Abstract: A method including receiving one or more datasets of audio data of a key child captured in a natural sound environment of the key child. The method also includes segmenting each of the one or more datasets of audio data to create audio segments. The audio segments include cry-related segments and non-cry segments. The method additionally includes determining periods of the cry-related segments that satisfy one or more threshold non-sparsity criteria. The method further includes performing a classification on the periods to classify each of the periods as either a cry period or a fussiness period. Other embodiments are described.

Abstract: A method including receiving one or more datasets of audio data of a key child captured in a natural sound environment of the key child. The method also includes segmenting each of the one or more datasets of audio data to create audio segments. The audio segments include cry-related segments and non-cry segments. The method additionally includes determining periods of the cry-related segments that satisfy one or more threshold non-sparsity criteria. The method further includes performing a classification on the periods to classify each of the periods as either a cry period or a fussiness period. Other embodiments are described.

Abstract: A method including receiving one or more datasets of audio data of a key child captured in a natural sound environment of the key child. The method also includes segmenting each of the one or more datasets of audio data to create audio segments. The audio segments include cry-related segments and non-cry segments. The method additionally includes determining periods of the cry-related segments that satisfy one or more threshold non-sparsity criteria. The method further includes performing a classification on the periods to classify each of the periods as either a cry period or a fussiness period. Other embodiments are described.

Abstract: A method including receiving one or more datasets of audio data of a key child captured in a natural sound environment of the key child. The method also includes segmenting each of the one or more datasets of audio data to create audio segments. The audio segments include cry-related segments and non-cry segments. The method additionally includes determining periods of the cry-related segments that satisfy one or more threshold non-sparsity criteria. The method further includes performing a classification on the periods to classify each of the periods as either a cry period or a fussiness period. Other embodiments are described.

Abstract: In a laser radar (lidar) system, a Doublet Pulse is generated by injection-seeded aborted cavity dumping of a solid-state laser. The doublet pulse provides coherent Doppler lidar systems a substantial time bandwidth product (TB) with a very modest processing requirement. The waveform format comprises a pair of pulselets, each of duration .tau., separated by T seconds. The range resolution is governed by the pulselet duration .tau., while the velocity precision is governed by one over the pulselet separation, T. Ambiguities in the velocity measurement arise as a result of the periodic structure of the waveform and occur every .lambda./2T m/sec, where .lambda. is the operating wavelength. These ambiguities are removed by conventional de-aliasing algorithms as well as through the generation and processing of higher order pulse waveforms.