Research on the imaging of concrete defect based on the pulse compression technique

When the synthetic aperture focusing technology （SAFT） is used for the detection of the concrete, the signal-to-noise ratio （SNR） and detection depth are not satisfactory. Therefore, the application of SAFT is usually limited. In this paper, we propose an improved SAFT technique for the detection of concrete based on the pulse compression technique used in the Radar domain. The proposed method first transmits a linear frequency modulation （LFM） signal, and then compresses the echo signal using the matched filtering method, after which a compressed signal with a narrower main lobe and higher SNR is obtained. With our improved SAFT, the compressed signals are manipulated in the imaging process and the image contrast is improved. Results show that the SNR is improved and the imaging resolution is guaranteed compared with the conventional short-pulse method. From theoretical and experimental results, we show that the proposed method can suppress noise and improve imaging contrast, and can also be used to detect multiple defects in concrete.

Preparation and coking properties of coal maceral concentrates

The concentrates with different maceral contents were obtained from Kailuan coking coals with different coal ranks(Ro;ranvarying from 0.88%to 1.73%)by float–sink separation in lab.Then these concentrates were characterized by proximate analysis,ultimate analysis,petrography analysis and coking index determination.The results show that the vitrinite is characterized as nature of lower carbon content,higher hydrogen content,higher volatile matter and stronger caking property compared to inertinite.The relationships between variation rate of volatile matter and maximum volatile matter and coal ranks are identified,and a linear model is developed for fast determination of the maceral contents.Compared to inertinite-rich concentrate,the blending ratio of vitrinite-rich concentrate is increased by 13%,which is considered to be a potential technique based on maceral separation for expanding the coking coal resources.

Complete Focal Plane Compression Based on CMOS Image Sensor Using Predictive Coding

In this paper, a CMOS image sensor(CIS) is proposed, which can accomplish both decorrelation and entropy coding of image compression directly on the focal plane. The design is based on predictive coding for image decorrelation. The predictions are performed in analog domain by 2×2 pixel units. Both the prediction residuals and original pixel values are quantized and encoded in parallel. Since the residuals have a peak distribution around zero,the output codewords can be replaced by the valid part of the residuals' binary mode. The compressed bit stream is accessible directly at the output of CIS without extra disposition. Simulation results show that the proposed approach achieves a compression rate of 2. 2 and PSNR of 51 on different test images.