Gaussian Fitting Method Research Articles

Development of a full field-of-view spectroscopic single-molecule fluorescence instrument with improved methods for calibration

Abstract We have developed a high-throughput, full field-of-view (FOV) spectroscopic single-molecule fluorescence (sSMF) instrument, overcoming distortion issues with gratings and prisms. Our calibration techniques ensure spectral uniformity across the whole FOV, and our two-step Gaussian fit method enables better than 2 nm precision in spectral peak determination for accurate molecule identification, making it a valuable tool for multi-color single-molecule detection and analysis.

Simulation of nitrate nitrogen concentrations and DOM characteristics in groundwater from Southwest China's Karst Wetlands using an improved GMS model.

Existing models often face limitations in the understanding and prediction of nitrate nitrogen (NO3--N) concentrations in karst groundwater. In this study, to tackle this issue, a Gaussian function model was coupled with the Groundwater Modeling System (GMS) to simulate NO3--N concentration changes in the southwest karst wetland of China. Additionally, fluorescence spectroscopy was employed to measure dissolved organic matter (DOM) components in the groundwater, providing insights into their variation and influence on NO3--N dynamics. The results demonstrated that coupling the Gaussian curve fitting method with the GMS model accurately simulated NO3--N concentration changes in the study area. The simulation revealed lower NO3--N levels in the northern region, with higher concentrations in the central area, peaking at 20.73mg/L at lower elevations. NO3--N was primarily distributed in the southwestern region and upper Mudong Lake, exhibiting a diffusion trend from west to east. DOM analysis indicated significant autochthonous contributions, particularly microbial metabolic by-products. The total fluorescence intensity and DOM components increased downstream, with the lowest values at the source and the highest values at river confluences. The humification index (HIX) was correlated with NO3--N concentrations, where lower NO3--N levels corresponded to lower HIX values, and higher NO3--N levels corresponded to higher HIX values. In conclusion, this study provides valuable insights into NO3--N prediction in groundwater and the role of DOM, offering a reference for groundwater protection in the southwest China karst basin.

Comparison of On-Sky Wavelength Calibration Methods for Integral Field Spectrograph

With advancements in technology, scientists are delving deeper in their explorations of the universe. Integral field spectrograph (IFS) play a significant role in investigating the physical properties of supermassive black holes at the centers of galaxies, the nuclei of galaxies, and the star formation processes within galaxies, including under extreme conditions such as those present in galaxy mergers, ultra-low-metallicity galaxies, and star-forming galaxies with strong feedback. IFS transform the spatial field into a linear field using an image slicer and obtain the spectra of targets in each spatial resolution element through a grating. Through scientific processing, two-dimensional images for each target band can be obtained. IFS use concave gratings as dispersion systems to decompose the polychromatic light emitted by celestial bodies into monochromatic light, arranged linearly according to wavelength. In this experiment, the working environment of a star was simulated in the laboratory to facilitate the wavelength calibration of the space integral field spectrometer. Tools necessary for the calibration process were also explored. A mercury–argon lamp was employed as the light source to extract characteristic information from each pixel in the detector, facilitating the wavelength calibration of the spatial IFS. The optimal peak-finding method was selected by contrasting the center of weight, polynomial fitting, and Gaussian fitting methods. Ultimately, employing the 4FFT-LMG algorithm to fit Gaussian curves enabled the determination of the spectral peak positions, yielding wavelength calibration coefficients for a spatial IFS within the range of 360 nm to 600 nm. The correlation of the fitting results between the detector pixel positions and corresponding wavelengths was >99.99%. The calibration accuracy during wavelength calibration was 0.0067 nm, reaching a very high level.

Overlapping microbubble localization based on multiscale statistical features for ultrasound super-resolution imaging

