The performance of brain-machine interfaces (BMIs) that continuously control upper limb neuroprostheses may benefit from distinguishing periods of posture and movement so as to prevent inappropriate movement of the prosthesis. Few studies, however, have investigated how decoding behavioral states and detecting the transitions between posture and movement could be used autonomously to trigger a ...

Neural machine translation has shown very promising results lately. Most NMT models follow the encoder-decoder framework. To make encoder-decoder models more flexible, attention mechanism was introduced to machine translation and also other tasks like speech recognition and image captioning. We observe that the quality of translation by attention-based encoder-decoder can be significantly damag...

This paper presents an approach to vector quantization of sources exhibiting intervector dependency. We present the optimal decoder based on a collection of received indices. We also present the optimal encoder for such decoding. The optimal decoder can be implemented as a table look-up decoder, however the size of the decoder codebook grows very fast with the size of the collection of utilized...

We describe a scalable decoder for parsing-based machine translation. e decoder is written in Java and implements all the essential algorithms described in (Chiang, 2007) and (Li and Khudanpur, 2008b): chart-parsing, n-gram language model integration, beamand cube-pruning, and k-best extraction. Additionally, parallel and distributed computing techniques are exploited to make it scalable. We d...

Applying a joint code and decoder design methodology, we develop a high-speed (3, k)-regular LDPC code partly parallel decoder architecture, based on which a 9216-bit, rate1/2 (3, 6)-regular LDPC code decoder is implemented on Xilinx FPGA device. When performing maximum 18 iterations for each code block decoding, this partly parallel decoder supports a maximum symbol throughput of 54 Mbps and a...