AI Face Changer Video

AI Face Changer Video — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Data preprocessing

  Data preprocessing can refer to manipulation, filtration or augmentation of data before it is analyzed, and is often an important step in the data mining process. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues. Preprocessing is the process by which unstructured data is transformed into intelligible representations suitable for machine-learning models. This phase of model deals with noise in order to arrive at better and improved results from the original data set which was noisy. This dataset also has some level of missing value present in it. The preprocessing pipeline used can often have large effects on the conclusions drawn from the downstream analysis. Thus, representation and quality of data is necessary before running any analysis. If there is a high proportion of irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase may be more difficult. Data preparation and filtering steps can take a considerable amount of processing time. Examples of methods used in data preprocessing include cleaning, instance selection, normalization, one-hot encoding, data transformation, feature extraction and feature selection. == Applications == === Data mining === Data preprocessing allows for the removal of unwanted data with the use of data cleaning, this allows the user to have a dataset to contain more valuable information after the preprocessing stage for data manipulation later in the data mining process. Editing such dataset to either correct data corruption or human error is a crucial step to get accurate quantifiers like true positives, true negatives, false positives and false negatives found in a confusion matrix that are commonly used for a medical diagnosis. Users are able to join data files together and use preprocessing to filter any unnecessary noise from the data which can allow for higher accuracy. Users use Python programming scripts accompanied by the pandas library which gives them the ability to import data from a comma-separated values as a data-frame. The data-frame is then used to manipulate data that can be challenging otherwise to do in Excel. Pandas (software) which is a powerful tool that allows for data analysis and manipulation; which makes data visualizations, statistical operations and much more, a lot easier. Many also use the R programming language to do such tasks as well. The reason why a user transforms existing files into a new one is because of many reasons. Aspects of data preprocessing may include imputing missing values, aggregating numerical quantities and transforming continuous data into categories (data binning). More advanced techniques like principal component analysis and feature selection are working with statistical formulas and are applied to complex datasets which are recorded by GPS trackers and motion capture devices. === Semantic data preprocessing === Semantic data mining is a subset of data mining that specifically seeks to incorporate domain knowledge, such as formal semantics, into the data mining process. Domain knowledge is the knowledge of the environment the data was processed in. Domain knowledge can have a positive influence on many aspects of data mining, such as filtering out redundant or inconsistent data during the preprocessing phase. Domain knowledge also works as constraint. It does this by using working as set of prior knowledge to reduce the space required for searching and acting as a guide to the data. Simply put, semantic preprocessing seeks to filter data using the original environment of said data more correctly and efficiently. There are increasingly complex problems which are asking to be solved by more elaborate techniques to better analyze existing information. Instead of creating a simple script for aggregating different numerical values into a single value, it make sense to focus on semantic based data preprocessing. The idea is to build a dedicated ontology, which explains on a higher level what the problem is about. In regards to semantic data mining and semantic pre-processing, ontologies are a way to conceptualize and formally define semantic knowledge and data. The Protégé (software) is the standard tool for constructing an ontology. In general, the use of ontologies bridges the gaps between data, applications, algorithms, and results that occur from semantic mismatches. As a result, semantic data mining combined with ontology has many applications where semantic ambiguity can impact the usefulness and efficiency of data systems. Applications include the medical field, language processing, banking, and even tutoring, among many more. There are various strengths to using a semantic data mining and ontological based approach. As previously mentioned, these tools can help during the per-processing phase by filtering out non-desirable data from the data set. Additionally, well-structured formal semantics integrated into well designed ontologies can return powerful data that can be easily read and processed by machines. A specifically useful example of this exists in the medical use of semantic data processing. As an example, a patient is having a medical emergency and is being rushed to hospital. The emergency responders are trying to figure out the best medicine to administer to help the patient. Under normal data processing, scouring all the patient’s medical data to ensure they are getting the best treatment could take too long and risk the patients’ health or even life. However, using semantically processed ontologies, the first responders could save the patient’s life. Tools like a semantic reasoner can use ontology to infer the what best medicine to administer to the patient is based on their medical history, such as if they have a certain cancer or other conditions, simply by examining the natural language used in the patient's medical records. This would allow the first responders to quickly and efficiently search for medicine without having worry about the patient’s medical history themselves, as the semantic reasoner would already have analyzed this data and found solutions. In general, this illustrates the incredible strength of using semantic data mining and ontologies. They allow for quicker and more efficient data extraction on the user side, as the user has fewer variables to account for, since the semantically pre-processed data and ontology built for the data have already accounted for many of these variables. However, there are some drawbacks to this approach. Namely, it requires a high amount of computational power and complexity, even with relatively small data sets. This could result in higher costs and increased difficulties in building and maintaining semantic data processing systems. This can be mitigated somewhat if the data set is already well organized and formatted, but even then, the complexity is still higher when compared to standard data processing. Below is a simple a diagram combining some of the processes, in particular semantic data mining and their use in ontology. The diagram depicts a data set being broken up into two parts: the characteristics of its domain, or domain knowledge, and then the actual acquired data. The domain characteristics are then processed to become user understood domain knowledge that can be applied to the data. Meanwhile, the data set is processed and stored so that the domain knowledge can applied to it, so that the process may continue. This application forms the ontology. From there, the ontology can be used to analyze data and process results. Fuzzy preprocessing is another, more advanced technique for solving complex problems. Fuzzy preprocessing and fuzzy data mining make use of fuzzy sets. These data sets are composed of two elements: a set and a membership function for the set which comprises 0 and 1. Fuzzy preprocessing uses this fuzzy data set to ground numerical values with linguistic information. Raw data is then transformed into natural language. Ultimately, fuzzy data mining's goal is to help deal with inexact information, such as an incomplete database. Currently fuzzy preprocessing, as well as other fuzzy based data mining techniques see frequent use with neural networks and artificial intelligence.

