Avatar (computing)In computing, an avatar is a graphical representation of a user or the user's character or persona. Avatars can be two-dimensional icons in Internet forums and other online communities, where they are also known as profile pictures, userpics, or formerly picons (personal icons, or possibly "picture icons"). Alternatively, an avatar can take the form of a three-dimensional model, as used in online worlds and video games, or an imaginary character with no graphical appearance, as in text-based games or worlds such as MUDs.
Virtual camera systemIn 3D video games, a virtual camera system aims at controlling a camera or a set of cameras to display a view of a 3D virtual world. Camera systems are used in video games where their purpose is to show the action at the best possible angle; more generally, they are used in 3D virtual worlds when a third-person view is required. As opposed to filmmakers, virtual camera system creators have to deal with a world that is interactive and unpredictable.
Action potentialAn action potential occurs when the membrane potential of a specific cell rapidly rises and falls. This depolarization then causes adjacent locations to similarly depolarize. Action potentials occur in several types of animal cells, called excitable cells, which include neurons, muscle cells, and in some plant cells. Certain endocrine cells such as pancreatic beta cells, and certain cells of the anterior pituitary gland are also excitable cells.
Virtual communityA virtual community is a social network of individuals who connect through specific social media, potentially crossing geographical and political boundaries in order to pursue mutual interests or goals. Some of the most pervasive virtual communities are online communities operating under social networking services. Howard Rheingold discussed virtual communities in his book, The Virtual Community, published in 1993. The book's discussion ranges from Rheingold's adventures on The WELL, computer-mediated communication, social groups and information science.
Interaction (statistics)In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the effect of one causal variable on an outcome depends on the state of a second causal variable (that is, when effects of the two causes are not additive). Although commonly thought of in terms of causal relationships, the concept of an interaction can also describe non-causal associations (then also called moderation or effect modification).
Detection theoryDetection theory or signal detection theory is a means to measure the ability to differentiate between information-bearing patterns (called stimulus in living organisms, signal in machines) and random patterns that distract from the information (called noise, consisting of background stimuli and random activity of the detection machine and of the nervous system of the operator). In the field of electronics, signal recovery is the separation of such patterns from a disguising background.
Multilevel modelMultilevel models (also known as hierarchical linear models, linear mixed-effect model, mixed models, nested data models, random coefficient, random-effects models, random parameter models, or split-plot designs) are statistical models of parameters that vary at more than one level. An example could be a model of student performance that contains measures for individual students as well as measures for classrooms within which the students are grouped.
Constant false alarm rateConstant false alarm rate (CFAR) detection refers to a common form of adaptive algorithm used in radar systems to detect target returns against a background of noise, clutter and interference. In the radar receiver, the returning echoes are typically received by the antenna, amplified, down-converted to an intermediate frequency, and then passed through detector circuitry that extracts the envelope of the signal, known as the video signal. This video signal is proportional to the power of the received echo.
Markov modelIn probability theory, a Markov model is a stochastic model used to model pseudo-randomly changing systems. It is assumed that future states depend only on the current state, not on the events that occurred before it (that is, it assumes the Markov property). Generally, this assumption enables reasoning and computation with the model that would otherwise be intractable. For this reason, in the fields of predictive modelling and probabilistic forecasting, it is desirable for a given model to exhibit the Markov property.
