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NASA AI Assisted-Air Quality Monitoring Project

The NASA Expert-System Ion Trap Mass Spectrometer (ES-ITMS) Project was a public-private partnership to develop an artificial intelligence assisted, air quality monitoring system and was qualified for use on the Space Shuttle. The partnership was also the first cost and intellectual property shared public-partnership implemented by NASA, which used the commercial Research and Development Limited Partnership (RDLP) model that had been adopted by the Reagan Administration for Department of Defense semiconductor development, and recommended for use by NASA for space commercialization. The project partners included NASA, the University of Florida and Finnigan MAT Corporation, was organized and administered by the NASA Joint Enterprise Institute (subsequently NASA Joint Sponsored Program) and ran from 1988 through 1990. The partnership concluded final testing in 1991, generating four patents, expert system software and application protocol reports. The system was space qualified for use on the Shuttle and elements of the ES-ITMS system were integrated into the product Improvements for Finnigan MAT corporation. The success of the partnership lead NASA to create a pilot program to develop partnership business models as an ongoing management practice. == Purpose and objectives == The need to monitor air quality in confined spaces represented an increasing challenge for NASA's planned space missions and private sector facility managers facing the increased scrutiny of possible air contaminants. Up to the early 1980's, air quality monitors generally required large spaces and human technicians to interpret readings. This created a need for miniaturized air quality monitors that could generate reliable and accurate analytic results without on-site technician presence. NASA initiated projects to develop..."mobile and/or portable mass spectrometers" that evaluated the "tradeoff between instrumentation capabilities and space, weight and power considerations." NASA selected a "commercial ITMS instrument capable of generating electron ionization, chemical ionization and mass spectrometry data", to develop a linked expert system to accomplish analysis without human intervention. The commercial instrumentation was from Finnigan MAT corporation while the scientific expertise to support expert system development was available at the University of Florida. The project managers at NASA Ames created a single, integrated project using the RDLP model with objectives to: Develop AI/expert system software for instrument control (NASA's role) Expand sensitivity, selectivity and speed of the spectrometer (Univ Florida role) Expand the spectrometer analytic capability and automate the screening (Finnigan role) == Membership == The partnership included seven specialists from five member organizations: Federal Government National Aeronautics and Space Administration (NASA) NASA Ames Research Center (ARC) NASA Kennedy Space Center (KSC) Commercial Finnigan MAT Corporation (Thermo-Fisher Scientific) TGS Technology, Inc. Research Management University of Florida == Organization, management and administration == The technical project was organized into two development teams, one located in at the NASA Ames Research Center covering expert systems and analytic capabilities and one in Florida covering improved sensitivity and testing. The partnership management and administration was provided by a non-profit, partnership support organization: the Joint Enterprise Institute operating through San Francisco State University Foundation (SFSUF) with a NASA employee liaison, Syed Shariq. == Public-private partnership == The partnership structure was as a prototype test of a pilot NASA program to develop public-private partnership business models. The pilot program was known as the NASA Joint Sponsored Research Program (JSRP), which operated as the NASA Joint Enterprise Institute between 1988 and 1991. The partnership was the first public-private, research and development partnership implemented by NASA in response to national policy shifts to increase technology transfer and space commercialization. The partnership structure included a two year technology development and testing plan that cost $610,000, of which NASA funded $310,000, Finnigan $175,000 and the University of Florida $95,000. == Results and commercialization == The project generated patents (4), software (2) and application protocol reports (8). NASA gained use of the patents and jointly development software while Finnigan received commercial utilization rights. The results were commercialized within eighteen months of project completion. == Recognition == NASA recognized the project as a space qualified instrument. Its achievements were reported to the NASA Administrator, directly leading to establishment of the agency-wide Joint Sponsored Research Program.
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ArcSoft ShowBiz

ShowBiz is a video editor by ArcSoft for the Windows operating system. It can create VCD and DVDs and can also export to the formats AVI, MPEG, WMV, and MOV. ShowBiz also contains a DVD burning and menu building feature. As of 2003, it was one of the three most dominant bundled titles. == Reception == PC Magazine reviewer Jan Ozer states: "ArcSoft's ShowBiz has evolved into a competent editor that's generally more usable than Dazzle's MovieStar program, providing more configuration controls, better preview features, and a much greater range of fun effects." John Virata, senior editor of Digital Media Online, says in his three page review of ShowBiz DVD 2, "It is an easy editor to work with and has a logically laid out interface that takes you step by step through the video creation and DVD creation process"
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NeoPaint

NeoPaint is a raster graphics editor for Windows and MS-DOS. It supports several file formats including JPEG, GIF, BMP, PNG, and TIFF. The developer, NeoSoft, advertises NeoPaint as "being simple enough for use by children while remaining powerful enough for the purposes of advanced image editing". The first version, NeoPaint 1.0, was released in 1992 on floppy disks. It supported video modes ranging from 640x350 to 1024x768 and multiple fonts. NeoPaint 2.2 came out for MS-DOS 3.1 in 1993, with support of for 2, 16, or 256 color images in Hercules, EGA, VGA, and Super VGA modes. NeoPaint 3.1 was released in 1995 supporting 24-bit images and formats like PCX, TIFF and BMP. NeoPaint 3.2 was released in 1996. An updated version, NeoPaint 3.2a, supported the GIF file format. NeoPaint 3.2d was released in 1998. A Windows 95 version named NeoPaint for Windows v4.0 was released in 1999 supporting the PNG file format. On September 1, 2018 the program was rebranded as PixelNEO, becoming one of the VisualNEO software products. Formats such as JPEG 2000, ICO, CUR, PSD and RAW are supported.
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Conduit (company)

