In this paper, a single-shot 360-degree cranial deformity detection system using digital image correlation (DIC) is presented to quickly obtain and detect accurate 3D data of infants’ cranium. By introducing plane mirrors into a stereo 3D DIC measurement system, a multi-view 3D imaging model is established to convert 3D data from real and virtual perspectives into 360-degree 3D data of the tested infant cranium, achieving single-shot and panoramic 3D measurement. Experimental results showed that the performance and measurement accuracy of the proposed system can meet the requirements for cranial deformity detection, which provides a fast, accurate, and low-cost solution medically.

The stockpiling, delivery, and provision of emergency material were in the public gaze of millions of people when the coronavirus disease 2019 (COVID-19) broke out. Civil-military integration emergency logistics silently opened up the “second battlefield” of anti-epidemic, and established a lifeline under that emergency situation. Research on the construction of civil-military integrated logistics system plays an extremely important role and occupies a significant position in ensuring social stability and security as well as the stable development of social economy in China. The modern economy driven by the Internet, Internet of Things, and big data demonstrates a rapid growing trend calling for efficient, fast, and convenient logistics. It is urgent to upgrade or build an intelligent logistics system with intelligent technology and unmanned technology as the core to meet the international and domestic market demand. As mentioned above, this paper analyzes and expounds the construction problem and practical significance of civil-military integration emergency logistics system based on unmanned technology, and puts forward the strategy of constructing civil-military integration emergency logistics system with unmanned technology under the new system.

Complicated electromagnetic environments of the space situational awareness facilities (i.e., satellite navigation systems, radar) would significantly impact normal operations. Effective monitoring and the corresponding diagnosis of the jamming signals are essential to normal operations and the innovations in anti-jamming equipment. This paper demonstrates a comprehensive survey on jamming monitoring algorithms and applications. The methods in dealing with jamming signals are summarized primarily. Subsequently, the jamming detection, identification, and direction finding techniques are addressed separately. Based on the established studies, we also provide some potential trends of the demonstrated jamming monitoring issues.

The complicated electromagnetic environment of the BeiDou satellites introduces various types of external jamming to communication links, in which recognition of jamming signals with uncertainties is essential. In this work, the jamming recognition framework proposed consists of feature fusion and a convolutional neural network (CNN). Firstly, the recognition inputs are obtained by prepossessing procedure, in which the 1-D power spectrum and 2-D time-frequency image are accessed through the Welch algorithm and short-time Fourier transform (STFT), respectively. Then, the 1D-CNN and residual neural network (ResNet) are introduced to extract the deep features of the two prepossessing inputs, respectively. Finally, the two deep features are concatenated for the following three fully connected layers and output the jamming signal classification results through the softmax layer. Results show the proposed method could reduce the impacts of potential feature loss, therefore improving the generalization ability on dealing with uncertainties.

Obtaining polarization information enables researchers to enhance underwater imaging quality by removing backscattering effect and to distinguish targets of different materials. However, due to the simplified assumption of unpolarized target light, most of the existing underwater polarimetric methods lose part of the polarization information, resulting in degraded imaging quality. In this work, a novel underwater polarimetric method is reported, which obtains the angle of polarization (AOP) map to improve imaging quality. Specifically, the Stokes vectors were exploited to remove the backscattering effect by obtaining two pairs of orthogonal polarization sub-images of the underwater scene. The target reflected light and the angle between the polarization directions of the target reflected light and the backscattered light were computed through the two groups of the orthogonal polarized sub-images. The AOP map of the target light could be derived from the Stokes vectors. Then, the transmission map of the target light was estimated by using the non-local color priorly combined with the properties of light propagating underwater. Experiments show that the reported technique enables distinguishing different targets when the colors are similar. The quantitative metrics validate that the reported technique produces state-of-the-art performance for underwater imaging.

In this paper, a fast-speed and real-time online rail inspection method based on half-cycle orthogonal power demodulation algorithm is proposed. For this method, the power characters of detection signal which represent the degree of rail track can be calculated using only half-cycle detection signal because of the symmetry characteristic of detected sine signal and reference signal. The theoretical analysis, simulation results and experiment results show that the demodulation precision of proposed method is almost equal to fast Fourier transform (FFT) demodulation method and orthogonal demodulation method, but has high demodulation efficiency and less FPGA resources cost. A high-speed experiment system based on three coils structured sensor is built for rail inspection experiment at a moving speed of 200 km/h. The experiment results show that proposed method is more effective for rail inspection and the time resolution of proposed method is double of classic method that based on FFT and orthogonal.

With the rapid advancement of technology, not only do we need to acquire a clear inverse synthetic aperture radar (ISAR) image, but also the real size of the target on the imaging plane, so it’s particularly important for the ISAR to rescale the images. That is, the ISAR image which is in the range-Doppler domain is converted into the range-azimuth domain. Actually, the key point to solving the problem is to estimate the rotation parameters. In this paper, a new scheme to rescale the images is proposed. For the sake of solving the problem of two-dimensional image blur and target high-speed, the instantaneous range instantaneous Doppler (IRID) method is used to obtain ISAR images, and the rotation parameters are estimated by comparing the rotation correlation of the two images. Using this method, the error of the estimated rotation parameters is greatly reduced, so that the target can be rescaled accurately. The simulation results verify the effectiveness of the proposed algorithm.

With the widespread application of electrification and intelligence of automobiles, the number of electric devices with small DC motors in automobiles has gradually increased, and the interior of electric vehicles is quieter. The sound quality (SQ) of small motor directly affects the passenger experience. Therefore, the research on the SQ of small motor is of great significance. In this paper, the objective quantification of small motor sound quality was investigated based on traditional psychoacoustic metrics. The time-frequency characteristics of sound signal was analyzed to quantify the subjective perception caused by the sound of small motor. And a new psychoacoustic metrics of objective evaluation which were suitable for small motor SQ evaluation were proposed, namely specific loudness energy (SLE), specific prominence ratio index (SPRI), relative pitch exceedance (RPE) and tremolo index (TI). Then, two objective evaluation models of small motor SQ were established to characterize the multi-dimensional subjective perception attributes by using multiple linear regression (MLR) and support vector regression (SVR) respectively, which can be used for the prediction and evaluation of the small motor SQ. The results show that the prediction accuracy of the model established by SVR method was higher than that of MLR, and SVR has stronger robustness. The objective evaluation model of small motors SQ established in this study is of great importance for improving the sound quality of small motors.