A Source Localization Method Using Complex Variational Mode Decomposition.

Source localization with a passive sensors array is a common topic in various areas. Among the popular source localization algorithms, the compressive sensing (CS)-based method has recently drawn considerable interest because it is a high-resolution method, robust with coherent sources and few snapshots, and applicable for mixed near-field and far-field source localization. However, the CS-based methods rely on the dense grid to ensure the required estimation precision, which is time-consuming and impractical. This paper applies the complex variational mode decomposition (CVMD) to source localization. Specifically, the signal model of the source localization problem is similar to the time-domain frequency-modulated signal model. Motivated by this, we extend CVMD, initially designed for nonstationary time-domain signal analysis, to array signal processing. The decomposition results of the array measurements can correspond to the potential sources at different locations. Then, the sources' direction and range can be estimated by model fitting with the decomposed subsignals. The simulation results show that the proposed CVMD-based method can locate the pure far-field, pure near-field, mixed far-field, and near-field sources. Notably, it can yield high-resolution localization for the coherent sources with one single snapshot with low computing time.

A Modified Complex Variational Mode Decomposition Method for Analyzing Nonstationary Signals with the Low-Frequency Trend.

Complex variational mode decomposition (CVMD) has been proposed to extend the original variational mode decomposition (VMD) algorithm to analyze complex-valued data. Conventionally, CVMD divides complex-valued data into positive and negative frequency components using bandpass filters, which leads to difficulties in decomposing signals with the low-frequency trend. Moreover, both decomposition number parameters of positive and negative frequency components are required as prior knowledge in CVMD, which is difficult to satisfy in practice. This paper proposes a modified complex variational mode decomposition (MCVMD) method. First, the complex-valued data are upsampled through zero padding in the frequency domain. Second, the negative frequency component of upsampled data are shifted to be positive. Properties of analytical signals are used to get the real-valued data for standard variational mode decomposition and the complex-valued decomposition results after frequency shifting back. Compared with the conventional method, the MCVMD method gives a better decomposition of the low-frequency signal and requires less prior knowledge about the decomposition number. The equivalent filter bank structure is illustrated to analyze the behavior of MCVMD, and the MCVMD bi-directional Hilbert spectrum is provided to give the time-frequency representation. The effectiveness of the proposed algorithm is verified by both synthetic and real-world complex-valued signals.

An Optimal Subspace Deconvolution Algorithm for Robust and High-Resolution Beamforming.

Utilizing the difference in phase and power spectrum between signals and noise, the estimation of direction of arrival (DOA) can be transferred to a spatial sample classification problem. The power ratio, namely signal-to-noise ratio (SNR), is highly required in most high-resolution beamforming methods so that high resolution and robustness are incompatible in a noisy background. Therefore, this paper proposes a Subspaces Deconvolution Vector (SDV) beamforming method to improve the robustness of a high-resolution DOA estimation. In a noisy environment, to handle the difficulty in separating signals from noise, we intend to initial beamforming value presets by incoherent eigenvalue in the frequency domain. The high resolution in the frequency domain guarantees the stability of the beamforming. By combining the robustness of conventional beamforming, the proposed method makes use of the subspace deconvolution vector to build a high-resolution beamforming process. The SDV method is aimed to obtain unitary frequency matrixes more stably and improve the accuracy of signal subspaces. The results of simulations and experiments show that when the input SNR is less than -27 dB, signals of decomposition differ unremarkably in the subspace while the SDV method can still obtain clear angles. In a marine background, this method works well in separating the noise and recruiting the characteristics of the signal into the DOA for subsequent processing.

The Minimum AC Signal Model of Bipolar Transistor in Amplification Region for Weak Signal Detection.

This paper presents a minimum signal model via the AC small-signal model and the uncertainty principle, which reveals the minimum AC signal that can be amplified by a bipolar transistor. The Ebers-Moll model (EM3) can describe the small signal amplification process, but it is difficult to define the minimum amplifiable signal of the bipolar transistor. In this study, the correspondence relationship between the non-equilibrium carrier and the electric injection is proved, and the relationship between the life of the non-equilibrium carrier and the measurable signal is proposed by the uncertainty principle. Next, the limit of perceived minimum voltage is also derived in this paper. Then, combining with EM3 model, the minimum AC signal model of bipolar transistor is presented to calculate the minimum voltage signal of bipolar transistor that can be amplified. Finally, a number of the simulation and experiment results show that when the minimum signal in the model is used as input, the carrier concentration of the bipolar transistor does not change and the base electrode cannot perceive the signal, which verifies the validity of the minimum AC signal model.

Development of a UPLC-MS/MS method for the determination of lomefloxacin in rabbit aqueous humor and its application to a pharmacokinetic study.

Lomefloxacin is a kind of synthetic fluoroquinolone antibiotic, which is used for the treatment of infectious diseases. In this study, a rapid and efficient liquid chromatography-tandem mass spectrometric assay was developed to determine the concentration of lomefloxacin in rabbit aqueous humor quantitatively. Aqueous humor samples were extracted by protein precipitation. Ofloxacin was chosen as internal standard. The chromatographic separation was achieved on a Kinetex C18 (50mm×2.10mm, 2.6µm, Phenomenex Corp, USA) column, with a gradient of methanol (0.1% formic acid) and water (0.1% formic acid). Multiple reaction monitoring (MRM) with positive ionization mode was used for the mass analysis. The validation of this method was based on the European Medicines Agency (2011) [1] and US FDA Guidelines (2001) [2]. The calibration range of aqueous humor samples was 5-1200ng/mL with r=0.9990 (n=6). For all QC samples, Inter-and intra-run precisions were less than 15% and accuracies were between 80%-120%. In conclusion, the assay was rapid, sensitive and able to determinate the lomefloxacin in rabbit aqueous humor accurately. At the same time, this method was successfully applied to study the pharmacokinetics of lomefloxacin hydrochloride eye drops and lomefloxacin hydrochloride ophthalmic gel in rabbit aqueous humor.