COVID-19 has spread rapidly worldwide, despite the availability of vaccines, the fear of the World Health Organization continues due to the mutation of the Coronavirus. This is what prompted us to propose this work of social distance and wearing a face mask to fight against this pandemic to save lives. In this work, we propose a real-time four-stage model with monocular camera and deep learning based framework for automating the task of monitoring social distancing and face mask detection using video sequences. This work based on Scaled-You Only Look Once (Scaled-YOLOv4) object detection model, Simple Online and Real-time Tracking with a deep association metric approach to tracking people. The perspective transformation is used to approximate the three-dimensional coordinates with Euclidean metric to compute distance between boxes. The Dual Shot Face Detector (DSFD) and MobileNetv2 face mask model used to detect faces of people who violate or cross the social distance. Accuracy of 56.2% and real-time performance of 32 frames per second are achieved by the Social-Scaled-YOLOv4 (Social-YOLOv4-P6) model trained on the MS COCO dataset and Google-Open-Image dataset. The results are compared with other popular state-of-the-art models in terms of Mean-Average-Precision, frame rate and loss of values. The DSFD&MobileNetv2 facemask detectors trained on Wider Face and Real Face mask dataset achieves an accuracy of 99.3%. The proposed approach is validated on indoor/outdoor public images and video sequences such as wider face dataset, Oxford Town Center dataset and open access sequences.
Mechanical metamaterials, also known as architected materials, are rationally designed composites, aiming at elastic behaviors and effective mechanical properties beyond (“meta”) those of their individual ingredients – qualitatively and/or quantitatively. Due to advances in computational science and manufacturing, this field has progressed considerably throughout the last decade. Here, we review its mathematical basis in the spirit of a tutorial, and summarize the conceptual as well as experimental state-of-the-art. This summary comprises disordered, periodic, quasi-periodic, and graded anisotropic functional architectures, in one, two, and three dimensions, covering length scales ranging from below one micrometer to tens of meters. Examples include extreme ordinary linear elastic behavior from artificial crystals, e.g., auxetics and pentamodes, “negative” effective properties, behavior beyond classical linear elasticity, e.g., arising from local resonances, chirality, beyond-nearest-neighbor interactions, quasi-crystalline mechanical metamaterials, topological band gaps, cloaking based on coordinate transformations and on scattering cancellation, seismic protection, nonlinear and programmable metamaterials, as well as space-time-periodic architectures.
Quantitative phase microscopy (QPM) represents a non-invasive alternative to fluorescence microscopy for cell observation with high contrast and for the quantitative measurement of dry mass (DM) and growth rate at the single cell level. While DM measurements using QPM have been widely conducted on mammalian cells, bacteria have been less investigated, presumably due to the high-resolution and high-sensitivity required by their smaller size. This article demonstrates the use of cross-grating wavefront microscopy (CGM), a high-resolution and high-sensitivity QPM, for accurate DM measurement and monitoring of single micro-organisms (bacteria and archaea). The article covers strategies for overcoming light diffraction and sample focusing, and introduces the concepts of normalized optical volume (OV) and optical polarizability (OP) to gain additional information beyond DM. The algorithms for DM, OV, and OP measurements are illustrated through two case studies: monitoring dry mass evolution in a microscale colony forming unit as a function of temperature, and using OP as a potential species-specific signature.
How cells move is a fundamental biological question. The directionality of adherent migrating cells depends on the assembly and disassembly (turnover) of focal adhesions (FAs). FAs are micron-sized actin-based structures that link cells to the extracellular matrix. Traditionally, microtubules have been considered key to triggering FA turnover. Through the years, advancements in biochemistry, biophysics, and bioimaging tools have been invaluable for many research groups to unravel a variety of mechanisms and molecular players that contribute to FA turnover, beyond microtubules. Here, we discuss recent discoveries of key molecular players that affect the dynamics and organization of the actin cytoskeleton to enable timely FA turnover and consequently proper directed cell migration.
