Link-richness is the quality, possessed by some websites, of having many hyperlinks. Classified advertising sites like Craigslist tend to be very link-rich, sometimes with hundreds of links on their main page. They help users find the links they are looking for by grouping links into clusters. Inadequate link richness has been described as frustrating to readers, as it reduces transparency of site content from the main page. Students new to wiki collaboration were found to need guidance in how to take full advantage of the medium's potential for creating link-rich content. Link-richness in some contexts can be distracting, as when an article is surrounded by extraneous links. Indeed, it is becoming accepted as a best practice for universities to have link-rich home pages that do not rely on user categorisation and exploration of long sequences of links and are not constrained by traditional boundaries between departments. Tools are sometimes needed to make the publishing of link-rich web sites tractable, and many people may lack the technical skills, time, or inclination to engage in hand- crafting new digital document forms. A link-rich site that is low on content is sometimes referred to as a "gateway site." Link-rich portals were popular on the Web in 2000. Yahoo! and other sites featuring categories with many links were heavily used and often required fewer than three clicks to reach the content. Web designers were creating flat sites with content positioned close to the top of pages.
Glossary of robotics
Robotics is the branch of technology that deals with the design, construction, operation, structural disposition, manufacture and application of robots. Robotics is related to the sciences of electronics, engineering, mechanics, and software. The following is a list of common definitions related to the Robotics field. == A == Actuator: a motor that translates control signals into mechanical movement. The control signals are usually electrical but may, more rarely, be pneumatic or hydraulic. The power supply may likewise be any of these. It is common for electrical control to be used to modulate a high-power pneumatic or hydraulic motor. Aerobot: a robot capable of independent flight on other planets. A type of aerial robot. Arduino: The current platform of choice for small-scale robotic experimentation and physical computing. Artificial intelligence: is the intelligence of machines and the branch of computer science that aims to create it. Aura (satellite): a robotic spacecraft launched by NASA in 2004 which collects atmospheric data from Earth. Automaton: an early self-operating robot, performing exactly the same actions, over and over. Autonomous vehicle: a vehicle equipped with an autopilot system, which is capable of driving from one point to another without input from a human operator. == B == Biomimetic: See Bionics. Bionics: also known as biomimetics, biognosis, biomimicry, or bionical creativity engineering is the application of biological methods and systems found in nature to the study and design of engineering systems and modern technology. == C == CAD/CAM (computer-aided design and computer-aided manufacturing): These systems and their data may be integrated into robotic operations. Čapek, Karel: Czech author who coined the term 'robot' in his 1921 play, Rossum's Universal Robots. Chandra X-ray Observatory: a robotic spacecraft launched by NASA in 1999 to collect astronomical data. Cloud robotics: robots empowered with more capacity and intelligence from cloud. Combat, robot: a hobby or sport event where two or more robots fight in an arena to disable each other. This has developed from a hobby in the 1990s to several TV series worldwide. Cruise missile: a robot-controlled guided missile that carries an explosive payload. Cyborg: also known as a cybernetic organism, a being with both biological and artificial (e.g. electronic, mechanical or robotic) parts. == D == Degrees of freedom: the extent to which a robot can move itself; expressed in terms of Cartesian coordinates (x, y, and z) and angular movements (yaw, pitch, and roll). Delta robot: a tripod linkage, used to construct fast-acting manipulators with a wide range of movement. Drive Power: The energy source or sources for the robot actuators. == E == Emergent behaviour, a complicated resultant behaviour that emerges from the repeated operation of simple underlying behaviours. Envelope (Space), Maximum The volume of space encompassing the maximum designed movements of all robot parts including the end-effector, workpiece, and attachments. Explosive ordnance disposal robot A mobile robot designed to assess whether an object contains explosives; some carry detonators that can be deposited at the object and activated after the robot withdraws. == F == FIRST(For Inspiration and Recognition of Science