CMMCAN:Lightweight Feature Extraction and Matching Network for Endoscopic Images Based on Adaptive Attention

In minimally invasive surgery,endoscopes or laparoscopes equipped with miniature cameras and tools are used to enter the human body for therapeutic purposes through small incisions or natural cavities.However,in clinical operating environments,endoscopic images often suffer from challenges such as low texture,uneven illumination,and non-rigid structures,which affect feature observation and extraction.This can severely impact surgical navigation or clinical diagnosis due to missing feature points in endoscopic images,leading to treatment and postoperative recovery issues for patients.To address these challenges,this paper introduces,for the first time,a Cross-Channel Multi-Modal Adaptive Spatial Feature Fusion(ASFF)module based on the lightweight architecture of EfficientViT.Additionally,a novel lightweight feature extraction and matching network based on attention mechanism is proposed.This network dynamically adjusts attention weights for cross-modal information from grayscale images and optical flow images through a dual-branch Siamese network.It extracts static and dynamic information features ranging from low-level to high-level,and from local to global,ensuring robust feature extraction across different widths,noise levels,and blur scenarios.Global and local matching are performed through a multi-level cascaded attention mechanism,with cross-channel attention introduced to simultaneously extract low-level and high-level features.Extensive ablation experiments and comparative studies are conducted on the HyperKvasir,EAD,M2caiSeg,CVC-ClinicDB,and UCL synthetic datasets.Experimental results demonstrate that the proposed network improves upon the baseline EfficientViT-B3 model by 75.4%in accuracy(Acc),while also enhancing runtime performance and storage efficiency.When compared with the complex DenseDescriptor feature extraction network,the difference in Acc is less than 7.22%,and IoU calculation results on specific datasets outperform complex dense models.Furthermore,this method increases the F1 score by 33.2%and accelerates runtime by 70.2%.It is noteworthy that the speed of CMMCAN surpasses that of comparative lightweight models,with feature extraction and matching performance comparable to existing complex models but with faster speed and higher cost-effectiveness.

Soft Tissue Feature Tracking Based on Deep Matching Network

Research in the field ofmedical image is an important part of themedical robot to operate human organs.Amedical robot is the intersection ofmulti-disciplinary research fields,in whichmedical image is an important direction and has achieved fruitful results.In this paper,amethodof soft tissue surface feature tracking basedonadepthmatching network is proposed.This method is described based on the triangular matching algorithm.First,we construct a self-made sample set for training the depth matching network from the first N frames of speckle matching data obtained by the triangle matching algorithm.The depth matching network is pre-trained on the ORL face data set and then trained on the self-made training set.After the training,the speckle matching is carried out in the subsequent frames to obtain the speckle matching matrix between the subsequent frames and the first frame.From this matrix,the inter-frame feature matching results can be obtained.In this way,the inter-frame speckle tracking is completed.On this basis,the results of this method are compared with the matching results based on the convolutional neural network.The experimental results show that the proposed method has higher matching accuracy.In particular,the accuracy of the MNIST handwritten data set has reached more than 90%.

Adaptive Recurrent Iterative Updating Stereo Matching Network

When training a stereo matching network with a single training dataset, the network may overly rely on the learned features of the single training dataset due to differences in the training dataset scenes, resulting in poor performance on all datasets. Therefore, feature consistency between matched pixels is a key factor in solving the network’s generalization ability. To address this issue, this paper proposed a more widely applicable stereo matching network that introduced whitening loss into the feature extraction module of stereo matching, and significantly improved the applicability of the network model by constraining the variation between salient feature pixels. In addition, this paper used a GRU iterative update module in the disparity update calculation stage, which expanded the model’s receptive field at multiple resolutions, allowing for precise disparity estimation not only in rich texture areas but also in low texture areas. The model was trained only on the Scene Flow large-scale dataset, and the disparity estimation was conducted on mainstream datasets such as Middlebury, KITTI 2015, and ETH3D. Compared with earlier stereo matching algorithms, this method not only achieves more accurate disparity estimation but also has wider applicability and stronger robustness.

