AI For Students With Adhd

AI For Students With Adhd — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Cloud printing

There are, in essence, three kinds of Cloud printing. == Benefits == 76% of IT teams have moved, or plan to move, their print workflows to the cloud due to its simplicity. Consumers can print easily to any printer from their PC, tablet or smartphone, while the Cloud print service monitors the supplies level. Many printer vendors such as Lexmark propose an automatic supplies shipment based on the real-time analysis of the printer supplies and user behavior to ensure printing will always be possible. For IT department, Cloud Printing eliminates the need for print servers and represents the only way to print from Cloud virtual desktops and servers. For consumers, cloud ready printers eliminate the need for PC connections and print drivers, enabling them to print from mobile devices. As for publishers and content owners, cloud printing allows them to "avoid the cost and complexity of buying and managing the underlying hardware, software and processes" required for the production of professional print products. Leveraging cloud print for print on demand also allows businesses to cut down on the costs associated with mass production. Moreover, cloud printing can be considered more eco-friendly, as it significantly reduces the amount of paper used (13% reduction in print jobs yearly) and lowers carbon emissions from transportation. As many companies move their IT to the Cloud, some adopting the Windows 365 and Azure Virtual Desktop services from Microsoft, the connection from the Cloud environment to the on-premise printers become an issue as opening ports for incoming print flow traffic is not an option. In 2020, at the exact same time Google discontinued its Google Print offer, Microsoft has announced its Universal Print service offer, aimed at making printing compatible with Cloud Desktop environments, making printing driver-free and simple with no client to install on PC. With Universal Print Microsoft has built a disrupting architecture with a value proposition commodifying printers, removing print servers and drivers, allowing to move printers to VLAN for security purpose and printing from anywhere. Clients are free to use any printer from any model as they all work the same, clients are not tied anymore to any printer brand and that gave a significant boost to the Cloud print market. That Microsoft Universal Print architecture provides APIs to third-party developers who can develop add-ons such as Celiveo 365 to extend Microsoft Cloud Print with added features such as access control on printers and copiers, follow-me pull print, data encryption, advanced usage reporting or charge back. == Providers of Consumer Cloud Printing Solutions == Before 2020 only a handful of providers used to work towards a professional cloud print solution, operating in their own niche or focus on mobile devices. In 2020 Microsoft has boosted that market by announcing its Universal Print Cloud printing service and since then many publishers have started to propose solutions for that growing market. The Covid pandemic also created the need for employees to be able to print at home when using the corporate IT software. Closed VPN often prevent accessing home network printers from corporate laptops and Full Public Cloud solutions are meant to be a solution to that problem. After the decision by Google to terminate Google Cloud Print service on 31 December 2020, most printer vendors released their own mobile cloud solution to fill the gap, while Hewlett-Packard implemented its own cloud print with their ePrint solution. Those solutions are often proprietary, only working on printers proposed by the vendor. Google has decided to let third-party developers develop Cloud Print solutions and to limit its scope to certifying the best Print Management offers compatible with its Chrome Enterprise Cloud ecosystem. == Providers of Corporate Cloud Printing solutions == While many print solutions claim to be "Cloud Printing", there are actually three categories: full Private Cloud, full Public Cloud, and Hybrid Cloud. Their differences are real and have an impact on the overall TCO as the more software there is on-site, the more hidden cost there are. In the Full Public Cloud category, independent SaaS vendors like Celiveo, ezeep , Printix , and Y Soft support a wide range of printer brands and models, allowing clients to buy the best printer without being locked on any brand. They are leveraging cloud computing technology to offer cloud-based print infrastructure and cloud-based printing software as a Service (SaaS). These solutions have integrations to cloud enabled printers or provide embedded printer agents. They feature allow users to print to any printer in any network, isolated network or not, even if that printer is otherwise not reachable from the user's computer. This also allows IT departments to move printers to VLAN for maximum security, like what they are doing with IP phones. Google Chrome Enterprise Cloud ecosystem has its own technical particularities and Google certifies Print Management solutions, ensuring they comply with Google technical requirement, yet letting each solution differentiate from others with specific features or security. Many of solutions for Chrome Enterprise are Hybrid, a few are Full Public Cloud. Industry experts believe that as these services become more popular, users will no longer consider printers as necessary assets but rather as devices that they can access on demand when the need to generate a printed page presents itself. == Caveats of Cloud Printing == == Security == Print jobs flow through Public Internet. It is therefore important to verify no Man-in-the-Middle attack can be performed. The only technical solution is to ensure each printer and PC uses a non-self-generated cryptographic token or certificate allowing TLS mutual authentication and specific data encryption. Self-generated printer certificates are unknown from the Cloud and prevent trusted authentication. Microsoft has implemented its Zero Trust Access security in its Universal Print service, it generates a unique certificate on printers compatible with its service. Other Cloud Printing SaaS providers have followed Microsoft on that High Security path. Print jobs data stored on the Cloud is sensitive as it contains user information as well as all information appearing on pages. Good practices require such data is encrypted at rest and in motion, using asymmetric PKI keys instead of fixed encryption keys. Some solutions require to open incoming traffic ports on the firewall to let Cloud services communicate with printers attached behind that firewall (most of the time for IPP/IPPS flows), some other solutions use a pull model where the communication is always initiated by the printer and no firewall port needs to be open. In terms of security the later is to be preferred.
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Objective vision