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  YrWall

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

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  Voice user interface

  A voice user interface (VUI) enables spoken human interaction with computers, using speech recognition to understand spoken commands and answer questions, and typically text to speech to play a reply. A voice command device is a device controlled with a voice user interface. Voice user interfaces have been added to automobiles, home automation systems, computer operating systems, home appliances like washing machines and microwave ovens, and television remote controls. They are the primary way of interacting with virtual assistants on smartphones and smart speakers. Older automated attendants (which route phone calls to the correct extension) and interactive voice response systems (which conduct more complicated transactions over the phone) can respond to the pressing of keypad buttons via DTMF tones, but those with a full voice user interface allow callers to speak requests and responses without having to press any buttons. Newer voice command devices are speaker-independent, so they can respond to multiple voices, regardless of accent or dialectal influences. They are also capable of responding to several commands at once, separating vocal messages, and providing appropriate feedback, accurately imitating a natural conversation. == Overview == A VUI is the interface to any speech application. Only a short time ago, controlling a machine by simply talking to it was only possible in science fiction. Until recently, this area was considered to be artificial intelligence. However, advances in technologies like text-to-speech, speech-to-text, natural language processing, and cloud services contributed to the mass adoption of these types of interfaces. VUIs have become more commonplace, and people are taking advantage of the value that these hands-free, eyes-free interfaces provide in many situations. VUIs rely on the ability to process input reliably, inconsistent performance often leads to decreased user engagement and negative feedback. Designing a good VUI requires interdisciplinary talents of computer science, linguistics and human factors such as psychology. Even with advanced development tools, constructing an effective VUI requires understanding of both the tasks to be performed, as well as the target audience that will use the final system. The closer the VUI matches the user's mental model of the task, the easier it will be to use with little or no training, resulting in both higher efficiency and higher user satisfaction. A VUI designed for the general public should emphasize ease of use and provide a lot of help and guidance for first-time callers. In contrast, a VUI designed for a small group of power users (including field service workers), should focus more on productivity and less on help and guidance. Such applications should streamline the call flows, minimize prompts, eliminate unnecessary iterations and allow elaborate "mixed initiative dialogs", which enable callers to enter several pieces of information in a single utterance and in any order or combination. In short, speech applications have to be carefully crafted for the specific business process that is being automated. Not all business processes render themselves equally well for speech automation. In general, the more complex the inquiries and transactions are, the more challenging they will be to automate, and the more likely they will be to fail with the general public. In some scenarios, automation is simply not applicable, so live agent assistance is the only option. A legal advice hotline, for example, would be very difficult to automate. On the flip side, speech is perfect for handling quick and routine transactions, like changing the status of a work order, completing a time or expense entry, or transferring funds between accounts. == History == Early applications for VUI included voice-activated dialing of phones, either directly or through a (typically Bluetooth) headset or vehicle audio system. In 2007, a CNN business article reported that voice command was over a billion dollar industry and that companies like Google and Apple were trying to create speech recognition features. In the years since the article was published, the world has witnessed a variety of voice command devices. Additionally, Google has created a speech recognition engine called Pico TTS and Apple released Siri. Voice command devices are becoming more widely available, and innovative ways for using the human voice are always being created. For example, Business Week suggests that the future remote controller is going to be the human voice. Currently Xbox Live allows such features and Jobs hinted at such a feature on the new Apple TV. == Voice command software products on computing devices == Both Apple Mac and Windows PC provide built in speech recognition features for their latest operating systems. === Microsoft Windows === Two Microsoft operating systems, Windows 7 and Windows Vista, provide speech recognition capabilities. Microsoft integrated voice commands into their operating systems to provide a mechanism for people who want to limit their use of the mouse and keyboard, but still want to maintain or increase their overall productivity. ==== Windows Vista ==== With Windows Vista voice control, a user may dictate documents and emails in mainstream applications, start and switch between applications, control the operating system, format documents, save documents, edit files, efficiently correct errors, and fill out forms on the Web. The speech recognition software learns automatically every time a user uses it, and speech recognition is available in English (U.S.), English (U.K.), German (Germany), French (France), Spanish (Spain), Japanese, Chinese (Traditional), and Chinese (Simplified). In addition, the software comes with an interactive tutorial, which can be used to train both the user and the speech recognition engine. ==== Windows 7 ==== In addition to all the features provided in Windows Vista, Windows 7 provides a wizard for setting up the microphone and a tutorial on how to use the feature. ==== Mac OS X ==== All Mac OS X computers come pre-installed with the speech recognition software. The software is user-independent, and it allows for a user to, "navigate menus and enter keyboard shortcuts; speak checkbox names, radio button names, list items, and button names; and open, close, control, and switch among applications." However, the Apple website recommends a user buy a commercial product called Dictate. === Commercial products === If a user is not satisfied with the built in speech recognition software or a user does not have a built speech recognition software for their OS, then a user may experiment with a commercial product such as Braina Pro or DragonNaturallySpeaking for Windows PCs, and Dictate, the name of the same software for Mac OS. == Voice command mobile devices == Any mobile device running Android OS, Microsoft Windows Phone, iOS 9 or later, or Blackberry OS provides voice command capabilities. In addition to the built-in speech recognition software for each mobile phone's operating system, a user may download third party voice command applications from each operating system's application store: Apple App store, Google Play, Windows Phone Marketplace (initially Windows Marketplace for Mobile), or BlackBerry App World. === Android OS === Google has developed an open source operating system called Android, which allows a user to perform voice commands such as: send text messages, listen to music, get directions, call businesses, call contacts, send email, view a map, go to websites, write a note, and search Google. The speech recognition software is available for all devices since Android 2.2 "Froyo", but the settings must be set to English. Google allows for the user to change the language, and the user is prompted when he or she first uses the speech recognition feature if he or she would like their voice data to be attached to their Google account. If a user decides to opt into this service, it allows Google to train the software to the user's voice. Google introduced the Google Assistant with Android 7.0 "Nougat". It is much more advanced than the older version. Amazon.com has the Echo that uses Amazon's custom version of Android to provide a voice interface. === Microsoft Windows === Windows Phone is Microsoft's mobile device's operating system. On Windows Phone 7.5, the speech app is user independent and can be used to: call someone from your contact list, call any phone number, redial the last number, send a text message, call your voice mail, open an application, read appointments, query phone status, and search the web. In addition, speech can also be used during a phone call, and the following actions are possible during a phone call: press a number, turn the speaker phone on, or call someone, which puts the current call on hold. Windows 10 introduces Cortana, a voice control system that replaces the formerly used voice control on Windows

