AI Data Center Map

AI Data Center Map — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • MySocialCloud

    MySocialCloud

    MySocialCloud is a cloud-based bookmark vault and password website that allows users to log into all of their online accounts from a single, secure website. The company's investors include Sir Richard Branson, Insight Venture Partners’ Jerry Murdock, and PhotoBucket founder Alex Welch. The company and its founders have been featured in TechCrunch and The Huffington Post. == History == MySocialCloud was co-founded by Scott Ferreira, Stacey Ferreira, and Shiv Prakash in 2011. The idea for a one-stop password storage and login tool came when a computer crash left Scott without documents he used to store access information to his online data. In 2013, the siblings sold MySocialCloud to Reputation.com. == Services == MySocialCloud is cloud-based, and the platform lets users securely store passwords and automatically log into several social media websites simultaneously. The website auto-populates password fields, letting the user log into all of the sites at the push of a button. The service also provides users with security updates for the websites they have included in their profile, and informs users if a website has been hacked. Security played a major role during development of the platform. Passwords stored on the service are salted and hashed with a two-way encryption method known as AES.

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  • Spatial anti-aliasing

    Spatial anti-aliasing

    In digital signal processing, spatial anti-aliasing is a technique for minimizing the distortion artifacts (aliasing) when representing a high-resolution image at a lower resolution. Anti-aliasing is used in digital photography, computer graphics, digital audio, and many other applications. Anti-aliasing means removing signal components that have a higher frequency than is able to be properly resolved by the recording (or sampling) device. This removal is done before (re)sampling at a lower resolution. When sampling is performed without removing this part of the signal, it causes undesirable artifacts such as black-and-white noise. In signal acquisition and audio, anti-aliasing is often done using an analog anti-aliasing filter to remove the out-of-band component of the input signal prior to sampling with an analog-to-digital converter. In digital photography, optical anti-aliasing filters made of birefringent materials smooth the signal in the spatial optical domain. The anti-aliasing filter essentially blurs the image slightly in order to reduce the resolution to or below that achievable by the digital sensor (the larger the pixel pitch, the lower the achievable resolution at the sensor level). == Examples == In computer graphics, anti-aliasing improves the appearance of "jagged" polygon edges, or "jaggies", so they are smoothed out on the screen. However, it incurs a performance cost for the graphics card and uses more video memory. The level of anti-aliasing determines how smooth polygon edges are (and how much video memory it consumes). Near the top of an image with a receding checker-board pattern, the image is difficult to recognise and often not considered aesthetically pleasing. In contrast, when anti-aliased the checker-board near the top blends into grey, which is usually the desired effect when the resolution is insufficient to show the detail. Even near the bottom of the image, the edges appear much smoother in the anti-aliased image. Multiple methods exist, including the sinc filter, which is considered a better anti-aliasing algorithm. When magnified, it can be seen how anti-aliasing interpolates the brightness of the pixels at the boundaries to produce grey pixels since the space is occupied by both black and white tiles. These help make the sinc filter antialiased image appear much smoother than the original. In a simple diamond image, anti-aliasing blends the boundary pixels; this reduces the aesthetically jarring effect of the sharp, step-like boundaries that appear in the aliased graphic. Anti-aliasing is often applied in rendering text on a computer screen, to suggest smooth contours that better emulate the appearance of text produced by conventional ink-and-paper printing. Particularly with fonts displayed on typical LCD screens, it is common to use subpixel rendering techniques like ClearType. Sub-pixel rendering requires special colour-balanced anti-aliasing filters to turn what would be severe colour distortion into barely-noticeable colour fringes. Equivalent results can be had by making individual sub-pixels addressable as if they were full pixels, and supplying a hardware-based anti-aliasing filter as is done in the OLPC XO-1 laptop's display controller. Pixel geometry affects all of this, whether the anti-aliasing and sub-pixel addressing are done in software or hardware. == Simplest approach to anti-aliasing == The most basic approach to anti-aliasing a pixel is determining what percentage of the pixel is occupied by a given region in the vector graphic - in this case a pixel-sized square, possibly transposed over several pixels - and using that percentage as the colour. A Python program producing a basic plot of a single, white-on-black anti-aliased point using the method is as follows: This method is generally best suited for simple graphics, such as basic lines or curves, and applications that would otherwise have to convert absolute coordinates to pixel-constrained coordinates, such as 3D graphics. It is a fairly fast function, but it is relatively low-quality, and gets slower as the complexity of the shape increases. For purposes requiring very high-quality graphics or very complex vector shapes, this will probably not be the best approach. Note: The plot_antialiased_point routine above cannot blindly set the colour value to the percent calculated. It must add the new value to the existing value at that location up to a maximum of 1. Otherwise, the brightness of each pixel will be equal to the darkest value calculated in time for that location which produces a very bad result. For example, if one point sets a brightness level of 0.90 for a given pixel and another point calculated later barely touches that pixel and has a brightness of 0.05, the final value set for that pixel should be 0.95, not 0.05. For more sophisticated shapes, the algorithm may be generalized as rendering the shape to a pixel grid with higher resolution than the target display surface (usually a multiple that is a power of 2 to reduce distortion), then using bicubic interpolation to determine the average intensity of each real pixel on the display surface. == Signal processing approach to anti-aliasing == In this approach, the ideal image is regarded as a signal. The image displayed on the screen is taken as samples, at each (x,y) pixel position, of a filtered version of the signal. Ideally, one would understand how the human brain would process the original signal, and provide an on-screen image that will yield the most similar response by the brain. The most widely accepted analytic tool for such problems is the Fourier transform; this decomposes a signal into basis functions of different frequencies, known as frequency components, and gives us the amplitude of each frequency component in the signal. The waves are of the form: cos ⁡ ( 2 j π x ) cos ⁡ ( 2 k π y ) {\displaystyle \ \cos(2j\pi x)\cos(2k\pi y)} where j and k are arbitrary non-negative integers. There are also frequency components involving the sine functions in one or both dimensions, but for the purpose of this discussion, the cosine will suffice. The numbers j and k together are the frequency of the component: j is the frequency in the x direction, and k is the frequency in the y direction. The goal of an anti-aliasing filter is to greatly reduce frequencies above a certain limit, known as the Nyquist frequency, so that the signal will be accurately represented by its samples, or nearly so, in accordance with the sampling theorem; there are many different choices of detailed algorithm, with different filter transfer functions. Current knowledge of human visual perception is not sufficient, in general, to say what approach will look best. == Two dimensional considerations == The previous discussion assumes that the rectangular mesh sampling is the dominant part of the problem. The filter usually considered optimal is not rotationally symmetrical, as shown in this first figure; this is because the data is sampled on a square lattice, not using a continuous image. This sampling pattern is the justification for doing signal processing along each axis, as it is traditionally done on one dimensional data. Lanczos resampling is based on convolution of the data with a discrete representation of the sinc function. If the resolution is not limited by the rectangular sampling rate of either the source or target image, then one should ideally use rotationally symmetrical filter or interpolation functions, as though the data were a two dimensional function of continuous x and y. The sinc function of the radius has too long a tail to make a good filter (it is not even square-integrable). A more appropriate analog to the one-dimensional sinc is the two-dimensional Airy disc amplitude, the 2D Fourier transform of a circular region in 2D frequency space, as opposed to a square region. One might consider a Gaussian plus enough of its second derivative to flatten the top (in the frequency domain) or sharpen it up (in the spatial domain), as shown. Functions based on the Gaussian function are natural choices, because convolution with a Gaussian gives another Gaussian whether applied to x and y or to the radius. Similarly to wavelets, another of its properties is that it is halfway between being localized in the configuration (x and y) and in the spectral (j and k) representation. As an interpolation function, a Gaussian alone seems too spread out to preserve the maximum possible detail, and thus the second derivative is added. As an example, when printing a photographic negative with plentiful processing capability and on a printer with a hexagonal pattern, there is no reason to use sinc function interpolation. Such interpolation would treat diagonal lines differently from horizontal and vertical lines, which is like a weak form of aliasing. == Practical real-time anti-aliasing approximations == There are only a handful of primitives used at the lowest level in a real-time rend

