Kalman-based compartmental estimation for covid-19 pandemic using advanced epidemic model.

The practicality of administrative measures for covid-19 prevention is crucially based on quantitative information on impacts of various covid-19 transmission influencing elements, including social distancing, contact tracing, medical facilities, vaccine inoculation, etc. A scientific approach of obtaining such quantitative information is based on epidemic models of S I R family. The fundamental S I R model consists of S-susceptible, I-infected, and R-recovered from infected compartmental populations. To obtain the desired quantitative information, these compartmental populations are estimated for varying metaphoric parametric values of various transmission influencing elements, as mentioned above. This paper introduces a new model, named S E I R R P V model, which, in addition to the S and I populations, consists of the E-exposed, R e -recovered from exposed, R-recovered from infected, P-passed away, and V-vaccinated populations. Availing of this additional information, the proposed S E I R R P V model helps in further strengthening the practicality of the administrative measures. The proposed S E I R R P V model is nonlinear and stochastic, requiring a nonlinear estimator to obtain the compartmental populations. This paper uses cubature Kalman filter (CKF) for the nonlinear estimation, which is known for providing an appreciably good accuracy at a fairly small computational demand. The proposed S E I R R P V model, for the first time, stochastically considers the exposed, infected, and vaccinated populations in a single model. The paper also analyzes the non-negativity, epidemic equilibrium, uniqueness, boundary condition, reproduction rate, sensitivity, and local and global stability in disease-free and endemic conditions for the proposed S E I R R P V model. Finally, the performance of the proposed S E I R R P V model is validated for real-data of covid-19 outbreak.

Securing Optical Networks Using Quantum-Secured Blockchain: An Overview.

The deployment of optical network infrastructure and development of new network services are growing rapidly for beyond 5/6G networks. However, optical networks are vulnerable to several types of security threats, such as single-point failure, wormhole attacks, and Sybil attacks. Since the uptake of e-commerce and e-services has seen an unprecedented surge in recent years, especially during the COVID-19 pandemic, the security of these transactions is essential. Blockchain is one of the most promising solutions because of its decentralized and distributed ledger technology, and has been employed to protect these transactions against such attacks. However, the security of blockchain relies on the computational complexity of certain mathematical functions, and because of the evolution of quantum computers, its security may be breached in real-time in the near future. Therefore, researchers are focusing on combining quantum key distribution (QKD) with blockchain to enhance blockchain network security. This new technology is known as quantum-secured blockchain. This article describes different attacks in optical networks and provides a solution to protect networks against security attacks by employing quantum-secured blockchain in optical networks. It provides a brief overview of blockchain technology with its security loopholes, and focuses on QKD, which makes blockchain technology more robust against quantum attacks. Next, the article provides a broad view of quantum-secured blockchain technology. It presents the network architecture for the future research and development of secure and trusted optical networks using quantum-secured blockchain. The article also highlights some research challenges and opportunities.

Comparative Analytical Study of SCMA Detection Methods for PA Nonlinearity Mitigation.

Non-orthogonal multiple access (NOMA) has emerged as a promising technology that allows for multiplexing several users over limited time-frequency resources. Among existing NOMA methods, sparse code multiple access (SCMA) is especially attractive; not only for its coding gain using suitable codebook design methodologies, but also for the guarantee of optimal detection using message passing algorithm (MPA). Despite SCMA's benefits, the bit error rate (BER) performance of SCMA systems is known to degrade due to nonlinear power amplifiers at the transmitter. To mitigate this degradation, two types of detectors have recently emerged, namely, the Bussgang-based approaches and the reproducing kernel Hilbert space (RKHS)-based approaches. This paper presents analytical results on the error-floor of the Bussgang-based MPA, and compares it with a universally optimal RKHS-based MPA using random Fourier features (RFF). Although the Bussgang-based MPA is computationally simpler, it attains a higher BER floor compared to its RKHS-based counterpart. This error floor and the BER's performance gap are quantified analytically and validated via computer simulations.

MSE-based analysis of circular grating self-images for testing beam collimation.

Mean square error (MSE) is used to detect variations in the period between a pair of self-images formed at two different Talbot planes of a circular grating (CG) using a beam splitter in a conventional collimation testing setup. By varying the position of the collimator with respect to the point source, the collimation state of the input beam is varied and the computed MSEs are analyzed to deduce the collimation state. The minimum value of the MSE indicates beam collimation. For equal sized images, the MSE relates to the sum of the squared difference between spatially correspondent pixel values of the images. Since comparison of the spatial information takes place at the pixels' level, any small spatial shift between patterns of the two self-images due to collimation error is detected with precision. The CG, comprising concentric circular structures, offers added advantage in terms of error-free alignment, which otherwise is error prone and cumbersome with widely used linear gratings. It is well known that self-images formed with circular grating have good fidelity with less optical distortions and irregularities, especially at distant Talbot planes. Also, the self-images formed with circular gratings are less affected by lens aberrations, tilts, misalignments, etc. Higher sensitivity in beam collimation is achievable, as self-images of a CG can be recorded at widely separated Talbot planes, and analyzed using an algorithm which is more responsive toward any minute difference between them. The suggested method is promising for a quick collimation setting with good accuracy and enhanced sensitivity.

Comparative analysis of image pre-filtering techniques for phase-shifted noise-affected interferograms.

