OpenPipeline

openPipeline is an open-source plug-in for Autodesk Maya that is designed to assist in a Production Pipeline structure and Computer animation. == Development == Created in Maya Embedded Language, openPipeline was initiated at Eyebeam Atelier and further developed at Pratt Institute in the Digital Arts Lab. The initial release date was December 28, 2006. == Contributors == Rob O'Neill (Creator) Paris Mavroidis Meng-Han Ho

IBM ALP

IBM Assembly Language Processor (ALP) is an assembler written by IBM for 32-bit OS/2 Warp (OS/2 3.0), which was released in 1994. ALP accepts source programs compatible with Microsoft Macro Assembler (MASM) version 5.1, which was originally used to build many of the device drivers included with OS/2. For OS/2 versions 3 and 4, ALP was distributed, along with other tools and documentation, as part of the Device Driver Kit (DDK). The DDK was withdrawn in 2004 as part of IBM's discontinuance of OS/2.

Aseprite

Aseprite ( ace-prite) is a proprietary, source-available image editor designed primarily for pixel art drawing and animation. It runs on Windows, macOS, and Linux, and features different tools for image and animation editing such as layers, frames, tilemap support, command-line interface, Lua scripting, among others. It is developed by Igara Studio S.A. and led by the developers David, Gaspar, and Martín Capello. Aseprite can be downloaded as freeware, (albeit it does not have the ability to save sprites) or purchased on Steam or Itch.io. Aseprite source code and binaries are distributed under EULA, educational, and Steam proprietary licenses. == History == Aseprite, formerly known as Allegro Sprite Editor, had its first release in 2001 as a free software project under the GPLv2 license. This license was kept until August 2016 with version v1.1.8, when the developers switched to a EULA, thus making the software proprietary. On the 1st of September 2016, the main developer, David Capello, wrote a post on the Aseprite Devblog explaining this change. The EULA permits others to download the Aseprite source code, compile it, and use it for personal purposes, but forbids its redistribution to third parties. After the license change, LibreSprite, a free and open source version of it, was created. Both before and after the license change, Aseprite was sold online, on Steam, itch.io, and the project's website. The project's code repository was hosted on Google Code until August 2014, when it was migrated to GitHub, where it remains hosted to date. As of October 2022, its repository has had 68 contributors and around 19 thousand stars. From 2014 to 2021, Aseprite had 66 different releases. Aseprite was used in the development of several notable games such as TowerFall (2013), Celeste (2018), Minit (2018), Wargroove (2019), Loop Hero (2021), Eastward (2021), Unpacking (2021), Haiku the Robot (2022) and Pizza Tower (2023). == Design and features == The main design purpose of Aseprite is to create animated 2D pixel-art sprites. Some of its features include: Layers and frames, with layer grouping and animation tagging Pixel-art specific transformations and tools (pixel-perfect modes, custom brushes, etc.) Animation real-time preview and onion skinning Tilemap and tileset modes Color palette managing, including 65 default palettes Color profiles and modes (RGBA, indexed and grayscale) Non-square pixels Command line interface (CLI) and Lua scripting Aseprite uses its own binary file type to store data, which is typically saved with .ase or .aseprite extensions. Different third-party projects were developed to support parsing of .ase files in programming languages including C#, Python and JavaScript, and in game engines such as Unity and Godot. Images and animations can be exported to different file formats including PNG, GIF, FLC, FLI, JPEG, PCX, TGA, ICO, SVG, and bitmap (BMP).

LCD crosstalk

LCD crosstalk is a visual defect in an LCD screen which occurs because of interference between adjacent pixels. Owing to the way rows and columns in the display are addressed, and charge is pushed around, the data on one part of the display has the potential to influence what is displayed elsewhere. This is generally known as crosstalk, and in matrix displays typically occurs in the horizontal and vertical directions. Crosstalk used to be a serious problem in the old passive-matrix (STN) displays, but is rarely discernable in modern active-matrix (TFT) displays. A fortunate side effect of inversion (see above) is that, for most display material, what little crosstalk there is largely cancelled out. For most practical purposes, the level of crosstalk in modern LCDs is negligible. Certain patterns, particularly those involving fine dots, can interact with the inversion and reveal visible crosstalk. If you try moving a small Window in front of the inversion pattern (above) which makes your screen flicker the most, you may well see crosstalk in the surrounding pattern. Different patterns are required to reveal crosstalk on different displays (depending on their inversion scheme).