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  • Pixelmator Pro

    Pixelmator Pro

    Pixelmator Pro is a photo, video, and vector graphic editor developed by Apple for macOS and iPadOS as part of its Pixelmator and pro apps platforms and as a part of their Apple Creator Studio suite of applications. Pixelmator Pro relies heavily on technologies from Apple platforms such as Metal, CoreML, Core Image, AVFoundation, GCD, and SwiftUI. == Features == GPU accelerated with Metal 50+ standard image editing tools Layer-based image editor Video editing support Vector graphic support (including SVG support) AI-powered editing features such as background removal ML Super Resolution and Smart Replace Supports a variety of media formats (JPEG, RAW, Apple ProRAW, PSD, PNG, GIF, MP4, HEIF, etc) == Reception == Pixelmator Pro was generally well-received by reviewers who praised its deep use of machine learning, fully macOS-native design, and relatively affordable one-time purchase compared to subscription software such as Adobe Photoshop. Some reviewers criticized that some features are hard to find or hard to use. It was awarded Apple's Mac App of the Year in 2018. Pixelmator Pro does not have support for panorama stitching. == Acquisition by Apple == On November 1, 2024, the Pixelmator Team announced that they were to be acquired by Apple, subject to regulatory approval. Their site promises "There will be no material changes to the Pixelmator Pro, Pixelmator for iOS, and Photomator apps at this time." The acquisition was completed in February 2025. On January 13, 2026, Apple announced that a new version of Pixelmator Pro with AI features would be included in its new Apple Creator Studio subscription, the app would be brought to the iPad and the Mac app would be redesigned with Liquid Glass. == Version history == == Applescript == In 2020 Pixelmator Pro added the ability to leverage Apple's automation language 'AppleScript' to automate many tasks in version 1.8 (Lynx). This enabled simple and advanced automation activities such as image resize, crop, color adjustments, format change, moving layers around, and more advanced actions like removing background, Gaussian blur, text replacement, shadows, color replacement, etc.

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  • Cyborg

    Cyborg

    A cyborg () is a being with both organic and biomechatronic body parts. It is a portmanteau of cybernetic and organism. The term was coined in 1960 by Manfred Clynes and Nathan S. Kline. In contrast to biorobots and androids, the term cyborg applies to a living organism that has undergone restoration of function or enhancements of abilities due to the integration of some artificial component or technology that relies on feedback. == Description and definition == Alternative names for a cyborg include cybernetic organism, cyber-organism, cyber-organic being, cybernetically enhanced organism, cybernetically augmented organism, technorganic being, techno-organic being, and techno-organism. Unlike bionics, biorobotics, or androids, a cyborg is an organism that has restored function or, especially, enhanced abilities due to the integration of some artificial component or technology that relies on some sort of feedback, for example: prostheses, artificial organs, implants or, in some cases, wearable technology. Cyborg technologies may enable or support collective intelligence. A related idea is the "augmented human". While cyborgs are commonly thought of as mammals, including humans, the term can apply to any organism. === Placement and distinctions === D. S. Halacy's Cyborg: Evolution of the Superman (1965) featured an introduction which spoke of a "new frontier" that was "not merely space, but more profoundly the relationship between 'inner space' to 'outer space' – a bridge...between mind and matter." In "A Cyborg Manifesto", Donna Haraway rejects the notion of rigid boundaries between humanity and technology, arguing that, as humans depend on more technology over time, humanity and technology have become too interwoven to draw lines between them. She believes that since we have allowed and created machines and technology to be so advanced, there should be no reason to fear what we have created, and cyborgs should be embraced because they are part of human identities. However, Haraway has also expressed concern over the contradictions of scientific objectivity and the ethics of technological evolution, and has argued that "There are political consequences to scientific accounts of the world." === Biosocial definition === According to some definitions of the term, the physical attachments that humans have with even the most basic technologies have already made them cyborgs. In a typical example, a human with an artificial cardiac pacemaker or implantable cardioverter-defibrillator would be considered a cyborg, since these devices measure voltage potentials in the body, perform signal processing, and can deliver electrical stimuli, using a synthetic feedback mechanism to keep that person alive. Implants, especially cochlear implants, that combine mechanical modification with any kind of feedback response are also cybernetic enhancements. Some theorists cite such modifications as contact lenses, hearing aids, smartphones, or intraocular lenses as examples of fitting humans with technology to enhance their biological capabilities. The emerging trend of implanting microchips inside the body (mainly the hands), to make financial operations like a contactless payment, or basic tasks like opening a door, has been erroneously marketed as more recent examples of cybernetic enhancement. The latter has not yet seen significant traction outside niche areas in Scandinavia and in actual function is little more than a pre-programmed Radio-frequency identification (RFID) microchip encased in glass that does not interact with the human body (it is the same technology used in the microchips injected into animals for ease of identification), thus not fitting the definition of a cybernetic implant. As cyborgs currently are on the rise, some theorists argue there is a need to develop new definitions of aging. For instance, a bio-techno-social definition of aging has been suggested. The term is also used to address human-technology mixtures in the abstract. This includes not only commonly used pieces of technology such as phones, computers, the Internet, and so on, but also artifacts that are not usually considered technology; for example, pen and paper, and speech and language. When augmented with these technologies and connected in communication with people in other times and places, a person becomes capable of more than they were before. An example is a computer, which gains power by using Internet protocols to connect with other computers. Another example is a social-media bot—either a bot-assisted human or a human-assisted-bot—used to target social media with likes and shares. Cybernetic technologies thus include highways, pipes, electrical wiring, buildings, electrical plants, libraries, and other infrastructural constructs. Bruce Sterling, in his Shaper/Mechanist universe, suggested an idea of an alternative cyborg called 'Lobster', which is made not by using internal implants, but by using an external shell (e.g. a powered exoskeleton). The computer game Deus Ex: Invisible War prominently features cyborgs called Omar, Russian for 'lobster'. === Evolutionary perspective === In 1994, Hans Hass formulated a scientific view of the human-machine hybrids he called "hypercells". They can expand their biological cell body with artificial artifacts and thus expand their performance body. The theory of hypercells or Homo proteus, as Hass called the human-machine hybrid to distinguish Homo sapiens, extends Charles Darwin's theory of evolution and deals with the course of evolution beyond humans. In his 2019 book Novacene, James Lovelock used the term "cyborgs" to refer to the next generation of beings who will become the "understanders of the future" and "lead the cosmos to self-knowledge". While acknowledging the organic component in Clynes' and Kline's definition, he proposed that these cyborgs "will have designed and built themselves from the artificial intelligence systems we have already constructed", and used the term cyborg "to emphasize that the new intelligent beings will have arisen, like us, from Darwinian evolution." == Origins == The concept of a man-machine mixture was widespread in science fiction before World War II. As early as 1843, Edgar Allan Poe described a man with extensive prostheses in the short story "The Man That Was Used Up". In 1911, Jean de La Hire introduced the Nyctalope, a science fiction hero who was perhaps the first literary cyborg, in Le Mystère des XV (later translated as The Nyctalope on Mars). Nearly two decades later, Edmond Hamilton presented space explorers with a mixture of organic and machine parts in his 1928 novel The Comet Doom. He later featured the talking, living brain of an old scientist, Simon Wright, floating in a transparent case, and in all the adventures of his famous hero, Captain Future. In 1944, in the short story "No Woman Born", C. L. Moore wrote of Deirdre, a dancer, whose body was burned completely and whose brain was placed in a faceless but beautiful and supple mechanical body. In 1960, the term "cyborg" was coined by Manfred E. Clynes and Nathan S. Kline to refer to their conception of an enhanced human being who could survive in extraterrestrial environments: For the exogenously extended organizational complex functioning as an integrated homeostatic system unconsciously, we propose the term 'Cyborg'. Their concept was the outcome of thinking about the need for an intimate relationship between human and machine as the new frontier of space exploration was beginning to develop. A designer of physiological instrumentation and electronic data-processing systems, Clynes was the chief research scientist in the Dynamic Simulation Laboratory at Rockland State Hospital in New York. The term first appears in print 5 months earlier when The New York Times reported on the "Psychophysiological Aspects of Space Flight Symposium" where Clynes and Kline first presented their paper: A cyborg is essentially a man-machine system in which the control mechanisms of the human portion are modified externally by drugs or regulatory devices so that the being can live in an environment different from the normal one. Thereafter, Hamilton would first use the term "cyborg" explicitly in the 1962 short story, "After a Judgment Day", to describe the "mechanical analogs" called "Charlies," explaining that "[c]yborgs, they had been called from the first one in the 1960s...cybernetic organisms." The 1972 novel Cyborg by Martin Caidin introduced the character of bionic government agent Steve Austin, and was adapted into the popular television series The Six Million Dollar Man, which ran from 1973 to 1978. In 2001, a book titled Cyborg: Digital Destiny and Human Possibility in the Age of the Wearable computer was published by Doubleday. Some of the ideas in the book were incorporated into the documentary film Cyberman that same year. == Cyborg tissues in engineering == Cyborg tissues structured with carbon nanotubes and plan

