 22 January 2026, with a consortium of investors—including Oracle, Silver Lake, MGX, and others including the personal investment entity for Michael Dell—owning more than 80% of the new venture. ByteDance retained 19.9% ownership. Under the deal, the app would remain the same, and the algorithm would be adjusted over time to favor American topics for those users. === Expansion in other markets === TikTok was downloaded over 104 million times on Apple's App Store during the first half of 2018, according to data provided to CNBC by Sensor Tower. After merging with musical.ly in August, downloads increased and TikTok subsequently became the most downloaded app in the US in October 2018, which musical.ly had done once before. In February 2019, TikTok, together with Douyin, hit one billion downloads globally, excluding Android

  Read more →
• 

  Gaumina

  Gaumina is the largest interactive agency in the Baltics, providing services of web design, web development, online advertising, video, multimedia, mobile and viral. The company works on projects for Procter & Gamble, Nokia, Nissan, Unilever, YX Energi, 7 Up, Vodafone, MTV, Dunnes Stores, Philip Morris, FIBA Europe as well as Irish public sector. == History == Founded in 1998, Gaumina accounts for 39 percent of the Lithuanian interactive market and has completed more than 2,000 online projects. Since 2004 the company has been operating in the UK and Ireland as Gaumina.co.uk. In 2007 Gaumina gained wide media coverage for winning three awards in three days. A website developed by Gaumina won the Best Social Networking website award at the same the Irish Golden Spiders awards. A website developed by Gaumina was named among the 21 best European multimedia projects of 2007 in the final of Europrix Top Talent Award in Austria. The company was also named one of the winners of the national Innovation Prize 2007, awarding the Lithuania's most innovative companies, in the category of Innovative Enterprise. The agency was named "Digital Agency of the Year" by International advertising festival Golden Hammer in September 2008. The agency also won the main prize at the best at Best Use of Film, Digital Animation or Motion Graphics category by the Irish Golden Spider awards in November 2008. Gaumina is currently managed by CEO Darius Bagdžiūnas.

  Read more →
• 

  Digital intermediate

  Digital intermediate (DI) is a motion picture finishing process which classically involves digitizing a motion picture and manipulating the color and other image characteristics. == Definition and overview == A digital intermediate often replaces or augments the photochemical timing process and is usually the final creative adjustment to a movie before distribution in theaters. It is distinguished from the telecine process in which film is scanned and color is manipulated early in the process to facilitate editing. However the lines between telecine and DI are continually blurred and are often executed on the same hardware by colorists of the same background. These two steps are typically part of the overall color management process in a motion picture at different points in time. A digital intermediate is also customarily done at higher resolution and with greater color fidelity than telecine transfers. Although originally used to describe a process that started with film scanning and ended with film recording, digital intermediate is also used to describe color correction and color grading and even final mastering when a digital camera is used as the image source and/or when the final movie is not output to film. This is due to recent advances in digital cinematography and digital projection technologies that strive to match film origination and film projection. In traditional photochemical film finishing, an intermediate is produced by exposing film to the original camera negative. The intermediate is then used to mass-produce the films that get distributed to theaters. Color grading is done by varying the amount of red, green, and blue light used to expose the intermediate. The digital intermediate process uses digital tools to color grade, which allows for much finer control of individual colors and areas of the image, and allows for the adjustment of image structure (grain, sharpness, etc.). The intermediate for film reproduction can then be produced by means of a film recorder. The physical intermediate film that is a result of the recording process is sometimes also called a digital intermediate, and is usually recorded to internegative (IN) stock, which is inherently finer-grain than original camera negative (OCN). One of the key technical achievements that made the transition to DI possible was the use of 3D look-up tables, which could be used to mimic how the digital image would look once it was printed onto release print stock. This removed a large amount of guesswork from the film-making process, and allowed greater freedom in the colour grading process while reducing risk. The digital master is often used as a source for a DCI-compliant distribution of the motion picture for digital projection. For archival purposes, the digital master created during the digital intermediate process can be recorded to very stable high dynamic range yellow-cyan-magenta (YCM) separations on black-and-white film with an expected 100-year or longer life. While still subject to the natural degradation of any analog chemical master, this archival format, long used in the industry prior to the invention of DI, was considered valuable for providing an archival medium that is independent of changes in digital data recording technologies and file formats that might otherwise render digitally archived material unreadable in the long term. A "film intermediate" is an analog variation of a digital intermediate, where a project shot on digital video is printed onto film stock and transferred back to digital video to emulate film. The term was coined after it was used on the Oscar-winning 2012 short film "Curfew". The process was also used on the films Dune (2021) and The Batman (2022). == History == Telecine tools to electronically capture film images are nearly as old as broadcast television, but the resulting images were widely considered unsuitable for exposing back onto film for theatrical distribution. Film scanners and recorders with quality sufficient to produce images that could be inter-cut with regular film began appearing in the 1970s, with significant improvements in the late 1980s and early 1990s. During this time, digitally processing an entire feature-length film was impractical because the scanners and recorders were extremely slow and the image files were too large compared to computing power available. Instead, individual shots or short sequences were processed for visual effects. In 1992, Visual Effects Supervisor/Producer Chris F. Woods broke through several "techno-barriers" in creating a digital studio to produce the visual effects for the 1993 release Super Mario Bros. It was the first feature film project to digitally scan a large number of VFX plates (over 700) at 2K resolution. It was also the first film scanned and recorded at Kodak's just launched Cinesite facility in Hollywood. This project based studio was the first feature film to use Discreet Logic's (now Autodesk) Flame and Inferno systems, which enjoyed early dominance as high resolution / high performance digital compositing systems. Digital film compositing for visual effects was immediately embraced, while optical printer use for VFX declined just as quickly. Chris Watts further revolutionized the process on the 1998 feature film Pleasantville, becoming the first visual effects supervisor for New Line Cinema to scan, process, and record the majority of a feature-length, live-action, Hollywood film digitally. The first Hollywood film to utilize a digital intermediate process from beginning to end was O Brother, Where Art Thou? in 2000 and in Europe it was Chicken Run released that same year. The process rapidly caught on in the mid-2000s. Around 50% of Hollywood films went through a digital intermediate in 2005, increasing to around 70% by mid-2007. This is due not only to the extra creative options the process affords film makers but also the need for high-quality scanning and color adjustments to produce movies for digital cinema. == Milestones == 1990: The Rescuers Down Under – First feature-length film to be entirely recorded to film from digital files; in this case animation assembled on computers using Walt Disney Feature Animation and Pixar's CAPS system. 1992: Visual effects supervisor and producer Chris F. Woods creates a VFX studio to produce the visual effects for the 1993 film Super Mario Bros. It was the first 35mm feature film to digitally scan a large number of VFX plates (over 700) at 2K resolution, as well as to output the finished VFX to 35mm negative at 2K. 1993: Snow White and the Seven Dwarfs – First film to be entirely scanned to digital files, manipulated, and recorded back to film at 4K resolution. The restoration project was done entirely at 4K resolution and 10-bit color depth using the Cineon system to digitally remove dirt and scratches and restore faded colors. 1998: Pleasantville – The first time the majority of a new feature film was scanned, processed, and recorded digitally. The black-and-white meets color world portrayed in the movie was filmed entirely in color and selectively desaturated and contrast adjusted digitally. The work was done in Los Angeles by Cinesite utilizing a Spirit DataCine for scanning at 2K resolution and a MegaDef color correction system from UK Company Pandora International 1998: Zingo - The first feature film to use digital color correction via digital intermediate in its entirety. The work was performed at the Digital Film Lab in Copenhagen, using a Spirit Datacine to transfer the entire film to digital files at 2K resolution. The digital intermediate process was also used to perform a digital blowup of the film's original Super 16 source format to a 35mm output. 1999: Pacific Ocean Post Film, a team led by John McCunn and Greg Kimble used Kodak film scanners & laser film printer, Cineon software as well as proprietary tools to rebuild and repair the first two reels of the 1968 Beatles' film Yellow Submarine for re-release. 1999: Star Wars: Episode I – The Phantom Menace - Industrial Light & Magic (ILM) scanned the entirety of the visual effects-laden film for the purposes of digital enhancement and the integration of thousands of separately filmed elements with computer generated characters and environments. Outside of the approximately 2000 effects shots that were digitally manipulated, the remaining 170 non-effects shots were also scanned for continuity. However, after the digital shots were manipulated at ILM, they were filmed out individually and sent to Deluxe Labs where they were processed and color timed photochemically. 2000: Sorted - The first feature-length, color 35mm motion picture to fully utilize the digital intermediate process in its entirety from inception to completion. The film was produced at Wave Pictures' digital intermediate film facility in London, England. It was scanned at 2K resolution with 8 bits color depth per color / per pixel using a pin registered, liquid gate Oxberry

