Digital Image Representation by Atomic Functions: The Compression and Protection of Data for Edge Computing in IoT Systems.

Digital images are used in various technological, financial, economic, and social processes. Huge datasets of high-resolution images require protected storage and low resource-intensive processing, especially when applying edge computing (EC) for designing Internet of Things (IoT) systems for industrial domains such as autonomous transport systems. For this reason, the problem of the development of image representation, which provides compression and protection features in combination with the ability to perform low complexity analysis, is relevant for EC-based systems. Security and privacy issues are important for image processing considering IoT and cloud architectures as well. To solve this problem, we propose to apply discrete atomic transform (DAT) that is based on a special class of atomic functions generalizing the well-known up-function of V.A. Rvachev. A lossless image compression algorithm based on DAT is developed, and its performance is studied for different structures of DAT. This algorithm, which combines low computational complexity, efficient lossless compression, and reliable protection features with convenient image representation, is the main contribution of the paper. It is shown that a sufficient reduction of memory expenses can be obtained. Additionally, a dependence of compression efficiency measured by compression ratio (CR) on the structure of DAT applied is investigated. It is established that the variation of DAT structure produces a minor variation of CR. A possibility to apply this feature to data protection and security assurance is grounded and discussed. In addition, a structure or file for storing the compressed and protected data is proposed, and its properties are considered. Multi-level structure for the application of atomic functions in image processing and protection for EC in IoT systems is suggested and analyzed.

Exhaustive Search of Correspondences between Multimodal Remote Sensing Images Using Convolutional Neural Network.

Finding putative correspondences between a pair of images is an important prerequisite for image registration. In complex cases such as multimodal registration, a true match could be less plausible than a false match within a search zone. Under these conditions, it is important to detect all plausible matches. This could be achieved by an exhaustive search using a handcrafted similarity measure (SM, e.g., mutual information). It is promising to replace handcrafted SMs with deep learning ones that offer better performance. However, the latter are not designed for an exhaustive search of all matches but for finding the most plausible one. In this paper, we propose a deep-learning-based solution for exhaustive multiple match search between two images within a predefined search area. We design a computationally efficient convolutional neural network (CNN) that takes as input a template fragment from one image, a search fragment from another image and produces an SM map covering the entire search area in spatial dimensions. This SM map finds multiple plausible matches, locates each match with subpixel accuracy and provides a covariance matrix of localization errors for each match. The proposed CNN is trained with a specially designed loss function that enforces the translation and rotation invariance of the SM map and enables the detection of matches that have no associated ground truth data (e.g., multiple matches for repetitive textures). We validate the approach on multimodal remote sensing images and show that the proposed "area" SM performs better than "point" SM.

Entropy-Based Combined Metric for Automatic Objective Quality Assessment of Stitched Panoramic Images.

Quality assessment of stitched images is an important element of many virtual reality and remote sensing applications where the panoramic images may be used as a background as well as for navigation purposes. The quality of stitched images may be decreased by several factors, including geometric distortions, ghosting, blurring, and color distortions. Nevertheless, the specificity of such distortions is different than those typical for general-purpose image quality assessment. Therefore, the necessity of the development of new objective image quality metrics for such type of emerging applications becomes obvious. The method proposed in the paper is based on the combination of features used in some recently proposed metrics with the results of the local and global image entropy analysis. The results obtained applying the proposed combined metric have been verified using the ISIQA database, containing 264 stitched images of 26 scenes together with the respective subjective Mean Opinion Scores, leading to a significant increase of its correlation with subjective evaluation results.

Scalable, Micro-Neutralization Assay for Assessment of SARS-CoV-2 (COVID-19) Virus-Neutralizing Antibodies in Human Clinical Samples.

As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic expanded, it was clear that effective testing for the presence of neutralizing antibodies in the blood of convalescent patients would be critical for development of plasma-based therapeutic approaches. To address the need for a high-quality neutralization assay against SARS-CoV-2, a previously established fluorescence reduction neutralization assay (FRNA) against Middle East respiratory syndrome coronavirus (MERS-CoV) was modified and optimized. The SARS-CoV-2 FRNA provides a quantitative assessment of a large number of infected cells through use of a high-content imaging system. Because of this approach, and the fact that it does not involve subjective interpretation, this assay is more efficient and more accurate than other neutralization assays. In addition, the ability to set robust acceptance criteria for individual plates and specific test wells provided further rigor to this assay. Such agile adaptability avails use with multiple virus variants. By February 2021, the SARS-CoV-2 FRNA had been used to screen over 5000 samples, including acute and convalescent plasma or serum samples and therapeutic antibody treatments, for SARS-CoV-2 neutralizing titers.

Scalable, Micro-Neutralization Assay for Qualitative Assessment of SARS-CoV-2 (COVID-19) Virus-Neutralizing Antibodies in Human Clinical Samples.

As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic was expanding, it was clear that effective testing for the presence of neutralizing antibodies in the blood of convalescent patients would be critical for development of plasma-based therapeutic approaches. To address the need for a high-quality neutralization assay against SARS-CoV-2, a previously established fluorescence reduction neutralization assay (FRNA) against Middle East respiratory syndrome coronavirus (MERS-CoV) was modified and optimized. The SARS-CoV-2 FRNA provides a quantitative assessment of a large number of infected cells through use of a high-content imaging system. Because of this approach, and the fact that it does not involve subjective interpretation, this assay is more efficient and more accurate than other neutralization assays. In addition, the ability to set robust acceptance criteria for individual plates and specific test wells provided further rigor to this assay. Such agile adaptability avails use with multiple virus variants. By February 2021, the SARS-CoV-2 FRNA had been used to screen over 5,000 samples, including acute and convalescent plasma or serum samples and therapeutic antibody treatments, for SARS-CoV-2 neutralizing titers.

