CamScanner is a Chinese mobile app first released in 2010 that allows iOS and Android devices to be used as image scanners. It allows users to 'scan' documents (by taking a photo with the device's camera) and share the photo as either a JPEG or PDF. This app is available free of charge on the Google Play Store and the Apple App Store. The app is based on freemium model, with ad-supported free version and a premium version with additional functions. == History == On August 27, 2019, Russian cyber security company Kaspersky Lab discovered that recent versions of the Android app distributed an advertising library containing a Trojan Dropper, which was also included in some apps preinstalled on several Chinese mobiles. The advertising library decrypts a Zip archive which subsequently downloads additional files from servers controlled by hackers, allowing the hackers to control the device, including by showing intrusive advertising or charging paid subscriptions. Google took the app down after Kaspersky reported its findings. An updated version of the app with the advertising library removed was made available on the Google Play Store as of September 5, 2019. Kaspersky later acknowledged "We appreciate the willingness to cooperate that we've seen from CamScanner representatives, as well as the responsible attitude to user safety they demonstrated while eliminating the threat…The malicious modules were removed from the app immediately upon Kaspersky's warning, and Google Play has restored the app." In June 2020, as tensions along the Line of Actual Control between China and India continued, the Government of India decided to ban 118 Chinese apps, including TikTok and CamScanner citing data and privacy issues. On January 5, 2021, US President Donald Trump signed Executive Order 13971 banning Alipay, Tencent's QQ, QQ Wallet, WeChat Pay, CamScanner, Shareit, VMate and WPS Office to conduct US transactions. The Trump administration explained this act by saying that this move helps prevent personal information such as text, phone calls and photos collected from rivals. However, the Biden administration did not meet the February 2021 deadline for implementing the executive order, allowing these apps to operate in the US and revoked the previous executive order Executive Order 14034 of June 9, 2021.
Fragment (computer graphics)
In computer graphics, a fragment is the data necessary to generate a single pixel's worth of a drawing primitive in the frame buffer. These data may include, but are not limited to: raster position depth interpolated attributes (color, texture coordinates, etc.) stencil alpha window ID As a scene is drawn, drawing primitives (the basic elements of graphics output, such as points, lines, circles, text etc.) are rasterized into fragments which are textured and combined with the existing frame buffer. How a fragment is combined with the data already in the frame buffer depends on various settings. In a typical case, a fragment may be discarded if it is further away than the pixel which is already at that location (according to the depth buffer). If it is nearer than the existing pixel, it may replace what is already there, or, if alpha blending is in use, the pixel's color may be replaced with a mixture of the fragment's color and the pixel's existing color, as in the case of drawing a translucent object. In general, a fragment can be thought of as the data needed to shade the pixel, plus the data needed to test whether the fragment survives to become a pixel (depth, alpha, stencil, scissor, window ID, etc.). Shading a fragment is done through a fragment shader (or pixel shaders in Direct3D). In computer graphics, a fragment is not necessarily opaque, and could contain an alpha value specifying its degree of transparency. The alpha is typically normalized to the range of [0, 1], with 0 denotes totally transparent and 1 denotes totally opaque. If the fragment is not totally opaque, then part of its background object could show through, which is known as alpha blending.
