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Discrete Time Signals and Systems Discrete Time Signals
"... The basic theory of discretetime signals and systems is similar to continuoustime signals and systems. However, there are some differences: ..."
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The basic theory of discretetime signals and systems is similar to continuoustime signals and systems. However, there are some differences:
DiscreteTime Signals and Systems Signal Symmetry
"... δ(n) = 1, for n = 0 0, for n 6 = 0 2. unit step signal: u(n) = 1, for n ≥ 0 0, for n < 0 3. unit ramp signal: ur (n) = n, for n ≥ 0 0, for n < 0 Note: δ(n) = u(n) − u(n − 1) = ur (n + 1) − 2ur (n) + ur (n − 1) u(n) = ur (n + 1) − ur (n) Professor Deepa Kundur (University of Toronto) Discret ..."
AN L1 METHOD FOR BANDLIMITING DISCRETETIME SIGNALS
"... This paper addresses the problem of bandlimiting discretetime signals using digital filters, which are optimal in an L1 sense. An efficient procedure for the design of such filters is proposed and it is shown that the resulting filters admit several desired properties. 1. ..."
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This paper addresses the problem of bandlimiting discretetime signals using digital filters, which are optimal in an L1 sense. An efficient procedure for the design of such filters is proposed and it is shown that the resulting filters admit several desired properties. 1.
DiscreteTime Signal Processing and Makhoul’s Conjecture
"... This report surveys the role that polynomials and algebra play in investigating some fundamental properties of allpass signals and systems. Allpass systems are an important concept in the theory of discretetime signal processing. In particular, we summarize work regarding Makhoul’s conjecture on ..."
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This report surveys the role that polynomials and algebra play in investigating some fundamental properties of allpass signals and systems. Allpass systems are an important concept in the theory of discretetime signal processing. In particular, we summarize work regarding Makhoul’s conjecture
COURSE CONTENT: 1. DiscreteTime Signals and Systems
, 2014
"... The course starts with a review of basic concepts of signal processing: classification of signals and systems, system modeling and analysis; to take on more advanced and modern topics in digital signal processing such as subNyquist sampling, timevarying analysis and filtering, linear prediction and ..."
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and power spectrum estimation. Lectures: 2 days each week, 1.5 hours each day COURSE OBJECTIVE: The course covers fundamentals of discretetime signals, systems and modern digital signal processing. Intended to be a first course on the topic at the graduate level to prepare students for subsequent courses
Discretetime Signals and SystemsiiDiscretetime Signals and Systems An Operator Approach
"... (authors) and?? (publisher) is licensed under the... license. Brief contents Preface ix ..."
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(authors) and?? (publisher) is licensed under the... license. Brief contents Preface ix
Discretetime signal design for maximizing separation in amplitude
 IEEE TRANS. INFORM. THEORY
, 1995
"... Given a discretetime, linear, shiftinvariant channel with finite impulse response, the problem of designing finitelength input signals with bounded amplitude (1, norm) such that the corresponding output signals are maximally separated in amplitude (2 sense) is considered. In general, this is a ..."
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Given a discretetime, linear, shiftinvariant channel with finite impulse response, the problem of designing finitelength input signals with bounded amplitude (1, norm) such that the corresponding output signals are maximally separated in amplitude (2 sense) is considered. In general, this is a
Characterizing Wavelet Coefficient Decay of DiscreteTime Signals
, 2005
"... We present an intrinsically discretetime characterization of wavelet coefficient decay. To be more precise, let f = (f(n))n∈Z be a sequence and denote by (d_j,l)j≥1,l∈Z the coefficients obtained by passing f through a subsampled wavelet filter bank. Then it is common practi ..."
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We present an intrinsically discretetime characterization of wavelet coefficient decay. To be more precise, let f = (f(n))n∈Z be a sequence and denote by (d_j,l)j≥1,l∈Z the coefficients obtained by passing f through a subsampled wavelet filter bank. Then it is common
A UNIFIED APPROACH To THE RESTORATION OF LOST SAMPLES IN DISCRETE—TIME SIGNALS
"... We consider the problem of estimating lost sample values in discrete—time signals. The problem is treated as a linear minimum variance estimation problem, which, in principle, requires knowledge of the signal’s autocorrelation coefficients. We show that, starting from this general principle, several ..."
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We consider the problem of estimating lost sample values in discrete—time signals. The problem is treated as a linear minimum variance estimation problem, which, in principle, requires knowledge of the signal’s autocorrelation coefficients. We show that, starting from this general principle
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