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  • Enonic XP

    Enonic XP

    Enonic XP is a free and open-source content platform. Developed by the Norwegian software company Enonic, the platform can be used to build websites, progressive web applications, or web-based APIs. Enonic XP uses an application framework for coding server logic with JavaScript, and has no need for SQL as it ships with an integrated content repository. The CMS is fully decoupled, meaning developers can create traditional websites and landing pages, or use XP in headless mode, that is without the presentation layer, for loading editorial content onto any device or client. Enonic is used by major organizations in Norway, including the national postal service Norway Post, the insurance company Gjensidige, the Norwegian Labour and Welfare Administration, and all the top football clubs in the national football league for men, Eliteserien. == Overview == Enonic XP ships with the content management system (CMS) Content Studio. This includes a visual drag and drop editor, a landing page editor, support for multi-site and multi-language, media and structured content, advanced image editing, responsive user interface, permissions and roles management, revision and version control, and bulk publishing. Integrations and applications can be directly installed via the "Applications" section in XP, where the platform finds apps approved in the official Enonic Market. There are no third-party databases in Enonic XP. Instead, the developers have built a distributed storage repository, avoiding the need to index content. The system brings together capabilities from Filesystem, NoSQL, document stores, and search in the storage technology, which automatically indexes everything put into the storage. Enonic XP supports deployment of server side JavaScript. The open-source framework runs on top of a JVM (Java virtual machine), and allows developers to run the same code in the browser and on the server, thus enabling them to employ JavaScript. While running on the Java virtual machine, Enonic XP can be deployed on most infrastructures. The dependency on a third-party application server to deploy code has been removed, as the platform is an application server by default. A developer can for instance insert his own modules and code straight into the system while it is running. JavaScript unifies all the technical elements, and Enonic XP features a MVC framework where everything on the back-end can be coded with server-side JavaScript. The Enonic platform can use any template engine. === Progressive web apps === Another feature of Enonic XP is the possibility for developers to create progressive web apps (PWA). A PWA is a web application that is a regular web page or website, but can appear to the user like a mobile application. === Headless CMS and integrations === Enonic XP is headless, which means it separates content and presentation. The platform supports GraphQL, provides several default APIs, and allows for building custom APIs through the Guillotine starter kit. Consequently, Enonic supports modern front-end frameworks, and offers integrations with e.g. Next.js and React. == History == Enonic AS was founded in 2000 by Morten Øien Eriksen and Thomas Sigdestad. The software company specialized in building services and solutions, including a content management system known as "Vertical Site", then "Enonic CMS". Being aware that they had application, database, and website teams working on separate silos toward the same goal, Enonic sought to combine the different elements into a single software. The resulting application platform Enonic XP, first released in 2015, includes a CMS as an optional surface layer. In March 2020, Enonic XP was ranked by SoftwareReviews, a division of Info-Tech Research Group, a Canadian IT research and analyst firm, as the "Leader" in Web Experience Management. The ranking is based on user reviews, and is featured in SoftwareReviews‘ Digital Experience Data Quadrant Report, a comprehensive evaluation and ranking of leading Web Experience Management vendors. Enonic was also ranked first in 2021 and 2022. === Release history === Enonic XP assumed the mantle from the previous content management system Enonic CMS, and thus began with "version 5.0.0." The following list only contains major releases. == Development and support == Enonic offers a user and developer community consisting of a forum, support system with tickets, documentation, codex, learning and training center with certifications, and various community groups. Writing about the support system, Mike Johnston of CMS Critic notes that "enterprise customers obviously get access to a higher level of personalized support, where the Enonic support team can respond as fast as two hours." The support system is divided in three levels: silver, gold and platinum—from next day business support to 24/7 support. As Enonic XP is open-source, known vulnerabilities, bugs and issues are listed on GitHub.

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  • AppyStore

    AppyStore

    AppyStore is a comprehensive learning videos and games app for kids up to the age of 8 years. The platform developed by Mauj Mobile, a mobile value-added services (VAS) provider curates content to help in child development by leveraging technology. Mauj is funded by Sequoia Capital, Westbridge Capital and Intel Capital. == Background == AppyStore was launched in 2014 as a platform providing content for kids between the ages of 1.5 and 6 years. AppyStore subsequently extended its services for kids up to 8 years of age. The company operates on a subscription-based model and claims to have 5,000 learning games and videos segregated in 18 learning areas developed to help children gain optimal skills and qualities. According to an article published in Business Standard, the application is claimed to be one of the top 5 apps that help to enhance the logical and imaginative capabilities of children. AppyStore was awarded the Best app for kids by Google Play in December 2017. == Service == The company provides content via a website and an Android app. The website and android app provide learning games, rhymes, phonics, reading, stories, science, numbers, maths, logic videos comprising puzzles, worksheets, videos and fun activities and the premium subscription also includes physical worksheets which are home delivered. This content is educational and has been handpicked by teachers and experts with an understanding of the major areas of child development milestones for children up to 8 years of age. The mobile application also allows parents to track the progress of their child on the basis of the number of videos viewed.

