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Abstract At present, the active control of gear vibration mostly relies on existing algorithms. In order to achieve effective vibration reduction of the gear system, particularly during the vibration process, this paper proposes a multi-channel VSMFxLMS algorithm based on the FxLMS algorithm. This novel approach takes into account the time-varying nature of the vibration signal during gear vibration. Adaptive filter power coefficients are updated in a skip-tongue variable-step manner using momentum factors. Firstly, the paper establishes the dynamics model of the gear system and analyzes the nonlinear dynamic characteristics of the system. It then examines the vibration damping effect of the FxLMS algorithm and analyzes its performance under different gear system motion states, considering different step lengths and momentum factors. Lastly, the proposed VSMFxLMS algorithm is compared with the FxLMS algorithm, highlighting the superiority of the former. Overall, this research highlights the potential of a multi-channel VSMFxLMS algorithm in reducing vibrations in gear systems. The study optimizes the performance of gear systems while using advanced control strategies.

Abstract Traditional image processing-based autofocusing techniques require the acquisition, storage, and processing of large amounts of image sequences, constraining focusing speed and cost. Here we propose an autofocusing technique, which directly and exactly acquires the geometric moments of the target object in real time at different locations by means of a proper image modulation and detection by a single-pixel detector. An autofocusing criterion is then formulated using the central moments, and the fast acquisition of the focal point is achieved by searching for the position that minimizes the criterion. Theoretical analysis and experimental validation of the method are performed and the results show that the method can achieve fast and accurate autofocusing. The proposed method requires only three single-pixel detections for each focusing position of the target object to evaluate the focusing criterion without imaging the target object. The method does not require any active object-to-camera distance measurement. Comparing to local differential methods such as contrast or gradient measurement, our method is more stable to noise and requires very little data compared with the traditional image processing methods. It may find a wide range of potential applications and prospects, particularly in low-light imaging and near-infra imaging, where the level of noise is typically high.

Objectives: To comprehensively analyze the copper metabolism in Breast cancer, we established a prognostic signature for breast cancer (BC) related to copper metabolism. Methods: Copper metabolism-related genes were sourced from previous literatures and were selected by the Univariate Cox regression. Cu-enrichment scores were calculated via ssGSEA. Differentially expressed genes were identified with limma between high and low Cu-enrichment scores group, then we used the Random Survival Forest and LASSO to build the CuScore for BC. Kaplan-Meier analysis, ROC curves, and Cox regression were used to evaluate CuScore. Genomic mutations were analyzed with GISTIC. Immune cells were examined using ESTIMATE, ssGSEA and TIMER. Enrichment analysis used clusterProfiler and GSVA. The GDSC database and oncoPredict package analyzed chemotherapeutic sensitivity. MMP13 was selected for in vitro assays. Results: Four copper metabolism-related genes (UBE2D2, SLC31A1, ATP7A, and MAPK1) with prognostic value were identified. Higher expression levels of these genes were associated with higher Cu-enrichment scores, a factor of malignancy in breast cancer. Among 115 differentially expressed genes, 19 prognostic genes were identified, with three (CEACAM5, MMP13, and CRISP3) highlighted by Random Survival Forest and LASSO. Higher CuScores correlated with worse prognoses and were effective in predicting breast cancer outcomes. CuScore and metastasis were independent prognostic factors. Tumor-infiltrating immune cells were associated with lower CuScores. GO-GSEA analysis indicated six immune-related pathways might be regulated by CuScore. Patients with higher CuScores had lower TMB and were more sensitive to Sapitinib and LCL161, while those with lower CuScores might respond better to anti-PD1 therapy. High MMP13 expression in breast cancer was linked to malignancy, affecting cell proliferation and migration. Conclusion: The identified copper metabolism-related gene signature has the potential to predict prognosis and guide clinical treatment for BC. Among these genes, MMP13 may act as a malignant factor in BC.