Design of a distributed power amplifier based on T-type matching networks

The impedance characteristics of distributed amplifiers are analyzed based on T-type matching networks, and a distributed power amplifier consisting of three gain cells is proposed. Non-uniform T-type matching networks are adopted to make the impedance of artificial transmission lines connected to the gate and drain change stage by stage gradually, which provides good impedance matching and improves the output power and efficiency. The measurement results show that the amplifier gives an average forward gain of 6 dB from 3 to 16. 5 GHz. In the desired band, the input return loss is typically less than - 9. 5 dB, and the output return loss is better than -8.5 dB. The output power at 1-dB gain compression point is from 3.6 to 10. 6 dBm in the band of 2 to 16 GHz while the power added efficiency （PAE） is from 2% to 12. 5% . The power consumption of the amplifier is 81 mW with a supply of 1.8 V, and the chip area is 0.91 mm × 0.45 mm.

OPTIMIZATION DESIGN METHOD FOR INPUT IMPEDANCE MATCHING NETWORK OF LOW NOISE AMPLIFIER

According to the theories of optimal noise match and optimal power match, a method for calculating the optimal source impedance of low noise amplifier （LNA） is proposed based on the input reflection coefficient S11. Moreover.with the help of Smith chart, the calculation process is detailed, and the trade-off between the lowest noise figure and the maximum power gain is obtained during the design of LNA input impedance matching network. Based on the Chart 0. 35-μm CMOS process, a traditional cascode LNA circuit is designed and manufactured. Simulation and experimental results have a good agreement with the theoretical analysis, thus proving the correctness of theoretical analysis and the feasibility of the method.

Artificial Immune Detection for Network Intrusion Data Based on Quantitative Matching Method

Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.

An improved particle filter indoor fusion positioning approach based on Wi-Fi/PDR/geomagnetic field

The existing indoor fusion positioning methods based on Pedestrian Dead Reckoning(PDR)and geomagnetic technology have the problems of large initial position error,low sensor accuracy,and geomagnetic mismatch.In this study,a novel indoor fusion positioning approach based on the improved particle filter algorithm by geomagnetic iterative matching is proposed,where Wi-Fi,PDR,and geomagnetic signals are integrated to improve indoor positioning performances.One important contribution is that geomagnetic iterative matching is firstly proposed based on the particle filter algorithm.During the positioning process,an iterative window and a constraint window are introduced to limit the particle generation range and the geomagnetic matching range respectively.The position is corrected several times based on geomagnetic iterative matching in the location correction stage when the pedestrian movement is detected,which made up for the shortage of only one time of geomagnetic correction in the existing particle filter algorithm.In addition,this study also proposes a real-time step detection algorithm based on multi-threshold constraints to judge whether pedestrians are moving,which satisfies the real-time requirement of our fusion positioning approach.Through experimental verification,the average positioning accuracy of the proposed approach reaches 1.59 m,which improves 33.2%compared with the existing particle filter fusion positioning algorithms.

From VIB‑to VB‑Group Transition Metal Disulfides:Structure Engineering Modulation for Superior Electromagnetic Wave Absorption

The laminated transition metal disulfides(TMDs),which are well known as typical two-dimensional(2D)semiconductive materials,possess a unique layered structure,leading to their wide-spread applications in various fields,such as catalysis,energy storage,sensing,etc.In recent years,a lot of research work on TMDs based functional materials in the fields of electromagnetic wave absorption(EMA)has been carried out.Therefore,it is of great significance to elaborate the influence of TMDs on EMA in time to speed up the application.In this review,recent advances in the development of electromagnetic wave(EMW)absorbers based on TMDs,ranging from the VIB group to the VB group are summarized.Their compositions,microstructures,electronic properties,and synthesis methods are presented in detail.Particularly,the modulation of structure engineering from the aspects of heterostructures,defects,morphologies and phases are systematically summarized,focusing on optimizing impedance matching and increasing dielectric and magnetic losses in the EMA materials with tunable EMW absorption performance.Milestones as well as the challenges are also identified to guide the design of new TMDs based dielectric EMA materials with high performance.

