AI Detector Gemini

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

  • Instance-based learning

    Instance-based learning

    In machine learning, instance-based learning (sometimes called memory-based learning) is a family of learning algorithms that, instead of performing explicit generalization, compare new problem instances with instances seen in training, which have been stored in memory. Because computation is postponed until a new instance is observed, these algorithms are sometimes referred to as "lazy." It is called instance-based because it constructs hypotheses directly from the training instances themselves. This means that the hypothesis complexity can grow with the data: in the worst case, a hypothesis is a list of n training items and the computational complexity of classifying a single new instance is O(n). One advantage that instance-based learning has over other methods of machine learning is its ability to adapt its model to previously unseen data. Instance-based learners may simply store a new instance or throw an old instance away. Examples of instance-based learning algorithms are the k-nearest neighbors algorithm, kernel machines and RBF networks. These store (a subset of) their training set; when predicting a value/class for a new instance, they compute distances or similarities between this instance and the training instances to make a decision. To battle the memory complexity of storing all training instances, as well as the risk of overfitting to noise in the training set, instance reduction algorithms have been proposed.

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  • Signal-to-noise ratio (imaging)

    Signal-to-noise ratio (imaging)

    Signal-to-noise ratio (SNR) is used in imaging to characterize image quality. The sensitivity of a (digital or film) imaging system is typically described in the terms of the signal level that yields a threshold level of SNR. Industry standards define sensitivity in terms of the ISO film speed equivalent, using SNR thresholds (at average scene luminance) of 40:1 for "excellent" image quality and 10:1 for "acceptable" image quality. SNR is sometimes quantified in decibels (dB) of signal power relative to noise power, though in the imaging field the concept of "power" is sometimes taken to be the power of a voltage signal proportional to optical power; so a 20 dB SNR may mean either 10:1 or 100:1 optical power, depending on which definition is in use. == Definition of SNR == Traditionally, SNR is defined to be the ratio of the average signal value μ s i g {\displaystyle \mu _{\mathrm {sig} }} to the standard deviation of the signal σ s i g {\displaystyle \sigma _{\mathrm {sig} }} : S N R = μ s i g σ s i g {\displaystyle \mathrm {SNR} ={\frac {\mu _{\mathrm {sig} }}{\sigma _{\mathrm {sig} }}}} when the signal is an optical intensity, or as the square of this value if the signal and noise are viewed as amplitudes (field quantities).

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  • Breakup Notifier

    Breakup Notifier

    Breakup Notifier was a web application written by product developer and programmer Dan Loewenherz that enabled its registered users to track the relationship status of their Facebook friends. An email notification was sent to the user when one of their Facebook friends changed their relationship status. The app was one of the most viral Facebook app's at the time of its release. It was mentioned in a skit on The Jay Leno Show and news of its popularity was published in Time magazine, The New York Post, CNET, and The Globe and Mail. == Popularity and Facebook controversy == Breakup Notifier gathered 100,000 users in less than 24 hours of its launch and reached a user base of more than 3,000,000 in February 2011. Facebook then blocked the app. Loewenherz later created an app named Crush Notifier, which differs from the original app in that users can check if they have a mutual crush. Breakup Notifier was later unblocked by Facebook and monetized.

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  • Fatsecret

    Fatsecret

    Fatsecret, commonly styled as fatsecret, is a mobile application, website and API that helps people achieve their weight loss goals and find accurate nutrition information. It also offers a weight loss clinic with coaching and medically supported programs. The platform powers global health apps. == History == Fatsecret was founded in 2006 in Melbourne, Australia by Lenny Moses and Rodney Moses. As of 2019, Lenny serves as the company's CEO. The company is known for its calorie counting and meal tracking app, and by April 2016, the company claimed to have 45 million users of its services. In August 2018, a premium version of its app was released. Since August 2009, the company has operated the Fatsecret Platform API, which allows access to its global food and nutrition database. Fatsecret reportedly had 900,000 downloads of its app in January 2020. In an analysis of several Health & Fitness app subcategories for the United States in January 2021, Fatsecret was reported to have the highest 30 day user retention rate of top Calorie Counter + Meal Planner for Weight Loss apps.