Conduit Ltd. is an international software company. From its founding in 2005 to 2013, its most well-known product was the Conduit toolbar, which was widely-described as malware. In 2013, it spun off its toolbar business; today, its main product is a mobile development platform that allows users to create native and web mobile applications for smartphones. == Products == From 2005 to 2013, the company's most well-known product was the Conduit toolbar, which is flagged by most antivirus software as potentially unwanted and adware. Conduit's toolbar software is often downloaded by malware packages from other publishers. The company spun off the toolbar division that manages the Conduit toolbar in 2013. Today, the company's main product is a mobile development platform that allows users to create native and web mobile applications for smartphones. App creation for its App Gallery is free, but it charges a monthly subscription fee to place apps on the App Store or Google Play. == History == Conduit was founded in 2005 by Shilo, Dror Erez, and Gaby Bilcyzk. Between years 2005 and 2013, it ran a successful but controversial toolbar platform business. Conduit was part of the so-called Download Valley companies monetizing free software and downloads by bundling adware. The toolbars were criticized by some as being very difficult to uninstall. The toolbar software was referred to as a "potentially unwanted program" by some in the computer industry because it could be used to change browser settings. The company had more than 400 employees in 2013. In September same year, Conduit spun off its entire website toolbar business division, which combined with Perion Network. After the deal, Conduit shareholders owned 81% of Perion's existing shares and both Perion and Conduit remained independent companies. The substantial size of the Conduit user base allowed Perion to immediately surpass AOL in U.S. searches. In 2015, Conduit announced it would purchase Keeprz, a mobile customer loyalty platform, for $45 million.
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Confusion matrix

In machine learning, a confusion matrix, also known as error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervised learning one. In unsupervised learning it is usually called a matching matrix. The term is used specifically in the problem of statistical classification. Each row of the matrix represents the instances in an actual class while each column represents the instances in a predicted class, or vice versa – both variants are found in the literature. The diagonal of the matrix therefore represents all instances that are correctly predicted. The name stems from the fact that it makes it easy to identify whether the system is confusing two classes (i.e., commonly mislabeling one class as another). The confusion matrix has its origins in human perceptual studies of auditory stimuli. It was adapted for machine learning studies and used by Frank Rosenblatt, among other early researchers, to compare human and machine classifications of visual (and later auditory) stimuli. It is a special kind of contingency table, with two dimensions ("actual" and "predicted"), and identical sets of "classes" in both dimensions (each combination of dimension and class is a variable in the contingency table). == Example == Given a sample of 12 individuals, 8 that have been diagnosed with cancer and 4 that are cancer-free, where individuals with cancer belong to class 1 (positive) and non-cancer individuals belong to class 0 (negative), we can display that data as follows: Assume that we have a classifier that distinguishes between individuals with and without cancer in some way, we can take the 12 individuals and run them through the classifier. The classifier then makes 9 accurate predictions and misses 3: 2 individuals with cancer wrongly predicted as being cancer-free (sample 1 and 2), and 1 person without cancer that is wrongly predicted to have cancer (sample 9). Notice, that if we compare the actual classification set to the predicted classification set, there are 4 different outcomes that could result in any particular column: The actual classification is positive and the predicted classification is positive (1,1). This is called a true positive result because the positive sample was correctly identified by the classifier. The actual classification is positive and the predicted classification is negative (1,0). This is called a false negative result because the positive sample is incorrectly identified by the classifier as being negative. The actual classification is negative and the predicted classification is positive (0,1). This is called a false positive result because the negative sample is incorrectly identified by the classifier as being positive. The actual classification is negative and the predicted classification is negative (0,0). This is called a true negative result because the negative sample gets correctly identified by the classifier. We can then perform the comparison between actual and predicted classifications and add this information to the table, making correct results appear in green so they are more easily identifiable. The template for any binary confusion matrix uses the four kinds of results discussed above (true positives, false negatives, false positives, and true negatives) along with the positive and negative classifications. The four outcomes can be formulated in a 2×2 confusion matrix, as follows: The color convention of the three data tables above were picked to match this confusion matrix, in order to easily differentiate the data. Now, we can simply total up each type of result, substitute into the template, and create a confusion matrix that will concisely summarize the results of testing the classifier: In this confusion matrix, of the 8 samples with cancer, the system judged that 2 were cancer-free, and of the 4 samples without cancer, it predicted that 1 did have cancer. All correct predictions are located in the diagonal of the table (highlighted in green), so it is easy to visually inspect the table for prediction errors, as values outside the diagonal will represent them. By summing up the 2 rows of the confusion matrix, one can also deduce the total number of positive (P) and negative (N) samples in the original dataset, i.e. P = T P + F N {\displaystyle P=TP+FN} and N = F P + T N {\displaystyle N=FP+TN} . == Table of confusion == In predictive analytics, a table of confusion (sometimes also called a confusion matrix) is a table with two rows and two columns that reports the number of true positives, false negatives, false positives, and true negatives. This allows more detailed analysis than simply observing the proportion of correct classifications (accuracy). Accuracy will yield misleading results if the data set is unbalanced; that is, when the numbers of observations in different classes vary greatly. For example, if there were 95 cancer samples and only 5 non-cancer samples in the data, a particular classifier might classify all the observations as having cancer. The overall accuracy would be 95%, but in more detail the classifier would have a 100% recognition rate (sensitivity) for the cancer class but a 0% recognition rate for the non-cancer class. F1 score is even more unreliable in such cases, and here would yield over 97.4%, whereas informedness removes such bias and yields 0 as the probability of an informed decision for any form of guessing (here always guessing cancer). According to Davide Chicco and Giuseppe Jurman, the most informative metric to evaluate a confusion matrix is the Matthews correlation coefficient (MCC). Other metrics can be included in a confusion matrix, each of them having their significance and use. Some researchers have argued that the confusion matrix, and the metrics derived from it, do not truly reflect a model's knowledge. In particular, the confusion matrix cannot show whether correct predictions were reached through sound reasoning or merely by chance (a problem known in philosophy as epistemic luck). It also does not capture situations where the facts used to make a prediction later change or turn out to be wrong (defeasibility). This means that while the confusion matrix is a useful tool for measuring classification performance, it may give an incomplete picture of a model’s true reliability. == Confusion matrices with more than two categories == Confusion matrix is not limited to binary classification and can be used in multi-class classifiers as well. The confusion matrices discussed above have only two conditions: positive and negative. For example, the table below summarizes communication of a whistled language between two speakers, with zero values omitted for clarity. == Confusion matrices in multi-label and soft-label classification == Confusion matrices are not limited to single-label classification (where only one class is present) or hard-label settings (where classes are either fully present, 1, or absent, 0). They can also be extended to Multi-label classification (where multiple classes can be predicted at once) and soft-label classification (where classes can be partially present). One such extension is the Transport-based Confusion Matrix (TCM), which builds on the theory of optimal transport and the principle of maximum entropy. TCM applies to single-label, multi-label, and soft-label settings. It retains the familiar structure of the standard confusion matrix: a square matrix sized by the number of classes, with diagonal entries indicating correct predictions and off-diagonal entries indicating confusion. In the single-label case, TCM is identical to the standard confusion matrix. TCM follows the same reasoning as the standard confusion matrix: if class A is overestimated (its predicted value is greater than its label value) and class B is underestimated (its predicted value is less than its label value), A is considered confused with B, and the entry (B, A) is increased. If a class is both predicted and present, it is correctly identified, and the diagonal entry (A, A) increases. Optimal transport and maximum entropy are used to determine the extent to which these entries are updated. TCM enables clearer comparison between predictions and labels in complex classification tasks, while maintaining a consistent matrix format across settings.
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Beauty.AI

