AI Code Meme

AI Code Meme — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Stevens Award

    Stevens Award

    The Stevens Award is a software engineering lecture award given by the Reengineering Forum, an industry association. The international Stevens Award was created to recognize outstanding contributions to the literature or practice of methods for software and systems development. The first award was given in 1995. The presentations focus on the current state of software methods and their direction for the future. This award lecture is named in memory of Wayne Stevens (1944-1993), a consultant, author, pioneer, and advocate of the practical application of software methods and tools. The Stevens Award and lecture is managed by the Reengineering Forum. The award was founded by International Workshop on Computer Aided Software Engineering (IWCASE), an international workshop association of users and developers of computer-aided software engineering (CASE) technology, which merged into The Reengineering Forum. Wayne Stevens was a charter member of the IWCASE executive board. == Recipients == 1995: Tony Wasserman 1996: David Harel 1997: Michael Jackson 1998: Thomas McCabe 1999: Tom DeMarco 2000: Gerald Weinberg 2001: Peter Chen 2002: Cordell Green 2003: Manny Lehman 2004: François Bodart 2005: Mary Shaw, Jim Highsmith 2006: Grady Booch 2007: Nicholas Zvegintzov 2008: Harry Sneed 2009: Larry Constantine 2010: Peter Aiken 2011: Jared Spool, Barry Boehm 2012: Philip Newcomb 2013: Jean-Luc Hainaut 2014: François Coallier 2015: Pierre Bourque

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  • ShowScoop

    ShowScoop

    ShowScoop is a website and mobile app platform on which users can rate and review artists, concerts, and music festivals that they have seen/attended. The reviews and ratings are designed to be informative of how well such performances are live. This helps concert-goers decide which live music events they want to attend. == History == ShowScoop was founded in August 2012 by Micah Smurthwaite and is based out of San Diego, CA. In February 2013, ShowScoop launched its mobile app at the SF Music Tech Summit. The application is currently available on the iPhone, with plans to expand into the Android market in the future. == Services == ShowScoop uses crowdsourcing to provide accurate ratings of live concert experiences. In addition to viewing ratings, users are encouraged to rate and review concerts they have attended. The ShowScoop database includes nearly one million artists and over 2.5 million live music events. ShowScoop users can rate artists on four aspects of the performance: stage presence, crowd interaction, sound quality, and visual effects. The rating system uses an ascending scale from one to five in each of the aspects, with five being the highest score. In addition to the quantitative ratings, ShowScoop users are also free to write qualitative reviews in a provided comment section. This allows users to explain their ratings and add further insight or opinion. ShowScoop incorporates several facets of social media into its services. Users can create a user profile to share limited personal information and store their ratings and reviews. Users are also given the option of sharing their evaluations with their social networks on Facebook and Twitter. Users can "like" reviews, follow artists, and follow other ShowScoop users. The mobile app allows users to take photos, apply filters, and share the final image in conjunction with reviews and through Instagram. == Road Crew == ShowScoop's "Road Crew" is a group made up of top contributors within the ShowScoop community. The Road Crew assists in curating artist pages, assuring information quality and accuracy. In return, members of the Road Crew are given incentives, including free tickets to concerts and personal invitations to exclusive shows. Applicants to the Road Crew are judged on the number and quality of their reviews, the photos and videos they have posted, and their general engagement with the ShowScoop community in following and liking users and reviews.

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  • Sahara Net

    Sahara Net

    Sahara Net is an information and communications technology provider (ICT) serving the Saudi market, the company has rapidly grown since 1989 to offer various complementary services such as connectivity, internet, hosting, cloud, optimization, cyber security, and managed services. == History == Sahara Net is a Saudi Joint Stock Company (JSC) and its history goes back to 1989 when Sahara Net established the 1st Saudi Bulletin Board Service (BBS) in the Kingdom. During this period, it operated as a hub for email exchange in the FidoNet network. And in 1994 Sahara Net started offering Internet connectivity and other related services like internet email, web design, web hosting, and Domain name registry services. These services made the first ISP in Saudi Arabia before the official licensing in 1998, when the Saudi Internet market was regulated and Sahara Net received Internet Service Provider (ISP) license and was appointed as the official Local Internet Registry (LIR) in the Kingdom of Saudi Arabia. == Today == The company grew over these years to become one of the main ICTs in the Saudi Arabian market, extending network coverage to all major cities in Saudi Arabia, and offering various connectivity options to business as well as home users. In 2009, the company was partially acquired by Telindus (the ICT investment arm of Belgacom), the famous telecom operator in Belgium and Europe. Then, in 2014, the company was fully acquired by its original founders. Recently, Sahara Net was converted from an LLC to a JSC with over 1200 shareholders by a capital raise (original founders still control 70% of the shares).

