Sentence examples for signals recognition from inspiring English sources

The BBC's move signals recognition of the importance of access diversification, and heralds a new phase of public awareness of social virtual worlds.

Consequently, in the presence of noise, the proposed algorithm performs well especially for MP and BPSK signals recognition.

In order to implement a high-accuracy of unit saccadic EOG signals recognition, we propose a new feature extraction algorithm based on Common Spatial Pattern (CSP).

The average recognition accuracy of unit saccadic signals achieved 99.0% (before sample optimization) and 99.57% (sample optimized) over 10 participants, which reveals that the proposed algorithm presents an excellent classification performance in saccadic signals recognition.

In this stage we investigate different learning algorithms of MLP neural networks that some of them, such as quick prop (QP) learning algorithm, extended delta-bar-delta (EDBD), super self adaptive back propagation (SuperSAB) and conjugate gradient (CG) are proposed for the first time in the area of communication signals recognition.

First several robust features of signals were extracted as the input of classifiers, then the kernel thought was used to map feature vectors impliedly to the high dimensional feature space, and multi-class RVM and SVM classifiers were designed to complete AM, CW, SSB, MFSK and MPSK signals recognition.

Meanwhile, signal recognition capability is retained.

Signal recognition and WID matching are processed by Matlab.

It will be also useful for other biomedical signal recognition.

Signal recognition in [18] is limited to HardDec.

The proposed approach comprises two steps, i.e., the primary signal recognition and the advanced signal recognition.