This paper studies the decoding performance of low-density parity-check(LDPC)codes in a serial concatenation system with polar codes employing the successive cancellation(SC)*** is known that the absolute incorrect lo...
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This paper studies the decoding performance of low-density parity-check(LDPC)codes in a serial concatenation system with polar codes employing the successive cancellation(SC)*** is known that the absolute incorrect log-likelihood ratio(LLR)values from the SC decoding can be very *** phenomenon dramatically deteriorates the error correcting performance of the outer LDPC *** this paper,the LLR values of polar codes are regulated by a log processing before being sent to the LDPC *** results show that the log processing is an efficient approach with a low optimization complexity compared with the existing procedures to improve the performance of the serial concatenation systems.
We explore a notion of bent sequence attached to the data consisting of an Hadamard matrix of order n defined over the complex qth roots of unity, an eigenvalue of that matrix, and a Galois automorphism from the cyclo...
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With the increasing scale of wireless sensor networks (WSN), it inevitably exists some problems in time synchronization, such as the sensitivity to the data of the normal error range, the large energy consumption and ...
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Deep learning methods can enhance the efficiency of tumor segmentation in breast ultrasound (BUS) images. However, noise interference, small tumors, and blurred boundaries can reduce segmentation accuracy. We design a...
Deep learning methods can enhance the efficiency of tumor segmentation in breast ultrasound (BUS) images. However, noise interference, small tumors, and blurred boundaries can reduce segmentation accuracy. We design a three-branch challenge-aware U-net (CAU-net) to address these main challenges in BUS images. Our CAU-net extracts the features from three challenge-aware encoders in parallel first. Secondly, we propose an adaptive aggregation layer (AAL) to merge the multi-scale features of three challenging branches, enabling the network to adaptively handle different breast lesion samples with these main challenges. To further enhance the accuracy of segmentation, we introduce the graph reasoning module (GRM) to the network to model the correlation between the channels of the features and acquire the global information in the features. The result of our experiment on two datasets demonstrates the superiority of CAU-net over the advanced medical image segmentation methods. Our code can be downloaded from https://***/tzz-ahu .
In additive white Gaussian noise(AWGN) channels, construction of polar codes is needed for every operating signal-to-noise ratio(SNR). Recently, the proposal of the design-SNR reduces the computation effort in con...
In additive white Gaussian noise(AWGN) channels, construction of polar codes is needed for every operating signal-to-noise ratio(SNR). Recently, the proposal of the design-SNR reduces the computation effort in constructing polar codes. In this paper, we prove that although the BER performance of the design-SNR construction is not affected, the packet-error-rate(PER) performance is degraded compared with the point-by-point construction. Therefore, a concatenation scheme is proposed to improve the degraded PER performance. Results show the validity of the proposed concatenation scheme when employing the design-SNR construction.
A finite metric space is called here distance degree regular if its distance degree sequence is the same for every vertex. A notion of designs in such spaces is introduced that generalizes that of designs in Q-polynom...
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In this paper, we propose a method of enhancing whisper, using whisper without any pretreatment combined with Wavenet. Our method is end-to-end, that is, inputing noised whisper to get clean whisper. The input to our ...
In this paper, we propose a method of enhancing whisper, using whisper without any pretreatment combined with Wavenet. Our method is end-to-end, that is, inputing noised whisper to get clean whisper. The input to our method is the original whisper without any processing, reducing the loss of features caused by other operations. We use speech denoising Wavenet to enhance whisper. Wavenet can not only enhance whisper well, but also tackle the issue of intelligibility. Specifically, use symmetric dilated convolution to obtain noisy speech context, help the model to enhance the speech for better denoising effect. Experimental results show that the enchanced whisper gains better performance both in the aspect of speech quality and intelligibility.
In this paper, we are interested in the conversion of whispered to normal speech. The baseline method uses standard bidirectional LSTM (BLSTM) RNN to predict both the spectral features and excitation parameters of the...
In this paper, we are interested in the conversion of whispered to normal speech. The baseline method uses standard bidirectional LSTM (BLSTM) RNN to predict both the spectral features and excitation parameters of the normal speech from whispered speech. Also, it employs STRAIGHT speech synthesizer. The BLSTM based whispered speech to normal speech conversion system is among the best systems in term of the naturalness of generated speech. However, in many cases, the model complexity and inference cost of BLSTM prevents its usage. As opposed to using standard BLSTM with sharing values, we propose a meta-network to generate non-shared weights for LSTM memory block in BLSTM (denote as meta-BLSTM). Besides, we use a low-rank approximation to generate the parameter matrix, which can reduce the model complexity. To our knowledge, this is the first study that uses meta-network to train a whispered to normal speech conversion system. To evaluate the performance of the proposed system, we performed experiments in the TIMIT dataset. Experimental results show that the proposed method achieves state-of-the-art performance.
This paper proposes a wide-band low-profile high-gain microstrip slot antenna loaded with a mushroom-type metasurface structure. By directly loading the metamaterial structure on the upper surface of the slot antenna,...
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ISBN:
(数字)9781728169668
ISBN:
(纸本)9781728169675
This paper proposes a wide-band low-profile high-gain microstrip slot antenna loaded with a mushroom-type metasurface structure. By directly loading the metamaterial structure on the upper surface of the slot antenna, low profile loading characteristics are realized and the antenna bandwidth is expanded. And by inserting the metal line array in the metamaterial dielectric board to improve the antenna gain. Change the gap width through the metasurface element with corner cut to produce a phase difference and realize circularly polarized. Simulation test and analysis of antenna through HFSS simulation software. The results show that the antenna with this metasurface has a profile height of only 0.09×λ(λ =27 mm), a relative impedance band-width of 20.4% (S 11
Due to the curse of dimensionality, two main issues remain challenging for applying evolutionary algorithms (EAs) to large-scale multiobjective optimization. The first issue is how to improve the efficiency of EAs for...
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ISBN:
(数字)9781728169293
ISBN:
(纸本)9781728169309
Due to the curse of dimensionality, two main issues remain challenging for applying evolutionary algorithms (EAs) to large-scale multiobjective optimization. The first issue is how to improve the efficiency of EAs for reducing computation cost. The second one is how to improve the diversity maintenance of EAs to avoid local optima. Nevertheless, these two issues are somehow conflicting with each other, and thus it is crucial to strike a balance between them in practice. Thereby, we propose an iterated problem reformulation based EA for large-scale multiobjective optimization, where the problem reformulation based method and the decomposition based method are used iteratively to address the aforementioned issues. The proposed method is compared with several state-of-the-art EAs on a variety of large-scale multiobjective optimization problems. Experimental results demonstrate the effectiveness of our proposed iterated method in large-scale multiobjective optimization.
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