AI Generator Za Darmo

AI Generator Za Darmo — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • List of robotics journals

    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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  • Prequel (mobile application)

    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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  • Nvidia Omniverse

    Nvidia Omniverse

    Omniverse is a real-time 3D graphics collaboration platform created by Nvidia. It has been used for applications in the visual effects and "digital twin" industrial simulation industries. Omniverse makes extensive use of the Universal Scene Description (USD) format. == Third-party Integrations == Omniverse supports integration with external computer-aided design tools through third-party connectors. For example, academic work has demonstrated a connector linking Omniverse with the open-source CAD system FreeCAD, enabling collaborative access to CAD geometry via the Omniverse Nucleus server and extending Omniverse usage beyond media and entertainment workflows.

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  • Photoanalysis

    Photoanalysis

    Photoanalysis (or photo analysis) refers to the study of pictures to compile various types of data, for example, to measure the size distribution of virtually anything that can be captured by photo. Photoanalysis technology has changed the way mines and mills quantify fragmented material. Images are an effective way to document conditions before, after, and even during blasting activities. The technology is advancing at a high rate, and lenses, storage media memory, light sensitivity and resolution have been improving steadily. Today's digital cameras and camcorders include high-resolution optics, compact size, automatic time and date stamps, good battery life, shutters to freeze motion, and computers to autofocus and eliminate jitter using image stabilization. == Mining == Photoanalysis in mining operations can provide an automated system that forewarns a company of potential problems with materials, leading to economies and reduced damage caused from over-sized materials. It can also help determine the effectiveness of blasts. A company can use this technology to monitor materials moving on a conveyor belt in an underground environment, to measure piles left over from a blast, and even measure the amount of material being carried by dump trucks or vessels to a destination. Photoanalysis is being used on SAG mills worldwide to control the size of rock being crushed. Companies are using this technology to determine the size of particles being processed in the SAG Mill.[1] Archived 2009-05-23 at the Wayback Machine Having oversize material entering the SAG mill makes an operation less efficient, costing companies money in electrical and maintenance costs. Photoanalysis technology can eliminate unwanted material before it enters the mill, keeping rock crushing costs low. == Forestry == Wood chip size can affect the overall quality of a product. With automated photoanalysis systems, companies can remove any unwanted wrong-size particles without stopping their mill process. Photoanalysis can affect how efficiently forestry companies operate. In mills worldwide, photoanalysis technology is improving the use of lumber products, cutting back on the amount of trees being used to operate, and saving companies money through quality control optimization.[2] With the current downturn in the North American forestry industry, operators are looking at making their mills more efficient and effective when processing materials. Photoanalysis technology helps identify any weaknesses in the process by continuously monitoring different sections of an operation. == Agriculture == Agricultural companies can, using photoanalysis, monitor conveyor belts of food without contaminating the product by touching it. Other benefits of photoanalysis systems include: Automated removal of any unwanted material on food conveyor Improved quality control for the most important parts of the agricultural process Pinpoint accuracy that helps the efficiency and effectiveness of product handling techniques The importance of photoanalysis technology is being noticed by the agricultural industry as it identifies any unwanted materials going through the process. In an example, if a mouse is on a conveyor of corn, photoanalysis technology would be able to identify the unwanted object and remove it before it contaminates the whole process. == Origins of photoanalysis technology == Photoanalysis technology was created by using the Waterloo Image Enhancement Process in the 1980s. After further development of the imaging process with explosives producer DuPont, engineers Tom Palangio and Takis Katsabanis began selling photoanalysis software commercially. They later renamed the process WipFrag, standing for Waterloo Image Process Fragmentation Today, photoanalysis technology has evolved into stabilized and portable systems that can automatically capture and analyze results instantly. Thousands of these products are currently being used around the world to measure fragmented material. == Photoanalysis equipment photos == == Fragmentation analysis == Fragmentation analysis is becoming a popular term in mining, agricultural and forestry industries. With the majority of money in these industries directed towards the proper sizing of materials, companies are using fragmentation analysis to determine various factors within an operation.[3] The two main ways a company keeps track of fragmented material are through manual and automated sieving procedures. Manual sieving involves extracting a sample of material to analyze the size distribution. The results can be tabulated within two days. Automated sieving is an advanced way of sieving materials running through a process. Without having to extract the material, photoanalysis can take place, allowing for immediate results with pinpoint accuracy. == Blast Fragmentation Software == Operators are using fragmentation analysis to determine the effectiveness of various blasts. With automated sieving technology, workers can track the success of these blasts and receive instant results. Companies are using these results to determine what blasting method yielded the best results for their specific operation. The common variables associated with blast optimization are the provided Particle Size Distribution (PSD) from a shovel fragmentation system, geology including rock type and fracturing, and energy factor. By using photoanalysis the fragmented materials can be monitored, offering pinpoint accuracy and allowing mine operators to make adjustments to future blasting procedures. See Optical Granulometry to view the automated sieving process. == Pre-crushing analysis == Maintenance costs can be significantly reduced if an operation focuses on the fragmentation of the particles passing through their process. Automated sieving systems can detect and help remove any oversize material before it enters the crusher and causes maintenance problems. It also helps determine the effectiveness of the mining process prior to crushing; the sizing of material is always a critical part of operations in the mining, forestry and agricultural industries. Having an analysis taking place at every major point in an operation allows for the proper tracking of material being processed. Engineers can then determine what part of the process needs improving based solely on the size of material. == Post-crushing analysis == Measuring how effective industrial crushers are, can help save a company millions of dollars in energy costs on an annual basis. There are two components that affect a typical crusher: the size of the material inputted, and the speed at which the crusher is moving. If the user can find a perfect balance between these two components, the materials will be crushed to the right size in the shortest time possible. Meeting the material standards set by governments and large companies can be hard. Having a post-crushing analysis taking place ensures that no oversize material gets shipped; eliminating the chance of getting fined for not meeting industry specifications.

