In recent years, survivability of optical communication networks against large-scale disaster failures has been studied. The survivability analyses done by most of the studies are based on arbitrary simplified disaster areas under the assumption of uniform disaster occurrence in a given area, and short-term evaluation of network robustness. However, for assessing the generic applicability, and evaluating the performance of any protection, recovery, and/or topology design scheme irrespective of the varying geographical region and network topology, a stochastic model for disaster occurrence and optical network topology would be useful. In this paper, we propose a stochastic model to estimate the impact of earthquake disasters on a backbone optical network, namely, earthquake risk and backbone optical network (ERBON) model. We consider various stochastic distributions, real statistics, stochastic geometry, and graph theory to model seismic zonation, epicenter location and density, earthquake magnitudes, link/node failures, and network topology/connectivity. Furthermore, we propose an earthquake risk minimized node relocation (ERMNR) scheme that can improve the optical network's survivability. Performance of the proposed ERMNR scheme is evaluated under the proposed ERBON model and for real-world earthquake risk data from the U.S. and India.

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.

We propose a quantitative approach toward the detection of collimation of a coherent optical beam. For detection of collimation position of the collimator, a temporal phase shifting technique has been incorporated into Talbot interferometry. The slope of the phase maps depicts the collimation position of the collimator. This technique offers high precision, accuracy, and provides automated output.

In this paper, we report an automated technique for collimation testing by incorporating Fourier fringe analysis of the recorded interferograms in Talbot interferometry. The triangular profile of Talbot interferometric fringes has been recorded using a CCD and computer system. The interferograms corresponding to the in-focus, at-focus, and out-of-focus positions of the collimating lens have been recorded. Direct phase measurement using the Fourier transform method has been used for detection of collimation positions. Good accuracy and precision in measurement have been achieved.

Charcoal rot is one of the most destructive fungal diseases of soybean, caused by the pathogen called Macrophomina phaseolina. This disease thrives in warm and dry conditions, affecting the yield of soybean and other important agronomic crops. Existing methods used to screen the disease suffer from several drawbacks including, manual rating, low accuracy, high operating time, and high system complexity. To circumvent these drawbacks, we developed a laser biospeckle based sensor to characterize the charcoal rot in soybean crop. Applicability of the proposed sensor was tested to analyze three major aspects of plant disease management, viz. characterization of disease progression, early identification of disease symptoms, and analysis of genetic resistance of the given cultivar towards the disease. The experiments were conducted during Kharif season for two consecutive years (2019 and 2020) on two cultivars of soybean, namely, JS 90-41 and AMS-MB-5-18. The proposed sensor as well as standard rating protocol (i.e. measuring the length of necrosis) were used to analyze the extent of disease. To characterize the disease progression and the genetic resistance of different cultivars against M. phaseolina, two new metrics, charcoal rot severity index and disease susceptibility index were introduced. Biospeckle activity was found to be strongly correlated with the lesion length of infected plant stems (r = +0.96, p <; .01, two-tailed (for JS 90-41) and (r = +0.95, p <; .01, two-tailed (for AMS-MB-5-18) for the year 2019; and r = +0.97, p <; .01, two-tailed (for JS 90-41) and r = +0.93, p <; .01, two-tailed (for AMS-MB-5-18) for the year 2020). Experimental results clearly indicate that the proposed sensor can be used as an efficient tool to detect the disease in its early stages of pathogen development. This study provides insights into development and implementation of disease control measures for increasing soybean crop production.