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  • Image warping

    Image warping

    Image warping is the process of digitally manipulating an image such that any shapes portrayed in the image have been significantly distorted. Warping may be used for correcting image distortion as well as for creative purposes (e.g., morphing). The same techniques are equally applicable to video. While an image can be transformed in various ways, pure warping means that points are mapped to points without changing the colors. This can be based mathematically on any function from (part of) the plane to the plane. If the function is injective the original can be reconstructed. If the function is a bijection any image can be inversely transformed. Some methods are: Images may be distorted through simulation of optical aberrations. Images may be viewed as if they had been projected onto a curved or mirrored surface. (This is often seen in ray traced images.) Images can be partitioned into image polygons and each polygon distorted. Images can be distorted using morphing. The most obvious approach to transforming a digital image is the forward mapping. This applies the transform directly to the source image, typically generating unevenly-spaced points that will then be interpolated to generate the required regularly-spaced pixels. However, for injective transforms reverse mapping is also available. This applies the inverse transform to the target pixels to find the unevenly-spaced locations in the source image that contribute to them. Estimating them from source image pixels will require interpolation of the source image. To work out what kind of warping has taken place between consecutive images, one can use optical flow estimation techniques. == Image warping toolbox == ImWIP is an open-source, image warping tool for modeling deformation and motion in digital images, which contains differentiable image warping operators, together with their exact adjoints and derivatives.

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  • Pixel-art scaling algorithms

    Pixel-art scaling algorithms

    Pixel art scaling algorithms are graphical filters that attempt to enhance the appearance of hand-drawn 2D pixel art graphics. These algorithms are a form of automatic image enhancement. Pixel art scaling algorithms employ methods significantly different than the common methods of image rescaling, which have the goal of preserving the appearance of images. As pixel art graphics are commonly used at very low resolutions, they employ careful coloring of individual pixels. This results in graphics that rely on a high amount of stylized visual cues to define complex shapes. Several specialized algorithms have been developed to handle re-scaling of such graphics. These specialized algorithms can improve the appearance of pixel-art graphics, but in doing so they introduce changes. Such changes may be undesirable, especially if the goal is to faithfully reproduce the original appearance. Since a typical application of this technology is improving the appearance of fourth-generation and earlier video games on arcade and console emulators, many pixel art scaling algorithms are designed to run in real-time for sufficiently small input images at 60-frames per second. This places constraints on the type of programming techniques that can be used for this sort of real-time processing. Many work only on specific scale factors. 2× is the most common scale factor, while 3×, 4×, 5×, and 6× exist but are less used. == Algorithms == === SAA5050 'Diagonal Smoothing' === The Mullard SAA5050 Teletext character generator chip (1980) used a primitive pixel scaling algorithm to generate higher-resolution characters on the screen from a lower-resolution representation from its internal ROM. Internally, each character shape was defined on a 5 × 9 pixel grid, which was then interpolated by smoothing diagonals to give a 10 × 18 pixel character, with a characteristically angular shape, surrounded to the top and the left by two pixels of blank space. The algorithm only works on monochrome source data, and assumes the source pixels will be logically true or false depending on whether they are 'on' or 'off'. Pixels 'outside the grid pattern' are assumed to be off. The algorithm works as follows: A B C --\ 1 2 D E F --/ 3 4 1 = B | (A & E & !B & !D) 2 = B | (C & E & !B & !F) 3 = E | (!A & !E & B & D) 4 = E | (!C & !E & B & F) Note that this algorithm, like the Eagle algorithm below, has a flaw: If a pattern of 4 pixels in a hollow diamond shape appears, the hollow will be obliterated by the expansion. The SAA5050's internal character ROM carefully avoids ever using this pattern. The degenerate case: becomes: === EPX/Scale2×/AdvMAME2× === Eric's Pixel Expansion (EPX) is an algorithm developed by Eric Johnston at LucasArts around 1992, when porting the SCUMM engine games from the IBM PC (which ran at 320 × 200 × 256 colors) to the early color Macintosh computers, which ran at more or less double that resolution. The algorithm works as follows, expanding P into 4 new pixels based on P's surroundings: 1=P; 2=P; 3=P; 4=P; IF C==A => 1=A IF A==B => 2=B IF D==C => 3=C IF B==D => 4=D IF of A, B, C, D, three or more are identical: 1=2=3=4=P Later implementations of this same algorithm (as AdvMAME2× and Scale2×, developed around 2001) are slightly more efficient but functionally identical: 1=P; 2=P; 3=P; 4=P; IF C==A AND C!=D AND A!=B => 1=A IF A==B AND A!=C AND B!=D => 2=B IF D==C AND D!=B AND C!=A => 3=C IF B==D AND B!=A AND D!=C => 4=D AdvMAME2× is available in DOSBox via the scaler=advmame2x dosbox.conf option. The AdvMAME4×/Scale4× algorithm is just EPX applied twice to get 4× resolution. ==== Scale3×/AdvMAME3× and ScaleFX ==== The AdvMAME3×/Scale3× algorithm (available in DOSBox via the scaler=advmame3x dosbox.conf option) can be thought of as a generalization of EPX to the 3× case. The corner pixels are calculated identically to EPX. 1=E; 2=E; 3=E; 4=E; 5=E; 6=E; 7=E; 8=E; 9=E; IF D==B AND D!=H AND B!=F => 1=D IF (D==B AND D!=H AND B!=F AND E!=C) OR (B==F AND B!=D AND F!=H AND E!=A) => 2=B IF B==F AND B!=D AND F!=H => 3=F IF (H==D AND H!=F AND D!=B AND E!=A) OR (D==B AND D!=H AND B!=F AND E!=G) => 4=D 5=E IF (B==F AND B!=D AND F!=H AND E!=I) OR (F==H AND F!=B AND H!=D AND E!=C) => 6=F IF H==D AND H!=F AND D!=B => 7=D IF (F==H AND F!=B AND H!=D AND E!=G) OR (H==D AND H!=F AND D!=B AND E!=I) => 8=H IF F==H AND F!=B AND H!=D => 9=F There is also a variant improved over Scale3× called ScaleFX, developed by Sp00kyFox, and a version combined with Reverse-AA called ScaleFX-Hybrid. === Eagle === Eagle works as follows: for every in pixel, we will generate 4 out pixels. First, set all 4 to the color of the pixel we are currently scaling (as nearest-neighbor). Next look at the three pixels above, to the left, and diagonally above left: if all three are the same color as each other, set the top left pixel of our output square to that color in preference to the nearest-neighbor color. Work similarly for all four pixels, and then move to the next one. Assume an input matrix of 3 × 3 pixels where the centermost pixel is the pixel to be scaled, and an output matrix of 2 × 2 pixels (i.e., the scaled pixel) first: |Then . . . --\ CC |S T U --\ 1 2 . C . --/ CC |V C W --/ 3 4 . . . |X Y Z | IF V==S==T => 1=S | IF T==U==W => 2=U | IF V==X==Y => 3=X | IF W==Z==Y => 4=Z Thus if we have a single black pixel on a white background it will vanish. This is a bug in the Eagle algorithm but is solved by other algorithms such as EPX, 2xSaI, and HQ2x. === 2×SaI === 2×SaI, short for 2× Scale and Interpolation engine, was inspired by Eagle. It was designed by Derek Liauw Kie Fa, also known as Kreed, primarily for use in console and computer emulators, and it has remained fairly popular in this niche. Many of the most popular emulators, including ZSNES and VisualBoyAdvance, offer this scaling algorithm as a feature. Several slightly different versions of the scaling algorithm are available, and these are often referred to as Super 2×SaI and Super Eagle. The 2xSaI family works on a 4 × 4 matrix of pixels where the pixel marked A below is scaled: I E F J G A B K --\ W X H C D L --/ Y Z M N O P For 16-bit pixels, they use pixel masks which change based on whether the 16-bit pixel format is 565 or 555. The constants colorMask, lowPixelMask, qColorMask, qLowPixelMask, redBlueMask, and greenMask are 16-bit masks. The lower 8 bits are identical in either pixel format. Two interpolation functions are described: INTERPOLATE(uint32 A, UINT32 B). -- linear midpoint of A and B if (A == B) return A; return ( ((A & colorMask) >> 1) + ((B & colorMask) >> 1) + (A & B & lowPixelMask) ); Q_INTERPOLATE(uint32 A, uint32 B, uint32 C, uint32 D) -- bilinear interpolation; A, B, C, and D's average x = ((A & qColorMask) >> 2) + ((B & qColorMask) >> 2) + ((C & qColorMask) >> 2) + ((D & qColorMask) >> 2); y = (A & qLowPixelMask) + (B & qLowPixelMask) + (C & qLowPixelMask) + (D & qLowPixelMask); y = (y >> 2) & qLowPixelMask; return x + y; The algorithm checks A, B, C, and D for a diagonal match such that A==D and B!=C, or the other way around, or if they are both diagonals or if there is no diagonal match. Within these, it checks for three or four identical pixels. Based on these conditions, the algorithm decides whether to use one of A, B, C, or D, or an interpolation among only these four, for each output pixel. The 2xSaI arbitrary scaler can enlarge any image to any resolution and uses bilinear filtering to interpolate pixels. Since Kreed released the source code under the GNU General Public License, it is freely available to anyone wishing to utilize it in a project released under that license. Developers wishing to use it in a non-GPL project would be required to rewrite the algorithm without using any of Kreed's existing code. It is available in DOSBox via scaler=2xsai option. === hqnx family === Maxim Stepin's hq2x, hq3x, and hq4x are for scale factors of 2:1, 3:1, and 4:1 respectively. Each work by comparing the color value of each pixel to those of its eight immediate neighbors, marking the neighbors as close or distant, and using a pre-generated lookup table to find the proper proportion of input pixels' values for each of the 4, 9 or 16 corresponding output pixels. The hq3x family will perfectly smooth any diagonal line whose slope is ±0.5, ±1, or ±2 and which is not anti-aliased in the input; one with any other slope will alternate between two slopes in the output. It will also smooth very tight curves. Unlike 2xSaI, it anti-aliases the output. hqnx was initially created for the Super NES emulator ZSNES. The author of bsnes has released a space-efficient implementation of hq2x to the public domain. A port to shaders, which has comparable quality to the early versions of xBR, is available. Before the port, a shader called "scalehq" has often been confused for hqx. === xBR family === There are 6 filters in this family: xBR , xBRZ, xBR-Hybrid, Super xBR, xBR+3D and Super xBR+3D. xBR ("scale by rules"), cre