Clinical, laboratory, and temporal predictors of neutralizing antibodies against SARS-CoV-2 among COVID-19 convalescent plasma donor candidates.

BACKGROUNDSARS-CoV-2-specific antibodies may protect from reinfection and disease, providing rationale for administration of plasma containing SARS-CoV-2-neutralizing antibodies (nAbs) as a treatment for COVID-19. Clinical factors and laboratory assays to streamline plasma donor selection, and the durability of nAb responses, are incompletely understood.METHODSPotential convalescent plasma donors with virologically documented SARS-CoV-2 infection were tested for serum IgG against SARS-CoV-2 spike protein S1 domain and against nucleoprotein (NP), and for nAb.RESULTSAmong 250 consecutive persons, including 27 (11%) requiring hospitalization, who were studied a median of 67 days since symptom onset, 97% were seropositive on 1 or more assays. Sixty percent of donors had nAb titers ≥1:80. Correlates of higher nAb titers included older age (adjusted OR [AOR] 1.03 per year of age, 95% CI 1.00-1.06), male sex (AOR 2.08, 95% CI 1.13-3.82), fever during illness (AOR 2.73, 95% CI 1.25-5.97), and disease severity represented by hospitalization (AOR 6.59, 95% CI 1.32-32.96). Receiver operating characteristic analyses of anti-S1 and anti-NP antibody results yielded cutoffs that corresponded well with nAb titers, with the anti-S1 assay being slightly more predictive. nAb titers declined in 37 of 41 paired specimens collected a median of 98 days (range 77-120) apart (P < 0.001). Seven individuals (2.8%) were persistently seronegative and lacked T cell responses.CONCLUSIONnAb titers correlated with COVID-19 severity, age, and sex. SARS-CoV-2 IgG results can serve as useful surrogates for nAb testing. Functional nAb levels declined, and a small proportion of convalescent individuals lacked adaptive immune responses.FUNDINGThe project was supported by the Frederick National Laboratory for Cancer Research with support from the NIAID under contract number 75N91019D00024, and was supported by the Fred Hutchinson Joel Meyers Endowment, Fast-Grants, a New Investigator award from the American Society for Transplantation and Cellular Therapy, and NIH contracts 75N93019C0063, 75N91019D00024, and HHSN272201800013C, and NIH grants T32-AI118690, T32-AI007044, K08-AI119142, and K23-AI140918.

Limitations of adaptive control efficiency due to singular points in the wavefront of a laser beam.

Optical vortices occur at light propagation in an inhomogeneous medium, disturbing the operation of adaptive optical systems and assuring a priori continuity of the phase fluctuation function. It is clear that the physical process of the light wave propagation has a threshold of complexity relative to the description and measurement of this process, after which the light wave contains points with zero intensity and there is no continuous wavefront. The appearance of zeros indicates the transition of phenomenon in a new condition. The results of numerous studies of phase fluctuations of optical waves in the atmosphere, first of all, provide a basis for estimating the efficiency of operation of adaptive optical systems, second, make it possible to determine the requirements on the wavefront sensors and adaptive mirrors, and, finally, make it possible to determine the structure and properties of phase-conjugated adaptive optical systems.

Beam spreading of vortex beams propagating in turbulent atmosphere.

We present some results obtained by numerical modeling of the propagation of vortex beams LG(0l) through a randomly inhomogeneous medium. The vortex beams are the lower order Laguerre-Gaussian modes. Such beams, if propagated under conditions of weak turbulence, also experience distortions, like a Gaussian beam. However, the statistically averaged vortex beams (LG(0l)) conserve the central intensity dip with a nonzero intensity on the beam axis. The beam broadening of vortex beams is analyzed. The average vortex beams are found to be broadened less than the Gaussian beam while propagated through a randomly inhomogeneous medium. The higher the topological charge l is, the smaller the beam broadening is.

Influence of the source spectrum on the optical measurements of turbulence.

It is considered how the source spectrum influences the measurement accuracy of optical wave arrival angles, as well as the estimation of the path-averaged structure parameter of the refractive index fluctuations. Two reasons that can cause the wavelength dependence of the variance of fluctuations of wave arrival angles are analyzed. The first one is connected with the fact that phases depend on a wavelength in the approximation of smooth perturbations. The second reason is associated with the wavelength dependence of the refractive index and, consequently, its fluctuations. Strict equations are obtained to take into account the influence of the source spectrum on the measurement accuracy of the variance of arrival angle fluctuations and, indirectly, on the estimation accuracy of the path-averaged refractive index structure parameter. It can be stated that for most radiation sources (even nonmonochromatic) the influence of the source spectral composition can be neglected.

Phase-correction of turbulent distortions of an optical wave propagating under conditions of strong intensity fluctuations.

Phase correction of a plane wave and a spatiolimited beam propagating through a turbulent layer of atmosphere were considered. The required adaptive corrector element size and the system bandwidth were found by numerical simulation. These requirements were determined to be the same as for a weak-intensity scintillation approximation. The size of the required segmented mirror element was found to be equal to Fried length r0, whereas the tolerable time lag was r0/V, where V is the wind velocity. However, the local slope sensors then became impractical, as did tip-tilt correction over the corrector subapertures.