Objective Vision (Object Oriented Visionary) is a project mainly aimed at real-time computer vision and simulation vision of living creatures. it has three sections containing an open-source library of programming functions for using inside the projects, Virtual laboratory for scholars to check the application of functions directly and by command-line code for external and instant access, and the research section consists of paperwork and libraries to expand the scientific prove of works. == Background == The process has been used in the OVC libraries is as same as what's happening when living see a picture, and it's designed to give the researchers to experience the brain's visual cortex most close simulation for picture perception. The OVC was designed to work as a simulated visual cortex that has a critical job in processing and classify the objects to make it easier to work with pictures and graphical perception and processing. The human brain is much more aware of how it solves complex problems such as playing chess or solving algebra equations, which is why computer programmers have had so much success building machines that emulate this type of activity. but when the whole process is still a riddle that how the entities visionary system works. The project was simulated the visionary system by how it starts to convert the signals to image(actually the edges and colors) and then recognizing the shapes to find a relation between brain's information and image. The Objective Visionary system actually is concentrating on the separable sections, this separation gives the application visionary system the excellence processing result, because with this method the system do not waste much time on processing non significant sections and signals. this operation in the Objective Vision project called objective processing and because the O.V. mission is focused on human visionary simulation, so the developer refers with Objective Vision. == History == Objective-Vision is a Human (Natural) Visionary simulation Project developed by Michael Bidollahkhany. Following an explosion of interest during the 21st century were characterized by the maturing of the field and the significant growth of active applications; simulation of visionary systems, visionary based autonomous vehicle guidance, medical imaging (2D and 3D) and automatic surveillance are the most rapidly developing areas. This progress can be seen in an increasing number of software and hardware products on the market, as well as in a number of digital image processing software and APIs and also machine vision courses offered at universities worldwide. Therefore, the OVC project has been released as a research software project in 2016. One of important parts of this project was O.V.C. (Objective Vision Class library), that was designed to able companies and scientists to use the brain's most likely functionalities as visionary libraries to simplify and accelerate the image processing algorithms developments. The project started under MIT copyright license, but since 2018 the project continued as classified based on sponsors opinion. == The Algorithm == As developers claimed the algorithm used in the class library and developer's kit of project has been developed based on natural visionary system, and the functionalities containing image processing, optimization and labeling etc. are mostly upgraded and near techniques. Suppose that we've a picture of a jungle, or somewhere else, with this library developer will be able to manipulate not only the pixel of images for data extraction, but automatically based on which algorithm is used and image quality, he can manipulate directly a list of objects, same pixels and every data project needs to have, said the developer in his lecture answering how the algorithm works. === Viewpoint === For long times digital image processing and storing, was actually by processing just pixels; this Project tries to present a new kind of image processing and even storing, "objective vision" or "object-oriented visionary" is called. This project officially launched in May 2016, with the aim of making more adaptation between Computer Vision (Include Visionary, Digital image processing, discernment and even Perception) and Human Visual System; about development of the project: "...so we decided to research on Human Vision System, besides we worked on Artificial Retinal image processing and new visionary optimization unit(Presented at Istanbul Technical University Conference(Turkey 2015-2016)) and grew our research to Visionary CORTEX of Brain", Michael Bidollahkhany said. == Applications == The OVC application areas include: 2D and 3D feature toolkits Egomotion estimation Human–computer interaction (HCI) Mobile robotics Motion understanding Object identification Segmentation and recognition Stereopsis stereo vision: depth perception from two cameras Structure from motion (SFM) Motion tracking == Programming language == In first initial release of Objective Visionary Project the algorithm has been written in C++ and C#, and the virtual laboratory has been developed in C# and Delphi. Based on developers last lecture since the second release the complete algorithm has been re-written in C# based on .Net Core 1.0 to make it easier to work on different operating systems.
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EasyA

EasyA is a web3 technology company and education platform based in London (United Kingdom), founded in 2022 by Phil Kwok and Dom Kwok. EasyA was officially launched in 2022, focusing on web3 technologies. This community was influenced by the founders' experiences during the COVID-19 pandemic and early collaborations with universities and other educational institutions. Subsequently, the community was used as a foundation for developing Web3-related initiatives, including the organisation of EasyA's first Web3 hackathon in 2022. The EasyA app has over one million users and provides educational content on various blockchain technologies. EasyA Labs is a separate initiative focused on developing products intended to improve accessibility to cryptocurrency for a broader audience.
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Final Cut Express