Beauty.AI is a mobile beauty pageant for humans and a contest for programmers developing algorithms for evaluating human appearance. The mobile app and website created by Youth Laboratories that uses artificial intelligence technology to evaluate people's external appearance through certain algorithms, such as symmetry, facial blemishes, wrinkles, estimated age and age appearance, and comparisons to actors and models. The Beauty.AI 2.0 contest caused great concern over important ethical issues with deep neural networks such as age, race and gender bias and lead to the creation of the Diversity.AI think tank dedicated to developing new methods for uncovering and managing bias in artificially intelligent systems. Beauty.AI was also an attempt to find approaches on how machines can perceive human face through evaluating particular features, commonly associated with health and beauty. == Concept == The Beauty.AI app was created by Youth Laboratories, a company based out of Russia and Hong Kong that focuses on facial skin analytics. The bioinformation company Insilico Medicine assists in the Beauty.AI app by testing its deep learning techniques to the app. One goal of the app is to reduce the need for human and animal testing as well as improving people's overall health. Its first contest was started in December 2016, and the results were announced in August 2016. More than 60,000 people submitted entries into the contest. The mobile app uses artificial intelligence technology to inspect photographs for certain facial features in order to both determine a person's beauty through artificial means by multiple robots. Part of the Beauty.AI app's purpose is to collect visual and anecdotal data to improve its creator's Youth Laboratories skin analyst skills. == Accusations of racism == There were a total of 44 individuals from different age groups and genders judged as the most attractive, with 37 white entrants, six Asian entrants, and one dark-skinned entrant. The app has received criticism from social justice advocates and computer science professionals. However, Alex Zhavoronkov, PhD, chief science officer of Youth Laboratories and chief technology officer Konstantin Kiselev, both for Youth Laboratories, noted that a lack of data may have contributed to these results. Also, Kiselev added that another issue was that approximately 75% of entrants were white Europeans, whereas only 7% and 1% were from India and Africa, respectively. Kiselev stated that they would work on doing more and better outreach to these areas to improve in this area. Despite this, it was said by Dr. Zhavoronkov that the AI would discard photos of dark-skinned people if the lighting is too poor. Dr. Zhavoronkov vowed to weed out the issues for the next beauty pageant and to try to avoid a similar controversy in the future.
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LumenVox

