Time-to-Collision (TTC) judgements with offsize objects show that Tau needs to be intergrated with familiar size to explain TTC performance

Observers judged TTC with computer-generated displays simulating an approaching object in three familiar-size conditions:

(i) Real-size (smaller, larger objects depicted as tennis, soccer balls respectively).
(ii) Off-size (smaller, larger objects depicted as soccer, tennis balls respectively).
(iii) Ambiguous-size (smaller, larger objects depicted as texture-less black balls of different size).

Displays simulated objects approaching observersí viewpoint from 24.96 m, and disappearing at 5.76 m. Manipulation of approach velocities (4.8-19.2 msec-1) produced viewing times from 1.0 to 4.0 sec, and delays between object disappearance and tau-based TTC ranging from 0.3 to 1.2 sec. Motion characteristics of smaller and larger objects in the three familiar-size conditions simulated those of approaching real-sized tennis and soccer balls respectively; that is, for each approach velocity, tau‚-based TTC was the same across the three conditions for smaller and larger objects.

Results showed that, consistent with the proposition of tau-determined TTC, TTC estimates in the real-size condition were uninfluenced by object size. This is contrary to previous reports that TTC for larger objects is underestimated relative to TTC for smaller objects. However, such size-dependent TTC differences were found in the ambiguous-size condition, with even larger differences in the off-size condition; TTCs for the ëlargerí tennis ball were much less than TTCs to the ësmallerí soccer ball compared to corresponding TTCs in the ambiguous-size condition. These results are problematic for the proposition that tau solely determines TTC. We discuss the role of perceptual learning in resolving this problem.

A stochastic time-domain model for burst data aggregation in IEEE 802.15.4 wireless sensor networks

In many network applications, the nature of traffic is of burst type. Often, the transient response of network to such traffics is the result of a series of interdependant events whose occurrence prediction is not a trivial task. The previous efforts in IEEE 802.15.4 networks often followed top-down approaches to model those sequences of events, i.e., through making top-view models of the whole network, they tried to track the transient response of network to burst packet arrivals. The problem with such approaches was that they were unable to give station-level views of network response and were usually complex. In this paper, we propose a non-stationary analytical model for the IEEE 802.15.4 slotted CSMA/CA medium access control (MAC) protocol under burst traffic arrival assumption and without the optional acknowledgements. We develop a station-level stochastic time-domain method from which the network-level metrics are extracted. Our bottom-up approach makes finding station-level details such as delay, collision and failure distributions possible. Moreover, network-level metrics like the average packet loss or transmission success rate can be extracted from the model. Compared to the previous models, our model is proven to be of lower memory and computational complexity order and also supports contention window sizes of greater than one. We have carried out extensive and comparative simulations to show the high accuracy of our model.