In signal processing, the chirplet transform is an inner product of an input signal with a family of analysis primitives called chirplets. Similar to the wavelet transform, chirplets are usually generated from (or can be expressed as being from) a single mother chirplet (analogous to the so-called mother wavelet of wavelet theory). == Definitions == The term chirplet transform was coined by Steve Mann, as the title of the first published paper on chirplets. The term chirplet itself (apart from chirplet transform) was also used by Steve Mann, Domingo Mihovilovic, and Ronald Bracewell to describe a windowed portion of a chirp function. In Mann's words: A wavelet is a piece of a wave, and a chirplet, similarly, is a piece of a chirp. More precisely, a chirplet is a windowed portion of a chirp function, where the window provides some time localization property. In terms of time–frequency space, chirplets exist as rotated, sheared, or other structures that move from the traditional parallelism with the time and frequency axes that are typical for waves (Fourier and short-time Fourier transforms) or wavelets. The chirplet transform thus represents a rotated, sheared, or otherwise transformed tiling of the time–frequency plane. Although chirp signals have been known for many years in radar, pulse compression, and the like, the first published reference to the chirplet transform described specific signal representations based on families of functions related to one another by time–varying frequency modulation or frequency varying time modulation, in addition to time and frequency shifting, and scale changes. In that paper, the Gaussian chirplet transform was presented as one such example, together with a successful application to ice fragment detection in radar (improving target detection results over previous approaches). The term chirplet (but not the term chirplet transform) was also proposed for a similar transform, apparently independently, by Mihovilovic and Bracewell later that same year. == Applications == The first practical application of the chirplet transform was in water-human-computer interaction (WaterHCI) for marine safety, to assist vessels in navigating through ice-infested waters, using marine radar to detect growlers (small iceberg fragments too small to be visible on conventional radar, yet large enough to damage a vessel). Other applications of the chirplet transform in WaterHCI include the SWIM (Sequential Wave Imprinting Machine). More recently other practical applications have been developed, including image processing (e.g. where there is periodic structure imaged through projective geometry), as well as to excise chirp-like interference in spread spectrum communications, in EEG processing, and Chirplet Time Domain Reflectometry. == Extensions == The warblet transform is a particular example of the chirplet transform introduced by Mann and Haykin in 1992 and now widely used. It provides a signal representation based on cyclically varying frequency modulated signals (warbling signals).
Intelligent agent
In artificial intelligence, an intelligent agent is an entity that perceives its environment, takes actions autonomously to achieve goals, and may improve its performance through machine learning or by acquiring knowledge. AI textbooks define artificial intelligence as the "study and design of intelligent agents," emphasizing that goal-directed behavior is central to intelligence. A specialized subset of intelligent agents, agentic AI (also known as an AI agent or simply agent), expands this concept by proactively pursuing goals, making decisions, and taking actions over extended periods. Intelligent agents can range from simple to highly complex. A basic thermostat or control system is considered an intelligent agent, as is a human being, or any other system that meets the same criteria—such as a firm, a state, or a biome. Intelligent agents operate based on an objective function, which encapsulates their goals. They are designed to create and execute plans that maximize the expected value of this function upon completion. For example, a reinforcement learning agent has a reward function, which allows programmers to shape its desired behavior. Similarly, an evolutionary algorithm's behavior is guided by a fitness function. Intelligent agents in artificial intelligence are closely related to agents in economics, and versions of the intelligent agent paradigm are studied in cognitive science, ethics, and the philosophy of practical reason, as well as in many interdisciplinary socio-cognitive modeling and computer social simulations. Intelligent agents are often described schematically as abstract functional systems similar to computer programs . To distinguish theoretical models from real-world implementations, abstract descriptions of intelligent agents are called abstract intelligent agents. Intelligent agents are also closely related to software agents—autonomous computer programs