A recording format is a format for encoding data for storage on a storage medium. The format can be container information such as sectors on a disk, or user/audience information (content) such as analog stereo audio. Multiple levels of encoding may be achieved in one format. For example, a text encoded page may contain HTML and XML encoding, combined in a plain text file format, using either EBCDIC or ASCII character encoding, on a UDF digitally formatted disk. In electronic media, the primary format is the encoding that requires hardware to interpret (decode) data; while secondary encoding is interpreted by secondary signal processing methods, usually computer software. == Recording container formats == A container format is a system for dividing physical storage space or virtual space for data. Data space can be divided evenly by a system of measurement, or divided unevenly with meta data. A grid may divide physical or virtual space with physical or virtual (dividers) borders, evenly or unevenly. Just as a physical container (such as a file cabinet) is divided by physical borders (such as drawers and file folders), data space is divided by virtual borders. Meta data such as a unit of measurement, address, or meta tags act as virtual borders in a container format. A template may be considered an abstract format for containing a solution as well as the content itself. Systems of measurement Metric system Geographic coordinate system Page grid Film formats Audio data format Video tape format Disk format File format Meta data Text formatting Template Data structure == Raw content formats == A raw content format is a system of converting data to displayable information. Raw content formats may either be recorded in secondary signal processing methods such as a software container format (e.g. digital audio, digital video) or recorded in the primary format. A primary raw content format may be directly observable (e.g. image, sound, motion, smell, sensation) or physical data which only requires hardware to display it, such as a phonographic needle and diaphragm or a projector lamp and magnifying glass.
Data cube
In computer programming, a data cube (or datacube) is a multi-dimensional array of values. Typically, the term "data cube" is applied in contexts where these arrays are massively larger than the hosting computer's main memory; examples include multi-terabyte/petabyte data warehouses and time series of image data. Even though it is called a cube, a data cube generally is a multi-dimensional concept which can be 1-dimensional, 2-dimensional, 3-dimensional, or higher-dimensional. The data cube is used to represent data (sometimes called facts) along some dimensions of interest. In satellite image timeseries, dimensions would be latitude and longitude coordinates and time; a fact (sometimes called measure) would be a pixel at a given space and time as taken by the satellite. For example, in online analytical processing, an OLAP cube about a company would have dimensions that could be the company subsidiaries, the company products, and time; in this setup, a fact would be a sales event where a particular product has been sold in a particular subsidiary at a particular time. In any case, every dimension divides data into groups of cells whereas each cell in the cube represents a single measure of interest. Sometimes cubes hold only a few values with the rest being empty, i.e. undefined, while sometimes most or all cube coordinates hold a cell value. In the first case such data are called sparse, and in the second case they are called dense, although there is no hard delineation between the two. Data cubes may be stored in database management systems (DBMS) as part of array DBMS. Spatio-temporal databases and geospatial databases may also be represented as coverage data. == History == Multi-dimensional arrays have long been familiar in programming languages. Fortran offers arbitrarily-indexed 1-D arrays and arrays of arrays, which allows the construction of higher-dimensional arrays, up to 15 dimensions. APL supports n-D arrays with a rich set of operations. All these have in common that arrays must fit into the main memory and are available only while the particular program maintaining them (such as image processing software) is running. A series of data exchange formats support storage and transmission of data cube-like data, often tailored towards particular application domains. Examples include MDX for statistical (in particular, business) data, Zarr and Hierarchical Data Format for general scientific data, and TIFF for imagery. In 1992, Peter Baumann introduced management of massive data cubes with high-level user functionality combined with an efficient software architecture. Datacube operations include subset extraction, processing, fusion, and in general queries in the spirit of data manipulation languages like SQL. Some years after, the data cube concept was applied to describe time-varying business data as data cubes by Jim Gray, et al., and by Venky Harinarayan, Anand Rajaraman and Jeff Ullman. Around that time, a working group on Multi-Dimensional Databases ("Arbeitskreis Multi-Dimensionale Datenbanken") was established at German Gesellschaft für