  Read more →
• 

  Data communication

  Data communication is the transfer of data over a point-to-point or point-to-multipoint communication channel. Data communication comprises data transmission and data reception and can be classified as analog transmission and digital communications. Analog data communication conveys voice, data, image, signal or video information using a continuous signal, which varies in amplitude, phase, or some other property. In baseband analog transmission, messages are represented by a sequence of pulses by means of a line code; in passband analog transmission, they are communicated by a limited set of continuously varying waveforms, using a digital modulation method. Passband modulation and demodulation are carried out by modem equipment. Digital transmission and digital reception are the transfer of either a digitized analog signal or a born-digital bitstream. Baseband digital transmission is regarded as comprising part of a digital signal, whereas passband transmission of digital data may also or alternatively be considered a form of digital-to-analog conversion. Data communication channels include copper wires, optical fibers, wireless communication using radio spectrum, storage media and computer buses. The data are represented as an electromagnetic signal, such as an electrical voltage, radiowave, microwave, or infrared signal. == Distinction between related subjects == Digital transmission or data transmission traditionally belongs to telecommunications and electrical engineering. Basic principles of data transmission may also be covered within the computer science or computer engineering topic of data communications, which also includes computer networking applications and communication protocols, for example, routing, switching and inter-process communication. Although the Transmission Control Protocol (TCP) involves transmission, TCP and other transport layer protocols are covered in computer networking but not discussed in a textbook or course about data transmission. In most textbooks, the term analog transmission only refers to the transmission of an analog message signal (without digitization) by means of an analog signal, either as a non-modulated baseband signal or as a passband signal using an analog modulation method such as AM or FM. It may also include analog-over-analog pulse modulated baseband signals such as pulse-width modulation. In a few books within the computer networking tradition, analog transmission also refers to passband transmission of bit-streams using digital modulation methods such as FSK, PSK and ASK. The theoretical aspects of data transmission are covered by information theory and coding theory. == Protocol layers and sub-topics == Courses and textbooks in the field of data transmission typically deal with the following OSI model protocol layers and topics: Layer 1, the physical layer: Channel coding including Digital modulation schemes Line coding schemes Forward error correction (FEC) codes Bit synchronization Multiplexing Equalization Channel models Layer 2, the data link layer: Channel access schemes, media access control (MAC) Packet mode communication and Frame synchronization Error detection and automatic repeat request (ARQ) Flow control Layer 6, the presentation layer: Source coding (digitization and data compression), and information theory. Cryptography (may occur at any layer) It is also common to deal with the cross-layer design of those three layers. == Applications and history == Data (mainly but not exclusively informational) has been sent via non-electronic (e.g. optical, acoustic, mechanical) means since the advent of communication. Analog signal data has been sent electronically since the advent of the telephone. However, the first data electromagnetic transmission applications in modern time were electrical telegraphy (1809) and teletypewriters (1906), which are both digital signals. The fundamental theoretical work in data transmission and information theory by Harry Nyquist, Ralph Hartley, Claude Shannon and others during the early 20th century, was done with these applications in mind. In the early 1960s, Paul Baran invented distributed adaptive message block switching for digital communication of voice messages using switches that were low-cost electronics. Donald Davies invented and implemented modern data communication during 1965–7, including packet switching, high-speed routers, communication protocols, hierarchical computer networks and the essence of the end-to-end principle. Baran's work did not include routers with software switches and communication protocols, nor the idea that users, rather than the network itself, would provide the reliability. Both were seminal contributions that influenced the development of computer networks. Data transmission is utilized in computers in computer buses and for communication with peripheral equipment via parallel ports and serial ports such as RS-232 (1969), FireWire (1995) and USB (1996). The principles of data transmission are also utilized in storage media for error detection and correction since 1951. The first practical method to overcome the problem of receiving data accurately by the receiver using digital code was the Barker code invented by Ronald Hugh Barker in 1952 and published in 1953. Data transmission is utilized in computer networking equipment such as modems (1940), local area network (LAN) adapters (1964), repeaters, repeater hubs, microwave links, wireless network access points (1997), etc. In telephone networks, digital communication is utilized for transferring many phone calls over the same copper cable or fiber cable by means of pulse-code modulation (PCM) in combination with time-division multiplexing (TDM) (1962). Telephone exchanges have become digital and software controlled, facilitating many value-added services. For example, the first AXE telephone exchange was presented in 1976. Digital communication to the end user using Integrated Services Digital Network (ISDN) services became available in the late 1980s. Since the end of the 1990s, broadband access techniques such as ADSL, Cable modems, fiber-to-the-building (FTTB) and fiber-to-the-home (FTTH) have become widespread to small offices and homes. The current tendency is to replace traditional telecommunication services with packet mode communication such as IP telephony and IPTV. Transmitting analog signals digitally allows for greater signal processing capability. The ability to process a communications signal means that errors caused by random processes can be detected and corrected. Digital signals can also be sampled instead of continuously monitored. The multiplexing of multiple digital signals is much simpler compared to the multiplexing of analog signals. Because of all these advantages, because of the vast demand to transmit computer data and the ability of digital communications to do so and because recent advances in wideband communication channels and solid-state electronics have allowed engineers to realize these advantages fully, digital communications have grown quickly. The digital revolution has also resulted in many digital telecommunication applications where the principles of data transmission are applied. Examples include second-generation (1991) and later cellular telephony, video conferencing, digital TV (1998), digital radio (1999), and telemetry. Data transmission, digital transmission or digital communications is the transfer of data over a point-to-point or point-to-multipoint communication channel. Examples of such channels include copper wires, optical fibers, wireless communication channels, storage media and computer buses. The data are represented as an electromagnetic signal, such as an electrical voltage, radio wave, microwave, or infrared light. While analog transmission is the transfer of a continuously varying analog signal over an analog channel, digital communication is the transfer of discrete messages over a digital or an analog channel. The messages are either represented by a sequence of pulses by means of a line code (baseband transmission) or by a limited set of continuously varying waveforms (passband transmission), using a digital modulation method. The passband modulation and corresponding demodulation (also known as detection) are carried out by modem equipment. According to the most common definition of a digital signal, both baseband and passband signals representing bit-streams are considered as digital transmission, while an alternative definition only considers the baseband signal as digital, and passband transmission of digital data as a form of digital-to-analog conversion. Data transmitted may be digital messages originating from a data source, for example, a computer or a keyboard. It may also be an analog signal, such as a phone call or a video signal, digitized into a bit-stream, for example,e using pulse-code modulation (PCM) or more advanced source coding (analog-to-digital conversion and