that carry out tasks on behalf of users. They are also referred to using a term borrowed from economics: a "rational agent". == Intelligent agents as the foundation of AI == The concept of intelligent agents provides a foundational lens through which to define and understand artificial intelligence. For instance, the influential textbook Artificial Intelligence: A Modern Approach (Russell & Norvig) describes: Agent: Anything that perceives its environment (using sensors) and acts upon it (using actuators). E.g., a robot with cameras and wheels, or a software program that reads data and makes recommendations. Rational Agent: An agent that strives to achieve the best possible outcome based on its knowledge and past experiences. "Best" is defined by a performance measure – a way of evaluating how well the agent is doing. Artificial Intelligence (as a field): The study and creation of these rational agents. Other researchers and definitions build upon this foundation. Padgham & Winikoff emphasize that intelligent agents should react to changes in their environment in a timely way, proactively pursue goals, and be flexible and robust (able to handle unexpected situations). Some also suggest that ideal agents should be "rational" in the economic sense (making optimal choices) and capable of complex reasoning, like having beliefs, desires, and intentions (BDI model). Kaplan and Haenlein offer a similar definition, focusing on a system's ability to understand external data, learn from that data, and use what is learned to achieve goals through flexible adaptation. Defining AI in terms of intelligent agents offers several key advantages: Avoids Philosophical Debates: It sidesteps arguments about whether AI is "truly" intelligent or conscious, like those raised by the Turing test or Searle's Chinese Room. It focuses on behavior and goal achievement, not on replicating human thought. Objective Testing: It provides a clear, scientific way to evaluate AI systems. Researchers can compare different approaches by measuring how well they maximize a specific "goal function" (or objective function). This allows for direct comparison and combination of techniques. Interdisciplinary Communication: It creates a common language for AI researchers to collaborate with other fields like mathematical optimization and economics, which also use concepts like "goals" and "rational agents." == Objective function == An objective function (or goal function) specifies the goals of an intelligent agent. An agent is deemed more intelligent if it consistently selects actions that yield outcomes better aligned with its objective function. In effect, the objective function serves as a measure of success. The objective function may be: Simple: For example, in a game of Go, the objective function might assign a value of 1 for a win and 0 for a loss. Complex: It might require the agent to evaluate and learn from past actions, adapting its behavior based on patterns that have proven effective. The objective function encapsulates all of the goals the agent is designed to achieve. For rational agents, it also incorporates the trade-offs between potentially conflicting goals. For instance, a self-driving car's objective function might balance factors such as safety, speed, and passenger comfort. Different terms are used to describe this concept, depending on the context. These include: Utility function: Often used in economics and decision theory, representing the desirability of a state. Objective function: A general term used in optimization. Loss function: Typically used in machine learning, where the goal is to minimize the loss (error). Reward Function: Used in reinforcement learning. Fitness Function: Used in evolutionary systems. Goals, and therefore the objective function, can be: Explicitly defined: Programmed directly into the agent. Induced: Learned or evolved over time. In reinforcement learning, a "reward function" provides feedback, encouraging desired behaviors and discouraging undesirable ones. The agent learns to maximize its cumulative reward. In evolutionary systems, a "fitness function" determines which agents are more likely to reproduce. This is analogous to natural selection, where organisms evolve to maximize their chances of survival and reproduction. Some AI systems, such as nearest-neighbor, reason by analogy rather than being explicitly goal-driven. However, even these systems can have goals implicitly defined within their training data. Such systems can still be benchmarked by framing the non-goal system as one whose "goal" is to accomplish its narrow classification task. Systems not traditionally considered agents, like knowledge-representation systems, are sometimes included in the paradigm by framing them as agents with a goal of, for example, answering questions accurately. Here, the concept of an "action" is extended to encompass the "act" of providing an answer. As a further extension, mimicry-driven systems