Practical State Machines for Computer Software and Engineering

This paper introduces methods for describing properties of possibly very large state machines in terms of solutions to recursive functions and applies those methods to computer systems

Directional acceleration vector-driven displacement of fluids (DAVD-DOF)

Centrifugal analyzer and method for staining biological or non-biological samples in microgravity, wherein the method utilizes an increase in weight of a fluid sample as a function of g-load, to overcome cohesive and frictional forces from preventing its movement in a preselected direction. Apparatus is characterized by plural specimen reservoirs and channels in a slide, each channel being of differing cross-section, wherein respective samples are selectively dispensed, from the reservoirs in response to an imposed g-factor, precedent to sample staining. Within the method, one thus employs microscope slides which define channels, each being of a differing cross-section dimension relative to others. In combination therewith, centrifugal slide mounting apparatus controllably imposes g-vectors of differing magnitudes within a defined structure of the centrifuge such as a chip array

A data-based hybrid driven control for networked-based remote control applications

This paper develops a data-based hybrid driven control (DHDC) approach for a class of networked nonlinear systems compromising delays, packet dropouts and disturbances. First, the delays and/or packet dropouts are detected and updated online using a network problem detector. Second, a single-variable first-order proportional-integral (PI) -based adaptive grey model is designed to predict in a near future the network problems. Third, a hybrid driven scheme integrated a small adaptive buffer is used to allow the system to operate without any interrupt due to the large delays or packet dropouts. Forth, a prediction-based model-free adaptive controller is developed to compensate for the network problems. Effectiveness of the proposed approach is demonstrated through a case study

Measuring information-transfer delays

In complex networks such as gene networks, traffic systems or brain circuits it is important to understand how long it takes for the different parts of the network to effectively influence one another. In the brain, for example, axonal delays between brain areas can amount to several tens of milliseconds, adding an intrinsic component to any timing-based processing of information. Inferring neural interaction delays is thus needed to interpret the information transfer revealed by any analysis of directed interactions across brain structures. However, a robust estimation of interaction delays from neural activity faces several challenges if modeling assumptions on interaction mechanisms are wrong or cannot be made. Here, we propose a robust estimator for neuronal interaction delays rooted in an information-theoretic framework, which allows a model-free exploration of interactions. In particular, we extend transfer entropy to account for delayed source-target interactions, while crucially retaining the conditioning on the embedded target state at the immediately previous time step. We prove that this particular extension is indeed guaranteed to identify interaction delays between two coupled systems and is the only relevant option in keeping with Wiener’s principle of causality. We demonstrate the performance of our approach in detecting interaction delays on finite data by numerical simulations of stochastic and deterministic processes, as well as on local field potential recordings. We also show the ability of the extended transfer entropy to detect the presence of multiple delays, as well as feedback loops. While evaluated on neuroscience data, we expect the estimator to be useful in other fields dealing with network dynamics