Ship As a Wave Buoy: Estimating Full Directional Wave Spectra From In-Service Ship Motion Measurements Using Deep Learning

The ocean wave directional spectrum is an important wave characteristic for maritime safety and navigation. Accurate estimation of directional spectra in real-time is a challenge. In this study we aim to reconstruct the directional spectra from ship motions using a deep convolutional encoding-decoding neural network. In-service measurements of ship motions and wave spectra from a WAMOS II wave scanning radar were used to train the neural network. The data was collected from a frigate type ship for a period of two years. We demonstrate that the deep convolutional encoding-decoding neural network is successful in predicting the directional spectra in real-time. At the same time, we conclude that more data is needed for a better prediction performance, including a more complete coverage of operational conditions.

Evaluating Deep Learning Methods for Word Segmentation of Scripta Continua Texts in Old French and Latin

International audience Tokenization of modern and old Western European languages seems to be fairly simple, as it stands on the presence mostly of markers such as spaces and punctuation. However, when dealing with old sources like manuscripts written in scripta continua, antiquity epigraphy or Middle Age manuscripts, (1) such markers are mostly absent, (2) spelling variation and rich morphology make dictionary based approaches difficult. Applying convolutional encoding to characters followed by linear categorization to word-boundary or in-word-sequence is shown to be effective at tokenizing such inputs. Additionally, the software is released with a simple interface for tokenizing a corpus or generating a training set.

Viterbi Decoder Design of Beidou RDSS Receiver

Firstly, the convolutional encoding with constraint length of 7 is used as the channel coding scheme in Beidou RDSS system. In addition, in receiver design Viterbi Decoder serves as the decoder of convolutional encoding. Finally, the optimized design of hardware construction in traditional Viterbi compared with that in decoder is introduced to reduce hardware complexity.

The Design of High Speed and Parallel Convolutional Encoder Based on FPGA

For the channel source of large capacity image data, the error correcting capability and coding efficiency of channel encoding is very important, in order to solve the real-time and parallel of encoding, a kind of code encoding method realized easily by FPGA is proposed. First, the coding principle of the convolutional code is introduced in detail; Secondly, the representation method of the convolutional encoder is elaborated, including the state transition diagram and the grid diagram of the convolutional code; Then, the convolutional encoding algorithm based on FPGA will be converting to achieve rapid, parallel processing method; Finally, (2,1,7) convolutional code algorithm is simulated and tested. The experimental results show that: the convolutional encoding module is capable of handling the input data stream of up to 160Mbps, processing speed, to meet the parallel and real-time of convolutional encoding for the channel source of large capacity image data, to improve the efficiency of convolutional encoding.

Convolutional Coding Using Booth Algorithm For Application in Wireless Communication

Convolutional codes are preferred types of error control codes which can achieve low BERs at signal to noise ratio (SNR) very close to Shannon limit. Here, a new method of convolutional encoding is proposed using the general Booth algorithm for multiplication. This algorithm follows a fast multiplication process and achieves a significantly less computational complexity over its conventional counterparts. It can be a useful technique for use in chip design as it provides significant improvements. In this work, the performance of conventional convolutional coding with Viterbi decoding in AWGN channel, is studied and the results show the effectiveness of the work described here.