Mirrorless cameraA mirrorless camera is a camera that does not have a mirror. Though most cameras, including smartphones, lack mirrors, the term mirrorless is primarily used to describe digital interchangeable lens cameras, in order to distinguish them from DSLRs, which have historically dominated interchangeable lens cameras. Lacking a mirror system allows the camera to be smaller, quieter, and lighter. In cameras with mirrors, light from the lens is directed to either the image sensor or the viewfinder.
Experimental psychologyExperimental psychology refers to work done by those who apply experimental methods to psychological study and the underlying processes. Experimental psychologists employ human participants and animal subjects to study a great many topics, including (among others) sensation & perception, memory, cognition, learning, motivation, emotion; developmental processes, social psychology, and the neural substrates of all of these.
CoefficientIn mathematics, a coefficient is a multiplicative factor involved in some term of a polynomial, a series, or an expression. It may be a number (dimensionless), in which case it is known as a numerical factor. It may also be a constant with units of measurement, in which it is known as a constant multiplier. In general, coefficients may be any expression (including variables such as a, b and c). When the combination of variables and constants is not necessarily involved in a product, it may be called a parameter.
Equal-area projectionIn cartography, an equivalent, authalic, or equal-area projection is a map projection that preserves relative area measure between any and all map regions. Equivalent projections are widely used for thematic maps showing scenario distribution such as population, farmland distribution, forested areas, and so forth, because an equal-area map does not change apparent density of the phenomenon being mapped. By Gauss's Theorema Egregium, an equal-area projection cannot be conformal.
Neural codingNeural coding (or neural representation) is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the individual or ensemble neuronal responses and the relationship among the electrical activity of the neurons in the ensemble. Based on the theory that sensory and other information is represented in the brain by networks of neurons, it is thought that neurons can encode both digital and analog information.
Recognition memoryRecognition memory, a subcategory of declarative memory, is the ability to recognize previously encountered events, objects, or people. When the previously experienced event is reexperienced, this environmental content is matched to stored memory representations, eliciting matching signals. As first established by psychology experiments in the 1970s, recognition memory for pictures is quite remarkable: humans can remember thousands of images at high accuracy after seeing each only once and only for a few seconds.
Regularization (mathematics)In mathematics, statistics, finance, computer science, particularly in machine learning and inverse problems, regularization is a process that changes the result answer to be "simpler". It is often used to obtain results for ill-posed problems or to prevent overfitting. Although regularization procedures can be divided in many ways, the following delineation is particularly helpful: Explicit regularization is regularization whenever one explicitly adds a term to the optimization problem.
Hierarchical temporal memoryHierarchical temporal memory (HTM) is a biologically constrained machine intelligence technology developed by Numenta. Originally described in the 2004 book On Intelligence by Jeff Hawkins with Sandra Blakeslee, HTM is primarily used today for anomaly detection in streaming data. The technology is based on neuroscience and the physiology and interaction of pyramidal neurons in the neocortex of the mammalian (in particular, human) brain. At the core of HTM are learning algorithms that can store, learn, infer, and recall high-order sequences.
Sensor nodeA sensor node (also known as a mote in North America), consists of an individual node from a sensor network that is capable of performing a desired action such as gathering, processing or communicating information with other connected nodes in a network. Although wireless sensor networks have existed for decades and used for diverse applications such as earthquake measurements or warfare, the modern development of small sensor nodes dates back to the 1998 Smartdust project and the NASA.
Support vector machineIn machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974).