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  • Web application firewall

    Web application firewall

    A Web application firewall (WAF) is a specific form of application firewall that filters, monitors, and blocks HTTP traffic to and from a web service. By inspecting HTTP traffic, it can prevent attacks exploiting a Web application's known vulnerabilities, such as SQL injection, cross-site scripting (XSS), file inclusion, and improper system configuration. Financial institutions often utilize WAFs to help in the mitigation of Web application zero-day vulnerabilities, as well as hard-to-patch bugs or weaknesses through custom attack signature strings. == History == Dedicated Web application firewalls entered the market in the late 1990s during a time when web server attacks were becoming more prevalent. Early WAF products, from Kavado and Gilian technologies, tried to solve the increasing amount of attacks on Web applications in the late 1990s. In 2002, the open-source project ModSecurity was formed in order to make WAF technology more accessible. They finalized a core rule set for protecting Web applications, based on OASIS Web Application Security Technical Committee’s (WAS TC) vulnerability work. In 2003, they expanded and standardized rules through the Open Web Application Security Project’s (OWASP) Top 10 List, an annual ranking for Web security vulnerabilities. This list would become the industry standard for Web application security compliance. Since then, the market has continued to grow and evolve, especially focusing on credit card fraud prevention. With the development of the Payment Card Industry Data Security Standard (PCI DSS), a standardization of control over cardholder data, security has become more regulated in this sector. == Description == A Web application firewall is a special type of application firewall that applies specifically to Web applications. It is deployed in front of Web applications and analyzes bi-directional web-based (HTTP) traffic – detecting and blocking anything malicious. The OWASP provides a broad technical definition for a WAF as “a security solution on the Web application level which – from a technical point of view – does not depend on the application itself”. According to the PCI DSS Information Supplement for requirement 6.6, a WAF is defined as “a security policy enforcement point positioned between a Web application and the client endpoint. This functionality can be implemented in software or hardware, running in an appliance device, or in a typical server running a common operating system. It may be a stand-alone device or integrated into other network components.” In other words, a WAF can be a virtual or physical appliance that prevents vulnerabilities in Web applications from being exploited by outside threats. These vulnerabilities may be because the application itself is a legacy type or was insufficiently coded by design. The WAF addresses these code shortcomings by special configurations of rule-sets, also known as policies. Previously unknown vulnerabilities can be discovered through penetration testing or via a vulnerability scanner. A Web application vulnerability scanner, also known as a web application security scanner, is defined in the SAMATE NIST 500-269 as “an automated program that examines Web applications for potential security vulnerabilities. In addition to searching for Web application-specific vulnerabilities, the tools also look for software coding errors.” Resolving vulnerabilities is commonly referred to as remediation. Corrections to the code can be made in the application, but typically a more prompt response is necessary. In these situations, the application of a custom policy for a unique Web application vulnerability to provide a temporary but immediate fix (known as a virtual patch) may be necessary. WAFs are not an ultimate security solution, rather they are meant to be used in conjunction with other network perimeter security solutions such as network firewalls and intrusion prevention systems to provide a holistic defense strategy. WAFs typically follow a positive security model, a negative security, or a combination of both as mentioned by the SANS Institute. WAFs use a combination of rule-based logic, parsing, and signatures to detect and prevent attacks such as cross-site scripting and SQL injection. In general, features like browser emulation, obfuscation and virtualization, and IP obfuscation are used to attempt to bypass WAFs. The OWASP produces a list of the top ten Web application security flaws. All commercial WAF offerings cover these ten flaws at a minimum. There are non-commercial options as well. As mentioned earlier, the well-known open-source WAF engine called ModSecurity is one of these options. A WAF engine alone is insufficient to provide adequate protection, therefore OWASP along with Trustwave's Spiderlabs help organize and maintain a Core-Rule Set via GitHub to use with the ModSecurity WAF engine. == Deployment options == Although the names for operating mode may differ, WAFs are basically deployed inline in three different ways. According to NSS Labs, deployment options are transparent bridge, transparent reverse proxy, and reverse proxy. "Transparent" refers to the fact that the HTTP traffic is sent straight to the Web application, therefore the WAF is transparent between the client and server. This is in contrast to reverse proxy, where the WAF acts as a proxy, and the client’s traffic is sent directly to the WAF. The WAF then separately sends filtered traffic to Web applications. This can provide additional benefits such as IP masking but may introduce disadvantages such as performance latencies. == JA3 fingerprint == JA3, developed by Salesforce in 2017, is a technique for generating a unique fingerprint for SSL/TLS traffic based on specific fields in the handshake, such as the version, cipher suites, and extensions used by the client. This fingerprint enables the identification and tracking of clients based on the characteristics of their encrypted traffic. In the context of distributed denial of service (DDoS) protection, JA3 fingerprints are used to detect and differentiate malicious traffic, often associated with attack bots, from legitimate traffic, allowing for more precise filtering of potential threats. In September 2023, AWS WAF announced built-in support for JA3, enabling customers to inspect the JA3 fingerprints of incoming requests. JA3 was deprecated in May 2025 in favor of JA4. JA4 is currently patent pending.

