Forward error correction (FEC) is as one of the key technologies enabling the next-generation high-speed fiber optical communications. In this paper, we propose a rate-adaptive scheme using a class of generalized low-density parity-check (GLDPC) codes with a Hamming code as local code. We show that with the proposed unified GLDPC decoder architecture, a variable net coding gains (NCGs) can be a...

A full-parallel architecture for turbo decoding, which achieves ultrahigh data rates when using product codes as error correcting codes, is proposed. This architecture is able to decode product codes using binary BCH or m-ary Reed-Solomon component codes. The major advantage of our architecture is that it enables the memory blocks between all half-iterations to be removed. Moreover, the latency...

We design a compact but effective CNN model for optical flow by exploiting the well-known design principles: pyramid, warping, and cost volume. Cast in a learnable feature pyramid, our network uses the current optical flow estimate to warp the CNN features of the second image. It then uses the warped features and features of the first image to construct the cost volume, which is processed by a ...

We present an error-control coding technique for optical communications. It consists of the serial concatenation of an outer convolutional code, an interleaver, a bit-accumulator, and pulse-position modulation (PPM). We refer to the coded modulation as serially concatenated PPM, or SCPPM. The encoding is accomplished with simple shift register operations and a table look-up to map code bits to ...

We have proposed, fabricated and demonstrated experimentally a set of Coherent Direct Sequence-OCDMA en/decoders based on Super Structured Fiber Bragg Gratings (SSFBGs) which are able to compensate the fiber chromatic dispersion at the same time that they perform the en/decoding task. The proposed devices avoid the use of additional dispersion compensation stages reducing system complexity and ...

Our network is a chain of encoder-decoder networks. Figures 15 and 16 explain the details of the two encoderdecoders used in the bootstrap and iterative net part. Fig. 17 gives implementation details for the refinement net. The encoder-decoders for the bootstrap and iterative net use additional inputs which come from previous predictions. Some of these inputs, like warped images or depth from o...