Jones–Stokes polarimetry is a robust in vitro polarimetric technique that can be used to investigate the anisotropic properties of a birefringent medium. The study of spatially resolved Jones matrix components of an object is a heuristic approach to extract its phase and polarization information. However, direct interpretation of Jones matrix elements and their decomposition into associated anisotropic properties of a sample is still a challenging research problem that needs to be investigated. In this paper, we experimentally demonstrate combined Jones–Stokes polarimetry to investigate the amplitude, phase, and polarization modulation characteristics of a twisted nematic liquid crystal spatial light modulator (TNLC-SLM). The anisotropic response of the SLM is calibrated for its entire grayscale range. We determine the inevitable anisotropic properties viz., diattenuation, retardance, isotropic absorption, birefringence, and dichroism, which are retrieved from the measured Jones matrices of the SLM using Jones polar decomposition and a novel algebraic approach for Jones matrix decomposition. The results of this study provide a complete polarimetric calibration of the SLM within the framework of its anisotropic characteristics.

Mueller–Stokes polarimetry is emerging as a prominent noninvasive imaging technique to study the structural characteristics of an anisotropic medium. Spatial light modulator (SLM) is a programmable liquid crystal device (LCD), which is used to modulate the amplitude, phase, and polarization of light. The compact design and cumbrous manufacturing process of SLM requires its polarimetric calibration prior to its utilization for various applications. In this study, we experimentally demonstrate Mueller–Stokes imaging of a reflective-type SLM (Holoeye, LCR-720) to calibrate its polarization modulation characteristics with respect to its dynamic gray value range (0–255) at different spatial locations of SLM screen. Mueller matrices at 18 different gray values of SLM at an interval of 15, that is, at gray values 0, 15, 30, up to 255 have been experimentally measured using an improvised Mueller matrix imaging polarimeter (MMIP). Crucial polarimetric characteristics, that is, diattenuation, polarizance, state of polarization (SOP), depolarization, and retardance have been estimated with respect to the gray value range of SLM. Significant polarization modulation characteristics [diattenuation (0.08–0.3), polarizance (0.02–0.2), and retardance (0 to π)] have been determined for the SLM. These results indicate that the SLM exhibits spatially variable depolarizing nature and hence it is not perfectly homogeneous in structure. Therefore, it is expected that the outcomes of this study would be helpful for exploring the applicability of Mueller–Stokes polarimetry in advancement of LC technology.

Liquid crystal spatial light modulators (LC-SLMs) have gained substantial interest of the research fraternity due to their remarkable light modulation characteristics in modern imaging applications. Replacing the conventional optical elements from the SLM-based computer-generated holograms (CGHs) is a trending approach in modern digital holographic applications due to the optimized phase shift depending on the phase modulation features of SLMs. Apparently; SLMs serve a crucial role in the experimental implementation of digital holographic techniques. However, the resolution of the CGHs are sometimes limited by the structural discrepancies (fill factor, spatial anomalies, refresh rate, etc.) of SLM. Therefore, it is recommended to calibrate the modulation characteristics of SLMs prior to their implementation for imaging applications. This chapter provides comprehensive literature (review) of the LC-SLMs along with their major calibration methods. In addition, recent interesting applications of LC-SLMs have been discussed thoroughly within the framework of polarization holography.

The COVID-19 is spreading very fast globally and various factors of it have to be analysed. The aim of this study is to analyze the effect of climatic parameters (Average Temperature (AT), Atmospheric Pressure (AP), Relative Humidity (RH), Solar Radiation (SR) and Wind Speed (WS)) on the COVID-19 epidemic during 25 March 2020 to 15 June 2020 in most affected states of India i.e. Maharashtra, Delhi and Tamilnadu. We quantitatively establish the correlation between these parameters by using Kendall & Spearman rank correlation test. The results indicate that the numbers of cases are highly correlated with the AT (r ² > 0.6, p < 0.001) in Delhi where as a moderate correlation (r ² > 0.6, p < 0.001) has been estimated for Maharashtra and Tamilnadu. Similarly, an intermediate range of correlation coefficient has been observed for other climatic parameters. A comparative study of climatic parameters in the current COVID-19 period with previous two years (2018-2019) has been carried out. Corresponding results imply a substantial trend for all three states. The range of climatic parameters have been found corresponding to maximum number of cases results as AT (25∼ 40 ° C), RH (40∼70%), AT (740∼965 mmHg), SR (200-250 W/mt ² ) and WS (.5∼14 m/sec). Time series analysis depicts that the number of cases and mortality are increasing rapidly. COVID-19 epidemic peak has been predicted by SIR model for capital of India (New Delhi) and it would be around October 2020. The outcomes of this study will be helpful for the containment of COVID- 19 not only in India but globally.

COVID-19 (SARS-CoV-2) is rapidly spreading in South Asian countries especially in India. India is the fourth most COVID-19 affected country at present (as on July 10, 2020). With limited medical facilities and high transmission rate, study of COVID-19 progression and its subsequence trajectory need to be analyzed in India. Epidemiologic mathematical models have the potential to predict the epidemic peak of COVID-19 under different scenario. Lockdown is one of the most effective mitigation policy adapted worldwide to control the transmission rate of COVID-19 cases. In this study, we use an improvised five compartment mathematical model i.e. Susceptible (S) - exposed (E)- infected (I)- recovered (R)- death (D) (SEIRD) to investigate the progression of COVID-19 and predict the epidemic peak under the impact of lockdown in India. The aim of this study to provide the most accurate prediction of epidemic peak and to evaluate the impact of lockdown on epidemic peak shift in India. For this purpose, we examine most recent data (up to July 10, 2020) to enhance the accuracy of outcomes from the proposed model. The obtained results indicate that COVID-19 epidemic peak would appear on around mid-August 2020 in India and corresponding estimated cases would be 2.5×10 ⁶ under current scenario. In addition, our study indicates that existence of under-reported cases (∼10 ⁵ ) during post-lockdown period in India. It is expected that nationwide lockdown would lead to epidemic peak suppression in India. The obtained results would be beneficial for determining further COVID-19 mitigation policies not only in India but globally.