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  • Marq (company)

    Marq (company)

    Marq (formerly Lucidpress) is a cloud-based software platform for brand management and templated content creation. The platform integrates with digital asset management (DAM) systems—including Aprimo and Bynder and customer relationship management (CRM) tools such as Salesforce and HubSpot. Marq also includes AI-assisted features for brand compliance and content automation. Trade publications have described the product as a brand templating and creative automation platform. == History == In October 2013, Lucid Software, Inc. announced Lucidpress as a public beta version. Following its release, Lucidpress was featured in TechCrunch, VentureBeat and PC World, with TechCrunch noting: "I had a chance to test the app before its launch and it is indeed very easy to use. If you've ever used a desktop publishing app in the past, you'll feel right at home with Marq, as it features the same kind of standard top-bar menu and layout options as most other publishing apps. In terms of features, it can also hold its own against similar desktop-based apps." In May 2021, Lucidpress announced that it had been acquired by Charles Thayne Capital ("CTC"), a growth-oriented and technology-focused private investment firm. In May 2021, following its acquisition by Charles Thayne Capital, Lucidpress became fully independent. Owen Fuller, who had served as General Manager since 2017, was appointed Chief Executive Officer. In 2022, Lucidpress was rebranded as Marq to reflect the company’s shift toward brand templating and creative automation tools, while continuing to support its publishing features. == Features == Marq integrates with customer relationship management (CRM) platforms such as Salesforce and HubSpot, enabling the creation of personalized, on-brand sales and marketing materials. The platform also connects with multiple digital asset management (DAM) systems, including Bynder, Aprimo, MediaValet, PhotoShelter, Acquia, and Canto. == Investment == Lucid Software raised $1 million in Seed in 2011, led by Google Ventures. In May 2014, the company received a $5 million investment. The round was led by Salt Lake-based Kickstart Seed Fund. In September 2016, the company received a $36 million investment from Spectrum Equity.

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  • Pronunciation assessment