Informatik. Datacube Inc. was an image processing company selling hardware and software applications for the PC market in 1996, however without addressing data cubes as such. The EarthServer initiative has established geo data cube service requirements. == Standardization == In 2018, the ISO SQL database language was extended with data cube functionality as "SQL – Part 15: Multi-dimensional arrays (SQL/MDA)". Web Coverage Processing Service is a geo data cube analytics language issued by the Open Geospatial Consortium in 2008. In addition to the common data cube operations, the language knows about the semantics of space and time and supports both regular and irregular grid data cubes, based on the concept of coverage data. An industry standard for querying business data cubes, originally developed by Microsoft, is MultiDimensional eXpressions. == Implementation == Many high-level computer languages treat data cubes and other large arrays as single entities distinct from their contents. These languages, of which Fortran, APL, IDL, NumPy, PDL, and S-Lang are examples, allow the programmer to manipulate complete film clips and other data en masse with simple expressions derived from linear algebra and vector mathematics. Some languages (such as PDL) distinguish between a list of images and a data cube, while many (such as IDL) do not. Array DBMSs (Database Management Systems) offer a data model which generically supports definition, management, retrieval, and manipulation of n-dimensional data cubes. This database category has been pioneered by the rasdaman system since 1994. == Applications == Multi-dimensional arrays can meaningfully represent spatio-temporal sensor, image, and simulation data, but also statistics data where the semantics of dimensions is not necessarily of spatial or temporal nature. Generally, any kind of axis can be combined with any other into a data cube. === Mathematics === In mathematics, a one-dimensional array corresponds to a vector, a two-dimensional array resembles a matrix; more generally, a tensor may be represented as an n-dimensional data cube. === Science and engineering === For a time sequence of color images, the array is generally four-dimensional, with the dimensions representing image X and Y coordinates, time, and RGB (or other color space) color plane. For example, the EarthServer initiative unites data centers from different continents offering 3-D x/y/t satellite image timeseries and 4-D x/y/z/t weather data for retrieval and server-side processing through the Open Geospatial Consortium WCPS geo data cube query language standard. A data cube is also used in the field of imaging spectroscopy, since a spectrally-resolved image is represented as a three-dimensional volume. Earth observation data cubes combine satellite imagery such as Landsat 8 and Sentinel-2 with Geographic information system analytics. === Business intelligence === In online analytical processing (OLAP), data cubes are a common arrangement of business data suitable for analysis from different perspectives through operations like slicing, dicing, pivoting, and aggregation.
Kernel (image processing)
In image processing, a kernel, convolution matrix, or mask is a small matrix used for blurring, sharpening, embossing, edge detection, and more. This is accomplished by doing a convolution between the kernel and an image. Or more simply, when each pixel in the output image is a function of the nearby pixels (including itself) in the input image, the kernel is that function. == Details == The general expression of a convolution is g x , y = ω ∗ f x , y = ∑ i = − a a ∑ j = − b b ω i , j f x − i , y − j , {\displaystyle g_{x,y}=\omega f_{x,y}=\sum _{i=-a}^{a}{\sum _{j=-b}^{b}{\omega _{i,j}f_{x-i,y-j}}},} where g ( x , y ) {\displaystyle g(x,y)} is the filtered image, f ( x , y ) {\displaystyle f(x,y)} is the original image, ω {\displaystyle \omega } is the filter kernel. Every element of the filter kernel is considered by − a ≤ i ≤ a {\displaystyle -a\leq i\leq a} and − b ≤ j ≤ b {\displaystyle -b\leq j\leq b} . Depending on the element values, a kernel can cause a wide range of effects: The above are just a few examples of effects achievable by convolving kernels and images. === Origin === The origin is the position of the kernel which is above (conceptually) the current output pixel. This could be outside of the actual kernel, though usually it corresponds to one of the kernel elements. For a symmetric kernel, the origin is usually the center element. == Convolution == Convolution is the process of adding each element of the image to its local neighbors, weighted by the kernel. This is related to a form of mathematical convolution. The matrix operation being performed—convolution—is not traditional matrix multiplication, despite being similarly denoted by . For example, if we have two three-by-three matrices, the first a kernel, and the second an image piece, convolution is the process of flipping both the rows and columns of the kernel and multiplying locally similar entries and summing. The element at coordinates [2, 2] (that is, the central element) of the resulting image would be a weighted combination of all the entries of the image matrix, with weights given by the kernel: ( [ a b c d e f g h i ] ∗ [ 1 2 3 4 5 6 7 8 9 ] ) [ 2 , 2 ] = {\displaystyle \left({\begin{bmatrix}a&b&c\\d&e&f\\g&h&i\end{bmatrix}}{\begin{bmatrix}1&2&3\\4&5&6\\7&8&9\end{bmatrix}}\right)[2,2]=} ( i ⋅ 1 ) + ( h ⋅ 2 ) + ( g ⋅ 3 ) + ( f ⋅ 4 ) + ( e ⋅ 5 ) + ( d ⋅ 6 ) + ( c ⋅ 7 ) + ( b ⋅ 8 ) + ( a ⋅ 9 ) . {\displaystyle (i\cdot 1)+(h\cdot 2)+(g\cdot 3)+(f\cdot 4)+(e\cdot 5)+(d\cdot 6)+(c\cdot 7)+(b\cdot 8)+(a\cdot 9).