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Objective. To evaluate the effects of cardiac rehabilitation on exercise tolerance and cardiac function in heart failure patients undergoing cardiac resynchronization therapy (CRT). Methods. Randomized controlled trials were initially identified from systematic reviews of the literature about cardiac rehabilitation and heart failure patients with CRT. We undertook updated literature searches of the Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, EMBASE, CBM, CNKI, and Wanfang databases until July 1, 2017. STATA12.0 software was used. Results. Four randomized controlled studies were included. The total sample size was 157 patients, including 77 in the control group. Cardiac rehabilitation treatment affected the peak VO2 in heart failure patients with CRT (Pheterogeneity=0.491, I2 = 0%). The results lacked heterogeneity, and the data were merged in a fixed-effects model (WMD = 2.17 ml/kg/min, 95% CI (1.42, 2.92), P<0.001). The peak VO2 was significantly higher in the cardiac rehabilitation group than in the control group. The sensitivity analysis showed that the results of the meta-analysis were robust. Cardiac rehabilitation treatment affected LVEF in heart failure patients with CRT (Pheterogeneity=0.064, I2 = 63.6%); the heterogeneity among the various research results meant that the data were merged in a random-effects model (WMD = 4.75%, 95% CI (1.53, 7.97), P=0.004). The LVEF was significantly higher in the cardiac rehabilitation group than in the control group. The sources of heterogeneity were analyzed, and it was found that one of the studies was the source of significant heterogeneity. After the elimination of that study, the data were reanalyzed, and the heterogeneity was significantly reduced. There were still significant differences in the WMD and 95% CI. Conclusion. Cardiac rehabilitation can improve exercise tolerance and cardiac function in heart failure patients with CRT. Future studies are needed to evaluate whether these beneficial effects of cardiac rehabilitation may translate into an improvement in long-term clinical outcomes among these patients.

Recent theory of Compressed Sensing (CS) suggests that exact recovery of an unknown sparse signal can be achieved from few measurements with overwhelming probability. In this paper, CS technology is combined with random noise radar and the concept of random noise radar is proposed based on CS. The block diagram of the radar system is presented. Detailed analysis show that the sensing matrix of the random noise radar based on CS exerts good Restricted Isometry Property (RIP). Given a sparse or transform sparse target scene, the random nose radar based on CS can get high accuracy image by collecting far less amount of echo data than conventional noise radar does. Finally, in this paper, the conclusions are all demonstrated by simulation experiments.

A novel approach is proposed for speckle reduction in multilook full polarimetric SAR images. In contrast to others, this approach adopts an enhanced structure detection method to estimate the parameters of the polarimetric covariance matrix for the multilook polarimetric whitening filtering (MPWF) algorithm and thus a structural adaptive and optimal specklefflter is developed. To evaluate the present approach, NASA SIR-C/XSAR, L band, four-look processed polarimetric SAR data of the Tian-Mountain Forest is used for simulation. Experimental results demonstrate the effectiveness of this novel filtering algorithm in case of both speckle reduction and preservation of texture information. Comparisons with other methods are also made.

This paper focus on the polarimetric synthetic aperture radar (SAR) tomography for forested areas based on compressive MUSIC. In the proposed method, full polarimetric SAR echo signal reflected from the imaging area is collected, the corresponding multiple measurement vector model is established according to the parameters of polarimetric channels, the wavelet basis is adopted for representing the sparse vertical structure of the imaging area, and finally, the backscattering coefficients of the area are reconstructed by compressive MUSIC algorithm. The necessary number of tracks for SAR tomography is reduced and the severity of spurious spikes is suppressed under the same measurement accuracy. Simulation results from the PolSARpro data validate the effectiveness.

Sparse microwave imaging radar is a newly developed concept of microwave imaging, which introduces the sparse signal processing theory to traditional microwave imaging. As a combination of sparse signal processing theory and microwave imaging, the sparse microwave imaging is attracting people's interest for its several potential advantages including better imaging performance and lower system complexity requirement. Airborne platform is an important application platform of sparse microwave imaging radar. On the airborne platform, the effect of motion phase error must be considered. Autofocus is an important technology to deal with the phase error in radar imaging, while traditional autofocus algorithms could not be directly applied under the sparse microwave imaging framework. In this paper, we introduce the phase recovery problem in the sparse signal processing theory to sparse microwave imaging, develop a novel autofocus algorithm under the sparse microwave imaging framework based on phase recovery. The phase recovery problem is solved via a greedy algorithm. A simulation is provided to demonstrate the effectiveness of the suggested algorithm.