can be framed as agents optimizing a "goal function" based on how closely the agent mimics the desired behavior. In generative adversarial networks (GANs) of the 2010s, an "encoder"/"generator" component attempts to mimic and improvise human text composition. The generator tries to maximize a function representing how well it can fool an antagonistic "predictor"/"discriminator" component. While symbolic AI systems often use an explicit goal function, the paradigm also applies to neural networks and evolutionary computing. Reinforcement learning can generate intelligent agents that appear to act in ways intended to maximize a "reward function". Sometimes, instead of setting the reward function directly equal to the desired benchmark evaluation function, machine learning programmers use reward shaping to initially give the machine rewards for incremental progress. Yann LeCun stated in 2018, "Most of the learning algorithms that people have come up with essentially consist of minimizing some objective function." AlphaZero chess had a simple objective function: +1 point for each win, and -1 point for each loss. A self-driving car's objective function would be more complex. Evolutionary computing can evolve intelligent agents that appear to act in ways intended to maximize a "fitness function" influencing how many descendants each agent is allowed to leave. The mathematical formalism of AIXI was proposed as a maximally intelligent agent in this paradigm. However, AIXI is uncomputable. In the real world, an intelligent agent is constrained by finite time and hardware resources, and scientists compete to produce algorithms that achieve progressively higher scores on benchmark tests with existing hardware. == Agent function == An intelligent agent's behavior can be described mathematically by an agent function. This function determines what the agent does based on what it has seen. A percept refers to the agent's sensory inputs at a single point in time. For example, a self-driving car's percepts might include camera images, lidar data, GPS coordinates, and speed r
Prix Ars Electronica
The Prix Ars Electronica is one of the best known and longest running yearly prizes in the field of electronic and interactive art, computer animation, digital culture and music. It has been awarded since 1987 by Ars Electronica (Linz, Austria). In 2005, the Golden Nica, the highest prize, was awarded in six categories: "Computer Animation/Visual Effects," "Digital Musics," "Interactive Art," "Net Vision," "Digital Communities" and the "u19" award for "freestyle computing." Each Golden Nica came with a prize of €10,000, apart from the u19 category, where the prize was €5,000. In each category, there are also Awards of Distinction and Honorary Mentions. The Golden Nica trophy is a replica of the Greek Nike of Samothrace. It is a handmade gold-plated wooden statuette that is approximately 35 cm high with a wingspan of about 20 cm. "Prix Ars Electronica" is a phrase composed of French, Latin and Spanish words, loosely translated as "Electronic Arts Prize." == Golden Nica winners == === Computer animation / film / vfx === The "Computer Graphics" category (1987–1994) was open to different kinds of computer images. The "Computer Animation" (1987–1997) was replaced by the current "Computer Animation/Visual Effects" category in 1998. ==== Computer Graphics ==== 1987 – Figur10 by Brian Reffin Smith, UK 1988 – The Battle by David Sherwin, US 1989 – Gramophone by Tamás Waliczky, HU 1990 – P-411-A by Manfred Mohr, Germany 1991 – Having encountered Eve for the second time, Adam begins to speak by Bill Woodard, US 1992 – RD Texture Buttons by Michael Kass and Andrew Witkin, US 1993 – Founders Series by Michael Tolson, US 1994 – Jellylife / Jellycycle / Jelly Locomotion by Michael Joaquin Grey, US ==== Computer Animation ==== 1987 – Luxo Jr. by John Lasseter, US 1988 – Red's Dream by John Lasseter, US 1989 – Broken Heart by Joan Staveley, US 1990 – Footprint by Mario Sasso and Nicola Sani, IT 1991 – Panspermia by Karl Sims, US 1992 – Liquid Selves / Primordial Dance by Karl Sims, US 1993 – Lakmé by Pascal Roulin, BE 1994 – Jurassic Park by Dennis Muren, Mark Dippé and Steve Williams, US/CA Distinction: Quarxs by Maurice Benayoun, FR Distinction: K.O. Kid by Marc Caro, FR 1995 – God's Little Monkey by David Atherton and Bob Sabiston, US 1996 – Toy Story by John Lasseter, Lee Unkrich and Ralph Eggleston, US 1997 – Dragonheart by Scott Squires, Industrial Light & Magic (ILM), US ==== Computer Animation/Visual Effects ==== 1998 – The Sitter by Liang-Yuan Wang, TW Titanic by Robert Legato and Digital Domain, US 1999 – Bunny by Chris Wedge, US What Dreams May Come by Mass Illusions, POP, Digital Domain, Vincent Ward, Stephen Simon and Barnet Bain, US 2000 – Maly Milos by Jakub Pistecky, CA Maaz by Christian Volckman, FR 2001 – Le Processus by Xavier de l’Hermuzičre and Philippe Grammaticopoulos, FR 2002 – Monsters, Inc. by Andrew Stanton, Lee