Related papers: Compressed Sensing of Field-resolved Molecular Fin…
Mid-infrared absorption spectroscopy is a well-established technique for non-destructive quantitative molecular analysis. Waveguide-integrated sensors provide a particularly compact solution operating with reduced sample volumes while…
Frequency combs have revolutionized the field of optical spectroscopy, enabling researchers to probe molecular systems with a multitude of accurate and precise optical frequencies. While there have been tremendous strides in direct…
A novel framework of compressed sensing, namely statistical compressed sensing (SCS), that aims at efficiently sampling a collection of signals that follow a statistical distribution, and achieving accurate reconstruction on average, is…
We report the first study of the micro-SQUID response of a molecular system to electromagnetic radiation. The advantages of our micro-SQUID technique in respect to pulsed electron paramagnetic resonance (EPR) techniques consist in the…
Ultrafast spectroscopy of molecular systems is fundamentally constrained by the Fourier uncertainty principle: high temporal resolution smears out electronic state signatures, while high spectral resolution obscures dynamic information.…
Accurate diagnostics of the combustor region of ramjet engines can improve engine design and create benchmarks for computational fluid dynamics models. Previous works demonstrate that dual frequency comb spectroscopy can provide low…
For the realization of magnonic devices, spin-wave dispersions need to be identified. Recently, the time-resolved pump-probe imaging method combined with the Fourier transform was demonstrated for obtaining the dispersions in the…
Infrared (IR) imaging has become a viable tool for visualizing various chemical bonds in a specimen. The performance, however, is limited in terms of spatial resolution and imaging speed. Here, instead of measuring the loss of the IR beam,…
This note complements the paper "The quest for optimal sampling: Computationally efficient, structure-exploiting measurements for compressed sensing" [2]. Its purpose is to present a proof of a result stated therein concerning the recovery…
The study is devoted to development and optimization of a metasurface-based sensor with graphene constituents for potential biosensing applications. A unit cell of the proposed metasurface consists of a thin flexible dielectric substrate…
The squeezed photons, as a quantum-correlated light with reduced noise, have emerged as a great resource for sensing the structures of matter. Here we study the transient absorption (TA) scheme using the squeezed photons whose spectral…
Compressed sensing (CS) is a powerful method routinely employed to accelerate image acquisition. It is particularly suited to situations when the image under consideration is sparse but can be sampled in a basis where it is non-sparse. Here…
We describe two different modes for electronically detecting an adsorbed molecule using a nanoscale transistor. The attachment of an ionic molecular target shifts the threshold voltage through modulation of the depletion layer…
Label-free optical microscopy through absorption or scattering spectroscopy provides fundamental insights across biology and materials science, yet its sensitivity remains fundamentally limited by photon shot noise. While recent…
X-Ray Fluorescence (XRF) scanning is a widespread technique of high importance and impact since it provides chemical composition maps crucial for several scientific investigations. There are continuous requirements for larger, faster and…
Compressed sensing is a signal processing technique whereby the limits imposed by the Shannon--Nyquist theorem can be exceeded provided certain conditions are imposed on the signal. Such conditions occur in many real-world scenarios, and…
Visualizing ultrafast dynamics at the atomic scale requires time-resolved pump-probe characterization with femtosecond temporal resolution. For single-shot ultrafast electron diffraction (UED) with fully relativistic electron bunch probes,…
This paper deals with the Compressive Sensing implementation in the Face Recognition problem. Compressive Sensing is new approach in signal processing with a single goal to recover signal from small set of available samples. Compressive…
This work addresses the problem of extracting deeply learned features directly from compressive measurements. There has been no work in this area. Existing deep learning tools only give good results when applied on the full signal, that too…
Compressed sensing is a novel technique where one can recover sparse signals from the undersampled measurements. In this paper, a $K \times N$ measurement matrix for compressed sensing is deterministically constructed via multiplicative…