The time-interleaved analog-to-digital converter (TIADC) is an architecture used to achieve a high sampling rate and high dynamic performance. However, estimation and compensation methods are required to maintain the dynamic performance of the constituent analog-to-digital converters (ADCs) due to channel mismatches. This paper proposes a blind adaptive method to calibrate the nonlinear mismatches in M-channel TIADCs (M-TIADCs). The nonlinearity-induced error signal is reconstructed by the proposed multiplier Hadamard transform (MHT) structure, and the nonlinear parameters are estimated by the filtered-X least-mean square (FxLMS) algorithm. The performance of cascade calibration is also analyzed. The numerical simulation results show that the proposed method consumes much less hardware resources while maintaining the calibration performance.

Electrical Impedance Tomography (EIT) is applied to acquire the impedance characteristic, which is correlative with the physiological and pathological status of a body, and reconstruct the images of impedance distribution.

Integral nonlinearity is an important parameter affecting the performance of Analog-Digital Converters(ADC). And the integral nonlinearity (INL) is used for the calibration of analog digital converter. In order to let high precision ADC perform excellent performance indicators, In this paper, we propose a method based on quadratic functions to piecewise fit and calibrate the nonlinear error of the high precision analog to digital converter chip (Analog Devices AD9680). The model consists of a static term that is dependent on the ADC output code and a dynamic term that has an additional dependence on the input frequency. Test results show that within the bandwidth of 200MHz, Spurious Free Dynamic Range, Signal to Noise and Distortion Ratio, Effective Number of Bits and other indicators of ADC were got better improvement. In addition, this calibration algorithm has a certain universality, the calibration algorithm is expected to apply to all pipelined ADC.