An Ultrahighly Pressure Sensitive Electronic Fish Skin for Underwater Wave Sensing.

Underwater flexible sensors have a future for wide application, which is promising for attaching them to underwater creatures to monitor vital signals and biomechanical analysis of their motion and perceive tiny environmental disturbances. However, the pressure waves induced by biological swimming are extremely weak and susceptible to undercurrents, making them difficult to sense. Here, we report an ultrahighly sensitive biomimetic electronic fish skin designed by embedding an artificial pseudocapacitive-based hair cell into a simulated canal neuromast encapsulation structure, in which the artificial hair cell, as the key sensitive unit, is assembled from hybrid film electrodes and polyurethane-acidic electrolyte foam. Such a film is prepared by inter-cross-linking MXene and holey reduced graphene oxide with the assistance of l-cysteine, effectively increasing the interfacial capacitance and alleviating the oxidation issues of MXene. Meanwhile, the acidic foam with high porosity shows great compressibility to adapt to a high-pressure underwater environment. Consequently, the device exhibits ultrahighly sensitivity (maximum sensitivity ∼173688 kPa-1) over a wide range of depths (0-100 m) and remains stable after 10000 repeated tests. As an example case, the device is integrated as a motion monitoring system to identify the minor disturbances triggered by instantaneous postural changes of fish. The electronic fish skin is expected to demonstrate enormous potentials in flow field monitoring, ocean current detecting, and even seismic waves warning.

Amplitude Control Method of Magnetic Flux Vertical Modulation Structure for TMR Magnetic Sensor Based on Harmonic Component of Modulated Signal.

Magnetic flux vertical modulation method based on piezoelectric resonance can reduce the 1/f noise of tunnel magnetoresistance (TMR) magnetic sensor and significantly improves the low-frequency magnetic field detectivity. However, the amplitude variation of the modulation structure will lead to the instability of the sensor output. In order to improve the amplitude stability of the modulation structure, an amplitude control method based on the amplitude ratio of the first and second harmonic components of the modulated signal was proposed. Compared with the piezoelectric or capacitive feedback method, this method does not require an independent amplitude conversion circuit, and has the advantages of simple structure, high control efficiency and strong anti-interference ability. The experimental results showed that the amplitude and temperature drift of the modulated structure was significantly suppressed, which is of great significance for enhancing the adaptability of the TMR magnetic sensor to the application environments.

Resolution improvement of low frequency AC magnetic field detection for modulated MR sensors.

Magnetic modulation methods especially Micro-Electro-Mechanical System (MEMS) modulation can improve the sensitivity of magnetoresistive (MR) sensors dramatically, and pT level detection of Direct Current (DC) magnetic field can be realized. While in a Low Frequency Alternate Current (LFAC) magnetic field measurement situation, frequency measurement is limited by a serious spectrum aliasing problem caused by the remanence in sensors and geomagnetic field, leading to target information loss because frequency indicates the magnetic target characteristics. In this paper, a compensation field produced with integrated coils is applied to the MR sensor to remove DC magnetic field distortion, and a LFAC magnetic field frequency estimation algorithm is proposed based on a search of the database, which is derived from the numerical model revealing the relationship of the LFAC frequency and determination factor [defined by the ratio of Discrete Fourier Transform (DFT) coefficients]. In this algorithm, an inverse modulation of sensor signals is performed to detect jumping-off point of LFAC in the time domain; this step is exploited to determine sampling points to be processed. A determination factor is calculated and taken into database to figure out frequency with a binary search algorithm. Experimental results demonstrate that the frequency measurement resolution of the LFAC magnetic field is improved from 12.2 Hz to 0.8 Hz by the presented method, which, within the signal band of a magnetic anomaly (0.04-2 Hz), indicates that the proposed method may expand the applications of magnetoresistive (MR) sensors to human healthcare and magnetic anomaly detection (MAD).

A fast and accurate frequency estimation algorithm for sinusoidal signal with harmonic components.

Frequency estimation is a fundamental problem in many applications, such as traditional vibration measurement, power system supervision, and microelectromechanical system sensors control. In this paper, a fast and accurate frequency estimation algorithm is proposed to deal with low efficiency problem in traditional methods. The proposed algorithm consists of coarse and fine frequency estimation steps, and we demonstrate that it is more efficient than conventional searching methods to achieve coarse frequency estimation (location peak of FFT amplitude) by applying modified zero-crossing technique. Thus, the proposed estimation algorithm requires less hardware and software sources and can achieve even higher efficiency when the experimental data increase. Experimental results with modulated magnetic signal show that the root mean square error of frequency estimation is below 0.032 Hz with the proposed algorithm, which has lower computational complexity and better global performance than conventional frequency estimation methods.