The gas pipeline network constitutes a critical component of urban infrastructure. Emergencies, such as leaks or explosions, can lead to severe casualties, extensive property damage, and long-lasting effects on societal and environmental safety. The genesis of such emergencies typically involves a complex interplay among various risk factors. Therefore, the meticulous identification of these risk factors and the effective modeling of their interrelations are crucial for assessing the risks associated with such emergencies and enhancing urban safety. In this research, we initially developed a specialized dataset tailored specifically to analyze emergencies within gas pipeline networks. Recognizing the significance of unstructured domain-specific knowledge, we introduced an innovative approach Fusing expert knowledge and pre-trained model. This approach facilitates the fusion of domain experts’ knowledge into the process of risk factor identification and extraction in different steps, ensuring a thorough and precise determination and filtration of risk factors. Subsequently, a network depicting the associations between risk factors was established, drawing on their co-occurrence relationships, followed by a quantitative analysis of this network. Supported by real-world gas network management and emergency cause investigations, the methods proposed in this study provide strong support for gas network safety management and emergency decision-making.

In recent years, urban floods have occurred frequently in China. Therefore, there is an urgent need to strengthen urban flood resilience. This paper proposed a hybrid multi-criteria group decision-making method to assess urban flood resilience based on heterogeneous data, group decision-making methodologies, the pressure-state–response model, and social–economic–natural complex ecosystem theory (PSR-SENCE model). A qualitative and quantitative indicator system is formulated using the PSR-SENCE model. Additionally, a new weighting method for indicators, called the synthesis weighting-group analytic hierarchy process (SW-GAHP), is proposed by considering both intrapersonal consistency and interpersonal consistency of decision-makers. Furthermore, an extensional group decision-making technology (EGDMT) based on heterogeneous data is proposed to evaluate qualitative indicators. The flexible parameterized mapping function (FPMF) is introduced for the evaluation of quantitative indicators. The normal cloud model is employed to handle various uncertainties associated with heterogeneous data. The evaluations for Beijing from 2017 to 2021 reveal a consistent annual improvement in urban flood resilience, with a 14.1% increase. Subsequently, optimization recommendations are presented not only for favorable indicators such as regional economic status, drainability, and public transportation service capacity but also for unfavorable indicators like flood risk and population density. This provides a theoretical foundation and a guide for making decisions about the improvement of urban flood resilience. Finally, our proposed method shows superiority and robustness through comparative and sensitivity analyses.

Magnetorheological fluid (MRF) porous fabric composite has been demonstrated to improve the shear properties of MRF. Non-woven fabric is manufactured from a multitude of fibers through spinning or melt-blown processing methods. As the fundamental unit of non-woven fabric, fibers without spinning or melt-blown directly influence the shear properties of magnetorheological (MR) materials. However, the effect of unprocessed fiber on the shear properties of MR grease remains uncertain. This study introduces a novel MR grease with fiber threads (MRG-FT) by incorporating fiber threads into MRG. The effects of fiber thread length, mass fraction, and material type on MRG shear stress are investigated. Compared to conventional MRG, the maximum shear stress of MRG-FT is increased by 31.8% under the magnetic field of 0.64 T. A novel Herschel–Bulkley–Fiber (H-B-F) model that considers fiber parameters (tenacity, mass fraction, etc.) is proposed based on the H–B model. To validate the enhancement of MRG by fiber threads, a linear damper based on shear mode has been designed and tested. The results demonstrate a 23.8% increase in the maximum damping force of MRG-FT compared to conventional MRG under an excitation current of 1.6 A. This study reveals the influence of fiber threads, which directly influences the shear properties of MRG upon the application of the magnetic field. The maximum damping force of the MRG can be increased by 23.8% by only 1.5% mass fraction of fibers.

One of the main goals in biogeography and ecology is the study of patterns of species diversity and the driving factors in these patterns. However, such studies have not focused on Sternorrhyncha in China, although this region hosts massive species distribution data. Here, based on the 15,450 distribution records of Sternorrhyncha species in China, we analyzed patterns in species richness and endemism at 1° × 1° grid size and determined the effects of environmental variables on these patterns using correlations analysis and the model averaging approach. We found that species richness and endemism of Sternorrhyncha species are unevenly distributed, with high values in the eastern and southeastern coastal regions of mainland China, as well as Taiwan Island. Furthermore, the key factors driving species richness and endemism patterns are inconsistent. Species richness patterns were strongly affected by the normalized difference vegetation index, which is closely related to the feeding habits of Sternorrhyncha, whereas endemism patterns were strongly affected by the elevation range. Therefore, our results indicate that the range size of species should be considered to understand the determinants of species diversity patterns.