    Pronunciation assessment

    Automatic pronunciation assessment uses computer speech recognition to determine how accurately speech has been pronounced, instead of relying on a human instructor or proctor. It is also called speech verification, pronunciation evaluation, and pronunciation scoring. This technology is used to grade speech quality, for language testing, for computer-aided pronunciation teaching (CAPT) in computer-assisted language learning (CALL), for speaking skill remediation, and for accent reduction. Pronunciation assessment is different from dictation or automatic transcription, because instead of determining unknown speech, it verifies learners' pronunciation of known word(s), often from prior transcription of the same utterance; ideally scoring the intelligibility of the learners' speech. Sometimes pronunciation assessment evaluates the prosody of the learners' speech, such as intonation, pitch, tempo, rhythm, and syllable and word stress, although those are usually not essential for being understood in most languages. Pronunciation assessment is also used in reading tutoring, for example in products from Google, Microsoft, and Amira Learning. Automatic pronunciation assessment can also be used to help diagnose and treat speech disorders such as apraxia. == Intelligibility == Intelligibility refers to how well a learner's utterance is understood by a listener, rather than how much it sounds like a native speaker. This is separate from measures of fluency, such as so-called "Goodness of Pronunciation" (GoP) scores, which estimate how closely an utterance aligns with those of native speakers. Intelligibility is widely regarded as the most important communicative goal in pronunciation teaching and assessment. For example, in the Common European Framework of Reference for Languages (CEFR) assessment criteria for "overall phonological control", intelligibility outweighs formally correct pronunciation at all levels. Studies in applied linguistics have shown that accent reduction does not always increase intelligibility because listeners can often comprehend heavily accented speech without difficulty. Pronunciation assessment systems often rely on acoustic methods such as GoP which compare learner speech to reference models to produce phoneme-level scores, which are in turn aggregated to produce word and phrase scores. While these methods are effective for identifying deviations from native speakers' utterances, they do not effectively measure how understandable speech is to human listeners. Intelligibility is influenced by broader linguistic and contextual factors such as stress placement, speech rate, and coarticulation, which are not represented in purely segmental scores. The earliest work on pronunciation assessment avoided measuring genuine listener intelligibility, a shortcoming corrected in 2011 at the Toyohashi University of Technology, and included in the Versant high-stakes English fluency assessment from Pearson and mobile apps from 17zuoye Education & Technology, but still missing in 2023 products from Google Search, Microsoft, Educational Testing Service, Speechace, and ELSA. Assessing authentic listener intelligibility is essential for avoiding inaccuracies from accent bias, especially in high-stakes assessments; from words with multiple correct pronunciations; and from phoneme coding errors in machine-readable pronunciation dictionaries. In 2022, researchers found that some newer speech-to-text systems, based on end-to-end reinforcement learning to map audio signals directly into words, produce word and phrase confidence scores (from 10-25ms audio frame logit aggregation) closely correlated with genuine listener intelligibility. Others have been able to assess intelligibility using Levenshtein or dynamic time warping distance measures from Wav2Vec2 representation of good speech. Further work through 2025 has focused specifically on measuring intelligibility. A 2025 study of 42 pronunciation and speech coaching apps (32 mobile and 10 web) found that none offered intelligibility assessment. Instead, most provided only segmental and accent-focused scoring. About two-thirds of the apps provided some form of specific pronunciation feedback, usually with phonetic transcriptions, but accompanied by visual cues (such as animations of the vocal tract or the lips and tongue from the front) in only about 5% of the apps. Less than a third provided feedback on learner perception of exemplar speech. == Evaluation == Although there are as yet no industry-standard benchmarks for evaluating pronunciation assessment accuracy, researchers occasionally release evaluation speech corpuses for others to use for improving assessment quality. Such evaluation databases often emphasize formally unaccented pronunciation to the exclusion of genuine intelligibility evident from blinded listener transcriptions. As of mid-2025, state of the art approaches for automatically transcribing phonemes typically achieve an error rate of about 10% from known good speech. The International Speech Communication Association (ISCA) 2025 Workshop on Speech and Language Technology in Education (SLaTE) administered a Speak & Improve Challenge: Spoken Language Assessment and Feedback, introducing benchmarks for evaluating pronunciation assessment and remediation systems across languages, accents, and learner populations. The challenge emphasized cross-lingual generalization and alignment with human intelligibility judgments, for more robust and interpretable assessment systems. Ethical issues in pronunciation assessment are present in both human and automatic methods. Authentic validity, fairness, and mitigating bias in evaluation are all crucial. Diverse speech data should be included in automatic pronunciation assessment models. Combining human judgments, especially blinded transcriptions from a wide diversity of listeners, with automated feedback can improve accuracy and fairness. Second language learners benefit substantially from their use of widely available speech recognition systems for dictation, virtual assistants, and AI chatbots. In such systems, users naturally try to correct their own errors evident in speech recognition results that they notice. Such use improves their grammar and vocabulary development along with their pronunciation skills. The extent to which explicit pronunciation assessment and remediation approaches improve on such self-directed interactions remains an open question. Similarly, automatic dictation results have been shown to reflect intelligibility about as well as human scorers. == Recent developments == During 2021–22, a smartphone-based CAPT system was used to sense articulation through both audible and inaudible signals, providing feedback at the phoneme level. Some promising areas for improvement which were being developed in 2024 include articulatory feature extraction and transfer learning to suppress unnecessary corrections. Other interesting advances under development include "augmented reality" interfaces for mobile devices using optical character recognition to provide pronunciation training on text found in user environments. In 2024, audio multimodal large language models were first described as assessing pronunciation. That work has been carried forward by other researchers in 2025 who report positive results. Subsequently, researchers demonstrated pronunciation scoring by providing a language model with textual descriptions of speech, including the speech-to-text transcript, phoneme sequences, pauses, and phoneme sequence matching; this approach can achieve performance similar to multimodal LLMs that analyze raw audio while avoiding their higher computational cost. In 2025, the Duolingo English Test authors published a description of their pronunciation assessment method, purportedly built to measure intelligibility rather than accent imitation. While achieving a correlation of 0.82 with expert human ratings, very close to inter-rater agreement and outperforming alternative methods, the method is nonetheless based on experts' scores along the six-point CEFR common reference levels scale, instead of actual blinded listener transcriptions. Further promising work in 2025 includes assessment feedback aligning learner speech to synthetic utterances using interpretable features, identifying continuous spans of words for remediation feedback; synthesizing corrected speech matching learners' self-perceived voices, which they prefer and imitate more accurately as corrections; and streaming such interactions. On January 21, 2026, Educational Testing Service's TOEFL iBT high-stakes English language test, required by US university admissions and employers from English as a foreign language applicants more often than all other internet-based tests combined, changed its speaking assessments. While official rubrics claim that the new scoring will be based primarily on intelligibility, the new test's technical description indicates that it ju

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  • Flektor

    Flektor

    Flektor was a web application that allowed users the ability to create and "mashup" their own content (photos, videos, music, etc.) and share it via email, on social networking websites MySpace, Facebook, Blogger, Digg, eBay or on personal blogs. The company's website (Flektor.com) launched on April 2, 2007, and over 40,000 people began utilizing its features just one month later. Flektor closed down in January 2009. Flektor offered tools and widgets that included audio, video, photos, text, and approximately 100 effects, transitions and filters to be used with media. Users could create personalized slideshows, polls, postcards, and streaming video projects which the website calls "fleks". Flektor also offered Chat (used as a MySpace addon) and Movie Editor, which provided the ability to edit content and assets together. Users of Flektor could import media from websites like Photobucket and Google's YouTube, and then edit their content with the site's editing tools. Flektor's erstwhile competitors include Slide.com (founded by PayPal co-founder Max Levchin), RockYou!, Yahoo's JumpCut and Brightcove. == History == Flektor was created by Jason Rubin, Andy Gavin and former HBO executive Jason R. Kay. Both Rubin and Gavin spent most of their careers in the video game industry developing games for publishers like Electronic Arts, Universal Interactive Studios and Sony Computer Entertainment America. They founded a successful game development studio called Naughty Dog and were responsible for games such as Crash Bandicoot and Jak and Daxter. After selling Naughty Dog to Sony, Rubin focused on a comic book series called Iron and the Maiden before teaming up again with Gavin to venture into the web industry with Flektor. Jason Kay spent four years at Home Box Office, working as a consultant to the EVP of Business Development. They recruited former employee and then Naughty Dog Lead Programmer Scott Shumaker to lead the technology team along with Gavin. Ryan Evans joined shortly thereafter, spearheading product development. Flektor is based in Culver City, California. In May 2007, the company was sold to Fox Interactive Media, which is a division of News Corp., for more than $20 million. The deal coincided with Fox's acquisition of Photobucket, an image-hosting and sharing website. Fox Interactive Media already holds possession of MySpace, IGN Entertainment, FOXSports.com, AmericanIdol.com and Rotten Tomatoes. After the acquisition, Rubin, Gavin and Kay departed, leaving the studio in the hands of Shumaker and Evans. In the fall of 2007, Flektor partnered with its sister company, MySpace, and MTV to provide instant audience feedback via polls for the interactive MySpace/ MTV Presidential Dialogues series with presidential candidates Senator Barack Obama, Senator John McCain and John Edwards. Use of Flektor's polling system, enabled hosts John McLaughlin and Geoffrey Garin to cater their questions towards subjects of voter-interest. In the fall of 2008, Flektor built the official site for the 2008 Presidential debates, hosted at MyDebates. In January 2009, due to a company directive to focus on the core MySpace property, Fox Interactive announced that Flektor would be shut down, with some of its technology being incorporated into MySpace.

