A synchronization technique for optical PPM signals

A technique for maintaining synchronization between optical PPM (pulse-position modulation) pulses and a receiver clock by means of a delay-tracking loop is described and analyzed. The tracking loop is driven by a doubly stochastic Poisson process that contains information about the location of the desired slot boundaries. The slot boundaries are subject to slowly varying random delays that are ultimately tracked by the loop. The concept of fractional rms delay error is introduced to quantify the effects of signal and background induced shot noise on the performance of the delay-tracking loop

Lagrangian block hydrodynamics of macro resistance in a river-flow model

River hydrodynamicsBed roughness and flow resistanc

Hysteretic and chaotic dynamics of viscous drops in creeping flows with rotation

It has been shown in our previous publication (Blawzdziewicz,Cristini,Loewenberg,2003) that high-viscosity drops in two dimensional linear creeping flows with a nonzero vorticity component may have two stable stationary states. One state corresponds to a nearly spherical, compact drop stabilized primarily by rotation, and the other to an elongated drop stabilized primarily by capillary forces. Here we explore consequences of the drop bistability for the dynamics of highly viscous drops. Using both boundary-integral simulations and small-deformation theory we show that a quasi-static change of the flow vorticity gives rise to a hysteretic response of the drop shape, with rapid changes between the compact and elongated solutions at critical values of the vorticity. In flows with sinusoidal temporal variation of the vorticity we find chaotic drop dynamics in response to the periodic forcing. A cascade of period-doubling bifurcations is found to be directly responsible for the transition to chaos. In random flows we obtain a bimodal drop-length distribution. Some analogies with the dynamics of macromolecules and vesicles are pointed out.Comment: 22 pages, 13 figures. submitted to Journal of Fluid Mechanic

Conservative Thirty Calendar Day Stock Prediction Using a Probabilistic Neural Network

We describe a system that predicts significant short-term price movement in a single stock utilizing conservative strategies. We use preprocessing techniques, then train a probabilistic neural network to predict only price gains large enough to create a significant profit opportunity. Our primary objective is to limit false predictions (known in the pattern recognition literature as false alarms). False alarms are more significant than missed opportunities, because false alarms acted upon lead to losses. We can achieve false alarm rates as low as 5.7% with the correct system design and parameterization