and Technology): an organization founded by inventor Dean Kamen in 1989 in order to develop ways to inspire students in engineering and technology fields. Forward chaining: a process in which events or received data are considered by an entity to intelligently adapt its behavior. == G == Gynoid: A humanoid robot designed to look like a human female. == H == Haptic: tactile feedback technology using the operator's sense of touch. Also sometimes applied to robot manipulators with their own touch sensitivity. Hexapod (platform): A movable platform using six linear actuators. Often used in flight simulators and fairground rides, they also have applications as a robotic manipulator. Hexapod (walker): A six-legged walking robot, using a simple insect-like locomotion. Human–computer interaction. Humanoid: A robotic entity designed to resemble a human being in form, function, or both. Hydraulics: the control of mechanical force and movement, generated by the application of liquid under pressure. cf. pneumatics. == I == Industrial robot: A reprogrammable, multifunctional manipulator designed to move material, parts, tools, or specialized devices through variable programmed motions for the performance of a variety of tasks. Insect robot: A small robot designed to imitate insect behaviors rather than complex human behaviors. == K == Kalman filter: a mathematical technique to estimate the value of a sensor measurement, from a series of intermittent and noisy values. Kinematics: the study of motion, as applied to robots. This includes both the design of linkages to perform motion, their power, control and stability; also their planning, such as choosing a sequence of movements to achieve a broader task. Inverse Kinematics: the process of determining joint angles required for a robot's end-effector to reach a desired position and orientation in space. Used in motion planning to calculate motor commands from target positions. == L == Linear actuator A form of motor that generates a linear movement directly. == M == Manipulator or gripper: A robotic 'hand'. Mobile robot: A self-propelled and self-contained robot that is capable of moving over a mechanically unconstrained course. Muting: The deactivation of a presence-sensing safeguarding device during a portion of the robot cycle. Mecanum wheel: A wheel fitted with angled rollers that enables a robot vehicle to move in multiple directions, including sideways. == O == Ornithopter – An aerial robot or drone that achieves flight through a flapping-wing mechanism rather than rotating blades or fixed wings, often utilized for highly maneuverable flight. == P == Parallel manipulator: an articulated robot or manipulator based on a number of kinematic chains, actuators and joints, in parallel. cf. serial manipulator. Pendant: Any portable control device that permits an operator to control the robot from within the restricted envelope (space) of the robot. Pneumatics: the control of mechanical force and movement, generated by the application of compressed gas. cf. hydraulics. Powered exoskeleton: is a wearable mobile machine that allow for limb movement with increased strength and endurance. Prosthetic robots: programmable manipulators or devices for missing human limbs. == R == Remote manipulator: A manipulator under direct human control, often used for work with hazardous materials. Robonaut: a development project conducted by NASA to create humanoid robots capable of using space tools and working in similar environments to suited astronauts. == S == Sensor fusion:The process of combining data from multiple sensors, such as LiDAR, cameras, global positioning systems (GPS), and inertial measurement units (IMUs), to produce a more accurate and reliable understanding of an environment than using a single sensor alone. It is widely used in robotics and autonomous systems to improve perception, localization, and decision-making. Serial manipulator: an articulated robot or manipulator with a single series kinematic chain of actuators. cf. parallel manipulator. Service robots are machines that extend human capabilities. Servo, a motor that moves to and maintains a set position under command, rather than continuously moving. Servomechanism An automatic device that uses error-sensing negative feedback to correct the performance of a mechanism. Single Point of Control The ability to operate the robot such that initiation or robot motion from one source of control is possible only from that source and cannot be overridden from another source. Slow Speed Control A mode of robot motion control where the velocity of the robot is limited to allow persons sufficient time either to withdraw the hazardous motion or stop the robot. Snake robot A robot component resembling a tentacle or elephant's trunk, where many small actuators are used to allow continuous