Effects of pulse parameters on the temperature distribution of a human head exposed to the electromagnetic pulse.

The presence of blood-brain barrier (BBB) is a major obstacle to effectively deliver therapeutics to the central nervous system (CNS); hence, the outcomes following treatment of CNS diseases remain unsatisfactory. Fortunately, electromagnetic pulses (EMPs) provide a non-invasive method to locally open the BBB. To obtain the optimal pulse parameters of EMP-induced BBB opening to ensure the effective delivery of CNS drugs, it is particularly important to measure and assess the effects of pulse parameters on the temperature distribution in the human head exposed to EMPs. In this paper, the specific anthropomorphic mannequin phantom was adopted and the temperature increase in the human head induced by EMPs of different parameters was estimated in the software "COMSOL Multiphysics". The results show that the temperature distribution profiles with different EMP parameters have almost similar characteristics, the highest temperature increase values in the human head are positively correlated with variations of EMP parameters, and potential hazards to the human head may occur when EMP parameters exceed the safety threshold, which will provide theoretical basis for seeking the optimal EMP parameters to open the BBB to the greatest extent within a safe range.

Suppression of Continuous Wave Interference in Loran-C Signal Based on Sparse Optimization Using Tunable Q-Factor Wavelet Transform and Discrete Cosine Transform.

Loran-C is the most essential backup and supplementary system for the global navigation satellite system (GNSS). Continuous wave interference (CWI) is one of the main interferences in the Loran-C system, which will cause errors in the measurement of the time of arrival, thereby affecting positioning performance. The traditional adaptive notch filter method needs to know the frequency of CWI when removing it, and the number is limited. This paper presents a method based on sparseness to suppress the CWI in the Loran-C signal. According to the different morphological characteristics of the Loran-C signal and the CWI, we construct dictionaries suitable for the two components, respectively. We use the tunable Q-factor wavelet transform and the discrete cosine transform to make the two components obtain a good sparse representation in their respective dictionaries. Then, the two components are separated using the morphological component analysis theory. We illustrate this method using both synthetic data and actual data. A huge advantage of the proposed method is that there is no need to know the frequencies of the CWI for it can better cope with frequency changes of the CWI in the actual environments. Compared with the adaptive notch filter method, the results of the proposed method show that our approach is more effective and convenient.