Stereoscopic video coding (SSVC) plays an important role in various 3D video applications. In SSVC, robust stereoscopic video transmission over error-prone networks is still a challenge problem to be solved. In this paper, we propose a joint encoder–decoder error control framework for SSVC, where error-resilient source coding, transmission network conditions, and error concealment scheme are jo...

A major hurdle to clinical translation of brain-machine interfaces (BMIs) is that current decoders, which are trained from a small quantity of recent data, become ineffective when neural recording conditions subsequently change. We tested whether a decoder could be made more robust to future neural variability by training it to handle a variety of recording conditions sampled from months of pre...

Neural network-based encoder-decoder models are among recent attractive methodologies for tackling natural language generation tasks. This paper investigates the usefulness of structural syntactic and semantic information additionally incorporated in a baseline neural attention-based model. We encode results obtained from an abstract meaning representation (AMR) parser using a modified version ...

The use of digital wireless communication systems has become more and more common during recent years. A multiple-input-multiple-output (MIMO) system using Space-Time Coding techniques can be implemented to enhance the capacity of a wireless link. The optimal decoder is based on the maximum likelihood principle. But as the number of the antennas in the system and the data rates increase, the ma...

In this paper, a directional bi-directional Soft-Output Viterbi algorithm (SOVA) based Turbo decoder architecture is proposed. By performing an extra pass through the decoder trellis, effectively bi-directional decoding, decoder performance can be improved. The bi-directional decoding results in a 60% increase in operations over the conventional SOVA decoder, while incurring no significant incr...

Many natural language generation tasks, such as abstractive summarization and text simplification, are paraphrase-orientated. In these tasks, copying and rewriting are two main writing modes. Most previous sequence-to-sequence (Seq2Seq) models use a single decoder and neglect this fact. In this paper, we develop a novel Seq2Seq model to fuse a copying decoder and a restricted generative decoder...

A major hurdle to clinical translation of brain–machine interfaces (BMIs) is that current decoders, which are trained from a small quantity of recent data, become ineffective when neural recording conditions subsequently change. We tested whether a decoder could be made more robust to future neural variability by training it to handle a variety of recording conditions sampled from months of pre...

In this paper, a modified FPGA scheme for the convolutional encoder and Viterbi decoder based on the IEEE 802.11a standards of WLAN is presented in OFDM baseband processing systems. The proposed design supports a generic, robust and configurable Viterbi decoder with constraint length of 7, code rate of 1/2 and decoding depth of 36 symbols. The Viterbi decoder uses full-parallel structure to imp...

In this paper we combined an optical mixer via photonic crystal ringresonator to propose an all optical 1 to 2 decoder. The main idea used in this paper isbased on controlling the optical behavior of the resonant ring via optical powerintensity. We know that resonant wavelength of the photonic crystal ring resonator isvery sensitive upon the refractive index of dielectric rods, on the other had...

Abstractive document summarization seeks to automatically generate a summary for a document, based on some abstract “understanding” of the original document. State-of-the-art techniques traditionally use attentive encoder–decoder architectures. However, due to the large number of parameters in these models, they require large training datasets and long training times. In this paper, we propose ...