curved motion of a robot component, with many degrees of freedom. This is usually applied to snake-arm robots, which use this as a flexible manipulator. A rarer application is the snakebot, where the entire robot is mobile and snake-like, so as to gain access through narrow spaces. Stepper motor Stewart platform A movable platform using six linear actuators, hence also known as a Hexapod. Subsumption architecture A robot architecture that uses a modular, bottom-up design beginning with the least complex behavioral tasks. Surgical robot, a remote manipulator used for keyhole surgery Swarm robotics involve large numbers of mostly simple physical robots. Their actions may seek to incorporate emergent behavior observed in social insects (swarm intelligence). Synchro == T == Teach Mode: The control state that al
Hybrid intelligent system
Hybrid intelligent system denotes a software system which employs, in parallel, a combination of methods and techniques from artificial intelligence subfields, such as: Neuro-symbolic systems Neuro-fuzzy systems Hybrid connectionist-symbolic models Fuzzy expert systems Connectionist expert systems Evolutionary neural networks Genetic fuzzy systems Rough fuzzy hybridization Reinforcement learning with fuzzy, neural, or evolutionary methods as well as symbolic reasoning methods. From the cognitive science perspective, every natural intelligent system is hybrid because it performs mental operations on both the symbolic and subsymbolic levels. For the past few years, there has been an increasing discussion of the importance of A.I. Systems Integration. Based on notions that there have already been created simple and specific AI systems (such as systems for computer vision, speech synthesis, etc., or software that employs some of the models mentioned above) and now is the time for integration to create broad AI systems. Proponents of this approach are researchers such as Marvin Minsky, Ron Sun, Aaron Sloman, Angelo Dalli and Michael A. Arbib. An example hybrid is a hierarchical control system in which the lowest, reactive layers are sub-symbolic. The higher layers, having relaxed time constraints, are capable of reasoning from an abstract world model and performing planning (even by hybrid wisdom). Intelligent systems usually rely on hybrid reasoning processes, which include induction, deduction, abduction and reasoning by analogy.
Knowledge integration
Knowledge integration is the process of synthesizing multiple knowledge models (or representations) into a common model (representation). Compared to information integration, which involves merging information having different schemas and representation models, knowledge integration focuses more on synthesizing the understanding of a given subject from different perspectives. For example, multiple interpretations are possible of a set of student grades, typically each from a certain perspective. An overall, integrated view and understanding of this information can be achieved if these interpretations can be put under a common model, say, a student performance index. The Web-based Inquiry Science Environment (WISE), from the University of California at Berkeley has been developed along the lines of knowledge integration theory. Knowledge integration has also been studied as the process of incorporating new information into a body of existing knowledge with an interdisciplinary approach. This process involves determining how the new information and the existing knowledge interact, how existing knowledge should be modified to accommodate the new information, and how the new information should be modified in light of the existing knowledge. A learning agent that actively investigates the consequences of new information can detect and exploit a variety of learning opportunities; e.g., to resolve knowledge conflicts and to fill knowledge gaps. By exploiting these learning opportunities the learning agent is able to learn beyond the explicit content of the new information. The machine learning program KI, developed by Murray and Porter at the University of Texas at Austin, was created to study the use of automated and semi-automated knowledge integration to assist knowledge engineers constructing a large knowledge base. A possible technique which can be used is semantic matching. More recently, a technique useful to minimize the effort in mapping validation and visualization has been presented which is based on Minimal Mappings. Minimal mappings are high quality mappings such that i) all the other mappings can be computed from them in time linear in the size of the input graphs, and ii) none of them can be dropped without losing property i). The University of Waterloo operates a Bachelor of Knowledge Integration undergraduate degree program as an academic major or minor. The program started in 2008.