YaDICs

YaDICs is a program written to perform digital image correlation on 2D and 3D tomographic images. The program was designed to be both modular, by its plugin strategy and efficient, by it multithreading strategy. It incorporates different transformations (Global, Elastic, Local), optimizing strategy (Gauss-Newton, Steepest descent), Global and/or local shape functions (Rigid-body motions, homogeneous dilatations, flexural and Brazilian test models)... == Theoretical background == === Context === In solid mechanics, digital image correlation is a tool that allows to identify the displacement field to register a reference image (called herein fixed image) to images during an experiment (mobile image). For example, it is possible to observe the face of a specimen with a painted speckle on it in order to determine its displacement fields during a tensile test. Before the appearance of such methods, researchers usually used strain gauges to measure the mechanical state of the material but strain gauges only measure the strain on a point and don't allow to understand material with an heterogeneous behavior. One can obtain a full in plane strain tensor by derivation of the displacement fields. Many methods are based upon the optical flow. In fluid mechanics a similar method is used, called Particle Image Velocimetry (PIV); the algorithms are similar to those of DIC but it is impossible to ensure that the optical flow is conserved so a vast majority of the software used the normalized cross correlation metric. In mechanics the displacement or velocity fields are the only concern, registering images is just a side effect. There is another process called image registration using the same algorithms (on monomodal images) but where the goal is to register images and thereby identifying the displacement field is just a side effect. YaDICs uses the general principle of image registration with a particular attention to the displacement fields basis. === Image registration principle === YaDICs can be explained using the classical image registration framework: === Image registration general scheme === The common idea of image registration and digital image correlation is to find the transformation between a fixed image and a moving one for a given metric using an optimization scheme. While there are many methods to achieve such a goal, Yadics focuses on registering images with the same modality. The idea behind the creation of this software is to be able to process data that comes from a μ-tomograph; i.e.: data cube over 10003 voxels. With such a size it is not possible to use naive approach usually used in a two-dimensional context. In order to get sufficient performances OpenMP parallelism is used and data are not globally stored in memory. As an extensive description of the different algorithms is given in. === Sampling === Contrary to image registration, Digital Image Correlation targets the transformation, one wants to extracted the most accurate transformation from the two images and not just match the images. Yadics uses the whole image as a sampling grid: it is thus a total sampling. === Interpolator === It is possible to choose between bilinear interpolation and bicubic interpolation for the grey level evaluation at non integer coordinates. The bi-cubic interpolation is the recommended one. === Metrics === ==== Sum of squared differences (SSD) ==== The SSD is also known as mean squared error. The equation below defines the SSD metric: S S D ( μ , I F , I M ) = 1 | Ω F | ∑ x i ∈ Ω F ( I F ( x i ) − I M ( T μ ( x i ) ) ) 2 , {\displaystyle SSD(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})={\dfrac {1}{\left|\Omega _{F}\right|}}\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\mathcal {I_{M}}}({T}_{\mu }(x_{i}))\right)^{2},} where I F {\displaystyle {\mathcal {I_{F}}}} is the fixed image, I M {\displaystyle {\mathcal {I_{M}}}} the moving one, Ω F {\displaystyle \Omega _{F}} the integration area | Ω F | {\displaystyle \left|\Omega _{F}\right|} the number of pi(vo)xels (cardinal) and T μ {\displaystyle {T}_{\mu }} the transformation parametrized by μ The transformation can be written as: T μ ( x ) = x + { Φ ( x ) } t { μ } . {\displaystyle T_{\mu }(x)=x+\left\{\Phi (x)\right\}^{t}\left\{\mu \right\}.} This metric is the main one used in the YaDICs as it works well with same modality images. One has to find the minimum of this metric ==== Normalized cross-correlation ==== The normalized cross-correlation (NCC) is used when one cannot assure the optical flow conservation; it happens in case of change of lighting or if particles disappear from the scene can occur in particle images velocimetry (PIV). The NCC is defined by: N C C ( μ , I F , I M ) = ∑ x i ∈ Ω F ( I F ( x i ) − I F ¯ ) ( I M ( T μ ( x i ) ) − I M ¯ ) ∑ x i ∈ Ω F ( I F ( x i ) − I F ¯ ) 2 ∑ x i ∈ Ω F ( I M ( T μ ( x i ) ) − I M ¯ ) 2 , {\displaystyle NCC(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})={\dfrac {\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\overline {\mathcal {I_{F}}}}\right)\left({\mathcal {I_{M}}}({T}_{\mu }(x_{i}))-{\overline {\mathcal {I_{M}}}}\right)}{\sqrt {\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\overline {\mathcal {I_{F}}}}\right)^{2}\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{M}}}({T}_{\mu }(x_{i}))-{\overline {\mathcal {I_{M}}}}\right)^{2}}}},} where I F ¯ {\displaystyle {\overline {\mathcal {I_{F}}}}} and I M ¯ {\displaystyle {\overline {\mathcal {I_{M}}}}} are the mean values of the fixed and mobile images. This metric is only used to find local translation in Yadics. This metric with translation transform can be solved using cross-correlation methods, which are non iterative and can be accelerated using Fast Fourier Transform . === Classification of transformations === There are three categories of parametrization: elastic, global and local transformation. The elastic transformations respect the partition of unity, there are no holes created or surfaces counted several times. This is commonly used in Image Registration by the use of B-Spline functions and in solid mechanics with finite element basis. The global transformations are defined on the whole picture using rigid body or affine transformation (which is equivalent to homogeneous strain transformation). More complex transformations can be defined such as mechanically based one. These transformations have been used for stress intensity factor identification by and for rod strain by. The local transformation can be considered as the same global transformation defined on several Zone Of Interest (ZOI) of the fixed image. ==== Global ==== Several global transforms have been implemented: Rigid and homogeneous (Tx,Ty,Rz in 2D; Tx,Ty,Tz,Rx,Ry,Rz,Exx,Eyy,Ezz,Eyz,Exz,Exy in 3D) Brazilian (Only in 2D), Dynamic Flexion, ==== Elastic ==== First-order quadrangular finite elements Q4P1 are used in Yadics. ===== Local ===== Every global transform can be used on a local mesh. === Optimization === The YaDICs optimization process follows a gradient descent scheme. The first step is to compute the gradient of the metric regarding the transform parameters ∂ S S D ( μ , I F , I M ) ∂ μ = 2 | Ω F | ∑ x i ∈ Ω F ( I F ( x i ) − I M ( T μ ( x i ) ) ) ∂ I M ( T μ ( x i ) ∂ μ = 2 | Ω F | ∑ x i ∈ Ω F ( I F ( x i ) − I M ( T μ ( x i ) ) ) ( ∂ T μ ( x i ) ∂ μ ) t ∂ I M ( T μ ( x i ) ) ∂ x {\displaystyle {\begin{array}{lcl}{\dfrac {\partial SSD(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})}{\partial \mu }}&=&{\dfrac {2}{\left|\Omega _{F}\right|}}\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\mathcal {I_{M}}}({T}_{\mu }(x_{i}))\right){\dfrac {\partial {\mathcal {I_{M}}}({T}_{\mu }(x_{i})}{\partial \mu }}\\&=&{\dfrac {2}{\left|\Omega _{F}\right|}}\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\mathcal {I_{M}}}({T}_{\mu }(x_{i}))\right)\left({\dfrac {\partial {T}_{\mu }(x_{i})}{\partial \mu }}\right)^{t}{\dfrac {\partial {\mathcal {I_{M}}}({T}_{\mu }(x_{i}))}{\partial x}}\\\end{array}}} ==== Gradient method ==== Once the metric gradient has been computed, one has to find an optimization strategy The gradient method principle is explained below: μ k + 1 = μ k + α k d k {\displaystyle \mu _{k+1}=\mu _{k}+\alpha _{k}d_{k}} The gradient step can be constant or updated at every iteration. d k = − γ k ∂ C ( μ , I F , I M ) ∂ μ {\displaystyle d_{k}=-\gamma _{k}{\dfrac {\partial {\mathcal {C}}(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})}{\partial \mu }}} , γ k {\displaystyle \gamma _{k}} allows one to choose between the following methods : γ k {\displaystyle \gamma _{k}} ⟹ {\displaystyle \Longrightarrow } steepest descent, γ k = [ ∂ C ( μ , I F , I M ) ∂ μ ∂ C ( μ , I F , I M ) ∂ μ t ] − 1 {\displaystyle \gamma _{k}=\left[{\dfrac {\partial {\mathcal {C}}(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})}{\partial \mu }}{\dfrac {\partial {\mathcal {C}}(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})}{\partial \mu }}^{t}\right]^{-1}} ⟹ {\displaystyle \Longrightarrow } Gauss-Newto