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  Desktop video

  Desktop video refers to a phenomenon lasting from the mid-1980s to the early 1990s when the graphics capabilities of personal computers such as the Amiga, Macintosh II, and specially-upgraded IBM PC compatibles had advanced to the point where individuals and local broadcasters could use them for analog non-linear editing and vision mixing in video production. Despite the use of computers, desktop video should not be confused with digital video since the video data remained analog, and it uses items like a VCR and a camcorder to record the video. Full-screen, full-motion video's vast storage requirements meant that the promise of digital encoding would not be realized on desktop computers for at least another decade. == Description == There were multiple models of genlock cards available to synchronize the content; the Newtek Video Toaster was commonly used in Amiga in countries that used NTSC (PAL-M in Brazil), while PCs had Truevision and Matrox Illuminator cards and Mac systems had the SuperMac Video Spigot and Radius VideoVision cards. Apple later introduced the Macintosh Quadra 840AV and Centris 660AV systems to specifically address this market. Desktop video was a parallel development to desktop publishing and enabled many small production houses and local TV stations to produce their own original content for the first time. Along with the advent of public-access cable channels, desktop video meant that television advertising became affordable for local businesses such as retailers, restaurants, real estate agents, contractors and auto dealers. As with the phrase desktop publishing, use of the term died out as the technologies to which it referred become the norm for any kind of video production.