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  • Super-resolution imaging

    Super-resolution imaging

    Super-resolution imaging (SR) is a class of techniques that improve the resolution of an imaging system. In optical SR the diffraction limit of systems is transcended, while in geometrical SR the resolution of digital imaging sensors is enhanced. In some radar and sonar imaging applications (e.g. magnetic resonance imaging (MRI), high-resolution computed tomography), subspace decomposition-based methods (e.g. MUSIC) and compressed sensing-based algorithms (e.g., SAMV) are employed to achieve SR over standard periodogram algorithm. Super-resolution imaging techniques are used in general image processing and in super-resolution microscopy. == Super-resolution principles == Several concepts are fundamental to super-resolution imaging: Diffraction limit: the capacity of an optical instrument to reproduce the details of an object in an image has limits that are imposed by laws of physics: the diffraction equations in the wave theory of light, or the uncertainty principle for photons in quantum mechanics. Information transfer can never be increased beyond this boundary, but packets outside the limits can be cleverly swapped for (or multiplexed with) some inside it. Super-resolution microscopy does not so much “break” as “circumvent” the diffraction limit. New procedures probing electro-magnetic disturbances at the molecular level (in the so-called near field) remain fully consistent with Maxwell's equations. Spatial frequency domain: A succinct expression of the diffraction limit is given in the spatial frequency domain. In Fourier optics light distributions are expressed as superpositions of a series of grating light patterns in a range of fringe widths - these widths represent the spatial frequencies. It is generally taught that diffraction theory stipulates an upper limit, the cut-off spatial-frequency, beyond which pattern elements fail to be transferred into the optical image, i.e., are not resolved. But in fact what is set by diffraction theory is the width of the passband, not a fixed upper limit. No laws of physics are broken when a spatial frequency band beyond the cut-off spatial frequency is swapped for one inside it: this has long been implemented in dark-field microscopy. Nor are information-theoretical rules broken when superimposing several bands, disentangling them in the received image needs assumptions of object invariance during multiple exposures, i.e., the substitution of one kind of uncertainty for another. Information: When the term super-resolution is used in techniques based on the inference of object details using a statistical treatment of the image within standard resolution limits (for example, averaging multiple exposures), it involves an exchange of one kind of information (extracting signal from noise) for another (the assumption that the target has remained invariant). Recent breakthroughs incorporate quantum-transformer hybrids into super-resolution, such as QUIET‑SR, a 2025 model that employs shifted quantum window attention within a transformer to enhance image detail while respecting diffraction and information-theory limits Similarly, frequency-integrated transformers (e.g., FIT) enrich super-resolution by explicitly combining spatial and frequency-domain information via FFT-based attention, improving reconstruction across scales Resolution and localization: True resolution involves the distinction of whether a target, e.g. a star or a spectral line, is single or double, ordinarily requiring separable peaks in the image. When a target is known to be single, its location can be determined with higher precision than the image width by finding the centroid (center of gravity) of its image light distribution. The word ultra-resolution had been proposed for this process but it did not catch on, and the high-precision localization procedure is typically referred to as super-resolution. == Techniques == === Optical or diffractive super-resolution === Substituting spatial-frequency bands: Though the bandwidth allowable by diffraction is fixed, it can be positioned anywhere in the spatial-frequency spectrum. Dark-field illumination in microscopy is an example. See also aperture synthesis. ==== Multiplexing spatial-frequency bands ==== An image is formed using the normal passband of the optical device. Then, some known light structure (for example, a set of light fringes) is superimposed on the target. The image now contains components resulting from the combination of the target and the superimposed light structure, e.g. moiré fringes, and carries information about target detail which simple unstructured illumination does not. The “superresolved” components, however, need disentangling to be revealed. For an example, see structured illumination (figure to left). ==== Multiple parameter use within traditional diffraction limit ==== If a target has no special polarization or wavelength properties, two polarization states or non-overlapping wavelength regions can be used to encode target details, one in a spatial-frequency band inside the cut-off limit the other beyond it. Both would use normal passband transmission but are then separately decoded to reconstitute target structure with extended resolution. ==== Probing near-field electromagnetic disturbance ==== Super-resolution microscopy is generally discussed within the realm of conventional optical imagery. However, modern technology allows the probing of electromagnetic disturbance within molecular distances of the source, which has superior resolution properties. See also evanescent waves and the development of the new super lens. === Geometrical or image-processing super-resolution === ==== Multi-exposure image noise reduction ==== When an image is degraded by noise, the resolution may be improved by averaging multiple exposures. See example on the right. ==== Single-frame deblurring ==== Known defects in a given imaging situation, such as defocus or aberrations, can sometimes be mitigated in whole or in part by suitable spatial-frequency filtering of even a single image. Such procedures all stay within the diffraction-mandated passband, and do not extend it. ==== Sub-pixel image localization ==== The location of a single source can be determined by computing the "center of gravity" (centroid) of the light distribution extending over several adjacent pixels (see figure on the left). Provided that there is enough light, this can be achieved with arbitrary precision, very much better than pixel width of the detecting apparatus and the resolution limit for the decision of whether the source is single or double. This technique, which requires the presupposition that all the light comes from a single source, is at the basis of what has become known as super-resolution microscopy, e.g. stochastic optical reconstruction microscopy (STORM), where fluorescent probes attached to molecules give nanoscale distance information. It is also the mechanism underlying visual hyperacuity. ==== Bayesian induction beyond traditional diffraction limit ==== Some object features, though beyond the diffraction limit, may be known to be associated with other object features that are within the limits and hence contained in the image. Then conclusions can be drawn, using statistical methods, from the available image data about the presence of the full object. The classical example is Toraldo di Francia's proposition of judging whether an image is that of a single or double star by determining whether its width exceeds the spread from a single star. This can be achieved at separations well below the classical resolution bounds, and requires the prior limitation to the choice "single or double?" The approach can take the form of extrapolating the image in the frequency domain, by assuming that the object is an analytic function, and that we can exactly know the function values in some interval. This method is severely limited by the ever-present noise in digital imaging systems, but it can work for radar, astronomy, microscopy or magnetic resonance imaging. More recently, a fast single image super-resolution algorithm based on a closed-form solution to ℓ 2 − ℓ 2 {\displaystyle \ell _{2}-\ell _{2}} problems has been proposed and demonstrated to accelerate most of the existing Bayesian super-resolution methods significantly. == Aliasing == Geometrical SR reconstruction algorithms are possible if and only if the input low resolution images have been under-sampled and therefore contain aliasing. Because of this aliasing, the high-frequency content of the desired reconstruction image is embedded in the low-frequency content of each of the observed images. Given a sufficient number of observation images, and if the set of observations vary in their phase (i.e. if the images of the scene are shifted by a sub-pixel amount), then the phase information can be used to separate the aliased high-frequency content from the true low-frequency content, and the full-resolution image can be accurate