Final Cut Express was a video editing software suite created by Apple Inc. It was the consumer version of Final Cut Pro and was designed for advanced editing of digital video as well as high-definition video, which was used by many amateur and professional videographers. Final Cut Express was considered a step above iMovie in terms of capabilities, but a step underneath Final Cut Pro and its suite of applications. As of June 21, 2011, Final Cut Express was discontinued in favor of Final Cut Pro X. == History == Final Cut Express 1.0, based on Final Cut Pro 3, was released at Macworld Conference and Expo in San Francisco in 2003. The second version, based on Final Cut Pro 4, was released at Macworld San Francisco in 2004. The third version, capable of editing high definition video, was also announced at Macworld San Francisco a year later, and was released as Final Cut Express HD in February 2005. It was based on Final Cut Pro HD (version 4.5) and included LiveType 1.2 and Soundtrack 1.2. Final Cut Express version 3.5 was released with little fanfare in May 2006 as a Universal Binary. In addition to improving real-time rendering with Dynamic RT, version 3.5 upgraded LiveType to version 2.0 and Soundtrack to version 1.5. In November 2007, Apple released Final Cut Express 4, which although it did not support real-time editing in the AVCHD format (it only allowed for transcoding AVCHD to Apple Intermediate Codec (AIC) provided that the camera was actually attached to the computer - it did not convert AVCHD files stored elsewhere and is currently for Intel processors only), imported iMovie '08 projects and included 50 new filters. It did not include Soundtrack 1.5, but it still included LiveType which enables users to create advanced text for the movies they created in Final Cut. The price was dropped from $299 for version 3.5 to $199 for version 4.0. In June 2011, Final Cut Express was officially discontinued, in favor of Final Cut Pro X. == Features == Final Cut Express' interface was identical to that of Final Cut Pro, but lacks some film-specific features, including Cinema Tools, multi-cam editing, batch capture, and a time code view. The program performed 32 undo operations, while Final Cut Pro did 99 [2]. Features the program did include were: The ability to keyframe filters Dynamic RT, which changes real-time settings on-the-fly Motion path keyframing Opacity keyframing Ripple, roll, slip, slide and blade edits Picture-in-picture and split-screen effects Up to 99 video tracks and 12 compositing modes Up to 99 audio tracks Motion project import Two-way color correction. Chroma key One feature of Final Cut Express that was not available in Final Cut Pro is the ability to import iMovie '08 projects (though transitions are not preserved). === RT Extreme === Inherited from Final Cut Pro, Final Cut Express features RT Extreme, which allows previews of some video filters and transitions without rendering. Audio that is not in the native AIFF file format needs rendering before it can be played back. RT Extreme has three modes: 'Safe', for seeing multiple video layers at a quality that more or less guarantees a smooth playback; 'Unlimited', which allows the maximum number of composited video layers to be viewed at the same time; and 'Dynamic', which alternates between these settings depending on how many simultaneous video tracks are present. Frame dropping may result from using 'Unlimited' on low-resource machines. === Boris Calligraphy === Like Final Cut Pro, Express also comes with Boris Calligraphy, a plugin for advanced titling and scrolling/crawling titles more sophisticated than the ones that can be created with the built-in title overlays. Calligraphy has a WYSIWYG interface and features wrapping, alignment, leading, kerning and tracking features, as well as allowing up to five custom outlines and five custom drop shadows to be defined for a selected portion of the title. == Soundtrack == Prior to version 4, Final Cut Express included Soundtrack 1.5, a music program similar to the consumer-level GarageBand, but designed for videographers who wish to add music to their films. Soundtrack comes with around 4,000 professionally recorded instrument loops and sound effects that can be arranged in multiple tracks beneath the video track. To use Soundtrack, users export their Final Cut Express sequence, or a marked portion thereof, as a reference file, which can include scoring markers defined in the timeline. This reference file can be imported as the video track in Soundtrack. Soundtrack is functionally and visually identical to Soundtrack Pro's multitrack editing mode, but includes fewer Logic plugins and lacks the highly regarded noise removal tool. Soundtrack was removed from Final Cut Express 4, which lowered its price and may have encouraged people to buy Logic Express.
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Expectation propagation

Expectation propagation (EP) is a technique in Bayesian machine learning. EP finds approximations to a probability distribution. It uses an iterative approach that uses the factorization structure of the target distribution. It differs from other Bayesian approximation approaches such as variational Bayesian methods. More specifically, suppose we wish to approximate an intractable probability distribution p ( x ) {\displaystyle p(\mathbf {x} )} with a tractable distribution q ( x ) {\displaystyle q(\mathbf {x} )} . Expectation propagation achieves this approximation by minimizing the Kullback–Leibler divergence K L ( p | | q ) {\displaystyle \mathrm {KL} (p||q)} . Variational Bayesian methods minimize K L ( q | | p ) {\displaystyle \mathrm {KL} (q||p)} instead. If q ( x ) {\displaystyle q(\mathbf {x} )} is a Gaussian N ( x | μ , Σ ) {\displaystyle {\mathcal {N}}(\mathbf {x} |\mu ,\Sigma )} , then K L ( p | | q ) {\displaystyle \mathrm {KL} (p||q)} is minimized with μ {\displaystyle \mu } and Σ {\displaystyle \Sigma } being equal to the mean of p ( x ) {\displaystyle p(\mathbf {x} )} and the covariance of p ( x ) {\displaystyle p(\mathbf {x} )} , respectively; this is called moment matching. == Applications == Expectation propagation via moment matching plays a vital role in approximation for indicator functions that appear when deriving the message passing equations for TrueSkill.
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Colors!

Colors! is a series of digital painting applications for handheld game consoles and mobile devices. Originally created as a homebrew application for Nintendo DS (as Colors!), which was since legitimately distributed on PlayStation Vita, iOS, and Android, the project eventually evolved into an officially licensed application for Nintendo 3DS (as Colors! 3D) and Nintendo Switch (as Colors Live). == History == === Colors! === Colors! was originally released in June 2007 as a simple homebrew painting application for the Nintendo DS. It was developed by Jens Andersson, a programmer and designer on sabbatical from the games industry who wanted to experiment with the potential of the new handheld platform. Shortly after, Rafał Piasek created an online gallery where users could upload paintings made with the program. Colors! quickly became one of the best-known homebrew applications on the Nintendo DS, and in September 2008, it was also released for the iPhone and iPod Touch. As of August 2010, it had been downloaded almost half a million times. It was voted the most popular homebrew application on the Nintendo DS by readers of the R4 for DS blog. Development of Colors! DS homebrew officially ended in December 2010 although the official gallery still accepted submissions from DS users until 2020 when Colors! Gallery was discontinued. === Colors! 3D === Colors! 3D is a successor to the application Colors! for the Nintendo 3DS. It was released as an officially licensed application for the Nintendo eShop in North America on April 5, 2012, and in the PAL region on April 19, 2012. It was later released in Japan on August 21, 2013, published by Arc System Works. Colors! 3D allows users to draw on five layers, each on their own stereoscopic 3D plane. Drawing is done on the bottom screen, while the top screen displays the painting in 3D. While drawing, players can use the various controls on the Nintendo 3DS to change layers, zoom and pan, and alter the pressure of their brush. Pressing the L button allows users to access a menu to change brush type, size, and opacity, modify the layers, use the camera to provide references, and more. When the user finishes their painting, they can export it to the SD card for viewing in the Nintendo 3DS Camera application. Users can also upload their finished creations to an online gallery, viewed on the 3DS or the official website. Gallery features include hashtags and the ability to follow artists and post comments. Each painting also features a replay feature that allows viewers to see how it was drawn. The application also features local multiplayer, allowing several people to work cooperatively on a painting. In April 2024, the developers of Colors! 3D collaborated with the Pretendo Network project to officially add support for the application, meaning Colors! 3D will continue to operate as normal when using Pretendo Network. ==== Reception ==== IGN gave the application a score of 9.0 and an Editor's Choice award, praising its simple interface and tutorials. Destructoid gave the app a 9.0, calling it "a simple and incredibly fun tool with an amazing community of artists proudly displaying their beautiful and funny 3D images." Nintendo Life gave the app a 9/10, stating, "Though lacking in any structured play, Colors! 3D’s robust free drawing system and unique ability to let anyone create their own three-dimensional artwork more than make up for this." === Colors Live === A Nintendo Switch successor called Colors Live (stylised as Colors L!ve) was released in 2020 after being funded via a Kickstarter campaign. This expanded upon the features of previous installments by adding new brushes, increasing the maximum number of layers to ten, and introducing blend modes. A new game mode called Colors Quest was also included. A pressure-sensitive pen called the Colors SonarPen was developed in collaboration with GreenBulb to facilitate drawing on the Nintendo Switch, and comes pre-bundled with physical copies of the game. ==== Colors Quest ==== This new mode acts as a story-driven adventure wherein players are given a daily drawing challenge with a specific theme and certain stipulations that must be fulfilled. Once the drawing is complete, players must anonymously score other players' submissions, these scores are then aggregated to produce a personal ranking that measures the improvement in the player's art skills over time.
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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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List of robotics journals