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  Scale space

  Scale-space theory is a framework for multi-scale signal representation developed by the computer vision, image processing and signal processing communities with complementary motivations from physics and biological vision. It is a formal theory for handling image structures at different scales, by representing an image as a one-parameter family of smoothed images, the scale-space representation, parametrized by the size of the smoothing kernel used for suppressing fine-scale structures. The parameter t {\displaystyle t} in this family is referred to as the scale parameter, with the interpretation that image structures of spatial size smaller than about t {\displaystyle {\sqrt {t}}} have largely been smoothed away in the scale-space level at scale t {\displaystyle t} . The main type of scale space is the linear (Gaussian) scale space, which has wide applicability as well as the attractive property of being possible to derive from a small set of scale-space axioms. The corresponding scale-space framework encompasses a theory for Gaussian derivative operators, which can be used as a basis for expressing a large class of visual operations for computerized systems that process visual information. This framework also allows visual operations to be made scale invariant, which is necessary for dealing with the size variations that may occur in image data, because real-world objects may be of different sizes and in addition the distance between the object and the camera may be unknown and may vary depending on the circumstances. == Definition == The notion of scale space applies to signals of arbitrary numbers of variables. The most common case in the literature applies to two-dimensional images, which is what is presented here. Consider a given image f {\displaystyle f} where f ( x , y ) {\displaystyle f(x,y)} is the greyscale value of the pixel at position ( x , y ) {\displaystyle (x,y)} . The linear (Gaussian) scale-space representation of f {\displaystyle f} is a family of derived signals L ( x , y ; t ) {\displaystyle L(x,y;t)} defined by the convolution of f ( x , y ) {\displaystyle f(x,y)} with the two-dimensional Gaussian kernel g ( x , y ; t ) = 1 2 π t e − ( x 2 + y 2 ) / 2 t {\displaystyle g(x,y;t)={\frac {1}{2\pi t}}e^{-(x^{2}+y^{2})/2t}\,} such that L ( ⋅ , ⋅ ; t ) = g ( ⋅ , ⋅ ; t ) ∗ f ( ⋅ , ⋅ ) , {\displaystyle L(\cdot ,\cdot ;t)\ =g(\cdot ,\cdot ;t)f(\cdot ,\cdot ),} where the semicolon in the argument of L {\displaystyle L} implies that the convolution is performed only over the variables x , y {\displaystyle x,y} , while the scale parameter t {\displaystyle t} after the semicolon just indicates which scale level is being defined. This definition of L {\displaystyle L} works for a continuum of scales t ≥ 0 {\displaystyle t\geq 0} , but typically only a finite discrete set of levels in the scale-space representation would be actually considered. The scale parameter t = σ 2 {\displaystyle t=\sigma ^{2}} is the variance of the Gaussian filter and as a limit for t = 0 {\displaystyle t=0} the filter g {\displaystyle g} becomes an impulse function such that L ( x , y ; 0 ) = f ( x , y ) , {\displaystyle L(x,y;0)=f(x,y),} that is, the scale-space representation at scale level t = 0 {\displaystyle t=0} is the image f {\displaystyle f} itself. As t {\displaystyle t} increases, L {\displaystyle L} is the result of smoothing f {\displaystyle f} with a larger and larger filter, thereby removing more and more of the details that the image contains. Since the standard deviation of the filter is σ = t {\displaystyle \sigma ={\sqrt {t}}} , details that are significantly smaller than this value are to a large extent removed from the image at scale parameter t {\displaystyle t} , see the following figures and for graphical illustrations. === Why a Gaussian filter? === When faced with the task of generating a multi-scale representation one may ask: could any filter g of low-pass type and with a parameter t which determines its width be used to generate a scale space? The answer is no, as it is of crucial importance that the smoothing filter does not introduce new spurious structures at coarse scales that do not correspond to simplifications of corresponding structures at finer scales. In the scale-space literature, a number of different ways have been expressed to formulate this criterion in precise mathematical terms. The conclusion from several different axiomatic derivations that have been presented is that the Gaussian scale space constitutes the canonical way to generate a linear scale space, based on the essential requirement that new structures must not be created when going from a fine scale to any coarser scale. Conditions, referred to as scale-space axioms, that have been used for deriving the uniqueness of the Gaussian kernel include linearity, shift invariance, semi-group structure, non-enhancement of local extrema, scale invariance and rotational invariance. In the works, the uniqueness claimed in the arguments based on scale invariance has been criticized, and alternative self-similar scale-space kernels have been proposed. The Gaussian kernel is, however, a unique choice according to the scale-space axiomatics based on causality or non-enhancement of local extrema. === Alternative definition === Equivalently, the scale-space family can be defined as the solution of the diffusion equation (for example in terms of the heat equation), ∂ t L = 1 2 ∇ 2 L , {\displaystyle \partial _{t}L={\frac {1}{2}}\nabla ^{2}L,} with initial condition L ( x , y ; 0 ) = f ( x , y ) {\displaystyle L(x,y;0)=f(x,y)} . This formulation of the scale-space representation L means that it is possible to interpret the intensity values of the image f as a "temperature distribution" in the image plane and that the process that generates the scale-space representation as a function of t corresponds to heat diffusion in the image plane over time t (assuming the thermal conductivity of the material equal to the arbitrarily chosen constant ⁠1/2⁠). Although this connection may appear superficial for a reader not familiar with differential equations, it is indeed the case that the main scale-space formulation in terms of non-enhancement of local extrema is expressed in terms of a sign condition on partial derivatives in the 2+1-D volume generated by the scale space, thus within the framework of partial differential equations. Furthermore, a detailed analysis of the discrete case shows that the diffusion equation provides a unifying link between continuous and discrete scale spaces, which also generalizes to nonlinear scale spaces, for example, using anisotropic diffusion. Hence, one may say that the primary way to generate a scale space is by the diffusion equation, and that the Gaussian kernel arises as the Green's function of this specific partial differential equation. == Motivations == The motivation for generating a scale-space representation of a given data set originates from the basic observation that real-world objects are composed of different structures at different scales. This implies that real-world objects, in contrast to idealized mathematical entities such as points or lines, may appear in different ways depending on the scale of observation. For example, the concept of a "tree" is appropriate at the scale of meters, while concepts such as leaves and molecules are more appropriate at finer scales. For a computer vision system analysing an unknown scene, there is no way to know a priori what scales are appropriate for describing the interesting structures in the image data. Hence, the only reasonable approach is to consider descriptions at multiple scales in order to be able to capture the unknown scale variations that may occur. Taken to the limit, a scale-space representation considers representations at all scales. Another motivation to the scale-space concept originates from the process of performing a physical measurement on real-world data. In order to extract any information from a measurement process, one has to apply operators of non-infinitesimal size to the data. In many branches of computer science and applied mathematics, the size of the measurement operator is disregarded in the theoretical modelling of a problem. The scale-space theory on the other hand explicitly incorporates the need for a non-infinitesimal size of the image operators as an integral part of any measurement as well as any other operation that depends on a real-world measurement. There is a close link between scale-space theory and biological vision. Many scale-space operations show a high degree of similarity with receptive field profiles recorded from the mammalian retina and the first stages in the visual cortex. In these respects, the scale-space framework can be seen as a theoretically well-founded paradigm for early vision, which in addition has been thoroughly tested by algorithms and experiments. == Gaussian derivatives == At any scale in scale space, we c

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  Firefox Lockwise

  Firefox Lockwise (formerly Lockbox) is a deprecated password manager for the Firefox web browser, as well as the mobile operating systems iOS and Android. On desktop, Lockwise was simply part of Firefox, whereas on iOS and Android it was available as a standalone app. If Firefox Sync was activated (with a Firefox account), then Lockwise synced passwords between Firefox installations across devices. It also featured a built-in random password generator. The application and branding have since been "phased out." == History == Developed by Mozilla, it was originally named Firefox Lockbox in 2018. It was renamed "Lockwise" in May 2019. It was introduced for iOS on 10 July 2018 as part of the Test Pilot program. On 26 March 2019, it was released for Android. On desktop, Lockwise started out as a browser addon. Alphas were released between March and August 2019. Since Firefox version 70, Lockwise has been integrated into the browser (accessible at about:logins), having replaced a basic password manager presented in a popup window. Mozilla ended support for Firefox Lockwise on December 13, 2021. As of January 2026, Lockwise is still fully functional on Android to this day.