LumenVox is a privately held speech recognition software company based in San Diego, California. LumenVox has been described as one of the market leaders in the speech recognition software industry. == History == LumenVox was founded in 2001 as subsidiary of Progressive Computing. According to LumenVox CEO Edward Miller, when Progressive had initially looked to add speech recognition to its own phone system, it found the existing offerings too expensive and recognized a niche in the market for a more affordable speech recognition product. This led to the development of LumenVox with an aim to bring speech recognition to small-to-midsized businesses. LumenVox is one of the major providers of automatic speech recognition for telephone systems, and as of 2006, became the second largest provider of speech recognition software. == Products == The primary LumenVox product is the LumenVox Speech Engine. It is a speaker-independent automatic speech recognizer that uses the Speech Recognition Grammar Specification for building and defining grammars. It has been integrated with several of the major voice platforms, including Avaya Voice Portal/Interactive Response, Aculab, and BroadSoft's BroadWorks. The Speech Engine was originally derived from CMU Sphinx, but LumenVox has added considerable development effort to make it a commercial-ready product. LumenVox also offers a product called the Speech Tuner, which provides a graphical means of testing and troubleshooting speech recognition applications. == Open source support == LumenVox was recognized as one of the top VoIP companies in 2008 for its work in providing its offerings to the open source community, an effort by the company that began in 2006 when it partnered with Digium. At that time, Digium, maintainer of the open source Asterisk PBX, integrated the LumenVox Speech Engine into Asterisk. This made LumenVox the first commercially available speech recognition engine for Asterisk. As one of the earlier commercial software integrations with Asterisk, the LumenVox integration has been described as one of the applications that helped to mainstream Asterisk. In 2009, LumenVox also began offering access to the Speech Engine as a monthly subscription, bringing the cost of entry down even lower for open source users. LumenVox is also integrated with the open source UniMRCP project, which provides open source client and server libraries for the Media Resource Control Protocol.
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Kai's Power Tools

Kai's Power Tools (KPT) are a set of API plugins created by the German computer scientist Kai Krause in 1992 that were designed for use with Adobe Photoshop and Corel Photo-Paint. Kai's Power Tools were sold to Corel in 2000 when MetaCreations was closed. There are various versions of Kai's Power Tools. KPT 3, 5, 6, and X sets are compilations of different filters. The program interface features a reward-based function in which a bonus function is revealed as the user moves towards more complex aspects of the tool. == Filters == The KPT Convolver is a mathematics based filter; the level of precision and varying effects can be achieved by using numerical values of colour, tint, hue, saturation, contrast, brightness, luminosity, and posterize. The KPT Projector takes the current image or selection and offers a number of interactive perspective warp effects. To a large extent, with its draggable distortion handles and its moving, scaling and rotating options, this simply duplicates Adobe Photoshop's Free Transform capabilities. What is completely different is the ability to rotate the bitmap image in 3D space and to tile the results if desired. It can also animate the distortions by dragging keyframes from the preview window into an animation palette. KPT 6 will then preview the animation and output it to various sizes in avi or mov format. This animation capability is even more useful with the KPT Turbulence filter. This is another distortion filter, but one that treats the image as if it was completely liquid. The preview panel shows the animation in real time. The KPT Goo filter is used to produce a single frame freeform liquid distortion. This filter is available both with KPT 6 and the standalone version. It works by effectively turning a bitmap image into a liquid that can be interactively smeared, smudged, twirled, and pinched with the range of tools on offer. The obvious use is to distort photographic portraits into caricatures. KPT Materializer can create advanced surface textures based on bump maps that define troughs and peaks. It can use any external image for the basis of the bump map or alternatively the user can pick out the hue, saturation, luminance or red, green, or blue channel of the current image. It can then offset, scale and rotate the texture map, control its lighting, and even blend in a reflection map. The filter can be used for anything from providing an oil-painting feel to an entire image, to giving the illusion of depth to a selection. Also producing the impression of depth is the KPT Gel filter which uses various paint tools to synthesize photo-realistic 3D materials such as metals, liquids, or plastics. Gel painting is very different from traditional 2D painting as the brush strokes pool together when they touch and refract the underlying image. It can also manipulate 3D paint—once it has been added—by twirling, pinching, and carving it. The opposite is true of the Equalizer filter, which is used for applying variations on sharpening effects. The filter has three modes. The first mode, Equalizer, looks and works rather like the graphic equalizer on a stereo system, enabling adjustment of the level of pixel contrast within nine bands of different visual frequencies. The second mode, Contrast Sharpen, allows for increasing the contrast between light and dark areas in an image. The third mode, Bounded Sharpen, can sharpen an image without causing oversharpening, which can lead to halo effects. This feature is particularly useful when pulling out the detail in an image softened by resizing. KPT SceneBuilder is used for producing photorealistic 3D scenes by importing and rendering 3DS files. The main image window offers three tabs for editing in 2D and 3D mode and for setting up the object's final texture. Many users regard this filter as being the most impressive because it acts as a standalone 3D rendering tool and provides control over everything from transparency, reflection, refraction, bump mapping through to multiple light sources, and so on but without the ability to create or edit objects. The final filter, KPT SkyEffects, also has its roots in Metacreations' experience with 3D programs such as Bryce and RayDream. This filter is designed to simulate the interaction between the light from the sun or moon with no less than six atmospheric layers of haze, fog and cloud. The filter is typical of the KPT 6 collection as a whole: at times the interface is inspired and offers the ability to create beautiful reddening sunsets simply by interactively dragging the sun toward the horizon, producing realistic sunsets and moonscapes. == Other effects == Kai's Power Tools 6 features a lens flare effect for precisely managing the type of glow, halo, streaks, and reflection. The addition of a library of preset effects helps to overcome this by allowing the user to choose a standard effect and then interactively position the flare in the image preview. KPT 6 provides a new engine in the form of the KPT Reaction, which takes a reaction seed and turns it into a seamlessly tiling pattern based on a reaction diffusion process. It offers random noise, regular dots or reticulated voronoi patterns or a bitmap image itself as the seed. Corel has no plans for any updates.
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Situational application