List of robotics journals includes notable academic and scientific journals that focus on research in the field of robotics and automation. == Journals == Acta Mechanica et Automatica Advanced Robotics Annual Review of Control, Robotics, and Autonomous Systems IEEE Robotics and Automation Letters IEEE Transactions on Robotics IEEE Transactions on Field Robotics The International Journal of Advanced Manufacturing Technology International Journal of Humanoid Robotics International Journal of Robotics Research Journal of Cognitive Engineering and Decision Making Journal of Field Robotics Journal of Intelligent & Robotic Systems Paladyn Robotics and Autonomous Systems Robotics Science Robotics SLAS Technology
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Odor source localization

Odor source localization (OSL) is the problem of locating the origin of an airborne or waterborne chemical plume using one or more mobile sensors, typically robots equipped with chemical sensors. The task sits at the intersection of robotics, fluid dynamics and machine olfaction. Chemical plumes in turbulent flows are intermittent and patchy, and most chemical sensors respond slowly and have limited selectivity, so the instantaneous reading available to a moving sensor is a poor proxy for the underlying time-averaged concentration field. Robotic OSL has been studied since the late 1980s and has applications including the detection of gas leaks, search and rescue after industrial accidents, and environmental monitoring of industrial emissions. == History == Robotic odor search emerged in the late 1980s and 1990s, drawing on earlier work in chemical ecology that had described how moths and other insects locate distant pheromone sources. R. A. Russell at Monash University was among the first to build mobile robots that followed chemical trails on the floor and tracked airborne odor plumes. Distributed and multi-robot odor search were investigated by Hayes, Martinoli and Goodman at the California Institute of Technology and EPFL, who studied cooperative plume-tracing on simulated and physical robot swarms. In 2007 Vergassola, Villermaux and Shraiman introduced infotaxis, an information-theoretic search strategy in which a sensor moves so as to maximize the expected information gain about source location, rather than following a chemical concentration gradient; the paper appeared in Nature and prompted substantial follow-up work in the robotics community. From the mid-2010s, multi-rotor unmanned aerial vehicles carrying lightweight chemical sensors became a common experimental platform for OSL research. == Problem formulation == OSL is generally decomposed into three sub-problems: plume detection (deciding whether a chemical signal is present), plume traversal (moving so as to remain in contact with the plume), and source declaration (deciding when the source has been reached). The mathematical difficulty depends strongly on the assumed dispersion model. In laminar or low-Reynolds number flows a Gaussian advection–diffusion model gives a smooth concentration field with a well-defined gradient. In turbulent flows, which dominate most realistic environments, the plume is filamentary: the sensor receives short, randomly spaced bursts of chemical separated by periods of zero signal, and the time-averaged field is not a useful guide on the time scales at which a robot must act. Source-term estimation, surveyed by Hutchinson and colleagues, additionally aims to recover both the position and the release rate of the source from the observed concentrations, often using probabilistic filters. == Biological inspiration == Many OSL strategies are explicitly modeled on the behavior of male moths flying upwind toward a pheromone source. As reviewed by Cardé and Willis, moths combine an upwind surge whenever they detect a filament of pheromone with a wider crosswind cast when contact is lost, producing a characteristic zig-zag trajectory that has been transposed onto mobile robots by several groups. Other biological models draw on the search behavior of dogs and of marine animals such as blue crabs and lobsters, which integrate chemical and bilateral hydrodynamic cues over much shorter ranges. == Algorithms and strategies == === Reactive strategies === Reactive strategies select the next motion as a direct function of the current sensor reading. Chemotaxis steers along the locally estimated concentration gradient, which is effective in laminar plumes but degrades severely in turbulence. Anemotaxis exploits a measured wind direction by surging upwind when chemical contact is made. The bio-inspired cast-and-surge family combines anemotaxis with a deterministic crosswind cast on contact loss, and is the dominant reactive approach for turbulent environments. === Probabilistic and information-theoretic strategies === Probabilistic methods maintain a posterior distribution over possible source locations and choose actions that improve that distribution. The infotaxis strategy of Vergassola, Villermaux and Shraiman selects the move that maximizes the expected reduction in entropy of the source-location posterior, and is effective in regimes where the spatial gradient is unusable. Bayesian source-term estimation extends this idea by inferring both source position and release rate, typically using particle filters or sequential Monte Carlo. === Map-based strategies === Map-based methods build a spatial model of the time-averaged gas distribution from sensor readings collected along the robot's trajectory and search for local maxima in that model. Lilienthal and colleagues describe a family of kernel-based gas distribution mapping techniques in which point measurements are convolved with a Gaussian kernel to produce a spatially extrapolated estimate. Such methods are most useful when the source can be assumed quasi-stationary and the robot is able to revisit locations. === Multi-robot and swarm strategies === Multiple robots searching cooperatively can shorten search times. Cooperative formations spread the sensors across the crosswind axis, making detection of an intermittent plume more likely. Swarm-based approaches, reviewed by Wang and colleagues, deploy larger numbers of simpler agents and rely on collective behavior rather than centralized planning; reported advantages include improved coverage of the search area and the possibility of locating multiple sources in parallel. == Sensors and platforms == Most OSL systems use metal-oxide semiconductor (MOX) sensors, photoionization detectors or electrochemical cells, which trade off sensitivity, selectivity, response time and power consumption. Ishida and colleagues describe how these sensors interact with airflow around the robot body, an effect that motivates careful aerodynamic design and active sampling. Mobile platforms include wheeled ground robots for indoor and structured outdoor environments, multi-rotor unmanned aerial vehicles for open spaces and elevated sources, and autonomous underwater vehicles for chemical plumes in the marine environment. == Notable systems == Among the early demonstrations, R. A. Russell's series of differential-drive robots at Monash University localized volatile sources in still and ventilated rooms during the 1990s. The Smelling Nano Aerial Vehicle reported by Burgués and colleagues used a Crazyflie nano-quadcopter (approximately 27 grams in mass and 10 cm across) carrying a custom MOX gas sensing board, and built three-dimensional gas distribution maps of indoor releases from sweeping flights of less than three minutes. The GADEN simulator, released by Monroy and colleagues, couples three-dimensional dispersion computed from an OpenFOAM CFD solver with models of MOX and photo-ionization gas sensors, and is widely used to test mobile-robot olfaction algorithms in simulation. == Applications == Reported applications include the localization of natural-gas and methane leaks in urban infrastructure, search for chemical contamination after industrial accidents, search and rescue, and environmental monitoring of industrial emissions. Drug- and explosives-detection robots are an adjacent application area, although these typically rely on close-range sniffing rather than long-range plume tracking. == Open challenges == Open challenges identified in recent reviews include the limited speed, selectivity and stability of available chemical sensors; the scarcity of standardized, large-scale benchmarks comparable to those available in computer vision; reliable handling of multi-source environments, where standard single-source assumptions fail; and the integration of OSL with other autonomous-vehicle subsystems such as obstacle avoidance and navigation in three-dimensional turbulent flow.
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Video renderer