  Read more →
• 

  Manifold regularization

  In machine learning, manifold regularization is a technique for using the shape of a dataset to constrain the functions that should be learned on that dataset. In many machine learning problems, the data to be learned do not cover the entire input space. For example, a facial recognition system may not need to classify any possible image, but only the subset of images that contain faces. The technique of manifold learning assumes that the relevant subset of data comes from a manifold, a mathematical structure with useful properties. The technique also assumes that the function to be learned is smooth: data with different labels are not likely to be close together, and so the labeling function should not change quickly in areas where there are likely to be many data points. Because of this assumption, a manifold regularization algorithm can use unlabeled data to inform where the learned function is allowed to change quickly and where it is not, using an extension of the technique of Tikhonov regularization. Manifold regularization algorithms can extend supervised learning algorithms in semi-supervised learning and transductive learning settings, where unlabeled data are available. The technique has been used for applications including medical imaging, geographical imaging, and object recognition. == Manifold regularizer == === Motivation === Manifold regularization is a type of regularization, a family of techniques that reduces overfitting and ensures that a problem is well-posed by penalizing complex solutions. In particular, manifold regularization extends the technique of Tikhonov regularization as applied to Reproducing kernel Hilbert spaces (RKHSs). Under standard Tikhonov regularization on RKHSs, a learning algorithm attempts to learn a function f {\displaystyle f} from among a hypothesis space of functions H {\displaystyle {\mathcal {H}}} . The hypothesis space is an RKHS, meaning that it is associated with a kernel K {\displaystyle K} , and so every candidate function f {\displaystyle f} has a norm ‖ f ‖ K {\displaystyle \left\|f\right\|_{K}} , which represents the complexity of the candidate function in the hypothesis space. When the algorithm considers a candidate function, it takes its norm into account in order to penalize complex functions. Formally, given a set of labeled training data ( x 1 , y 1 ) , … , ( x ℓ , y ℓ ) {\displaystyle (x_{1},y_{1}),\ldots ,(x_{\ell },y_{\ell })} with x i ∈ X , y i ∈ Y {\displaystyle x_{i}\in X,y_{i}\in Y} and a loss function V {\displaystyle V} , a learning algorithm using Tikhonov regularization will attempt to solve the expression arg min f ∈ H 1 ℓ ∑ i = 1 ℓ V ( f ( x i ) , y i ) + γ ‖ f ‖ K 2 {\displaystyle {\underset {f\in {\mathcal {H}}}{\arg \!\min }}{\frac {1}{\ell }}\sum _{i=1}^{\ell }V(f(x_{i}),y_{i})+\gamma \left\|f\right\|_{K}^{2}} where γ {\displaystyle \gamma } is a hyperparameter that controls how much the algorithm will prefer simpler functions over functions that fit the data better. Manifold regularization adds a second regularization term, the intrinsic regularizer, to the ambient regularizer used in standard Tikhonov regularization. Under the manifold assumption in machine learning, the data in question do not come from the entire input space X {\displaystyle X} , but instead from a nonlinear manifold M ⊂ X {\displaystyle M\subset X} . The geometry of this manifold, the intrinsic space, is used to determine the regularization norm. === Laplacian norm === There are many possible choices for the intrinsic regularizer ‖ f ‖ I {\displaystyle \left\|f\right\|_{I}} . Many natural choices involve the gradient on the manifold ∇ M {\displaystyle \nabla _{M}} , which can provide a measure of how smooth a target function is. A smooth function should change slowly where the input data are dense; that is, the gradient ∇ M f ( x ) {\displaystyle \nabla _{M}f(x)} should be small where the marginal probability density P X ( x ) {\displaystyle {\mathcal {P}}_{X}(x)} , the probability density of a randomly drawn data point appearing at x {\displaystyle x} , is large. This gives one appropriate choice for the intrinsic regularizer: ‖ f ‖ I 2 = ∫ x ∈ M ‖ ∇ M f ( x ) ‖ 2 d P X ( x ) {\displaystyle \left\|f\right\|_{I}^{2}=\int _{x\in M}\left\|\nabla _{M}f(x)\right\|^{2}\,d{\mathcal {P}}_{X}(x)} In practice, this norm cannot be computed directly because the marginal distribution P X {\displaystyle {\mathcal {P}}_{X}} is unknown, but it can be estimated from the provided data. === Graph-based approach of the Laplacian norm === When the distances between input points are interpreted as a graph, then the Laplacian matrix of the graph can help to estimate the marginal distribution. Suppose that the input data include ℓ {\displaystyle \ell } labeled examples (pairs of an input x {\displaystyle x} and a label y {\displaystyle y} ) and u {\displaystyle u} unlabeled examples (inputs without associated labels). Define W {\displaystyle W} to be a matrix of edge weights for a graph, where W i j {\displaystyle W_{ij}} is a similarity built from distance measure between the data points x i {\displaystyle x_{i}} and x j {\displaystyle x_{j}} (so that more close implies higher W i j {\displaystyle W_{ij}} ). Define D {\displaystyle D} to be a diagonal matrix with D i i = ∑ j = 1 ℓ + u W i j {\displaystyle D_{ii}=\sum _{j=1}^{\ell +u}W_{ij}} and L {\displaystyle L} to be the Laplacian matrix D − W {\displaystyle D-W} . Then, as the number of data points ℓ + u {\displaystyle \ell +u} increases, L {\displaystyle L} converges to the Laplace–Beltrami operator Δ M {\displaystyle \Delta _{M}} , which is the divergence of the gradient ∇ M {\displaystyle \nabla _{M}} . Then, if f {\displaystyle \mathbf {f} } is a vector of the values of f {\displaystyle f} at the data, f = [ f ( x 1 ) , … , f ( x l + u ) ] T {\displaystyle \mathbf {f} =[f(x_{1}),\ldots ,f(x_{l+u})]^{\mathrm {T} }} , the intrinsic norm can be estimated: ‖ f ‖ I 2 = 1 ( ℓ + u ) 2 f T L f {\displaystyle \left\|f\right\|_{I}^{2}={\frac {1}{(\ell +u)^{2}}}\mathbf {f} ^{\mathrm {T} }L\mathbf {f} } As the number of data points ℓ + u {\displaystyle \ell +u} increases, this empirical definition of ‖ f ‖ I 2 {\displaystyle \left\|f\right\|_{I}^{2}} converges to the definition when P X {\displaystyle {\mathcal {P}}_{X}} is known. === Solving the regularization problem with graph-based approach === Using the weights γ A {\displaystyle \gamma _{A}} and γ I {\displaystyle \gamma _{I}} for the ambient and intrinsic regularizers, the final expression to be solved becomes: arg min f ∈ H 1 ℓ ∑ i = 1 ℓ V ( f ( x i ) , y i ) + γ A ‖ f ‖ K 2 + γ I ( ℓ + u ) 2 f T L f {\displaystyle {\underset {f\in {\mathcal {H}}}{\arg \!\min }}{\frac {1}{\ell }}\sum _{i=1}^{\ell }V(f(x_{i}),y_{i})+\gamma _{A}\left\|f\right\|_{K}^{2}+{\frac {\gamma _{I}}{(\ell +u)^{2}}}\mathbf {f} ^{\mathrm {T} }L\mathbf {f} } As with other kernel methods, H {\displaystyle {\mathcal {H}}} may be an infinite-dimensional space, so if the regularization expression cannot be solved explicitly, it is impossible to search the entire space for a solution. Instead, a representer theorem shows that under certain conditions on the choice of the norm ‖ f ‖ I {\displaystyle \left\|f\right\|_{I}} , the optimal solution f ∗ {\displaystyle f^{}} must be a linear combination of the kernel centered at each of the input points: for some weights α i {\displaystyle \alpha _{i}} , f ∗ ( x ) = ∑ i = 1 ℓ + u α i K ( x i , x ) {\displaystyle f^{}(x)=\sum _{i=1}^{\ell +u}\alpha _{i}K(x_{i},x)} Using this result, it is possible to search for the optimal solution f ∗ {\displaystyle f^{}} by searching the finite-dimensional space defined by the possible choices of α i {\displaystyle \alpha _{i}} . === Functional approach of the Laplacian norm === The idea beyond the graph-Laplacian is to use neighbors to estimate the Laplacian. This method is akin to local averaging methods, that are known to scale poorly in high-dimensional problems. Indeed, the graph Laplacian is known to suffer from the curse of dimensionality. Luckily, it is possible to leverage expected smoothness of the function to estimate thanks to more advanced functional analysis. This method consists of estimating the Laplacian operator using derivatives of the kernel reading ∂ 1 , j K ( x i , x ) {\displaystyle \partial _{1,j}K(x_{i},x)} where ∂ 1 , j {\displaystyle \partial _{1,j}} denotes the partial derivatives according to the j-th coordinate of the first variable. This second approach to the Laplacian norm is to put in relation with meshfree methods, that contrast with the finite difference method in PDE. == Applications == Manifold regularization can extend a variety of algorithms that can be expressed using Tikhonov regularization, by choosing an appropriate loss function V {\displaystyle V} and hypothesis space H {\displaystyle {\mathcal {H}}} . Two commonly used examples are the families of support vector machines and regularized least squares algorithm

