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Chapter 14 Review of Quantization - Chester F. Carlson ...
hold circuits. The simplest quantizer converts an analog input voltage to a 1-bit digital output and can be constructed from an ideal di fferential amplifier, where the output voltage Voutis proportional to the difference of two voltages Vinand Vref: Vout= α(Vin−Vref) Vref is a reference voltage provided by a known source. If αis large ...
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Traveling Waves - Chester F. Carlson Center for Imaging ...
www.cis.rit.edu32 CHAPTER 4. TRAVELING WAVES amplitudes over a discrete set of frequencies: y[z,t]= X∞ n=1 y n X∞ n=1 Ancos[knz−ωnt+φ], where An,kn,andωnare the amplitude, angular spatial frequency, and angular spatial frequency of the nthwave.Therefore, we can define the phase velocity of the nthwave as: (vφ)n ωn kn Now suppose that a particular anharmonic oscillation is …
Chapter 6 Maxwell’s Equations for Electromagnetic Waves
www.cis.rit.eduChapter 6 Maxwell’s Equations for Electromagnetic Waves 6.1 Vector Operations Any physical or mathematical quantity whose amplitude may be decomposed into “directional” components often is represented conveniently as a vector. In this dis-cussion, vectors are denoted by bold-faced underscored lower-case letters, e.g., x.The
Propagation of Waves - RIT Center for Imaging Science
www.cis.rit.eduPROPAGATION OF WAVES 7.1.2 Cylindrical Waves If a wave is emitted from a line source, the wavefronts are cylindrical. Since the wave expands to Þll a cylinder of radius r0, the wavefront crosses a cylindrical area that grows as Area =2πrh ∝ r.
Correlation in Random Variables
www.cis.rit.eduRandom Process • A random variable is a function X(e) that maps the set of ex- periment outcomes to the set of numbers. • A random process is a rule that maps every outcome e of an experiment to a function X(t,e). • A random process is usually conceived of as a function of time, but there is no reason to not consider random processes that are
Lecture 3: Basic Morphological Image Processing
www.cis.rit.eduSep 13, 2005 · Morphological processing is described almost entirely as operations on sets. In this discussion, a set is a collection of pixels in the context of an image. Our sets will be collections of points on an image grid G of size N × M pixels. DIP Lecture 3 1. Pixel Location
Poisson and Normal Distributions
www.cis.rit.eduPoisson Distribution • The Poisson∗ distribution can be derived as a limiting form of the binomial distribution in which n is increased without limit as the product λ =np is kept constant. • This corresponds to conducting a very large number of Bernoulli trials with the probability p of success on any one trial being very small. • The Poisson distribution can also be derived …
Lecture 2: Geometric Image Transformations
www.cis.rit.eduSep 08, 2005 · Rochester Institute of Technology [email protected] September 8, 2005 Abstract Geometric transformations are widely used for image registration and the removal of geometric distortion. Common applications include construction of mosaics, geographical mapping, stereo and video. DIP Lecture 2
Binary Images - Chester F. Carlson Center for Imaging Science
www.cis.rit.eduIndexed color images store a fixed number of colors limited by the bit-depth: 3 bits/pixel : 8 colors 4 bits/pixel : 16 colors 5 bits/pixel:64 colors 8 bits/pixel : 256 colors. File Size Calculation 100 pixels 100 pixels Bit depth = 8 bits per pixel (256 gray levels)
Functions of Random Variables - College of Science | RIT
www.cis.rit.eduSuppose that a random variable U can take on any one of L ran-dom values, say u1,u2,...uL. Imagine that we make n indepen-dent observations of U and that the value uk is observed nk times, k =1,2,...,L.Of course, n1 +n2 +···+nL = n. The emperical average can be computed by u = 1 n L k=1 nkuk = L k=1 nk n uk The concept of statistical ...
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