MobileNet
MobileNet is a family of convolutional neural network (CNN) architectures designed for image classification, object detection, and other computer vision tasks. They are designed for small size, low latency, and low power consumption, making them suitable for on-device inference and edge computing on resource-constrained devices like mobile phones and embedded systems. They were originally designed to be run efficiently on mobile devices with TensorFlow Lite. The need for efficient deep learning models on mobile devices led researchers at Google to develop MobileNet. As of June 2025, the family has five versions, each improving upon the previous one in terms of performance and efficiency. == Features == === V1 === MobileNetV1 was published in April 2017. Its main architectural innovation was incorporation of depthwise separable convolutions. It was first developed by Laurent Sifre during an internship at Google Brain in 2013 as an architectural variation on AlexNet to improve convergence speed and model size. The depthwise separable convolution decomposes a single standard convolution into two convolutions: a depthwise convolution that filters each input channel independently and a pointwise convolution ( 1 × 1 {\displaystyle 1\times 1} convolution) that combines the outputs of the depthwise convolution. This factorization significantly reduces computational cost. The MobileNetV1 has two hyperparameters: a width multiplier α {\displaystyle \alpha } that controls the number of channels in each layer. Smaller values of α {\displaystyle \alpha } lead to smaller and faster models, but at the cost of reduced accuracy, and a resolution multiplier ρ {\displaystyle \rho } , which controls the input resolution of the images. Lower resolutions result in faster processing but potentially lower accuracy. === V2 === MobileNetV2 was published in March 2019. It uses inverted residual layers and linear bottlenecks. Inverted residuals modify the traditional residual block structure. Instead of compressing the input channels before the depthwise convolution, they expand them. This expansion is followed by a 1 × 1 {\displaystyle 1\times 1} depthwise convolution and then a 1 × 1 {\displaystyle 1\times 1} projection layer that reduces the number of channels back down. This inverted structure helps to maintain representational capacity by allowing the depthwise convolution to operate on a higher-dimensional feature space, thus preserving more information flow during the convolutional process. Linear bottlenecks removes the typical ReLU activation function in the projection layers. This was rationalized by arguing that that nonlinear activation loses information in lower-dimensional spaces, which is problematic when the number of channels is already small. === V3 === MobileNetV3 was published in 2019. The publication included MobileNetV3-Small, MobileNetV3-Large, and MobileNetEdgeTPU (optimized for Pixel 4). They were found by a form of neural architecture search (NAS) that takes mobile latency into account, to achieve good trade-off between accuracy and latency. It used piecewise-linear approximations of swish and sigmoid activation functions (which they called "h-swish" and "h-sigmoid"), squeeze-and-excitation modules, and the inverted bottlenecks of MobileNetV2. === V4 === MobileNetV4 was published in September 2024. The publication included a large number of architectures found by NAS. Inspired by Vision Transformers, the V4 series included multi-query attention. It also unified both inverted residual and inverted bottleneck from the V3 series with the "universal inverted bottleneck", which includes these two as special cases. === V5 === MobileNetV5's architecture was published shortly after the release of Gemma 3n in June 2025. While the announcement stated a technical report on MobileNetV5 would be available soon, this has not yet materialised. The network is 10 times larger than the largest V4 variant.
Automated parking system
An automated (car) parking system (APS) is a mechanical system designed to minimize the area and/or volume required for parking cars. Like a multi-story parking garage, an APS provides parking for cars on multiple levels stacked vertically to maximize the number of parking spaces while minimizing land usage. The APS, however, utilizes a mechanical system to transport cars to and from parking spaces (rather than the driver) in order to eliminate much of the space wasted in a multi-story parking garage. While a multi-story parking garage is similar to multiple parking lots stacked vertically, an APS is more similar to an automated storage and retrieval system for cars. Parking systems are generally powered by electric motors or hydraulic pumps that move vehicles into a storage position.The paternoster (shown animated at the right) is an example of one of the earliest and most common types of APS. APS are also generically known by a variety of other names, including:automated parking facility (APF), automated vehicle storage and retrieval system (AVSRS), car parking system, mechanical parking, and robotic parking garage. == History == The concept for the automated parking system was and is driven by two factors: a need for parking spaces and a scarcity of available land. The earliest use of an APS was in Paris, France in 1905 at the Garage Rue de Ponthieu. The APS consisted of a groundbreaking multi-story concrete structure with an internal car elevator to transport cars