} The other entries would be similarly weighted, where we position the center of the kernel on each of the boundary points of the image, and compute a weighted sum. The values of a given pixel in the output image are calculated by multiplying each kernel value by the corresponding input image pixel values. This can be described algorithmically with the following pseudo-code: for each image row in input image: for each pixel in image row: set accumulator to zero for each kernel row in kernel: for each element in kernel row: if element position corresponding to pixel position then multiply element value corresponding to pixel value add result to accumulator endif set output image pixel to accumulator corresponding input image pixels are found relative to the kernel's origin. If the kernel is symmetric then place the center (origin) of the kernel on the current pixel. The kernel will overlap the neighboring pixels around the origin. Each kernel element should be multiplied with the pixel value it overlaps with and all of the obtained values should be summed. This resultant sum will be the new value for the current pixel currently overlapped with the center of the kernel. If the kernel is not symmetric, it has to be flipped both around its horizontal and vertical axis before calculating the convolution as above. The general form for matrix convolution is [ x 11 x 12 ⋯ x 1 n x 21 x 22 ⋯ x 2 n ⋮ ⋮ ⋱ ⋮ x m 1 x m 2 ⋯ x m n ] ∗ [ y 11 y 12 ⋯ y 1 n y 21 y 22 ⋯ y 2 n ⋮ ⋮ ⋱ ⋮ y m 1 y m 2 ⋯ y m n ] = ∑ i = 0 m − 1 ∑ j = 0 n − 1 x ( m − i ) ( n − j ) y ( 1 + i ) ( 1 + j ) {\displaystyle {\begin{bmatrix}x_{11}&x_{12}&\cdots &x_{1n}\\x_{21}&x_{22}&\cdots &x_{2n}\\\vdots &\vdots &\ddots &\vdots \\x_{m1}&x_{m2}&\cdots &x_{mn}\\\end{bmatrix}}{\begin{bmatrix}y_{11}&y_{12}&\cdots &y_{1n}\\y_{21}&y_{22}&\cdots &y_{2n}\\\vdots &\vdots &\ddots &\vdots \\y_{m1}&y_{m2}&\cdots &y_{mn}\\\end{bmatrix}}=\sum _{i=0}^{m-1}\sum _{j=0}^{n-1}x_{(m-i)(n-j)}y_{(1+i)(1+j)}} === Edge handling === Kernel convolution usually requires values from pixels outside of the image boundaries. There are a variety of methods for handling image edges. Extend The nearest border pixels are conceptually extended as far as necessary to provide values for the convolution. Corner pixels are extended in 90° wedges. Other edge pixels are extended in lines. Wrap The image is conceptually wrapped (or tiled) and values are taken from the opposite edge or corner. Mirror The image is conceptually mirrored at the edges. For example, attempting to read a pixel 3 units outside an edge reads one 3 units inside the edge instead. Crop / Avoid overlap Any pixel in the output image which would require values from beyond the edge is skipped. This method can result in the output image being slightly smaller, with the edges having been cropped. Move kernel so that values from outside of image is never required. Machine learning mainly uses this approach. Example: Kernel size 10x10, image size 32x32, result image is 23x23. Kernel Crop Any pixel in the kernel that extends past the input image isn't used and the normalizing is adjusted to compensate. Constant Use constant value for pixels outside of image. Usually black or sometimes gray is used. Generally this depends on application. === Normalization === Normalization is defined as the division of each element in the kernel by the sum of all kernel elements, so that the sum of the elements of a normalized kernel is unity. This will ensure the average pixel in the modified image is as bright as the average pixel in the original image. === Optimization === Fast convolution algorithms include: separable convolution ==== Separable convolution ==== 2D convolution with an M × N kernel requires M × N multiplications for each sample (pixel). If the kernel is separable, then the computation can be reduced to M + N multiplications. Using separable convolutions can significantly decrease the computation by doing 1D convolution twice instead of one 2D convolution. === Implementation === Here a concrete convolution implementation done with the GLSL shading language :
Workplace robotics safety
