Classical light analogue of the nonlocal Aharonov-Bohm effect

We demonstrate the existence of a non-local geometric phase in the intensity-intensity correlations of classical incoherent light, that is not seen in the lower order correlations. This two-photon Pancharatnam phase was observed and modulated in a Mach-Zehnder interferometer. Using acousto-optic interaction, independent phase noise was introduced to light in the two arms of the interferometer to create two independent incoherent classical sources from laser light. The experiment is the classical optical analogue of the multi-particle Aharonov-Bohm effect. As the trajectory of light over the Poincare sphere introduces a phase shift observable only in the intensity-intensity correlation, it provides a means of deflecting the two-photon wavefront, while having no effect on single photons.Comment: To appear in Europhys. Let

Optical phase noise engineering via acousto-optic interaction and its interferometric applications

We exercise rapid and fine control over the phase of light by transferring digitally gen- erated phase jumps from radio frequency (rf) electrical signals onto light by means of acousto-optic interaction. By tailoring the statistics of phase jumps in the electrical signal and thereby engineering the optical phase noise, we manipulate the visibil- ity of interference fringes in a Mach-Zehnder interferometer that incorporates two acousto-optic modulators. Such controlled dephasing finds applications in modern experiments involving the spread or diffusion of light in an optical network. Further, we analytically show how engineered partial phase noise can convert the dark port of a stabilised interferometer to a weak source of highly correlated photons.Comment: 5 figure

Remote Sensing of Cloud Ice Water Path from SAPHIR Microwave Sounder Onboard Megha- Tropiques

This study derives the ice water path of the atmospheric column from the microwave sounder SAPHIR onboard Megha-Tropiques. SAPHIR (Sondeur Atmospherique du Profil d'Humidite Intertropicale par Radiometrie) is a cross-track, multichannel microwave humidity sounder with six channels ranging from 183.3 +/- 0.2 to 183.3 +/- 11GHz near the 183.31GHz water vapor absorption line. It measures the earth emitted radiation at these six frequencies. In this paper, Concurrent and collocated observations of Channel 183.31 +/- 6.6GHz, and 183.3 +/- 11GHz from SAPHIR and IWP (Ice water Path) from CloudSat have been used in the development the algorithm. A total of five sets of neural network model, each for 10 degrees of incidence angle of SAPHIR have been developed. The model shows a correlation of 0.83 and RMSE of 195g/m(2) with an independent test dataset. The validation of the algorithm has been done by comparing the retrieval with various satellite derived IWP products such as CloudSat, GMI (Global precipitation measuring mission Microwave Imager) and MSPPS (Microwave Surface and Precipitation Products System). The instantaneous comparisons of IWP over a cyclonic storm ROANU demonstrate a good agreement between NN (Neural Network) derived IWP and CloudSat. A probability distribution of IWP indicates consistency between SAPHIR and CloudSat. A comparison of zonal mean between all the IWP products shows that SAPHIR performs better than GMI, and MSPPS

Enhancing Road Safety and Cybersecurity in Traffic Management Systems: Leveraging the Potential of Reinforcement Learning

With the increasing reliance on technology in traffic management systems, ensuring road safety and protecting the integrity of these systems against cyber threats have become critical concerns. This research paper investigates the potential of reinforcement learning techniques in enhancing both road safety and cyber security of traffic management systems. The paper explores the theoretical foundations of reinforcement learning, discusses its applications in traffic management, and presents case studies and empirical evidence demonstrating its effectiveness in improving road safety and mitigating cyber security risks. The findings indicate that reinforcement learning can contribute to the development of intelligent and secure traffic management systems, thus minimizing accidents and protecting against cyber-attacks