Ultrasound localization microscopy enables visualization of the microvasculature through the localization and tracking of isolated microbubbles (MBs). Currently, improving the precision for localizing overlapping MBs at high concentration to decrease the acquisition time remains a challenge. This study introduces multiscale statistical feature localization (MSFL) to address this challenge. First, the overlapping MBs are separated by local energy and blobness filters constructed from the Hessian matrix of texture features and its eigenvalues. Then, multiscale and orientation kernels are modeled to adapt to the appearance of various MBs; their amplitude and gradient convolution response results jointly determine the credibility of the MBs. Finally, precise MB localization is obtained by the weighted average of the local maxima of the response maps at each scale to achieve super-resolution (SR) imaging. The proposed method is evaluated using simulation and in-vivo datasets. Simulation results demonstrate that MSFL can adapt to asymmetric Gaussian distributions and improve overlapping MB localization, with a 9.28 % improvement in localization precision and a 5 % greater Jaccard index compared to the Gaussian fitting method. Additionally, the strong robustness of MSFL under noise interference maintains its ability to locate more MBs, and reconstruct the same saturation vessels with 36 % acquisition time reduction compared to Radial symmetry method. For SR images, MSFL can draw smoother interlaced vessels when more overlapping MBs are present, and quickly reconstructs more saturated in vivo microvasculature with a resolution is 10.5 μm(λ/10). These results validate the advantages of MSFL in achieving precise SR imaging with a short acquisition time.

Computational analysis of GCaMP fluorescence data in neuronal activity

The background and motivation of our research is to explore how to use computational methods to analyze GCaMP fluorescence data. GCaMP is a genetically encoded calcium indicator that can be used to observe the activity of thousands of neurons simultaneously using modern fluorescence imaging techniques. Our research includes the application of principles such as basic signal processing, statistical inference, hypothesis testing, and graph theory to help understand raw GCaMP fluorescence data recorded from awake, behavioral mice. The data set includes GCaMP fluorescence trace and correlation coefficient matrix among neurons. Our methods include high-pass filtering, Gaussian mixture model fitting and online active set method peak inference (oasis). By analyzing the correlation between neurons, we can understand the connection and centrality between neurons.

Fast Three-Dimensional Profilometry with Large Depth of Field.

By applying a high projection rate, the binary defocusing technique can dramatically increase 3D imaging speed. However, existing methods are sensitive to the varied defocusing degree, and have limited depth of field (DoF). To this end, a time-domain Gaussian fitting method is proposed in this paper. The concept of a time-domain Gaussian curve is firstly put forward, and the procedure of determining projector coordinates with a time-domain Gaussian curve is illustrated in detail. The neural network technique is applied to rapidly compute peak positions of time-domain Gaussian curves. Relying on the computing power of the neural network, the proposed method can reduce the computing time greatly. The binary defocusing technique can be combined with the neural network, and fast 3D profilometry with a large depth of field is achieved. Moreover, because the time-domain Gaussian curve is extracted from individual image pixel, it will not deform according to a complex surface, so the proposed method is also suitable for measuring a complex surface. It is demonstrated by the experiment results that our proposed method can extends the system DoF by five times, and both the data acquisition time and computing time can be reduced to less than 35 ms.

Understanding the effects of microbubble concentration on localization accuracy in super-resolution ultrasound imaging.

Super-resolution ultrasound (SRUS) through localising and tracking of microbubbles (MBs) can achieve sub-wavelength resolution for imaging microvascular structure and flow dynamics in deep tissuein vivo. The technique assumes that signals from individual MBs can be isolated and localised accurately, but this assumption starts to break down when the MB concentration increases and the signals from neighbouring MBs start to interfere. The aim of this study is to gain understanding of the effect of MB-MB distance on ultrasound images and their localisation. Ultrasound images of two MBs approaching each other were synthesised by simulating both ultrasound field propagation and nonlinear MB dynamics. Besides the distance between MBs, a range of other influencing factors including MB size, ultrasound frequency, transmit pulse sequence, pulse amplitude and localisation methods were studied. The results show that as two MBs approach each other, the interference fringes can lead to significant and oscillating localisation errors, which are affected by both the MB and imaging parameters. When modelling a clinical linear array probe operating at 6 MHz, localisation errors between 20 and 30μm (∼1/10 wavelength) can be generated when MBs are ∼500μm (2 wavelengths or ∼1.7 times the point spread function (PSF)) away from each other. When modelling a cardiac probe operating at 1.5 MHz, the localisation errors were as high as 200μm (∼1/5 wavelength) even when the MBs were more than 10 wavelengths apart (2.9 times the PSF). For both frequencies, at smaller separation distances, the two MBs were misinterpreted as one MB located in between the two true positions. Cross-correlation or Gaussian fitting methods were found to generate slightly smaller localisation errors than centroiding. In conclusion, caution should be taken when generating and interpreting SRUS images obtained using high agent concentration with MBs separated by less than 1.7 to 3 times the PSF, as significant localisation errors can be generated due to interference between neighbouring MBs.