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  Avid DS

  Avid DS (which was called Avid DS Nitris until early 2008) is a high-end offline and finishing system comprising a non-linear editing system and visual effects software. It was developed by Softimage (this company was owned by Microsoft at the time of DS v1.0's launch before being acquired from Microsoft by Avid Technology, Inc. shortly thereafter) in Montreal. DS was discontinued on September 30, 2013 with support ending on the same date the following year. == Software == DS was called ‘Digital Studio’ in development. It was envisioned to be a complete platform for video/audio work. The first previews of the system were on the SGI platform, but this version was never released. The system was rewritten on Windows NT with different video hardware platforms (Matrox DigiSuite or Play Trinity running on a NetPower system) before the final system was released on Intergraph/StudioZ hardware in January 1998. After its acquisition by Avid, DS was always positioned as a high end video finishing tool. However, many users found it to be uniquely soup-to-nuts in its capabilities. From version 1.0 of the product, it competed with products like Autodesk Smoke, Quantel and Avid Symphony. The toolset in DS offered video timeline editing, an object-oriented vector-based paint tool, 2D layer compositing, sample based audio and starting with version 3.01 of the product, a 3D environment. Originally, a subset of the Softimage|XSI 3D software was planned to become part of the DS toolset, both were built on the same software foundation, but over time the code bases divided between the applications and the integration never happened. While the first version of the DS still lacked a few key features (no 3D, poor keying, no real-time effects), it had some significant features compared to the competing products at the time. It offered a large number of built in effects. Avid OMF import was available, positioning Softimage DS as a strong finishing tool for then typical off-line Avid systems. Lastly the integration of the toolset of Softimage DS was beyond what other product offered. A Softimage DS user could quickly go from editing, to paint, to compositing with a few mouse clicks all inside the same interface. Some of the lacking features were quickly resolved, within months of version 1.0 a new chroma keyer was released. Early versions of the software (up thru 4.0) added additional key features. Development continued with one of the first uncompressed HD editing systems (version 4.01) and an attempt to make the system more friendly to Media Composer editors in version 6. In later versions (v7.5 on beyond) DS was criticized for slow development of compositing tools, mainly lack of a new 3D environment and better tracking tools. Many DS users felt that Avid had not been giving DS the attention that it deserved. On July 7, 2013, Avid sent out an email marking the end of life of the DS product. "To Our Avid DS customers, We are writing to inform you that Avid will be realigning our business strategy to focus on a core suite of products to best leverage our developmental and creative resources. As part of this transition, we will be ceasing future development of Avid DS with a final sale date of September 30th, 2013" == Hardware == Up until version 10.5, DS was sold as a turn-key system; the software was not available without purchasing CPU, I/O and storage hardware from Avid. Beginning with 10.5, customers were able to configure their own systems using widely available components, based on recommended system requirements. In turn-key systems, there were many hardware refreshes over time. StudioZ single stream: Intergraph TDZ-425 with 30 minutes of uncompressed SCSI storage. CPUs at the time were Pentium II/300 MHz. StudioZ dual stream: Intergraph TDZ-2000 GT1 with one hour of fibre channel storage. CPUs on first systems were Pentium II/400 MHz, but last shipping systems had Pentium III/1 GHz. DS was one of the first applications to show that real-time effects could be processed with just the CPUs of the system, not requiring special video cards with real-time effect hardware. Equinox: Developed by Avid, it was one of the first uncompressed HD video cards available. Systems were available on CPUs from Pentium III/1 GHz to Pentium 4/2.8 GHz. Storage was typically SCSI, but fibre channel was also supported. Nitris DNA: Developed by Avid, the Nitris hardware was probably the largest hardware update to the system since it was released. 10-bit HD and SD support was standard. Real-time down and cross convert. This was the only hardware for DS that had on-board effect processing. This allowed a system at the time to play back dual-stream uncompressed HD effects in real-time at 16-bit precision. This was also the first hardware from Avid to support the DNxHD codec. Starting with Pentium 4, Intel Core Xeons were supported. SCSI storage was primarily used. AJA Video Systems: First available as a 4:4:4 option to be used in conjunction with Nitris hardware. Final-generation DS systems used the AJA Video Systems Kona 3 (Xena 2K) card as the only I/O for the system. The last systems shipped with two Intel Core Xeon 6-core processors. SAS is the recommended storage for these systems. == History ==