Phase-shifting techniques are one of the most promising strategies to extract the phase information and retrieve the parameters of interest (e.g., refractive index, beam collimation, object shape, deformations, thickness, focal length, etc.) from interferograms. However, traditional phase-shifting techniques suffer from both internal and external noise, which reduce measurement accuracy. This paper reports a comparative analysis of the three commonly used filtering techniques, namely, Fourier, windowed Fourier, and wavelet filtering for noise reduction and accurate extraction of phase information from phase-shifted interferograms. Toward this, two basic types of noise (additive and multiplicative noise) are introduced in the simulated interferograms and processed using the pre-filtering strategies. The effect of second-order harmonics in the presence of noise is also examined. In addition, experimental demonstrations of the real-life applicability of the analyses are provided using the interferograms recorded on coherent (Talbot) and incoherent (Lau) grating shearing interferometers. High accuracy in the measurement of defocusing error of the lens is obtained using the filtering strategies. Further inferences and insights are drawn in favor of the pre-filtering techniques.

Thresholdless Detection of Symbols in Nano-Communication Systems.

Molecular communication (MC) can play an indispensable role in nanonetworking and internet of bio-nano things based applications. In most MC receivers, the detection of data symbols requires the optimal threshold, which depends on the accurate diffusion channel impulse response (DCIR), and statistics of noise and interference (SNI). In order to estimate these parameters, a training phase must be carried out. Further, DCIR and SNI can change due to diffusion, drift, temperature and interference variation in the MC system, which results in complex training or sub-optimal MC performance. In this paper, we propose a coded modulation scheme (CMS) for MC, which does not require threshold estimation at the receiver. Hence, the proposed CMS is completely free from any training, and also independent of noise and interferences variation in MC systems. The proposed CMS has optimal system performance with negligible system complexity as verified through the numerical results. Further, the impact of various parameters such as diffusion coefficient, transmission distance, symbol duration, etc. are also examined for CMS.

Automated collimation testing by determining the statistical correlation coefficient of Talbot self-images.

In this paper, we propose a simple, fast, and accurate technique for detection of collimation position of an optical beam using the self-imaging phenomenon and correlation analysis. Herrera-Fernandez et al. [J. Opt.18, 075608 (2016)JOOPDB0150-536X10.1088/2040-8978/18/7/075608] proposed an experimental arrangement for collimation testing by comparing the period of two different self-images produced by a single diffraction grating. Following their approach, we propose a testing procedure based on correlation coefficient (CC) for efficient detection of variation in the size and fringe width of the Talbot self-images and thereby the collimation position. When the beam is collimated, the physical properties of the self-images of the grating, such as its size and fringe width, do not vary from one Talbot plane to the other and are identical; the CC is maximum in such a situation. For the de-collimated position, the size and fringe width of the self-images vary, and correspondingly the CC decreases. Hence, the magnitude of CC is a measure of degree of collimation. Using the method, we could set the collimation position to a resolution of 1 µm, which relates to ±0.25 µ radians in terms of collimation angle (for testing a collimating lens of diameter 46 mm and focal length 300 mm). In contrast to most collimation techniques reported to date, the proposed technique does not require a translation/rotation of the grating, use of complicated phase evaluation algorithms, or an intricate method for determination of period of the grating or its self-images. The technique is fully automated and provides high resolution and precision.

Collimation testing using deflectometry in conjunction with windowed Fourier transform analysis.

In this paper, we demonstrate a simple automated procedure for the detection of collimation of an optical beam by incorporating the windowed Fourier fringe analysis technique into a deflectometric setup. The experimental arrangement consists of a deflectometry-based system in which light from a laser is expanded and passed through a collimating lens. The transmitted light illuminates a coarse sinusoidal grating. The grating image is directly captured through a charge-coupled device. Typical image patterns corresponding to "in-focus," "at-focus," and "out-of-focus" positions of an optical beam are recorded. Depending on the position of the collimating lens, the grating line spacing and the resulting phase of the emerging wavefront varies. Direct phase measurement using the windowed Fourier transform method has been used to obtain the slope map of the wavefront. The slope of the phase map depicts the diverging, collimated, or converging nature of the optical beam. The positioning error of light beam collimation was approximately 1 µm. The experimental arrangement is simple, low cost, and compact. The technique is fully automatic and provides high resolution, high precision, and good sensitivity.

Fingerprint detection and mapping using a phase shifted coherent gradient sensing technique.

In this paper, a full field technique for mapping a latent fingerprint using a coherent gradient sensing (CGS) sensor is proposed. Collimated light from an He-Ne laser illuminates a specimen comprising a fingerprint implanted onto a reflecting surface. Reflected light from the specimen is analyzed using the CGS sensor comprising a pair of gratings. Reflected light carries information regarding the depth and orientation of furrows and ridges in the fingerprint. The topological information of the fingerprint is retrieved using four-step phase shifting interferometry. Well-defined 2D and 3D phase plots have been reconstructed to map the topography of the human fingerprint. The recorded slope data reconstructs the information regarding the separation and depth of the ridges in the latent fingerprint. The proposed technique is noninvasive and full field and does not require any kind of chemical or physical treatment. The sensor is very simple, yields interferometric sensitivity, and has the advantages of easy alignment, compactness, and low cost.