  Read more →
• 

  CSS box model

  In web development, the CSS box model refers to how HTML elements are modeled in browser engines and how the dimensions of those HTML elements are derived from CSS properties. It is a fundamental concept for the composition of HTML webpages. The guidelines of the box model are described by web standards World Wide Web Consortium (W3C) specifically the CSS Working Group. For much of the late-1990s and early 2000s there had been non-standard compliant implementations of the box model in mainstream browsers. With the advent of CSS2 in 1998, which introduced the box-sizing property, the problem had mostly been resolved. == Specifics == The Cascading Style Sheets (CSS) specification describes how elements of web pages are displayed by graphical browsers. Section 4 of the CSS1 specification defines a "formatting model" that gives block-level elements—such as p and blockquote—a width and height, and three levels of boxes surrounding it: padding, borders, and margins. While the specification never uses the term "box model" explicitly, the term has become widely used by web developers and web browser vendors. All HTML elements can be considered "boxes", this includes div tag, p tag, or a tag. Each of those boxes has five modifiable dimensions: the height and width describe dimensions of the actual content of the box (text, images, ...) the padding describes the space between this content and the border of the box the border is any kind of line (solid, dotted, dashed...) surrounding the box, if present the margin is the space around the border According to the CSS1 specification, released by W3C in 1996 and revised in 1999, when a width or height is explicitly specified for any block-level element, it should determine only the width or height of the visible element, with the padding, borders, and margins applied afterward. Before CSS3, this box model was known as W3C box model, in CSS3, it is known as the content-box. The total width of a box is therefore margin-left + border-left + padding-left + width + padding-right + border-right + margin-right. Similarly, the total height of a box equals margin-top + border-top + padding-top + height + padding-bottom + border-bottom + margin-bottom. For example, the following CSS code would specify the box dimensions of each block belonging to 'my-class'. Moreover, each such box will have total height 140px and width 240px. CSS3 introduced the Internet Explorer box model to the standard, known referred to as border-box. == History == Before HTML 4 and CSS, very few HTML elements supported both border and padding, so the definition of the width and height of an element was not very contentious. However, it varied depending on the element. The HTML width attribute of a table defined the width of the table including its border. On the other hand, the HTML width attribute of an image defined the width of the image itself (inside any border). The only element to support padding in those early days was the table cell. Width for the cell was defined as "the suggested width for a cell content in pixels excluding the cell padding." In 1996, CSS introduced margin, border and padding for many more elements. It adopted a definition width in relation to content, border, margin and padding similar to that for a table cell. This has since become known as the W3C box model. At the time, very few browser vendors implemented the W3C box model to the letter. The two major browsers at the time, Netscape 4.0 and Internet Explorer 4.0 both defined width and height as the distance from border to border. This has been referred to as the traditional or the Internet Explorer box model. Internet Explorer in "quirks mode" includes the content, padding and borders within a specified width or height; this results in a narrower or shorter rendering of a box than would result following the standard behavior. The Internet Explorer box model behavior was often considered a bug, because of the way in which earlier versions of Internet Explorer handle the box model or sizing of elements in a web page, which differs from the standard way recommended by the W3C for the Cascading Style Sheets language. As of Internet Explorer 6, the browser supports an alternative rendering mode (called the "standards-compliant mode") which solves this discrepancy. However, for backward compatibility reasons, all versions still behave in the usual, non-standard way by default (see quirks mode). Internet Explorer for Mac is not affected by this non-standard behavior. === Workarounds === Internet Explorer versions 6 and onward are not affected by the bug if the page contains certain HTML document type declarations. These versions maintain the buggy behavior when in quirks mode for reasons of backward compatibility. For example, quirks mode is triggered: When the document type declaration is absent or incomplete; When an HTML 3 or earlier document is encountered; When an HTML 4.0 Transitional or Frameset document type declaration is used and a system identifier (URI) is not present; When an SGML comment or other unrecognized content appears before the document type declaration Internet Explorer 6 also uses quirks mode if there is an XML declaration prior to the document type declaration. Various workarounds have been devised to force Internet Explorer versions 5 and earlier to display Web pages using the W3C box model. These workarounds generally exploit unrelated bugs in Internet Explorer's CSS selector processing in order to hide certain rules from the browser. The best known of these workarounds is the "box model hack" developed by Tantek Çelik, a former Microsoft employee who developed this idea while working on Internet Explorer for the Macintosh. It involves specifying a width declaration for Internet Explorer for Windows, and then overriding it with another width declaration for CSS-compliant browsers. This second declaration is hidden from Internet Explorer for Windows by exploiting other bugs in the way that it parses CSS rules. The implementation of these CSS “hacks” has been further complicated by the public release of Internet Explorer 7, which has had some issues fixed, but not others, causing undesired results in pages using these hacks. Box model hacks have proven unreliable because they rely on bugs in browsers' CSS support that may be fixed in later versions. For this reason, some Web developers have instead recommended either avoiding specifying both width and padding for the same element or using conditional comment and/or CSS filters to work around the box model bug in older versions of Internet Explorer. == Support for Internet Explorer's box model == Web designer Doug Bowman has said that the original Internet Explorer box model represents a better, more logical approach. Peter-Paul Koch gives the example of a physical box, whose dimensions always refer to the box itself, including potential padding, but never its content. He says that this box model is more useful for graphic designers, who create designs based on the visible width of boxes rather than the width of their content. Bernie Zimmermann says that the Internet Explorer box model is closer to the definition of cell dimensions and padding used in the HTML table model. The W3C has included a "box-sizing" property in CSS3. When box-sizing: border-box; is specified for an element, any padding or border of the element is drawn inside the specified width and height, "as commonly implemented by legacy HTML user agents". Internet Explorer 8, WebKit browsers such as Apple Safari 5.1+ and Google Chrome, Gecko-based browsers such as Mozilla Firefox 29.0 and later, Opera 7.0 and later, and Konqueror 3.3.2 and later support the CSS3 box-sizing property. Gecko browsers previous than 29.0 support the same functionality using the browser-specific -moz-box-sizing property. border-box is the default box model used in Bootstrap framework.