In computing, a situational application is "good enough" software created for a narrow group of users with a unique set of needs. The application typically (but not always) has a short life span, and is often created within the group where it is used, sometimes by the users themselves. As the requirements of a small team using the application change, the situational application often also continues to evolve to accommodate these changes. Although situational applications are specifically designed to embrace change, significant changes in requirements may lead to an abandonment of the situational application altogether – in some cases it is just easier to develop a new one than to evolve the one in use. == Characteristics == Situational applications are developed fast, easy to use, uncomplicated, and serve a unique set of requirements. They have a narrow focus on a specific business problem, and they are written in a way where if the business problem changes rapidly, so can the situational application. This contrasts with more common enterprise applications, which are designed to address a large set of business problems, require meticulous planning, and impose a sometimes-slow and often-meticulous change process. == Origination == Clay Shirky in his essay entitled "Situated Software" described a type of software that "...is designed for use by a specific social group, rather than for a generic set of "users"." IBM later morphed the term into "situational applications". == Evolution == The successful large-scale implementation of a situational application environment in an organization requires a strategy, mindset, methodology and support structure quite different from traditional application development. This is now evolving as more companies learn how to best leverage the ideas behind situational applications. In addition, the advent of cloud-based application development and deployment platforms makes the implementation of a comprehensive situational application environment much more feasible. == Examples == A structured wiki that can host wiki applications lends itself to creation of situational applications. Some mashups can also be considered situational applications. A forms application such as a Microsoft Access Database (MDB file) can be considered a situational application. The latest implementations of situational application environments include Longjump, Force.com and WorkXpress.
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Arattai

Arattai Messenger (or simply Arattai) is an encrypted messaging service for instant messaging, voice calls, and video calls, developed by Zoho Corporation. The name Arattai means "chat" or "conversation" in Tamil. The app was soft-launched in January 2021. The app saw a sharp surge in downloads in September 2025, partially fueled by endorsements from Indian government officials. However, the app dropped from the top rankings in October 2025. == History == Arattai was initially tested internally among Zoho employees before being released publicly in early 2021. The launch coincided with a surge in interest for privacy-focused and messaging services, triggered by concerns over WhatsApp's updated terms of service. In September 2025, Arattai experienced a major surge in adoption, with daily sign-ups reportedly increasing 100-fold, from around 3,000 to more than 350,000 in three days. The surge in downloads was attributed to Zoho products being promoted by Indian government officials as part of their Make in India push for homegrown alternatives to foreign‐owned apps, amid deteriorating India–US relations. The growth temporarily strained Zoho's infrastructure, prompting rapid scaling of servers and capacity expansion. During the same period, the app reached the top position in Apple's App Store charts for the "Social Networking" category in India. The app dropped from the top ranking in late October 2025. == Reception == At launch, Arattai was positioned as a potential domestic rival to WhatsApp in India, but analysts noted that it faced challenges with encryption, ecosystem, and network effect. Critics pointed to occasional sync delays.
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Drops (app)

Drops is a language learning app that was created in Estonia by Daniel Farkas and Mark Szulyovszky in 2015. It is the second product from the company, after their first app, LearnInvisible, had issues in retaining a user's engagement over the required time period. The languages available include Native Hawaiian and Māori, and was classified as one of the fifty "Most Innovative Companies" for 2019 by Fast Company. The company partnered with Global Eagle Entertainment to include Travel Talk, a feature intended to focus on words and phrases frequently used by travelers. At the beginning of the COVID-19 pandemic in March 2020, the number of users increased by 55 percent in the United States and 92 percent in the United Kingdom. Droplets, a language app for children, includes profiles for multiple teachers working with remote students. The company also produces an app called Scripts, intended to help users learn to write alphabets. The app was purchased by the Norwegian company Kahoot! on 24 November 2020.
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Video editing software

