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2023-04-28
Lotfollahi, Mahsa, Tran, Nguyen, Gajjela, Chalapathi, Berisha, Sebastian, Han, Zhu, Mayerich, David, Reddy, Rohith.  2022.  Adaptive Compressive Sampling for Mid-Infrared Spectroscopic Imaging. 2022 IEEE International Conference on Image Processing (ICIP). :2336–2340.
Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free, biochemically quantitative technologies targeting digital histopathology. Conventional histopathology relies on chemical stains that alter tissue color. This approach is qualitative, often making histopathologic examination subjective and difficult to quantify. MIRSI addresses these challenges through quantitative and repeatable imaging that leverages native molecular contrast. Fourier transform infrared (FTIR) imaging, the best-known MIRSI technology, has two challenges that have hindered its widespread adoption: data collection speed and spatial resolution. Recent technological breakthroughs, such as photothermal MIRSI, provide an order of magnitude improvement in spatial resolution. However, this comes at the cost of acquisition speed, which is impractical for clinical tissue samples. This paper introduces an adaptive compressive sampling technique to reduce hyperspectral data acquisition time by an order of magnitude by leveraging spectral and spatial sparsity. This method identifies the most informative spatial and spectral features, integrates a fast tensor completion algorithm to reconstruct megapixel-scale images, and demonstrates speed advantages over FTIR imaging while providing spatial resolutions comparable to new photothermal approaches.
ISSN: 2381-8549
2017-02-21
Y. Y. Won, D. S. Seo, S. M. Yoon.  2015.  "Improvement of transmission capacity of visible light access link using Bayesian compressive sensing". 2015 21st Asia-Pacific Conference on Communications (APCC). :449-453.

A technical method regarding to the improvement of transmission capacity of an optical wireless orthogonal frequency division multiplexing (OFDM) link based on a visible light emitting diode (LED) is proposed in this paper. An original OFDM signal, which is encoded by various multilevel digital modulations such as quadrature phase shift keying (QPSK), and quadrature amplitude modulation (QAM), is converted into a sparse one and then compressed using an adaptive sampling with inverse discrete cosine transform, while its error-free reconstruction is implemented using a L1-minimization based on a Bayesian compressive sensing (CS). In case of QPSK symbols, the transmission capacity of the optical wireless OFDM link was increased from 31.12 Mb/s to 51.87 Mb/s at the compression ratio of 40 %, while It was improved from 62.5 Mb/s to 78.13 Mb/s at the compression ratio of 20 % under the 16-QAM symbols in the error free wireless transmission (forward error correction limit: bit error rate of 10-3).