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  • Tweak programming environment

    Tweak programming environment

    Tweak is a graphical user interface (GUI) layer written by Andreas Raab for the Squeak development environment, which in turn is an integrated development environment based on the Smalltalk-80 computer programming language. Tweak is an alternative to an earlier graphic user interface layer called Morphic. Development began in 2001. Applications that use the Tweak software include Sophie (version 1), a multimedia and e-book authoring system, and a family of virtual world systems: Open Cobalt, Teleplace, OpenQwaq, 3d ICC's Immersive Terf and the Croquet Project. == Influences == An experimental version of Etoys, a programming environment for children, used Tweak instead of Morphic. Etoys was a major influence on a similar Squeak-based programming environment known as Scratch.

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  • Mobile cloud computing

    Mobile cloud computing

    Mobile Cloud Computing (MCC) is the combination of cloud computing and mobile computing to bring rich computational resources to mobile users, network operators, as well as cloud computing providers. The ultimate goal of MCC is to enable execution of rich mobile applications on a plethora of mobile devices, with a rich user experience. MCC provides business opportunities for mobile network operators as well as cloud providers. More comprehensively, MCC can be defined as "a rich mobile computing technology that leverages unified elastic resources of varied clouds and network technologies toward unrestricted functionality, storage, and mobility to serve a multitude of mobile devices anywhere, anytime through the channel of Ethernet or Internet regardless of heterogeneous environments and platforms based on the pay-as-you-use principle." == Architecture == MCC uses computational augmentation approaches (computations are executed remotely instead of on the device) by which resource-constraint mobile devices can utilize computational resources of varied cloud-based resources. In MCC, there are four types of cloud-based resources, namely distant immobile clouds, proximate immobile computing entities, proximate mobile computing entities, and hybrid (combination of the other three model). Giant clouds such as Amazon EC2 are in the distant immobile groups whereas cloudlet or surrogates are member of proximate immobile computing entities. Smartphones, tablets, handheld devices, and wearable computing devices are part of the third group of cloud-based resources which is proximate mobile computing entities. Vodafone, Orange and Verizon have started to offer cloud computing services for companies. == Challenges == In the MCC landscape, an amalgam of mobile computing, cloud computing, and communication networks (to augment smartphones) creates several complex challenges such as Mobile Computation Offloading, Seamless Connectivity, Long WAN Latency, Mobility Management, Context-Processing, Energy Constraint, Vendor/data Lock-in, Security and Privacy, Elasticity that hinder MCC success and adoption. === Open research issues === Although significant research and development in MCC is available in the literature, efforts in the following domains is still lacking: Architectural issues: A reference architecture for heterogeneous MCC environment is a crucial requirement for unleashing the power of mobile computing towards unrestricted ubiquitous computing. Energy-efficient transmission: MCC requires frequent transmissions between cloud platform and mobile devices, due to the stochastic nature of wireless networks, the transmission protocol should be carefully designed. Context-awareness issues: Context-aware and socially-aware computing are inseparable traits of contemporary handheld computers. To achieve the vision of mobile computing among heterogeneous converged networks and computing devices, designing resource-efficient environment-aware applications is an essential need. Live VM migration issues: Executing resource-intensive mobile application via Virtual Machine (VM) migration-based application offloading involves encapsulation of application in VM instance and migrating it to the cloud, which is a challenging task due to additional overhead of deploying and managing VM on mobile devices. Mobile communication congestion issues: Mobile data traffic is tremendously hiking by ever increasing mobile user demands for exploiting cloud resources which impact on mobile network operators and demand future efforts to enable smooth communication between mobile and cloud endpoints. Trust, security, and privacy issues: Trust is an essential factor for the success of the burgeoning MCC paradigm. It is because the data along with code/component/application/complete VM is offloaded to the cloud for execution. Moreover, just like software and mobile application piracy, the MCC application development models are also affected by the piracy issue. Pirax is known to be the first specialized framework for controlling application piracy in MCC requirements == MCC research groups and activities == Several academic and industrial research groups in MCC have been emerging since last few years. Some of the MCC research groups in academia with large number of researchers and publications include: MDC, Mobile and Distributed Computing research group is at Faculty of Computer and Information Science, King Saud University. MDC research group focuses on architectures, platforms, and protocols for mobile and distributed computing. The group has developed algorithms, tools, and technologies which offer energy efficient, fault tolerant, scalable, secure, and high performance computing on mobile devices. MobCC lab, Faculty of Computer Science and Information Technology, University Malaya. The lab was established in 2010 under the High Impact Research Grant, Ministry of Higher Education, Malaysia. It has 17 researchers and has track of 22 published articles in international conference and peer-reviewed CS journals. ICCLAB, Zürich University of Applied Sciences has a segment working on MCC. The InIT Cloud Computing Lab is a research lab within the Institute of Applied Information Technology (InIT) of Zürich University of Applied Sciences (ZHAW). It covers topic areas across the entire cloud computing technology stack. Mobile & Cloud Lab, Institute of Computer Science, University of Tartu. Mobile & Cloud Lab conducts research and teaching in the mobile computing and cloud computing domains. The research topics of the group include cloud computing, mobile application development, mobile cloud, mobile web services and migrating scientific computing and enterprise applications to the cloud. SmartLab, Data Management Systems Laboratory, Department of Computer Science, University of Cyprus. SmartLab is a first-of-a-kind open cloud of smartphones that enables a new line of systems-oriented mobile computing research. Mobile Cloud Networking: Mobile Cloud Networking (MCN) was an EU FP7 Large-scale Integrating Project (IP, 15m Euro) funded by the European Commission. The MCN project was launched in November 2012 for the period of 36 month. The project was coordinated by SAP Research and the ICCLab at the Zurich University of Applied Science. In total 19 partners from industry and academia established the first vision of Mobile Cloud Computing. The project was primarily motivated by an ongoing transformation that drives the convergence between the Mobile Communications and Cloud Computing industry enabled by the Internet and is considered the first pioneer in the area of Network Function Virtualization.