Source-code editor
A source-code editor is a text editor program designed specifically for editing the source code of computer programs. It includes basic functionality such as syntax highlighting, and sometimes debugging. It may be a standalone application or it may be built into an integrated development environment (IDE). == Features == Source-code editors have features specifically designed to simplify and speed up typing of source code, such as syntax highlighting(syntax error highlighting), auto indentation, autocomplete and brace matching functionality. These editors may also provide a convenient way to run a compiler, interpreter, debugger, or other program relevant for the software-development process. While many text editors like Notepad can be used to edit source code, if they do not enhance, automate or ease the editing of code, they are not defined as source-code editors. Structure editors are a different form of a source-code editor, where instead of editing raw text, one manipulates the code's structure, generally the abstract syntax tree. In this case features such as syntax highlighting, validation, and code formatting are easily and efficiently implemented from the concrete syntax tree or abstract syntax tree, but editing is often more rigid than free-form text. Structure editors also require extensive support for each language, and thus are harder to extend to new languages than text editors, where basic support only requires supporting syntax highlighting or indentation. For this reason, strict structure editors are not popular for source code editing, though some IDEs provide similar functionality. A source-code editor can check syntax dynamically while code is being entered and immediately warn of syntax problems, as well as suggest code autocomplete snippets. A few source-code editors compress source code, typically converting common keywords into single-byte tokens, removing unnecessary whitespace, and converting numbers to a binary form. Such tokenizing editors later uncompress the source code when viewing it, possibly prettyprinting it with consistent capitalization and spacing. A few source-code editors do both. The Language Server Protocol, first used in Microsoft's Visual Studio Code, allows for source code editors to implement an LSP client that can read syntax information about any language with a LSP server. This allows for source code editors to easily support more languages with syntax highlighting, refactoring, and reference finding. Many source code editors such as Neovim and Brackets have added a built-in LSP client while other editors such as Emacs, Vim, and Sublime Text have support for an LSP Client via a separate plug-in. == History == In 1985, Mike Cowlishaw of IBM created LEXX while seconded to the Oxford University Press. LEXX used live parsing and used color and fonts for syntax highlighting. IBM's LPEX (Live Parsing Extensible Editor) was based on LEXX and ran on VM/CMS, OS/2, OS/400, Windows, and Java Although the initial public release of vim was in 1991, the syntax highlighting feature was not introduced until version 5.0 in 1998. On November 1, 2015, the first version of NeoVim was released. In 2003, Notepad++, a source code editor for Windows, was released by Don Ho. The intention was to create an alternative to the java-based source code editor, JEXT In 2015, Microsoft released Visual Studio Code as a lightweight and cross-platform alternative to their Visual Studio IDE. The following year, Visual Studio Code became the Microsoft product using the Language Server Protocol. This code editor quickly gained popularity and emerged as the most widely used source code editor. == Comparison with IDEs == A source-code editor is one component of a Integrated Development Environment. In contrast to a standalone source-code editor, an IDE typically also includes several tools which enhance the software development process. Such tools include syntax highlighting, code autocomplete suggestions, version control, automatic formatting, integrated runtime environments, debugger, and build tools. Standalone source code editors are preferred over IDEs by some developers when they believe the IDEs are bloated with features they do not need. == Notable examples == == Controversy == Many source-code editors and IDEs have been involved in ongoing user arguments, sometimes referred to jovially as "holy wars" by the programming community. Notable examples include vi vs. Emacs and Eclipse vs. NetBeans. These arguments have formed a significant part of internet culture and they often start whenever either editor is mentioned anywhere.
Web container
A web container (also known as a servlet container; and compare "webcontainer") is the component of a web server that interacts with Jakarta Servlets. A web container is responsible for managing the lifecycle of servlets, mapping a URL to a particular servlet and ensuring that the URL requester has the correct access-rights. A web container handles requests to servlets, Jakarta Server Pages (JSP) files, and other types of files that include server-side code. The Web container creates servlet instances, loads and unloads servlets, creates and manages request and response objects, and performs other servlet-management tasks. A web container implements the web component contract of the Jakarta EE architecture. This architecture specifies a runtime environment for additional web components, including security, concurrency, lifecycle management, transaction, deployment, and other services. == List of Servlet containers == The following is a list of notable applications which implement the Jakarta Servlet specification from Eclipse Foundation, divided depending on whether they are directly sold or not. === Open source Web containers === Apache Tomcat (formerly Jakarta Tomcat) is an open source web container available under the Apache Software License. Apache Tomcat 6 and above are operable as general application container (prior versions were web containers only) Apache Geronimo is a full Java EE 6 implementation by Apache Software Foundation. Enhydra, from Lutris Technologies. GlassFish from Eclipse Foundation (an application server, but includes a web container). Jetty, from the Eclipse Foundation. Also supports SPDY and WebSocket protocols. Open Liberty, from IBM, is a fully compliant Jakarta EE server Virgo from Eclipse Foundation provides modular, OSGi based web containers implemented using embedded Tomcat and Jetty. Virgo is available under the Eclipse Public License. WildFly (formerly JBoss Application Server) is a full Java EE implementation by Red Hat, division JBoss. === Commercial Web containers === iPlanet Web Server, from Oracle. JBoss Enterprise Application Platform from Red Hat, division JBoss is subscription-based/open-source Jakarta EE-based application server. WebLogic Application Server, from Oracle Corporation (formerly developed by BEA Systems). Orion Application Server, from IronFlare. Resin Pro, from Caucho Technology. IBM WebSphere Application Server. SAP NetWeaver.
