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`AQUILA - Ex. 2010
`AQUILA - EX. 2010
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`

`

`S E C O N D E D I T I O N
`
`COMPREHENSIVE
`
`DICTIONARY
`
`OF
`
`© 2005 by Taylor & Francis Group, LLC
`
`

`

`S E C O N D E D I T I O N
`
`COMPREHENSIVE
`DICTIONARY
`OF
`
`EDITOR-IN-CHIEF
`Phillip A. Laplante
`
`Boca Raton London New York Singapore
`
`CRC PRESS, a Taylor & Francis title, part of the Taylor and Francis Group.
`
`© 2005 by Taylor & Francis Group, LLC
`
`

`

`finger stick
`
`finger stick
`an insulated stick like a hot-stick
`used to actuate a disconnect-switch atop a pole.
`
`finite difference method
`a numerical tech-
`nique for solving a differential equation wherein
`the differential equation is replaced by a finite
`difference equation that relates the value of the
`solution at a point to the values at neighboring
`points.
`
`finite difference time domain (FDTD)
`a nu-
`merical technique for the solution of electromag-
`netic wave problems that involves the mapping
`of the Maxwell equations onto a finite difference
`mesh and then following the time evolution of
`an initial value problem. This technique is widely
`used to investigate the performance of a complex
`RF structures.
`
`finite differences
`a method used to numerically
`solve partial differential equations by replacing
`the derivatives with finite increments.
`
`finite element
`a numerical technique for the
`solution of boundary value problems that involves
`the replacement of the set of differential equations
`describing the problem under consideration with a
`corresponding set of integral equations. The area
`or volume of the problem is then subdivided with
`simple shapes such as triangles and an approxima-
`tion to the desired solution with free parameters is
`written for each subregion and the resulting set of
`equations is minimized to find the final solution.
`This approach is useful for solving a variety of
`problems on complex geometries.
`
`finite-extent sequence
`the discrete-time sig-
`nals with finite duration. The finite-extent se-
`quence {x (n)} is zero for all values of n outside a
`finite interval.
`
`finite field
`a finite set of elements and two
`operations, usually addition and multiplication,
`that satisfy a number of specific algebraic proper-
`ties. In honor of the pioneering work by Evariste
`
`262
`
`© 2005 by Taylor & Francis Group, LLC
`
`Galois, finite fields are often called Galois fields
`and denoted G F (q), where q is the number of
`elements in the field. Finite fields exist for all q
`which are prime or the power of a prime.
`
`finite impulse response (FIR) filter
`any filter
`having an impulse response which is nonzero for
`only a finite period of time (therefore having a
`frequency response consisting only of zeros, no
`poles). For example, every moving average pro-
`cess can be written as the output of a FIR filter
`driven by white noise. Impulse response function,
`moving average, infinite impulse response (IIR)
`filter.
`
`finite state machine (FSM)
`a mathematical
`model that is defined in discrete time and has a
`finite number of possible states it can reside in.
`At each time instance, an input, x, is accepted
`and an output, y, and a transition from the current
`state, Sc, to a new state, Sn , are generated based
`on separate functions of the input and the cur-
`rent state. A finite state machine can be uniquely
`defined by a set of possible states, S, an output
`function, y = f (x , Sc), and a transition function,
`Sn = g(x , Sc). An FSM describes many differ-
`ent concepts in communications such as convo-
`lutional coding/decoding, CPM modulation, ISI
`channels, CDMA transmission, shift-register se-
`quence generation, data transmission and com-
`puter protocols. Also known as finite state au-
`tomata (FSA), state machine.
`
`finite state VQ (FSVQ)
`a vector quantizer with
`memory. FSVQ form a subset of the general class
`of recursive vector quantization. The next state is
`determined by the current state Sn together with
`the previous channel symbol un by some mapping
`function.
`
`Sn+1 = f (un , Sn ) , n = 0, 1, . . .
`
`This also obeys the minimum distortion property
`
`α(x, s) =
`
`−1
`min d(x, β(u, s))
`
`