Unkrich, Pete Docter and David Silverman, US 2003 – Tim Tom by Romain Segaud and Cristel Pougeoise, FR 2004 – Ryan by Chris Landreth, US. Distinction: Parenthèse from Francois Blondeau, Thibault Deloof, Jérémie Droulers, Christophe Stampe, France Distinction: Birthday Boy from Sejong Park, Australia 2005 – Fallen Art by Tomek Baginski, Poland. Distinction: The Incredibles from Pixar Distinction: City Paradise by Gaëlle Denis (UK), Passion Pictures (FR) 2006 – 458nm by Jan Bitzer, Ilija Brunck, Tom Weber, Filmakademie Baden-Württemberg, Germany. Distinction: Kein platz Für Gerold by Daniel Nocke / Studio Film Bilder, Germany Distinction: Negadon, the monster from Mars, by Jun Awazu, Japan 2007 – Codehunters by Ben Hibon, (UK) 2008 – Madame Tutli-Putli by Chris Lavis, Maciek Szczerbowski. (Directors), Jason Walker (Special Visual Effects), National Film Board of Canada 2009 – HA'Aki by Iriz Pääbo, National Film Board of Canada 2010 – Nuit Blanche by Arev Manoukian (Director), Marc-André Gray (Visual Effects Artist), National Film Board of Canada 2011 – Metachaos by Alessandro Bavari (IT) 2012 – Rear Window Loop by Jeff Desom (LU) Distinction: Caldera by Evan Viera/Orchid Animation (US) Distinction: Rise of the Planet of the Apes by Weta Digital (NZ)/Twentieth Century Fox 2013 – Forms by Quayola (IT), Memo Akten (TR) Distinction: Duku Spacemarines by La Mécanique du Plastique (FR) Distinction: Oh Willy… by Emma De Swaef (BE), Marc James Roels (BE) / Beast Animation 2014 – Walking City by Universal Everything (UK) 2015 – Temps Mort by Alex Verhaest (BE)[1] Distinction: Bär by Pascal Floerks (DE) Distinction: The Reflection of Power by Mihai Grecu (RO/HU) === Digital Music === This category is for those making electronic music and sound art through digital means. From 1987 to 1998 the category was known as "Computer music." Two Golden Nicas were awarded in 1987, and none in 1990. There was no Computer Music category in 1991. 1987 – Peter Gabriel and Jean-Claude Risset 1988 – Denis Smalley 1989 – Kaija Saariaho 1990 – None 1991 – Category omitted 1992 – Alejandro Viñao 1993 – Bernard Parmegiani 1994 – Ludger Brümmer Distinction: Jonathan Impett 1995 – Trevor Wishart 1996 – Robert Normandeau 1997 – Matt Heckert 1998 – Peter Bosch and Simone Simons (joint award) 1999 – Come to Daddy by Aphex Twin (Richard D. James) and Chris Cunningham (joint award) Distinction: Birthdays by Ikue Mori (JP) Distinction: Mego (label), Hotel Paral.lel by Christian Fennesz, Seven Tons For Free by Peter Rehberg (a.k.a. Pita) 2000 – 20' to 2000 by Carsten Nicolai Distinction: Minidisc by Gescom Distinction: Outside the Circle of Fire by Chris Watson 2001 – Matrix by Ryoji Ikeda 2002 – Man'yo Wounded 2001 by Yasunao Tone 2003 – Ami Yoshida, Sachiko M and Utah Kawasaki (joint award) 2004 – Banlieue du Vide by Thomas Köner 2005 – TEO! A Sonic Sculpture by Maryanne Amacher 2006 – L'île ré-sonante by Éliane Radigue 2007 – Reverse-Simulation Music by Mashiro Miwa 2008 – Reactable by Sergi Jordà (ES), Martin Kaltenbrunner (AT), Günter Geiger (AT) and Marcos Alonso (ES) 2009 – Speeds of Time versions 1 and 2 by Bill Fontana (US) 2010 – rheo: 5 horizons by Ryoichi Kurokawa (JP) 2011 – Energy Field by Jana Winderen (NO) 2012 – "Crystal Sounds of a Synchrotron" by Jo Thomas (GB) 2013 – frequencies (a) by Nicolas Bernier (CA) Distinction: SjQ++ by SjQ++ (JP) Distinction: Borderlands Granular by Chris Carlson (US) 2015 – Chijikinkutsu by Nelo Akamatsu (JP) Distinction: Drumming is an elastic concept by Josef Klammer (AT) Distinction: Under Way by Douglas Henderson (DE) 2017 – Not Your World Music: Noise In South East Asia by Cedrik Fermont (CD/BE/DE), Dimitri della Faille (BE/CA) Distinction: Gamelan Wizard by Lucas Abela (AU), Wukir Suryadi (ID) und Rully Shabara (ID) Distinction: Corpus Nil by Marco Donnarumma (DE/IT) === Hybrid art === 2007 – Symbiotica 2008 – Pollstream – Nuage Vert by Helen Evans (FR/UK) and Heiko Hansen (FR/DE) HeHe 2009 – Natural History of the Enigma by Eduardo Kac (US) 2010 – Ear on Arm by Stelarc (AU) 2011 – May the Horse Live in me by Art Orienté Objet (FR) 2012 – Bacterial radio by Joe Davis (US) Distinction: Free Universal Construction Kit (F.U.C.K.) by Golan Levin and Shawn Sims 2013 – Cosmopolitan Chicken Project, Koen Vanmechelen (BE) 2015 – Plantas Autofotosintéticas, Gilberto Esparza (MX) 2017 – K-9_topology, Maja Smrekar (SI) === [the next idea] voestalpine Art and Technology Grant === 2009 – Open_Sailing by Open_Sailing Crew led by Cesar Harada. 2010 – Hostage by [Frederik De Wilde]. 2011 – Choke Point Project by P2P Foundation (NL). 2012 – qaul.net – tools for the next revolution by Christoph Wachter & Mathias Jud 2013 – Hyperform by Marcelo Coelho (BR), Skylar Tibbits (US), Natan Linder (IL), Yoav Reaches (IL) Honorary Mentions: GravityLight by Martin Riddiford (GB), Jim Reeves (GB) 2014 – BlindMaps by Markus Schmeiduch, Andrew Spitz and Ruben van der Vleuten 2015 – SOYA C(O)U(L)TURE by XXLab (ID) – Irene Agrivina Widyaningrum, Asa Rahmana, Ratna Djuwita, Eka Jayani Ayuningtias, Atinna Rizqiana === Interactive Art === Prizes in the category of interactive art have been awarded since 1990. This category applies to many categories of works, including installations and performances, characterized by audience participation, virtual reality, multimedia and telecommunication. 