Abstract-The emerging field of compressed sensing provides sparse reconstruction, which has demonstrated promising results in the areas of signal processing and pattern recognition. In this paper, a new approach for synthetic aperture radar (SAR) target classification is proposed based on Bayesian compressive sensing (BCS) with scattering centers features. Scattering centers features is extracted as a l 1 -norm sparse problem on the basis of SAR observation physical model, which can improve discrimination ability compared with original SAR image. Using an overcomplete dictionary constructed by training samples, BCS is utilized to design targets classifier. For target classification performance evaluation, the proposed method is compared with several state-of-art methods through experiments on Moving and Stationary Target Acquisition and Recognition (MSTAR) public release database. Experiment results illustrate the effectiveness and robustness of the proposed approach.

Abstract-A new approach to classify synthetic aperture radar (SAR) targets is presented based on high range resolution (HRR) profiles time-frequency matrix non-negative sparse coding (NNSC). Firstly, SAR target images have been converted into HRR profiles. And the non-negative time-frequency matrix for each of the profiles is obtained by using an adaptive Gaussian representation (AGR). Secondly, NNSC is applied to learn target time-frequency basis of the training set. Feature vectors are constructed by projecting each HRR profile time-frequency matrix to low dimensional time-frequency basis space. Finally, the target classification decision is found with support vector machine and nearest neighbor algorithm respectively. To demonstrate the performance of the proposed approach, experiments are performed with Moving and Stationary Target Acquisition and Recognition (MSTAR) public release SAR database. The experimental results support the effectiveness of the proposed technique for SAR target classification.

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Objective: To investigate the application of cystatin C combined with homocysteine detection in AIDS and tuberculosis complicated with hypertension. Methods: 57 patients with AIDS complicated with hypertension and 52 patients with tuberculosis complicated with hypertension from Guangxi Infectious Diseases Hospital Nanning Fourth People's Hospital/Guangxi AIDS Clinical Treatment Center (Nanning) from October 2022 to March 2023, and 196 patients with simple hypertension from Guangxi Cardiovascular Diseases Hospital Nanning Third People's Hospital were selected as research objects. And then the difference in the detection results of cystatin C and homocysteine among the three groups was compared. Results: The detection results of serum cystatin C and homocysteine in AIDS patients with hypertension and tuberculosis patients with hypertension were higher than those in the simple hypertension group, and the difference was statistically significant (P < 0.05). However, there was no significant difference in the detection results of cystatin C or homocysteine between the AIDS hypertension group and the tuberculosis hypertension group (P > 0.05). Conclusion: The detection of cystatin C combined with homocysteine has high clinical application value in AIDS with hypertension and tuberculosis with hypertension. When AIDS is combined with hypertension or tuberculosis is combined with hypertension, cystatin C and homocysteine are at a high level, while the concentration levels of cystatin C and homocysteine are relatively low in simple hypertension. Therefore, cystatin C combined with homocysteine detection can provide better laboratory evidence for clinical diagnosis and differential diagnosis, and is worth promoting and applying.