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  • Rake (software)

    Rake (software)

    Rake is a software task management and a build automation tool created by Jim Weirich. It allows the user to specify tasks and to describe dependencies as well as to group tasks into namespaces. It is similar to SCons and Make. Rake was written in Ruby and has been part of the standard library of Ruby since version 1.9. == Examples == The tasks that should be executed need to be defined in a configuration file called Rakefile. A Rakefile has no special syntax and contains executable Ruby code. === Tasks === The basic unit in Rake is the task. A task has a name and an action block, that defines its functionality. The following code defines a task called greet that will output the text "Hello, Rake!" to the console. When defining a task, you can optionally add dependencies, that is one task can depend on the successful completion of another task. Calling the "seed" task from the following example will first execute the "migrate" task and only then proceed with the execution of the "seed" task.Tasks can also be made more versatile by accepting arguments. For example, the "generate_report" task will take a date as argument. If no argument is supplied the current date is used.A special type of task is the file task, which can be used to specify file creation tasks. The following task, for example, is given two object files, i.e. "a.o" and "b.o", to create an executable program.Another useful tool is the directory convenience method, that can be used to create directories upon demand. === Rules === When a file is named as a prerequisite but it does not have a file task defined for it, Rake will attempt to synthesize a task by looking at a list of rules supplied in the Rakefile. For example, suppose we were trying to invoke task "mycode.o" with no tasks defined for it. If the Rakefile has a rule that looks like this: This rule will synthesize any task that ends in ".o". It has as a prerequisite that a source file with an extension of ".c" must exist. If Rake is able to find a file named "mycode.c", it will automatically create a task that builds "mycode.o" from "mycode.c". If the file "mycode.c" does not exist, Rake will attempt to recursively synthesize a rule for it. When a task is synthesized from a rule, the source attribute of the task is set to the matching source file. This allows users to write rules with actions that reference the source file. === Advanced rules === Any regular expression may be used as the rule pattern. Additionally, a proc may be used to calculate the name of the source file. This allows for complex patterns and sources. The following rule is equivalent to the example above: NOTE: Because of a quirk in Ruby syntax, parentheses are required around a rule when the first argument is a regular expression. The following rule might be used for Java files: === Namespaces === To better organize big Rakefiles, tasks can be grouped into namespaces. Below is an example of a simple Rake recipe:

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  • Charge-coupled device

    Charge-coupled device

    A charge-coupled device (CCD) is an integrated circuit containing an array of linked, or coupled, capacitors. Under the control of an external circuit, each capacitor can transfer its electric charge to a neighboring capacitor. CCD sensors are a major technology used in digital imaging. In a CCD image sensor, pixels are represented by p-doped metal–oxide–semiconductor (MOS) capacitors. These MOS capacitors, the basic building blocks of a CCD, are biased above the threshold for inversion when image acquisition begins, allowing the conversion of incoming photons into electron charges at the semiconductor-oxide interface; the CCD is then used to read out these charges. Although CCDs are not the only technology to allow for light detection, CCD image sensors are widely used in professional, medical, and scientific applications where high-quality image data are required. In applications with less exacting quality demands, such as consumer and professional digital cameras, active pixel sensors, also known as CMOS sensors (complementary MOS sensors), are generally used. However, the large quality advantage CCDs enjoyed early on has narrowed over time and since the late 2010s CMOS sensors are the dominant technology, having largely if not completely replaced CCD image sensors. == History == The basis for the CCD is the metal–oxide–semiconductor (MOS) structure, with MOS capacitors being the basic building blocks of a CCD, and a depleted MOS structure used as the photodetector in early CCD devices. In the late 1960s, Willard Boyle and George E. Smith at Bell Labs were researching MOS technology while working on semiconductor bubble memory. They realized that an electric charge was the analog of the magnetic bubble and that it could be stored on a tiny MOS capacitor. As it was fairly straightforward to fabricate a series of MOS capacitors in a row, they connected a suitable voltage to them so that the charge could be stepped along from one to the next. This led to the invention of the charge-coupled device by Boyle and Smith in 1969. They conceived of the design of what they termed, in their notebook, "Charge 'Bubble' Devices". The initial paper describing the concept in April 1970 listed possible uses as memory, a delay line, and an imaging device. The device could also be used as a shift register. The essence of the design was the ability to transfer charge along the surface of a semiconductor from one storage capacitor to the next. The first experimental device demonstrating the principle was a row of closely spaced metal squares on an oxidized silicon surface electrically accessed by wire bonds. It was demonstrated by Gil Amelio, Michael Francis Tompsett and George Smith in April 1970. This was the first experimental application of the CCD in image sensor technology, and used a depleted MOS structure as the photodetector. The first patent (U.S. patent 4,085,456) on the application of CCDs to imaging was assigned to Tompsett, who filed the application in 1971. The first working CCD made with integrated circuit technology was a simple 8-bit shift register, reported by Tompsett, Amelio and Smith in August 1970. This device had input and output circuits and was used to demonstrate its use as a shift register and as a crude eight pixel linear imaging device. Development of the device progressed at a rapid rate. By 1971, Bell researchers led by Michael Tompsett were able to capture images with simple linear devices. Several companies, including Fairchild Semiconductor, RCA and Texas Instruments, picked up on the invention and began development programs. Fairchild's effort, led by ex-Bell researcher Gil Amelio, was the first with commercial devices, and by 1974 had a linear 500-element device and a 2D 100 × 100 pixel device. Peter L. P. Dillon, a scientist at Kodak Research Labs, invented the first color CCD image sensor by overlaying a color filter array on this Fairchild 100 x 100 pixel Interline CCD starting in 1974. Steven Sasson, an electrical engineer working for the Kodak Apparatus Division, invented a digital still camera using this same Fairchild 100 × 100 CCD in 1975. The interline transfer (ILT) CCD device was proposed by L. Walsh and R. Dyck at Fairchild in 1973 to reduce smear and eliminate a mechanical shutter. To further reduce smear from bright light sources, the frame-interline-transfer (FIT) CCD architecture was developed by K. Horii, T. Kuroda and T. Kunii at Matsushita (now Panasonic) in 1981. The first KH-11 KENNEN reconnaissance satellite equipped with charge-coupled device array (800 × 800 pixels) technology for imaging was launched in December 1976. Under the leadership of Kazuo Iwama, Sony started a large development effort on CCDs involving a significant investment. Eventually, Sony managed to mass-produce CCDs for their camcorders. Before this happened, Iwama died in August 1982. Subsequently, a CCD chip was placed on his tombstone to acknowledge his contribution. The first mass-produced consumer CCD video camera, the CCD-G5, was released by Sony in 1983, based on a prototype developed by Yoshiaki Hagiwara in 1981. Early CCD sensors suffered from shutter lag. This was largely resolved with the invention of the pinned photodiode (PPD). It was invented by Nobukazu Teranishi, Hiromitsu Shiraki and Yasuo Ishihara at NEC in 1980. They recognized that lag can be eliminated if the signal carriers could be transferred from the photodiode to the CCD. This led to their invention of the pinned photodiode, a photodetector structure with low lag, low noise, high quantum efficiency and low dark current. It was first publicly reported by Teranishi and Ishihara with A. Kohono, E. Oda and K. Arai in 1982, with the addition of an anti-blooming structure. The new photodetector structure invented at NEC was given the name "pinned photodiode" (PPD) by B.C. Burkey at Kodak in 1984. In 1987, the PPD began to be incorporated into most CCD devices, becoming a fixture in consumer electronic video cameras and then digital still cameras. Since then, the PPD has been used in nearly all CCD sensors and then CMOS sensors. In January 2006, Boyle and Smith were awarded the National Academy of Engineering Charles Stark Draper Prize, and in 2009 they were awarded the Nobel Prize for Physics for their invention of the CCD concept. Michael Tompsett was awarded the 2010 National Medal of Technology and Innovation, for pioneering work and electronic technologies including the design and development of the first CCD imagers. He was also awarded the 2012 IEEE Edison Medal for "pioneering contributions to imaging devices including CCD Imagers, cameras and thermal imagers". == Basics of operation == In a CCD for capturing images, there is a photoactive region (an epitaxial layer of silicon), and a transmission region made out of a shift register (the CCD, properly speaking). An image is projected through a lens onto the capacitor array (the photoactive region), causing each capacitor to accumulate an electric charge proportional to the light intensity at that location. A one-dimensional array, used in line-scan cameras, captures a single slice of the image, whereas a two-dimensional array, used in video and still cameras, captures a two-dimensional picture corresponding to the scene projected onto the focal plane of the sensor. Once the array has been exposed to the image, a control circuit causes each capacitor to transfer its contents to its neighbor (operating as a shift register). The last capacitor in the array dumps its charge into a charge amplifier, which converts the charge into a voltage. By repeating this process, the controlling circuit converts the entire contents of the array in the semiconductor to a sequence of voltages. In a digital device, these voltages are then sampled, digitized, and usually stored in memory; in an analog device (such as an analog video camera), they are processed into a continuous analog signal (e.g. by feeding the output of the charge amplifier into a low-pass filter), which is then processed and fed out to other circuits for transmission, recording, or other processing. == Detailed physics of operation == === Charge generation === Before the MOS capacitors are exposed to light, they are biased into the depletion region; in n-channel CCDs, the silicon under the bias gate is slightly p-doped or intrinsic. The gate is then biased at a positive potential, above the threshold for strong inversion, which will eventually result in the creation of an n channel below the gate as in a MOSFET. However, it takes time to reach this thermal equilibrium: up to hours in high-end scientific cameras cooled at low temperature. Initially after biasing, the holes are pushed far into the substrate, and no mobile electrons are at or near the surface; the CCD thus operates in a non-equilibrium state called deep depletion. Then, when electron–hole pairs are generated in the depletion region, they are separated by the electric field, the elec