1990 – Videoplace installation by Myron Krueger 1991 – Think About the People Now project by Paul Sermon 1992 – Home of the Brain installation by Monika Fleischmann and Wolfgang Strauss 1993 – Simulationsraum-Mosaik mobiler Datenklänge (smdk) installation by Knowbotic Research 1994 – A-Volve environment by Christa Sommerer and Laurent Mignonneau 1995 – the concept of Hypertext, attributed to Tim Berners-Lee 1996 – Global Interior Project installation by Masaki Fujihata 1997 – Music Plays Images X Images Play Music concert by Ryuichi Sakamoto and Toshio Iwai 1998 – World Skin, a Photo Safari in the Land of War installation by Jean-Baptiste Barrière and Maurice Benayoun 1999 – Difference Engine #3 by construct and Lynn Hershman 2000 – Vectorial Elevati
Democratization of technology
Democratization of technology is the process by which access to technology rapidly extends to an ever-broader audience, especially from a select group of people to the average public. New technologies and improved user experiences have empowered those outside of the technical industry to access and use technological products and services. At an increasing scale, consumers have greater access to use and purchase technologically sophisticated products, as well as to participate meaningfully in the development of these products. Industry innovation and user demand have been associated with more affordable, user-friendly products. This is an ongoing process, beginning with the development of mass production and increasing dramatically as digitization became commonplace. Thomas Friedman argued that the era of globalization has been characterized by the democratization of technology, democratization of finance, and democratization of information. Technology has been critical in the latter two processes, facilitating the rapid expansion of access to specialized knowledge and tools, as well as changing the way that people view and demand such access. A counter argument is that this is just a process of 'massification' - more people can use banks, technology, have access to information, but it does not mean there is any more democratic influence over its production, or that this massification promotes Democracy. == History == Scholars and social critics often cite the invention of the printing press as a major invention that changed the course of history. The force of the printing press rested not in its impact on the printing industry or inventors, but on its ability to transmit information to a broader public by way of mass production. This event is so widely recognized because of its social impact – as a democratizing force. The printing press is often seen as the historical counterpart to the Internet. After the development of the Internet in 1969, its use remained limited to communications between scientists and within government, although use of email and boards gained popularity among those with access. It did not become a popular means of communication until the 1990s. In 1993 the US federal government opened the Internet to commerce and the creation of HTML formed the basis for universal accessibility. === Major innovations === The Internet has played a critical role in modern life as a typical feature of most Western households, and has been key in the democratization of knowledge. It not only constitutes arguably the most critical innovation in this trend thus far; it has also allowed users to gain knowledge of and access to other technologies. Users can learn of new developments more quickly, and purchase high-tech products otherwise only actively marketed to recognized experts. Social media has also empowered and emboldened users to become contributors and critics of technological developments. Some have argued that cloud computing is having a major effect by allowing users greater access through mobility and pay-as-you-use capacity. The open-source model allows users to participate directly in development of software, rather than indirect participation, through contributing opinions. By being shaped by the user, development is directly responsive to user demand and can be obtained for free or at a low cost. In a comparable trend, arduino and littleBits have made electronics more accessible to users of all backgrounds and ages. The development of 3D printers has the potential to increasingly democratize production. Generative artificial intelligence tools have the potential to democratize the process of innovation by improving the ability of individuals to specify and visualize ideas. The democratization of artificial intelligence refers to the transition from