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  • Ray tracing (graphics)

    Ray tracing (graphics)

    In 3D computer graphics, ray tracing is a technique for modeling light transport for use in a wide variety of rendering algorithms for generating digital images. On a spectrum of computational cost and visual fidelity, ray tracing-based rendering techniques, such as ray casting, recursive ray tracing, distribution ray tracing, photon mapping and path tracing, are generally slower and higher fidelity than scanline rendering methods. Thus, ray tracing was first deployed in applications where taking a relatively long time to render could be tolerated, such as still CGI images, and film and television visual effects (VFX), but was less suited to real-time applications such as video games, where speed is critical in rendering each frame. Since 2018, however, hardware acceleration for real-time ray tracing has become standard on new commercial graphics cards, and graphics APIs have followed suit, allowing developers to use hybrid ray tracing and rasterization-based rendering in games and other real-time applications with a lesser hit to frame render times. Ray tracing is capable of simulating a variety of optical effects, such as reflection, refraction, soft shadows, scattering, depth of field, motion blur, caustics, ambient occlusion and dispersion phenomena (such as chromatic aberration). It can also be used to trace the path of sound waves in a similar fashion to light waves, making it a viable option for more immersive sound design in video games by rendering realistic reverberation and echoes. In fact, any physical wave or particle phenomenon with approximately linear motion can be simulated with ray tracing. Ray tracing–based rendering techniques that sample light over a domain typically generate multiple rays and often rely on denoising to reduce the resulting noise. == History == The idea of ray tracing comes from as early as the 16th century, when it was described by Albrecht Dürer, who is credited for its invention. Dürer described multiple techniques for projecting 3-D scenes onto an image plane. Some of these project chosen geometry onto the image plane, as is done with rasterization today. Others determine what geometry is visible along a given ray, as is done with ray tracing. Using a computer for ray tracing to generate shaded pictures was first accomplished by Arthur Appel in 1968. Appel used ray tracing for primary visibility (determining the closest surface to the camera at each image point) by tracing a ray through each point to be shaded into the scene to identify the visible surface. The closest surface intersected by the ray was the visible one. This non-recursive ray tracing-based rendering algorithm is today called "ray casting". His algorithm then traced secondary rays to the light source from each point being shaded to determine whether the point was in shadow or not. Later, in 1971, Goldstein and Nagel of MAGI (Mathematical Applications Group, Inc.) published "3-D Visual Simulation", wherein ray tracing was used to make shaded pictures of solids. At the ray-surface intersection point found, they computed the surface normal and, knowing the position of the light source, computed the brightness of the pixel on the screen. Their publication describes a short (30-second) film "made using the University of Maryland's display hardware outfitted with a 16mm camera. The film showed the helicopter and a simple ground-level gun emplacement. The helicopter was programmed to undergo a series of maneuvers including turns, take-offs, and landings, etc., until it eventually is shot down and crashed." A CDC 6600 computer was used. MAGI produced an animation video called MAGI/SynthaVision Sampler in 1974. Another early instance of ray casting came in 1976, when Scott Roth created a flip book animation in Bob Sproull's computer graphics course at Caltech. The scanned pages are shown as a video in the accompanying image. Roth's computer program noted an edge point at a pixel location if the ray intersected a bounded plane different from that of its neighbors. Of course, a ray could intersect multiple planes in space, but only the surface point closest to the camera was noted as visible. The platform was a DEC PDP-10, a Tektronix storage-tube display, and a printer which would create an image of the display on rolling thermal paper. Roth extended the framework, introduced the term ray casting in the context of computer graphics and solid modeling, and in 1982 