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  ALL-IN-1

  ALL-IN-1 was an office automation product developed and sold by Digital Equipment Corporation in the 1980s. It was one of the first purchasable off the shelf electronic mail products. It was later known as Office Server V3.2 for OpenVMS Alpha and OpenVMS VAX systems before being discontinued. == Overview == ALL-IN-1 was advertised as an office automation system including functionality in Electronic Messaging, Word Processing and Time Management. It offered an application development platform and customization capabilities that ranged from scripting to code-level integration. ALL-IN-1 was designed and developed by Skip Walter, John Churin and Marty Skinner from Digital Equipment Corporation who began work in 1977. Sheila Chance was hired as the software engineering manager in 1981. The first version of the software, called CP/OSS, the Charlotte Package of Office System Services, named after the location of the developers, was released in May 1982. In 1983, the product was renamed ALL-IN-1 and the Charlotte group continued to develop versions 1.1 through 1.3. Digital then made the decision to move most of the development activity to its central engineering facility in Reading, United Kingdom, where a group there took responsibility for the product from version 2.0 (released in field test in 1984 and to customers in 1985) onward. The Charlotte group continued to work on the Time Management subsystem until version 2.3 and other contributions were made from groups based in Sophia Antipolis, France (System for Customization Management and the integration with VAX Notes), Reading (Message Router and MAILbus), and Nashua, New Hampshire (FMS). ALL-IN-1 V3.0 introduced shared file cabinets and the File Cabinet Server (FCS) to lay the foundation for an eventual integration with TeamLinks, Digital's PC office client. Previous integrations with PCs included PC ALL-IN-1, a DOS-based product introduced in 1989 that never proved popular with customers. Bob Wyman was the first product manager. He oversaw the growth of the product culminating in over $2 billion per year in revenue and market leadership in the proprietary office automation sector. Other consultants from Digital Equipment Corporation involved include Frank Nicodem, Donald Vickers and Tony Redmond.

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  Night Sky (app)

  Night Sky (app) is an application developed and published by indie studio iCandi Apps Ltd. from the UK. Night Sky is a stargazing reference app, where the user can explore a virtual representation of the night sky to identify stars, planets, constellations and satellites. The app is developed specifically for iOS, tvOS and watchOS devices. Night Sky was first released on November 1, 2011 for iOS, and has had multiple updates since launch. Night Sky was mentioned in the September 2016 Apple Keynote during the Apple Watch Series 2 announcement. In October 2016, Night Sky was featured as the Free App of The Week on the Apple App Store. == Reception == Night Sky was featured in Apple's 'Best of 2012' and has also been pre-installed onto iPads in Apple retail stores worldwide.

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  CinePlayer

  CinePlayer is a software based media player used to review Digital Cinema Packages (DCP) without the need for a digital cinema server by Doremi Labs. CinePlayer can play back any DCP, not just those created by Doremi Mastering products. In addition to playing DCPs, CinePlayer can also playback JPEG2000 image sequences and many popular multimedia file types. There are two versions of CinePlayer available, standard and Pro. The standard version supports playback of non-encrypted, 2D DCP's up to 2K resolution. The Pro version supports playback of encrypted, 2D or 3D DCP's with subtitles up to 4K resolution. == Supported formats == === Containers === AVI MOV MXF MPG TS WMV M2TS MTS MP4 MKV === Video codecs === JPEG2000 ProRes 422 DNxHD YUV Uncompressed 8-10 bits DIVX XVID MPEG4 AVC / H-264 VC-1 MPEG2 === Supported image sequences === BMP TIFF TGA DPX JPG J2C === Supported audio files === WAV MP3 WMA MP2