AI as a high-cost, specialized field to one accessible to non-experts and smaller organizations. This process is driven by the release of open-weights models, the availability of cloud computing for model training, and the emergence of no-code development platforms. While early AI development was concentrated within Big Tech firms and elite research universities, the 2020s saw a proliferation of public tools like ChatGPT and repositories such as Hugging Face, which lowered the technical barriers to entry. However, the trend has faced criticism as the "illusion of democratization," as the underlying GPU hardware remains concentrated among a few global providers. == Cultural impact == This trend is linked to the spread of knowledge of and ability to perform high-tech tasks, challenging previous conceptions of expertise. Widespread access to technology, including lower costs, was critical to the transition to the new economy. Similarly, democratization of technology was also fuelled by this economic transition, which produced demands for technological innovation and optimism in technology-driven progress. Since the 1980s, a spreading constructivist conception of technology has emphasized that the social and technical domains are critically intertwined. Scholars have argued that technology is non-neutral, defined contextually and locally by a certain relationship with society. Andrew Feenberg, a central thinker in the philosophy of technology, argued that democratizing technology means expanding technological design to include alternative interests and values. When successful in doing so, this can be a tool for increasing inclusiveness. This also suggests an important participatory role for consumers if technology is to be truly democratic. Feenberg asserts that this must be achieved by consumer intervention in a liberated design process. Improved access to specialized knowledge and tools has been associated with an increase in the "do it yourself" (DIY) trend. This has also been associated with consumerization, whereby personal or privately owned devices and software are also used for business purposes. Some have argued that this is linked to reduced dependence on traditional information technology departments. Astra Taylor, the author of the book The People's Platform: Taking Back Power and Culture in the Digital Age, argues, "The promotion of Internet-enabled amateurism is a lazy substitute for real equality of opportunity." === Industry impact === In some ways, democratization of technology has strengthened this industry. Markets have broadened and diversified. Consumer feedback and input is available at a very low or no cost. However, related industries are experiencing decreased demand for qualified professionals as consumers are able to fill more of their demands themselves. Users of a range of types and status have access to increasingly similar technology. Because of the decreased costs and expertise necessary to use products and software, professionals (e.g. in the audio industry) may experience loss of work. In some cases, technology is accessible but sufficiently complex that most users without specialized training are able to operate it without necessarily understanding how it works. Additionally, the process of consumerization has led to an influx in the number of devices in businesses and accessing private networks that IT departments cannot control or access. While this can lead to lowered operating costs and increased innovation, it is also associated with security concerns that most businesses are unable to address at the pace of the spread of technology. === Political impact === Some scholars have argued that technological change will bring about a third wave of democracy. The Internet has been recognized for its role in promoting increased citizen advocacy and government transparency. Jesse Chen, a leading thinker in democratic engagement technologies, distinguishes the democratizing effects of technology from democracy itself. Chen has argued that, while the Internet may have democratizing effects, the Internet alone cannot deliver democracy at all levels of society unless technologies are purposely designed for the nuances of democracy, specifically the engagement of large groups of people in between elections in and beyond government. The spread of the Internet and other forms of technology has led to increased global connectivity. Many scholars believe that it has been associated in the developing world not only with increased Western influence, but also with the spread of democracy through increased communication, efficiency, and access to information. Scholars have drawn associations between the level of technological connectedness and democracy in many nations. Technology can enhance democracy in the developed world as well. In addition to increased communication and transparency, some electorates have implemented online voting to accommodate an increased number of citizens.