published his work while at GM Research Labs. Turner Whitted was the first to show recursive ray tracing for mirror reflection and for refraction through translucent objects, with an angle determined by the solid's index of refraction, and to use ray tracing for anti-aliasing. Whitted also showed ray traced shadows. He produced a recursive ray traced film called The Compleat Angler in 1979 while an engineer at Bell Labs. Whitted's deeply recursive ray tracing algorithm reframed rendering from being primarily a matter of surface visibility determination to being a matter of light transport. His paper inspired a series of subsequent work by others that included distribution ray tracing and finally unbiased path tracing, which provides the rendering equation framework that has allowed computer-generated imagery to be faithful to reality. For decades, global illumination in major films using computer-generated imagery was approximated with additional lights. Ray tracing-based rendering eventually changed that by enabling physically based light transport. Early feature films rendered entirely using path tracing include Monster House (2006), Cloudy with a Chance of Meatballs (2009), and Monsters University (2013). == Algorithm overview == Optical ray tracing describes a method for producing visual images constructed in 3D computer graphics environments, with more photorealism than either ray casting or scanline rendering techniques. It works by tracing a path from an imaginary eye through each pixel in a virtual screen, and calculating the color of the object visible through it. Scenes in ray tracing are described mathematically by a programmer or by a visual artist (normally using intermediary tools). Scenes may also incorporate data from images and models captured by means such as digital photography. Typically, each ray must be tested for intersection with some subset of all the objects in the scene. Once the nearest object has been identified, the algorithm will estimate the incoming light at the point of intersection, examine the material properties of the object, and combine this information to calculate the final color of the pixel. Certain illumination algorithms and reflective or translucent materials may require more rays to be re-cast into the scene. It may at first seem counterintuitive or "backward" to send rays away from the camera, rather than into it (as actual light does in reality), but doing so is many orders of magnitude more efficient. Since the overwhelming majority of light rays from a given light source do not make it directly into the viewer's eye, a "forward" simulation could potentially waste a tremendous amount of computation on light paths that are never recorded. Therefore, the shortcut taken in ray tracing is to presuppose that a given ray intersects the view frame. After either a maximum number of reflections or a ray traveling a certain distance without intersection, the ray ceases to travel and the pixel's value is updated. === Calculate rays for rectangular viewport === On input we have (in calculation we use vector normalization and cross product): E ∈ R 3 {\displaystyle E\in \mathbb {R^{3}} } eye position T ∈ R 3 {\displaystyle T\in \mathbb {R^{3}} } target position θ ∈ [ 0 , π ] {\displaystyle \theta \in [0,\pi ]} field of view - for humans, we can assume ≈ π / 2 rad = 90 ∘ {\displaystyle \approx \pi /2{\text{ rad}}=90^{\circ }} m , k ∈ N {\displaystyle m,k\in \mathbb {N} } numbers of square pixels on viewport vertical and horizontal direction i , j ∈ N , 1 ≤ i ≤ k ∧ 1 ≤ j ≤ m {\displaystyle i,j\in \mathbb {N} ,1\leq i\leq k\land 1\leq j\leq m} numbers of actual pixel v → ∈ R 3 {\displaystyle {\vec {v}}\in \mathbb {R^{3}} } vertical vector which indicates where is up and down, usually v → = [ 0 , 1 , 0 ] {\displaystyle {\vec {v}}=[0,1,0]} - roll component which determine viewport rotation around point C (where the axis of rotation is the ET section) The idea is to find the position of each viewport pixel center P i j {\displaystyle P_{ij}} which allows us to find the line going from eye E {\displaystyle E} through that pixel and finally get the ray described by point E {\displaystyle E} and vector R → i j = P i j − E {\displaystyle {\vec {R}}_{ij}=P_{ij}-E} (or its normalization r → i j {\displaystyle {\vec {r}}_{ij}} ). First we need to find the coordinates of the bottom left viewport pixel P 1 m {\displaystyle P_{1m}} and find the next pixel by making a shift along directions parallel to viewport (vectors b → n {\displaystyle {\vec {b}}_{n

