Microwave interferometric technique for characterizing few mode fibers

We propose and experimentally demonstrate a simple and accurate technique for measuring differential mode group delay (DMGD) in few mode fibers (FMF). A frequency-swept microwave signal is modulated on a filtered optical incoherent source. The microwave signals carried on different fiber modes experience different time delays and interfere with each other in the photodetector. Optical interference between propagating fiber modes is avoided by the use of an incoherent optical source. A mathematical model is established to analyze the interference pattern and extract the DMGD values. A 456-m two-mode fiber and a 981-m FMF, which supports four LP modes, are measured. The measurement covers the whole C-band and the results coincide well with those obtained by the time-of-flight method and the numerical simulations. A precision of ±0.002 ps/m is achieved.

Synthesis and optical property of MgMoO4 crystals

In this work, we report on the synthesis of MgMoO4 crystals by oxide mixed method. The powder was calcined at 1100 degrees C for 4h and analyzed by X-ray diffraction (XRD), Fourier transform infrared (FT-IR), Field emission gun scanning electron microscopy (FEG-SEM), Ultraviolet-visible (UV-vis) absorption spectroscopy and Photoluminescence (PL) measurement. XRD analyses revealed that the MgMoO4 powders crystallize in a monoclinic structure and are free secondary phases. UV-vis technique was employed to determine the optical band gap of this material. MgMoO4 crystals exhibit an intense PL emission at room temperature with maximum peak at 579 nm (yellow region) when excited by 350 nm wavelength at room temperature.

Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments

Optical flow methods are accurate algorithms for estimating the displacement and velocity fields of objects in a wide variety of applications, being their performance dependent on the configuration of a set of parameters. Since there is a lack of research that aims to automatically tune such parameters, in this work we have proposed an evolutionary-based framework for such task, thus introducing three techniques for such purpose: Particle Swarm Optimization, Harmony Search and Social-Spider Optimization. The proposed framework has been compared against with the well-known Large Displacement Optical Flow approach, obtaining the best results in three out eight image sequences provided by a public dataset. Additionally, the proposed framework can be used with any other optimization technique.