Computationally efficient expressions for the collision efficiency between electrically charged aerosol particles and cloud droplets

A multiple factor parametrization is described to permit the efficient calculation of collision efficiency (E) between electrically charged aerosol particles and neutral cloud droplets in numerical models of cloud and climate. The four-parameter representation summarizes the results obtained from a detailed microphysical model of E, which accounts for the different forces acting on the aerosol in the path of falling cloud droplets. The parametrization's range of validity is for aerosol particle radii of 0.4 to 10 mu m, aerosol particle densities of I to 2.0 g cm(-3), aerosol particle charges from neutral to 100 elementary charges and drop radii from 18.55 to 142 mu m. The parametrization yields values of E well within an order of magnitude of the detailed model's values, from a dataset of 3978 E values. Of these values 95% have modelled to parametrized ratios between 0.5 and 1.5 for aerosol particle sizes ranging between 0.4 and 2.0 mu m, and about 96% in the second size range. This parametrization speeds up the calculation of E by a factor of similar to 10(3) compared with the original microphysical model, permitting the inclusion of electric charge effects in numerical cloud and climate models.

Perceiving time to collision activates the sensorimotor cortex

The survival of many animals hinges upon their ability to avoid collisions with other animals or objects or to precisely control the timing of collisions. Optical expansion provides a compelling impression of object approach and in principle can provide the basis for judgments of time to collision (TTC) [1]. It has been demonstrated that pigeons [2] and houseflies [3] have neural systems that can initiate rapid coordinated actions on the basis of optical expansion. In the case of humans, the linkage between judgments of TTC and coordinated action has not been established at a cortical level. Using functional magnetic resonance imaging (fMRI), we identified superior-parietal and motor-cortex areas that are selectively active during perceptual TTC judgments, some of which are normally involved in producing reach-to-grasp responses. These activations could not be attributed to actual movement of participants. We demonstrate that networks involved in the computational problem of extracting TTC from expansion information have close correspondence with the sensorimotor systems that would be involved in preparing a timed motor response, such as catching a ball or avoiding collision.

An FLIR video surveillance system to avoid bridge-ship collision

In this paper, a forward-looking infrared (FLIR) video surveillance system is presented for collision avoidance of moving ships to bridge piers. An image pre-processing algorithm is proposed to reduce clutter noises by multi-scale fractal analysis, in which the blanket method is used for fractal feature computation. Then, the moving ship detection algorithm is developed from image differentials of the fractal feature in the region of surveillance between regularly interval frames. Experimental results have shown that the approach is feasible and effective. It has achieved real-time and reliable alert to avoid collisions of moving ships to bridge piers

Neural processing of imminent collision in humans

Detecting a looming object and its imminent collision is imperative to survival. For most humans, it is a fundamental aspect of daily activities such as driving, road crossing and participating in sport, yet little is known about how the brain both detects and responds to such stimuli. Here we use functional magnetic resonance imaging to assess neural response to looming stimuli in comparison with receding stimuli and motion-controlled static stimuli. We demonstrate for the first time that, in the human, the superior colliculus and the pulvinar nucleus of the thalamus respond to looming in addition to cortical regions associated with motor preparation. We also implicate the anterior insula in making timing computations for collision events.

A bridge-ship collision avoidance system based on FLIR image sequences

In this paper, a forward-looking infrared (FLIR) video surveillance system is presented for collision avoidance of moving ships to bridge piers. An image preprocessing algorithm is proposed to reduce clutter background by multi-scale fractal analysis, in which the blanket method is used for fractal feature computation. Then, the moving ship detection algorithm is developed from image differentials of the fractal feature in the region of surveillance between regularly interval frames. When the moving ships are detected in region of surveillance, the device for safety alert is triggered. Experimental results have shown that the approach is feasible and effective. It has achieved real-time and reliable alert to avoid collisions of moving ships to bridge piers.