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  • Outline of the Python programming language

    Outline of the Python programming language

    The following outline is provided as an overview of and topical guide to Python: Python is a general-purpose, interpreted, object-oriented, functional, multi-paradigm, and dynamically typed programming language known for its emphasis on code readability and broad standard library. Python was created by Guido van Rossum and first released in 1991. It emphasizes code readability and developer productivity. == What type of language is Python? == Programming language — artificial language designed to communicate instructions to a machine. Object-oriented programming — built primarily around objects and classes. Functional programming — supports functions as first-class objects. Scripting language — often used for automation and small programs. General-purpose programming language — designed for a wide variety of application domains. Dynamically typed — type checking occurs at runtime. Interpreted language — code is executed by an interpreter. Multi-paradigm — supports procedural, object-oriented, and functional programming. == History of Python == ABC (programming language) – precursor to Python Python was started by Guido van Rossum in 1989 and first released in 1991. Python 2 — major version released in 2000, officially retired in 2020. Python 3 — released in 2008 == General Python concepts == == Issues and limitations == Performance — generally slower than many compiled languages such as C or Java can be mitigated by C extensions or JIT compilers (PyPy). Global interpreter lock — limits parallel CPU-bound threads in CPython Memory consumption — high memory use compared to some lower-level languages Version compatibility — Python 2 vs Python 3 differences caused migration issues == Python implementations == CPython — reference implementation in C IronPython — Python for .NET Jython — Python for the JVM MicroPython — Python for microcontrollers and embedded systems Nuitka — compiler that packages user code with CPython into a static binary PyPy — JIT-compiled Python interpreter for speed PythonAnywhere — freemium hosted Python installation that runs in the browser Stackless Python — Python with lightweight concurrency features == Python toolchain == List of Python software Comparison of Python IDEs Comparison of server-side web frameworks for Python List of Python frameworks List of Python libraries List of unit testing frameworks for Python Python Package Index == Notable projects using Python == YouTube (backend) Instagram (backend) Dropbox Reddit OpenStack Blender (scripting and plugins) SageMath NumPy Pandas TensorFlow == Python development communities == ActiveState — commercial Python distributions and support Anaconda, Inc. — Python data science ecosystem GitHub Python Software Foundation Python Package Index (PyPI) — third-party software repository for Python == Example source code == Articles with example Python code == Python publications == === Books about Python === Automate the Boring Stuff with Python – Creative Commons Python book Alex Martelli — Python in a Nutshell and Python Cookbook Mark Pilgrim – Dive into Python Naomi Ceder — The Quick Python Book Wes McKinney — Python for Data Analysis Zed Shaw – Learn Python the Hard Way === Textbooks === Core Python Programming == Python programmers == == Python conferences == EuroPython – annual Python conference in Europe PyCon – the largest annual convention for the Python community PyData – conference series focused on data analysis, machine learning, and scientific computing with Python SciPy Conferences – focused on the use of Python in scientific computing and research DjangoCon – a conference dedicated to the Django web framework PyOhio – a free regional Python conference held in Ohio == Python learning resources == Codecademy – interactive Python programming lessons GeeksforGeeks – tutorials, coding examples, and interactive programming for Python concepts and data structures. Kaggle – free Python courses focused on data science and machine learning. Python.org Tutorial – the official Python tutorial from the Python Software Foundation. Real Python – articles, tutorials, and courses for Python developers. W3Schools – beginner-friendly Python tutorials. Wikibooks Python Programming – free open-content textbook on Python. === Competitive programming === Codeforces – an online platform for programming contests that supports Python submissions Codewars – gamified coding challenges supporting Python HackerRank – competitive programming and interview preparation site with Python challenges Kaggle – while focused on data science competitions, it also includes Python-based problem solving. LeetCode – online judge and problem-solving platform where Python is widely used