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  • Lawbot

    Lawbot

    Lawbots are a broad class of customer-facing legal AI applications that are used to automate specific legal tasks, such as document automation and legal research. The terms robot lawyer and lawyer bot are used as synonyms to lawbot. A robot lawyer or a robo-lawyer refers to a legal AI application that can perform tasks that are typically done by paralegals or young associates at law firms. However, there is some debate on the correctness of the term. Some commentators say that legal AI is technically speaking neither a lawyer nor a robot and should not be referred to as such. Other commentators believe that the term can be misleading and note that the robot lawyer of the future will not be one all-encompassing application but a collection of specialized bots for various tasks. Lawbots use various artificial intelligence techniques or other intelligent systems to limit humans' direct ongoing involvement in certain steps of a legal matter. The user interfaces on lawbots vary from smart searches and step-by-step forms to chatbots. Consumer and enterprise-facing lawbot solutions often do not require direct supervision from a legal professional. Depending on the task, some client-facing solutions used at law firms operate under an attorney supervision. == Levels of autonomy == The following levels of autonomy (LoA) are suggested for automated AI legal reasoning: Level 0 (LoA0): No automation for AI legal reasoning Level 1 (LoA1): Simple assistance automation Level 2 (LoA2): Advanced assistance automation Level 3 (LoA3): Semi-autonomous automation Level 4 (LoA4): Domain automation Level 5 (LoA5): Fully-autonomous automation Level 6 (LoA6): Superhuman automation == Examples == Some legal AI solutions are developed and marketed directly to the customers or consumers, whereas other applications are tools for the attorneys at law firms. There are already hundreds of legal AI solutions that operate in multitude of ways varying in sophistication and dependence on scripted algorithms. One notable legal technology chatbot application is DoNotPay. It had started off as an app for contesting parking tickets, but has since expanded to include features that help users with many different types of legal issues, ranging from consumer protection to immigration rights and other social issues. == Impact on the legal industry == In the 2016 report, Deloitte estimated that more than 110,000 law jobs in just the United Kingdom alone could disappear within the next twenty years due to automation. This change could result in the creation of more highly skilled jobs and in the reduction of paralegal and temporary positions. Deloitte's report asserts that "there is significant potential for high-skilled roles that involve repetitive processes to be automated by smart and self-learning algorithms". According to Lawyers to Engage, between 22% of a lawyer’s work and 35% of a legal assistant’s work can be automated in the US. Top law schools like Harvard have already begun to integrate Artificial Intelligence into the curriculum. Legal tech start-up companies have begun developing applications that assist law firms with completing low-risk legal processes. These applications can enable lawyers to focus on more work that requires their specific expertise. The automation of processes like contract reviewing, enforcement of negotiations (smart contracts) and client intake (expert systems) allows law firms to streamline their procedures and improve efficiency. In addition, automation benefits small-to-medium law firms that do not have the resources to utilize junior talent on such routine tasks. The increase of law firms utilizing automated applications could result into legal tech becoming a necessity in the industry. Digital Reason CEO, Tim Estes, stated that those who refuse the opportunity to integrate AI in their workflow are “most at risk.” In 2018, Forbes reported a 713% increase in investments in legal tech. This rapid growth is reflective of law firms beginning to “cede business to… new model legal providers… that meld technological, business and legal expertise.” == Access to law and justice == It has been widely estimated for at least the last generation that all the programs and resources devoted to ensuring access to justice address only 20% of the civil legal needs of low-income people in the United States. Drawing on this experience, in late 2011, the U.S. government-funded Legal Services Corporation decided to convene a summit of leaders to explore how best to use technology in the access-to-justice community. The group adopted a mission for The Summit on the Use of Technology to Expand Access to Justice (Summit) consistent with the magnitude of the challenge: "to explore the potential of technology to move the United States toward providing some form of effective assistance to 100% of persons otherwise unable to afford an attorney for dealing with essential civil legal needs". In April 2017, joined by Microsoft and Pro Bono Net, the Legal Services Corporation (LSC) announced a pilot program to develop online, statewide legal portals to direct individuals with civil legal needs to the most appropriate forms of assistance. == Technological limitations == Current research in subjects such as computational privacy, explainable machine learning, Bayesian deep learning, knowledge-intensive machine learning, and transfer learning reveals that we do not yet have the technology to enable Level 4 to 6 AI lawbots. In 2023, OpenLaw began developing a model called Law Bot, which interacts in a conversational way as an attorney. The dialogue format makes it possible for Law Bot to answer follow-up questions, challenge incorrect premises, and reject inappropriate requests. Currently, they try to ensure it is in full compliance with all laws and regulations while conducting further beta testing before releasing it to the general public.

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  • Alexis Spectral Data

    Alexis Spectral Data

    Alexis Spectral Data is a software developed for colour matching processes that calculates from available spectral data the colour numbers used by computers to display colours on screen. It displays the colour for each spectral reflectance curve and records the calculated trichromatic values and colour numbers along with the spectral curves. This eliminates the need to scan the samples separately with a truecolour Scanner while creating the database. The spectral data can be introduced manually as a series of reflectance values at wavelengths measured in different standard illuminants with an arbitrary but fixed increment that must be kept for each spectral curve throughout the creation of the whole database. Therefore, older UV-VIS Spectrophotometers that can't be interfaced with computers can also be used for creating the database needed for colour matching. Alexis Spectral Data determines the whiteness degree in a less time-consuming method, which permits storage and easier handling of the obtained data. Alexis Spectral Data can export the trichromatic values, calculated from the spectral curves, to Alexis Analyser, software that handles only trichromatic data. The earliest information about the development of this software comes from a paper published by a student at the University Politehnica Bucharest in 1993. The software runs on Windows based computers but not on other operating systems.