Web Intents
Web Intents was an experimental framework for web-based inter-application communication and service discovery. Web Intents consists of a discovery mechanism and a very light-weight RPC system between web applications, modelled after the Intents system in Android. In the context of the framework an Intent equals an action to be performed by a provider. Web Intents allow two web applications to communicate with each other, without either of them having to actually know what the other one is. == Support == === Client === Google Chrome versions 18 to 23 natively supported Web Intents. This support was disabled in version 24, citing the existence of a "number of areas for development in both the API and specific user experience in Chrome". There is a JavaScript shim with support for IE 8, IE 9, Opera, Safari, Firefox 3+ and Chrome 3+. === Server === There are some Web Intents proxy pages that make available some real services that don't yet support intents. AddThis supports Web Intents by their sharing tools regardless of browser support. == History == Paul Kinlan of Google announced the Web Intents project in December 2010. He soon released a prototype API to GitHub. In August 2011 Google announced that Chrome would support Web Intents. Google and Mozilla have started co-operating to unify Web Intents and Mozilla's Web Activities (which tries to solve the same problem) into one proposal. In November 2012, Greg Billock of Google announced that experimental support of Web Intents had been removed from Chrome.
Deep learning in photoacoustic imaging
Photoacoustic imaging (PA) is based on the photoacoustic effect, in which optical absorption causes a rise in temperature, which causes a subsequent rise in pressure via thermo-elastic expansion. This pressure rise propagates through the tissue and is sensed via ultrasonic transducers. Due to the proportionality between the optical absorption, the rise in temperature, and the rise in pressure, the ultrasound pressure wave signal can be used to quantify the original optical energy deposition within the tissue. Photoacoustic imaging has applications of deep learning in both photoacoustic computed tomography (PACT) and photoacoustic microscopy (PAM). PACT utilizes wide-field optical excitation and an array of unfocused ultrasound transducers. Similar to other computed tomography methods, the sample is imaged at multiple view angles, which are then used to perform an inverse reconstruction algorithm based on the detection geometry (typically through universal backprojection, modified delay-and-sum, or time reversal ) to elicit the initial pressure distribution within the tissue. PAM on the other hand uses focused ultrasound detection combined with weakly focused optical excitation (acoustic resolution PAM or AR-PAM) or tightly focused optical excitation (optical resolution PAM or OR-PAM). PAM typically captures images point-by-point via a mechanical raster scanning pattern. At each scanned point, the acoustic time-of-flight provides axial resolution while the acoustic focusing yields lateral resolution. == Applications of deep learning in PACT == The first application of deep learning in PACT was by Reiter et al. in which a deep neural network was trained to learn spatial impulse responses and locate photoacoustic point sources. The resulting mean axial and lateral point location errors on 2,412 of their randomly selected test images were 0.28 mm and 0.37 mm respectively. After this initial implementation, the applications of deep learning in PACT have branched out primarily into removing artifacts from acoustic reflections, sparse sampling, limited-view, and limited-bandwidth. There has also been some recent work in PACT toward using deep learning for wavefront localization. There have been networks based on fusion of information from two different reconstructions to improve the reconstruction using deep learning fusion based networks. === Using deep learning to locate photoacoustic point sources === Traditional photoacoustic beamforming techniques modeled photoacoustic wave propagation by using detector array geometry and the time-of-flight to account for differences in the PA signal arrival time. However, this technique failed to account for reverberant acoustic signals caused by acoustic reflection, resulting in acoustic reflection artifacts that corrupt the true photoacoustic point source location information. In Reiter et al., a convolutional neural network (similar to a simple VGG-16 style architecture) was used that took pre-beamformed photoacoustic data as input and outputted a classification result specifying the 2-D point source location. ==== Deep learning for PA wavefront localization ==== Johnstonbaugh et al. was able to localize the source of photoacoustic wavefronts with a deep neural network. The network used was an encoder-decoder style convolutional neural network. The encoder-decoder network was made of residual convolution, upsampling, and