Lexxe

Lexxe is an internet search engine that applies Natural Language Processing in its semantic search technology. Founded in 2005 by Dr. Hong Liang Qiao, Lexxe is based in Sydney, Australia. Today, Lexxe's key focus is on sentiment search with the launch of a news sentiment search site at News & Moods (www.newsandmoods.com). Lexxe has experienced several stages of change of focus in search technology: Lexxe launched its Alpha version in 2005, featuring Natural Language question answering (i.e. users could ask questions in English to the search engine apart from keyword searches — this feature has been suspended for redevelopment since 2010). It used only algorithms to extract answers from web pages, with no question-answer pair databases prepared in advance. In 2011, Lexxe launched a beta version with a new search technology called Semantic Key. Semantic Keys enable users to query with a conceptual keyword (or a keyword with a special meaning, hence the term Semantic Key) in order to find instances under the concept, e.g. price → $5.95 or €200, color → red, yellow, white. For example, “price: a pound of apples”, “color: ferrari”. With initial 500 Semantic Keys at the Beta launch, Lexxe became the first search engine in the world to offer this unique and useful search technology to the users. The cost of building Semantic Keys was too heavy though. In 2017, Lexxe launched News & Moods (www.newsandmoods.com), an open platform for news sentiment search, a first step towards sentiment search feature for the entire Internet search in Lexxe search engine. News & Moods also comes with smartphone apps in Android and iOS.

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