Video editing software or a video editor is software used for performing the post-production video editing of digital video sequences on a non-linear editing system (NLE). It has replaced traditional flatbed celluloid film editing tools and analog video tape editing machines. Video editing software serves a lot of purposes, such as filmmaking, audio commentary, and general editing of video content. In NLE software, the user manipulates sections of video, images, and audio on a sequence. These clips can be trimmed, cut, and manipulated in many different ways. When editing is finished, the user exports the sequence as a video file. == Components == === Timeline === NLE software is typically based on a timeline interface where sections moving image video recordings, known as clips, are laid out in sequence and played back. The NLE offers a range of tools for trimming, splicing, cutting, and arranging clips across the timeline. Another kind of clip is a text clip, used to add text to a video, such as title screens or movie credits. Audio clips can additionally be mixed together, such as mixing a soundtrack with multiple sound effects. Typically, the timeline is divided into multiple rows on the y-axis for different clips playing simultaneously, whereas the x-axis represents the run time of the video. Effects such as transitions can be performed on each clip, such as a crossfade effect going from one scene to another. === Exporting === Since video editors represent a project with a file format specific to the program, one needs to export the video file in order to publish it. Once a project is complete, the editor can then export to movies in a variety of formats in a context that may range from broadcast tape formats to compressed video files for web publishing (such as on an online video platform or personal website), optical media, or saved to mobile devices. To facilitate editing, source video typically has a higher resolution than the desired output. Therefore, higher resolution video needs to be downscaled during exporting, or after exporting in a process known as transsizing. === Visual effects === As digital video editing advanced, visual effects became possible, and is part of the standard toolkit, usually found in prosumer and professional grade software. A common ability is to do compositing techniques such as chroma keying or luma keying, among others, which allow different objects to look as if they are in the same scene. A different kind of visual effects is motion capture. Software such as Blender can perform motion capture to make animated objects follow an actor's movements. === Additional features === Most professional video editors are able to do color grading, which is to manipulate visual attributes of a video such as contrast to enhance output, and improve emotional impact. Some video editors such as iMovie include stock footage available for use. == Hardware requirements == As video editing puts great demands on storage and graphics performance, especially at high resolutions such as 4K, and for videos with many visual effects, powerful hardware is often required. It is not uncommon for a computer built for video editing to have a lot of drive capacity, and a powerful graphics processing unit, which optimally has hardware accelerated video encoding. Having sufficient disk space is important since videos can take up large amounts of storage, depending on the resolution and compression format used. Each minute of a Full HD (1080p) video at 30 fps takes up 60MB of space. When visual effects are used, a server farm can be employed to speed up the rendering process. == Examples == Video editing software can be divided into consumer grade, which focuses on ease-of-use, along with professional grade software, which focuses on feature availability, and advanced editing techniques. The typical use case for the former is to edit personal videos on the go, when more advanced editing is not required. === Consumer grade === Photos (Apple) Google Photos YouTube Create === Prosumer grade === ==== Proprietary software ==== iMovie CyberLink PowerDirector === Professional grade === ==== Proprietary software ==== Final Cut Pro Adobe Premiere Pro DaVinci Resolve Vegas Pro Lightworks Camtasia Media Composer ==== Free and open source software ==== Avidemux Blender Cinelerra Flowblade Kdenlive OpenShot Shotcut While most video editing software has been separate from the operating systems, some operating systems have had a video editor installed by default, such as Windows Movie Maker in Windows XP, or as a component of the default photo viewer, such as the Photos app on iOS. Some social media platforms, such as TikTok and Instagram may include a rudimentary video editor to trim clips.
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Application enablement

Application enablement is an approach which brings telecommunications network providers and developers together to combine their network and web abilities in creating and delivering high demand advanced services and new intelligent applications. Network providers, in addition to bandwidth, provide abilities such as billing, location, presence, and security, which have allowed them to establish long-term relationships with end-users. By offering these select abilities as application programming interfaces (APIs), providers give developers access to a set of tools to create (mashup) new applications and services to run on provider networks. Unifying the strengths of providers and developers facilitates the creation of mash-up applications, and in turn, a better end user quality of experience (QoE) for improved profit margins. Apple's iOS with App Store, and Google's Android with Android Market exemplify this approach. Both have introduced mobile platforms that are supported by a comprehensive ecosystem in order to perpetuate innovation in product design, content and service offerings, and overall consumer behavior. By the end of April 2010, downloadable applications numbered over 200,000 for iPhone and over 50,000 for Android. == Background == Historically, telecommunication providers primarily based their business models on network performance, emphasizing connectivity, availability, and quality of service (QoS) as key sources of revenue and customer value. With the increasing demand for bandwidth-intensive data and video applications, maintaining service continuity has required substantial infrastructure investments. To address rising operational costs and declining average revenue per user (ARPU), providers have increasingly adopted customer-oriented strategies and diversified business models to expand their roles within the telecommunications value chain. Application enablement supports providers in making this transition by providing an environment, or ecosystem, where providers and developers can collaborate to build, test, manage, and distribute applications across networks including television, broadband, Internet, and mobile. This cooperative effort produces mutually beneficial results for all parties, opening up new revenue streams while enhancing value and rate of return (ROI). The following are some examples of key network abilities which function as application enablers in the telecommunications market: Billing systems Security for private transactions Network-based storage of digital content End-to-end bandwidth for high-quality transmissions Scoring abilities to identify end-user preferences and behaviors Subscriber data to customize the end-user experience Context information, such as location and presence, to localize services. == New business models == As network providers work toward effective collaboration with application and content developers, several new business models are emerging to help facilitate the business relationships: === Vendor-led === A type of business model driven by telecommunications vendors, who assist network providers in building relationships with application and content developers to lower the cost and complexity of managing third parties. Examples of this model include: Forum Nokia IBM Technology Partner Ecosystem Ng Connect Huawei Intouch program === Operator-led === Characterized by network providers who want to maintain a high degree of flexibility and control over applications created for their end-consumers, this model lets them create and manage their own developer program, development platform, and application store. Under this arrangement, independent developers provide their own branding, marketing communications, pricing and customer care. Network providers pursuing this model will often seek to partner with a large number of third parties using standardized on-boarding processes. Examples of this model include: o2 Litmus Orange Partner Joint Innovation Lab === Aggregator === Network providers who choose not to create/manage their own developer relationships will partner with one or multiple aggregators, to administer a portion of or their entire application strategy. Examples of this model include: Ovi Operator Partnership Blackberry Operator Partnership Cellmania Buongiorno === Mass wholesale === Select network providers also participate in wholesale models that exist primarily for applications (BT's Ribbit- an Internet Protocol (IP) based calling and messaging platform) and devices (Verizon's Open Device initiative). This business-to-business approach reduces a large portion of the potential costs of third party application enablement (marketing, acquisition and support). Examples of this model include: BT's Ribbit Verizon Wireless ODI AT&T Synaptic Hosting === The enterprise customer === Some network providers are focusing on enabling applications in the enterprise space. In this model, the network provider establishes a platform for their large enterprise customers who want to blend custom software with enhanced abilities, and will provide standardized processes around mobilizing enterprise applications, and exposing core back-office abilities to allow for dynamic customer interaction. Examples of this model include: Vodafone Applications Service Verizon Private Network Sprint Solution Launchpad === Trusted partner === In this model, the network provider builds one-on-one relationships with trusted third-party developers by exposing customized network abilities, bringing a greater variety of brands to the network provider's portfolio. Network providers using this model tend to only have a few partners (in contrast to the operator led model). Under this scenario, network providers benefit from a pre-established customer base and the developer's marketing resources. Examples of this model include: 3/Skype Partnership (UK) Virgin Media and BBC iPlayer == Network operator developer resources == Operator led model o2 Litmus Orange Partner Joint Innovations Lab Aggregator model Ovi Operator Partnership Cellmania Buongiorno Mass wholesale model BT Ribbit Verizon Wireless ODI AT&T Synaptic Hosting Enterprise customer model Vodafone Applications Service Verizon Private Network Sprint Solution Launchpad == Rerencesfe ==
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Prequel (mobile application)