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  • Machine vision

    Machine vision

    Machine vision is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance, usually in industry. Machine vision refers to many technologies, software and hardware products, integrated systems, actions, methods and expertise. Machine vision as a systems engineering discipline can be considered distinct from computer vision, a form of computer science. It attempts to integrate existing technologies in new ways and apply them to solve real world problems. The term is the prevalent one for these functions in industrial automation environments but is also used for these functions in other environment vehicle guidance. The overall machine vision process includes planning the details of the requirements and project, and then creating a solution. During run-time, the process starts with imaging, followed by automated analysis of the image and extraction of the required information. == Definition == Definitions of the term "Machine vision" vary, but all include the technology and methods used to extract information from an image on an automated basis, as opposed to image processing, where the output is another image. The information extracted can be a simple good-part/bad-part signal, or more a complex set of data such as the identity, position and orientation of each object in an image. The information can be used for such applications as automatic inspection and robot and process guidance in industry, for security monitoring and vehicle guidance. This field encompasses a large number of technologies, software and hardware products, integrated systems, actions, methods and expertise. Machine vision is practically the only term used for these functions in industrial automation applications; the term is less universal for these functions in other environments such as security and vehicle guidance. Machine vision as a systems engineering discipline can be considered distinct from computer vision, a form of basic computer science; machine vision attempts to integrate existing technologies in new ways and apply them to solve real world problems in a way that meets the requirements of industrial automation and similar application areas. The term is also used in a broader sense by trade shows and trade groups such as the Automated Imaging Association and the European Machine Vision Association. This broader definition also encompasses products and applications most often associated with image processing. The primary uses for machine vision are automatic inspection and industrial robot/process guidance. In more recent times the terms computer vision and machine vision have converged to a greater degree. See glossary of machine vision. == Imaging based automatic inspection and sorting == The primary uses for machine vision are imaging-based automatic inspection and sorting and robot guidance.; in this section the former is abbreviated as "automatic inspection". The overall process includes planning the details of the requirements and project, and then creating a solution. This section describes the technical process that occurs during the operation of the solution. === Methods and sequence of operation === The first step in the automatic inspection sequence of operation is acquisition of an image, typically using cameras, lenses, and lighting that has been designed to provide the differentiation required by subsequent processing. MV software packages and programs developed in them then employ various digital image processing techniques to extract the required information, and often make decisions (such as pass/fail) based on the extracted information. === Equipment === The components of an automatic inspection system usually include lighting, a camera or other imager, a processor, software, and output devices. === Imaging === The imaging device (e.g. camera) can either be separate from the main image processing unit or combined with it in which case the combination is generally called a smart camera or smart sensor. Inclusion of the full processing function into the same enclosure as the camera is often referred to as embedded processing. When separated, the connection may be made to specialized intermediate hardware, a custom processing appliance, or a frame grabber within a computer using either an analog or standardized digital interface (Camera Link, CoaXPress). MV implementations also use digital cameras capable of direct connections (without a framegrabber) to a computer via FireWire, USB or Gigabit Ethernet interfaces. While conventional (2D visible light) imaging is most commonly used in MV, alternatives include multispectral imaging, hyperspectral imaging, imaging various infrared bands, line scan imaging, 3D imaging of surfaces and X-ray imaging. Key differentiations within MV 2D visible light imaging are monochromatic vs. color, frame rate, resolution, and whether or not the imaging process is simultaneous over the entire image, making it suitable for moving processes. Though the vast majority of machine vision applications are solved using two-dimensional imaging, machine vision applications utilizing 3D imaging are a growing niche within the industry. The most commonly used method for 3D imaging is scanning based triangulation which utilizes motion of the product or image during the imaging process. A laser is projected onto the surfaces of an object. In machine vision this is accomplished with a scanning motion, either by moving the workpiece, or by moving the camera & laser imaging system. The line is viewed by a camera from a different angle; the deviation of the line represents shape variations. Lines from multiple scans are assembled into a depth map or point cloud. Stereoscopic vision is used in special cases involving unique features present in both views of a pair of cameras. Other 3D methods used for machine vision are time of flight and grid based. One method is grid array based systems using pseudorandom structured light system as employed by the Microsoft Kinect system circa 2012. === Image processing === After an image is acquired, it is processed. Central processing functions are generally done by a CPU, a GPU, a FPGA or a combination of these. Deep learning training and inference impose higher processing performance requirements. Multiple stages of processing are generally used in a sequence that ends up as a desired result. A typical sequence might start with tools such as filters which modify the image, followed by extraction of objects, then extraction (e.g. measurements, reading of codes) of data from those objects, followed by communicating that data, or comparing it against target values to create and communicate "pass/fail" results. Machine vision image processing methods include; Stitching/Registration: Combining of adjacent 2D or 3D images. Filtering (e.g. morphological filtering) Thresholding: Thresholding starts with setting or determining a gray value that will be useful for the following steps. The value is then used to separate portions of the image, and sometimes to transform each portion of the image to simply black and white based on whether it is below or above that grayscale value. Pixel counting: counts the number of light or dark pixels Segmentation: Partitioning a digital image into multiple segments to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Edge detection: finding object edges Color Analysis: Identify parts, products and items using color, assess quality from color, and isolate features using color. Blob detection and extraction: inspecting an image for discrete blobs of connected pixels (e.g. a black hole in a grey object) as image landmarks. Neural network / deep learning / machine learning processing: weighted and self-training multi-variable decision making Circa 2019 there is a large expansion of this, using deep learning and machine learning to significantly expand machine vision capabilities. The most common result of such processing is classification. Examples of classification are object identification,"pass fail" classification of identified objects and OCR. Pattern recognition including template matching. Finding, matching, and/or counting specific patterns. This may include location of an object that may be rotated, partially hidden by another object, or varying in size. Barcode, Data Matrix and "2D barcode" reading Optical character recognition: automated reading of text such as serial numbers Gauging/Metrology: measurement of object dimensions (e.g. in pixels, inches or millimeters) Comparison against target values to determine a "pass or fail" or "go/no go" result. For example, with code or bar code verification, the read value is compared to the stored target value. For gauging, a measurement is compared against the proper value and tolerances. For verification of alpha-numberic codes, the