high field-of-view convolution modules. A Nyquist convolution layer and differentiable spatial-to-numerical transform layer were also used within the architecture. Simulated PA wavefronts served as the input for training the model. To create the wavefronts, the forward simulation of light propagation was done with the NIRFast toolbox and the light-diffusion approximation, while the forward simulation of sound propagation was done with the K-Wave toolbox. The simulated wavefronts were subjected to different scattering mediums and Gaussian noise. The output for the network was an artifact free heat map of the targets axial and lateral position. The network had a mean error rate of less than 30 microns when localizing target below 40 mm and had a mean error rate of 1.06 mm for localizing targets between 40 mm and 60 mm. With a slight modification to the network, the model was able to accommodate multi target localization. A validation experiment was performed in which pencil lead was submerged into an intralipid solution at a depth of 32 mm. The network was able to localize the lead's position when the solution had a reduced scattering coefficient of 0, 5, 10, and 15 cm−1. The results of the network show improvements over standard delay-and-sum or frequency-domain beamforming algorithms and Johnstonbaugh proposes that this technology could be used for optical wavefront shaping, circulating melanoma cell detection, and real-time vascular surgeries. === Removing acoustic reflection artifacts (in the presence of multiple sources and channel noise) === Building on the work of Reiter et al., Allman et al. utilized a full VGG-16 architecture to locate point sources and remove reflection artifacts within raw photoacoustic channel data (in the presence of multiple sources and channel noise). This utilization of deep learning trained on simulated data produced in the MATLAB k-wave library, and then later reaffirmed their results on experimental data. === Ill-posed PACT reconstruction === In PACT, tomographic reconstruction is performed, in which the projections from multiple solid angles are combined to form an image. When reconstruction methods like filtered backprojection or time reversal, are ill-posed inverse problems due to sampling under the Nyquist-Shannon's sampling requirement or with limited-bandwidth/view, the resulting reconstruction contains image artifacts. Traditionally these artifacts were removed with slow iterative methods like total variation minimization, but the advent of deep learning approaches has opened a new avenue that utilizes a priori knowledge from network training to remove artifacts. In the deep learning methods that seek to remove these sparse sampling, limited-bandwidth, and limited-view artifacts, the typical workflow involves first performing the ill-posed reconstruction technique to transform the pre-beamformed data into a 2-D representation of the initial pressure distribution that contains artifacts. Then, a convolutional neural network (CNN) is trained to remove the artifacts, in order to produce an artifact-free representation of the ground truth initial pressure distribution. ==== Using deep learning to remove sparse sampling artifacts ==== When the density of uniform tomographic view angles is under what is prescribed by the Nyquist-Shannon's sampling theorem, it is said that the imaging system is performing sparse sampling. Sparse sampling typically occurs as a way of keeping production costs low and improving image acquisition speed. The typical network architectures used to remove these sparse sampling artifacts are U-net and Fully Dense (FD) U-net. Both of these architectures contain a compression and decompression phase. The compression phase learns to compress the image to a latent representation that lacks the imaging artifacts and other details. The decompression phase then combines with information passed by the residual connections in order to add back image details without adding in the details associated with the artifacts. FD U-net modifies the original U-net architecture by including dense blocks that allow layers to utilize information learned by previous layers within the dense block. Another technique was proposed using a simple CNN based architecture for removal of artifacts and improving the k-wave image reconstruction. ==== Removing limited-view artifacts with deep learning ==== When a region of partial solid angles are not captured, generally due to geometric limitations, the image acquisition is said to have limited-view. As illustrated by the experiments of Davoudi et al., limited-view corruptions can be directly observed as missing information in the frequency domain of the reconstructed image. Limited-view, similar to sparse sampling, makes the initial reconstruction algorithm ill-posed. Prior to deep learning, the limited-view problem was addressed with complex hardware such as acoustic deflectors and full ring-shaped transducer arrays, as well as solutions like compressed sensing, weighted factor, and iterative filtered backprojection. The result of this ill-posed reconstruction is imaging artifacts that can be removed by CNNs. The deep learning algorithms used to remove limited-view artifacts include U-net and FD U-net, as well as generative adversarial networks (GANs) and volumetric versions of U-net. One GAN implementation of note improved upon U-net by using U-net as a generator and VGG as a discriminator, with the Wasserstein metric and gradient penalty to stabilize training (WGAN-GP). ==== Pixel-wise interpolation