Improved HardNet and Stricter Outlier Filtering to Guide Reliable Matching

As the fundamental problem in the computer vision area,image matching has wide applications in pose estimation,3D reconstruction,image retrieval,etc.Suffering from the influence of external factors,the process of image matching using classical local detectors,e.g.,scale-invariant feature transform(SIFT),and the outlier filtering approaches,e.g.,Random sample consensus(RANSAC),show high computation speed and pool robustness under changing illumination and viewpoints conditions,while image matching approaches with deep learning strategy(such as HardNet,OANet)display reliable achievements in large-scale datasets with challenging scenes.However,the past learning-based approaches are limited to the distinction and quality of the dataset and the training strategy in the image-matching approaches.As an extension of the previous conference paper,this paper proposes an accurate and robust image matching approach using fewer training data in an end-to-end manner,which could be used to estimate the pose error This research first proposes a novel dataset cleaning and construction strategy to eliminate the noise and improve the training efficiency;Secondly,a novel loss named quadratic hinge triplet loss(QHT)is proposed to gather more effective and stable feature matching;Thirdly,in the outlier filtering process,the stricter OANet and bundle adjustment are applied for judging samples by adding the epipolar distance constraint and triangulation constraint to generate more outstanding matches;Finally,to recall the matching pairs,dynamic guided matching is used and then submit the inliers after the PyRANSAC process.Multiple evaluation metrics are used and reported in the 1st place in the Track1 of CVPR Image-Matching Challenge Workshop.The results show that the proposed method has advanced performance in large-scale and challenging Phototourism benchmark.

Shared genetic architecture highlights the bidirectional association between major depressive disorder and fracture risk

Background There is limited evidence suggesting that osteoporosis might exacerbate depressive symptoms,while more studies demonstrate that depression negatively affects bone density and increases fracture risk.Aims To explore the relationship between major depressive disorder(MDD)and fracture risk.Methods We conducted a nested case-control analysis(32670 patients with fracture and 397017 individuals without fracture)and a matched cohort analysis(16496 patients with MDD and 435492 individuals without MDD)in the same prospective UK Biobank data set.Further,we investigated the shared genetic architecture between MDD and fracture with linkage disequilibrium score regression and the MiXeR statistical tools.We used the conditional/conjunctional false discovery rate approach to identify the specific shared loci.We calculated the weighted genetic risk score for individuals in the UK Biobank and logistic regression was used to confirm the association observed in the prospective study.Results We found that MDD was associated with a 14%increase in fracture risk(hazard ratio(HR)1.14,95%CI 1.14 to 1.15,p<0.001)in the nested case-control analysis,while fracture was associated with a 72%increase in MDD risk(HR 1.72,95%CI 1.64 to 1.79,p<0.001)in the matched cohort analysis,suggesting a longitudinal and bidirectional relationship.Further,genetic summary data suggested a genetic overlap between MDD and fracture.Specifically,we identified four shared genomic loci,with the top signal(rs7554101)near SGIP1.The protein encoded by SGIP1 is involved in cannabinoid receptor type 1 signalling.We found that genetically predicted MDD was associated with a higher risk of fracture and vice versa.In addition,we found that the higher expression level of SGIP1 in the spinal cord and muscle was associated with an increased risk of fracture and MDD.Conclusions The genetic pleiotropy between MDD and fracture highlights the bidirectional association observed in the epidemiological analysis.The shared genetic components(such as SGIP1)between the diseases suggest that modulating the endocannabinoid system could be a potential therapeutic strategy for both MDD and bone loss.

Distributed Matching Theory-Based Task Re-Allocating for Heterogeneous Multi-UAV Edge Computing

Many efforts have been devoted to efficient task scheduling in Multi-Unmanned Aerial Vehicle(UAV)edge computing.However,the heterogeneity of UAV computation resource,and the task re-allocating between UAVs have not been fully considered yet.Moreover,most existing works neglect the fact that a task can only be executed on the UAV equipped with its desired service function(SF).In this backdrop,this paper formulates the task scheduling problem as a multi-objective task scheduling problem,which aims at maximizing the task execution success ratio while minimizing the average weighted sum of all tasks’completion time and energy consumption.Optimizing three coupled goals in a realtime manner with the dynamic arrival of tasks hinders us from adopting existing methods,like machine learning-based solutions that require a long training time and tremendous pre-knowledge about the task arrival process,or heuristic-based ones that usually incur a long decision-making time.To tackle this problem in a distributed manner,we establish a matching theory framework,in which three conflicting goals are treated as the preferences of tasks,SFs and UAVs.Then,a Distributed Matching Theory-based Re-allocating(DiMaToRe)algorithm is put forward.We formally proved that a stable matching can be achieved by our proposal.Extensive simulation results show that Di Ma To Re algorithm outperforms benchmark algorithms under diverse parameter settings and has good robustness.