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  • AARON

    AARON

    AARON is the collective name for a series of computer programs written by artist Harold Cohen that create original artistic images autonomously, which set it apart from previous programs. Proceeding from Cohen's initial question "What are the minimum conditions under which a set of marks functions as an image?", AARON was in development between 1972 and the 2010s. As the software is not open source, its development effectively ended with Cohen's death in 2016. The name "AARON" does not seem to be an acronym; rather, it was a name chosen to start with the letter "A" so that the names of successive programs could follow it alphabetically. However, Cohen did not create any other major programs. Initial versions of AARON created abstract drawings that grew more complex through the 1970s. More representational imagery was added in the 1980s; first rocks, then plants, then people. In the 1990s more representational figures set in interior scenes were added, along with color. AARON returned to more abstract imagery, this time in color, in the early 2000s. Cohen used machines that allowed AARON to produce physical artwork. The first machines drew in black and white using a succession of custom-built "turtle" and flatbed plotter devices. Cohen would sometimes color these images by hand in fabric dye (Procion), or scale them up to make larger paintings and murals. In the 1990s Cohen built a series of digital painting machines to output AARON's images in ink and fabric dye. His later work used a large-scale inkjet printer on canvas. Development of AARON began in the C programming language then switched to Lisp in the early 1990s. Cohen credits Lisp with helping him solve the challenges he faced in adding color capabilities to AARON. An article about Cohen appeared in Computer Answers that describes AARON and shows two line drawings that were exhibited at the Tate gallery. The article goes on to describe the workings of AARON, then running on a DEC VAX 750 minicomputer. Raymond Kurzweil's company has produced a downloadable screensaver of AARON for Microsoft Windows PCs. This version of AARON can also produce printable images. AARON's source code is not publicly available, but Cohen has described AARON's operations in various essays and it is discussed in abstract in Pamela McCorduck's book. AARON cannot learn new styles or imagery on its own; each new capability must be hand-coded by Cohen. It is capable of producing a practically infinite supply of distinct images in its own style. Examples of these images have been exhibited in galleries worldwide. AARON's artwork has been used as an artistic equivalent of the Turing test. It does seem however that AARON's output follows a noticeable formula (figures standing next to a potted plant, framed within a colored square is a common theme). Cohen is very careful not to claim that AARON is creative. But he does ask "If what AARON is making is not art, what is it exactly, and in what ways, other than its origin, does it differ from the 'real thing?' If it is not thinking, what exactly is it doing?" — The further exploits of AARON, Painter. The Whitney Museum featured AARON in 2024, showcasing the evolution of AARON as the earliest artificial intelligence (AI) program for artmaking.

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  • Butler in a Box

    Butler in a Box

    Butler in a Box was an early voice-controlled home automation device developed in 1983 by magician Gus Searcy and programmer Franz Kavan. The device allowed users to control various home electronics, such as lights and phones, using voice commands. It predated modern smart speakers and virtual assistants by several decades. == History == The idea for the Butler in a Box originated in 1983 when Searcy was asked by friends why he couldn't simply command lights to turn on and off if he could pull rabbits out of hats, given his background as a professional magician. Searcy partnered with former IBM programmer Kavan to develop the device, with their first prototype being named "Sidney". The Butler in a Box combined remote control technology with voice recognition to enable control of home devices. However, it faced challenges due to the technological limitations of the era and its high price point of nearly $1,500 (equivalent to around $3,700 in 2021). == Features and functionality == Users could activate the Butler in a Box by speaking a wake word, typically a traditional butler name, and the device would address the user as "boss". It was capable of performing tasks such as: Turning lights on and off, controlling individual zones if lights were connected to remote control modules Making and receiving phone calls Setting timers Pairing with sensors to function as a security alarm system However, the device required extensive voice training for each user, a time-consuming process compared to modern voice recognition. Additionally, settings and trained commands would be lost if power was out for over 3 hours due to the volatile memory technology used at the time. == Reception and legacy == While innovative for its time, the Butler in a Box did not achieve widespread commercial success due to its high price and the technical limitations of the 1980s. Nevertheless, it served as an important early step in the development of home automation and showcased the potential for voice-controlled technology to enhance accessibility and convenience in the home. Decades later, products like Amazon Alexa, Google Home, and Apple's Siri would make voice-controlled smart home devices commonplace and affordable, building on the groundwork laid by early attempts like the Butler in a Box.

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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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  • Clubdjpro

    Clubdjpro

    ClubDJPro (often referred to as ClubDJ) is a DJ console and video mixing tool developed by Cube Software Solutions Inc. software. It was released in June 2005. == User interface == ClubDJPro has a GUI that was designed to allow aesthetic revisions via Skins. The skin engine that ClubDJPro uses allows for the ability to expand the software to take up the entire screen. As of 4.4.3.3 there are 3 user changeable skins included in the program which are changeable in the preferences tab. They are called 'AquaLung', 'Eleanor', and 'Grabber'. == Editions == ClubDJPro is available in two different editions, with separate features depending upon their target consumer group. DJ Edition - Can play audio files only. VJ Edition - Contains all of the features of the DJ Edition, in addition to support for video, karaoke, and visualizations. == Supported MIDI Controllers == Supported since version 2.0: Hercules Console Hercules Console MK2 Hercules Control MP3 PCDJ DAC-2 Controller == History == The initial "final release" of ClubDJPro was released on June 24, 2005. On June 26, 2009, the 4th iteration of the ClubDJPro software was released. The development of the software and website appears to have halted. As of March 2018 the website continues to show a new version "Coming Spring 2016".