Onshape
Onshape is a computer-aided design (CAD) software system, delivered over the Internet via a software as a service (SaaS) model. It makes extensive use of cloud computing, with compute-intensive processing and rendering performed on Internet-based servers, and users are able to interact with the system via a web browser or the iOS and Android apps. As a SaaS system, Onshape upgrades are released directly to the web interface, and the software does not require maintenance by the user. Onshape allows teams to collaborate on a single shared design, the same way multiple writers can work together editing a shared document via cloud services. It is primarily focused on mechanical CAD (MCAD) and is used for product and machinery design across many industries, including consumer electronics, mechanical machinery, medical devices, 3D printing, machine parts, and industrial equipment. As of 2025, Onshape is popularly used as a CAD suite for the FIRST Robotics Competition (FRC) alongside the MKCad application available in the Onshape App Store. == Company history == Onshape was developed by a company with the same name. Founded in 2012, Onshape was based in Cambridge, Massachusetts (USA), with offices in Singapore and Pune, India. Its leadership team includes several engineers and executives who originated from SolidWorks, a popular 3D CAD program that runs on Microsoft Windows. Onshape’s co-founders include two former SolidWorks CEOs, Jon Hirschtick and John McEleney. In November 2012, former SolidWorks CEOs Jon Hirschtick and John McEleney led six co-founders launching Belmont Technology, a placeholder name that was later changed to Onshape. The company’s first round of funding was $9 million from North Bridge Venture Partners and Commonwealth Capital. In March 2015, Onshape released the public beta version of its cloud CAD software, after pre-production testing with more than a thousand CAD professionals in 52 countries. Included in the beta launch was Onshape for iPhone. In August 2015, the company released its Onshape for Android app. In December 2015, Onshape launched its full commercial release. The company also launched the Onshape App Store, offering CAM, simulation, rendering and other cloud-based engineering tools. The Onshape App Store was launched with 24 developer partners. In April 2016, Onshape introduced its Education Plan, with a free version of Onshape Professional geared for college students and educators. In May 2016, Onshape released FeatureScript, a new open source (MIT licensed) programming language for creating and customizing CAD features. In October 2019, Onshape agreed to be acquired by PTC. The acquisition closed in November 2019 for $470 million. In February 2024, Onshape released iOS support for the Apple Vision Pro, allowing for real world applications of CAD models and prototypes. In January 2025, Onshape released the CAM studio, allowing users to generate G-code for up to 5-axis Simultaneous milling. == Funding == Onshape was a venture-backed company with investments from firms including Andreessen Horowitz, Commonwealth Capital Ventures, New Enterprise Associates (NEA) and North Bridge Venture Partners. Total venture funding amounted to $169 million. == Supported file formats == === Modelling === ==== Importing ==== As of May 2025, Onshape supported importing (opening) the following common CAD file formats: Parasolid X_T (Preferred) STEP (ISO 10303) ISO JT (ISO 14306) ACIS IGES CATIA v4, v5, v6 Autodesk Inventor Part (.IPT) Assembly (.IAM) Presentation (.IPN) Drawing (.IDW) Pro/ENGINEER, Creo Rhinoceros 3D: .3dm .STL .OBJ SolidWorks file formats Siemens NX file formats Drawings (.DXF/.DWG) ==== Exporting ==== Onshape supports exporting to the following formats: STEP (ISO 10303) Parasolid XT ACIS IGES SolidWorks file formats .STL Rhinoceros 3D: .3dm Collada XML-spec based textual file === Drawing === Ordinary engineering or technical drawing can be exported as .PDF file. === Other Formats === In addition to CAD file formats, Onshape supports importing some Non-CAD file formats for viewing and referencing. === Assembly === Assemblies can be imported and exported to: STEP (ISO 10303) Parasolid XT ACIS Pro/ENGINEER, Creo ISO JT Rhinoceros 3D: .3dm Siemens NX file formats SolidWorks Pack and Go zip file File formats that assemblies can be only-exported to, are: IGES .STL Collada XML-spec based textual file