`

`LASCR
`
`Laplace transform
`f (t ) given by
`
`the transform of a function
`
`F (s) = ∞
`
`−∞
`
`f (t )e−st dt
`
`where s = a + j ω is a complex variable. The one
`sided or unilateral Laplace transform is given by
`the same equation except that the lower limit is
`0 and not −∞. The region of convergence of the
`Laplace integral is a vertical strip R in the s-plane.
`The inverse Laplace transform is given by
`
`theoretical work was heavily in the field of celes-
`tial mechanics. He helped to establish the math-
`ematical basis for the field and in doing so con-
`firmed significant parts of Newton’s work.
`
`Laplacian
`the second-order operator, defined
`in Rn as ∇2 = ∂ 2/∂ x 2
`+ · · · + ∂ 2/∂ 2
`n . The zero
`1
`crossings of an image to which the Laplacian oper-
`ator has been applied usually correspond to edges,
`as in such points a peak (trough) of the first deriva-
`tive components can be found.
`
`f (t ) = 1/(2π j ) L
`
`F (s)est ds
`
`large cell
`cell with the radius of 5–35 km (such
`as those found in Groupe Special Mobile systems).
`See also cell.
`
`where L is a vertical line in R. The Fourier Trans-
`form of f (t ) is given by F ( j ω).
`Laplacian pyramid a set of Laplacian images at
`multiple scales used in pyramid coding. An input
`image G1 is Gaussian lowpass filtered and down-
`sampled to form G2. Typically G2 is one quarter
`the size of G1, i.e. it is downsampled by a fac-
`tor of 2 in each direction. G2 is upsampled and
`Gaussian lowpass filtered to form R1 which is then
`subtracted from G1 to give L 1. The process then
`repeats using G2 as input. The sets of multireso-
`lution images so generated are called “pyramids”:
`G1 . . . Gn form a Gaussian pyramid; L 1 . . . L n
`form a Laplacian pyramid.
`
`Laplace’s equation
`a partial differential equa-
`tion mathematically described by ∇2φ = 0,
`where ∇2 is the Laplacian and φ is the equation’s
`solution.
`
`Laplace, Pierre-Simon, Marquis de
`(1749–
`1827) Born: Beaumont-en-Auge, Normandy,
`France
`Best known for his development of basic
`tools of mathematical analysis including the
`Laplace transform, the Laplace theorem, and the
`Laplace coefficients. Laplace studied in Paris with
`the great mathematician Jean d’Alembert. Laplace
`was heavily involved in politics throughout his ca-
`reer and held many government posts. Laplace’s
`
`© 2005 by Taylor & Francis Group, LLC
`
`large disturbance
`a disturbance for which the
`equation for dynamic operation cannot be lin-
`earized for analysis.
`
`large-scale integration (LSI)
`(1) term usually
`used to describe the level of integration at which
`entire integrated circuits can be placed on a single
`chip.
`(2) an integrated circuit made of hundreds to
`thousands of transistors.
`
`large-scale process (system)
`partitioned com-
`plex process (system) composed of several sub-
`processes (subsystems) that are either physically
`interconnected or must be considered jointly due
`to the nature of the control objectives.
`
`lapped orthogonal transform (LOT)
`a criti-
`cally sampled block transform, where the blocks
`overlap, typically by half a block. Equivalently the
`LOT is a critically sampled filter bank, where typ-
`ically the filter lengths are equal to twice the num-
`ber of channels or filters. The LOT was motivated
`by reducing the blocking effect in transform cod-
`ing by using overlapping blocks. A cosine modu-
`lated filter bank is a type of LOT.
`
`LASCR
`rectifier.
`
`See light-activated silicon controlled
`
`385
`
`

