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Signals and Systems Lecture Filter Structure and Quantization Effects
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Implementation PART II
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Structure for discrete-time
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Realization of Filters
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Digital Filter
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Direct Form I Implementation
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Block Diagram IIR DF-I
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Realization of Filters: Ex.
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Block Diagrams/ signal Flowgraphs
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Signal Flow Graph: DF-I
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Multiple Structures
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Discrete Form II
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Signal Flow Graph: IIR DF-II
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Discrete Form II (canonic)
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Cascade Form
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Cascade Form: Real Case
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IIR Cascade Form
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Parallel Form
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Transposed Forms
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Structures for FIR Filters
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Structures for LP FIR Filters
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Quantization Effects discrete-time filters, not digital filters. Most DSP systems are implemented using fixed-point arithmetic Floating-point arithmetic helps alleviate this problem, but consumes too much power and costs more Due to the very nature of DSP, where digital data are obtained through an A/D converter, floating-point precision is usually not required
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Coefficient Quantization first design a discrete-time filter with double floating-point precision, such as the use of Matlab Truncate (or round) the filter coefficients to implement the fixed- point HW/SW
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Finite Precision effects
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Quantization effects on the FIR systems
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Unquantized FIR Filter Effect
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16-bit Quantization FIR Filter
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8-bit Quantization of FIR Filter
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Finite Precision effects- Example
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Coefficient Quantization
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Coefficient Quantization(cont.)
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DFII vs. 2 nd order sections for IIR
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2 nd Order Filter
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cos and - 2 must be computed and rounded to the number of bits available Suppose that we use a 4-bit quantizer b0.b1b2b3. Both cos and 2 can take on the numbers from 1.000 to 0.111 (-1 to 0.875) Poles and zeros of a 2-nd order filter can only occur at the intersection of the lines representing cos and the semi-circles representing 2
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Quantization in 2 nd order Section
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Evenly Spaced Quantization Non-uniform density of poles and zeros of a 2nd-order section can be mitigated by using a “coupled form” structure The quantized poles and zeros are at the intersections of evenly spaced horizontal and vertical lines.
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