A video renderer is software that processes a video file and sends it sequentially to the video display controller card for display on a computer screen. An example of a video renderer, is the VMR-7 that was used by Microsoft's DirectShow. An example of a UNIX video renderer is the one container within GStreamer. Commonly used video renderers are: Enhanced Video Renderer VMR9 Renderless Haali's Video Renderer Madvr Video Renderer JRVR, a part of JRiver Media Center
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Captions (app)

Mirage (formerly known as Captions) is a video-generating, video-editing and AI research company headquartered in New York City. Their first app, Captions, is available on iOS, Android, and Web and offers a suite of tools aimed at streamlining the creation and editing of videos. Their enterprise platform, Mirage Studio, generates AI actors and videos for marketing assets and video campaigns. == History == Mirage was co-founded by Gaurav Misra and Dwight Churchill. During Misra's time leading design engineering at Snap Inc., he followed the rise of a new category of video, the "talking video." In 2021, Misra left Snap to found Mirage with his former colleague Churchill. Later that year, the Captions app launched with early backing from venture capital firms Sequoia Capital and Andreessen Horowitz as well as individual investors. In 2023, the company released Lipdub, an Al dubbing app which translates any video with spoken audio into 28 languages. In October 2023, Captions shared that it maintained over 100,000 daily active users with "about a million" videos being created monthly. In November 2024, Captions acquired AlpacaML, a generative AI company that focused on art and other images. In June 2025, Captions launched Mirage Studio, for marketers and advertising agencies. In September 2025, Captions rebranded their company to Mirage. This change reflects the company's focus on developing their proprietary foundation model and future video products. == Products == The Captions app offers features to automate common production tasks including captioning, editing, dubbing, script creation, and music integration. Mirage Studio allows users to generate AI avatars and create short-form videos from prompts or audio. == Awards == In 2023, the company was recognized as part of Fast Company's "Next Big Things In Tech" series. In 2024, the company won 2 Webby Awards for Best Use of AI & Machine Learning and Creative Production.
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Microsoft Fresh Paint

Fresh Paint is a painting app developed by Microsoft and released on May 25, 2012. == History == Fresh Paint originated from a Microsoft Research project known as Project Gustav, an endeavor to reproduce the behavior of physical oil paint on a digital medium. To push the boundaries of simulating oil on a digital medium, the research team created a physics model that precisely replicated on a screen what would happen in the real world if you combined oil, a surface and a tool such as a paint brush. Two publications, Detail-Preserving Paint Modeling for 3D Brushes and Simple Data-Driven Modeling of Brushes, were released as a result of the team’s findings. After a variety of internal testing Project, Gustav was codenamed Digital Art. Partnering with The Museum of Modern Art, Digital Art was tested for a year by 60,000 people. With feedback culled from MoMA, developers expanded the existing physics model, experimenting with how real oil paint blended and reacted to the texture of a canvas. After final adjustments were made, Digital Art was rebranded as Fresh Paint. It was released to the public on 25 May 2012.
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Hierarchical control system

