RESUMO
Recently, random lasing in complex networks has shown efficient lasing over more than 50 localised modes, promoted by multiple scattering over the underlying graph. If controlled, these network lasers can lead to fast-switching multifunctional light sources with synthesised spectrum. Here, we observe both in experiment and theory high sensitivity of the network laser spectrum to the spatial shape of the pump profile, with some modes for example increasing in intensity by 280% when switching off 7% of the pump beam. We solve the nonlinear equations within the steady state ab-initio laser theory (SALT) approximation over a graph and we show selective lasing of around 90% of the strongest intensity modes, effectively programming the spectrum of the lasing networks. In our experiments with polymer networks, this high sensitivity enables control of the lasing spectrum through non-uniform pump patterns. We propose the underlying complexity of the network modes as the key element behind efficient spectral control opening the way for the development of optical devices with wide impact for on-chip photonics for communication, sensing, and computation.
RESUMO
This paper presents an analytic expression for optimizing a monostatic, incoherent pulsed LiDAR scanner for small object detection. Using a hexagonal raster-scan pattern, we constrained the link budget by the need of detecting a distant object at a defined refresh rate with a 100% probability of detection, independently of its position on the scene. From the obtained expression, we minimized the needed laser mean power by playing on the beam divergence and collection efficiency via a drilled-hole mirror diameter. An expression for the probability of detection of said object was also deduced. The impact of both the refresh rate and the distance on the probability of detection was then studied, defining an expression for the cutoff distance and giving a complete overview of the system capabilities.