Workplace robotics safety is an aspect of occupational safety and health when robots are used in the workplace. This includes traditional industrial robots as well as emerging technologies such as drone aircraft and wearable robotic exoskeletons. Types of accidents include collisions, crushing, and injuries from mechanical parts. Hazard controls include physical barriers, good work practices, and proper maintenance. == Background == Many workplace robots are industrial robots used in manufacturing. According to the International Federation of Robotics, 1.7 million new robots are expected to be used in factories between 2017 and 2020. Emerging robot technologies include collaborative robots, personal care robots, construction robots, exoskeletons, autonomous vehicles, and drone aircraft (also known as unmanned aerial vehicles or UAVs). Advances in automation technologies (e.g. fixed robots, collaborative and mobile robots, and exoskeletons) have the potential to improve work conditions but also to introduce workplace hazards in manufacturing workplaces. Fifty-six percent of robot injuries are classified as pinch injuries and 44% of injuries are classified as impact injuries. A 1987 study found that line workers are at the greatest risk, followed by maintenance workers, and programmers. Poor workplace design and human error caused most injuries. Despite the lack of occupational surveillance data on injuries associated specifically with robots, researchers from the US National Institute for Occupational Safety and Health (NIOSH) identified 61 robot-related deaths between 1992 and 2015 using keyword searches of the Bureau of Labor Statistics (BLS) Census of Fatal Occupational Injuries research database (see info from Center for Occupational Robotics Research). Using data from the Bureau of Labor Statistics, NIOSH and its state partners have investigated 4 robot-related fatalities under the Fatality Assessment and Control Evaluation Program. In addition the Occupational Safety and Health Administration (OSHA) has investigated robot-related deaths and injuries, which can be reviewed at OSHA Accident Search page. Injuries and fatalities could increase over time because of the increasing number of collaborative and co-existing robots, powered exoskeletons, and autonomous vehicles into the work environment. Safety standards are being developed by the Robotic Industries Association (RIA) in conjunction with the American National Standards Institute (ANSI). On October 5, 2017, OSHA, NIOSH and RIA signed an alliance to work together to enhance technical expertise, identify and help address potential workplace hazards associated with traditional industrial robots and the emerging technology of human-robot collaboration installations and systems, and help identify needed research to reduce workplace hazards. On October 16 NIOSH launched the Center for Occupational Robotics Research to "provide scientific leadership to guide the development and use of occupational robots that enhance worker safety, health, and well being". So far, the research needs identified by NIOSH and its partners include: tracking and preventing injuries and fatalities, intervention and dissemination strategies to promote safe machine control and maintenance procedures, and on translating effective evidence-based interventions into workplace practice. == Hazards == Many hazards and injuries can result from the use of robots in the workplace. Some robots, notably those in a traditional industrial environment, are fast and powerful. This increases the potential for injury as one swing from a robotic arm, for example, could cause serious bodily harm. There are additional risks when a robot malfunctions or is in need of maintenance. A worker who is working on the robot may be injured because a malfunctioning robot is typically unpredictable. For example, a robotic arm that is part of a car assembly line may experience a jammed motor. A worker who is working to fix the jam may suddenly get hit by the arm the moment it becomes unjammed. Additionally, if a worker is standing in a zone that is overlapping with nearby robotic arms, he or she may get injured by other moving equipment. There are four types of accidents that can occur with robots: impact or collision accidents, crushing and trapping accidents, mechanical part accidents, and other accidents. Impact or collision accidents occur generally from malfunctions and unpredicted changes. Crushing and trapping accidents occur when a part of a worker's body becomes trapped or caught on robotic equipment. Mechanical part accidents can occur when a robot malfunctions and starts to "break down", where the ejection of parts or exposed wire can cause serious injury. Other accidents at just general accidents that occur from working with robots. There are seven sources of hazards that are associated with human interaction with robots and machines: human errors, control errors, unauthorized access, mechanical failures, environmental sources, power systems, and improper installation. Human errors could be anything from one line of incorrect code to a loose bolt on a robotic arm. Many hazards can stem from human-based error. Control errors are intrinsic and are usually not controllable nor predictable. Unauthorized access hazards occur when a person who is not familiar with the area enters the domain of a robot. Mechanical failures can happen at any time, and a faulty unit is usually unpredictable. Environmental sources are things such as electromagnetic or radio interference in the environment that can cause a robot to malfunction. Power systems are pneumatic, hydraulic, or electrical power sources; these power sources can malfunction and cause fires, leaks, or electrical shocks. Improper installation is fairly self-explanatory; a loose bolt or an exposed wire can lead to inherent hazards. === Emerging technologies === Emerging robotic technologies