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  • Final Cut Express

    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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  • Spatial anti-aliasing

    Spatial anti-aliasing

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

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  • PhotoWorks (ray tracing software)

    PhotoWorks (ray tracing software)

    PhotoWorks is a raytrace rendering program created by Dassault Systèmes SolidWorks Corporation, formerly supplied as a photorealistic rendering add-in for SolidWorks. The program is based on the Mental Ray rendering engine. It has a library of scenes and materials that can be used with user-created SolidWorks files to create still frame images within the SolidWorks GUI. Since the 2011 release of SolidWorks, PhotoWorks has been replaced by the PhotoView 360 rendering utility. A 2010 review comparing PhotoWorks with three other rendering programs for SolidWorks (including PhotoView 360) gave the program high marks for render speed and built-in materials, but low marks for realism and user interface. Appearance File Type: .p2m

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  • LumenVox

    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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  • Necrobotics

    Necrobotics

    Necrobotics is the practice of using biotic materials (or dead organisms) as robotic components. Necrobotics can serve as an alternative to mechanical components that are difficult to manufacture by using biological components designed by natural selection in order to exploit the highly developed selective design implemented in biological lifeforms via the process of evolution. In July 2022, researchers in the Preston Innovation Lab at Rice University in Houston, Texas published a paper in Advanced Science introducing the concept and demonstrating its capability by repurposing dead spiders as robotic grippers and applying pressurized air to activate their gripping arms. In April 2025 researchers at Shinshu University created a “bio-hybrid drone” using silk-worm moth antennae to detect the source of a smell. In November 2025 researchers at McGill University demonstrated the use of a mosquito proboscis as a fine nozzle in experimental 3D printing. Necrobotics utilizes the spider's organic hydraulic system and their compact legs to create an efficient and simple gripper system. The necrobotic spider gripper is capable of lifting small and light objects, thereby serving as an alternative to complex and costly small mechanical grippers. == Background == The main appeal of the spider's body in necrobotics is its compact leg mechanism and use of hydraulic pressure. The spider's anatomy utilizes a simple hydraulic (fluid) pressure system. Spider legs have flexor muscles that naturally constrict their legs when relaxed. A force is required to straighten and extend their legs, which spiders accomplish by pumping hemolymph fluid (blood) through their joints as a means of hydraulic pressure. It takes no external power to curl their legs due to their flexor muscles' natural curled state. In July 2022, researchers in the Preston Innovation Lab at Rice University published a paper detailing their experiments with the gripper. Although dead spiders no longer produce hemolymph, Te Faye Yap (lead author and mechanical engineering graduate) found that pumping air through a needle into the spider's cephalothorax (main body) accomplishes the same results as hemolymph. The original hydraulic (fluid) system is essentially converted into a pneumatic (air) system. == Fabrication == Obtain a spider Euthanize the spider using a cold temperature of around -4°C for 5-7 days Insert a 25 gauge hypodermic needle into the spider's cephalothorax (main body) Apply glue around the needle to form a seal and allow it to dry Connect a syringe or pump to the needle Extend the spider's legs by pumping air in == Testing and Data == === Internal Force Versus Gripping Force === The typical pressure in a resting spider's legs ranges from 4 kPa to 6.1 kPa. Researchers extended the legs by increasing the spider's internal pressure to 5.5 kPa. Pumping air into the body increases the internal pressure, causing the legs to expand. Pumping air out of the body decreases internal pressure, causing the legs to contract due to their flexor leg muscles. When the internal pressure decreases to 0 kPa, the gripper would be fully closed, allowing for the gripper to grasp objects. This action demonstrates that as internal pressure decreases, the gripping force increases. Inversely, when internal pressure increases, the gripping force decreases. By gripping individual weighted acetate beads, it is found that the necrobotic gripper achieves a maximum gripping force of 0.35 milinewtons. === Spider Weight Versus Gripping Force === To estimate the gripping forces of smaller and larger spiders, researchers created a plot to predict the gripping force relative to the size of the spider. The wolf spider's body weight is relatively equal to the gripping force of its legs. The mass of the gripper is 33.5 mg and can lift 1.3 times its body weight (43.6 mg or 0.35 mN). However, with larger spiders, the gripping force relative to body weight decreases. For example, a 200-gram goliath birdeater is predicted to lift 10% of its weight (20 grams or 196 mN). Though there is an inverse relationship between spider mass and gripping force, larger spiders exert greater gripping forces than smaller spiders. === Gripper Lifespan === The necrobotic gripper's functionality is entirely reliant on the structural integrity of the spider. If the spider were to break down easily and frequently, the gripper would not be practical. Using cyclic testing, a series of repeated actions, it is found that the necrobotic gripper can actuate 700 to 1000 times. After 1000 cycles, cracks begin forming on the membrane of the leg joints due to dehydration. Weakened and decomposing joints lead to frequent breakage and replacement, thereby serving as an obstacle in applying necrobotics to real-world scenarios. One theorized fix to this issue is applying beeswax or a lubricant to the joints. Researchers found that over 10 days, the mass of an uncoated spider decreased 17 times more than the mass of a spider coated with beeswax. Lubricating joints combats dehydration and slows the loss of organic material. == Constraints == With the usage of organic material, there is a higher chance of the component decomposing and breaking down as opposed to traditional mechanical systems. There may be additional work and management required to replace these grippers if they fail. Additionally, organic inconsistencies with the spiders will yield inaccurate results. Not all wolf spiders develop the same, so gripping force and leg contraction can vary between grippers. There are moral implications behind euthanizing spiders for robotics. The ethical boundaries that necrobotics push in the pursuit of biohybrid systems raise concerns, as opponents say it may lead to the hybridization of mammals and is intrusive to nature. Proponents respond that repurposing dead animals has been human practice for millennia and that necrobotics should be pursued to advance science.

