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This review reports the most current evidence on the effects of exercise interventions on improving physical function, daily living activities and quality of life in community-dwelling frail older adults. CBMdisc, CNKI, Wanfang, VIP, MEDLINE, Cochrane Central Register of Controlled Trials (Central), Pubmed, Embase, Web of Science and EBSCO were searched from inception to July 2019. Exercise interventions were found to improve knee extension strength and normal speed and were beneficial in lower Time up and go test (TUG) and improving semi-tandem, Berg balance scale (BBS), Short Physical Performance Battery (SPPB), Physical Performance Test (PPT) and Activities of daily living (ADL). Further well-designed studies are needed to explore the most effective intervention type and dose.

In this study, by employing the intra-pulse cross-correlation (IPCC) operation, an unambiguous velocity estimation method is proposed for narrow-band long-range radars with high carrier frequency and low pulse repetition frequency. This estimation algorithm is simple and could be easily implemented in existing radar systems without changing the radar hardware or the pulse transmitting scheme. Comparing with the slow time dimension correlation algorithm, the accuracy of the proposed intra-pulse frequency domain method is greatly improved, and the brute-force search for the unknown motion parameters is also unnecessary. By first setting a small frequency offset of the IPCC operation, the unambiguous velocity region could be significantly enlarged. Using the relatively coarse but unambiguous estimates and increasing the frequency offset step by step, the IPCC is repeatedly applied to obtain more accurate estimates. Note that the estimation results of the IPCC algorithm could be used in the maximum-likelihood estimator for ambiguity resolution. The Cramer-Rao bound for the proposed algorithm is derived, and the optimal frequency offset in the sense of estimation accuracy is also analysed. Through numerical simulations for both synthetic and real radar data, the effectiveness of the proposed estimation algorithm is verified.

The progress brought by the deep learning technology over the last decade has inspired many research domains, such as radar signal processing, speech and audio recognition, etc., to apply it to their respective problems. Most of the prominent deep learning models exploit data representations acquired with either Lidar or camera sensors, leaving automotive radars rarely used. This is despite the vital potential of radars in adverse weather conditions, as well as their ability to simultaneously measure an object’s range and radial velocity seamlessly. As radar signals have not been exploited very much so far, there is a lack of available benchmark data. However, recently, there has been a lot of interest in applying radar data as input to various deep learning algorithms, as more datasets are being provided. To this end, this paper presents a survey of various deep learning approaches processing radar signals to accomplish some significant tasks in an autonomous driving application, such as detection and classification. We have itemized the review based on different radar signal representations, as it is one of the critical aspects while using radar data with deep learning models. Furthermore, we give an extensive review of the recent deep learning-based multi-sensor fusion models exploiting radar signals and camera images for object detection tasks. We then provide a summary of the available datasets containing radar data. Finally, we discuss the gaps and important innovations in the reviewed papers and highlight some possible future research prospects.

A new numerical simulation method is developed for the evaluation of thermal and mechanical analyses of manufactured structures with Metal Inert Gas (MIG) welding on the basis of the peridynamics. The peridynamic formulations of the weak coupled thermo-mechanical problems are derived based on a heat transfer model and a microplastic model. To simulate the MIG welding process, a birth–death material point method in the peridynamic framework is proposed to model the addition of weld metal to the workpiece. To simplify the calculation process and improve the computational efficiency due to no need of updating the neighbourhoods of material points, an additional scalar field is introduced in the equations to describe the birth and death states of the material points. In addition, the present method is applied to simulate a classical model of two plates being joined together by the MIG welding. The results indicate that the residual tensile deformations are formed in the welded area while the residual compressive deformations are formed far from the wild line after the release of the clamps on account of the plastic deformation. These results also demonstrate the validity of the present method for the simulation of the MIG welding by comparing with the reference solution.

Millimeter-wave radar has proven to have a good range estimation accuracy and is less influenced by weather conditions. However, it is difficult for radar to recognize objects, and it is prone to cause a false alarm. In this paper, we present an object detection and classification by jointly using a radar and camera sensors for traffic surveillance applications. The proposed method fuses the Regions of Interest (ROIs) generated on each of the detection results obtained independently from radar and camera sensors. Reducing the high false alarm of a radar sensor is the main aim of the fusion method. Then, a Convolutional Neural Network (CNN) is used to classify the final fused detected objects into one of the six-vehicle categories; Sedan, Truck, Minivan, Bus, Microbus, and SUV. The proposed method was verified using real data. Results obtained demonstrate the good performance of the proposed fusion approach in traffic surveillance context.

The sea clutter modeling is critical to the radar design and assessment of relevant detection algorithms. In this paper, we investigate a family of generalized autoregressive conditional heteroscedastic (GARCH) processes to model the sea clutter as a time series, in which the current variance is dependent on historical information. The most general model (so-called the ALLGARCH model) provides more flexible variance structures to model non-Gaussian, asymmetry, and nonlinear properties of the clutter. However, after going through the usage of the ALLGARCH model, we find that it is not very suitable because the coefficients of the model, which are numerous, would be difficult to estimate in a real-time operating environment. Meanwhile, we find that some of the coefficients are negligible under almost all kinds of sea environments and weather conditions. Motivated by these observations, we propose a novel GARCH model for sea clutter modeling, which is a generalization of the nonlinear-asymmetric GARCH (NAGARCH) model. Considering the correlation between adjacent clutter returns, autoregressive terms are also introduced. By systematically analyzing practical sea clutter data under different sea environments, we demonstrate that the proposed model achieves comparable fitting effect to some commonly used statistical models. Also, we develop the corresponding generalized likelihood ratio test (GLRT) algorithm for the new model. Numerical simulations exhibit that the proposed detector achieves higher probability of detection, comparing with the AR-GARCH detector.