can reduce hazards to workers, but can also introduce new hazards. For example, robotic exoskeletons can be used in construction to reduce load to the spine, improve posture, and reduce fatigue; however, they can also increase chest pressure, limit mobility when moving out of the way of a falling object, and cause balance problems. Unmanned aerial vehicles are being used in the construction industry to do monitoring and inspections of buildings under construction. This reduces the need for humans to be in hazardous locations, but the risk of a UAV collision presents a hazard to workers. For collaborative robots, isolation is not possible. Possible hazard controls include collision avoidance systems, and making the robot less stiff to lessen the impact force. Robotic tech vest is a wearable device for humans, worn in Amazon warehouses. == Hazard controls == There are a few ways to prevent injuries by implementing hazard controls. There can be risk assessments at each of the various stages of a robot's development. Risk assessments can help gather information about a robot's status, how well it is being maintained, and if repairs are needed soon. By being aware of the status of a robot, injuries can be prevented and hazards reduced. Safeguarding devices can be implemented to reduce the risk of injuries. These can include engineering controls such as physical barriers, guard rails, presence-sensing safeguarding devices, etc. Awareness devices are usually used in conjunction with safeguarding devices. They are usually a system of rope or chain barriers with lights, signs, whistles, and horns. Their purpose it to be able to alert workers or personnel of certain dangers. Operator safeguards can also be in place. These usually utilize safeguarding devices to protect the operator and reduce risk of injury. Additionally, when an operator is within close proximity of a robot, the working speed of the robot can be reduced to ensure that the operator is in full control. This can be done by placing the robot in the manual or teach mode. It is also crucial to inform the programmer of the robot of what type of work the robot will be doing, how it will interact with other robots, and how it will work in relation to an operator. Proper maintenance of robotic equipment is also critical in order to reduce hazards. Maintaining a robot insures that it continues to function properly, thereby reducing the risks associated with a malfunction. One common safeguard used in industrial settings is the installation of robot safety fencing. These barriers, often made from durable materials such as mesh or polycarbonate, prevent accidental interactions between workers and robotic systems, reducing the risk of injury. Robot safety fencing is particularly important in environments where high-speed or powerful robots are used. == Regulations == Some existing regulations regarding robots and robotic systems include: ANSI/RIA R15.06 OSHA 29 CFR 1910.333 OSHA 29 CFR 1910.147 ISO 10218 ISO/TS 15066 ISO/DIS 13482
Imaging
Imaging is the process of creating visual representations of objects, scenes, or phenomena. The term encompasses both the formation of images through physical processes and the technologies used to capture, store, process, and display them. While traditional imaging relies on visible light, modern imaging systems can visualize information across the electromagnetic spectrum and through other physical phenomena such as sound waves, magnetic fields, and particle emissions, enabling the visualization of subjects invisible to the human eye. Imaging science is the multidisciplinary field concerned with the theoretical foundations and practical applications of image creation and analysis. The field draws on physics, mathematics, electrical engineering, computer science, computer vision, and perceptual psychology to develop systems that generate, collect, duplicate, analyze, modify, and visualize images. == Principles == === The imaging chain === The imaging chain is a conceptual framework describing the interconnected components of any imaging system. Understanding each link in this chain allows engineers and scientists to optimize system performance for specific applications. The chain begins with the subject and its observable properties, typically energy that is emitted, reflected, or transmitted. A light source or other energy source may illuminate the subject to make these properties detectable. The capture device then collects this energy using appropriate sensors: optical systems for electromagnetic radiation, transducers for acoustic waves, or antenna arrays for radio frequencies. In digital systems, a processor converts the captured signals into a format suitable for rendering, applying algorithms for noise reduction, enhancement, or reconstruction. Finally, a display renders the processed information as a visible image on media such as paper, screens, or projection surfaces. Throughout this process, the characteristics of the human visual system inform design decisions, as the ultimate purpose of most imaging systems is to convey information to human observers. === Coherent and non-coherent imaging === Imaging systems are often classified by whether they use coherent or non-coherent