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  • Adobe GoLive

    Adobe GoLive

    Adobe GoLive was a WYSIWYG HTML editor and web site management application from Adobe Systems. It replaced Adobe PageMill as Adobe's primary HTML editor and was itself discontinued in favor of Dreamweaver. The last version of GoLive that Adobe released was GoLive 9. == History == GoLive originated as the flagship product of a company named GoNet Communication, Inc. then based in Menlo Park, California, and the development company GoNet Communications GmbH in Hamburg, Germany, in 1996. Later GoNet changed its name to GoLive Systems, Inc, and the name of its product to GoLive CyberStudio. Adobe acquired GoLive in 1999 and re-branded the GoLive CyberStudio product to what became Adobe GoLive. Adobe took over the Hamburg office as an Adobe development site to continue to develop the product. At the time of the acquisition, CyberStudio was a Macintosh-only application. In the spring of 1999 Adobe released Adobe GoLive for both Macintosh and Microsoft Windows. The first versions of Dreamweaver and CyberStudio were released in a similar timeframe. However, Dreamweaver eventually became the dominant WYSIWYG HTML editor in market share. After the Adobe acquisition of Macromedia (the company that had owned Dreamweaver), GoLive was progressively re-targeted toward Adobe's traditional design market, and the product became better integrated with Adobe's existing suite of design-oriented software products and less focused on the professional web development market. The Adobe CS2 Premium suite contained GoLive CS2. With the release of Creative Suite 3, Adobe integrated Dreamweaver as a replacement for GoLive and released GoLive 9 as a standalone product. In April 2008, Adobe announced that sales and development of GoLive would cease in favor of Dreamweaver. == General description and distinctive aspects == GoLive incorporated a largely modeless workflow that relied heavily on drag-and-drop. Most user interaction was done via a contextual inspector rather than the modal workflow found in Dreamweaver. Among its features were a separate editor for tables that supported nesting, and a two-dimensional panel for applying CSS styles to elements. GoLive supported drag-and-drop of native Adobe Photoshop and Adobe Illustrator files via what the company called "Smart Objects", which then automatically guided the user through saving those files in web-supported formats. Updates to the original Photoshop or Illustrator assets were automatically tracked by GoLive. It also implemented a tool called "Components" which allowed updates to interface elements throughout a site to be updated globally by changing one single file. As a website management tool, GoLive allowed users to transfer and publish content directly from within the application, and allowed individual files to be excluded from uploading. == Features == One of the new features of GoLive version 5 was Dynamic Link, which was a method of creating dynamic, database-driven web content without the need to know a server-side language and with full WYSIWYG support in the GoLive user interface. GoLive had a powerful set of extensibility API which could be used to add additional functionality to the product. The GoLive SDK provided interfaces which allowed developers to use a combination of XML, JavaScript and C/C++ to create plugins for the product. The extensibility API allowed developers access to custom drawing and event handling using JavaScript, as well as a full JavaScript debugger and command line interpreter. This allowed intermediate-level developers using interpreted JavaScript to create sophisticated user interfaces. == Language and framework structure == Adobe GoLive is coded in the C++ programming language. It uses a custom C++ framework called SCL (Simple Class Library) which was initially built from scratch by the engineers at GoLive Systems Inc. The SCL framework was also used in the short-lived Adobe Atmosphere 3D software. == Release history == As the final version, GoLive 9 was discontinued in April 2008.

