On the two classes of global primary modal instability in laminar separation bubbles

The self-excited global instability mechanisms existing in flat-plate laminar separation bubbles are studied here, in order to shed light on the causes of unsteadiness and three- dimensionality of unforced, nominally two-dimensional separated flows. The presence of two known linear global mechanisms, namely an oscillator behavior driven by local regions of absolute inflectional instability and a centrifugal instability giving rise to a steady three- dimensionalization of the bubble, is studied in a series of model separation bubbles. Present results indicate that absolute instability, and consequently a global oscillator behavior, does not exist for two-dimensional bubbles with a peak reversed-flow velocity below 12% of the free-stream velocity. However, the three-dimensional instability becomes active for recirculation levels as low as urev ≈ 7%. These findings suggest a route to the three-dimensionality and unsteadiness observed in experiments and simulations substantially different from that usually found in the literature, in which two-dimensional vortex shedding is followed by three-dimensionalization.

Multi-channel signal separation

The separation of independent sources from mixed observed data is a fundamental and challenging problem. In many practical situations, observations may be modelled as linear mixtures of a number of source signals, i.e. a linear multi-input multi-output system. A typical example is speech recordings made in an acoustic environment in the presence of background noise and/or competing speakers. Other examples include EEG signals, passive sonar applications and cross-talk in data communications. In this paper, we propose iterative algorithms to solve the n × n linear time invariant system under two different constraints. Some existing solutions for 2 × 2 systems are reviewed and compared.

Bayesian separation and recovery of convolutively mixed autoregressive sources

In this paper we address the problem of the separation and recovery of convolutively mixed autoregressive processes in a Bayesian framework. Solving this problem requires the ability to solve integration and/or optimization problems of complicated posterior distributions. We thus propose efficient stochastic algorithms based on Markov chain Monte Carlo (MCMC) methods. We present three algorithms. The first one is a classical Gibbs sampler that generates samples from the posterior distribution. The two other algorithms are stochastic optimization algorithms that allow to optimize either the marginal distribution of the sources, or the marginal distribution of the parameters of the sources and mixing filters, conditional upon the observation. Simulations are presented.

High peak power femtosecond pulse VECSELs for terahertz time domain spectroscopy

We report on a high peak power femtosecond modelocked VECSEL and its application as a drive laser for an all semiconductor terahertz time domain spectrometer. The VECSEL produced near-transform-limited 335 fs sech2 pulses at a fundamental repetition rate of 1 GHz, a centre wavelength of 999 nm and an average output power of 120 mW. We report on the effect that this high peak power and short pulse duration has on our generated THz signal.