Atmospheric turbulence is a key factor contributing to data distortion in mid-to-long-range target observation tasks. Neural networks have become a powerful tool for dealing with such problems due to their strong ability to fit nonlinearities in the spatial domain. However, the degradation in data is not confined solely to the spatial domain but is also present in the frequency domain. In recent years, the academic community has come to recognize the significance of frequency domain information within neural networks. There remains a gap in research on how to combine dual-domain information to reconstruct high-quality images in the field of blind turbulence image restoration. Drawing upon the close association between spatial and frequency domain degradation information, we introduce a novel neural network architecture, termed Dual-Domain Removal Turbulence Network (DDRTNet), designed to improve the quality of reconstructed images. DDRTNet incorporates multiscale spatial and frequency domain attention mechanisms, combined with a dual-domain collaborative learning strategy, effectively integrating global and local information to achieve efficient restoration of atmospheric turbulence-degraded images. Experimental findings demonstrate significant advantages in performance for DDRTNet compared to existing methods, validating its effectiveness in the task of blind turbulence image restoration.

The real-time tracking of moving objects has extensive applications in various domains. Existing tracking methods typically utilize video image processing, but their performance is limited due to the high information throughput and computational requirements associated with processing continuous images. Additionally, imaging in certain spectral bands can be costly. This paper proposes a non-imaging real-time three-dimensional tracking technique for distant moving targets using single-pixel LiDAR. This novel approach involves compressing scene information from three-dimensional to one-dimensional space using spatial encoding modulation and then obtaining this information through single-pixel detection. A LiDAR system is constructed based on this method, where the peak position of the detected full-path one-dimensional echo signal is used to obtain the target distance, while the peak intensity is used to obtain the azimuth and pitch information of the moving target. The entire process requires minimal data collection and a low computational load, making it feasible for the real-time three-dimensional tracking of single or multiple moving targets. Outdoor experiments confirmed the efficacy of the proposed technology, achieving a distance accuracy of 0.45 m and an azimuth and pitch angle accuracy of approximately 0.03° in localizing and tracking a flying target at a distance of 3 km.

Over the past three decades, China has seen aerosol levels substantially surpass the global average, significantly impacting regional climate. This study investigates the long-term and seasonal variations of aerosols in the Huai River Basin (HRB) using MODIS, CALIOP observations from 2007 to 2021, and ground-based measurements. A notable finding is a significant decline in the annual mean Aerosol Optical Depth (AOD) across the HRB, with MODIS showing a decrease of approximately 0.023 to 0.027 per year, while CALIOP, which misses thin aerosol layers, recorded a decrease of about 0.016 per year. This downward trend is corroborated by improvements in air quality, as evidenced by PM2.5 measurements and visibility-based aerosol extinction coefficients. Aerosol decreases occurred at all heights, but for aerosols below 800 m, with an annual AOD decrease of 0.011. The study also quantifies the long-term trends of five major aerosol types, identifying Polluted Dust (PD) as the predominant frequency type (46%), which has significantly decreased, contributing to about 68% of the total AOD reduction observed by CALIOP (0.011 per year). Despite this, Dust and Polluted Continental (PC) aerosols persist, with PC showing no clear trend of decrease. Seasonal analysis reveals aerosol peaks in summer, contrary to surface measurements, attributed to variations in the Boundary Layer (BL) depth, affecting aerosol distribution and extinction. Furthermore, the study explores the influence of seasonal wind patterns on aerosol type variation, noting that shifts in wind direction contribute to the observed changes in aerosol types, particularly affecting Dust and PD occurrences. The integration of satellite and ground measurements provides a comprehensive view of regional aerosol properties, highlighting the effectiveness of China’s environmental policies in aerosol reduction. Nonetheless, the persistence of high PD and PC levels underscores the need for continued efforts to reduce both primary and secondary aerosol production to further enhance regional air quality.