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  • Spherical basis

    Spherical basis

    In pure and applied mathematics, particularly quantum mechanics and computer graphics and their applications, a spherical basis is the basis used to express spherical tensors. The spherical basis closely relates to the description of angular momentum in quantum mechanics and spherical harmonic functions. While spherical polar coordinates are one orthogonal coordinate system for expressing vectors and tensors using polar and azimuthal angles and radial distance, the spherical basis are constructed from the standard basis and use complex numbers. == In three dimensions == A vector A in 3D Euclidean space R3 can be expressed in the familiar Cartesian coordinate system in the standard basis ex, ey, ez, and coordinates Ax, Ay, Az: or any other coordinate system with associated basis set of vectors. From this extend the scalars to allow multiplication by complex numbers, so that we are now working in C 3 {\displaystyle \mathbb {C} ^{3}} rather than R 3 {\displaystyle \mathbb {R} ^{3}} . === Basis definition === In the spherical bases denoted e+, e−, e0, and associated coordinates with respect to this basis, denoted A+, A−, A0, the vector A is: where the spherical basis vectors can be defined in terms of the Cartesian basis using complex-valued coefficients in the xy plane: in which i {\displaystyle i} denotes the imaginary unit, and one normal to the plane in the z direction: e 0 = e z {\displaystyle \mathbf {e} _{0}=\mathbf {e} _{z}} The inverse relations are: === Commutator definition === While giving a basis in a 3-dimensional space is a valid definition for a spherical tensor, it only covers the case for when the rank k {\displaystyle k} is 1. For higher ranks, one may use either the commutator, or rotation definition of a spherical tensor. The commutator definition is given below, any operator T q ( k ) {\displaystyle T_{q}^{(k)}} that satisfies the following relations is a spherical tensor: [ J ± , T q ( k ) ] = ℏ ( k ∓ q ) ( k ± q + 1 ) T q ± 1 ( k ) {\displaystyle [J_{\pm },T_{q}^{(k)}]=\hbar {\sqrt {(k\mp q)(k\pm q+1)}}T_{q\pm 1}^{(k)}} [ J z , T q ( k ) ] = ℏ q T q ( k ) {\displaystyle [J_{z},T_{q}^{(k)}]=\hbar qT_{q}^{(k)}} === Rotation definition === Analogously to how the spherical harmonics transform under a rotation, a general spherical tensor transforms as follows, when the states transform under the unitary Wigner D-matrix D ( R ) {\displaystyle {\mathcal {D}}(R)} , where R is a (3×3 rotation) group element in SO(3). That is, these matrices represent the rotation group elements. With the help of its Lie algebra, one can show these two definitions are equivalent. D ( R ) T q ( k ) D † ( R ) = ∑ q ′ = − k k T q ′ ( k ) D q ′ q ( k ) {\displaystyle {\mathcal {D}}(R)T_{q}^{(k)}{\mathcal {D}}^{\dagger }(R)=\sum _{q'=-k}^{k}T_{q'}^{(k)}{\mathcal {D}}_{q'q}^{(k)}} === Coordinate vectors === For the spherical basis, the coordinates are complex-valued numbers A+, A0, A−, and can be found by substitution of (3B) into (1), or directly calculated from the inner product ⟨, ⟩ (5): A 0 = ⟨ e 0 , A ⟩ = ⟨ e z , A ⟩ = A z {\displaystyle A_{0}=\left\langle \mathbf {e} _{0},\mathbf {A} \right\rangle =\left\langle \mathbf {e} _{z},\mathbf {A} \right\rangle =A_{z}} with inverse relations: In general, for two vectors with complex coefficients in the same real-valued orthonormal basis ei, with the property ei·ej = δij, the inner product is: where · is the usual dot product and the complex conjugate must be used to keep the magnitude (or "norm") of the vector positive definite. == Properties (three dimensions) == === Orthonormality === The spherical basis is an orthonormal basis, since the inner product ⟨, ⟩ (5) of every pair vanishes meaning the basis vectors are all mutually orthogonal: ⟨ e + , e − ⟩ = ⟨ e − , e 0 ⟩ = ⟨ e 0 , e + ⟩ = 0 {\displaystyle \left\langle \mathbf {e} _{+},\mathbf {e} _{-}\right\rangle =\left\langle \mathbf {e} _{-},\mathbf {e} _{0}\right\rangle =\left\langle \mathbf {e} _{0},\mathbf {e} _{+}\right\rangle =0} and each basis vector is a unit vector: ⟨ e + , e + ⟩ = ⟨ e − , e − ⟩ = ⟨ e 0 , e 0 ⟩ = 1 {\displaystyle \left\langle \mathbf {e} _{+},\mathbf {e} _{+}\right\rangle =\left\langle \mathbf {e} _{-},\mathbf {e} _{-}\right\rangle =\left\langle \mathbf {e} _{0},\mathbf {e} _{0}\right\rangle =1} hence the need for the normalizing factors of 1 / 2 {\displaystyle 1/\!{\sqrt {2}}} . === Change of basis matrix === The defining relations (3A) can be summarized by a transformation matrix U: ( e + e − e 0 ) = U ( e x e y e z ) , U = ( − 1 2 − i 2 0 + 1 2 − i 2 0 0 0 1 ) , {\displaystyle {\begin{pmatrix}\mathbf {e} _{+}\\\mathbf {e} _{-}\\\mathbf {e} _{0}\end{pmatrix}}=\mathbf {U} {\begin{pmatrix}\mathbf {e} _{x}\\\mathbf {e} _{y}\\\mathbf {e} _{z}\end{pmatrix}}\,,\quad \mathbf {U} ={\begin{pmatrix}-{\frac {1}{\sqrt {2}}}&-{\frac {i}{\sqrt {2}}}&0\\+{\frac {1}{\sqrt {2}}}&-{\frac {i}{\sqrt {2}}}&0\\0&0&1\end{pmatrix}}\,,} with inverse: ( e x e y e z ) = U − 1 ( e + e − e 0 ) , U − 1 = ( − 1 2 + 1 2 0 + i 2 + i 2 0 0 0 1 ) . {\displaystyle {\begin{pmatrix}\mathbf {e} _{x}\\\mathbf {e} _{y}\\\mathbf {e} _{z}\end{pmatrix}}=\mathbf {U} ^{-1}{\begin{pmatrix}\mathbf {e} _{+}\\\mathbf {e} _{-}\\\mathbf {e} _{0}\end{pmatrix}}\,,\quad \mathbf {U} ^{-1}={\begin{pmatrix}-{\frac {1}{\sqrt {2}}}&+{\frac {1}{\sqrt {2}}}&0\\+{\frac {i}{\sqrt {2}}}&+{\frac {i}{\sqrt {2}}}&0\\0&0&1\end{pmatrix}}\,.} It can be seen that U is a unitary matrix, in other words its Hermitian conjugate U† (complex conjugate and matrix transpose) is also the inverse matrix U−1. For the coordinates: ( A + A − A 0 ) = U ∗ ( A x A y A z ) , U ∗ = ( − 1 2 + i 2 0 + 1 2 + i 2 0 0 0 1 ) , {\displaystyle {\begin{pmatrix}A_{+}\\A_{-}\\A_{0}\end{pmatrix}}=\mathbf {U} ^{\mathrm {} }{\begin{pmatrix}A_{x}\\A_{y}\\A_{z}\end{pmatrix}}\,,\quad \mathbf {U} ^{\mathrm {} }={\begin{pmatrix}-{\frac {1}{\sqrt {2}}}&+{\frac {i}{\sqrt {2}}}&0\\+{\frac {1}{\sqrt {2}}}&+{\frac {i}{\sqrt {2}}}&0\\0&0&1\end{pmatrix}}\,,} and inverse: ( A x A y A z ) = ( U ∗ ) − 1 ( A + A − A 0 ) , ( U ∗ ) − 1 = ( − 1 2 + 1 2 0 − i 2 − i 2 0 0 0 1 ) . {\displaystyle {\begin{pmatrix}A_{x}\\A_{y}\\A_{z}\end{pmatrix}}=(\mathbf {U} ^{\mathrm {} })^{-1}{\begin{pmatrix}A_{+}\\A_{-}\\A_{0}\end{pmatrix}}\,,\quad (\mathbf {U} ^{\mathrm {} })^{-1}={\begin{pmatrix}-{\frac {1}{\sqrt {2}}}&+{\frac {1}{\sqrt {2}}}&0\\-{\frac {i}{\sqrt {2}}}&-{\frac {i}{\sqrt {2}}}&0\\0&0&1\end{pmatrix}}\,.} === Cross products === Taking cross products of the spherical basis vectors, we find an obvious relation: e q × e q = 0 {\displaystyle \mathbf {e} _{q}\times \mathbf {e} _{q}={\boldsymbol {0}}} where q is a placeholder for +, −, 0, and two less obvious relations: e ± × e ∓ = ± i e 0 {\displaystyle \mathbf {e} _{\pm }\times \mathbf {e} _{\mp }=\pm i\mathbf {e} _{0}} e ± × e 0 = ± i e ± {\displaystyle \mathbf {e} _{\pm }\times \mathbf {e} _{0}=\pm i\mathbf {e} _{\pm }} === Inner product in the spherical basis === The inner product between two vectors A and B in the spherical basis follows from the above definition of the inner product: ⟨ A , B ⟩ = A + B + ⋆ + A − B − ⋆ + A 0 B 0 ⋆ {\displaystyle \left\langle \mathbf {A} ,\mathbf {B} \right\rangle =A_{+}B_{+}^{\star }+A_{-}B_{-}^{\star }+A_{0}B_{0}^{\star }}

