Compressed sensing in spectroscopy for chemical analysis

Gamez, Gerardo

doi:10.1039/c6ja00262e

Cited by 28 publications

(10 citation statements)

References 68 publications

(67 reference statements)

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“…Of course, these approaches can be extended to other kinds of microscopies or analytical techniques, as suggested or explored by other groups [72][73][74][75].…”

Section: Compressive Sensingmentioning

confidence: 99%

Soft X-ray Microscopy Techniques for Medical and Biological Imaging at TwinMic—Elettra

et al. 2021

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Progress in nanotechnology calls for material probing techniques of high sensitivity and resolution. Such techniques are also used for high-impact studies of nanoscale materials in medicine and biology. Soft X-ray microscopy has been successfully used for investigating complex biological processes occurring at micrometric and sub-micrometric length scales and is one of the most powerful tools in medicine and the life sciences. Here, we present the capabilities of the TwinMic soft X-ray microscopy end-station at the Elettra synchrotron in the context of medical and biological imaging, while we also describe novel uses and developments.

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“…Of course, these approaches can be extended to other kinds of microscopies or analytical techniques, as suggested or explored by other groups [72][73][74][75].…”

Section: Compressive Sensingmentioning

confidence: 99%

Soft X-ray Microscopy Techniques for Medical and Biological Imaging at TwinMic—Elettra

et al. 2021

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“…The structure of the paper proceeds as follows. (1) The first part is a brief review of the CS theory. (2) The second part describes the EBP transform.…”

Section: F I G U R Ementioning

confidence: 99%

Task‐adaptive eigenvector‐based projection (EBP) transform for compressed sensing: A case study of spectroscopic profiling sensor

Zhang

Wang

Cheng

et al. 2021

Analytical Science Advances

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The compressed sensing (CS) theory requires the signal to be sparse under some transform. For most signals (e.g., speech and photos), the non-adaptive transform bases, such as discrete cosine transform (DCT), discrete Fourier transform (DFT), and Walsh-Hadamard transform (WHT), can meet this requirement and perform quite well. However, one limitation of these non-adaptive transforms is that we cannot leverage domain-specific knowledge to improve CS efficiency. This study presents a task-adaptive eigenvector-based projection (EBP) transform. The EBP basis has an equivalent effect of the principal component loading matrix and can generate a sparse representation in the latent space. In a Raman spectroscopic profiling case study, EBP demonstrates better performance than its non-adaptive counterparts. At the 1% CS sampling ratio (k), the reconstruction relative mean square errors of DCT, DFT, WHT and EBP are 0.33, 0.68, 0.32, and 0.00, respectively. At a fixed k, EBP achieves much better reconstruction quality than the non-adaptive counterparts. For specific domain tasks, EBP can significantly lower the CS sampling ratio and reduce the overall measurement cost.

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“…MC methods exploit an underlying property of the system, such as rank or sparsity, to complete/recover missing information. Methods that exploit sparsity, known as compressed sensing (CS), have found widespread applications in imaging, 31 analytical chemistry, [32][33][34] thermodynamics, 35 and quantum chemistry. 1,[36][37][38][39] In the latter domain, CS has been employed to compute lattice couplings, 36 determine large Hessians, 37 construct anharmonic potential energy surfaces, 381 and identify scaling relationships in heterogeneous catalysis.…”

Section: Introductionmentioning

confidence: 99%

A matrix completion algorithm for efficient calculation of quantum and variational effects in chemical reactions

Bac

Quiton

Kron

et al. 2022

The Journal of Chemical Physics

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This work examines the viability of matrix completion methods as cost-effective alternatives to full nuclear Hessians for calculating quantum and variational effects in chemical reactions. The harmonic variety-based matrix completion (HVMC) algorithm, developed in a previous study (https://doi.org/10.1063/5.0018326), exploits the low-rank character of the polynomial expansion of potential energy to recover, using a small sample, vibrational frequencies (square roots of nuclear Hessian eigenvalues) constituting the reaction path. These frequencies are essential for calculating rate coefficients using variational transition state theory with multidimensional tunneling (VTST-MT). HVMC performance is examined for four SN2 reactions and five hydrogen transfer reactions, with each H-transfer reaction consisting of at least one vibrational mode strongly coupled to the reaction coordinate. HVMC is robust and captures zero-point energies, vibrational free energies, zero-curvature tunneling, and adiabatic ground state and free energy barriers as well as their positions on the reaction coordinate. For medium to large reactions involving H-transfer, with the exception of the most complex Ir catalysis system, less than 35% of total eigenvalue information is necessary for accurate recovery of key VTST-MT observables.

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Compressed sensing in spectroscopy for chemical analysis

Abstract: An intuitive view of compressed sensing is presented with selected examples to highlight its potential impact in atomic spectrometry.

Cited by 28 publications

References 68 publications

Soft X-ray Microscopy Techniques for Medical and Biological Imaging at TwinMic—Elettra

Soft X-ray Microscopy Techniques for Medical and Biological Imaging at TwinMic—Elettra

Task‐adaptive eigenvector‐based projection (EBP) transform for compressed sensing: A case study of spectroscopic profiling sensor

A matrix completion algorithm for efficient calculation of quantum and variational effects in chemical reactions

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