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Table 3 shows the complexity of the Log-MAP decoding algorithm for a single decoder of the convolutional code with memory.
Further, we put the whole coding and decoding system into GPU rather than a single decoder.
The multiple microphone-array processing using a single decoder needs about one-third the computational time of that using multiple decoders without degrading speech recognition performance.
The system runs at 100 MHz clock rate, with the overall output rate of 495 Mb/s for a single decoder.
Each camera j encodes a small number of random measurements y j of its observed image x j, and a single decoder jointly reconstructs all images {x j } from the ensemble of compressive measurements {y j }.
The second method is to calculate the output probability of each input at frame level, and a single decoder using these output probabilities is used to perform speech recognition.
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Despite the high error associated with each single decoder, the fusion algorithms successfully produce highly accurate decodes, significantly improving over all three individual decoders.
A single UFA/BFA decoder with a finite input buffer can be treated as a D/G/1/B queue [21], in which D means that the input data rate is deterministic, G means that the decoding time is generic, 1 means that there is one decoder and B is the number of codewords the input buffer can hold.
The Kalman filter framework as a single neural decoder was very similar to that of the fusion implementation.
Each single decoder (PVA, Kalman filter, and optimal linear decoder) was trained on an identical dataset composed of 50 simulated neuron spike observations with a corresponding endpoint path.
As most decoding algorithms are optimized for specific modalities, we employ techniques for decision fusion, where we incorporate the estimates from each individual decoder into a single device state estimate.
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