illumination. Coherent imaging employs an active source that produces waves with a consistent phase relationship, as in radar, synthetic aperture radar, medical ultrasound, and optical coherence tomography. These systems can capture phase information in addition to amplitude, enabling techniques such as holography and interferometry. Non-coherent imaging systems, including conventional photography, fluorescence microscopy, and telescopes, rely on illumination sources where light waves have random phase relationships. == Methods and applications == Imaging methods span a wide range of physical principles, each suited to particular applications. Optical imaging encompasses photography, cinematography, microscopy, and telescopic observation. These methods capture electromagnetic radiation in or near the visible spectrum and form the basis of most consumer and scientific imaging. Extensions include thermography, which visualizes infrared radiation to reveal temperature distributions, and multispectral imaging, which captures data across multiple wavelength bands for applications in remote sensing and materials analysis. Medical imaging comprises techniques designed to visualize the interior of the human body for diagnostic and therapeutic purposes. Radiography and computed tomography use X-rays to image dense structures such as bone. Magnetic resonance imaging exploits nuclear magnetic properties to produce detailed soft-tissue images without ionizing radiation. Ultrasound imaging uses high-frequency sound waves and is particularly valuable for real-time imaging and fetal monitoring. Nuclear medicine techniques such as positron emission tomography track radioactive tracers to reveal metabolic activity. Emerging modalities include photoacoustic imaging, which combines optical and acoustic principles, and Magneto-acousto-electrical tomography, which maps electrical conductivity in biological tissues. Acoustic imaging uses sound waves to create images. Beyond medical ultrasound, applications include sonar for underwater navigation and mapping, seismic imaging for geological exploration, and industrial non-destructive testing. Radar and microwave imaging employ radio waves to detect and image objects. Synthetic aperture radar produces high-resolution images from aircraft or satellites regardless of weather or lighting conditions, making it essential for Earth observation and reconnaissance. Ground-penetrating radar images subsurface structures for archaeological and engineering applications. Electron and particle imaging use beams of electrons or other particles to achieve resolutions far beyond the diffraction limit of visible light. Electron microscopes can image individual atoms, enabling advances in materials science and structural biology. Chemical imaging combines spectroscopy with spatial imaging to map the chemical composition of samples, with applications in pharmaceutical development, food safety, and forensics. LIDAR (Light Detection and Ranging) measures distances using laser pulses to create three-dimensional representations of surfaces and objects, widely used in autonomous vehicles, topographic mapping, and forestry. Computational and digital imaging encompasses image processing, computer graphics, three-dimensional rendering, and digital image restoration. Computer vision applies algorithmic analysis to extract information from images automatically. == History == Photography and imaging have always been intertwined. When Joseph Nicéphore Niépce created the first permanent photograph using heliography in 1826, and Louis Daguerre refined the process into the daguerreotype a decade later, they weren't just inventing a new art form, they were laying the groundwork for an entire scientific discipline built on silver halide chemistry. For most of the nineteenth century, photography remained the province of specialists. That changed with George Eastman's Kodak camera, introduced in 1888 with the slogan "You press the button, we do the rest." Suddenly, anyone could take pictures. Around the same time, Wilhelm Röntgen stumbled onto X-rays in 1895, an accident that would spawn the entire field of medical imaging. World War II proved to be a turning point. Radar technology, developed frantically on both sides of the conflict, introduced concepts that engineers would later adapt for synthetic aperture radar and medical ultrasound. Then the charge-coupled device came: Willard Boyle and George E. Smith built the first one at Bell Labs in 1969, and within a few decades it had made film nearly obsolete. Magnetic resonance imaging arrived in the 1970s, offering doctors something X-rays never could, detailed views of soft tissue without any radiation. Digital cameras took over fast. By the 2000s, film was already in decline; by the 2010s, smartphones had put a surprisingly capable camera in nearly every pocket. Features that once required real skill, proper exposure, sharp focus, accurate color, became automatic. Today, billions of photos get uploaded to social media every day. As a result, a growing issue is that generative artificial intelligence can fabricate photorealistic images from scratch. What counts as a "real" photograph is no longer necessarily obvious.