Acute myeloid leukemia (AML) is a complex mixed entity composed of malignant tumor cells, immune cells and stromal cells, with intra-tumor and inter-tumor heterogeneity. Single-cell RNA sequencing enables a comprehensive study of the highly complex tumor microenvironment, which is conducive to exploring the evolutionary trajectory of tumor cells. Herein, we carried out comprehensive analyses of aggrephagy-related cell clusters based on single-cell sequencing for patients with acute myeloid leukemia. A total of 11 specific cell types (T, NK, CMP, Myeloid, GMP, MEP, Promono, Plasma, HSC, B, and Erythroid cells) using t-SNE dimension reduction analysis. Several aggrephagy-related genes were highly expressed in the 11 specific cell types. Using Monocle analysis and NMF clustering analysis, six aggrephagy-related CD8+ T clusters, six aggrephagy-related NK clusters, and six aggrephagy-related Mac clusters were identified. We also evaluated the ligand-receptor links and Cell–cell communication using CellChat package and CellChatDB database. Furthermore, the transcription factors (TFs) of aggrephagy-mediated cell clusters for AML were assessed through pySCENIC package. Prognostic analysis of the aggrephagy-related cell clusters based on R package revealed the differences in prognosis of aggrephagy-mediated cell clusters. Immunotherapy of the aggrephagy-related cell clusters was investigated using TIDE algorithm and public immunotherapy cohorts. Our study revealed the significance of aggrephagy-related patterns in tumor microenvironment, prognosis, and immunotherapy for AML.

Abstract Background Ovarian cancer is one of the most lethal malignancies, with a 1.9% mortality rate worldwide. The dysregulation of the FEN1 gene and miR-4324 has been associated with cancer progression. However, the relationship between miR-4324 and-FEN1 requires further investigation. Methods miR-4324 and FEN1 expressions in ovarian cancer tissues and cell lines were measured via RT-qPCR. The interaction between miR-4324 and FEN1 was assessed using luciferase and RNA pull-down assays. The effects of miR-4324 and FEN1 on cell proliferation, adhesion and apoptosis were determined by CCK-8, BrdU, colony formation, cell adhesion, Caspase-3 and western blot assays in ovarian cancer cell lines CaOV3 and OVCAR3, respectively. Results The results showed that miR-4324 expression was significantly decreased and FEN1 expression was enhanced in ovarian cancer tissues and cell lines. miR-4324 inhibitor promoted cell proliferation, adhesion and migration, and prevented apoptosis. Furthermore, the downregulation of FEN1 inhibited ovarian cancer cell growth and increased apoptosis. miR-4324 inhibited FEN1 expression and repressed ovarian cancer progression. Conclusion Our study found that miR-4324 inhibited FEN1 expression, suppressed cell growth, and increased apoptosis in ovarian cancer cells. Therefore, we identified miR-4324 and FEN1 as potential therapeutic targets for ovarian cancer treatment.

The majority of edge detection methods are applied after the capture of object photos. Thus, edge detection quality suffers when disturbances occur during imaging. This work proposes an effective edge detection technique for single-pixel imaging (SI). A sequence of edge-sensitive single-pixel imaging (ESI) and single-round edge-sensitive single-pixel imaging (SESI) modulation patterns is specially designed to extract the edges of unknown objects directly without the need for any previous images. The modulation patterns are formed by convolving the SI basis patterns with a second-order differential operator. Compared with existing published edge detection methods, experimental results revealed that the proposed SESI increased the signal-to-noise ratio by at least 228%, thereby reducing the edge detection time by at least half. The edge detection performance of the SESI scheme was also demonstrated on moving objects, with SESI detecting clear edges even when the target was in motion. Moreover, unlike traditional methods, ESI and SESI are immune to light interference and can detect clear edges of objects even if the objects are corrupted by severe interference from laser or light-emitting diode light sources, whereas traditional methods exhibit substantial noise contamination. Consequently, ESI and SESI can lay the groundwork for fast and robust edge detection operations without imaging.

The rapid extinction of species is of considerable concern for biodiversity conservation. Identifying the drivers of species diversity and hotspots is beneficial for developing conservation strategies. Studies on insects have mainly focused on terrestrial species and rarely on semiaquatic species. Using 135,208 georeferenced occurrence records of 296 damselflies across North America, their species richness and endemism (represented by weighted endemism) patterns were mapped in a 100 × 100-km grid size, and the effects of environmental variables on species richness and endemism were investigated using generalized linear models and hierarchical partitioning. Subsequently, the top 5% grids with species richness and weighted endemism were separately selected as hotspots and their congruence was evaluated. Finally, species diversity hotspots were identified by integrating two types of hotspot grids, and gap analysis was performed to evaluate their conservation status. Temperature conditions and water availability had the strongest influence on species richness and endemism, respectively. Low congruence among species richness and endemism hotspots was observed. Moreover, four species diversity hotspots were identified, namely, region of the eastern United States and southeastern Canada, southwestern United States, central Mexico, and southernmost North America. Approximately 69.31% of the hotspot grids are not a part of the existing protected areas, presenting a significant conservation gap. The habitats of taxonomic groups should be considered while identifying the most common driving mechanisms of endemism. Strengthening the establishment of protected areas in regions with conservation gaps is urgently needed to promote the conservation of damselflies in North America.