A hierarchical control system (HCS) is a form of control system in which a set of devices and governing software is arranged in a hierarchical tree. When the links in the tree are implemented by a computer network, then that hierarchical control system is also a form of networked control system. == Overview == A human-built system with complex behavior is often organized as a hierarchy. For example, a command hierarchy has among its notable features the organizational chart of superiors, subordinates, and lines of organizational communication. Hierarchical control systems are organized similarly to divide the decision making responsibility. Each element of the hierarchy is a linked node in the tree. Commands, tasks and goals to be achieved flow down the tree from superior nodes to subordinate nodes, whereas sensations and command results flow up the tree from subordinate to superior nodes. Nodes may also exchange messages with their siblings. The two distinguishing features of a hierarchical control system are related to its layers. Each higher layer of the tree operates with a longer interval of planning and execution time than its immediately lower layer. The lower layers have local tasks, goals, and sensations, and their activities are planned and coordinated by higher layers which do not generally override their decisions. The layers form a hybrid intelligent system in which the lowest, reactive layers are sub-symbolic. The higher layers, having relaxed time constraints, are capable of reasoning from an abstract world model and performing planning. A hierarchical task network is a good fit for planning in a hierarchical control system. Besides artificial systems, an animal's control systems are proposed to be organized as a hierarchy. In perceptual control theory, which postulates that an organism's behavior is a means of controlling its perceptions, the organism's control systems are suggested to be organized in a hierarchical pattern as their perceptions are constructed so. == Control system structure == The accompanying diagram is a general hierarchical model which shows functional manufacturing levels using computerised control of an industrial control system. Referring to the diagram; Level 0 contains the field devices such as flow and temperature sensors, and final control elements, such as control valves Level 1 contains the industrialised Input/Output (I/O) modules, and their associated distributed electronic processors. Level 2 contains the supervisory computers, which collate information from processor nodes on the system, and provide the operator control screens. Level 3 is the production control level, which does not directly control the process, but is concerned with monitoring production and monitoring targets Level 4 is the production scheduling level. == Applications == === Manufacturing, robotics and vehicles === Among the robotic paradigms is the hierarchical paradigm in which a robot operates in a top-down fashion, heavy on planning, especially motion planning. Computer-aided production engineering has been a research focus at NIST since the 1980s. Its Automated Manufacturing Research Facility was used to develop a five layer production control model. In the early 1990s DARPA sponsored research to develop distributed (i.e. networked) intelligent control systems for applications such as military command and control systems. NIST built on earlier research to develop its Real-Time Control System (RCS) and Real-time Control System Software which is a generic hierarchical control system that has been used to operate a manufacturing cell, a robot crane, and an automated vehicle. In November 2007, DARPA held the Urban Challenge. The winning entry, Tartan Racing employed a hierarchical control system, with layered mission planning, motion planning, behavior generation, perception, world modelling, and mechatronics. === Artificial intelligence === Subsumption architecture is a methodology for developing artificial intelligence that is heavily associated with behavior based robotics. This architecture is a way of decomposing complicated intelligent behavior into many "simple" behavior modules, which are in turn organized into layers. Each layer implements a particular goal of the software agent (i.e. system as a whole), and higher layers are increasingly more abstract. Each layer's goal subsumes that of the underlying layers, e.g. the decision to move forward by the eat-food layer takes into account the decision of the lowest obstacle-avoidance layer. Behavior need not be planned by a superior layer, rather behaviors may be triggered by sensory inputs and so are only active under circumstances where they might be appropriate. Reinforcement learning has been used to acquire behavior in a hierarchical control system in which each node can learn to improve its behavior with experience. James Albus, while at NIST, developed a theory for intelligent system design named the Reference Model Architecture (RMA), which is a hierarchical control system inspired by RCS. Albus defines each node to contain these components. Behavior generation is responsible for executing tasks received from the superior, parent node. It also plans for, and issues tasks to, the subordinate nodes. Sensory perception is responsible for receiving sensations from the subordinate nodes, then grouping, filtering, and otherwise processing them into higher level abstractions that update the local state and which form sensations that are sent to the superior node. Value judgment is responsible for evaluating the updated situation and evaluating alternative plans. World Model is the local state that provides a model for the controlled system, controlled process, or environment at the abstraction level of the subordinate nodes. At its lowest levels, the RMA can be implemented as a subsumption architecture, in which the world model is mapped directly to the controlled process or real world, avoiding the need for a mathematical abstraction, and in which time-constrained reactive planning can be implemented as a finite-state machine. Higher levels of the RMA however, may have sophisticated mathematical world models and behavior implemented by automated planning and scheduling. Planning is required when certain behaviors cannot be triggered by current sensations, but rather by predicted or anticipated sensations, especially those that come about as result of the node's actions.
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Automated restaurant