Generative art
Generative art is post-conceptual art that has been created (in whole or in part) with the use of an autonomous system. An autonomous system in this context is generally one that is non-human and can independently determine features of an artwork that would otherwise require decisions made directly by the artist. In some cases the human creator may claim that the generative system represents their own artistic idea, and in others that the system takes on the role of the creator. "Generative art" often refers to algorithmic art (algorithmically determined computer generated artwork) and synthetic media (general term for any algorithmically generated media), but artists can also make generative art using systems of chemistry, biology, mechanics and robotics, smart materials, manual randomization, mathematics, data mapping, symmetry, and tiling. Generative algorithms, algorithms programmed to produce artistic works through predefined rules, stochastic methods, or procedural logic, often yielding dynamic, unique, and contextually adaptable outputs—are central to many of these practices. == History == The use of the word "generative" in the discussion of art has developed over time. The use of "Artificial DNA" defines a generative approach to art focused on the construction of a system able to generate unpredictable events, all with a recognizable common character. The use of autonomous systems, required by some contemporary definitions, focuses a generative approach where the controls are strongly reduced. This approach is also named "emergent". Margaret Boden and Ernest Edmonds have noted the use of the term "generative art" in the broad context of automated computer graphics in the 1960s, beginning with artwork exhibited by Georg Nees and Frieder Nake in 1965: A. Michael Noll did his initial computer art, combining randomness with order, in 1962, and exhibited it along with works by Bell Julesz in 1965. The terms "generative art" and "computer art" have been used in tandem, and more or less interchangeably, since the very earliest days. The first such exhibition showed the work of Nees in February 1965, which some claim was titled "Generative Computergrafik". While Nees does not himself remember, this was the title of his doctoral thesis published a few years later. The correct title of the first exhibition and catalog was "computer-grafik". "Generative art" and related terms was in common use by several other early computer artists around this time, including Manfred Mohr and Ken Knowlton. Vera Molnár (born 1924) is a French media artist of Hungarian origin. Molnar is widely considered to be a pioneer of generative art, and is also one of the first women to use computers in her art practice. The term "Generative Art" with the meaning of dynamic artwork-systems able to generate multiple artwork-events was clearly used the first time for the "Generative Art" conference in Milan in 1998. The term has also been used to describe geometric abstract art where simple elements are repeated, transformed, or varied to generate more complex forms. Thus defined, generative art was practiced by the Argentinian artists Eduardo Mac Entyre and Miguel Ángel Vidal in the late 1960s. In 1972 the Romanian-born Paul Neagu created the Generative Art Group in Britain. It was populated exclusively by Neagu using aliases such as "Hunsy Belmood" and "Edward Larsocchi". In 1972 Neagu gave a lecture titled 'Generative Art Forms' at the Queen's University, Belfast Festival. In 1970 the School of the Art Institute of Chicago created a department called Generative Systems. As described by Sonia Landy Sheridan the focus was on art practices using the then new technologies for the capture, inter-machine transfer, printing and transmission of images, as well as the exploration of the aspect of time in the transformation of image information. Also noteworthy is John Dunn, first a student and then a collaborator of Sheridan. In 1988 Clauser identified the aspect of systemic autonomy as a critical element in generative art: It should be evident from the above description of the evolution of generative art that process (or structuring) and change (or transformation) are among its most definitive features, and that these features and the very term 'generative' imply dynamic development and motion. (the result) is not a creation by the artist but rather the product of the generative process - a self-precipitating structure. In 1989 Celestino Soddu defined the Generative Design approach to Architecture and Town Design in his book Citta' Aleatorie. In 1989 Franke referred to "generative mathematics" as "the study of mathematical operations suitable for generating artistic images." From the mid-1990s