A imaging passive localization method for wideband signal is proposed in this paper. By introducing synthetic aperture radar (SAR) imaging method, the location of the signal emitter is directly given in SAR image. The...
ISBN:
(数字)9781728129129
ISBN:
(纸本)9781728129136
A imaging passive localization method for wideband signal is proposed in this paper. By introducing synthetic aperture radar (SAR) imaging method, the location of the signal emitter is directly given in SAR image. The key of this method is to focus unknown wideband data in range and azimuth domain. To focus the data in range domain without signal parameter, a new pulse compress method is proposed by constructing reference signal from raw data. To focus the data in azimuth domain without knowing range, a range-searching azimuth focus method is proposed by constructing azimuth focus functions with different range. Simulation result validate the effectiveness of the proposed method.
It is a great challenge to design effective protocols for underwater acoustic communication networks due to the specific characteristics of underwater acoustic channel, such as long propagation delay and limited avail...
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It is a great challenge to design effective protocols for underwater acoustic communication networks due to the specific characteristics of underwater acoustic channel, such as long propagation delay and limited available bandwidth. In order to reduce the average end-to-end delay and improve the network throughput, a multi-node cooperative MAC protocol joint with space-time encoding of physical layer for multi-user MIMO centralized network is proposed in this paper. It is receiver-initiated, where the receiver groups the reserved nodes into a series of cells according to the propagation delay between individual reserved node and itself. By scheduling the transmission time, the data packets sent by the nodes in the same cell are guaranteed to reach the receiver within a predetermined time deviation, realizing cooperative parallel transmission of multiple nodes. Simulation results show that the proposed MAC protocol achieves the shortest end-to-end delay and optimal normalized network throughput under different network sizes, traffic loads and data packet lengths compared with other three existing protocols. The performance improvement is significant especially when the data packet length is longer and the load is heavier.
How to perform effective information fusion of different modalities is a core factor in boosting the performance of RGBT tracking. This paper presents a novel deep fusion algorithm based on the representations from an...
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Existing Lexical Punctuation Prediction methods are mainly trained on the standard clean data while losing the generalization in practical automatic speech recognition (ASR) system with ubiquitous transcription errors...
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Existing Lexical Punctuation Prediction methods are mainly trained on the standard clean data while losing the generalization in practical automatic speech recognition (ASR) system with ubiquitous transcription errors. To bridge the gap between clean training data and noisy testing data, we propose three random (3R) data augmentation strategies: random word deletion (RWD), random word substitution (RWS), and random phoneme edition (RPE) in both word and phoneme levels on the training dataset. Specifically, we contribute an acoustically similar vocabulary with phoneme level editions for acoustically similar word substitution. In addition, we first introduce the RoBERTa-large model into a punctuation prediction task to capture the semantics and the long-distance dependencies in language. Extensive experiments on the English dataset IWSLT2011 yield to a new state-of-the-art comparing to the prevalent punctuation prediction methods.
Synthetic aperture radar (SAR) images of metal targets are highly sensitive to the observation angle, which is unbeneficial for target recognition. To solve this problem, a novel two-step image registration method is ...
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At present, the Ethernet has been widely used for data transmission in embedded real-time systems, including TCP (Transmission Control Protocol) and UDP (User Datagram Protocol). However, as embedded real-time systems...
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Due to low imaging cost and robustness, the distributed passive radars using multiple transmitters and multiple receivers to observe targets have become a hot research. In the case of low SNR, the imaging accuracy of ...
ISBN:
(数字)9781728129129
ISBN:
(纸本)9781728129136
Due to low imaging cost and robustness, the distributed passive radars using multiple transmitters and multiple receivers to observe targets have become a hot research. In the case of low SNR, the imaging accuracy of the distributed passive radar imaging model via Orthogonal Matching Pursuit (OMP) sparse reconstruction is low. For this problem, a framework consisting of sparse representation of the received multi-snapshot radarsignal covariance matrix, Sparse Bayesian Learning (SBL) based reconstruction algorithm has been built. At the end of paper, through the simulation experiment, the imaging results of the original data and covariance data at low SNR are compared, and the reconstruction errors under different SNR are used to verify the effectiveness of the proposed algorithm.
Semi-supervised learning is adopted for fine-grained image classification with convolutional networks. Compared with the traditional approach of distillation, we obtain accuracy improvement of ~3 percent points under ...
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ISBN:
(纸本)9781943580705
Semi-supervised learning is adopted for fine-grained image classification with convolutional networks. Compared with the traditional approach of distillation, we obtain accuracy improvement of ~3 percent points under the upper limit of supervised learning on cassava-disease dataset.
The current automatic decoding method of the Morse telegram has limited accuracy, and can't adapt to signal distortion and code length deviation of the manual telegram. This paper introduces the deep learning meth...
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ISBN:
(数字)9781728161068
ISBN:
(纸本)9781728161075
The current automatic decoding method of the Morse telegram has limited accuracy, and can't adapt to signal distortion and code length deviation of the manual telegram. This paper introduces the deep learning method and constructs an automatic decoding model, which integrates feature extraction, sequence modeling and transcription into an end-to-end training neural network. The time-frequency diagrams of signals are used for training and testing. Experimental results show that the decoding system has strong adaptability to manual deviation and frequency drift, and is robust in a noisy environment.
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