A semi-analytical approach for estimating inland water inherent optical properties and chlorophyll a using airborne hyperspectral imagery

The inversion of inherent optical properties (IOPs) and chlorophyll a (Chla) is one of the key objectives in water color remote sensing, and hyperspectral remote sensing with rich spectral information makes precise inversion possible. In this study, we developed a semi-analytical estimation method for inland water IOPs based on the quasi-analytical algorithm (QAA). Considering the complex optical characteristics of inland waters, empirical parameter regional optimization was conducted. Furthermore, a dual-band joint inversion strategy and Gaussian function fitting method were utilized to optimize the solution processes for the backscattering coefficient of particles (bbp) and the absorption coefficient of phytoplankton pigments (aph), respectively. This approach overcomes the limitations of single-band bbp inversion in inland waters. In addition, it directly decomposes the absorption coefficient to obtain aph using a Gaussian function, which can reduce the intermediate steps and errors caused by indirect inversion. For Chla, we constructed a binary inversion model using aph(677) and remote sensing reflectance (Rrs), where the coefficient of determination (R2) exceeded 0.8. We also constructed an airborne hyperspectral image correction process, including vicarious calibration, atmospheric correction, and bidirectional reflectance distribution function (BRDF) correction, obtaining high-precision Rrs images. The ground models were successfully applied to the airborne hyperspectral images, mapping the spatial distribution of IOPs and Chla concentration in the study area. The experiments demonstrated that the proposed semi-analytical method using airborne hyperspectral imagery exhibits a good performance in terms of modeling accuracy and mapping analysis, and successfully applied to long-term monitoring using satellite hyperspectral images, highlighting the significant potential of hyperspectral remote sensing for high-precision monitoring of regional water bodies.

The influence and forecast of three industries and energy structure on regional carbon emission

ABSTRACT Carbon emission reduction is an important part of regional low-carbon economic development. In this paper, gray correlation analysis, neural network model, Gaussian multi-mode fitting and other methods were used to analyze the relationship between total carbon emissions and regional economic development, industrial structure, and energy consumption in Henan Province. On this basis, the future development of carbon emissions is predicted. The calculation results showed that the correlation between the three industries and carbon emissions in Henan Province is more than .7, among which the secondary industry has the highest correlation (.77). In the secondary industry, the correlation coefficient between coal and carbon emissions is the highest .87, while the correlation coefficient between other energy sources is about .5. In the neural network prediction model, the correlation coefficient between the prediction curve and the actual total carbon emission curve is .989, and the prediction results have a good degree of fit. The carbon emission prediction curve was divided into two parts: a linear decline stage from 2018 to 2024, and a rapid decline stage after 2024.The results showed that more efforts should be made in industrial structure, energy consumption structure and environmental protection to achieve low-carbon development in Henan province.

A robust and precise centroid positioning method for the weak beacon spot in long-distance inter-satellite optical wireless communications.

The centroid estimation of the beacon spot is crucial to the pointing, acquisition, and tracking subsystem in inter-satellite optical wireless communication (IsOWC), especially for the received very weak beacon caused by a long link distance. In this work, we propose an accurate centroid positioning method to calculate the centroid of such a weak beacon with a low peak signal-to-noise ratio. The proposed method is based on the idea that uses the normalized amplitude of the gray gradient to enhance the weights near the center of the beacon spot. Both comparative numerical simulation and experimental verification are implemented, which demonstrate the effectiveness and feasibility of the proposed method. Compared to the gray centroid method, interpolation-based method, Hough transform method, and Gaussian fitting method, the proposed method has stronger robustness and higher accuracy, which could be helpful to applications in IsOWC as well as beacon-based pointing and tracking systems.

Quantitative Analysis of the Interface between Titanium Dioxide Support and Noble Metal by Electron Energy Loss Spectroscopy.