The radar echo model used to estimate the motion parameters of a moving target is found to be inaccurate and biased. In order to improve the parameter estimation accuracy of high-speed targets, we extend the maximum likelihood estimation (MLE) method from slow-speed echo model to high-speed echo model and propose an MLE estimator based on Newton's method for high-speed targets. Newton's iteration formula and Cramer-Rao lower bound (CRLB) are derived in closed form for high-speed echo signal and the convergence region is briefly analyzed. The results of Monte Carlo simulation show the proposed approach achieves the CRLB and outperforms the existing estimators. The bias of the slow-speed echo model and the accuracy of the high-speed echo model are verified with the real radar data and simulation data.

When using a long range radar (LRR) to track a target with micromotion, the micro-Doppler embodied in the radar echoes may suffer from ambiguity problem. In this paper, we propose a novel method based on compressed sensing (CS) to solve micro-Doppler ambiguity. According to the RIP requirement, a sparse probing pulse train with its transmitting time random is designed. After matched filtering, the slow-time echo signals of the micromotion target can be viewed as randomly sparse sampling of Doppler spectrum. Select several successive pulses to form a short-time window and the CS sensing matrix can be built according to the time stamps of these pulses. Then performing Orthogonal Matching Pursuit (OMP), the unambiguous micro-Doppler spectrum can be obtained. The proposed algorithm is verified using the echo signals generated according to the theoretical model and the signals with micro-Doppler signature produced using the commercial electromagnetic simulation software FEKO.

We studied the flow-induced vibration (FIV) and fretting wear of fuel rod with grid relaxation. According to the flow distribution around a type of pressurized water reactor (PWR) fuel rod, the power spectral density (PSD) is obtained to characterize the turbulence excitation. By combining the correlation of PSD test parameters, the mean square value of the vibration displacement of each rod mode is found, and then the wear depth of dimple position is calculated based on the ARCHARD wear formula. The grids may relax due to inaccurate manufacturing, fuel transportation and in-core irradiation. The absence of grid clamping force would significantly influence the rod mode and thereby changes its FIV responses. Simulation results show that the failure of the leaf spring has negligible effect on the rod natural frequency whereas the dimple failure near the location with larger FIV amplitude has a much significant effect. The lateral flow velocities at the inlet and outlet of the core are larger. For the fully clamped fuel rod, the responses amplitude of turbulent excitation at the bottom and top of the fuel rod are larger. This is even more obvious with a failed dimple at these locations. Comparatively, the effect of dimple support failure in the middle is less influential. The influence of dimple support failure on the rod wear depth depicts basically the same trend as on the maximum FIV amplitude.

We have published a ground-coupled GPR antenna array, which consists of one transmitter and five receivers, for in situ estimation of the thickness and dielectric permittivity of an asphalt pavement layer [1]. In this paper, we optimize the design of the antipodal Vivaldi antenna to pursue a higher resolution through reducing the late-time ringing. Furthermore, an off-ground antenna array is assembled, and can effectively accelerate the acquisition of common-source GPR datasets. The antenna phase center was calibrated to estimate the velocity and thickness of both the air gap and asphalt pavement layer, through velocity spectrum analysis. Numerical experimental results show that the off-ground antenna array is one ideal method for inspecting asphalt pavements.

This letter proposes a dual-polarized frequency selective rasorber (FSR), which exhibits bandpass filtering response together with absorption performance at both sides of the passband. Although the design concept is modified from a three-dimensional FSR, the proposed FSR is implemented by cross-inserting a few identical printed circuit board pieces etched with planar slotline structures, thus releasing difficulties in fabrication and assembly. The bandpass characteristic is provided by lossless resonators constructed in the slotlines with center-loaded U-type strips, whereas the absorption bands are realized by lossy resonators formed in short-circuited slotlines with lumped resistors. Surface current distributions are illustrated to explain the operation mechanism. A prototype of the designed FSR is fabricated and measured. The measured results show that the minimum insertion loss in the passband is about 0.3 dB, and the absorption bandwidths are ranged from 1.6 to 2.3 GHz and from 3.6 to 4.6 GHz, respectively. To further demonstrate the design concept, another FSR with wider absorption bands is developed by introducing more lossy resonators. Moreover, the proposed FSRs feature stable performance under oblique incident angles.

The surface of haemoglobin (Hb) is chemically modified to produce molecular dispersions of discrete core–shell Hb–polymer surfactant bionanoconjugates in water and organic solvents. The hybrid nanoconstructs exhibit peroxidase-like catalytic activity with enhanced turnover rates compared with native Hb in water.

Our aim is to investigate the significance of electrogastrography in the treatment of esophageal variceal hemorrhage with octreotide. Electrogastrography was performed in patients with esophageal variceal hemorrhage before and during the treatment consisting of various doses of octreotide (25 ug h(-1) group and 50 ug h(-1) group). The dominant power of electrogastrography and its relationship with the hemostatic efficacy of octreotide treatment were evaluated. Dominant power of electrogastrography decreased significantly during treatment with octreotide (P0.05). The reduction in the amplitude of dominant power in the 50 ug h(-1) group was significantly larger than in the 25 ug h(-1) group (P0.05), and it was correlated with hemostatic efficacy of octreotide treatment. We conclude that octreotide treatment in patients with esophageal variceal hemorrhage can result in a significant decrease of dominant power, which correlates with the hemostatic efficacy of octreotide, so the change of dominant power could be used as a predictor of evaluating the treatment efficacy of octreotide in esophageal variceal hemorrhage patients.