The timing characteristics, including propagation delay, overshooting time and transition time, as well as its variability, are accurately modeled in analytical expressions.
In the neural context it is natural to include propagation delay between neurons explicitly, for example in models such as frac{mathrm{d}}{mathrm{d}t}x_{i}(t) = f_{i}bigl(x_{i}(t) bigr)+ epsilonsum_{j=1}^{N} w_{ij} G_{ij}bigl(x_{i}(t),x_{j} t- tau bigr), where the delay time (tau>0) represents the time of propagation of the signal from one neuron to another.
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where p k)(t) is the downlink (reader-tag) channel response to g(t) which includes also the propagation delay.
This [4] protocol incorporates the channel delay of the link including queuing delay, propagation delay and protocol processing time, available bandwidth, and cost of each path to choose the least delayed path.
Typical fault effects include functional changes, propagation delay increases, sequential logic faults, stuck-at faults, reduced noise margins, and increased IDDQ.
Compared with radio transmissions in wireless sensor networks (WSNs), the challenges of acoustic transmissions include (1) long propagation delay: The propagation speed for an acoustic link is 1500 m/s, 2 × 105 times lower than the speed of a radio link.
Traditional delay fault models, the transition fault, gate-delay fault, and path-delay fault, as well as newer models are also presented, including inline delay fault, propagation delay fault, segment delay fault, and defect-based delay fault models.
where T prop is the propagation delay, which includes the round trip time for one transmission (includes the propagation delay for a block as well as for the ACK/NACK packet); T transition is the modem transition delay (for a single transmission, the modem needs to change the state two times).