`

`R− < |z| < R+, where R− and R+ denote real
`parameters that are related to the causal and an-
`ticausal components, respectively, for the signal
`whose z-transform is being sought.
`(3) an area on a display device where the im-
`age displayed meets an accepted criteria for raster
`coordinate deviation. See region of absolute con-
`vergence.
`
`region of interest (ROI)
`a restricted set of im-
`age pixels upon which image processing opera-
`tions are performed. Such a set of pixels might be
`those representing an object that is to be analyzed
`or inspected.
`
`region of support
`the region of variable or vari-
`ables where the function has non-zero value.
`
`register
`a circuit formed from identical flip-
`flops or latches and capable of storing several bits
`of data.
`
`regular form
`
`register transfer notation
`a mathematical no-
`tation to show the movement of data from one
`register to another register by using a backward
`arrow. Notation used to describe elementary op-
`erations that take place during the execution of a
`machine instruction.
`
`register window in the SPARC architecture, a
`set or window of registers selected out of a larger
`group.
`
`registration
`(1) See overlay.
`(2) the process of aligning multiple images ob-
`tained from different modalities, at different time-
`points, or with different image acquisition param-
`eters. See fusion.
`
`regression
`the methods which use backward
`prediction error as input to produce an estimation
`of a desired signal. Quantitatively, the regression
`of y on X , denoted by r (y), is defined as the first
`conditional moment, i.e.,
`
`register alias table
`
`See virtual register.
`
`r (y) = E (X |y)
`
`register direct addressing
`an instruction ad-
`dressing method in which the memory address of
`the data to be accessed or stored is found in a gen-
`eral purpose register.
`
`regular controllability
`a dynamical system is
`said to be regularly controllable in time interval
`[t0, t1] if every dynamical system of the form
`
`register file
`a collection of CPU registers ad-
`dressable by number.
`
`x (t ) = Ax (t )x (t ) + b j u j (t )
`j = 1, 2, . . . , m
`
`register indirect addressing
`an instruction ad-
`dressing method in which the register field con-
`tains a pointer to a memory location that contains
`the memory address of the data to be accessed or
`stored.
`
`register renaming
`dynamically allocating a lo-
`cation in a special register file for an instance of
`a destination register appearing in an instruction
`prior to its execution. Used to remove antidepen-
`dencies and output dependencies. See also reorder
`buffer.
`
`is controllable where b j is the j th column of the
`matrix B and u j (t ) is the j th scalar admissible
`control.
`
`regular cue
`any regular recurring point/element
`of a signal that can be used to signal the start of
`a new signal sequence; e.g., the leading edge of a
`60-Hz square wave is a regular cue.
`
`regular form a particular form of the state
`space description of a dynamical system. This
`form is obtained by a suitable transformation of
`
`© 2005 by Taylor & Francis Group, LLC
`
`583
`
`

`

`model by
`
`G(s) = C[s I − A]−1 B + D
`
`assuming that the initial conditions on all internal
`variables are zero. The Laplace variable is denoted
`by s. Similar equations, based on difference and
`algebraic equations, define state space model for
`linear discrete-time (digital) dynamic systems.
`
`xt +1 = Axt + But
`
`yt = C xt + Dut
`
`See also transfer function.
`
`state space variable
`the internal variable (or
`state) in a state space model description of a dy-
`namic process. These internal variables effectively
`define the status or energy locked up in the system
`at any given instant in time and hence influence
`its behavior for future time.
`
`state transition diagram a component of the
`essential model; it describes event-response be-
`haviors.
`
`state variable
`one of a set of variables that com-
`pletely determine the system’s status in the follow-
`ing sense: if all the state variables are known at
`some time t0, then the values of the state variables
`at any time t1 > t0 can be determined uniquely
`provided the system input is known for. The vec-
`tor whose components are state variables is called
`the state vector. The state space is the vector space
`whose elements are state vectors.
`
`state vector
`ables.
`
`a vector formed by the state vari-
`
`state-space averaging
`a method of obtaining a
`state-model representation of a circuit containing
`switching elements by averaging the state models
`of all the switched topologies.
`
`state-space averaging model
`a small-signal
`dynamic modeling method for PWM switching
`
`state equations
`
`(2) a simple diagram representing the input–
`output relationship and all possible states of a
`convolutional encoder together with the possible
`transitions from one state to another. Distance
`properties and error rate performance can be de-
`rived from the state diagram.
`
`state equations
`equations formed by the state
`equation and the output equation.
`
`state feedback
`the scheme whereby the con-
`trol signal is generated by feeding back the state
`variables through the control gains.
`
`state machine
`a software or hardware structure
`that can be in one of a finite collection of states.
`Used to control a process by stepping from state
`to state as a function of its inputs. See also finite
`state machine.
`
`state plane
`
`See phase plane.
`
`state space conditional codec
`an approach
`where the number of codes is much less than with
`conditional coding. The previous N − 1 pixels are
`used to determine the state s j . Then the j th vari-
`able word-length is used to code the value.
`
`state space model
`a set of differential and al-
`gebraic equations defining the dynamic behavior
`of systems. Its generic form for linear continuous-
`time systems is given by
`
`x (t ) = Ax (t ) + Bu(t )
`
`y(t ) = C x (t ) + Du(t )
`
`d d
`
`t
`
`where u(t ) is the system input signal, x (t ) is its
`internal or state space variable, and y(t ) is its out-
`put. Matrices A, B, C, D of real constants define
`the model. The internal variable is often a vector
`of internal variables, while in the general multi-
`variable case all the input and output signals are
`also vectors of signals. Although not identically
`equivalent, the state space model can be related to
`the transfer function (or transfer function matrix)
`
`656
`
`© 2005 by Taylor & Francis Group, LLC
`
`