Reshaping Li–Mg hybrid batteries:Epitaxial electrodeposition and spatial confinement on MgMOF substrates via the lattice‐matching strategy

The emergence of Li–Mg hybrid batteries has been receiving attention,owing to their enhanced electrochemical kinetics and reduced overpotential.Nevertheless,the persistent challenge of uneven Mg electrodeposition remains a significant impediment to their practical integration.Herein,we developed an ingenious approach that centered around epitaxial electrocrystallization and meticulously controlled growth of magnesium crystals on a specialized MgMOF substrate.The chosen MgMOF substrate demonstrated a robust affinity for magnesium and showed minimal lattice misfit with Mg,establishing the crucial prerequisites for successful heteroepitaxial electrocrystallization.Moreover,the incorporation of periodic electric fields and successive nanochannels within the MgMOF structure created a spatially confined environment that considerably promoted uniform magnesium nucleation at the molecular scale.Taking inspiration from the“blockchain”concept prevalent in the realm of big data,we seamlessly integrated a conductive polypyrrole framework,acting as a connecting“chain,”to interlink the“blocks”comprising the MgMOF cavities.This innovative design significantly amplified charge‐transfer efficiency,thereby increasing overall electrochemical kinetics.The resulting architecture(MgMOF@PPy@CC)served as an exceptional host for heteroepitaxial Mg electrodeposition,showcasing remarkable electrostripping/plating kinetics and excellent cycling performance.Surprisingly,a symmetrical cell incorporating the MgMOF@PPy@CC electrode demonstrated impressive stability even under ultrahigh current density conditions(10mAcm–2),maintaining operation for an extended 1200 h,surpassing previously reported benchmarks.Significantly,on coupling the MgMOF@PPy@CC anode with a Mo_(6)S_(8) cathode,the assembled battery showed an extended lifespan of 10,000 cycles at 70 C,with an outstanding capacity retention of 96.23%.This study provides a fresh perspective on the rational design of epitaxial electrocrystallization driven by metal–organic framework(MOF)substrates,paving the way toward the advancement of cuttingedge batteries.

Feature Matching via Topology-Aware Graph Interaction Model

Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive outliers.This paper attempts to tackle the outlier filtering problem from two aspects. First, a robust and efficient graph interaction model,is proposed, with the assumption that matches are correlated with each other rather than independently distributed. To this end, we construct a graph based on the local relationships of matches and formulate the outlier filtering task as a binary labeling energy minimization problem, where the pairwise term encodes the interaction between matches. We further show that this formulation can be solved globally by graph cut algorithm. Our new formulation always improves the performance of previous localitybased method without noticeable deterioration in processing time,adding a few milliseconds. Second, to construct a better graph structure, a robust and geometrically meaningful topology-aware relationship is developed to capture the topology relationship between matches. The two components in sum lead to topology interaction matching(TIM), an effective and efficient method for outlier filtering. Extensive experiments on several large and diverse datasets for multiple vision tasks including general feature matching, as well as relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multi-modal image matching, demonstrate that our TIM is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code is publicly available at http://github.com/YifanLu2000/TIM.

Secure Transmission of Compressed Medical Image Sequences on Communication Networks Using Motion Vector Watermarking

Medical imaging plays a key role within modern hospital management systems for diagnostic purposes.Compression methodologies are extensively employed to mitigate storage demands and enhance transmission speed,all while upholding image quality.Moreover,an increasing number of hospitals are embracing cloud computing for patient data storage,necessitating meticulous scrutiny of server security and privacy protocols.Nevertheless,considering the widespread availability of multimedia tools,the preservation of digital data integrity surpasses the significance of compression alone.In response to this concern,we propose a secure storage and transmission solution for compressed medical image sequences,such as ultrasound images,utilizing a motion vector watermarking scheme.The watermark is generated employing an error-correcting code known as Bose-Chaudhuri-Hocquenghem(BCH)and is subsequently embedded into the compressed sequence via block-based motion vectors.In the process of watermark embedding,motion vectors are selected based on their magnitude and phase angle.When embedding watermarks,no specific spatial area,such as a region of interest(ROI),is used in the images.The embedding of watermark bits is dependent on motion vectors.Although reversible watermarking allows the restoration of the original image sequences,we use the irreversible watermarking method.The reason for this is that the use of reversible watermarks may impede the claims of ownership and legal rights.The restoration of original data or images may call into question ownership or other legal claims.The peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)serve as metrics for evaluating the watermarked image quality.Across all images,the PSNR value exceeds 46 dB,and the SSIM value exceeds 0.92.Experimental results substantiate the efficacy of the proposed technique in preserving data integrity.

