An improved proton magnetometer for Earth's magnetic field observation
As a precision instrument to measure the earth magnetic field, proton magnetometer is widely used in different fields such as geological survey, buried objects detection and earth field variations. Due to poor signal to noise ratio (SNR) of the system, proton magnetometer suffers from low sensitivity which directly affects the performance. In order to increase the sensitivity, we present an improved proton magnetometer. First, the effect of matching resistance on Q value is discussed to enhance SNR, and high matching resistance has been chosen to improve the Q value of the resonant circuit. Second, noise induced by pre-amplifier is investigated in order to obtain low noise signal, and we adopt the JFET with noise figure less than 0.5dB as the pre-amplifier. Third, by using band-pass filter, low-noise output signal is obtained. Fourth, the method of period measurement based on CPLD is employed to measure frequency of the square wave shaped from the output sinusoidal signal. High precision temperature compensate crystal oscillator (TCXO) has been used to improve the frequency measurement accuracy. Last, experimental data is obtained through field measurements. By calculating the standard deviation, the sensitivity of the improved proton magnetometer is 0.15nT for Earth’s magnetic field observation. Experimental results show that the new magnetometer is sensitive to earth field measurement.
A comprehensive study on the weak magnetic sensor character of different geometries for proton precession magnetometer
The measurement precision or uncertainty of proton precession magnetometer (PPM) depends on the signal-to-noise ratio (SNR) of the free induction decay (FID) signal induced in the sensing coil. A diversity of weak magnetic measurement applications has benefited from various coils through a more comprehensive and reliable design. Likewise, numerous optimized sensors for PPM have been proposed and published in literature. However, due to lack of commonly accepted assessment measures and benchmark resources, it is hard to identify the performance of the proposed sensors and corresponding implementations. This paper investigates and categorizes sensing coils for PPM which are well used and accepted. Three state-of-the-art geometries of the coil including solenoid, toroid, and cylindric, are considered to evaluate the significance of these designs in terms of sensing signal strength analyses within their respective applications. Furthermore, the electromotive force (EMF) of FID signal is calculated as a function of sensing coils' geometrical parameters, and numerous critical factors such as the polarizing current, the orientation of the coil axis with respect to the direction of the external magnetic field, etc., are considered. Although the results of these calculations are limited by prerequisites, e.g. the sensor should be in a uniform magnetic field, it is a critical step for future sensor research. This paper aims to advance weak magnetic sensor development by establishing a comparative study to allow the optimum geometrical shapes to be chosen for different applications.
Speci fi cation Test for Panel Data Models with Interactive Fixed E ff ects ∗
In this paper, we propose a consistent nonparametric test for linearity in a large dimensional panel data model with interactive fixed effects. Both lagged dependent variables and conditional heteroskedasticity of unknown form are allowed in the model. We estimate the model under the null hypothesis of linearity to obtain the restricted residuals which are then used to construct the test statistic. We show that after being appropriately centered and standardized, the test statistic is asymptotically normally distributed under both the null hypothesis and a sequence of Pitman local alternatives by using the concept of conditional strong mixing that was recently introduced by Prakasa Rao (2009). To improve the finite sample performance, we propose a bootstrap procedure to obtain the bootstrap -value. A small set of Monte Carlo simulations illustrates that our test performs well in finite samples. An application to an economic growth panel dataset indicates significant nonlinear relationships between economic growth, initial income level and capital accumulation.