As the evolution of traditional electroencephalogram (EEG) monitoring unit for epilepsy diagnosis, wearable ambulatory EEG (WAEEG) system transmits EEG data wirelessly, and can be made miniaturized, discrete and social acceptable. To prolong the battery lifetime, analog wavelet filter is used for epileptic event detection in WAEEG system to achieve on-line data reduction. For mapping continuous wavelet transform to analog filter implementation with low-power consumption and high approximation accuracy, this paper proposes a novel approximation method to construct the wavelet base in analog domain, in which the approximation process in frequency domain is considered as an optimization problem by building a mathematical model with only one term in the numerator. The hybrid genetic algorithm consisting of genetic algorithm and quasi-Newton method is employed to find the globally optimum solution, taking required stability into account. Experiment results show that the proposed method can give a stable analog wavelet base with simple structure and higher approximation accuracy compared with existing method, leading to a better spike detection accuracy. The fourth-order Marr wavelet filter is designed as an example using Gm-C filter structure based on LC ladder simulation, whose power consumption is only 33.4 pW at 2.1Hz. Simulation results show that the design method can be used to facilitate low power and small volume implementation of on-line epileptic event detector.
Analog filter implementation of continuous wavelet transform is considered as a promising technique for on-line spike detection applied in wearable electroencephalogram system. This Letter proposes a novel method to construct analog wavelet base for analog wavelet filter design, in which the mathematical approximation model in frequency domain is built as an optimization problem and the genetic algorithm is used to find the global optimum resolution. Also, the Gm-C filter structure based on LC ladder simulation is employed to synthesize the obtained analog wavelet base. The Marr wavelet filter is designed as an example using SMIC 1V 0.35µm CMOS technology. Simulation results show that the proposed method can give a stable analog wavelet filter with higher approximation accuracy and excellent circuit performance, which is well suited for the design of low-frequency low-power spike detector.
This paper presents a real-time output 56 GS/s 8 bit time-interleaved analog-to-digital converter (ADC), where the full-speed converted data are output by 16-lane transmitters. A 64-way 8 bit asynchronous SAR array using monotonous and split switching strategy with 1 bit redundancy is utilized to achieve a high linearity and high-power efficiency. A low-power ring voltage-controlled oscillator-based injection-locked phase-locked loop combining with a phase interpolator-based time-skew adjuster is developed to generate the 8 equally spaced sampling phases. Digital gain correction, digital-detection-analog-correction offset calibration, and coarse–fine two-step time-skew calibration are combined to optimize the ADC’s performances. An edge detector and phase selector associated with a common near-end data-transmission position and far-end data-collection instant are designed to avoid reset competition and implement deterministic latency. Fabricated in a 28 nm CMOS process, the prototype ADC achieves an outstanding SNDR of 36.38 dB at 56 GS/s with a 19.9 GHz input, where 7.25 dB and 9.33 dB are optimized by offset-gain calibration and time-skew calibration, respectively. The ADC core occupies an area of 1.2 mm2 and consumes 432 mW power consumption.
To provide multiple options for specific application in biosignal processing, the programmable Gaussian‐derived Gm‐C wavelet filter has been proposed. To realize the programmable characteristic, the analog wavelet base with one numerator term is constructed by using hybrid artificial fish swarm algorithm. Also, the inverse follow‐the‐leader feedback Gm‐C filter structure with a switch array is employed. By programming switches only, Gaussian and Marr wavelet transforms can be realized flexibly with all component parameters unchanged. The seventh‐order programmable wavelet filter is designed as an example. Simulation results show that power consumption is only 141.68 pW at scale a = 0.1, with dynamic range of 42.6 dB and figure‐of‐merit of 2.05 × 10−13. Due to the programmability, the proposed design method can implement two wavelet filters with very low circuit complexity.
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