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  • Fully probabilistic design

    Fully probabilistic design

    Decision making (DM) can be seen as a purposeful choice of action sequences. It also covers control, a purposeful choice of input sequences. As a rule, it runs under randomness, uncertainty and incomplete knowledge. A range of prescriptive theories have been proposed how to make optimal decisions under these conditions. They optimise sequence of decision rules, mappings of the available knowledge on possible actions. This sequence is called strategy or policy. Among various theories, Bayesian DM is broadly accepted axiomatically based theory that solves the design of optimal decision strategy. It describes random, uncertain or incompletely known quantities as random variables, i.e. by their joint probability expressing belief in their possible values. The strategy that minimises expected loss (or equivalently maximises expected reward) expressing decision-maker's goals is then taken as the optimal strategy. While the probabilistic description of beliefs is uniquely and deductively driven by rules for joint probabilities, the composition and decomposition of the loss function have no such universally applicable formal machinery. Fully probabilistic design (of decision strategies or control, FPD) removes the mentioned drawback and expresses also the DM goals of by the "ideal" probability, which assigns high (small) values to desired (undesired) behaviours of the closed DM loop formed by the influenced world part and by the used strategy. FPD has axiomatic basis and has Bayesian DM as its restricted subpart. FPD has a range of theoretical consequences , and, importantly, has been successfully used to quite diverse application domains.

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  • Shepp–Logan phantom

    Shepp–Logan phantom

    The Shepp–Logan phantom is a standard test image created by Larry Shepp and Benjamin F. Logan for their 1974 paper "The Fourier Reconstruction of a Head Section". It serves as the model of a human head in the development and testing of image reconstruction algorithms. == Definition == The function describing the phantom is defined as the sum of 10 ellipses inside a 2×2 square:

