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In practical applications, communication emitters are often carried by low orbit satellites aircraft, ships, and other moving carriers. Doppler phenomenon usually exists due to the relative motion between the emitter and the receiver, which seriously affects the identification performance. Given this situation, a method based on the signal vector decomposition and the multi-feature fusion is proposed. By normalizing and demodulating the target signal, the signal vector diagram is decomposed into four parts. We present a multi-feature fusion model with varying features extracted from each diagram by a deep learning network. Experimental results show that the proposed method is effective in the presence of Doppler effect.

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Email is the most common and effective source of communication for most enterprises and individuals. In the corporate sector the volume of email received daily is significant while timely reply of each email is important. This generates a huge amount of work for the organisation, in particular for the staff located in the help-desk role. In this paper we present a novel Smart E-mail Management System (SEMS) for handling the issue of E-mail overload. The Term Frequency-Inverse Document Frequency (TF-IDF) model was used for designing a Smart Email Client in previous research. Since TF-IDF does not consider semantics between words, the replies suggested by the model are not very accurate. In this paper we apply Document to Vector (Doc2Vec) and introduce a novel Gated Recurrent Unit Sentence to Vector (GRU-Sent2Vec), which is a hybrid model by combining GRU and Sent2Vec. Both models are more intelligent as compared to TF-IDF. We compare our results from both models with TF-IDF. The Doc2Vec model performs the best on predicting a response for a similar new incoming Email. In our case, since the dataset is too small to require a deep learning algorithm model, the GRU-Sent2Vec hybrid model cannot produce ideal results, whereas in our understanding it is a robust method for long-text prediction.

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