The interface structure of supported catalysts plays a significant role in the strong metal-support interactions (SMSI). However, it remains limited on interpreting interface structures, thus affecting the understanding of SMSI origin and impact on catalytic performance. Herein, electronic energy loss spectroscopy was adopted to characterize the interface microstructures of Pt/TiO2 materials. After high-temperature reduction processing, it was observed that the coating on the surface of the Pt metal particles was TiOx. Then, based on Gaussian function fitting, an effective and valid method was established for quantitative analysis on Ti L edge loss spectrum. This method allowed us to accurately determine the stoichiometric number of TiOx phases. In order to probe the classical phenomenon of strong metal-support interactions in more detail, we also discussed and analyzed the origin of TiOx and its effect on the electronic structure of the material using density functional theory calculations. The structure of surfaces and interfaces as well as the chemical evolution of supported catalysts on a microscale have been revealed, thereby providing a new analysis method and research perspectives for the future study of metal-support interactions.

Observational Signatures of Electron-driven Chromospheric Evaporation in a White-light Flare

We investigate observational signatures of explosive chromospheric evaporation during a white-light flare (WLF) that occurred on 2022 August 27. Using the moment analysis, bisector techniques, and the Gaussian fitting method, redshifted velocities of less than 20 km s−1 are detected in low-temperature spectral lines of Hα, C i, and Si iv at the conjugated flare kernels, which could be regarded as downflows caused by chromospheric condensation. Blueshifted velocities of ∼30−40 km s−1 are found in the high-temperature line of Fe xxi, which can be interpreted as upflows driven by chromospheric evaporation. A nonthermal hard X-ray (HXR) source is cospatial with one of the flare kernels, and the Doppler velocities are temporally correlated with the HXR fluxes. The nonthermal energy flux is estimated to be at least (1.3 ± 0.2) × 1010 erg s−1 cm−2. The radiation enhancement at Fe i 6569.2 Å and 6173 Å suggests that the flare is a WLF. Moreover, the while-light emission at Fe i 6569.2 Å is temporally and spatially correlated with the blueshift of the Fe xxi line, suggesting that both the white-light enhancement and the chromospheric evaporation are triggered and driven by nonthermal electrons. All of our observations support the scenario of an electron-driven explosive chromospheric evaporation in the WLF.

Research on H7006C Angular Contact Ball Bearing Slipping Behavior under Operating Conditions

During operation, angular contact ball bearings exhibit slipping behavior, which accelerates bearing wear and reduces the bearing’s overall service life. This paper focuses on studying the degree of slippage and the variation in slippage rate in the H7006C angular contact ball bearing under constant inner ring speed and angular acceleration conditions. To achieve this objective, the study defines the contact between rolling elements, inner and outer rings, and the cage as flexible. A rigid–flexible coupling slippage dynamic simulation model is then developed, which utilizes cage slippage rate as the evaluation index. The analysis method of cage slippage rate change is established through the Gaussian function fitting method. By comparing simulation and testing results, the effectiveness of the analysis method of angular contact ball bearing slip behavior is validated. On this basis, the influence law of bearing speed and acceleration conditions on the slipping behavior of angular contact ball bearings is further studied. The results of the study show that as the axial load increases at different inner ring speeds, the cage speed increases until it reaches a stable state, while the cage slip rate continuously decreases until reaching a stable state. The higher the inner ring speed, the greater the axial load required for the bearing to enter the steady state. Under different angular accelerations, the cage speed increases with time; when the cage speed increases to stable and constant, the cage slip rate decreases to a stable state, and the higher the angular acceleration, the longer it takes for the bearing to enter the stable state.

Who Is behind a Trend? Temporal Analysis of Interactions among Trend Participants on Twitter

Trends are a fundamental component of today's fast-evolving media landscape. Still, a lot of questions about who participates in such trends remain unanswered. Are trends driven by individual actors, or do interactions between actors reveal community structures? If so, do those structures change during the life cycle of a trend or between topically similar trends? In short: Who is behind a trend? This paper contributes to a better understanding of these questions and, in general, actor networks underlying trends on social media. As a case study, we leverage a large Twitter dataset from the EURO2020 soccer competition to detect and analyze topical trends. Our novel Gaussian fitting method allows separating trend life cycles into up- and down-trend components, as well as determining the duration of trends. An event-based evaluation proves good performance results. Given separate trend stages and topically similar trends at different points in time, we conduct a temporal analysis of the actor networks during trends. Our findings not only reveal a large overlap of participants between successive trends but also indicate large variations within a trend life cycle. Furthermore, actor networks seem to be centred around a small number of dominant users and communities. Those users also show large stability across similar trends over time. In contrast, temporally stable community structures are neither found within nor across topically similar trends.