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  • Likewise, Inc.

    Likewise, Inc.

    Likewise, Inc., is an American technology startup company which provides a social networking service for finding and saving content recommendations for movies, TV shows, books, and podcasts. A team of ex-Microsoft employees founded Likewise in October 2017 with financial investment from Microsoft co-founder Bill Gates. The company is led by CEO Ian Morris and as of 2020 had a team of about 35 employees. Its headquarters operates in Bellevue, Washington. As of July 2020, 1 million users had joined the platform. == History == === Ideation (October 2017) === In 2017, former Microsoft Communications Chief Larry Cohen came up with the idea for Likewise in Bill Gates’ private office, Gates Ventures. Cohen currently serves as Gates Ventures’ CEO and managing partner. Cohen collaborated with colleagues Michael Dix and Ian Morris to co-found what would become Likewise, with Morris as its CEO. Gates funded the company's early development. The company developed its platform in stealth mode before launching publicly in October 2018. === Release (October 2018) === Likewise officially released its platform in the US and Canada on October 3, 2018. === Growth (2020 COVID-19 pandemic) === Likewise experienced accelerated growth alongside the COVID-19 pandemic. From March 2020 to July 2020, the platform's monthly active users tripled in numbers. The company reached one million users in July 2020. == Applications == === Mobile === Likewise is available as a mobile app for the Android and iOS mobile operating systems. Users receive recommendations from the Likewise algorithm, people they follow, and the Likewise editorial team. === Likewise TV === In October 2019, the company launched its Apple TV app called Likewise TV. The television app organizes shows across streaming services under one watchlist. On July 20, 2020, Likewise TV expanded to Android TV and Amazon Fire TV users.

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  • 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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  • Hamilton C shell

    Hamilton C shell

    Hamilton C shell is a clone of the Unix C shell and utilities for Microsoft Windows created by Nicole Hamilton at Hamilton Laboratories as a completely original work, not based on any prior code. It was first released on OS/2 on December 12, 1988 and on Windows NT in July 1992. The OS/2 version was discontinued in 2003 but the Windows version continues to be actively supported. == Design == Hamilton C shell differs from the Unix C shell in several respects. These include its compiler architecture, its use of threads, and the decision to follow Windows rather than Unix conventions. === Parser === The original C shell uses an ad hoc parser. This has led to complaints about its limitations. It works well enough for the kinds of things users type interactively but not very well for the more complex commands a user might take time to write in a script. It is not possible, for example, to pipe the output of a foreach statement into grep. There was a limit to how complex a command it could handle. By contrast, Hamilton uses a top-down recursive descent parser that allows it to compile statements to an internal form before running them. As a result, statements can be nested or piped arbitrarily. The language has also been extended with built-in and user-defined procedures, local variables, floating point and additional expression, editing and wildcarding operators, including an "indefinite directory" wildcard construct written as "..." that matches zero or more directory levels as required to make the rest of the pattern match. === Threads === Lacking fork or a high performance way to recreate that functionality, Hamilton uses the Windows threads facilities instead. When a new thread is created, it runs within the same process space and it shares all of the process state. If one thread changes the current directory or the contents of memory, it's changed for all the threads. It's much cheaper to create a thread than a process but there's no isolation between them. To recreate the missing isolation of separate processes, the threads cooperate to share resources using locks. === Windows conventions === Hamilton differs from other Unix shells in that it also directly supports Windows conventions for drive letters, filename slashes, escape characters, etc.

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  • Conservative morphological anti-aliasing

    Conservative morphological anti-aliasing

    Conservative morphological anti-aliasing (CMAA) is an antialiasing technique originally developed by Filip Strugar at Intel. CMAA is an image-based, post processing technique similar to that of morphological antialiasing. CMAA uses 4 main steps which are image analysis for color discontinuities, locally dominant edge detection, simple shape handling, and lastly symmetrical long edge shape handling. A couple of years after CMAA was introduced, Intel unveiled an updated version which they named CMAA2.

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