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  • SWIG

    SWIG

    The Simplified Wrapper and Interface Generator (SWIG) is an open-source software tool used to connect computer programs or libraries written in C or C++ with scripting languages such as Lua, Perl, PHP, Python, R, Ruby, Tcl, and other language implementations like C#, Java, JavaScript, Go, D, OCaml, Octave, Scilab and Scheme. Output can also be in the form of XML. == Function == The aim is to allow the calling of native functions (that were written in C or C++) by other programming languages, passing complex data types to those functions, keeping memory from being inappropriately freed, inheriting object classes across languages, etc. The programmer writes an interface file containing a list of C/C++ functions to be made visible to an interpreter. SWIG will compile the interface file and generate code in regular C/C++ and the target programming language. SWIG will generate conversion code for functions with simple arguments; conversion code for complex types of arguments must be written by the programmer. The SWIG tool creates source code that provides the glue between C/C++ and the target language. Depending on the language, this glue comes in three forms: a shared library that an extant interpreter can link to as some form of extension module, or a shared library that can be linked to other programs compiled in the target language (for example, using Java Native Interface (JNI) in Java). a shared dynamic library source code that should be compiled and dynamically loaded (e.g. Node.js native extensions) SWIG is not used for calling interpreted functions by native code; this must be done by the programmer manually. == Example == SWIG wraps simple C declarations by creating an interface that closely matches the way in which the declarations would be used in a C program. For example, consider the following interface file: In this file, there are two functions sin() and strcmp(), a global variable Foo, and two constants STATUS and VERSION. When SWIG creates an extension module, these declarations are accessible as scripting language functions, variables, and constants respectively. In Python: == Purpose == There are two main reasons to embed a scripting engine in an existing C/C++ program: The program can then be customized far faster, via a scripting language instead of C/C++. The scripting engine may even be exposed to the end-user, so that they can automate common tasks by writing scripts. Even if the final product is not to contain the scripting engine, it may nevertheless be very useful for writing test scripts. There are several reasons to create dynamic libraries that can be loaded into extant interpreters, including: Provide access to a C/C++ library which has no equivalent in the scripting language. Write the whole program in the scripting language first, and after profiling, rewrite performance-critical code in C or C++. == History == SWIG is written in C and C++ and has been publicly available since February 1996. The initial author and main developer was David M. Beazley who developed SWIG while working as a graduate student at Los Alamos National Laboratory and the University of Utah and while on the faculty at the University of Chicago. Development is currently supported by an active group of volunteers led by William Fulton. SWIG has been released under a GNU General Public License. == Google Summer of Code == SWIG was a successful participant of Google Summer of Code in 2008, 2009, 2012. In 2008, SWIG got four slots. Haoyu Bai spent his summers on SWIG's Python 3.0 Backend, Jan Jezabek worked on Support for generating COM wrappers, Cheryl Foil spent her time on Comment 'Translator' for SWIG, and Maciej Drwal worked on a C backend. In 2009, SWIG again participated in Google Summer of Code. This time four students participated. Baozeng Ding worked on a Scilab module. Matevz Jekovec spent time on C++0x features. Ashish Sharma spent his summer on an Objective-C module, Miklos Vajna spent his time on PHP directors. In 2012, SWIG participated in Google Summer of Code. This time four out of five students successfully completed the project. Leif Middelschulte worked on a C target language module. Swati Sharma enhanced the Objective-C module. Neha Narang added the new module on JavaScript. Dmitry Kabak worked on source code documentation and Doxygen comments. == Alternatives == For Python, similar functionality is offered by SIP, Pybind11, and Boost's Boost.python library. == Projects using SWIG == ZXID (Apache License, Version 2.0) Symlabs SFIS (commercial) LLDB GNU Radio up to (including) version 3.8.x.x; later versions use Pybind11 Xapian TensorFlow Apache SINGA QuantLib Babeltrace