Weak information extraction of gamma spectrum based on a two-dimensional wavelet transform

This paper studies the spectrum of gamma rays based on two-dimensional wavelet transforms using Haar, Daubechies, and Symlets wavelets. A two-level two-dimensional transform is carried out using these three wavelets. The paper focuses on the amplitude and extreme horizontal components of high-frequency components after each stage of transform and extracts the weak information from the gamma-ray spectrum data. This paper analyzes three wavelets, two moments, and two energy levels. The analysis incorporates a small support torque and a two-dimensional transform. The standard traversal comparison peak search approach, the symmetric zero area method, the Gaussian fitting method, and the contemporary popular SNIP algorithms are also compared and analyzed in this work. The outcomes were also contrasted with the NaI(Tl) detector's measured peak levels that were calibrated in a lab. The analysis's findings demonstrate that, in the presence of high radioactive background, standard analysis techniques are unable to assess weak information. The chosen Daubechies and Symlets wavelets were used for the first-level 2D wavelet transform processing, and their peak search results surpassed those obtained from SNIP-processed analysis. Additionally, the standard deviation approach was applied to the outcomes of the 2D wavelet and SNIP transform to identify transform fluctuations in the local peaks of the complete spectrum and assess the algorithm's stability. The outcomes demonstrate that the local peaks of the 2D Symlets wavelet transform and 2D Daubechies transform, as well as the full spectrum fluctuations, are very tiny and steady. This technology can extract weak information from the data and can be applied to other gamma-ray data sets. It lays the foundation for a shielding study of special radiation protection materials.

AXanthoceras sorbifoliumcrack segmentation method based on an improved level set

AbstractThe dehiscence of theXanthoceras sorbifolium (X. sorbifolium)may lead to seeds jump out and economic loss. The shape and the degree of the crack will provide the relevant elements for the study of theX. sorbifoliumdehiscence and duly picked. Therefore, an improved level set method is proposed forX. sorbifoliumcrack segmentation. The problems of intensity inhomogeneity and so on, which pose challenges for accurate crack segmentation. The local Gaussian distribution fitting method has a good segmentation effect, but it is sensitive to the initial contour and does not use gradient information, which affects the accurate location of the edge. Aiming at the above problems and the scene of crack segmentation, this paper firstly adopts histogram threshold method to obtain the initial contour automatically. Secondly, the energy function is constructed by combining local and edge information. Finally, the double‐well potential function is used to reduce the oscillation and distortion of the method. In this paper, the experiment results show that the average boundary precision is 88.25% and average segmentation time of each image is 12.7s of the proposed method. After comprehensive analysis of IoU and boundary recall, the method in this paper achieves better results.

Water Bottom and Surface Classification Algorithm for Bathymetric LiDAR Point Clouds of Very Shallow Waters

The absence of accurate point classification limits the effective use of airborne bathymetric LiDAR (ABL) data for coastal zone mapping. In this study, we propose a classification approach using a custom waveform decomposition technique with the pseudo-waveform generated from ABL point cloud data. Initially, the input point clouds were organized into a 2D grid. Next, the points that fall into a grid cell were organized into a histogram using Z-values to generate the pseudo-waveform. Subsequently, the pseudo-waveform was decomposed into water bottom, column, surface, and noise components using a custom multiple Gaussian curve fitting method. The proposed approach was evaluated with datasets acquired in Florida, USA, using a Riegl VQ-880-G ABL system. With an optimized parameter set, the proposed approach achieved F1 score of 98.944% for the classification of water bottom and an overall accuracy of 91.234% for all the classes. Further, the proposed approach was evaluated with datasets acquired in South Korea using a Seahawk system and compared against MBES data, demonstrating that the water bottom was successfully classified with a vertical error of 0.049 ± 0.167 m.

Ultrafast Ultrasound Localization Microscopy by Conditional Generative Adversarial Network.