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  • Phase stretch transform

    Phase stretch transform

    Phase stretch transform (PST) is a computational approach to signal and image processing. One of its utilities is for feature detection and classification. PST is related to time stretch dispersive Fourier transform. It transforms the image by emulating propagation through a diffractive medium with engineered 3D dispersive property (refractive index). The operation relies on symmetry of the dispersion profile and can be understood in terms of dispersive eigenfunctions or stretch modes. PST performs similar functionality as phase-contrast microscopy, but on digital images. PST can be applied to digital images and temporal (time series) data. It is a physics-based feature engineering algorithm. == Operation principle == Here the principle is described in the context of feature enhancement in digital images. The image is first filtered with a spatial kernel followed by application of a nonlinear frequency-dependent phase. The output of the transform is the phase in the spatial domain. The main step is the 2-D phase function which is typically applied in the frequency domain. The amount of phase applied to the image is frequency dependent, with higher amount of phase applied to higher frequency features of the image. Since sharp transitions, such as edges and corners, contain higher frequencies, PST emphasizes the edge information. Features can be further enhanced by applying thresholding and morphological operations. PST is a pure phase operation whereas conventional edge detection algorithms operate on amplitude. == Physical and mathematical foundations of phase stretch transform == Photonic time stretch technique can be understood by considering the propagation of an optical pulse through a dispersive fiber. By disregarding the loss and non-linearity in fiber, the non-linear Schrödinger equation governing the optical pulse propagation in fiber upon integration reduces to: E o ( z , t ) = 1 2 π ∫ − ∞ ∞ E ~ i ( 0 , ω ) ⋅ e − i β 2 z ω 2 2 ⋅ e i ω t d ω {\displaystyle E_{o}(z,t)={\frac {1}{2\pi }}\int _{-\infty }^{\infty }{\tilde {E}}_{i}(0,\omega )\cdot e^{\frac {-i\beta _{2}z\omega ^{2}}{2}}\cdot e^{i\omega {t}}\,d\omega } (1) where β 2 {\displaystyle \beta _{2}} = GVD parameter, z is propagation distance, E o ( z , t ) {\displaystyle E_{o}(z,t)} is the reshaped output pulse at distance z and time t. The response of this dispersive element in the time-stretch system can be approximated as a phase propagator as presented in H ( ω ) = e i φ ( ω ) = e i ∑ m = 0 ∞ φ m ( ω ) = ∏ m = 0 ∞ H m ( ω ) {\displaystyle H(\omega )=e^{i\varphi (\omega )}=e^{i\sum _{m=0}^{\infty }\varphi _{m}(\omega )}=\prod _{m=0}^{\infty }H_{m}(\omega )} (2) Therefore, Eq. 1 can be written as following for a pulse that propagates through the time-stretch system and is reshaped into a temporal signal with a complex envelope given by E o ( t ) = 1 2 π ∫ − ∞ ∞ E ~ i ( ω ) ⋅ H ( ω ) ⋅ e i ω t d ω {\displaystyle E_{o}(t)={\frac {1}{2\pi }}\int _{-\infty }^{\infty }{\tilde {E}}_{i}(\omega )\cdot H(\omega )\cdot e^{i\omega t}\,d\omega } (3) The time stretch operation is formulated as generalized phase and amplitude operations, S { E i ( t ) } = ∫ − ∞ + ∞ F { E i ( t ) } ⋅ e i φ ( ω ) ⋅ L ~ ( ω ) ⋅ e i ω t d ω {\displaystyle \mathbb {S} \{E_{i}(t)\}=\int _{-\infty }^{+\infty }{\mathcal {F}}\{E_{i}(t)\}\cdot e^{i\varphi (\omega )}\cdot {\tilde {L}}(\omega )\cdot e^{i\omega {t}}d\omega } (4) where e i φ ( ω ) {\displaystyle e^{i\varphi (\omega )}} is the phase filter and L ~ ( ω ) {\displaystyle {\tilde {L}}(\omega )} is the amplitude filter. Next the operator is converted to discrete domain, S { E i [ n ] } = 1 N ∑ u = 0 N − 1 F F T { E i ( n ) } ⋅ K ~ ( u ) ⋅ L ~ ( u ) ⋅ e i 2 π N u n {\displaystyle \mathbb {S} \{E_{i}[n]\}={\frac {1}{N}}\sum _{u=0}^{N-1}FFT\{E_{i}(n)\}\cdot {\tilde {K}}(u)\cdot {\tilde {L}}(u)\cdot e^{i{\frac {2\pi }{N}}un}} (5) where u {\displaystyle u} is the discrete frequency, K ~ ( u ) {\displaystyle {\tilde {K}}(u)} is the phase filter, L ~ ( u ) {\displaystyle {\tilde {L}}(u)} is the amplitude filter and FFT is fast Fourier transform. The stretch operator S { } {\displaystyle \mathbb {S} \{\}} for a digital image is then S { E i [ n , m ] } = 1 M N ∑ v = 0 N − 1 ∑ u = 0 M − 1 F F T 2 { E i ( n , m ) } ⋅ K ~ ( u , v ) ⋅ L ~ ( u , v ) ⋅ e i 2 π M u m ⋅ e i 2 π N v n {\displaystyle \mathbb {S} \{E_{i}[n,m]\}={\frac {1}{MN}}\sum _{v=0}^{N-1}\sum _{u=0}^{M-1}FFT^{2}\{E_{i}(n,m)\}\cdot {\tilde {K}}(u,v)\cdot {\tilde {L}}(u,v)\cdot e^{i{\frac {2\pi }{M}}um}\cdot e^{i{\frac {2\pi }{N}}vn}} (6) In the above equations, E i [ n , m ] {\displaystyle E_{i}[n,m]} is the input image, n {\displaystyle n} and m {\displaystyle m} are the spatial variables, F F T 2 {\displaystyle FFT^{2}} is the two-dimensional fast Fourier transform, and u {\displaystyle u} and v {\displaystyle v} are spatial frequency variables. The function K ~ ( u , v ) {\displaystyle {\tilde {K}}(u,v)} is the warped phase kernel and the function L ~ ( u , v ) {\displaystyle {\tilde {L}}(u,v)} is a localization kernel implemented in frequency domain. PST operator is defined as the phase of the Warped Stretch Transform output as follows P S T { E i [ n , m ] } ≜ ∡ { S { E i [ x , y ] } } {\displaystyle PST\{E_{i}[n,m]\}\triangleq \measuredangle \{\mathbb {S} \{E_{i}[x,y]\}\}} (7) where ∡ { } {\displaystyle \measuredangle \{\}} is the angle operator. == PST kernel implementation == The warped phase kernel K ~ ( u , v ) {\displaystyle {\tilde {K}}(u,v)} can be described by a nonlinear frequency dependent phase K ~ ( u , v ) = e i φ ( u , v ) {\displaystyle {\tilde {K}}(u,v)=e^{i\varphi (u,v)}} While arbitrary phase kernels can be considered for PST operation, here we study the phase kernels for which the kernel phase derivative is a linear or sublinear function with respect to frequency variables. A simple example for such phase derivative profiles is the inverse tangent function. Consider the phase profile in the polar coordinate system φ ( u , v ) = φ polar ( r , θ ) = φ polar ( r ) {\displaystyle \varphi (u,v)=\varphi _{\text{polar}}(r,\theta )=\varphi _{\text{polar}}(r)} From d φ ( r ) d r = tan − 1 ⁡ ( r ) {\displaystyle {\frac {d\varphi (r)}{dr}}=\tan ^{-1}(r)} we have φ ( r ) = r tan − 1 ⁡ ( r ) − 1 2 log ⁡ ( r 2 + 1 ) {\displaystyle \varphi (r)=r\tan ^{-1}(r)-{\frac {1}{2}}\log(r^{2}+1)} Therefore, the PST kernel is implemented as φ ( r ) = S ⋅ ( W r ) ⋅ tan − 1 ⁡ ( W r ) − 1 2 log ⁡ ( 1 + ( W r ) 2 ) ( W r max ) ⋅ tan − 1 ⁡ ( W r max ) − 1 2 log ⁡ ( 1 + ( W r max ) 2 ) {\displaystyle \varphi (r)=S\cdot {\frac {(Wr)\cdot \tan ^{-1}(Wr)-{\frac {1}{2}}\log(1+(Wr)^{2})}{(Wr_{\max })\cdot \tan ^{-1}(Wr_{\max })-{\frac {1}{2}}\log(1+(Wr_{\max })^{2})}}} where S {\displaystyle S} and W {\displaystyle W} are real-valued numbers related to the strength and warp of the phase profile == Applications == PST has been used for edge detection in biological and biomedical images as well as synthetic-aperture radar (SAR) image processing, as well as detail and feature enhancement for digital images. PST has also been applied to improve the point spread function for single molecule imaging in order to achieve super-resolution. The transform exhibits intrinsic superior properties compared to conventional edge detectors for feature detection in low contrast visually impaired images. The PST function can also be performed on 1-D temporal waveforms in the analog domain to reveal transitions and anomalies in real time. == Open source code release == On February 9, 2016, a UCLA Engineering research group has made public the computer code for PST algorithm that helps computers process images at high speeds and "see" them in ways that human eyes cannot. The researchers say the code could eventually be used in face, fingerprint, and iris recognition systems for high-tech security, as well as in self-driving cars' navigation systems or for inspecting industrial products. The Matlab implementation for PST can also be downloaded from Matlab Files Exchange. However, it is provided for research purposes only, and a license must be obtained for any commercial applications. The software is protected under a US patent. The code was then significantly refactored and improved to support GPU acceleration. In May 2022, it became one algorithm in PhyCV: the first physics-inspired computer vision library.