  Read more →
• 

  MADI

  Multichannel Audio Digital Interface (MADI) standardized as AES10 by the Audio Engineering Society (AES) defines the data format and electrical characteristics of an interface that carries multiple channels of digital audio. The AES first documented the MADI standard in AES10-1991 and updated it in AES10-2003 and AES10-2008. The MADI standard includes a bit-level description and has features in common with the two-channel AES3 interface. MADI supports serial digital transmission over coaxial cable or fibre-optic lines of 28, 56, 32, or 64 channels; and sampling rates to 96 kHz and beyond with an audio bit depth of up to 24 bits per channel. Like AES3 and ADAT Lightpipe, it is a unidirectional interface from one sender to one receiver. == Development and applications == MADI was developed by AMS Neve, Solid State Logic, Sony and Mitsubishi and is widely used in the audio industry, especially in the professional audio sector. It provides advantages over other audio digital interface protocols and standards such as AES3, ADAT Lightpipe, TDIF (Tascam Digital Interface), and S/PDIF (Sony/Philips Digital Interface). These advantages include: Support for a greater number of channels per line Use of coaxial and optical fiber media that support transmission of audio signals over 100 meters, up to 3000 meters over multi-mode and 40,000 meters over single-mode optical fiber The original specification (AES10-1991) defined the MADI link as a 56-channel transport for linking large-format mixing consoles to digital multitrack recording devices. Large broadcast studios also adopted it for routing multi-channel audio throughout their facilities. The 2003 revision (AES10-2003) adds a 64-channel capability by removing varispeed operation and supports 96 kHz sampling frequency with reduced channel capacity. The latest AES10-2008 standard includes minor clarifications and updates to correspond to the current AES3 standard. Audio over Ethernet of various types is the primary alternative to MADI for transport of many channels of professional digital audio. == Transmission format == MADI links use a transmission format similar to Fiber Distributed Data Interface (FDDI) networking. Since MADI is most often transmitted on copper links via 75-ohm coaxial cables, it more closely compares to the FDDI specification for copper-based links, called CDDI. AES10-2003 recommends using BNC connectors with coaxial cables and SC connectors with optic fibers. MADI over fibre can support a range of up to 2 km. The basic data rate is 100 Mbit/s of data using 4B5B encoding to produce a 125 MHz physical baud rate. Unlike AES3, this clock is not synchronized to the audio sample rate, and the audio data payload is padded using JK sync symbols. Sync symbols may be inserted at any subframe boundary, and must occur at least once per frame. Though the standard disassociates the transmission clock from the audio sample rate, and thus requires a separate word clock connection to maintain synchronization, some vendors do give the option of locking to parts of the transmission timing information for purposes of deriving a word clock. The audio data is almost identical to the AES3 payload, though with more channels. Rather than letters, MADI assigns channel numbers from 0–63. Frame synchronization is provided by sync symbols outside the data itself, rather than an embedded preamble sequence, and the first four time slots of each sub-channel are encoded as normal data, used for sub-channel identification: Bit 0: Set to 1 to mark channel 0, the first channel in each frame Bit 1: Set to 1 to indicate that this channel is active (contains interesting data) Bit 2: notA/B channel marker, used to mark left (0) and right (1) channels. Generally, even channels are A and odd channels are B. Bit 3: Set to 1 to mark the beginning of a 192-sample data block == Sampling frequency == The original AES10-1991 specification allowed 56 channels at sample rates from 32 to 48 kHz with an additional vari-speed range of ± 12.5%. This leads to a total range of 28 to 54 kHz. At the highest frequency, this produces a total of 56 × 32 × 54 = 96768 kbit/s, leaving 3.232% of the channel for synchronization marks and transmit clock error. The 2003 revision specifies different relations between sampling frequency and number of channels. 32 kHz to 48 kHz ± 12.5%, 56 channels; 32 kHz to 48 kHz nominal, 64 channels; 64 kHz to 96 kHz ± 12.5%, 28 channels. With a 48 kHz sampling frequency, 64 channels take 64 × 32 × 48000 = 98.304 Mbit/s. Adding the minimum 8 × 58 kbit/s of framing produces 98688 bit/s, leaving 1.312% free for timing variation and other overhead. Both versions of the standard accommodate higher sampling frequencies (for example, 96 kHz or 192 kHz) by using two or more channels per audio sample on the link.

  Read more →
• 

  Media Block

  A Media Block or Integrated Media Block (IMB) is a component in a digital cinema projection system. Its purpose is to convert the Digital Cinema Package (DCP) content into data that ultimately produces picture and sound in a theater in compliance with DCI anti-piracy encryption requirements. == Terminology == DCI specification allows for two different security system architectures. In the first the Media Block is outside of the projector. This design is simply referred to as a "Media Block" and is typically a device attached directly to the motherboard of a Digital Cinema server. The media block is usually connected to the projector by dual-link SDI cables. Such media block is limited to processing 2K output, downscaling 4K DCPs if necessary. The second architecture describes an "Integrated Media Block". This refers to a device attached and integrated directly into the projector, which receives image data from the server, usually via a cat6 Ethernet connection. They can process 2K and 4K output. Some hardware implementations integrate the entire server on a single board and are able to work both as a MB as well as an IMB. == Security features == All security functions are contained within a Secure Processing Block (SPB), a tamper-proof physical device. Upon ingestion into a DCP server, Key Delivery Messages (KDM) are stored on flash memory in the media block or IMB. A KDM is written to enable the playback of a specific DCP during a specific time window and on a specific media block or IMB, identified by its serial number during the authoring process. Media blocks and IMBs also contain a secure clock that is set in the factory cannot be altered by the end user, which the DCP servers to which they are attached use to determine showtimes. The secure clock prevents theaters from showing encrypted movies outside the times authorized by the KDM (e.g. after it has expired) by simply changing the date and time in the server's BIOS. Media blocks and IMBs also typically include anti-tamper devices, designed to self-destruct the unit if unauthorized modification of its hardware, software or secure clock is attempted.

