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https://www.selleckchem.com/products/etc-159.html This paper presents an error-tolerant and power-efficient impedance measurement scheme for bioimpedance acquisition. The proposed architecture measures the magnitude and the real part of the target complex impedance, unlike other impedance measurement architectures measuring either the real/imaginary components or the magnitude and phase. The phase information of the target impedance is obtained by using the ratio between the magnitude and the real components. This can allow for avoiding direct phase measurements, which require fast, power-hungry circuit blocks. A reference resistor is connected in series with the target impedance to compensate for the errors caused by the delay in the sinusoidal signal generator and the amplifier at the front. Moreover, an additional magnitude measurement path is connected to the reference resistor to cancel out the nonlinearity of the proposed system and enhance the settling speed of the low-pass filter by a ratio-based detection. Thanks to this ratio-based detection, the accuracy is enhanced by 30%, and the settling time is improved by 87.7% compared to the conventional single-path detection. The proposed integrated circuit consumes only 513 μW for a wide frequency range of 10 Hz to 1 MHz, with the maximum magnitude and phase errors of 0.3% and 2.1°, respectively.Rock lithology recognition plays a fundamental role in geological survey research, mineral resource exploration, mining engineering, etc. However, the objectivity of researchers, rock variable natures, and tedious experimental processes make it difficult to ensure the accurate and effective identification of rock lithology. Additionally, multitype hybrid rock lithology identification is challenging, and few studies on this issue are available. In this paper, a novel multitype hybrid rock lithology detection method was proposed based on convolutional neural network (CNN), and neural network model compression technology was

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