We develop a method called binary shaping for transmitting low-duty-cycle sequences robustly over binary-input channels, that employs a convolutional shaping code and a serially-concatenated convolutional code (SCCC) for forward error correction. Binary shaping is closely related to the nested linear coding scheme for coding over channels with known interference.

Aspects of the duality between the information-embedding problem and the Wyner-Ziv (1976) problem of source coding with side information at the decoder are developed and used to establish a spectrum new results on these and related problems, with implications for a number of important applications. The single-letter characterization of the information-embedding problem is developed and related to the corresponding characterization of the Wyner-Ziv problem, both of which correspond to optimization of a common mutual information difference. Dual variables and dual Markov conditions are identified, along with the dual role of noise and distortion in the two problems. For a Gaussian context with quadratic distortion metric, a geometric interpretation of the duality is developed. From such insights, we develop a capacity-achieving information-embedding system based on nested lattices. We show the resulting encoder-decoder has precisely the same decoder-encoder structure as the corresponding Wyner-Ziv system based on nested lattices that achieves the rate-distortion limit. For a binary context with Hamming distortion metric, the information-embedding capacity is developed, along with its relationship to the corresponding Wyner-Ziv rate-distortion function. In turn, an information-embedding system for this case based on nested linear codes is constructed having an encoder-decoder that is identical to the decoder-encoder structure for the corresponding system that achieves the Wyner-Ziv rate-distortion limit. Finally, based on these results, a simple layered joint source-channel coding system is developed with a perfectly symmetric encoder-decoder structure. Its application and performance is discussed in a broadcast setting in which there is a need to control the fidelity experienced by different receivers. Among other results, we show that such systems and their multilayer extensions retain attractive optimality properties in the Gaussian-quadratic case, but not in the binary-Hamming case.

This paper proposes several methods for noise reduction using deterministic side information about the desired signal as a constraint on the reconstruction. Two forms of side information are considered separately: short-time linear predictive coefficients, and short-time zero-phase impulse response coefficients. We derive general expressions for the ML, MAP and MMSE estimators, and develop algorithms that yield the ML estimators with the above side information for speech corrupted by additive white Gaussian noise. We also explore the use of these methods in the traditional noise reduction problem with no side information.

Most strategies proposed for utilizing chaotic signals for communications exploit the self-synchronization property of a class of chaotic systems. Any realistic communication channel will introduce distortion including time-dependent fading, dispersion, and modification of the frequency content due to channel filtering and multipath effects. All of these distortions will affect the ability of the chaotic receiver to properly synchronize. This paper develops and illustrates some specific approaches to channel equalization to compensate for these distortions for self-synchronizing chaotic systems. The approaches specifically exploit the properties of chaotic drive signals and the self-synchronization properties of the receiver.