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  • Aphelion (software)

    Aphelion (software)

    The Aphelion Imaging Software Suite is a software suite that includes three base products - Aphelion Lab, Aphelion Dev, and Aphelion SDK for addressing image processing and image analysis applications. The suite also includes a set of extension programs to implement specific vertical applications that benefit from imaging techniques. The Aphelion software products can be used to prototype and deploy applications, or can be integrated, in whole or in part, into a user's system as processing and visualization libraries whose components are available as both DLLs or .Net components. == History and evolution == The development of Aphelion started in 1995 as a joint project of a French company, ADCIS S.A., and an American company, Amerinex Applied Imaging, Inc. (AAI) Aphelion's image processing and analysis functions were made from operators available from the KBVision software developed and sold by Amerinex's predecessor, Amerinex Artificial Intelligence Inc. In the 1990s, the XLim software library was developed at the Center of Mathematical Morphology of Mines ParisTech, and both companies carried out its development tasks. The first version of Aphelion was completed and released in April 1996. Successive versions were released before the first official stable release in December 1996 at the Photonics East conference in Boston and the Solutions Vision show in Paris in January 1997, where at the latter it competed with Stemmer Imaging's CVB imaging toolbox. In 1998, version 2.3 of Aphelion for Windows 98 was released, and its user base was growing in both France and the United States. Version 3.0, totally rewritten to take advantage of Microsoft's then-recent ActiveX technology, was officially released in 2000. It also became available as a « Developer » version, for rapid prototyping of applications using its intuitive GUI and the macro recording capability, and a « Core » version, including the full library as a set of ActiveX components to be used by software developers, integrators and original equipment manufacturers (OEM). As AAI turned its focus to security, in 2001, ADCIS took the lead on developing Aphelion. AAI focused on millimeter wave scanners for concealed weapon detection at airports, and eventually merged with Millimetrics to become Millivision. In 2004, ADCIS specified version 4.0 of Aphelion. The set of image processing/analysis functions was rewritten one more time to be compatible with the .NET technology and the emergence of 64 bit architecture PCs. In addition, the GUI was redesigned to address two usage types: a semi-automatic use where the user is guided through the different steps of functions, and a fully automatic use where the expert user can quickly invoke imaging functions. Its first release was presented at the IPOT exhibition in Birmingham, UK the same year. During the Vision Show in Paris in October 2008, the new Aphelion Lab product was launched for users that are not specialists in image processing. It is easier to use, and only includes fewer image processing functions. It was then included in the Aphelion Image Processing Suite, consisting of Aphelion Dev (replacing Aphelion Developer), Aphelion Lab, Aphelion SDK (replacing Aphelion Core), and a set of extensions. Nowadays, ADCIS is still working on the suite, and updated versions with new extensions and functionalities continually become available from the websites of both companies. In 2015, support was added for very large images and scan microscope images (virtual slides compound into a very large JPEG 2000 image) for high throughput imaging, and new specific extensions were also added. In late 2015, ADCIS announced Aphelion's port for tablets and smartphones, for vertical applications. The name "Aphelion" comes from the astronomical term of the same name, meaning the point on a planet rotating around the Sun where it lies farthest from it, applying the term in a metaphorical sense. Unix was the operating system used on scientific workstations in the 1990s, such as on the workstations manufactured by market leader Sun Microsystems, which Windows suite Aphelion was quite removed from. == Description == Aphelion is a software suite to be used for image processing and image analysis. It supports 2D and 3D, monochrome, color, and multi-band images. It is developed by ADCIS, a French software house located in Saint-Contest, Calvados, Normandy. Aphelion is widely used in the scientific/industry community to solve basic and complex imaging applications. First, the imaging application is quickly developed from the Graphical User Interface, involving a set of functions that can be automatically recorded into a macro command. The macro languages available in Aphelion (i.e. BasicScript, Python, and C#) help to process batch of images, and prompt the user if needed for specific parameters that are applied to the imaging functions. All Aphelion image processing functions are written in C++, and the Aphelion user interface is written in C#. C++ functions can be called from the C# language thanks the use of dedicated wrappers. The main principle of image processing is to automatically process pixels of a digital image, then extract one or more objects of interest (i.e. cells in the field of biology, inclusions in the field of material science) and compute one or more measurements on those objects to quantify the image and generate a verdict (good image, image with defects, cancerous cells). In other words, starting from an image, pixels are processed by a set of successive functions or operators until only measurements are computed and used as the input of a 3rd party system or a classification software that will classify objects of interest that have been extracted during the imaging process. An acquisition system such as a digital camera, a video camera, an optical or electron microscope, a medical scanner, or a smartphone can be used to capture images. The set of values or pixels can be processed as a 1D image (1D signal), a 2D image (array of pixel values corresponding to a monochrome or color image), or a 3D image displayed using volume rendering (array of voxels in the 3D space) or displaying surfaces by using 3D rendering. A 2D color image is made of 3 value pixels (typically Red, Green, and Blue information or another color space), and a 3D image is made of monochrome, color (indexed color are often used), multispectral, or hyperspectral data. When dealing with videos, an additional band is added corresponding to temporal information. The Aphelion Software Suite includes three base products, and a set of optional extensions for specific applications: Aphelion Lab: Entry-level package for non-experts in image processing. It helps to quickly segment an image in a semi-automatic or manual ways, and compute a set of measurements computed on objects of interest that have been extracted during the segmentation process. A set of wizards guides the user from image acquisition to report generation. Aphelion Dev: Full imaging environment including over 450 functions to develop and deploy an application that involves image processing and analysis. It also includes a set of macro-command languages to automate any application to be invoked from the user interface. It also helps to run the imaging algorithm on more than one image that are stored on disk, available on the network, or captured by an acquisition device. Aphelion libraries for image processing and visualization are provided in Aphelion Dev as DLLs and .Net components. Aphelion SDK: A set of libraries to develop a stand-alone application with a custom interface based on the Aphelion libraries. This software development kit including display, processing and analysis functions that can be used by software developers and OEMs. It is provided as DLLs and .Net components. The stand-alone application is typically developed in C# on one computer, and then deployed on multiple PCs and systems. A set of optional extensions can be added to the « Aphelion Dev » product, depending on the application. An evaluation version of Aphelion can be run on a PC for 30 days. A permanent version of Aphelion is available based on a perpetual license. Upgrades are available through a maintenance agreement based on a yearly fee. Technical support is provided by the engineers who are developing the product. The goal of image processing is usually to extract object(s) of interest in an image, and then to classify them based on some characteristics such as shape, density, position, etc. Using Aphelion, this goal is achieved by performing the following tasks: Load an image from disk or acquire an image using an acquisition device. Enhance the image removing noise or modifying its contrast. Segment the image extracting objects of interest to be measured and analyzed. Typically, for simple applications, a threshold is performed to generate a binary image. Then, morphological operators are applied to clean the image and only keep obj