Ultrasound localization microscopy (ULM) overcomes the acoustic diffraction limit and enables the visualization of microvasculature at subwavelength resolution. However, challenges remain in ultrafast ULM implementation, where short data acquisition time, efficient data processing speed, and high imaging resolution need to be considered simultaneously. Recently, deep learning (DL)-based methods have exhibited potential in speeding up ULM imaging. Nevertheless, a certain number of ultrasound (US) data ( L frames) are still required to accumulate enough localized microbubble (MB) events, leading to an acquisition time within a time span of tens of seconds. To further speed up ULM imaging, in this article, we present a new DL-based method, termed as ULM-GAN. By using a modified conditional generative adversarial network (cGAN) framework, ULM-GAN is able to reconstruct a superresolution image directly from a temporal mean low-resolution (LR) image generated by averaging l -frame raw US images with l being significantly smaller than L . To evaluate the performance of ULM-GAN, a series of numerical simulations and phantom experiments are both implemented. The results of the numerical simulations demonstrate that when performing ULM imaging, ULM-GAN allows ∼40 -fold reduction in data acquisition time and ∼61 -fold reduction in computational time compared with the conventional Gaussian fitting method, without compromising spatial resolution according to the resolution scaled error (RSE). For the phantom experiments, ULM-GAN offers an implementation of ULM with ultrafast data acquisition time ( ∼0.33 s) and ultrafast data processing speed ( ∼0.60 s) that makes it promising to observe rapid biological activities in vivo.

Estimation of OH in urban plumes using TROPOMI-inferred NO2 ∕ CO

Abstract. A new method is presented for estimating urban hydroxyl radical (OH) concentrations using the downwind decay of the ratio of nitrogen dioxide over carbon monoxide column-mixing ratios (XNO2/XCO) retrieved from the Tropospheric Monitoring Instrument (TROPOMI). The method makes use of plumes simulated by the Weather Research and Forecast model (WRF-Chem) using passive-tracer transport, instead of the encoded chemistry, in combination with auxiliary input variables such as Copernicus Atmospheric Monitoring Service (CAMS) OH, Emission Database for Global Atmospheric Research v4.3.2 (EDGAR) NOx and CO emissions, and National Center for Environmental Protection (NCEP)-based meteorological data. NO2 and CO mixing ratios from the CAMS reanalysis are used as initial and lateral boundary conditions. WRF overestimates NO2 plumes close to the center of the city by 15 % to 30 % in summer and 40 % to 50 % in winter compared to TROPOMI observations over Riyadh. WRF-simulated CO plumes differ by 10 % with TROPOMI in both seasons. The differences between WRF and TROPOMI are used to optimize the OH concentration, NOx, CO emissions and their backgrounds using an iterative least-squares method. To estimate OH, WRF is optimized using (a) TROPOMI XNO2/XCO and (b) TROPOMI-derived XNO2 only. For summer, both the NO2/CO ratio optimization and the XNO2 optimization increase the prior OH from CAMS by 32 ± 5.3 % and 28.3 ± 3.9 %, respectively. EDGAR NOx and CO emissions over Riyadh are increased by 42.1 ± 8.4 % and 101 ± 21 %, respectively, in summer. In winter, the optimization method doubles the CO emissions while increasing OH by ∼ 52 ± 14 % and reducing NOx emissions by 15.5 ± 4.1 %. TROPOMI-derived OH concentrations and the pre-existing exponentially modified Gaussian function fit (EMG) method differ by 10 % in summer and winter, confirming that urban OH concentrations can be reliably estimated using the TROPOMI-observed NO2/CO ratio. Additionally, our method can be applied to a single TROPOMI overpass, allowing one to analyze day-to-day variability in OH, NOx and CO emission.

A deterministic collision avoidance decision-making system for multi-MASS encounter situation

Efficient identification of multi-ship encounter situation concerning action priority analysis is of vital significance for making effective and practical collision avoidance manoeuvres. However, action priority analysis is strongly involved in conflict urgency quantification, collision candidates relevance analysis as well as the contribution analysis within the encountering ships. In this paper, considering Maritime Autonomous Surface Ship, a deterministic collision avoidance decision-making system is established to estimate multi-MASS encounter situation. To this end, the approach index and asymmetrical Gaussian fitting method are deployed to assess collision risk, while the encountering ships are analytically distinguished into different clusters based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. By virtue of the improved Sharpley value method, the collision avoidance action priority is elaboratively sorted for different clusters. Accordingly, each individual collision avoidance manoeuvres are collaboratively generated by the modified velocity obstacle algorithm with certain time delay. Eventually, the proposed decision-making system is synthesized by functional modules including data-processing, conflict assessment detection, relevance analysis, action priority analysis, path planning and performance monitor. Simulation results demonstrate that this proposed decision-making system can perform significant superiority in various maritime environment in line with the practice of coordination and navigation.