Autonomous ship collision free trajectory navigation and control algorithms

A new autonomous ship collision free (ASCF) trajectory navigation and control system has been introduced with a new recursive navigation algorithm based on analytic geometry and convex set theory for ship collision free guidance. The underlying assumption is that the geometric information of ship environment is available in the form of a polygon shaped free space, which may be easily generated from a 2D image or plots relating to physical hazards or other constraints such as collision avoidance regulations. The navigation command is given as a heading command sequence based on generating a way point which falls within a small neighborhood of the current position, and the sequence of the way points along the trajectory are guaranteed to lie within a bounded obstacle free region using convex set theory. A neurofuzzy network predictor which in practice uses only observed input/output data generated by on board sensors or external sensors (or a sensor fusion algorithm), based on using rudder deflection angle for the control of ship heading angle, is utilised in the simulation of an ESSO 190000 dwt tanker model to demonstrate the effectiveness of the system.

Two-photon excitation with pico-second fluorescence lifetime imaging to detect nuclear association of flavanols

Two-photon excitation enabled for the first time the observation and measurement of excited state fluorescence lifetimes from three flavanols in solution, which were ∼1.0 ns for catechin and epicatechin, but <45 ps for epigallocatechin gallate (EGCG). The shorter lifetime for EGCG is in line with a lower fluorescence quantum yield of 0.003 compared to catechin (0.015) and epicatechin (0.018). In vivo experiments with onion cells demonstrated that tryptophan and quercetin, which tend to be major contributors of background fluorescence in plant cells, have sufficiently low cross sections for two-photon excitation at 630 nm and therefore do not interfere with detection of externally added or endogenous flavanols in Allium cepa or Taxus baccata cells. Applying two-photon excitation to flavanols enabled 3-D fluorescence lifetime imaging microscopy and showed that added EGCG penetrated the whole nucleus of onion cells. Interestingly, EGCG and catechin showed different lifetime behaviour when bound to the nucleus: EGCG lifetime increased from <45 to 200 ps, whilst catechin lifetime decreased from 1.0 ns to 500 ps. Semi-quantitative measurements revealed that the relative ratios of EGCG concentrations in nucleoli associated vesicles: nucleus: cytoplasm were ca. 100:10:1. Solution experiments with catechin, epicatechin and histone proteins provided preliminary evidence, via the appearance of a second lifetime (τ2 = 1.9–3.1 ns), that both flavanols may be interacting with histone proteins. We conclude that there is significant nuclear absorption of flavanols. This advanced imaging using two-photon excitation and biophysical techniques described here will prove valuable for probing the intracellular trafficking and functions of flavanols, such as EGCG, which is the major flavanol of green tea.

Collision prediction of a moving object within a virtual world

This paper provides a solution for predicting moving/moving and moving/static collisions of objects within a virtual environment. Feasible prediction in real-time virtual worlds can be obtained by encompassing moving objects within a sphere and static objects within a convex polygon. Fast solutions are then attainable by describing the movement of objects parametrically in time as a polynomial.

Balanced excitation and inhibition: Model based analysis of local field potentials

We have developed a model of the local field potential (LFP) based on the conservation of charge, the independence principle of ionic flows and the classical Hodgkin–Huxley (HH) type intracellular model of synaptic activity. Insights were gained through the simulation of the HH intracellular model on the nonlinear relationship between the balance of synaptic conductances and that of post-synaptic currents. The latter is dependent not only on the former, but also on the temporal lag between the excitatory and inhibitory conductances, as well as the strength of the afferent signal. The proposed LFP model provides a method for decomposing the LFP recordings near the soma of layer IV pyramidal neurons in the barrel cortex of anaesthetised rats into two highly correlated components with opposite polarity. The temporal dynamics and the proportional balance of the two components are comparable to the excitatory and inhibitory post-synaptic currents computed from the HH model. This suggests that the two components of the LFP reflect the underlying excitatory and inhibitory post-synaptic currents of the local neural population. We further used the model to decompose a sequence of evoked LFP responses under repetitive electrical stimulation (5 Hz) of the whisker pad. We found that as neural responses adapted, the excitatory and inhibitory components also adapted proportionately, while the temporal lag between the onsets of the two components increased during frequency adaptation. Our results demonstrated that the balance between neural excitation and inhibition can be investigated using extracellular recordings. Extension of the model to incorporate multiple compartments should allow more quantitative interpretations of surface Electroencephalography (EEG) recordings into components reflecting the excitatory, inhibitory and passive ionic current flows generated by local neural populations.