  Read more →
• 

  Model-based clustering

  In statistics, cluster analysis is the algorithmic grouping of objects into homogeneous groups based on numerical measurements. Model-based clustering based on a statistical model for the data, usually a mixture model. This has several advantages, including a principled statistical basis for clustering, and ways to choose the number of clusters, to choose the best clustering model, to assess the uncertainty of the clustering, and to identify outliers that do not belong to any group. == Model-based clustering == Suppose that for each of n {\displaystyle n} observations we have data on d {\displaystyle d} variables, denoted by y i = ( y i , 1 , … , y i , d ) {\displaystyle y_{i}=(y_{i,1},\ldots ,y_{i,d})} for observation i {\displaystyle i} . Then model-based clustering expresses the probability density function of y i {\displaystyle y_{i}} as a finite mixture, or weighted average of G {\displaystyle G} component probability density functions: p ( y i ) = ∑ g = 1 G τ g f g ( y i ∣ θ g ) , {\displaystyle p(y_{i})=\sum _{g=1}^{G}\tau _{g}f_{g}(y_{i}\mid \theta _{g}),} where f g {\displaystyle f_{g}} is a probability density function with parameter θ g {\displaystyle \theta _{g}} , τ g {\displaystyle \tau _{g}} is the corresponding mixture probability where ∑ g = 1 G τ g = 1 {\displaystyle \sum _{g=1}^{G}\tau _{g}=1} . Then in its simplest form, model-based clustering views each component of the mixture model as a cluster, estimates the model parameters, and assigns each observation to cluster corresponding to its most likely mixture component. === Gaussian mixture model === The most common model for continuous data is that f g {\displaystyle f_{g}} is a multivariate normal distribution with mean vector μ g {\displaystyle \mu _{g}} and covariance matrix Σ g {\displaystyle \Sigma _{g}} , so that θ g = ( μ g , Σ g ) {\displaystyle \theta _{g}=(\mu _{g},\Sigma _{g})} . This defines a Gaussian mixture model. The parameters of the model, τ g {\displaystyle \tau _{g}} and θ g {\displaystyle \theta _{g}} for g = 1 , … , G {\displaystyle g=1,\ldots ,G} , are typically estimated by maximum likelihood estimation using the expectation-maximization algorithm (EM); see also EM algorithm and GMM model. Bayesian inference is also often used for inference about finite mixture models. The Bayesian approach also allows for the case where the number of components, G {\displaystyle G} , is infinite, using a Dirichlet process prior, yielding a Dirichlet process mixture model for clustering. === Choosing the number of clusters === An advantage of model-based clustering is that it provides statistically principled ways to choose the number of clusters. Each different choice of the number of groups G {\displaystyle G} corresponds to a different mixture model. Then standard statistical model selection criteria such as the Bayesian information criterion (BIC) can be used to choose G {\displaystyle G} . The integrated completed likelihood (ICL) is a different criterion designed to choose the number of clusters rather than the number of mixture components in the model; these will often be different if highly non-Gaussian clusters are present. === Parsimonious Gaussian mixture model === For data with high dimension, d {\displaystyle d} , using a full covariance matrix for each mixture component requires estimation of many parameters, which can result in a loss of precision, generalizabity and interpretability. Thus it is common to use more parsimonious component covariance matrices exploiting their geometric interpretation. Gaussian clusters are ellipsoidal, with their volume, shape and orientation determined by the covariance matrix. Consider the eigendecomposition of a matrix Σ g = λ g D g A g D g T , {\displaystyle \Sigma _{g}=\lambda _{g}D_{g}A_{g}D_{g}^{T},} where D g {\displaystyle D_{g}} is the matrix of eigenvectors of Σ g {\displaystyle \Sigma _{g}} , A g = diag { A 1 , g , … , A d , g } {\displaystyle A_{g}={\mbox{diag}}\{A_{1,g},\ldots ,A_{d,g}\}} is a diagonal matrix whose elements are proportional to the eigenvalues of Σ g {\displaystyle \Sigma _{g}} in descending order, and λ g {\displaystyle \lambda _{g}} is the associated constant of proportionality. Then λ g {\displaystyle \lambda _{g}} controls the volume of the ellipsoid, A g {\displaystyle A_{g}} its shape, and D g {\displaystyle D_{g}} its orientation. Each of the volume, shape and orientation of the clusters can be constrained to be equal (E) or allowed to vary (V); the orientation can also be spherical, with identical eigenvalues (I). This yields 14 possible clustering models, shown in this table: It can be seen that many of these models are more parsimonious, with far fewer parameters than the unconstrained model that has 90 parameters when G = 4 {\displaystyle G=4} and d = 9 {\displaystyle d=9} . Several of these models correspond to well-known heuristic clustering methods. For example, k-means clustering is equivalent to estimation of the EII clustering model using the classification EM algorithm. The Bayesian information criterion (BIC) can be used to choose the best clustering model as well as the number of clusters. It can also be used as the basis for a method to choose the variables in the clustering model, eliminating variables that are not useful for clustering. Different Gaussian model-based clustering methods have been developed with an eye to handling high-dimensional data. These include the pgmm method, which is based on the mixture of factor analyzers model, and the HDclassif method, based on the idea of subspace clustering. The mixture-of-experts framework extends model-based clustering to include covariates. == Example == We illustrate the method with a dateset consisting of three measurements (glucose, insulin, sspg) on 145 subjects for the purpose of diagnosing diabetes and the type of diabetes present. The subjects were clinically classified into three groups: normal, chemical diabetes and overt diabetes, but we use this information only for evaluating clustering methods, not for classifying subjects. The BIC plot shows the BIC values for each combination of the number of clusters, G {\displaystyle G} , and the clustering model from the Table. Each curve corresponds to a different clustering model. The BIC favors 3 groups, which corresponds to the clinical assessment. It also favors the unconstrained covariance model, VVV. This fits the data well, because the normal patients have low values of both sspg and insulin, while the distributions of the chemical and overt diabetes groups are elongated, but in different directions. Thus the volumes, shapes and orientations of the three groups are clearly different, and so the unconstrained model is appropriate, as selected by the model-based clustering method. The classification plot shows the classification of the subjects by model-based clustering. The classification was quite accurate, with a 12% error rate as defined by the clinical classification. Other well-known clustering methods performed worse with higher error rates, such as single-linkage clustering with 46%, average link clustering with 30%, complete-linkage clustering also with 30%, and k-means clustering with 28%. == Outliers in clustering == An outlier in clustering is a data point that does not belong to any of the clusters. One way of modeling outliers in model-based clustering is to include an additional mixture component that is very dispersed, with for example a uniform distribution. Another approach is to replace the multivariate normal densities by t {\displaystyle t} -distributions, with the idea that the long tails of the t {\displaystyle t} -distribution would ensure robustness to outliers. However, this is not breakdown-robust. A third approach is the "tclust" or data trimming approach which excludes observations identified as outliers when estimating the model parameters. == Non-Gaussian clusters and merging == Sometimes one or more clusters deviate strongly from the Gaussian assumption. If a Gaussian mixture is fitted to such data, a strongly non-Gaussian cluster will often be represented by several mixture components rather than a single one. In that case, cluster merging can be used to find a better clustering. A different approach is to use mixtures of complex component densities to represent non-Gaussian clusters. == Non-continuous data == === Categorical data === Clustering multivariate categorical data is most often done using the latent class model. This assumes that the data arise from a finite mixture model, where within each cluster the variables are independent. === Mixed data === These arise when variables are of different types, such as continuous, categorical or ordinal data. A latent class model for mixed data assumes local independence between the variable. The location model relaxes the local independence assumption. The clustMD approach assumes that the observed variables are manifestations of underlying continuous Gaussian latent

