Purpose: The purpose of this paper is to study the effects of relational mechanisms and market contracts on cross-enterprise knowledge trading in supply chain and to examine the role of market contracts. Relational mechanism is categorized into indirect and direct relational mechanism in this paper. Cross-enterprise knowledge trading is categorized into explicit and tacit knowledge trading. The indirect relational mechanism is mainly expressed by knowledge brokers, while the direct relational mechanism consists of shared goals and trust. Design/methodology/approach: Multiple regression analysis was performed on questionnaire data from 256 Chinese manufacturing enterprises in supply chain in order to assess the relationships between relational mechanisms, market contracts and cross-enterprise knowledge trading. Findings: The results show that knowledge brokers and market contracts have significant and positive effects on explicit knowledge trading, but the effects on tacit knowledge trading are not significant. Shared goals and trust have significant and positive effects not only on explicit knowledge trading but also on tacit knowledge trading, while trust has a stronger positive effect on tacit knowledge trading than explicit knowledge trading. Finally, the moderating effects of market contracts are proven in the relationships between relational mechanisms and knowledge trading, excluding the relationship between knowledge brokers and tacit knowledge trading. Originality/value: Previous studies about the cross-enterprise knowledge trading in supply chain focused on theoretical research which did not match with reality, especially in China, where the relational mechanism in trading activities is strong. Based on relational exchange theory and transaction cost theory, a conceptual model for the effects of relational mechanisms and market contracts on cross-enterprise knowledge trading in supply chain is proposed in this paper, and then empirically tested using the data collected from 256 Chinese manufacturing enterprises in supply chain with multiple regression models. The findings provide a theoretical basis for knowledge trading participants selecting an appropriate governance mechanism to promote knowledge trading, and these also guide the knowledge trading among members of supply chain in practice.

Mismatches affect the dynamic performance of time-interleaved analog-to-digital converters (TIADCs). Linear mismatches can be calibrated by many mature methods, but if higher performance is required, nonlinearity mismatches have to be suppressed. The background calibration method based on input-free band (IFB) functions poorly for narrow-band signals. This brief proposes a correlation-based calibration method for nonlinearity mismatches in dual-channel TIADCs which behaves well for both wide-band and narrow-band signals. The output samples are calibrated by reducing the residual distortions which are approximated by multiplying the pseudo distortions and the estimated mismatch coefficients. The pseudo distortions are acquired by using a frequency-shifter, a differentiator, and multipliers. The coefficients which indicate the mismatch strength are estimated by eliminating the cross-correlation of the calibrated output samples and the calibrated pseudo distortions at zero lag. Simulations show that the proposed method can improve the SFDR by dozens of dBc for narrow-band input signals, compared with the IFB method. For the 16-QAM signal, the error vector magnitude improvement over the IFB method is 35.48 dB.

Time-interleaved analog-to-digital converter (TIADC) is a good option for high sampling rate applications. However, the inevitable sample-and-hold (S/H) mismatches between channels incur undesirable error and then affect the TIADC’s dynamic performance. Several calibration methods have been proposed for S/H mismatches which either need training signals or have less extensive applicability for different input signals and different numbers of channels. This paper proposes a statistics-based calibration algorithm for S/H mismatches in M-channel TIADCs. Initially, the mismatch coefficients are identified by eliminating the statistical differences between channels. Subsequently, the mismatch-induced error is approximated by employing variable multipliers and differentiators in several Richardson iterations. Finally, the error is subtracted from the original output signal to approximate the expected signal. Simulation results illustrate the effectiveness of the proposed method, the selection of key parameters and the advantage to other methods.

The output frequency response for most DACs rolls off according to the sin(x)/x frequency-response envelope [1]. This paper derives a FIR filter which is designed in the minimax sense with the ripple constraints as the design criteria for the frequency response compensation of DACs. The filter order estimation function under the criterion gives a detailed filter design configuration method, which can effectively shorten the design time and provide an accurate resource configuration reference for the top-level system design [2]. The simulation example verifies the advantages of the compensation filter in minimax sense and shows the performance of the compensation filter and the accuracy of the order estimation function.

This paper presented the design of a data acquisition system for Cellular Impedance Measurement (CIM) application. The hardware architecture of the system mainly consists of a micro electrode array, a stimulation circuit, a sensing circuit, a multi-function card and a PXI-E based Chassis. The test results prove that the proposed system has excellent phase accuracy (error <0.05%) and magnitude accuracy (error <0.2%). It is suitable for real-time cell monitoring and analysis applications in cell research.

Datasets of ARBic: an all-round biclustering algorithm for analyzing gene expression data