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  Blackmagic Design

  Blackmagic Design Pty Ltd is an Australian company that develops digital cinema technology and manufactures professional video production hardware and software. Headquartered in South Melbourne, it is known for producing high-end digital movie cameras and a range of broadcast and post-production equipment. The company also develops software applications, including the DaVinci Resolve application for non-linear video editing, color correction, color grading, visual effects, and audio post-production. == History == Blackmagic Design Pty Ltd was founded on 7 September 2001 by Grant Petty. Its first product, DeckLink, introduced in 2002, was a video capture card for macOS that supported uncompressed 10-bit video, marking a shift toward professional-grade yet affordable video workflows. Subsequent versions—including the DeckLink 2, Pro SDI, HD Plus, and Multibridge—added capabilities such as color correction, Windows support, and compatibility with major editing software like Adobe Premiere Pro, to broaden the product's appeal. At the 2012 NAB Show, Blackmagic announced its first Cinema Camera, a digital movie camera. Blackmagic made several acquisitions over the next decade. In 2009, it acquired da Vinci Systems, known for its color-grading tools. In 2010, it acquired Echolab's ATEM switcher line, in 2014, it added eyeon Software (developer of the Blackmagic Fusion compositing software) and London's Cintel (film scanning and restoration), and in 2016, it acquired Fairlight, an audio technology company known for its CMI synthesizers as well as mixing consoles. == Products == List of all products developed by the company. Editing, Color Correction and Audio Post Production DaVinci Resolve (free version) and DaVinci Resolve Studio (paid version), computer software for non-linear video editing, color correction, color grading, visual effects, and audio post-production. Audio/Video Controller Consoles: Editor Keyboard, Speed Editor, DaVinci Resolve Replay Editor, Micro Panel, Mini Panel, DaVinci Resolve Micro Color Panel, Advanced Panel, Fairlight Console Channel Fader, Fairlight Console Channel Control, Fairlight Console LCD Monitor, Fairlight Console Audio Editor, Fairlight Desktop Audio Editor, Fairlight Desktop Console, Fairlight Audio Interface Cintel Film Scanner (Generations 1-3) Live Production Home Streaming: ATEM Mini, ATEM Mini Pro/ISO, ATEM Mini Extreme, ATEM Mini Extreme ISO (The ATEM Mini series has both HDMI and SDI variants) Production Switchers: ATEM 1,2 & 4 M/E Constellation HD, ATEM 1,2 & 4 M/E Constellation 4K, ATEM Constellation 8K, ATEM 1,2 & 4 M/E Production Studio 4K, ATEM Television Studio HD8 & HD8 ISO Switcher & Camera Controllers: ATEM Camera Control Panel, ATEM 1 M/E Advanced Panel, ATEM 2 M/E Advanced Panel, ATEM 4 M/E Advanced Panel Chroma Keyers: Ultimatte 12 HD Mini, Ultimatte 12 HD, Ultimatte 12 4K, Ultimatte 12 8K Recording and Storage: HyperDeck Studio HD Mini, HyperDeck Studio HD Plus, HyperDeck Studio HD Plus, HyperDeck Studio 4K Pro, HyperDeck Extreme 8K HDR, HyperDeck Extreme 4K HDR, HyperDeck Extreme Control, HyperDeck Shuttle HD, Duplicator 4K, MultiDock 10G, Video Assist 7" 12G HDR, Video Assist 5" 12G HDR Capture and Playback UltraStudio: 3G, HD Mini, 4K Mini, 4K Extreme 3 DeckLink (PCIe cards): Mini Recorder, Mini Monitor, Mini Monitor 4K, Mini Recorder 4K, Duo 2 Mini, Duo 2, Quad 2, SDI 4K, Studio 4K, 4K Extreme 12G, 8K Pro, Quad HDMI Recorder Network Storage Cloud Store Cloud Pod Broadcast Converters Micro Converter: BiDirectional SDI/HDMI 3G wPSU, HDMI to SDI 3G wPSU, SDI to HDMI 3G wPSU, BiDirectional SDI/HDMI 3G, HDMI to SDI 3G, SDI to HDMI 3G Mini Converters: Audio to SDI, Optical Fiber 12G, SDI Multiplex 4K, Quad SDI to HDMI 4K, SDI Distribution 4K, SDI to Analog 4K, Audio to SDI 4K, SDI to Audio 4K, HDMI to SDI 6G, SDI to HDMI 6G Teranex Mini: SDI Distribution 12G, SDI to HDMI 12G, Audio to SDI 12G, SDI to Analog 12G, SDI to HDMI 8K HDR, SDI to DisplayPort 8K HDR 2110 IP Converters Routing and Distribution Videohub

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  PNGOUT

  PNGOUT is a freeware command line optimizer for PNG images written by Ken Silverman. The transformation is lossless, meaning that the resulting image is visually identical to the source image. According to its author, this program can often get higher compression than other optimizers by 5–10%. It is possible to compress some inflated PNGs to a size below 1% of the original file. PNGOUT was also available as a plug-in for the freeware image viewer IrfanView and can be enabled as an option when saving files. It allows editing of various PNGOUT settings via a dialog box. PNGOUT integration was removed in IrfanView version 4.58 in favour of OptiPNG. In 2006, a commercial version of PNGOUT with a graphical user interface, known as PNGOUTWin, was released by Ardfry Imaging, a small company Silverman co-founded in 2005. There is also a freeware GUI frontend to PNGOUT available, known as PNGGauntlet. == Main operation == The main function of PNGOUT is to reduce the size of image data contained in the IDAT chunk. This chunk is compressed using the deflate algorithm. Deflate algorithms can vary in speed and compression ratio, with higher compression ratios generally implying lower speed. Ken Silverman wrote a deflate compressor for PNGOUT that is slower than the ones used in most graphics software, but produces smaller files. PNGOUT also performs automatic bit depth, color, and palette reduction where appropriate.

