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Discrete-Time Signal Pro-cessing
TLDR
The definitive, authoritative text on DSP, written by prominent, DSP pioneers, it provides thorough treatment of the fundamental theorems and properties of discrete- time linear systems, filtering, sampling, and discrete-time Fourier Analysis.
Digital Signal Processing
TLDR
The Digital Signal Processing Group develops signal processing algorithms that span a wide variety of application areas including speech and image processing, sensor networks, communications, radar and sonar, and the approach to new algorithms includes some unconventional directions.
The importance of phase in signals
TLDR
Specific conditions under which a sequence can be exactly reconstructed from phase are reviewed, both for one-dimensional and multi-dimensional sequences, and algorithms for both approximate and exact reconstruction of signals from phase information are presented.
Enhancement and bandwidth compression of noisy speech
TLDR
An overview of the variety of techniques that have been proposed for enhancement and bandwidth compression of speech degraded by additive background noise is provided to suggest a unifying framework in terms of which the relationships between these systems is more visible and which hopefully provides a structure which will suggest fruitful directions for further research.
Signals and Systems
TLDR
This chapter briefly summarize and review this assumed background in the representation of linear, time-invariant systems and the associated continuous-time and discrete-time signals, through Fourier analysis, Laplace transforms and Z-transforms.
Signal processing with fractals: a wavelet-based approach
Wavelet transmission statistically self-similar signals detection and estimation with 1/processes deterministically self-similar signals fractal modulation linear self-similar signals.
Synchronization of Lorenz-based chaotic circuits with applications to communications
TLDR
An analogy between synchronization in chaotic systems, nonlinear observers for deterministic systems, and state estimation in probabilistic systems is established and the performance of the Lorenz SCS is compared to an extended Kalman filter for providing state estimates when the measurement consists of a single noisy transmitter component.
Linear Programming Algorithms for Sparse Filter Design
TLDR
This paper describes several approximate polynomial-time algorithms that use linear programming to design filters having a small number of nonzero coefficients, i.e., filters that are sparse.
Signal reconstruction from phase or magnitude
In this paper, we develop a set of conditions under which a sequence is uniquely specified by the phase or samples of the phase of its Fourier transform, and a similar set of conditions under which a
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