to upper levels where attendants parked the cars. In the 1920s, a Ferris wheel-like APS (for cars rather than people) called a paternoster system became popular as it could park eight cars in the ground space normally used for parking two cars. Mechanically simple with a small footprint, the paternoster was easy to use in many places, including inside buildings. At the same time, Kent Automatic Garages was installing APS with capacities exceeding 1,000 cars. The “ferris-wheel,” or paternoster system — was created by the Westinghouse Corporation in 1923 and subsequently built in 1932 on Chicago's Monroe Street. The Nash Motor Company created the first glass-enclosed version of this system for the Chicago Century of Progress Exhibition in 1933 The first driverless parking garage opened in 1951 in Washington, D.C., but was replaced with office space due to increasing land values. APS saw a spurt of interest in the U.S. in the late 1940s and 1950s with the Bowser, Pigeon Hole and Roto Park systems. In 1957, 74 Bowser, Pigeon Hole systems were installed, and some of these systems remain in operation. However, interest in APS in the U.S. waned due to frequent mechanical problems and long waiting times for patrons to retrieve their cars. In the United Kingdom, the Auto Stacker opened in 1961 in Woolwich, south east London, but proved equally difficult to operate. Interest in APS in the U.S. was renewed in the 1990s, and there were 25 major current and planned APS projects (representing nearly 6,000 parking spaces) in 2012. The first American robotic parking garage opened in 2002 in Hoboken, New Jersey. While interest in the APS in the U.S. languished until the 1990s, Europe, Asia and Central America had been installing more technically advanced APS since the 1970s. In the early 1990s, nearly 40,000 parking spaces were being built annually using the paternoster APS in Japan. In 2012, there are an estimated 1.6 million APS parking spaces in Japan. The ever-increasing scarcity of available urban land (urbanization) and increase of the number of cars in use (motorization) have combined with sustainability and other quality-of-life issues to renew interest in APS as alternatives to multi-storey car parks, on-street parking, and parking lots. == Largest systems == The largest Automated Parking Facility in the world is in Al Jahra, Kuwait, and provides 2,314 parking spaces. The world's fastest Automated Parking System is in Wolfsburg, Germany, with a retrieval time of 1 minute and 44 seconds. The largest APS in Europe is at Dokk1 in Aarhus, Denmark, and provides 1,000 parking spaces via 20 car lifts. == Space saving == All APS take advantage of a common concept to decrease the area of parking spaces - removing the driver and passengers from the car before it is parked. With either fully automated or semi-automated APS, the car is driven up to an entry point to the APS and the driver and passengers exit the car. The car is then moved automatically or semi-automatically (with some attendant action required) to its parking space. The space-saving provided by the APS, compared to the multi-story parking garage, is derived primarily from a significant reduction in space not directly related to the parking of the car: Parking space width and depth (and distances between parking spaces) are dramatically reduced since no allowance need be made for driving the car into the parking space or for the opening of car doors (for drivers and passengers) No driving lanes or ramps are needed to drive the car to/from the entrance/exit to a parking space Ceiling height is minimized since there is no pedestrian traffic (drivers and passengers) in the parking area, and No walkways, stairways or elevators are needed to accommodate pedestrians in the parking area. With the elimination of ramps, driving lanes, pedestrians and the reduction in ceiling heights, the APS requires substantially less structural material than the multi-story parking garage. Many APS utilize a steel framework (some use thin concrete slabs) rather than the monolithic concrete design of the multi-story parking garage. These factors contribute to an overall volume reduction and further space savings for the APS. == Other considerations == In addition to the space saving, many APS designs provide a number of secondary benefits: The parked cars and their contents are more secure since there is no public access to parked cars Minor parking lot damage such as scrapes and dents are eliminated Drivers and passengers are safer not having to walk through parking lots or garages Driving around in search of a parking space is eliminated, thereby reducing engine emissions and wasted time Only minimal ventilation and lighting systems are needed Handicap access is improved The volume and visual impact of the parking structure is minimized Shorter construction time === Problems === There have been a number of problems with robotic parking systems, particularly in the United States. The systems work well in balanced throughput situations like shopping malls and train stations, but they are unsuited to high peak volume applications like rush hour usage or stadiums and they suffer from technical problems. Further, parkers not familiar with the system may cause problems, for example by failing to push the button to alert a fully automated system to the presence of a car to be parked. In London around 40 vehicles were trapped for two years in CBRE's system. == Fully