This study aimed to identify the main factors driving species richness and endemism patterns of Chinese wild Rhododendron as well as to assess the hotspots of species diversity and their conservation status. We initially mapped the species richness and endemism (expressed by weighted endemism) patterns of 546 wild Rhododendron in China in 100 × 100 km grids using 13,969 occurrence records. Subsequently, the effects of environmental variables on species richness and endemism patterns were assessed using regression models, and hotspots were identified based on the areas overlapping in 10% of the grids with the highest species richness and endemism. Finally, the conservation status of the hotspots was evaluated via gap analysis. The key environmental variables affecting species richness and endemism patterns differed. Species richness patterns were significantly influenced by moisture index, whereas endemism patterns were significantly influenced by elevation range. Moreover, the following five species diversity hotspots were identified: southern Xizang, Hengduan Mountains, south-central Sichuan, eastern Yungui Plateau, and central Gansu; however, these hotspots are not fully covered by the existing nature reserves. Our results indicate that the establishment of nature reserves should be actively promoted to effectively protect wild Rhododendron in hotspots with a conservation gap.

BackgroundProgrammed death-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors displayed considerable advantages in neoadjuvant therapy of non-small cell lung cancer (NSCLC), but the specific application of neoadjuvant immunotherapy has not been well determined, and the long-term prognostic data of neoadjuvant immunochemotherapy combined with surgical resection of NSCLC remains limited. In this study, we intended to assess the efficacy of the neoadjuvant therapy of the PD-1 inhibitor and long-term prognosis in patients with resectable NSCLC.MethodsWe retrospectively analyzed NSCLC surgical patients treated with neoadjuvant therapy in our hospital, and divided them into a neoadjuvant chemotherapy group and a neoadjuvant immunotherapy combined with chemotherapy group. The propensity score matching method was used to evaluate the effectiveness of immunotherapy combined with chemotherapy in the treatment of resectable lung cancer, and the long-term prognosis of these two groups was compared.ResultsA total of 62 cases were enrolled, including 20 patients (20/62, 32.26%) in the immunotherapy group and 42 patients (42/62, 67.74%) in the chemotherapy group. The clinical baseline data of these two groups were balanced. In the immunotherapy group, all patients had tumor regression in imaging finding (tumor regression ratio: 11.88% - 75.00%). In the chemotherapy group, 30 patients had tumor regression (tumor regression ratio: 2.70% - 58.97%). The R0 removal rates of cancers were comparable between the immunotherapy group and chemotherapy group (19/20, 95.00% vs. 39/42, 92.86%, P=1.000). The two groups were balanced in complete minimally invasive surgery, pneumonectomy, operative duration, blood loss, postoperative complications, and hospital stay. The immunotherapy group had more sleeve resection (36.84% vs. 10.26%, p=0.039) including bronchial sleeve and vascular sleeve, higher pathological complete response (pCR) rate (57.89% vs. 5.13%, P

Nowadays, AC charging piles are widely used, and with the increasing number of charging piles, the harmonic pollution generated by them becomes more serious and affects the power quality of the grid. Aiming at the problem of harmonic control of the single-phase AC charging pile, it is decided to apply the active filter technology. The single-phase parallel active filter (PAPF) is selected according to the characteristics of the charging pile. The single harmonic detection method is studied based on instantaneous reactive power theory, and a new adaptive low-pass filter with fixed-step size is proposed to improve the performance of harmonic detection by combining the least mean square (LMS) and the least fourth-order moment (LMF) adaptive algorithms. The PAPF control part adopts the composite control strategy of inner and outer loop combined with repetitive control to complete the voltage stabilization control, harmonic tracking, and compensation. Based on MATLAB/SIMULINK, the output waveforms of the low-pass filter link are simulated and analyzed, and the prototype experiment is carried out. The results verify that the algorithm can improve the performance of harmonic detection. Finally, the single-phase PAPF system simulation is established and the harmonic compensation capability of system is analyzed by the Fast Fourier Transform (FFT). The results demonstrate that single-phase PAPF can effectively manage harmonics and the total harmonic distortion (THD) is reduced to less than 5%.