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  • Office automation

    Office automation

    Office automation refers to the varied computer machinery and software used to digitally create, collect, store, manipulate, and relay office information needed for accomplishing basic tasks. Raw data storage, electronic transfer, and the management of electronic business information comprise the basic activities of an office automation system. Office automation helps in optimizing or automating existing office procedures. The backbone of office automation is a local area network, which allows users to transfer data, mail and voice across the network. All office functions, including dictation, typing, filing, copying, fax, telex, microfilm and records management, telephone and telephone switchboard operations, fall into this category. Office automation was a popular term in the 1970s and 1980s as the desktop computer exploded onto the scene. Advantages of office automation include that it can get many tasks accomplished faster, it eliminates the need for a large staff, less storage is required to store data, and multiple people can update data simultaneously in the event of changes in schedule. == Outline == Businesses can easily purchase and stock their wares with the aid of technology. Many of the manual tasks that used to be done by hand can now be done through hand held devices and UPC and SKU coding. In the retail setting, automation also increases choice. Customers can easily process their payments through automated credit card machines and no longer have to wait in line for an employee to process and manually type in the credit card numbers. Office payrolls have been automated, which means no one has to manually cut checks, and those checks that are cut can be printed through computer programs. Direct deposit can be automatically set up and this further reduces the manual process, and most employees who participate in direct deposit often find their paychecks come earlier than if they'd have to wait for their checks to be written and then cleared by the bank. Other ways automation has reduced employee manpower on tasks is automated voice direction. Through the use of prompts, automated phone menus and directed calls, the need for employees to be dedicated to answer the phones has been reduced, and in some cases, eliminated.

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