Wearing masks in public areas is one of the effective protection methods for people. Although it is essential to wear the facemask correctly, there are few research studies about facemask detection and tracking based on image processing. In this work, we propose a new high performance two stage facemask detector and tracker with a monocular camera and a deep learning based framework for automating the task of facemask detection and tracking using video sequences. Furthermore, we propose a novel facemask detection dataset consisting of 18,000 images with more than 30,000 tight bounding boxes and annotations for three different class labels namely respectively: face masked/incorrectly masked/no masked. We based on Scaled-You Only Look Once (Scaled-YOLOv4) object detection model to train the YOLOv4-P6-FaceMask detector and Simple Online and Real-time Tracking with a deep association metric (DeepSORT) approach to tracking faces. We suggest using DeepSORT to track faces by ID assignment to save faces only once and create a database of no masked faces. YOLOv4-P6-FaceMask is a model with high accuracy that achieves 93% mean average precision, 92% mean average recall and the real-time speed of 35 fps on single GPU Tesla-T4 graphic card on our proposed dataset. To demonstrate the performance of the proposed model, we compare the detection and tracking results with other popular state-of-the-art models of facemask detection and tracking.
Quantitative phase microscopies (QPMs) enable label-free, non-invasive observation of living cells in culture, for arbitrarily long periods of time. One of the main benefits of QPMs compared with fluorescence microscopy is the possibility to measure the dry mass of individual cells or organelles. While QPM dry mass measurements on neural cells have been reported this last decade, dry mass measurements on their neurites has been very little addressed. Because neurites are tenuous objects, they are difficult to precisely characterize and segment using most QPMs. In this article, we use cross-grating wavefront microscopy (CGM), a high-resolution wavefront imaging technique, to measure the dry mass of individual neurites of primary neurons in vitro . CGM is based on the simple association of a cross-grating positioned in front of a camera, and can detect wavefront distortions smaller than a hydrogen atom (∼0.1 nm). In this article, an algorithm for dry-mass measurement of neurites from CGM images is detailed and provided. With objects as small as neurites, we highlight the importance of dealing with the diffraction rings for proper image segmentation and accurate biomass measurements. The high precision of the measurements we obtain using CGM and this semi-manual algorithm enabled us to detect periodic oscillations of neurites never observed before, demonstrating the sufficient degree of accuracy of CGM to capture the cell dynamics at the single neurite level, with a typical precision of 2%, i.e., 0.08 pg in most cases, down to a few fg for the smallest objects.
We design sources for the two-dimensional Helmholtz equation that can cloak an object by cancelling out the incident field in a region, without the sources completely surrounding the object to hide. As in previous work for real positive wavenumbers, the sources are also determined by the Green identities. The novelty is that we prove that the same approach works for complex wavenumbers which makes it applicable to a variety of media, including media with dispersion, loss and gain. Furthermore, by deriving bounds on Graf’s addition formulas with complex arguments, we obtain new estimates that allow to quantify the quality of the cloaking effect. We illustrate our results by applying them to achieve active exterior cloaking for the heat equation. This article is part of the theme issue ‘Wave generation and transmission in multi-scale complex media and structured metamaterials (part 2)’.
Thermophiles are microorganisms that thrive at high temperature. Studying them can provide valuable information on how life has adapted to extreme conditions. However, high temperature conditions are difficult to achieve on conventional optical microscopes. Some home-made solutions have been proposed, all based on local resistive electric heating, but no simple commercial solution exists. In this article, we introduce the concept of microscale laser heating over the field of view of a microscope to achieve high temperature for the study of thermophiles, while maintaining the user environment in soft conditions. Microscale heating with moderate laser intensities is achieved using a substrate covered with gold nanoparticles, as biocompatible, efficient light absorbers. The influences of possible microscale fluid convection, cell confinement and centrifugal thermophoretic motion are discussed. The method is demonstrated with two species: (i) Geobacillus stearothermophilus , a motile thermophilic bacterium thriving around 65 °C, which we observed to germinate, grow and swim upon microscale heating and (ii) Sulfolobus shibatae , a hyperthermophilic archaeon living at the optimal temperature of 80 °C. This work opens the path toward simple and safe observation of thermophilic microorganisms using current and accessible microscopy tools.
Resonances, also known as quasi normal modes (QNM) in the non-Hermitian case, play an ubiquitous role in all domains of physics ruled by wave phenomena, notably in continuum mechanics, acoustics, electrodynamics, and quantum theory. In this paper, we present a QNM expansion for dispersive systems, recently applied to photonics but based on sixty year old techniques in mechanics. The resulting numerical algorithm appears to be physically agnostic, that is independent of the considered physical problem and can therefore be implemented as a mere toolbox in a nonlinear eigenvalue computation library.
