As one of the most promising methods in the next generation of neuromorphic systems, memristor-based spiking neural networks (SNNs) show great advantages in terms of power efficiency, integration density, and biological plausibility. However, because of the nondifferentiability of discrete spikes, it is difficult to train SNNs with gradient descent and error backpropagation online. In this article, we propose an improved training algorithm for multilayer memristive SNN (MSNN) with three methods spontaneously, supporting in situ learning in hardware: 1) temporal order encoding is applied to generate different pulse trains in neurons; 2) a simplified homeostasis is realized by the activation state and refractory period to regulate hidden neurons spontaneously; and 3) spiking-timing-dependent plasticity (STDP) in memristive synapses is adopted to update weights in situ . Correspondingly, we provide a circuitry example and verify it in LTSPICE. Then the MSNN is benchmarked with the MNIST data set and analyzed with visualization methods, showing better recognition accuracy (95.15%) than existing SNNs with comparable scales and bio-inspired learning rules. We also consider some nonideal effects in memristor crossbar array and peripheral circuits. Evaluation results show that the proposed MSNN is robust to finite resolution, circuit noise and writing noise; and larger network scale will help the MSNN alleviate the negative impacts of other nonideal factors, including yield and device-to-device variation. Moreover, the energy efficiency of a MSNN system is estimated to achieve 7.6TOPS/W, showing great potential in low-power applications.

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.

This paper presents a novel bioimpedance emulation method designed for electrical impedance tomography system validation. The proposed method can emulate the impedance frequency characteristics of various biological samples from user configurations. The bioimpedance emulation system is realized in a hardware prototype comprising current sensing circuitry, voltage generating circuitry, a USB controller and a field-programmable gate array (FPGA) for reconfigurable digital control of emulated impedance. Experimental validation shows that the emulation system exhibits good accuracy ( > 97% at 1 kOhm magnitude) in the frequency range 1 kHz to 1 MHz. The digitally configurability offers advantages in flexibility, repeatability, and cost-efficient compared to more traditional approaches, simplifying the validation process of electrical impedance tomography systems.

The spectra of biological impedance can reveal physiological conditions and biological events of biological samples. This paper presents the development of a novel biological impedance spectroscopy system using an improved digital lock-in technique. The hardware of the system mainly consists of a current source, a voltage detector, a clock generator, and a field-programmable gate array (FPGA) device. Digital phase-sensitive detection algorithms including direct digital synthesis, digital multipliers, and digital filters were implemented in the FPGA. Test results show that the proposed spectroscopy has good performance from low frequency to 5 MHz with 1% accuracy. It will be suitable for high-accuracy biological impedance spectra analysis in either in vitro or in vivo conditions.

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.