Denoising micro-seismic signals is paramount for ensuring reliable data for localizing mining-related seismic events and analyzing the state of rock masses during mining operations. However, micro-seismic signals are commonly contaminated by various types of complex noise, which can hinder micro-seismic accurate P-wave pickup and analysis. In this study, we propose the Multiscale Dilated Convolutional Attention denoising method, referred to as MSDCAN, to eliminate complex noise interference. The MSDCAN denoising model consists of an encoder, an improved attention mechanism, and a decoder. To effectively capture the neighborhood features and multiscale features of the micro-seismic signal, we construct an initial dilated convolution block and a multiscale dilated convolution block in the encoder, and the encoder focuses on extracting the relevant feature information, thus eliminating the noise interference and improving the signal-to-noise ratio (SNR). In addition, the attention mechanism is improved and introduced between the encoder and decoder to emphasize the key features of the micro-seismic signal, thus removing the complex noise and further improving the denoising performance. The MSDCAN denoising model is trained and evaluated using micro-seismic data from Stanford University. Experimental results demonstrate an impressive increase in SNR by 11.237 dB and a reduction in root mean square error (RMSE) by 0.802. Compared to the denoising results of the DeepDenoiser, CNN-denoiser and Neighbor2Neighbor methods, the MSDCAN denoising model outperforms them by enhancing the SNR by 2.589 dB, 1.584 dB and 2dB, respectively, and reducing the RMSE by 0.219, 0.050 and 0.188, respectively. The MSDCAN denoising model presented in this study effectively improves the SNR of micro-seismic signals, offering fresh insights into micro-seismic signal denoising methodologies.

It is of great significance to acquire seismic signal accurately in seismic observation and research. A seismic data acquisition system based on high-precision data acquisition card NI USB-4432 and LabVIEW platform is designed and implemented. Firstly, the seismic signal is modulated by the front-end conditioning circuit composed of AD620 and MAX292, then, it is obtained and analyzed by data acquisition system based on USB-4432 and LabVIEW. To improve the signal quality and promote the signal-to-noise ratio (SNR), an improved wavelet threshold de-noising algorithm is proposed, which can de-noise adaptively according to the intensity of the collected signal. Practical test results show that, the designed system characterizes high stability, can effectively improve the SNR, and possesses a higher applied value.

It is very important that the radar sensor network has autonomous capabilities such as self-managing, etc. Quite often, MIMO interference channels are applied to radar sensor networks, and for self-managing purpose, interference management in MIMO interference channels is critical. Interference alignment (IA) has the potential to dramatically improve system throughput by effectively mitigating interference in multi-user networks at high signal-to-noise (SNR). However, the implementation of IA predominantly relays on perfect and global channel state information (CSI) at all transceivers. A large amount of CSI has to be fed back to all transmitters, resulting in a proliferation of feedback bits. Thus, IA with limited feedback has been introduced to reduce the sum feedback overhead. In this paper, by exploiting the advantage of heterogeneous path loss, we first investigate the throughput of IA with limited feedback in interference channels while each user transmits multi-streams simultaneously, then we get the upper bound of sum rate in terms of the transmit power and feedback bits. Moreover, we propose a dynamic feedback scheme via bit allocation to reduce the throughput loss due to limited feedback. Simulation results demonstrate that the dynamic feedback scheme achieves better performance in terms of sum rate.

Single-cell spatial transcriptomes have demonstrated molecular and cellular diversity in the brain, but gene regulatory mechanisms underlying transcriptomic profiles and disease pathogenesis remain largely unknown in primates. Here we performed single-nucleus Assay for Transposase-Accessible Chromatin followed by sequencing (snATAC-seq) for ~1.6 million cells from 142 cortical regions of two male cynomolgus monkeys (Macaca fascicularis), and identified distinct chromatin accessibility profiles of cis-regulatory elements (CREs) for various cell types. By integrative analysis with large-scale spatial transcriptome data, we found that these CREs showed laminar and regional preferences, with their regional accessibility exhibiting striking dependence on the region's hierarchical level. Cross-species comparison of snATAC-seq data revealed human/macaque-enriched layer-4 glutamatergic neurons and LAMP5/LHX6-expressing GABAergic neurons as well as human/macaque-biased CREs for genes related to neurodevelopment and psychiatric diseases. Importantly, risk single-nucleotide polymorphisms for many brain disorders strongly associated with human/macaque-biased CREs in glutamatergic neuronal types and those for Alzheimer's disease strongly associated with CREs exclusively in microglia. Our results provided the basis for understanding the spatial gene regulatory mechanisms underlying cellular diversity and disease pathogenesis in the primate cortex.

Gyri and sulci are key anatomical features of the primate cerebral cortex, distinguished by their structural and functional specialization, yet their cellular and molecular foundations remain poorly understood. Here we utilized single-cell spatial transcriptomics data from adult male macaque monkey brains and developed gsModel, a customized deep learning framework that distinguishes sulci from gyri. Relying solely on local cell-type composition, gsModel achieved over 90% accuracy in the classification task, outperforming alternative machine learning models and predictors based on anatomical features. By integrative analysis with diffusion MRI tractography, we found that these compositional distinctions align with systematic variations in structural connectivity, linking local molecular architecture to macroscale wiring. Embedding-guided analyses further revealed key cell types and marker genes contributing to sulcus-gyrus differences, highlighting prominent contributions from upper-layer glutamatergic neurons and oligodendrocytes, as well as distinct gene regulatory networks underlying their expression profiles. Together, this work establishes a molecular framework linking microscale molecular architecture to macroscale cortical organization, providing a basis for mechanistic studies of region-specific structural and functional specialization in the primate cortex.