In the past decade, visible light communication (VLC) technology has received increasing attention for numerous applications, including for indoor visible light positioning (VLP). The transmission medium for indoor VLP systems in industrial environments could include smoke particles, oil vapors, water mist, and industrial fumes. This work investigates the indoor atmospheric attenuation on the performance of VLP for industrial environments. The considered VLP technique uses trilateration based on the Cayley-Menger Determinant algorithm. The positioning method uses received signal strength (RSS) to estimate a drone's position. Smoke and fog effects for the indoor atmospheric attenuations have been considered based on visibility (V) ranging from 0.3 km to 1 km. The results show that the positioning error increases from an average value of 6.53 cm for clear air to 46.64 cm in smoke and 46.27 cm in fog attenuation with reduced visibility (V=0.3 km), respectively. The results also show that there is a slightly lower received power in the presence of smoke, as compared to fog, for a given visibility range.
In order to map the migration and introduction of farming into Europe during the seventh and sixth millennia Before Common Era, archeologists have made a connection between the study of pottery and farming migration. We are interested here in the classification of pottery into coiling and spiral types based on their manufacturing techniques. To distinguish between these two techniques, we look for the lines formed by air bubbles embedded in the pottery samples. Current methods make use of bulky systems, such as computerized tomography scanners or synchrotrons. Microwave acquisition and processing offer an interesting alternative, due to the possibility to have compact and portable systems. In this article, we investigate the classification of pottery based on low-terahertz measurements in the D-band. We process the measurements with 3-D fast Fourier transform. The resulting matrix is classified with an artificial neural network, multilayer perceptron, which is optimized with the gray wolf optimizer, a bioinspired algorithm. The first results show that the accuracy reaches up to 99% using all the acquired spatial and frequency measurements. Then, we optimize the millimeter-wave (mm-Wave) measurement system with a critical criterion on accuracy in two different scenarios. In the first scenario, we reduce the spatial acquisition but maintain the wideband operation and the results show that the accuracy is between 85% and 96%. In the second one, we reduce the spatial acquisition and use a single frequency. For this second scenario, we achieve a classification accuracy, which is between 77% and 100%.
Optically-assisted large-scale assembly of nanoparticles have been of recent interest owing to their potential in applications to assemble and manipulate colloidal particles and biological entities. In the recent years, plasmonic heating has been the most popular mechanism to achieve temperature hotspots needed for extended assembly and aggregation. In this work, we present an alternative route to achieving strong thermal gradients that can lead to non-equilibrium transport and assembly of matter. We utilize the excellent photothermal properties of graphene oxide to form a large-scale assembly of silica beads. The formation of the assembly using this scheme is rapid and reversible. Our experiments show that it is possible to aggregate silica beads (average size 385 nm) by illuminating thin graphene oxide microplatelet by a 785 nm laser at low intensities of the order of 50–100 µW/µm ² . We further extend the study to trapping and photoablation of E. coli bacteria using graphene oxide. We attribute this aggregation process to optically driven thermophoretic forces. This scheme of large-scale assembly is promising for the study of assembly of matter under non-equilibrium processes, rapid concentration tool for spectroscopic studies such as surface-enhanced Raman scattering and for biological applications.
Since the early 2000s, the experimental and theoretical studies of photothermal effects in plasmonics have been mainly oriented toward systems composed of nanoparticles, mostly motivated by applications in biomedecine, and overlooked the case of plasmonic resonances of nanoholes in metal layers (also called nanopores or nano-apertures). Yet, more and more applications based on plasmonic nanoholes have been reported these last years (e.g., optical trapping, molecular sensing, surface-enhanced Raman scattering), and photothermal effects can be unexpectedly high for this kind of systems, mainly because of the very large amount of metal under illumination, compared with nanoparticle systems. Nanoholes in metal layers involve a fully different photothermodynamical picture and few of what is known about nanoparticles can be applied with nanoholes. A plasmonic nanohole mixes localized and surfaces plasmons, along with heat transport in a two-dimensional highly conductive layer, making the underlying photothermodynamical physics particularly complex. This article is aimed to provide a comprehensive description of the photothermal effects in plasmonics when metal layers are involved, based on experimental, theoretical and numerical results (we share in Suppl. Mater. all the numerical codes used in this article). Photothermal effects in metal layers (embedded or suspended) are first described in detail, followed by the study of nanoholes, where we revisit the concept of absorption cross section, and discuss the influences of parameters such as layer thickness, layer composition, nanohole size and geometry, adhesion layer, thermal radiation, and illumination wavelength.
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