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  • Network Abstraction Layer

    Network Abstraction Layer

    The Network Abstraction Layer (NAL) is a part of the H.264/AVC and HEVC video coding standards. The main goal of the NAL is the provision of a "network-friendly" video representation addressing "conversational" (video telephony) and "non conversational" (storage, broadcast, or streaming) applications. NAL has achieved a significant improvement in application flexibility relative to prior video coding standards. == Introduction == An increasing number of services and growing popularity of high definition TV are creating greater needs for higher coding efficiency. Moreover, other transmission media such as cable modem, xDSL, or UMTS offer much lower data rates than broadcast channels, and enhanced coding efficiency can enable the transmission of more video channels or higher quality video representations within existing digital transmission capacities. Video coding for telecommunication applications has diversified from ISDN and T1/E1 service to embrace PSTN, mobile wireless networks, and LAN/Internet network delivery. Throughout this evolution, continued efforts have been made to maximize coding efficiency while dealing with the diversification of network types and their characteristic formatting and loss/error robustness requirements. The H.264/AVC and HEVC standards are designed for technical solutions including areas like broadcasting (over cable, satellite, cable modem, DSL, terrestrial, etc.) interactive or serial storage on optical and magnetic devices, conversational services, video-on-demand or multimedia streaming, multimedia messaging services, etc. Moreover, new applications may be deployed over existing and future networks. This raises the question about how to handle this variety of applications and networks. To address this need for flexibility and customizability, the design covers a NAL that formats the Video Coding Layer (VCL) representation of the video and provides header information in a manner appropriate for conveyance by a variety of transport layers or storage media. The NAL is designed in order to provide "network friendliness" to enable simple and effective customization of the use of VCL for a broad variety of systems. The NAL facilitates the ability to map VCL data to transport layers such as: RTP/IP for any kind of real-time wire-line and wireless Internet services. File formats, e.g., ISO MP4 for storage and MMS. H.32X for wireline and wireless conversational services. MPEG-2 systems for broadcasting services, etc. The full degree of customization of the video content to fit the needs of each particular application is outside the scope of the video coding standardization effort, but the design of the NAL anticipates a variety of such mappings. Some key concepts of the NAL are NAL units, byte stream, and packet formats uses of NAL units, parameter sets, and access units. A short description of these concepts is given below. == NAL units == The coded video data is organized into NAL units, each of which is effectively a packet that contains an integer number of bytes. The first byte of each H.264/AVC NAL unit is a header byte that contains an indication of the type of data in the NAL unit. For HEVC the header was extended to two bytes. All the remaining bytes contain payload data of the type indicated by the header. The NAL unit structure definition specifies a generic format for use in both packet-oriented and bitstream-oriented transport systems, and a series of NAL units generated by an encoder is referred to as a NAL unit stream. == NAL Units in Byte-Stream Format Use == Some systems require delivery of the entire or partial NAL unit stream as an ordered stream of bytes or bits within which the locations of NAL unit boundaries need to be identifiable from patterns within the coded data itself. For use in such systems, the H.264/AVC and HEVC specifications define a byte stream format. In the byte stream format, each NAL unit is prefixed by a specific pattern of three bytes called a start code prefix. The boundaries of the NAL unit can then be identified by searching the coded data for the unique start code prefix pattern. The use of emulation prevention bytes guarantees that start code prefixes are unique identifiers of the start of a new NAL unit. A small amount of additional data (one byte per video picture) is also added to allow decoders that operate in systems that provide streams of bits without alignment to byte boundaries to recover the necessary alignment from the data in the stream. Additional data can also be inserted in the byte stream format that allows expansion of the amount of data to be sent and can aid in achieving more rapid byte alignment recovery, if desired. == NAL Units in Packet-Transport System Use == In other systems (e.g., IP/RTP systems), the coded data is carried in packets that are framed by the system transport protocol, and identification of the boundaries of NAL units within the packets can be established without use of start code prefix patterns. In such systems, the inclusion of start code prefixes in the data would be a waste of data carrying capacity, so instead the NAL units can be carried in data packets without start code prefixes. == VCL and Non-VCL NAL Units == NAL units are classified into VCL and non-VCL NAL units. VCL NAL units contain the data that represents the values of the samples in the video pictures. Non-VCL NAL units contain any associated additional information such as parameter sets (important header data that can apply to a large number of VCL NAL units) and supplemental enhancement information (timing information and other supplemental data that may enhance usability of the decoded video signal but are not necessary for decoding the values of the samples in the video pictures). == Parameter Sets == A parameter set contains shared configuration data that is carried in non-VCL NAL units. Parameter sets are typically reused when decoding many coded pictures within a video sequence. Each VCL NAL unit references a picture parameter set (PPS), which in turn references a sequence parameter set (SPS). There are two types of parameter sets: Sequence parameter set (SPS), which specifies mostly constant configuration such as resolution, bit depth, or chroma format. (For a concrete implementation, see FFmpeg's SPS struct.) Picture parameter set (PPS), which applies on top of an SPS, and specifies configuration such as QP offsets. (For a concrete implementation, see FFmpeg's PPS struct.) The sequence and picture parameter-set mechanism decouples the transmission of infrequently changing information from the transmission of coded representations of the values of the samples in the video pictures. Each VCL NAL unit contains an identifier that refers to the content of the relevant picture parameter set and each picture parameter set contains an identifier that refers to the content of the relevant sequence parameter set. In this manner, a small amount of data (the identifier) can be used to refer to a larger amount of information (the parameter set) without repeating that information within each VCL NAL unit. Sequence and picture parameter sets can be sent well ahead of the VCL NAL units that they apply to, and can be repeated to provide robustness against data loss. In some applications, parameter sets may be sent within the channel that carries the VCL NAL units (termed "in-band" transmission). In other applications, it can be advantageous to convey the parameter sets "out-of-band" using a more reliable transport mechanism than the video channel itself. == Access Units == A set of NAL units in a specified form is referred to as an access unit. The decoding of each access unit results in one decoded picture. Each access unit contains a set of VCL NAL units that together compose a primary coded picture. It may also be prefixed with an access unit delimiter to aid in locating the start of the access unit. Some supplemental enhancement information containing data such as picture timing information may also precede the primary coded picture. The primary coded picture consists of a set of VCL NAL units consisting of slices or slice data partitions that represent the samples of the video picture. Following the primary coded picture may be some additional VCL NAL units that contain redundant representations of areas of the same video picture. These are referred to as redundant coded pictures, and are available for use by a decoder in recovering from loss or corruption of the data in the primary coded pictures. Decoders are not required to decode redundant coded pictures if they are present. Finally, if the coded picture is the last picture of a coded video sequence (a sequence of pictures that is independently decodable and uses only one sequence parameter set), an end of sequence NAL unit may be present to indicate the end of the sequence; and if the coded picture is the last coded picture in the entire NAL unit stream, an end of stream NAL unit may be present to

