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The precise architecture of TDNNs (time-delays, number of layers) is mostly determined by the designer depending on the classification problem and the most useful context sizes. The delays or context windows are chosen specific to each application. Work has also been done to create adaptable time-delay TDNNs [10] where this manual tuning is ...
The time-to-digital converter measures the time between a start event and a stop event. There is also a digital-to-time converter or delay generator. The delay generator converts a number to a time delay. When the delay generator gets a start pulse at its input, then it outputs a stop pulse after the specified delay.
The Smith predictor (invented by O. J. M. Smith in 1957) is a type of predictive controller designed to control systems with a significant feedback time delay. The idea can be illustrated as follows. Suppose the plant consists of () followed by a pure time delay .
The signal is disabled (carrier attenuated at least 3×, DC signal level lowered, or Manchester 0 bits transmitted), at one of three times during the bit interval: After 0.2 of a bit time, to encode a binary 0; After 0.5 of a bit time, to encode a binary 1; After 0.8 of a bit time, to encode a marker bit; Bit 0 is the frame marker bit P r.
Feedforward comb filter structure in discrete time. The general structure of a feedforward comb filter is described by the difference equation: [] = [] + []where is the delay length (measured in samples), and α is a scaling factor applied to the delayed signal.
A digital delay generator (also known as digital-to-time converter) is a piece of electronic test equipment that provides precise delays for triggering, syncing, delaying, and gating events. These generators are used in many experiments, controls, and processes where electronic timing of a single event or multiple events to a standard timing ...
The time delay is usually measured in slots, which are fixed-length periods (or slices) of time on the network. In a binary exponential backoff algorithm (i.e. one where b = 2 ), after c collisions, each retransmission is delayed by a random number of slot times between 0 and 2 c − 1 .
For example, multilayer perceptron (MLPs) and time delay neural network (TDNNs) have limitations on the input data flexibility, as they require their input data to be fixed. Standard recurrent neural network (RNNs) also have restrictions as the future input information cannot be reached from the current state.