This study introduces a novel method to reduce the radar cross section (RCS) of wideband antennas. A novel wideband radar absorbing material (RAM) with frequency selective surfaces (FSSs) is proposed. The wideband RAM is used as the ground plane of a Yagi-Uda and a Vivaldi antenna for in-band RCS reduction. The measurement result shows that the radiation pattern of the antennas over the whole band is preserved when wideband RAM is used. The RCS of the Yagi-Uda and Vivaldi antenna with RAM can be reduced by more than 10 dB over the operating band with this method.

In this paper, the potential sensitivity in Kelvin probe force microscopy (KPFM) was investigated in frequency modulation (FM) and heterodyne amplitude modulation (AM) modes. We showed theoretically that the minimum detectable contact potential difference (CPD) in FM-KPFM is higher than in heterodyne AM-KPFM. We experimentally confirmed that the signal-to-noise ratio in FM-KPFM is lower than that in heterodyne AM-KPFM, which is due to the higher minimum detectable CPD dependence in FM-KPFM. We also compared the corrugations in the local contact potential difference on the surface of Ge (001), which shows atomic resolution in heterodyne AM-KPFM. In contrast, atomic resolution cannot be obtained in FM-KPFM under the same experimental conditions. The higher potential resolution in heterodyne AM-KPFM was attributed to the lower crosstalk and higher potential sensitivity between topographic and potential measurements.

Abstract-Air is not the only medium that can spread and can be used to detect speech. In our previous paper, another valuable medium -millimeter wave (MMW) was introduced to develop a new kind of speech acquisition technique [6]. Because of the special features of the MMW radar, this speech acquisition method may provide some exciting possibilities for a wide range of applications. In the proposed study, we have designed a new kind of speech acquisition radar system. The super-heterodyne receiver was used in the new system so that to mitigate the severe DC offset problem and the associated 1/f noise at baseband. Furthermore, in order to decrease the harmonic noise, electro-circuit noise, and ambient noise which were combined in the MMW detected speech, an adaptive wavelet packet entropy algorithm is also proposed in this study, which incorporates the wavelet packet entropy based voice/unvoiced radar speech adaptive detection method and the human ear perception properties in a wavelet packet timescale adaptation speech enhancement process. The performance of the proposed method is evaluated objectively by signal-to-noise ratio and subjectively by mean-opinion-score. The results confirm that the proposed method offers improved effects over other traditional speech enhancement methods for MMW radar speech.

This paper presents a novel formulation for dispersive media computation in finite-difference time-domain (FDTD). Motivated by conventional recursive convolution (RC) methods in handling convolution integral, the method name quadratic RC (QRC) makes improvement in the approximation of electric field in convolution integral. The electric field is approximated by quadratic function determined by the fields at three time steps at current, next and former. Via quadratic interpolation, the convolution integral result is approximated by the linear combination of three electric fields, rather than two fields in trapezoidal RC (TRC) or piecewise linear RC (PLRC) and one field in constant RC (CRC). Because three electric fields is required for the convolution integral, the method needs two more back level storage of the electric fields to fulfill the recursion process. Numerical demonstrations of Debye and Drude slab's transmission and reflection coefficients demonstrate the efficiency and accuracy of the novel method.

Through-the-wall imaging (TWI) of human vital signs by bioradar is a hot research topic in recent years. Unknown wall parameters (mainly thickness and dielectric constant) are huge challenges for TWI. Ambiguities in wall parameters will degrade the image focusing quality, lower signal-to-noiseclutter ratio (SNCR) of vital signs, cause vital signs to be imaged away from their true positions and blur the close vital signs from multiple humans caused by the imaging resolution declination. A through-thewall propagation model of vital signs for multiple-input and multiple-output (MIMO) bioradar is first built to analyze the influence of wall on imaging. In order to obtain focused image of vital signs quickly, an imaging model and a novel autofocusing imaging method of vital signs are proposed in this paper. Since vital signs of human are weak and sensitive to interferences, the SNCR-enhanced imagery of vital signs after change detection (CD) is applied to evaluate the focusing quality of image. Reflections of wall in the stationary targets imaging result are line structure approximately, so Hough transform is used to extract the positions of the front edge and rear edge of wall automatically. Propagation time in the wall of electromagnetic waves is estimated and used to build the constraint relationship of wall parameters. The number of unknown parameters is reduced to only one and the efficiency of autofocusing imaging improves. Several cases, including the case of single human, multiple human objects close to each other and the case of non-human objects, are simulated. The magnetic resonance imaging (MRI) image of human chest is put into simulation scene. And then the simulation data of human vital signs are calculated by the finite-difference time-domain (FDTD) method. The results show that the proposed method can effectively estimate the wall parameters and improve the focusing performance of human vital signs. And also the kurtosis of image can be used as a feature to efficiently decide the human vital signs are existed or not. Thus the SNCR of vital signs and resolution of imaging are improved, which are beneficial for detection of vital signs. The position errors of human vital signs are also corrected.