Comparison of JavaScript-based web frameworks
This is a comparison of web frameworks for front-end web development that are reliant on JavaScript code for their behavior. == General information == == High-level framework comparison == JavaScript-based web application frameworks, such as React and Vue, provide extensive capabilities but come with associated trade-offs. These frameworks often extend or enhance features available through native web technologies, such as routing, component-based development, and state management. While native web standards, including Web Components, modern JavaScript APIs like Fetch and ES Modules, and browser capabilities like Shadow DOM, have advanced significantly, frameworks remain widely used for their ability to enhance developer productivity, offer structured patterns for large-scale applications, simplify handling edge cases, and provide tools for performance optimization. Frameworks can introduce abstraction layers that may contribute to performance overhead, larger bundle sizes, and increased complexity. Modern frameworks, such as React 18 and Vue 3, address these challenges with features like concurrent rendering, tree-shaking, and selective hydration. While these advancements improve rendering efficiency and resource management, their benefits depend on the specific application and implementation context. Lightweight frameworks, such as Svelte and Preact, take different architectural approaches, with Svelte eliminating the virtual DOM entirely in favor of compiling components to efficient JavaScript code, and Preact offering a minimal, compatible alternative to React. Framework choice depends on an application’s requirements, including the team’s expertise, performance goals, and development priorities. A newer category of web frameworks, including enhance.dev, Astro, and Fresh, leverages native web standards while minimizing abstractions and development tooling. These solutions emphasize progressive enhancement, server-side rendering, and optimizing performance. Astro renders static HTML by default while hydrating only interactive parts. Fresh focuses on server-side rendering with zero runtime overhead. Enhance.dev prioritizes progressive enhancement patterns using Web Components. While these tools reduce reliance on client-side JavaScript by shifting logic to build-time or server-side execution, they still use JavaScript where necessary for interactivity. This approach makes them particularly suitable for performance-critical and content-focused applications. == Features == == Browser support ==
Line detection
In image processing, line detection is an algorithm that takes a collection of n edge points and finds all the lines on which these edge points lie. The most popular line detectors are the Hough transform and convolution-based techniques. == Hough transform == The Hough transform can be used to detect lines and the output is a parametric description of the lines in an image, for example ρ = r cos(θ) + c sin(θ). If there is a line in a row and column based image space, it can be defined ρ, the distance from the origin to the line along a perpendicular to the line, and θ, the angle of the perpendicular projection from the origin to the line measured in degrees clockwise from the positive row axis. Therefore, a line in the image corresponds to a point in the Hough space. The Hough space for lines has therefore these two dimensions θ and ρ, and a line is represented by a single point corresponding to a unique set of these parameters. The Hough transform can then be implemented by choosing a set of values of ρ and θ to use. For each pixel (r, c) in the image, compute r cos(θ) + c sin(θ) for each values of θ, and place the result in the appropriate position in the (ρ, θ) array. At the end, the values of (ρ, θ) with the highest values in the array will correspond to strongest lines in the image == Convolution-based technique == In a convolution-based technique, the line detector operator consists of a convolution masks tuned to detect the presence of lines of a particular width n and a θ orientation. Here are the four convolution masks to detect horizontal, vertical, oblique (+45 degrees), and oblique (−45 degrees) lines in an image. a) Horizontal mask(R1) (b) Vertical (R3) (C) Oblique (+45 degrees)(R2) (d) Oblique (−45 degrees)(R4) In practice, masks are run over the image and the responses are combined given by the following equation: R(x, y) = max(|R1 (x, y)|, |R2 (x, y)|, |R3 (x, y)|, |R4 (x, y)|) If R(x, y) > T, then discontinuity As can be seen below, if mask is overlay on the image (horizontal line), multiply the coincident values, and sum all these results, the output will be the (convolved image). For example, (−1)(0)+(−1)(0)+(−1)(0) + (2)(1) +(2)(1)+(2)(1) + (−1)(0)+(−1)(0)+(−1)(0) = 6 pixels on the second row, second column in the (convolved image) starting from the upper left corner of the horizontal lines. page 82 == Example == These masks above are tuned for light lines against a dark background, and would give a big negative response to dark lines against a light background. == Code example == The code was used to detect only the vertical lines in an image using Matlab and the result is below. The original image is the one on the top and the result is below it. As can be seen on the picture on the right, only the vertical lines were detected
Digital exhibition
Digital Exhibition includes both the projection technologies, such as High Definition, and delivery technologies of a film to a movie theater. Delivery technologies include disk drives, satellite relay, and fiber optics. This can save money in distribution but is usually more expensive overall due to maintenance and standardization of technology. However, there are benefits to digital exhibition, for example it requires less assembly by the exhibitor and can contain the trailers that the distributor wishes.