The vertical-cavity surface-emitting laser (VCSEL) is a novel type of semiconductor laser that is having a dramatic influence on many optical applications, including computing, communication, and sensing. Unfortunately, VCSELs suffer from a randomly fluctuating polarization whose dynamics are not fully understood. The model of stochastic polarization dynamics developed by San Miguel is rather complicated, and comparisons between experiment and simulations are quite difficult. One of the approaches to solve this problem was suggested in, where a simplified spin-eliminated linearized model (i.e., with dynamics reduced to that of a class-A laser) is used to analyze experimentally measured fluctuations and fluctuational switchings. In this talk, we present a recently developed technique that estimates the parameters of a nonlinear stochastic dynamical model by Bayesian inference, and demonstrate its application to the characterization of VCSELs. We start by considering the problem of diffusion of polarization in a potential well, onto which the dynamics of a class-A laser are usually mapped. We demonstrate the ability to infer laser parameters in numerical and analogue simulations, with the emphasis being placed on the role of large deviations. We specifically show that, contrary to one's intuition, the quality of inference can be improved by neglecting those data points in experimental time series that correspond to the rising part of large deviations. We then extend our technique to the full set of equations describing the polarization dynamics of a VCSEL in terms of
the motion of its Stokes vector on the Poincare sphere. Application of this technique to other standard problems encountered in characterizing semiconductor lasers, such as the identification of laser parameters from measurements of relaxational oscillations, is also discussed.
We suggest a fresh approach to the modeling of the human cardiovascular system. Taking advantage of a new Bayesian inference technique, able to deal with stochastic nonlinear systems, we show that one can estimate parameters for models of the cardiovascular system directly from measured time series. We present preliminary results of inference of parameters of a model of coupled oscillators from measured cardiovascular data addressing cardiorespiratory interaction. We argue that the inference technique offers a very promising tool for the modeling, able to contribute significantly towards the solution of a long standing challenge -- development of new diagnostic techniques based on noninvasive measurements.
Photochromic films made from bacteriorhodopsin (BR) possess many desirable characteristics for a candidate holographic optical data storage medium. These properties include optical erasability, high spatial resolution, adequate diffraction efficiency, flexible film formats, durability, an optimal recording/readout wavelength of about 680 - 690 nm, and potentially inexpensive cost. In this paper, we experimentally study the raw bit-error-rate (BER) achievable with BR films made from the genetic variant known as D85N. Experimental data is collected for digital bit patterns fabricated as chrome-on- glass masks, at two different spatial resolutions. The results show that films fabricated from D85N have good potential for use in holographic data storage systems, but that further effort must be devoted to the film fabrication process in order to minimize optical nonuniformity and scattering losses.
Gaussian-beam propagation in the bio-optical material bacteriorhodopsin is studied with the consideration of the material's intensity-dependent absorption and refractive index modulation. The beam focusing size, focusing position and their dependence on the incident beam parameters are simulated.
A generic three-plane optical processor is investigated from a statistical viewpoint. The means and the mutual coherence functions of the output field amplitude and intensity are derived. The photodetection process is then studied, and the mean and the autocorrelation function of the output current are found, thus establishing the functional form of the signal dependence of noise at the processor output. An integral equation and a series expression are also presented for the probability density function of the output signal. A special case is then analyzed, and the use of these expressions is demonstrated. Finally, device models to be used within this framework are summarized.