An automated restaurant or robotic restaurant is a restaurant that uses robots to do tasks such as delivering food and drink to the tables or cooking the food. Restaurant automation means the use of a restaurant management system to automate some or occasionally all of the major operations of a restaurant establishment. More recently, restaurants are opening that have completely or partially automated their services. These may include: taking orders, preparing food, serving, and billing. A few fully automated restaurants operate without any human intervention whatsoever. Robots are designed to help and sometimes replace human labour (such as waiters and chefs). The automation of restaurants may also allow for the option for greater customization of an order. == History == === Vending machines === In the late 19th and early 20th century a number of restaurants served food solely through vending machines. These restaurants were called automats or, in Japan, shokkenki. Customers ordered their food directly through the machines. === Sushi conveyors === Yoshiaki Shiraishi is a Japanese innovator who is known for the creation of conveyor belt sushi. He had the idea following difficulty staffing his small sushi restaurant and managing the restaurant on his own. He was inspired seeing beer bottles on a conveyor belt in an Asahi brewery. Yoshiaki's restaurants are an early example of restaurant automation; they used a conveyor belt to distribute dishes around the restaurant, eliminating the need for waiters. This example of automation dates back to the Japanese economic miracle; the first of Yoshiaki's conveyor belt sushi restaurants was opened under the name Mawaru Genroku Sushi in 1958, in Osaka. === Partial automation === As of 2011, across Europe, McDonald's had already begun implementing 7,000 touch screen kiosks that could handle cashiering duties. From 2015 to 2020, Zume had an automated pizza parlor. Later companies would try to produce smaller, less ambitious devices, with one robotics company producing a machine that could automate the slowest and most repetitive parts of assembling a pizza, such as spreading pizza sauce or placing slices of pepperoni, while leaving other customizations to employees. In 2020, a restaurant in the Netherlands began trialling the use of a robot to serve guests. In September 2021, Karakuri's 'Semblr' food service robot served personalised lunches for the 4,000 employees of grocery technology solutions provider ocado Group's head offices in Hatfield, UK. 2,700 different combinations of dishes were on offer. Customers could specify in grams what hot and cold items, proteins, sauces and fresh toppings they wanted. In 2021, Columbia University School of Engineering and Applied Science engineers developed a method of cooking 3D printed chicken with software-controlled robotic lasers. The “Digital Food” team exposed raw 3D printed chicken structures to both blue and infrared light. They then assessed the cooking depth, colour development, moisture retention and flavour differences of the laser-cooked 3D printed samples in comparison to stove-cooked meat. In June 2022 a California nonprofit chain of residential communities, Front Porch, experimented with robots in dining rooms at two locations to supplement wait staff by carrying plated food and drink to tables, and removing dishes. 65% of residents found the robots helpful, with 51% saying they let the staff spend more quality time with diners. 51% of staff were "excited" and 58% said they enabled more quality time with diners. The chain has 19 senior living communities (and 35 affordable housing communities), so it has potential to expand robots to more dining rooms. It is shifting to memory care, which may affect plans. == Rationales == === Advantages === Efficiency: Automated restaurants can significantly enhance operational efficiency by minimizing human error and reducing service time. With automated ordering, payment, and food preparation systems, customers can enjoy faster service and reduced waiting times. Cost savings: By reducing the need for human staff, automated restaurants can potentially lower labor costs. This can be particularly beneficial in areas with high labor expenses, as it allows for better resource allocation and cost management. Consistency: Automation ensures consistency in food quality and presentation. With precise portion control and standardized cooking methods, customers can expect the same quality and taste in their meals every time they visit. Enhanced customer experience: Self-service kiosks and automated systems provide customers with control and convenience. They can customize their orders, browse through menu options, and pay seamlessly, creating a more interactive and satisfying dining experience. === Disadvantages === Lack of personal touch: Automated restaurants may lack the personal interaction and warmth that traditional restaurants provide. Some customers prefer the human touch, personalized recommendations, and the social aspect of dining out. Technical issues: Reliance on technology means that technical glitches and malfunctions can occur, resulting in service disruptions or delays. Maintenance and technical support become critical in ensuring smooth operations. Limited menu complexity: The automation process may be better suited for standardized menu items rather than complex or customized dishes. The ability to cater to unique dietary preferences or accommodate special requests may be limited. Employment implications: Automated restaurants may result in job losses for traditional restaurant staff, potentially impacting the local workforce. It is important to consider the social and economic implications of adopting such technology. == Locations == Automated restaurants have been opening in many countries. Examples include: Nala Restaurant in Naperville, Illinois Fritz's Railroad Restaurant in Kansas City, Kansas Výtopna, a Railway Restaurant using model trains: franchise of various restaurants and coffeehouses in the Czech Republic Bagger's Restaurant in Nuremberg, Germany FuA-Men Restaurant, a ramen restaurant located in Nagoya, Japan Fōster Nutrition in Buenos Aires, Argentina Dalu Robot Restaurant in Jinan, China Haohai Robot Restaurant in Harbin, China Robot Kitchen Restaurant in Hong Kong Robo-Chef restaurant in Tehran, Iran, started in 2017, is the first robotic and "waiterless" restaurant of the Middle East. MIT graduates opened Spyce Kitchens in downtown Boston, Massachusetts, in 2018 Foodom, under Country Garden Holdings, opened January 12, 2020, in Guangzhou, China Robot Chacha, the first robot restaurant of India, is planning to open in the capital city of New Delhi. Kura Revolving Sushi Bar, with a number of locations in the United States, uses a tablets at tables for ordering, a conveyor belt to deliver food, and robots to deliver drinks and condiments. Chipotle Mexican Grill is beginning to deploy the Hyphen Makeline, which assembles up to 350 bowls and salads automatically per hour, and Chippy, an automatic tortilla chip fryer made by Miso Robotics. Serious Dumplings in Boca Raton, Florida
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Ordered dithering

