Reverberation chambers (RCs) are used widely in the electromagnetic measurement area. An RC is designed to have a long reverberation time, generate numerous modes, and provide good field uniformity within the chamber. The purpose of this paper is to describe the design process and measurement of the KRISS Reverberation Chamber (KRC). KRC models with 4.5m × 3.4m × 2.8m dimensions are simulated by 3D numerical simulation software. The field uniformity and correlation coefficient are then analyzed at 200MHz to obtain the optimized model. The simulation results show good performance in terms of field uniformity and are confirmed by measurement from 200MHz to 1GHz. The lowest usable frequency (LUF) of KRC was confirmed by field uniformity to be 200MHz. However, the stirrer correlation coefficient results show good performance above 300MHz.

Autonomous Underwater Vehicle (AUV) can be utilized to directly measure the geomagnetic map in deep sea. The traditional map interpolation algorithms based on sampling continuation above the sea level yield low resolution and accuracy, which restricts the applications such as the deep sea geomagnetic positioning, navigation, searching and surveillance, etc. In this letter, we propose a Three-Dimensional (3D) Compressive Sensing (CS) algorithm in terms of the real trajectory of AUV which can be optimized with the required accuracy. The geomagnetic map recovered with the CS algorithm shows high precision compared with traditional interpolation schemes, by which the magnetic positioning accuracy can be greatly improved.

The voltage biased SQUID Bootstrap Circuit (SBC) was recently demonstrated for direct readout of SQUID signals. The SBC combines current- and voltage-feedbacks in one circuit to suppress the preamplifier noise. It offers not only a good noise performance, but also wide tolerance of SQUID parameters. Using SBC gradiometer, the bio-magnetic signals were successfully measured. In this paper, we overview the concept of SBC and its applications.

Correction factors are presented for estimating the RF electromagnetic field strength in the compliance assessment of human exposure from an indoor RF radio source in the frequency range from 800 MHz to 3.5 GHz. The correction factors are derived from the increase in the spatial average electric field strength distribution, which is dependent on the building materials. The spatial average electric field strength is calculated using relative complex dielectric constants of building materials. The relative complex dielectric constant is obtained through measurement of the transmission and reflection losses for eleven kinds of building materials used in business office buildings and single family dwellings.

The jaw movements can be measured by estimating the position and orientation of two small permanent magnets attached on the upper and lower jaws. It is a difficult problem to estimate the positions and orientations of the magnets from magnetic field because it is a typical inverse problem. The back propagation neural networks (BPNN) are applicable to solve this problem in short processing time. But its precision is not enough to apply to practical measurement. In the other hand, precise estimation is possible by using the nonlinear least-square (NLS) method. However, it takes long processing time for iterative calculation, and the solutions may be trapped in the local minima. In this paper, we propose a precise and fast measurement system which makes use of the estimation algorithm combining BPNN with NLS method. In this method, the BPNN performs an approximate estimation of magnet parameters in short processing time, and its result is used as the initial value of iterative calculation of NLS method. The cost function is solved by Gauss-Newton iteration algorithm. Precision, processing time and noise immunity were examined by computer simulations. These results shows the proposed system has satisfactory ability to be applied to practical measurement.

In this paper, we propose a method to reconstruct current distributions in the human brain from neuromagnetic measurements. The proposed method is based on the weighted lead-field synthetic (WLFS) filtering technique with the weighting factors calculated from the results of previous source space scanning. In this method, in addition to the depth normalization technique, weighting factors of the WLFS are determined by the cost values previously calculated based on the multiple signal classification (MUSIC) scan. We performed computer simulations of this method under noisy measurement conditions and compared the results to those obtained with the conventional WLFS method. The results of the simulations indicate that the proposed method is effective for the reconstruction of the current distributions in the human brain using magnetoencephalographic (MEG) measurements, even if the signal-to-noise ratio of the measured data is relatively low. We applied the proposed method to the magnetoencephalographic data obtained during a mental image processing task that included object recognition and mental rotation operations. The results suggest that the proposed method can extract the neural activity in the extrastriate visual region and the parietal region. These results are in agreement with the results of previous positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) studies.

As the track becomes narrower, the effects of track edge become more significant. These effects, such as the amplitude reduction, variations in the shape of the isolated pulse, the partial erasure and the amplitude asymmetry, are dependent on head/disk combination and the off-track position. These relationships are discussed in detail in this paper. More importantly, an off-track model is proposed to study the off-track BER performance for the head with narrow track width. The BER performances of EPRML channel for different off-track cases and different head/media combinations are studied based on this model. Simulation results have proved that this model is a useful tool for simulation of system performance.