Fourier ptychography imaging technology is a method developed in recent years to achieve high-resolution imaging. In the traditional macro Fourier ptychography technology, the scanning method when the camera captures low-resolution images mostly uses the rectangular linear grid format. These acquired images contain a small amount of complementary information, and a large number of low-resolution images are needed to achieve high-resolution imaging. Redundant measurements will extend the sampling and reconstruction time, and require more computing resources. In this paper, we propose to obtain the target image spectral energy distribution by pre-sampling. And according to the energy distribution, we use irregular and non-uniform sampling modes to restore the target image. With the same number of samples and same reconstruction time, higher resolution imaging can be achieved compared with traditional methods. Simulation and experimental studies are carried out in this paper, and the results confirm the effectiveness of the proposed methods. Compared with the traditional sampling mode, the two sampling modes proposed in this paper increase the resolution from 4.49 lp/mm to 5.66 lp/mm and 5.04 lp/mm respectively.

ABO blood group system is the most important blood group system in transfusion and transplantation medicine. Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphomas (NHLs) worldwide. There have been some studies that lymphoma could affect ABO blood group system and thus affect blood transfusion strategy. However, the mechanisms lymphoma affecting ABO blood group system have not been fully elucidated so far. Here, we report a case of a patient who was a 72-year-old Chinese man came to our hospital for medical advice because of cervical lymphadenophathy. The patient was subsequently diagnosed with diffuse large B-cell lymphoma by lymph-node biopsy. His ABO blood group was initially typed as B on November 7, 2020. He was transfusing B type leukocyte poor RBCs (LPR) before we found the patient’s ABO blood group discrepancy on December 2, 2020 by forward and reverse typing methods, which the discrepancy was verified by genotyping. The patient began to transfuse O type washed RBCs (WRBC) since then. Compared to transfuse B type leukocyte poor RBCs (LPR), the efficiency of transfusing O type washed RBCs (WRBC) was better. Although hemoglobin level did not greatly improve, indirect bilirubin level evidently decreased. Furthermore, we found B-cell lymphoma affected blood transfusion strategy by producing IgM anti-B antibody in this case. Clinicians should need to be aware of the effect of B-cell lymphoma on blood transfusion strategy.

For ground-based lidars in atmospheric observation, their data acquisition unit and control unit usually work independently. They usually require the cooperation of large-volume, high-power-consumption Industrial Personal Computer (IPC). However, the space-borne lidar has high requirements on the stability and integration of the acquisition control system. In this paper, a new data acquisition and lidar control system (DALCS) was developed based on System-on-Chip Field-Programmable Gate Array (SoC FPGA) technology. It can be used in lidar systems with high repetition rate and photon-counting mode and has functions such as data storage, laser control, automatic collimation, wireless communication, and fault self-test. DALCS has two working modes: in online mode, the echo data collected by DALCS are transmitted to the computer for display in real-time and then stored with the current time as the file name; in offline mode, the data are stored in local non-volatile memory, which can be read remotely and can work autonomously when there is no IPC. The test results showed that in the frequency range of 0–70 M, the counting linearity of DALCS reached 0.9999, and the maximum relative error between the DALCS card and the standard signal source was 0.211%. The comparison results showed that the correlation coefficient between DALCS and MCS-PCI was as high as 0.99768. The DALCS was placed in a self-developed lidar sensor system for continuous observation, and the system worked stably under different weather conditions. The range-squared-corrected signal profiles obtained from the observations reflect the spatial and temporal distribution characteristics of aerosols and clouds well. This provides scheme verification and experimental support for the development of space-borne lidar data acquisition and control system.

Single-pixel imaging (SPI) is a new type of imaging technology that uses a non-scanning single-pixel detector to image objects and has important application prospects and value in many fields. Most of the modulators currently used in SPI systems are digital micromirror device (DMD) modulators, which use a higher frequency for binary modulation than other alternatives. When modulating grayscale information, the modulation frequency is significantly reduced. This paper conducts research on multiple discrete objects in a scene and proposes using binary patterns to locate and image these objects. Compared with the existing methods of using gray patterns to locate and image multiple objects, the method proposed in this paper is more suitable for DMD-type SPI systems and has wider applicability and greater prospects. The principle of the proposed method is introduced, and the effectiveness of the method is experimentally verified. The experimental results show that, compared to traditional SPI methods, the number of patterns required by the proposed method is reduced by more than 85%.