  Read more →
• 

  Timeline of operating systems

  This article presents a timeline of events in the history of computer operating systems from 1951 to the current day. For a narrative explaining the overall developments, see the History of operating systems. == 20th Century == == 1940s == 1949 EDSAC was considered the first operating system developed by Maurice Wilkes and manufactured by the University of Cambridge == 1950s == 1951 LEO I 'Lyons Electronic Office' was the commercial development of EDSAC computing platform, supported by British firm J. Lyons and Co. 1953 DYSEAC - an early machine capable of distributing computing 1955 General Motors Operating System made for IBM 701 MIT's Tape Director operating system made for UNIVAC 1103 1956 GM-NAA I/O for IBM 704, based on General Motors Operating System 1957 Atlas Supervisor (Manchester University) (Atlas computer project start) BESYS (Bell Labs), for IBM 704, later IBM 7090 and IBM 7094 1958 University of Michigan Executive System (UMES), for IBM 704, 709, and 7090 1959 SHARE Operating System (SOS), based on GM-NAA I/O == 1960s == 1960 IBSYS (IBM for its 7090 and 7094) 1961 CTSS demonstration (MIT's Compatible Time-Sharing System for the IBM 7094) MCP (Burroughs Master Control Program) for B5000 1962 Atlas Supervisor (Manchester University) (Atlas computer commissioned) BBN Time-Sharing System GCOS (GE's General Comprehensive Operating System, originally GECOS, General Electric Comprehensive Operating Supervisor) 1963 ADMIRAL AN/FSQ-32, another early time-sharing system begun CTSS becomes operational (MIT's Compatible Time-Sharing System for the IBM 7094) JOSS, an interactive time-shared system that did not distinguish between operating system and language Titan Supervisor, early time-sharing system begun 1964 Berkeley Timesharing System (for Scientific Data Systems' SDS 940) Chippewa Operating System (for CDC 6600 supercomputer) Dartmouth Time-Sharing System (Dartmouth College's DTSS for GE computers) EXEC 8 (UNIVAC) KDF9 Timesharing Director (English Electric) – an early, fully hardware secured, fully pre-emptive process switching, multi-programming operating system for KDF9 (originally announced in 1960) OS/360 (IBM's primary OS for its S/360 series) (announced) PDP-6 Monitor (DEC) descendant renamed TOPS-10 in 1970 SCOPE (CDC 3000 series) 1965 BOS/360 (IBM's Basic Operating System) DECsys TOS/360 (IBM's Tape Operating System) Livermore Time Sharing System (LTSS) Multics (MIT, GE, Bell Labs for the GE-645) (announced) Pick operating system SIPROS 66 (Simultaneous Processing Operating System) THE multiprogramming system (Technische Hogeschool Eindhoven) development TSOS (later VMOS) (RCA) 1966 DOS/360 (IBM's Disk Operating System) GEORGE 1 & 2 for ICT 1900 series Mod 1 Mod 2 Mod 8 MS/8 (Richard F. Lary's DEC PDP-8 system) MSOS (Mass Storage Operating System) OS/360 (IBM's primary OS for its S/360 series) PCP and MFT (shipped) RAX Remote Users of Shared Hardware (RUSH), a time-sharing system developed by Allen-Babcock for the IBM 360/50 SODA for Elwro's Odra 1204 Universal Time-Sharing System (XDS Sigma series) 1967 CP-40, predecessor to CP-67 on modified IBM System/360 Model 40 CP-67 (IBM, also known as CP/CMS) Conversational Programming System (CPS), an IBM time-sharing system under OS/360 Michigan Terminal System (MTS) (time-sharing system for the IBM S/360-67 and successors) ITS (MIT's Incompatible Timesharing System for the DEC PDP-6 and PDP-10) OS/360 MVT ORVYL (Stanford University's time-sharing system for the IBM S/360-67) TSS/360 (IBM's Time-sharing System for the S/360-67, never officially released, canceled in 1969 and again in 1971) WAITS (SAIL, Stanford Artificial Intelligence Laboratory, time-sharing system for DEC PDP-6 and PDP-10, later TOPS-10) 1968 Airline Control Program (ACP) (IBM) B1 (NCR Century series) CALL/360, an IBM time-sharing system for System/360 HP Real-Time Executive (HP RTE) – Hewlett-Packard HP Time-Shared BASIC (HP TSB) – Hewlett-Packard (time-sharing system for the HP 2000) THE multiprogramming system (Eindhoven University of Technology) publication TSS/8 (DEC for the PDP-8) VP/CSS 1969 B2 (NCR Century series) B3 (NCR Century series) GEORGE 3 For ICL 1900 series MINIMOP Multics (MIT, GE, Bell Labs for the GE-645 and later the Honeywell 6180) (opened for paying customers in October) RC 4000 Multiprogramming System (RC) TENEX (Bolt, Beranek and Newman for DEC systems, later TOPS-20) Unics (later Unix) (AT&T, initially on DEC computers) Xerox Operating System == 1970s == 1970 DOS-11 (PDP-11) 1971 EMAS Kronos RSTS-11 2A-19 (First released version; PDP-11) RSX-15 OS/8 1972 B4 (NCR Century series) COS-300 Data General RDOS Edos MUSIC/SP OS/4 OS 1100 OS/2000 (Honeywell 2000-series) Operating System/Virtual Storage 1 (OS/VS1) Operating System/Virtual Storage 2 R1 (OS/VS2 SVS) PRIMOS (written in FORTRAN IV, that didn't have pointers, while later versions, around version 18, written in a version of PL/I, called PL/P) Virtual Machine/Basic System Extensions Program Product (BSEPP or VM/SE) Virtual Machine/System Extensions Program Product (SEPP or VM/BSE) Virtual Machine Facility/370 (VM/370), sometimes known as VM/CMS 1973 Эльбрус-1 (Elbrus-1) – Soviet computer – created using high-level language uЭль-76 (AL-76/ALGOL 68) Alto OS CP-V (Control Program V) RSX-11D RT-11 VME – implementation language S3 (ALGOL 68) 1974 ACOS-2 (NEC) ACOS-4 ACOS-6 CP/M DOS-11 V09-20C (Last stable release, June 1974) Hydra – capability-based, multiprocessing OS kernel MONECS Multi-Programming Executive (MPE) – Hewlett-Packard Operating System/Virtual Storage 2 R2 (MVS) OS/7 OS/16 OS/32 Sintran III 1975 BS2000 V2.0 (First released version) COS-350 ISIS NOS (Control Data Corporation) OS/3 (Univac) VS/9 (formerly RCA's TSOS, later named VMOS) Version 6 Unix XVM/DOS XVM/RSX 1976 Cambridge CAP computer – all operating system procedures written in ALGOL 68C, with some closely associated protected procedures in BCPL Cray Operating System DX10 FLEX TOPS-20 TX990/TXDS Tandem Nonstop OS v1 Thoth 1977 1BSD AMOS KERNAL OASIS operating system OS68 OS4000 RMX-80 System 88 (Exec) System Support Program (IBM System/34 and System/36) TRSDOS Virtual Memory System (VMS) V1.0 (Initial commercial release, October 25) VRX (Virtual Resource eXecutive) VS Virtual Memory Operating System 1978 2BSD Apple DOS Control Program Facility (IBM System/38) Cray Time Sharing System (CTSS) DPCX (IBM) DPPX (IBM) HDOS KSOS – secure OS design from Ford Aerospace KVM/370 – security retro-fit of IBM VM/370 Lisp machine (CADR) MVS/System Extensions (MVS/SE) OS4 (Naked Mini 4) PTDOS TRIPOS UCSD p-System (First released version) Z80-RIO 1979 Atari DOS 3BSD CP-6 Idris MP/M MVS/System Extensions R2 (MVS/SE2) NLTSS POS Sinclair BASIC Transaction Processing Facility (TPF) (IBM) UCLA Secure UNIX – an early secure UNIX OS based on security kernel UNIX/32V DOS/VSE Version 7 Unix == 1980s == 1980 86-DOS AOS/VS (Data General) Business Operating System CTOS DOSPLUS (TRS-80) MVS/System Product (MVS/SP) V1 NewDos/80 OS-9 RMX-86 RS-DOS SOS Virtual Machine/System Product (VM/SP) Xenix 1981 Acorn MOS Aegis SR1 (First Apollo/DOMAIN systems shipped on March 27) CP/M-86 DRX (Distributed Resource Executive) iMAX – OS for Intel's iAPX 432 capability machine MCS (Multi-user Control System) MS-DOS PC DOS Pilot (Xerox Star operating system) UNOS UTS V VERSAdos VRTX VSOS (Virtual Storage Operating System) Xinu first release 1982 Commodore DOS LDOS (By Logical Systems, Inc. – for the Radio Shack TRS-80 Models I, II & III) PCOS (Olivetti M20) pSOS QNX Stratus VOS Sun UNIX (later SunOS) 0.7 Ultrix Unix System III VAXELN 1983 Coherent DNIX EOS GNU (project start) Lisa Office System 7/7 LOCUS – UNIX compatible, high reliability, distributed OS MVS/System Product V2 (MVS/Extended Architecture, MVS/XA) Novell NetWare (S-Net) PERPOS ProDOS RTU (Real-Time Unix) STOP – TCSEC A1-class, secure OS for SCOMP hardware SunOS 1.0 VSE/System Package (VSE/SP) Version 1 1984 AMSDOS CTIX (Unix variant) DYNIX Mac OS (System 1.0) MSX-DOS NOS/VE PANOS PC/IX ROS Sinclair QDOS SINIX UNICOS Venix 2.0 Virtual Machine/Extended Architecture Migration Assistance (VM/XA MA) 1985 AmigaOS Atari TOS DG/UX DOS Plus Graphics Environment Manager Harmony MacOS 2 MIPS RISC/os Oberon – written in Oberon SunOS 2.0 Version 8 Unix Virtual Machine/Extended Architecture System Facility (VM/XA SF) Windows 1.0 Windows 1.01 Xenix 2.0 1986 AIX 1.0 Cronus distributed OS FlexOS GEMSOS – TCSEC A1-class, secure kernel for BLACKER VPN & GTNP GEOS Genera 7.0 HP-UX MacOS 3 SunOS 3.0 TR-DOS TRIX Version 9 Unix 1987 Arthur (much improved version came in 1989 under the name RISC OS) BS2000 V9.0 IRIX (3.0 is first SGI version) MacOS 4 MacOS 5 MDOS MINIX 1.0 OS/2 (1.0) PC-MOS/386 Topaz – semi-distributed OS for DEC Firefly workstation written in Modula-2+ and garbage collected VxWorks Windows 2.0 1988 A/UX (Apple Computer) AOS/VS II (Data General) CP/M rebranded as DR-DOS Flex machine – tagged, capability machine with OS and other software written