A Non-Parametric Scheme for Identifying Data Characteristic Based on Curve Similarity Matching

For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity matching.In the framework of the pro-posed scheme,a Parzen window(kernel density estimation,KDE)method on sliding window technology is applied for roughly esti-mating the sample probability density,a precise data probability density function(PDF)model is constructed with the least square method on K-fold cross validation,and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape,abruptness and symmetry.Some com-parison simulations with classical methods and UAV flight exper-iment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data,which provides better reference for the design of Kalman filter(KF)in complex water environment.

Preparation of CIP@TiO_(2) composite with broadband electromagnetic wave absorption properties

Scholars aim for the improved impedance matching (Z) of materials while maintaining their excellent wave absorption properties. Based on the hydrolysis characteristics of isopropyl titanate, a simple preparation process for the coating of carbonyl iron powder(CIP) with TiO_(2) was designed. Given the TiO2coating, the Z of the CIP@TiO_(2) composite was adjusted well by decreasing the dielectric constant. Moreover, the interfacial polarization of CIP@TiO_(2) was enhanced. Ultimately, the electromagnetic-wave (EMW) absorption property of the CIP@TiO_(2)composite was improved substantially, the minimum reflection loss reached-46.07 dB, and the effective absorption bandwidth can reach 8 GHz at the composite thickness of 1.5 mm. Moreover, compared with CIP, the oxidation resistance of CIP@TiO_(2)showed remarkable improvement. The results revealed that the oxidation starting temperature of CIP@TiO_(2) as about 400℃,whereas the uncoated CIP had an oxidation starting temperature of approximately 250℃. Moreover, the largest oxidation rate temperature of CIP@TiO_(2) increased to around 550℃. This work opens up a novel strategy for the production of high-performance EMW absorbers via structural design.

Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

A Practical Approach for Missing Wireless Sensor Networks Data Recovery

In wireless sensor networks(WSNs),the performance of related applications is highly dependent on the quality of data collected.Unfortunately,missing data is almost inevitable in the process of data acquisition and transmission.Existing methods often rely on prior information such as low-rank characteristics or spatiotemporal correlation when recovering missing WSNs data.However,in realistic application scenarios,it is very difficult to obtain these prior information from incomplete data sets.Therefore,we aim to recover the missing WSNs data effectively while getting rid of the perplexity of prior information.By designing the corresponding measurement matrix that can capture the position of missing data and sparse representation matrix,a compressive sensing(CS)based missing data recovery model is established.Then,we design a comparison standard to select the best sparse representation basis and introduce average cross-correlation to examine the rationality of the established model.Furthermore,an improved fast matching pursuit algorithm is proposed to solve the model.Simulation results show that the proposed method can effectively recover the missing WSNs data.

Inversion of Seabed Geotechnical Properties in the Arctic Chukchi Deep Sea Basin Based on Time Domain Adaptive Search Matching Algorithm

The chirp sub-bottom profiler,for its high resolution,easy accessibility and cost-effectiveness,has been widely used in acoustic detection.In this paper,the acoustic impedance and grain size compositions were obtained based on the chirp sub-bottom profiler data collected in the Chukchi Plateau area during the 11th Arctic Expedition of China.The time-domain adaptive search matching algorithm was used and validated on our established theoretical model.The misfit between the inversion result and the theoretical model is less than 0.067%.The grain size was calculated according to the empirical relationship between the acoustic impedance and the grain size of the sediment.The average acoustic impedance of sub-seafloor strata is 2.5026×10^(6) kg(s m^(2))^(-1)and the average grain size(θvalue)of the seafloor surface sediment is 7.1498,indicating the predominant occurrence of very fine silt sediment in the study area.Comparison of the inversion results and the laboratory measurements of nearby borehole samples shows that they are in general agreement.