Prequel, Inc. is an American technology company and mobile app developer known for developing the Prequel mobile application, which enables editing photos and videos with filters and effects generated using artificial intelligence. Prequel was founded in 2018 by Serge Aliseenko and Timur Khabirov, who currently serves as the company's CEO. It is headquartered in New York City. As of August 2022, it had been downloaded more than 100 million times. == History == In 2016, entrepreneur Timur Khabirov and investor Serge Aliseenko registered a US corporation named AIAR Labs Inc, which was developing AR solutions as an outsourced contractor. Of several proprietary products, Prequel was selected for beta-testing as a product focused on editing photos and videos. In 2018, Prequel was released on the Apple App Store. The launch cost $3 million USD, financed with the founders’ personal funds. The first release included approximately 10 filters for photos and the same amount of effects that augmented images with rose petals, rain and snow, VHS and film reel simulations, glitch, grain, sun puddles, and lomography. By June 2020, the app had also been released for Android. In 2021, Prequel founders Timur Khabirov and Serge Aliseenko launched a venture studio for startups working with artificial, computer vision, and AR-based visual art. In December 2022, Prequel reached the number 14 slot on the global rankings for Apple App Store’s Top Charts and the number 5 slot on the App Store’s U.S. charts. In March 2023, Prequel launched a new app called Artique, which is an AI-powered image editing app for businesses. Artique provides advertising and marketing graphic design using ready-made templates that users can customize, while giving suggestions and visual cues through artificial intelligence. Prequel was also one of the companies participating in discussions about artificial intelligence at SXSW 2023. == Features == Prequel describes its app as an "Aesthetic Pic Editor. The app uses artificial intelligence to create and edit content. Prequel can be used to touch up faces on images and videos and can also tie various decorative elements to certain points on the human body and face. Prequel filters include the "Cartoon" filter, which converts selfies into cartoon-style pictures. Other filters include Kidcore, Dust, Grain, Fisheye, Retro Style, Miami, Disco, and VHS-style filters, as well as the ability to create Renaissance-style pictures. Prequel also gives users the ability to apply color correction tools and to make moving images with 3D effects out of 2D images. Prequel allows users to take photos and videos directly through the app and apply filters and effects in real time. The app also comes with manual editing options for photos, such as adjusting the brightness and/or exposure and cropping photos, as well as an option to automatically apply adjustments. The Prequel app uses the Core ML, MNN, and TFLight frameworks to work with its neural networks. Some AI solutions are launched server-side, and some on the user's mobile device. A resulting photo or video edited with the app is called "a prequel." The app daily generates over 2 million such prequels, which are published by users in Instagram, TikTok, and other social media. As of 2022, the app has more than 800 filters and effects, along with video templates and support for GIFs and stickers. Prequel is free-to-use, but has a premium version that gives users access to more effects, filters, and beauty tools. Since its launch in 2018, Prequel has been downloaded more than 100 million times.
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Optical sorting