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  AI-complete

  In the field of artificial intelligence (AI), tasks that are hypothesized to require artificial general intelligence to solve are informally known as AI-complete or AI-hard. Calling a problem AI-complete reflects the belief that it cannot be solved by a simple specific algorithm. Prior to 2013, problems supposed to be AI-complete included computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real-world problem. AI-complete tasks were notably considered useful for distinguishing humans from automated agents, as CAPTCHAs aim to do. == History == The term was coined by Fanya Montalvo by analogy with NP-complete and NP-hard in complexity theory, which formally describes the most famous class of difficult problems. Early uses of the term are in Erik Mueller's 1987 PhD dissertation and in Eric Raymond's 1991 Jargon File. Expert systems, that were popular in the 1980s, were able to solve very simple and/or restricted versions of AI-complete problems, but never in their full generality. When AI researchers attempted to "scale up" their systems to handle more complicated, real-world situations, the programs tended to become excessively brittle without commonsense knowledge or a rudimentary understanding of the situation: they would fail as unexpected circumstances outside of its original problem context would begin to appear. When human beings are dealing with new situations in the world, they are helped by their awareness of the general context: they know what the things around them are, why they are there, what they are likely to do and so on. They can recognize unusual situations and adjust accordingly. Expert systems lacked this adaptability and were brittle when facing new situations. DeepMind published a work in May 2022 in which they trained a single model to do several things at the same time. The model, named Gato, can "play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens." Similarly, some tasks once considered to be AI-complete, like machine translation, are among the capabilities of large language models. == AI-complete problems == AI-complete problems have been hypothesized to include: AI peer review (composite natural language understanding, automated reasoning, automated theorem proving, formalized logic expert system) Bongard problems Computer vision (and subproblems such as object recognition) Natural language understanding (and subproblems such as text mining, machine translation, and word-sense disambiguation) Autonomous driving Dealing with unexpected circumstances while solving any real world problem, whether navigation, planning, or even the kind of reasoning done by expert systems. == Formalization == Computational complexity theory deals with the relative computational difficulty of computable functions. By definition, it does not cover problems whose solution is unknown or has not been characterized formally. Since many AI problems have no formalization yet, conventional complexity theory does not enable a formal definition of AI-completeness. == Research == Roman Yampolskiy suggests that a problem C {\displaystyle C} is AI-Complete if it has two properties: It is in the set of AI problems (Human Oracle-solvable). Any AI problem can be converted into C {\displaystyle C} by some polynomial time algorithm. On the other hand, a problem H {\displaystyle H} is AI-Hard if and only if there is an AI-Complete problem C {\displaystyle C} that is polynomial time Turing-reducible to H {\displaystyle H} . This also gives as a consequence the existence of AI-Easy problems, that are solvable in polynomial time by a deterministic Turing machine with an oracle for some problem. Yampolskiy has also hypothesized that the Turing Test is a defining feature of AI-completeness. Groppe and Jain classify problems which require artificial general intelligence to reach human-level machine performance as AI-complete, while only restricted versions of AI-complete problems can be solved by the current AI systems. For Šekrst, getting a polynomial solution to AI-complete problems would not necessarily be equal to solving the issue of artificial general intelligence, while emphasizing the lack of computational complexity research being the limiting factor towards achieving artificial general intelligence. For Kwee-Bintoro and Velez, solving AI-complete problems would have strong repercussions on society.

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  Signal transfer function

  The signal transfer function (SiTF) is a measure of the signal output versus the signal input of a system such as an infrared system or sensor. There are many general applications of the SiTF. Specifically, in the field of image analysis, it gives a measure of the noise of an imaging system, and thus yields one assessment of its performance. == SiTF evaluation == In evaluating the SiTF curve, the signal input and signal output are measured differentially; meaning, the differential of the input signal and differential of the output signal are calculated and plotted against each other. An operator, using computer software, defines an arbitrary area, with a given set of data points, within the signal and background regions of the output image of the infrared sensor, i.e. of the unit under test (UUT), (see "Half Moon" image below). The average signal and background are calculated by averaging the data of each arbitrarily defined region. A second order polynomial curve is fitted to the data of each line. Then, the polynomial is subtracted from the average signal and background data to yield the new signal and background. The difference of the new signal and background data is taken to yield the net signal. Finally, the net signal is plotted versus the signal input. The signal input of the UUT is within its own spectral response. (e.g. color-correlated temperature, pixel intensity, etc.). The slope of the linear portion of this curve is then found using the method of least squares. == SiTF curve == The net signal is calculated from the average signal and background, as in signal to noise ratio (imaging)#Calculations. The SiTF curve is then given by the signal output data, (net signal data), plotted against the signal input data (see graph of SiTF to the right). All the data points in the linear region of the SiTF curve can be used in the method of least squares to find a linear approximation. Given n {\displaystyle n\,} data points ( x i , y i ) {\displaystyle (x_{i}\,,y_{i}\,)} a best fit line parameterized as y = m x + b {\displaystyle y=mx+b\,} is given by: m = ∑ x i y i n − ∑ x i n ∑ y i n ∑ x i 2 n − ( ∑ x i n ) 2 b = ∑ y i n − m ∑ x i n {\displaystyle m={\frac {{\frac {\sum x_{i}y_{i}}{n}}-{\frac {\sum x_{i}}{n}}{\frac {\sum y_{i}}{n}}}{{\frac {\sum x_{i}^{2}}{n}}-({\frac {\sum x_{i}}{n}})^{2}}}\qquad \qquad b={\frac {\sum y_{i}}{n}}-m{\frac {\sum x_{i}}{n}}}

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  Multiple satellite imaging

  Multiple satellite imaging is the process of using multiple satellites to gather more information than a single satellite so that a better estimate of the desired source is possible. Something that cannot be resolved with one telescope might be visible with two or more telescopes. == Background == Interferometry is the process of combining waves in such a way that they constructively interfere. When two or more independent sources detect a signal at the same given frequency those signals can be combined and the result is better than each one individually. An overview of Astronomical interferometers and a History of astronomical interferometry can be referenced from their respective pages. The NASA Origins Program was created in the 1990s to ultimately search for the origin of the universe. The theory that the Origins Program is based on is: since light travels at a constant speed until it is absorbed by something; there is still light that was part of the first light ever created traveling about the universe and ultimately some of that light is coming in the general direction of Earth. So a satellite system capable of collecting light from the beginning of the universe would be able to tell us more about where we came from. There is also the constant search for life in other worlds. A satellite system using the interferometric technologies mentioned above would be able to have a much higher resolution than any of the current deep space imaging systems. == Future == NASA is currently focused on the Vision for Space Exploration and has reduced current funding for scientific unmanned space exploration in favor of human exploration. These budget cuts have slowed the multiple satellite imaging development and relevant scientific missions as Project Prometheus and Terrestrial Planet Finder have ended as well but research continues.

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