Ordered dithering is any image dithering algorithm which uses a pre-set threshold map tiled across an image. It is commonly used to display a continuous image on a display of smaller color depth. For example, Microsoft Windows uses it in 16-color graphics modes. With the most common "Bayer" threshold map, the algorithm is characterized by noticeable crosshatch patterns in the result. == Threshold map == The algorithm reduces the number of colors by applying a threshold map M to the pixels displayed, causing some pixels to change color, depending on the distance of the original color from the available color entries in the reduced palette. The first threshold maps were designed by hand to minimise the perceptual difference between a grayscale image and its two-bit quantisation for up to a 4x4 matrix. An optimal threshold matrix is one that for any possible quantisation of color has the minimum possible texture so that the greatest impression of the underlying feature comes from the image being quantised. It can be proven that for matrices whose side length is a power of two there is an optimal threshold matrix. The map may be rotated or mirrored without affecting the effectiveness of the algorithm. This threshold map (for sides with length as power of two) is also known as a Bayer matrix or, when unscaled, an index matrix. For threshold maps whose dimensions are a power of two, the map can be generated recursively via: M 2 n = 1 ( 2 n ) 2 [ 4 M n 4 M n + 2 J n 4 M n + 3 J n 4 M n + J n ] = J 2 ⊗ M n + 1 n 2 M 2 ⊗ J n , {\displaystyle \mathbf {M} _{2n}={\frac {1}{(2n)^{2}}}{\begin{bmatrix}4\mathbf {M} _{n}&4\mathbf {M} _{n}+2\mathbf {J} _{n}\\4\mathbf {M} _{n}+3\mathbf {J} _{n}&4\mathbf {M} _{n}+\mathbf {J} _{n}\end{bmatrix}}=\mathbf {J} _{2}\otimes \mathbf {M} _{n}+{\frac {1}{n^{2}}}\mathbf {M} _{2}\otimes \mathbf {J} _{n},} where J n {\displaystyle \mathbf {J} _{n}} are n × n {\displaystyle n\times n} matrices of ones and ⊗ {\displaystyle \otimes } is the Kronecker product. While the metric for texture that Bayer proposed could be used to find optimal matrices for sizes that are not a power of two, such matrices are uncommon as no simple formula for finding them exists, and relatively small matrix sizes frequently give excellent practical results (especially when combined with other modifications to the dithering algorithm). This function can also be expressed using only bit arithmetic: M(i, j) = bit_reverse(bit_interleave(bitwise_xor(i, j), i)) / n ^ 2 == Pre-calculated threshold maps == Rather than storing the threshold map as a matrix of n {\displaystyle n} × n {\displaystyle n} integers from 0 to n 2 {\displaystyle n^{2}} , depending on the exact hardware used to perform the dithering, it may be beneficial to pre-calculate the thresholds of the map into a floating point format, rather than the traditional integer matrix format shown above. For this, the following formula can be used: Mpre(i,j) = Mint(i,j) / n^2 This generates a standard threshold matrix. for the 2×2 map: this creates the pre-calculated map: Additionally, normalizing the values to average out their sum to 0 (as done in the dithering algorithm shown below) can be done during pre-processing as well by subtracting 1⁄2 of the largest value from every value: Mpre(i,j) = Mint(i,j) / n^2 – 0.5 maxValue creating the pre-calculated map: == Algorithm == The ordered dithering algorithm renders the image normally, but for each pixel, it offsets its color value with a corresponding value from the threshold map according to its location, causing the pixel's value to be quantized to a different color if it exceeds the threshold. For most dithering purposes, it is sufficient to simply add the threshold value to every pixel (without performing normalization by subtracting 1⁄2), or equivalently, to compare the pixel's value to the threshold: if the brightness value of a pixel is less than the number in the corresponding cell of the matrix, plot that pixel black, otherwise, plot it white. This lack of normalization slightly increases the average brightness of the image, and causes almost-white pixels to not be dithered. This is not a problem when using a gray scale palette (or any palette where the relative color distances are (nearly) constant), and it is often even desired, since the human eye perceives differences in darker colors more accurately than lighter ones, however, it produces incorrect results especially when using a small or arbitrary palette, so proper normalization should be preferred. In other words, the algorithm performs the following transformation on each color c of every pixel: c ′ = n e a r e s t _ p a l e t t e _ c o l o r ( c + r × ( M ( x mod n , y mod n ) − 1 / 2 ) ) {\displaystyle c'=\mathrm {nearest\_palette\_color} {\mathopen {}}\left(c+r\times \left(M(x{\bmod {n}},y{\bmod {n}})-1/2\right){\mathclose {}}\right)} where M(i, j) is the threshold map on the i-th row and j-th column, c′ is the transformed color, and r is the amount of spread in color space. Assuming an RGB palette with 23N evenly distanced colors where each color (a triple of red, green and blue values) is represented by an octet from 0 to 255, one would typically choose r ≈ 255 N {\textstyle r\approx {\frac {255}{N}}} . (1⁄2 is again the normalizing term.) Because the algorithm operates on single pixels and has no conditional statements, it is very fast and suitable for real-time transformations. Additionally, because the location of the dithering patterns always stays the same relative to the display frame, it is less prone to jitter than error-diffusion methods, making it suitable for animations. Because the patterns are more repetitive than error-diffusion method, an image with ordered dithering compresses better. Ordered dithering is more suitable for line-art graphics as it will result in straighter lines and fewer anomalies. The values read from the threshold map should preferably scale into the same range as the minimal difference between distinct colors in the target palette. Equivalently, the size of the map selected should be equal to or larger than the ratio of source colors to target colors. For example, when quantizing a 24 bpp image to 15 bpp (256 colors per channel to 32 colors per channel), the smallest map one would choose would be 4×2, for the ratio of 8 (256:32). This allows expressing each distinct tone of the input with different dithering patterns. === A variable palette: pattern dithering === == Non-Bayer approaches == The above thresholding matrix approach describes the Bayer family of ordered dithering algorithms. A number of other algorithms are also known; they generally involve changes in the threshold matrix, which changes the distribution of the "noise" introduced by all kinds of dithering (the difference between the original image and the dithered image). === Halftone === Halftone dithering performs a form of clustered dithering, creating a look similar to halftone patterns, using a specially crafted matrix. === Void and cluster === The Void and cluster algorithm uses a pre-generated blue noise as the matrix for the dithering process. The blue noise matrix keeps the Bayer's good high frequency content, but with a more uniform coverage of all the frequencies involved shows a much lower amount of patterning. The "voids-and-cluster" method gets its name from the matrix generation procedure, where a black image with randomly initialized white pixels is gaussian-blurred to find the brightest and darkest parts, corresponding to voids and clusters. After a few swaps have evenly distributed the bright and dark parts, the pixels are numbered by importance. It takes significant computational resources to generate the blue noise matrix: on a modern computer a 64×64 matrix requires a couple seconds using the original algorithm. This algorithm can be extended to make animated dither masks which also consider the axis of time. This is done by running the algorithm in three dimensions and using a kernel which is a product of a two-dimensional gaussian kernel on the XY plane, and a one-dimensional Gaussian kernel on the Z axis. === Simulated Annealing === Simulated annealing can generate dither masks by starting with a flat histogram and swapping values to optimize a loss function. The loss function controls the spectral properties of the mask, allowing it to make blue noise or noise patterns meant to be filtered by specific filters. The algorithm can also be extended over time for animated dither masks with chosen temporal properties.
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