Brian Eno popularized the terms generative music and generative systems, making a connection with earlier experimental music by Terry Riley, Steve Reich and Philip Glass. From the end of the 20th century, communities of generative artists, designers, musicians and theoreticians began to meet, forming cross-disciplinary perspectives. The first meeting about generative Art was in 1998, at the inaugural International Generative Art conference at Politecnico di Milano University, Italy. In Australia, the Iterate conference on generative systems in the electronic arts followed in 1999. On-line discussion has centered around the eu-gene mailing list, which began late 1999, and has hosted much of the debate which has defined the field. These activities have more recently been joined by the Generator.x conference in Berlin starting in 2005. In 2012 the new journal GASATHJ, Generative Art Science and Technology Hard Journal was founded by Celestino Soddu and Enrica Colabella jointing several generative artists and scientists in the editorial board. Some have argued that as a result of this engagement across disciplinary boundaries, the community has converged on a shared meaning of the term. As Boden and Edmonds put it in 2011: Today, the term "Generative Art" is still current within the relevant artistic community. Since 1998 a series of conferences have been held in Milan with that title (Generativeart.com), and Brian Eno has been influential in promoting and using generative art methods (Eno, 1996). Both in music and in visual art, the use of the term has now converged on work that has been produced by the activation of a set of rules and where the artist lets a computer system take over at least some of the decision-making (although, of course, the artist determines the rules). In the call of the Generative Art conferences in Milan (annually starting from 1998), the definition of Generative Art by Celestino Soddu: Generative Art is the idea realized as genetic code of artificial events, as construction of dynamic complex systems able to generate endless variations. Each Generative Project is a concept-software that works producing unique and non-repeatable events, like music or 3D Objects, as possible and manifold expressions of the generating idea strongly recognizable as a vision belonging to an artist / designer / musician / architect /mathematician. Discussion on the eu-gene mailing list was framed by the following definition by Adrian Ward from 1999: Generative art is a term given to work which stems from concentrating on the processes involved in producing an artwork, usually (although not strictly) automated by the use of a machine or computer, or by using mathematic or pragmatic instructions to define the rules by which such artworks are executed. A similar definition is provided by Philip Galanter: Generative art refers to any art practice where the artist creates a process, such as a set of natural language rules, a computer program, a machine, or other procedural invention, which is then set into motion with some degree of autonomy contributing to or resulting in a completed work of art. Around the 2020s, generative AI models learned to imitate the distinct style of particular authors. For example, a generative image model such as Stable Diffusion is able to model the stylistic characteristics of an artist like Pablo Picasso (including his particular brush strokes, use of colour, perspective, and so on), and a user can engineer a prompt such as "an astronaut riding a horse, by Picasso" to cause the model to generate a novel image applying the artist's style to an arbitrary subject. Generative image models have received significant backlash from artists who object to their style being imitated without their permission, arguing that this harms their ability to profit from their own work. The emergence of text-to-image generative AI systems has expanded debates over authorship, copyright, and artistic labor. The main issues in these debates include the eligibility of AI-generated outputs for copyright protection and the legal and ethical questions of using existing copyrighted works as training data for generative AI systems. == Types == === Music === Johann Kirnberger's Mu
Bandhan Tod
Bandhan Tod is a mobile app to stop child marriage in India's Bihar state through SOS button in the app. When the SOS on Bandhan Tod is activated, the nearest small NGO will attempt to resolve the issue. If the family resists, then the police gets notified. Till now so many child marriages has been cancelled through Bandhan Tod interventions. Bandhan Tod is an initiative of Gender Alliance managed by Prashanti Tiwari to support the state government's efforts to end child marriage and dowry.