Optical sorting (sometimes called digital sorting) is the automated process of sorting solid products using cameras and/or lasers. Depending on the types of sensors used and the software-driven intelligence of the image processing system, optical sorters can recognize an object's color, size, shape, structural properties and chemical composition. The sorter compares objects to user-defined accept/reject criteria to identify and remove defective products and foreign material (FM) from the production line, or to separate product of different grades or types of materials. Optical sorters are in widespread use in the food industry worldwide, with the highest adoption in processing harvested foods such as potatoes, fruits, vegetables and nuts where it achieves non-destructive, 100 percent inspection in-line at full production volumes. The technology is also used in pharmaceutical manufacturing and nutraceutical manufacturing, tobacco processing, waste recycling and other industries. Compared to manual sorting, which is subjective and inconsistent, optical sorting helps improve product quality, maximize throughput and increase yields while reducing labor costs. == History == Optical sorting is an idea that first came out of the desire to automate industrial sorting of agricultural goods like fruits and vegetables. Before automated optical sorting technology was conceived in the 1930s, companies like Unitec were producing wooden machinery to assist in the mechanical sorting of fruit processing. In 1931, a company known as “the Electric Sorting Company” was incorporated and began the creation of the world’s first color sorters, which were being installed and used in Michigan’s bean industry by 1932. In 1937, optical sorting technology had advanced to allow for systems based on a two-color principle of selection. The next few decades saw the installation of new and improved sorting mechanisms, like gravity feed systems and the implementation of optical sorting in more agricultural industries. In the late 1960s, optical sorting began to be implemented to new industries beyond agriculture, like the sorting of ferrous and non-ferrous metals. By the 1990s, optical sorting was being used heavily in the sorting of solid wastes. With the large technological revolution happening in the late 1990s and early 2000s, optical sorters were being made more efficient via the implementation of new optical sensors, like CCD, UV, and IR cameras. Today, optical sorting is used in a wide variety of industries and, as such, is implemented with a varying selection of mechanisms to assist in that specific sorter’s task. == The sorting system == In general, optical sorters feature four major components: the feed system, the optical system, image processing software, and the separation system. The objective of the feed system is to spread products into a uniform monolayer so products are presented to the optical system evenly, without clumps, at a constant velocity. The optical system includes lights and sensors housed above and/or below the flow of the objects being inspected. The image processing system compares objects to user-defined accept/reject thresholds to classify objects and actuate the separation system. The separation system — usually compressed air for small products and mechanical devices for larger products, like whole potatoes — pinpoints objects while in-air and deflects the objects to remove into a reject chute while the good product continues along its normal trajectory. The ideal sorter to use depends on the application. Therefore, the product's characteristics and the user's objectives determine the ideal sensors, software-driven capabilities and mechanical platform. == Sensors == Optical sorters require a combination of lights and sensors to illuminate and capture images of the objects so the images can be processed. The processed images will determine if the material should be accepted or rejected. There are camera sorters, laser sorters and sorters that feature a combination of the two on one platform. Lights, cameras, lasers and laser sensors can be designed to function within visible light wavelengths as well as the infrared (IR) and ultraviolet (UV) spectrums. The optimal wavelengths for each application maximize the contrast between the objects to be separated. Cameras and laser sensors can differ in spatial resolution, with higher resolutions enabling the sorter to detect and remove smaller defects. === Cameras === Monochromatic cameras detect shades of gray from black to white and can be effective when sorting products with high-contrast defects. Sophisticated color cameras with high color resolution are capable of detecting millions of colors to better distinguish more subtle color defects. Trichromatic color cameras (also called three-channel cameras) divide light into three bands, which can include red, green and/or blue within the visible spectrum as well as IR and UV. The interaction of different materials with parts of the electromagnetic spectrum make these contrasts more evident than how they appear to the naked human eye. Coupled with intelligent software, sorters that feature cameras are capable of recognizing each object's color, size and shape; as well as the color, size, shape and location of a defect on a product. Some intelligent sorters even allow the user to define a defective product based on the total defective surface area of any given object. === Lasers === While cameras capture product information based primarily on material reflectance, lasers and their sensors are able to distinguish a material's structural properties along with their color. This structural property inspection allows lasers to detect a wide range of organic and inorganic foreign material such as insects, glass, metal, sticks, rocks and plastic; even if they are the same color as the good product. Lasers can be designed to operate within specific wavelengths of light; whether on the visible spectrum or beyond. For example, lasers can detect chlorophyll by stimulating fluorescence using specific wavelengths; which is a process that is very effective for removing foreign material from green vegetables. === Camera/laser combinations === Sorters equipped with cameras and lasers on one platform are generally capable of identifying the widest variety of attributes. Cameras are often better at recognizing color, size and shape while laser sensors identify differences in structural properties to maximize foreign material detection and removal. === Hyperspectral Imaging === Driven by the need to solve previously impossible sorting challenges, a new generation of sorters that feature multispectral and hyperspectral imaging Optical Sorters. Like trichromatic cameras, multispectral and hyperspectral cameras collect data from the electromagnetic spectrum. Unlike trichromatic cameras, which divide light into three bands, hyperspectral systems can divide light into hundreds of narrow bands over a continuous range that covers a vast portion of the electromagnetic spectrum. This opens the door for more detailed analysis that leads to a more consistent product. Using IR alone might detect some defects, but combining it with a broader range of the spectrum makes it more effective. Compared to the three data points per pixel collected by trichromatic cameras, hyperspectral cameras can collect hundreds of data points per pixel, which are combined to create a unique spectral signature (also called a fingerprint) for each object. When complemented by capable software intelligence, a hyperspectral sorter processes those fingerprints to enable sorting on the chemical composition of the product. This is an emerging area of chemometrics. == Software-driven intelligence == Once the sensors capture the object's response to the energy source, image processing is used to manipulate the raw data. The image processing extracts and categorizes information about specific features. The user then defines accept/reject thresholds that are used to determine what is good and bad in the raw data flow. The art and science of image processing lies in developing algorithms that maximize the effectiveness of the sorter while presenting a simple user-interface to the operator. Object-based recognition is a classic example of software-driven intelligence. It allows the user to define a defective product based on where a defect lies on the product and/or the total defective surface area of an object. It offers more control in defining a wider range of defective products. When used to control the sorter's ejection system, it can improve the accuracy of ejecting defective products. This improves product quality and increases yields. New software-driven capabilities are constantly being developed to address the specific needs of various applications. As computing hardware becomes more powerful, new software-driven advancements become possible. Some of these advancements enhance the effectivene
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