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  • Dispo

    Dispo

    Dispo (formerly David's Disposable) is an American photo sharing and social networking app owned by Dispo, Inc. and co-founded by CEO Daniel Liss, YouTuber David Dobrik, and Natalie Mariduena. When the app initially launched on iOS in December 2019, it briefly charted as the most downloaded free app on the App Store, ahead of both Disney+ and Instagram. The app was rebranded and relaunched as Dispo, expanding from a simple camera app to a full social network in March 2021. It is based on the disposable camera. == History == On December 21, 2019, the app was first launched on the App Store under the name "David's Disposable." In its first week of release, it was downloaded more than a million times, reaching number one among free apps in the App Store. In June 2020, the team decided to rename the app to Dispo, purchasing the Dispo.fun domain on June 21, 2020. The company announced the change in September 2020. The early Dispo team consisted of Dobrik's longtime friend and business associate Natalie Mariduena as its treasurer, entrepreneur and venture capitalist Daniel Liss as chief executive officer, Regynald Augustin as first engineer, and Briana Hokanson as lead designer. In October 2020, the company raised a $4M seed round with backing from Alexis Ohanian's venture fund Seven Seven Six alongside other investors including Unshackled Ventures, Shrug Capital, and Weekend Fund. In February 2021, Axios reported that the app had generated US$20 million in its series A round, led by Spark Capital. At this time, the app was valued at US$200 million. A New York Times profile asked, "Are Disposables the Future of Photosharing?" In March 2021, the app was officially relaunched with new social network features and its invite-only feature was dropped. On March 21, 2021, it was announced that Spark Capital would sever all ties with Dispo in light of several disparaging allegations against David Dobrik and The Vlog Squad. The same day, it was announced that Dobrik would leave the company and step down from the company's board of directors. On March 22, 2021, Seven Seven Six and Unshackled Ventures announced they would be standing by the company and its remaining employees but donating profits to charity. In June, 2021, CEO Daniel Liss announced Dispo's official Series A. Investors and advisors in the new Dispo include Ohanian's Seven Seven Six, Unshackled, Endeavor, photographers Annie Leibovitz and Raven B. Varona, NBA stars Kevin Durant and Andre Iguodala (through their 35 Ventures and F9 Strategies venture firms, respectively). Other participants include Cara Delevingne, Sofia Vergara, Shade Room CEO Angelica Nwandu, Latin World Entertainment CEO Luis Balaguer, and Amplify Africa co-founders Damilare Kujembola and Timi Adeyeba. == Overview == Dispo has been compared to other image sharing and social networking services, most notably Instagram and VSCO, although users cannot immediately see the photos they have taken using the app. When a user attempts to take a photo, the interface mimics the developing process of a disposable camera. Users can take as many photos on the app as they want; they do not appear on the app however, until 9 am the next day. Once the set of photos appear on the app, users can choose to save them or share them with other users in a "roll". == Reception == Screen Rant has called the app "like Clubhouse [referring to the app] but for photos," comparing the early invite-only features of the apps. As it greatly restricts the user's editing options and sets out to offer a more authentic social networking experience, the app has been widely dubbed the "anti-Instagram". Between March 2021 and June 2021, the app reached the top ten in the App Store's photo/video rankings on 5 continents including in the US, Japan, Spain, Germany, Brazil, and Australia. It has been a notable success in Japan, where it opened its first international office in July 2021. In July 2021, NBA number one draft pick Cade Cunningham announced he had selected Dispo as his exclusive social media partner for the NBA draft.

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  • WHATWG

    WHATWG

    The Web Hypertext Application Technology Working Group (WHATWG) was founded by representatives from Apple Inc., the Mozilla Foundation and Opera Software, leading web browser vendors in 2004. WHATWG is responsible for maintaining multiple web-related technical standards, including the specifications for the HyperText Markup Language (HTML) and the Document Object Model (DOM). The central organizational membership and control of WHATWG – its "Steering Group" – consists of Apple, Mozilla, Google, and Microsoft. WHATWG editors of the specifications ensure correct implementation, in consultation with participants, but ultimately in accordance with Steering Group member objectives. == History == The WHATWG was formed in response to the slow development of World Wide Web Consortium (W3C) Web standards and W3C's decision to abandon HTML in favor of XML-based technologies. The WHATWG mailing list was announced on 4 June 2004, two days after the initiatives of a joint Opera–Mozilla position paper had been voted down by the W3C members at the W3C Workshop on Web Applications and Compound Documents. On 10 April 2007, the Mozilla Foundation, Apple, and Opera Software proposed that the new HTML working group of the W3C adopt the WHATWG's HTML5 as the starting point of its work and name its future deliverable as "HTML5" (though the WHATWG specification was later renamed HTML Living Standard). On 9 May 2007, the new HTML working group of the W3C resolved to do that. An Internet Explorer platform architect from Microsoft was invited but did not join, citing the lack of a patent policy to ensure all specifications can be implemented on a royalty-free basis. Since then, the W3C and the WHATWG had been developing HTML independently, at times causing specifications to diverge. In 2017, the WHATWG established an intellectual property rights agreement that includes a patent policy. This spurred a renewed attempt to allow the W3C and the WHATWG to work together on specifications. In 2019, the W3C and WHATWG agreed to a memorandum of understanding where development of HTML and DOM specifications would be done principally in the WHATWG. The editor has significant control over the specification, but the community can influence the decisions of the editor. In one case, editor Ian Hickson proposed replacing the

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  • Bandhan Tod

    Bandhan Tod

    Bandhan Tod is a mobile app to stop child marriage in India's Bihar state through SOS button in the app. When the SOS on Bandhan Tod is activated, the nearest small NGO will attempt to resolve the issue. If the family resists, then the police gets notified. Till now so many child marriages has been cancelled through Bandhan Tod interventions. Bandhan Tod is an initiative of Gender Alliance managed by Prashanti Tiwari to support the state government's efforts to end child marriage and dowry.

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  • BeHafizh

    BeHafizh

    BeHafizh is a mobile application to assist in the effort to memorize Qur'anic verses. The software runs on the Android operating system. This application was made by a team from Gadjah Mada University (UGM) consisting of Farid Amin Ridwanto, Rian Adam Rajagede and Alfian Try Putranto in order to participate in the National Student Musabaqoh Tilawatil Quran (MTQ) held at University of Indonesia (UI) on 1- August 8, 2015. This application then won a gold medal in the branch of Computer Application Design in the competition. == Features == === Audio Player === Audio player, paragraph can be played repeatedly, with pause, and can be done on a certain range of Quranic verses. === Memorization Test === Memorization testing continues users to improve their memorization. Memorization Recorders improves user's ability to recite Quran. === Colour indicators === === Achievements === === Reminders ===

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