Identification of urban road targets using radar systems is usually heavily dependent on the aspect angle between the target velocity and line of sight of the radar. To improve the performance of the classification result when the target is in a cross range position relative to the radar, a method based on range micro Doppler signature is proposed in this paper. Joint time-frequency analysis is applied in every range cell to extract the time Doppler signature. The spectrograms from all of the target range cells are combined to form the range micro Doppler signature to allow further identification. Experiments were conducted to investigate the performance of the proposed method, and the results proved the effectiveness of the method presented.

In this paper, Integration of Fractional Gaussian Window transform (IFRGWT) is proposed for the parameter estimation of linear FM (LFM) signal; the proposal is based on the integration of the Fractional Fourier transform modified by Gaussian Window. The peak values can be detected by adjusting the standard deviation of Gaussian function and locating the optimal rotated angles. And also the parameters of the signal can be estimated well. As an application, detection and parameter estimation of multiple LFM signals are investigated in low signal-to-noise ratios (SNRs). The analytic results and simulations clearly demonstrate that the method is effective.

Question answering (QA) systems are designed to answer questions based on given information or with the help of external information. Recent advances in QA systems are overwhelmingly contributed by deep learning techniques, which have been employed in a wide range of fields such as finance, sports and biomedicine. For generative QA in open-domain QA, although deep learning can leverage massive data to learn meaningful feature representations and generate free text as answers, there are still problems to limit the length and content of answers. To alleviate this problem, we focus on the variant YNQA of generative QA and propose a model CasATT (cascade prompt learning framework with the sentence-level attention mechanism). In the CasATT, we excavate text semantic information from document level to sentence level and mine evidence accurately from large-scale documents by retrieval and ranking, and answer questions with ranked candidates by discriminative question answering. Our experiments on several datasets demonstrate the superior performance of the CasATT over state-of-the-art baselines, whose accuracy score can achieve 93.1% on IR&QA Competition dataset and 90.5% on BoolQ dataset.

Until today, digital image watermarking has not been large-scale used in the industry. The first reason is that the watermarking efficiency is low and the real-time performance cannot be satisfied. The second reason is that the watermarking scheme cannot cope with various attacks. To solve above problems, this paper presents a multi-domain based digital image watermarking scheme, where a fast DFT (Discrete Fourier Transform) based watermarking method is proposed for synchronization correction and an IWT-DCT (Integer Wavelet Transform-Discrete Cosine Transform) based watermarking method is proposed for information embedding. The proposed scheme has high efficiency during embedding and extraction. Compared with five existing schemes, the robustness of our scheme is very strong and our scheme can cope with many common attacks and compound attacks, and thus can be used in wide application scenarios.