Results 141 to 150 of about 2,410 (185)

Optimizing multicompression approaches to elasticity imaging. [PDF]

IEEE Trans Ultrason Ferroelectr Freq Control, 2006
Du H, Liu J, Pellot-Barakat C, Insana MF.   +3 more
europepmc   +1 more source

Cascaded Tuning to Amplitude Modulation for Natural Sound Recognition. [PDF]

J Neurosci, 2019
Koumura T, Terashima H, Furukawa S.
europepmc   +1 more source

Molding compands and hybrid technology.

HYBRIDS, 1987
Chikao Krosawa, Hiroshi Suzuki
openaire   +2 more sources

A distributed sensor network for the control of a bioclimatic house in Spain. [PDF]

Sensors (Basel), 2009
Gutiérrez A, Jiménez-Leube J, Magdalena L.   +2 more
europepmc   +1 more source

Reconstruction of audio waveforms from spike trains of artificial cochlea models. [PDF]

Front Neurosci, 2015
Zai AT, Bhargava S, Mesgarani N, Liu SC.
europepmc   +1 more source

Enhanced EEG Forecasting: A Probabilistic Deep Learning Approach

Pankka H, Lehtinen J, Ilmoniemi RJ, Roine T.   +3 more
europepmc   +1 more source

Analytical Approach for PAPR Reduction and Mitigating Companding Distortion Using Piecewise Linear Companding

International Journal Of Engineering And Computer Science, 2016
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Log-linear companding—A digital companding technique

Proceedings of the IEEE, 1969
A new method of near-logarithmic companding is proposed. The method consists of first uniformly quantizing each sample and then processing the resulting binary number digitally. This is in contrast to the usual scheme of first compressing the input and then uniformly quantizing it. The method presented here is extremely simple to implement and requires
G.G. Apple, P.A. Wintz
openaire   +1 more source

Autoencoder-based Image Companding

2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), 2020
This paper presents a deep learning-based method for effective image companding. The autoencoder inherits the effectiveness of Convolutional Neural Networks (CNN) and residual learning framework to transform High Dynamic Range (HDR) images to Low Dynamic Range (LDR) and its reverse process.
Alim Wicaksono H.P., Heri Prasetyo, Jing-Ming Guo   +2 more
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Spectral quantization by companding

IEEE International Conference on Acoustics Speech and Signal Processing, 2002
Recent advances in spectral coding for speech suggest Gaussian mixture modeling (GMM) as a tool for designing close to optimal, single stage quantizers. This combined with companding techniques promises to give flexible, no-memory methods that are essentially rate universal.
null Shabestary, null Hedelin
openaire   +2 more sources