We have introduced a novel time-frequency decomposition approach for analyzing seismic data. This method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can further decrease redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) and improved CEEMD (ICEEMD). Moreover, VMD is an adaptive signal decomposition technique, which can nonrecursively decompose a multicomponent signal into several quasi-orthogonal intrinsic mode functions. This new tool, in contrast to empirical mode decomposition (EMD) and its variations, such as EEMD, CEEMD, and ICEEMD, is based on a solid mathematical foundation and can obtain a time-frequency representation that is less sensitive to noise. Two tests on synthetic data showed the effectiveness of our VMD-based time-frequency analysis method. Application on field data showed the potential of the proposed approach in highlighting geologic characteristics and stratigraphic information effectively. All the performances of the VMD-based approach were compared with those from the CEEMD- and ICEEMD-based approaches.

a b s t r a c tTwo new hybrid lead halides (H 2 BDA)[PbI 4 ] (1) (H 2 BDA ¼1,4-butanediammonium dication) and (HNPEIM)[PbI 3 ] (2) (HNPEIM¼ N-phenyl-ethanimidamidine cation) have been synthesized and structurally characterized. X-ray diffraction analyses reveal that compound 1 features a two-dimensional cornersharing perovskite layer whereas compound 2 contains one-dimensional edge-sharing double chains. The N-phenyl-ethanimidamidine cation within compound 2 was generated in-situ under solvothermal conditions. The optical absorption spectra collected at room temperature suggest that both compounds are semiconductors having direct band gaps, with estimated values of 2.64 and 2.73 eV for 1 and 2, respectively. Results from the density functional theory (DFT) calculations are consistent with the experimental data. Density of states (DOS) analysis reveals that in both compounds 1 and 2, the energy states in the valence band maximum region are iodine 5p atomic orbitals with a small contribution from lead 6s, while in the region of conduction band minimum, the major contributions are from the inorganic (Pb 6p atomic orbitals) and organic components (C and N 2p atomic orbitals) in compound 1 and 2, respectively.

The perovskite microlaser is a competitive candidate of light source for optical communication and integrated photonic circuits. Understanding the fundamental mechanism of lasing is crucial for the upcoming devices. The ultrafast establishment of lasing modes within a 2D perovskite microplate cavity at room temperature is investigated. The transient red shift of lasing modes can be found. Analysis based on electron–hole plasma (EHP) and a dynamic Drude‐like model are carried out with simulations on the transient dielectric response and the mode shift. In addition, the integrated lasing gain profile have a red shift at high excitation intensity, which is explained with EHP‐induced bandgap renormalization. The conflict between the experimental phenomena and the exciton–polariton theory confirms that under intense excitation, the exciton–polariton is ruled out for the origin of lasing. These results provide a direct understanding of the lasing evolving in a 2D perovskite microcavity. Suppressing the EHP‐related transient shift of cavity modes will advance the lasing applications.

A modern pulsar survey generates a large number of pulsar candidates. Filtering these pulsar candidates in a large astronomical dataset is an important step towards discovering new pulsars. In this paper, a novel adaptive boosting algorithm based on deep self normalized neural network (Adaboost-DSNN) is proposed to accurately classify pulsar and non-pulsar signals. To train the proposed method on a highly-imbalanced dataset, the Synthetic Minority Oversampling Technique (SMOTE) was initially employed for balancing the dataset. Then, a deep ensemble network combined with a deep self-normalized neural network and adaptive boosting was developed to train and learn the processed pulsar data. The design of the proposed Adaboost-DSNN method significantly reduced the computational time when dealing with large astronomical datasets, while also improving the classification performance. The scaled exponential liner units (SELU) activation function was used to normalize the data. Considering their neighbor information and the special dropout technique (α-dropout), Adaboost-DSNN displayed good pulsar classification performance, while preserving the data properties across subsequent layers. The proposed Adaboost-DSNN method was tested on the High Time Resolution Universe Survey datasets (HTRU-1 and HTRU-2). According to experimental results, Adaboost-DSNN outperform other state-of-the-art methods with respect to training time and F1-score. The training time of the Adaboost-DSNN model is 10x times faster compared to other models of this kind.

In pulsar observation, dispersion occurs due to the interstellar medium. The dispersion significantly affects the detection of pulsar signals. To overcome the dispersion effect, incoherent dedispersion methods are often applied. The tranditional inchoherent dedispersion methods are computationally expensive and troublesome. To deal with this problem, in this paper, we developed a Real-Time, Pipelined Incoherent Dedispersion Method (RT-PIDM). RT-PIMD only caches the summed-up time series, instead of all the frequency spectra, so the memory consumption is determined by the number of DM trails, whereas the traditional method’s memory consumption is determined by the number of frequency channels. In most of the situations, the number of frequency channels is several times more than that of DM trails, which means the memory consumption of traditional methods is more than that of RT-PIDM. With RT-PIDM, we designed a 1.2 GHz bandwidth prototype digital backend, and we finished pulsar observation with the 40 m radio telescope at Yunnan Observatory. The results demonstrate that the RT-PIDM can be implemented inside a single FPGA chip with less Block RAM, and the proposed RT-PIDM dedisperses the pulsar signal in real time and achieves the same result as compared to traditional incoherent dedispersion.