  Read more →
• 

  Nanonetwork

  A nanonetwork or nanoscale network is a set of interconnected nanomachines (devices a few hundred nanometers or a few micrometers at most in size) which are able to perform only very simple tasks such as computing, data storing, sensing and actuation. Nanonetworks are expected to expand the capabilities of single nanomachines both in terms of complexity and range of operation by allowing them to coordinate, share and fuse information. Nanonetworks enable new applications of nanotechnology in the biomedical field, environmental research, military technology and industrial and consumer goods applications. Nanoscale communication is defined in IEEE P1906.1. == Communication approaches == Classical communication paradigms need to be revised for the nanoscale. The two main alternatives for communication in the nanoscale are based either on electromagnetic communication or on molecular communication. === Electromagnetic === This is defined as the transmission and reception of electromagnetic radiation from components based on novel nanomaterials. Recent advancements in carbon and molecular electronics have opened the door to a new generation of electronic nanoscale components such as nanobatteries, nanoscale energy harvesting systems, nano-memories, logical circuitry in the nanoscale and even nano-antennas. From a communication perspective, the unique properties observed in nanomaterials will decide on the specific bandwidths for emission of electromagnetic radiation, the time lag of the emission, or the magnitude of the emitted power for a given input energy, amongst others. For the time being, two main alternatives for electromagnetic communication in the nanoscale have been envisioned. First, it has been experimentally demonstrated that is possible to receive and demodulate an electromagnetic wave by means of a nanoradio, i.e., an electromechanically resonating carbon nanotube which is able to decode an amplitude or frequency modulated wave. Second, graphene-based nano-antennas have been analyzed as potential electromagnetic radiators in the terahertz band. === Molecular === Molecular communication is defined as the transmission and reception of information by means of molecules. The different molecular communication techniques can be classified according to the type of molecule propagation in walkaway-based, flow-based or diffusion-based communication. In walkway-based molecular communication, the molecules propagate through pre-defined pathways by using carrier substances, such as molecular motors. This type of molecular communication can also be achieved by using E. coli bacteria as chemotaxis. In flow-based molecular communication, the molecules propagate through diffusion in a fluidic medium whose flow and turbulence are guided and predictable. The hormonal communication through blood streams inside the human body is an example of this type of propagation. The flow-based propagation can also be realized by using carrier entities whose motion can be constrained on the average along specific paths, despite showing a random component. A good example of this case is given by pheromonal long range molecular communications. In diffusion-based molecular communication, the molecules propagate through spontaneous diffusion in a fluidic medium. In this case, the molecules can be subject solely to the laws of diffusion or can also be affected by non-predictable turbulence present in the fluidic medium. Pheromonal communication, when pheromones are released into a fluidic medium, such as air or water, is an example of diffusion-based architecture. Other examples of this kind of transport include calcium signaling among cells, as well as quorum sensing among bacteria. Based on the macroscopic theory of ideal (free) diffusion the impulse response of a unicast molecular communication channel was reported in a paper that identified that the impulse response of the ideal diffusion based molecular communication channel experiences temporal spreading. Such temporal spreading has a deep impact in the performance of the system, for example in creating the intersymbol interference (ISI) at the receiving nanomachine. In order to detect the concentration-encoded molecular signal two detection methods named sampling-based detection (SD) and energy-based detection (ED) have been proposed. While the SD approach is based on the concentration amplitude of only one sample taken at a suitable time instant during the symbol duration, the ED approach is based on the total accumulated number of molecules received during the entire symbol duration. In order to reduce the impact of ISI a controlled pulse-width based molecular communication scheme has been analysed. The work presented in showed that it is possible to realize multilevel amplitude modulation based on ideal diffusion. A comprehensive study of pulse-based binary and sinus-based, concentration-encoded molecular communication system have also been investigated.

  Read more →