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  • Find It, Fix It

    Find It, Fix It

    Find It, Fix It is a mobile app developed by the city of Seattle to report non-emergency issues. == History == The City of Seattle launched Find It, Fix It in 2013 for Android and iOS phones to let citizens report potholes, graffiti, and other problems they observe to the city. The app did not support Windows Phone, making it inaccessible to Microsoft employees in the city who used the company's then-supported mobile operating system. In 2015, Mayor Ed Murray led a Find It, Fix It walk with about 100 other people, including police officers, in the University District. Participants were encouraged to use the app to report problems they observed in the neighborhood. Later Find It, Fix It walks have taken place in neighborhoods including Crown Hill, First Hill, Belltown, Wallingford, and Highland Park. In 2020, Find It, Fix It added support for reporting issues with the dockless bicycle sharing systems in the city. Citing the success of Seattle’s app, the nearby city of Kent, Washington, announced that it would create a similar customer service app. == Usage == Users of Find It, Fix It can submit reports about graffiti, potholes, parking violations, broken street signs, and other issues. The app is designed to use a smartphone’s camera and GPS features to make it easier for users to file reports. The Atlantic reported in 2018 that Find It, Fix It was being used by neighborhood groups to report homeless encampments with the intention of having authorities remove them, citing examples of campaigns in Ravenna and Ballard. The executive director of Ballard Alliance, a local chamber of commerce for businesses in the neighborhood, used a private Facebook group to encourage business owners to use the app to report homeless encampments. In response to a poster campaign in the summer of 2019 with the slogan “See a tent? Report a tent”, a representative for the mayor’s office and two Seattle City Council members said that it was inappropriate to encourage use of Find It, Fix It to displace homeless people. As a backlash to these campaigns, people living far from Seattle filed hoax complaints using the app, such as by using photos of tents on display at REI stores. According to the Seattle Times, between January 1, 2020, and November 15, 2021, the city had received over 230,000 service requests, of which 77% were submitted via Find It, Fix It. The largest category of these, numbering over 55,000, concerned illegal dumping. Of complaints categorized as "parking", 3,000 had comments explicitly mentioning issues around homelessness. The ZIP code 98134, covering an industrial area south of Pioneer Square and north of Georgetown, had 5,559 service requests per 1,000 residents, by far the highest in the city.

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