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Deterministic jitter has a known non-normal distribution. Deterministic jitter can either be correlated to the data stream (data-dependent jitter) or uncorrelated to the data stream (bounded uncorrelated jitter). Examples of data-dependent jitter are duty-cycle dependent jitter (also known as duty-cycle distortion) and intersymbol interference.
Data-dependent jitter (DDJ) is a specific class of timing jitter. In particular, it is a form of deterministic jitter which is correlated with the sequence of bits in the data stream. It is also a form of ISI .
Deterministic Networking (DetNet) is an effort by the IETF DetNet Working Group to study implementation of deterministic data paths for real-time applications with extremely low data loss rates, packet delay variation (jitter), and bounded latency, such as audio and video streaming, industrial automation, and vehicle control.
The IETF Deterministic Networking (DetNet) Working Group is focusing to define deterministic data paths with high reliability and bounds on latency, loss, and packet delay variation (jitter), such as audio and video streaming, industrial automation, and vehicle control.
The first step of computing an eye pattern is normally to obtain the waveform being analyzed in a quantized form. This may be done by measuring an actual electrical system with an oscilloscope of sufficient bandwidth, or by creating synthetic data with a circuit simulator in order to evaluate the signal integrity of a proposed design.
Certain workloads demand their data be delivered at particular times - video and audio are good examples. On lightly used networks this traffic can be sent as normal data, but on busy networks, retransmission delays can push delivery outside the jitter limits of the data. VG introduced the concept of Demand Priority to address this problem.
Example of a biological network between genes and proteins that controls entry into S phase. However, with knowledge of network interactions and a set of parameters for the proteins and protein interactions (usually obtained through empirical research), it is often possible to construct a model of the network as a dynamical system .
Hence, the function of coincidence detection is to reduce the jitter caused by spontaneous neuronal activity, and while random sub-threshold stimulations from cells may not often fire coincidentally, coincident synaptic inputs derived from a unitary external stimulus ensure that a target neuron will fire as a result of the stimulus.