automated vs semi-automated == Fully automated parking systems operate much like robotic valet parking. The driver drives the car into an APS entry (transfer) area. The driver and all passengers exit the car. The driver uses an automated terminal nearby for payment and receipt of a ticket. When driver and passengers have left the entry area, the mechanical system lifts the car and transports it to a pre-determined parking space in the system. More sophisticated fully automated APS will obtain the dimensions of cars on entry in order to place them in the smallest available parking space. The driver retrieves a car by inserting a ticket or code into an automated terminal. The APS lifts the car from its parking space and delivers it to an exit area. Most often, the retrieved car has been oriented to eliminate the need for the driver to back out. Fully automated APS theoretically eliminate the need for parking attendants. Semi-automated APS also use a mechanical system of some type to move a car to its parking space, however putting the car into and/or the operation of the system requires some action by an attendant or the driver. The choice between fully and semi-automated APS is often a matter of space and cost, however large capacity (> 100 cars) tend to be fully automated. == Applications == By virtue of their relatively smaller volume and mechanized parking systems, APS are often used in locations where a multi-story parking garage would be too large, too costly or impractical. Examples of such applications include, under or inside existing or new structures, between existing structures and in irregularly shaped areas. APS can also be applied in situations similar to multi-storey parking garages such as freestanding above ground, under buildings above grade and under buildings below grade. == Costs == The direct comparison of costs between an APS and a multi-story parking garage can be complicated by many variables such as capacity, land costs, area shape, number and location of entranc
Organoid intelligence
Organoid intelligence (OI) is an emerging field of study in computer science and biology that develops and studies biological wetware computing using 3D cultures of human brain cells (or brain organoids) and brain-machine interface technologies. Such technologies may be referred to as OIs or the nervous filesystem. Organoid intelligent computer systems can be an example of biohybrid systems. == Differences with non-organic computing == As opposed to traditional non-organic silicon-based approaches, OI seeks to use lab-grown cerebral organoids to serve as "biological hardware". While these structures are still far from being able to think like a regular human brain and do not yet possess strong computing capabilities, OI research currently offers the potential to improve the understanding of brain development, learning and memory, potentially finding treatments for neurological disorders such as dementia. Thomas Hartung, a professor from Johns Hopkins University, argued in 2023 that "while silicon-based computers are certainly better with numbers, brains are better at learning." He noted that transistor density in computer chip may be approaching its limits, whereas brains, being wired differently, are more energy-efficient and can store large amounts of information. Some researchers claim that even though human brains are slower than machines at processing simple information, they are far better at processing complex information as brains can deal with fewer and more uncertain data, perform both sequential and parallel processing, being highly heterogenous, use incomplete datasets, and is said to outperform non-organic machines in decision-making. Training OIs involve the process of biological learning (BL) as opposed to machine learning (ML) for AIs. == Bioinformatics in OI == OI generates complex biological data, necessitating sophisticated methods for processing and analysis. Bioinformatics provides the tools and techniques to decipher raw data, uncovering the patterns and insights. Researchers have developed a platform named Neuroplatform for experimenting remotely with brain organoids via an API. == Intended functions == Brain-inspired computing hardware aims to emulate the structure and working principles of the brain and could be used to address current limitations in AI technologies. However, brain-inspired silicon chips are still limited in their ability to fully mimic brain function, as most examples are built on digital electronic principles. One study performed OI computation (which they termed Brainoware) by sending and receiving information from the brain organoid using a high-density multielectrode array. By applying spatiotemporal electrical stimulation, nonlinear dynamics, and fading memory properties, as well as unsupervised learning from training data by reshaping the organoid functional connectivity, the study showed the potential of this technology by using it for speech recognition and nonlinear equation prediction in a reservoir computing framework. == Ethical concerns == While researchers are hoping to use OI and biological computing to complement traditional silicon-based computing, there are also questions about the ethics of such an approach. Concerns include the possibility that an organoid could develop sentience or consciousness, and the question of the relationship between a stem cell donor (for growing the organoid) and the respective OI system.