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Respirators, medical masks, and barrier face coverings all filter airborne particles using similar physical principles. However, they are tested for certification using a variety of standardized test methods, creating challenges for the comparison of differently certified products. We have performed systematic experiments to quantify and understand the differences between standardized test methods for N95 respirators (NIOSH TEB-APR-STP-0059 under US 42 CFR 84), medical face masks (ASTM F2299/F2100), and COVID-19-related barrier face coverings (ASTM F3502-21). Our experiments demonstrate the role of face velocity, particle properties (mean size, size variability, electric charge, density, and shape), measurement techniques, and environmental preconditioning. The measured filtration efficiency was most sensitive to changes in face velocity and particle charge. Relative to the NIOSH method, users of the ASTM F2299/F2100 method have commonly used non-neutralized (highly charged) aerosols as well as smaller face velocities, each of which may result in approximately 10% higher measured filtration efficiencies. In the NIOSH method, environmental conditioning at elevated humidity increased filtration efficiency in some commercial samples while decreasing it in others, indicating that measurement should be performed both with and without conditioning. More generally, our results provide an experimental basis for the comparison of respirators certified under various international methods, including FFP2, KN95, P2, Korea 1st Class, and DS2.

Two-dimensional correlation between a reference template and an input scene is a powerful pattern recognition technique but is demanding of computational power. Coherent optical correlators, exploiting the Fourier transforming properties of a lens and the capability to impart a phase modulation on a wavefront with an appropriate spatial light modulator (SLM), hold the promise of real-time implementation of two-dimensional correlation for realistic pattern recognition problems. However, their practical use has been delayed in many applications by the lack of availability of suitable SLM devices with the required speed and dynamic range, with different needs for input and frequency plane modulators. It is now possible to compute a two-dimensional Fourier transform at video-rates with various digital signal processing chip sets. Thus a hybrid correlator is proposed in which the input scene is digitally Fourier transformed at video-rate, and multiple templates searched during the next video frame interval by optical mixing and Fourier transformation at a speed at least two orders of magnitude faster than possible with digital methods. In this way, the input SLM is avoided and a precise spectrum is available for subsequent digital or optical mixing with the stored templates. The speed advantage over all-digital processing allows unconstrained pattern recognition problems to be tackled that require many template searches to match the input with a reference function. Different hybrid correlator configurations are considered, together with discussion of the various digital chip sets available to perform the videorate FFT, as well as the SLM devices currently available that are suitable as frequency domain phase modulators.

We present the implementation of a clutter-tolerant filter in a hybrid correlator system. Wiener filters were mapped with a complex encoding technique onto a smectic A(*) liquid-crystal spatial light modulator (SLM). The technique overcomes the problem of representing high-dynamic-range data on SLM's that have limited modulation capabilities. It also provides a compact image recognition system that is robust enough for many real-world applications. Experimental results are presented.

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Current methods to characterise specific properties of polymeric nanocomposites (PNCs), such as particle loading and dispersion profile, rely on a number of techniques that require special sample preparation and treatment, are very expensive, require long measurement times and quite often produce ambiguous results that are difficult to evaluate and interpret. In addition, given their complexity, they are not entirely suited for in-situ industrial environments. This paper presents alternative techniques based on optical diffraction and diffusion mechanisms combined with signal processing that can successfully discriminate between different particle loadings and levels of dispersion. The techniques discussed in this paper are Fourier-domain optical coherence tomography in the infra-red, Fraunhofer wavefront correlation in the visible red and oscillatory photon correlation spectroscopy in the visible green parts of the spectrum. Most importantly, they are non-invasive, are compact, fast and efficient, can potentially analyse large areas of the material and therefore suited for a wide variety of research and industrial situations.