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Portability and low cost make the PXIe system the best choice to reduce the cost of integrated circuit(IC) testing. However, due to the limitation of the PXIe module volume, the number of channels in a single module cannot be enough. And the testing of an IC with more pins will require multiple modules to achieve. In general, the synchronization performance between channels will be one of the most important indicators which affect the test capability of an IC test system. In this paper, a method which based on calibration to achieve synchronization between test channels in the IC test system is proposed. Through testing, toe time delay between board-level channels is

The generation of high-speed digital vector is one of the main problems in IC testing. With the increasing of the working frequency of digital circuits, the speed and accuracy of digital test vector are also required to be higher and higher. Based on FPGA high-speed transceiver, this paper provides a method of generating high-speed digital integrated circuit test vectors. It also introduces the specific implementation of high-speed digital pattern synthesis and broadening, and explains the possible situations in it. The resulting test vector speed is up to 800Mbps and the edge positioning accuracy can reach 40ps.

Camera calibration is the first and the most important step of spatial position measurement. Aiming at improving the measurement precision over a large field of view and long distance, the internal and external parameters of the camera need to be calibrated accurately. Therefore, a simple and accurate calibration method is proposed to solve the problem that a small calibration board cannot appear in the field of view of multiple cameras at the same time. We calibrate the internal and external parameters of camera separately. For the internal parameters, we calculate it using analytical solution estimation method after collecting calibration plate images for feature point detection. For the external parameters, we accurately obtain the relative positions of the cameras with the help of the total station, and then unify the pose relationship of each camera to the same coordinate system through rigid body transformation to obtain the parameters. Then, the maximum likelihood method is used to optimize the estimation of both internal and external parameters to improve the calibration accuracy. Finally, the three-dimensional coordinates of target can be obtained by triangulation. The experimental results show that this method meets requirement of calibration accuracy, and the error of the 3D spatial position of the target is in the centimeter level.

DC regulated power supplies are widely used in teaching, scientific research, industrial production and other fields. The accuracy of the power supply in the automatic test system will affect the quality of the test results. In order to design a high-precision DC regulated power supplies, based on closed-loop negative feedback and self-calibration technology, we use four-wire Kelvin sense and fuzzy adaptive PID control algorithm to improve the accuracy of the output's power, while floating output method has also been used to simplify the design. A microcomputer has been used to control a DAC which outputs a analog voltage to drive a power amplifier, in this way, the power amplifier could output a adjustable voltage with continuously. At the same time, a ADC has been used to sample the amplifier's output and send the sample value to the microcomputer for negative feedback control. A power dissipation control circuit has been designed based on the junction temperature characteristics of the power amplifier to improve power conversion efficiency and accuracy. The experiment test results show that the designed power supply's output voltage range is $0\sim\pm 30\mathrm{V}$ , accuracy is ± ( $0.25\% \ast \mathrm{U}\pm 10\text{mV}$ ), current range is $0\sim\pm 6\mathrm{A}$ , accuracy is ± ( $0.2\%^{\ast}\mathrm{I}\pm 5\text{mA}$ ), maximum power is 65W, which meets the expectations target.

Large amounts of wastewater are generated from stone processing, which are toxic and cause serious environmental and health risks. To quantify the content of stone processing wastewater and estimate its effects on plant growth, we collected water samples from sewage outfall of four stone processing factories and nearby water bodies. The concentration of potential toxic metals were much higher in the wastewater than background controls. Wastewater inhibited plant primary root elongation, lateral root formation, and growth of aerial part. Seedlings treated with the effluents were unhealthy with deep purple leaves and usually died before flowering. Chlorophyll a/b contents and chloroplast number were reduced in those abnormal mesophyll cells. Transcriptional levels were decreased for chloroplast formation genes, but increased for those participated in chloroplast degradation and catabolism. Six out of nine tested senescence-associated genes were up-regulated. Furthermore, our results show that endogenous toxic metal levels indeed increased after wastewater treatment. Altogether, these results indicated that the potential toxic metals rich wastewater had significant inhibition on plant growth and led to senescence-associated program cell death, which could be helpful for the government and enterprises to understand the environmental risks and formulate reasonable wastewater emission standards for the stone processing industry.

This paper utilizes administrative data to analyze expenditure responses to the health insurance policy in rural China, and clear visual evidence of bunching is observed at the kink point. A static response model with optimization frictions estimates that a complete elimination of the reimbursement would cause the total expenditure per visit to decrease by 34.5%, and approximately one-third of the studied population makes decisions with errors. Heterogeneous expenditure responses and optimization frictions are observed across demographic groups. Cost-benefit and counterfactual analyses indicate that the current policy generates the greatest welfare gains.

Research on Computer-aided diagnosis (CAD) of breast cancer in the last decade is reported tremendous. In this paper, a breast cancer diagnosis system based on rough set theory, SOM neutral work, and Genetic Algorithm is proposed. With this system, we transform data to knowledge which is very important to early diagnosis and further medical research of breast cancer. The system first discretizes breast cancer medical data by training SOM neural network; then reduce condition attributes by GA; finally induce diagnosis rules from decision table. Prior knowledge is not required by system. Specific diagnosis rules are available, and result is objective. Testing system on WDBC data set, we get 48 breast cancer diagnosis rules. Use these rules as classifier to evaluate performance of diagnosis system. The experiment result shows that diagnosis accuracy is substantially increased compared with traditional diagnosis method and other CAD methods.