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Stochastic Modeling for Photoplethysmography Compression
2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020Photoplethysmography (PPG) has been widely involved in health monitoring for clinical medicine and wearable devices. To make full use of PPG signals for diagnosis and health care, raw PPG waveforms have to be stored and transmitted in a storage and power-efficient way, which is data compression.
Ke, Xu +3 more
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Modeling dynamic skinfold compression
American Journal of Human Biology, 1999Real time compression of skinfolds was measured at three sites (triceps, abdominal medial calf), using a Slim Guide skinfold caliper adapted by the addition of a potentiometer, on eight males and eight females (age range 18-40 years). An average of eight trials for each subject at each site was used in modeling the compression curves.
R., Ward, R., Rempel, G.S., Anderson
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Compressible models of equilibrium polymerization
The Journal of Chemical Physics, 2005Flory-Huggins-type models of equilibrium polymerization are extended to describe compressible systems and, hence, the pressure dependence of thermodynamic properties. The theory is developed for three different mechanisms of equilibrium polymerization (the free association, monomer-activated polymerization, and chemically initiated polymerization ...
Maxim N, Artyomov, Karl F, Freed
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Models for the Compressible Web
2009 50th Annual IEEE Symposium on Foundations of Computer Science, 2009Graphs resulting from human behavior (the web graph, friendship graphs, etc.) have hitherto been viewed as a monolithic class of graphs with similar characteristics; for instance, their degree distributions are markedly heavy-tailed. In this paper we take our understanding of behavioral graphs a step further by showing that an intriguing empirical ...
CHIERICHETTI, FLAVIO +4 more
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ECG Data Compression by Modeling
Computers and Biomedical Research, 1993This paper presents a novel algorithm for data compression of single lead Electrocardiogram (ECG) data. The method is based on Parametric modeling of the Discrete Cosine Transformed ECG signal. Improved high frequency reconstruction is achieved by separately modeling the low and the high frequency regions of the transformed signal.
B, Madhukar, I S, Murthy
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Bayesian Automatic Model Compression
IEEE Journal of Selected Topics in Signal Processing, 2020Model compression has drawn great attention in deep learning community. A core problem in model compression is to determine the layer-wise optimal compression policy, e.g., the layer-wise bit-width in network quantization. Conventional hand-crafted heuristics rely on human experts and are usually sub-optimal, while recent reinforcement learning based ...
Jiaxing Wang +3 more
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Modeling Delta Encoding of Compressed Files
Data Compression Conference (DCC'06), 2006The Compressed Delta Encoding paradigm is introduced, i.e., delta encoding directly in two given compressed files without decompressing. Here we explore the case where the two given files are compressed using LZW, and devise the theoretical framework for modeling delta encoding of compressed files.
Klein, Shmuel T. +2 more
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Compression Quality Prediction Model for JPEG2000
IEEE Transactions on Image Processing, 2010A compression quality prediction model is proposed for grey images coding with JPEG2000. With this model, the compression quality (PSNR) could be estimated according to the given compression ratio (CR) and the image activity measures (IAM) without coding images.
Li, Ling, Wang, Zhen-Song
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Oldroyd Model for Compressible Fluids
Journal of Mathematical Sciences, 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Speech Recognition Model Compression
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020Deep Neural Network-based speech recognition systems are widely used in most speech processing applications. To achieve better model robustness and accuracy, these networks are constructed with millions of parameters, making them storage and compute-intensive. In this paper, we propose Bin & Quant (B&Q), a compression technique using which we were able
Madhumitha Sakthi +2 more
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