Spatial pattern recognition for near-surface high temperature increases in mountain areas using MODIS and SRTM DEM

Abrupt near-surface temperature changes in mountainous areas are a special component of the mountain climate system.Fast and accurate measurements of the locations,intensity,and width of the near-surface changes are necessary but highly difficult due to the complicated environmental conditions and instrumental issues.This paper develops a spatial pattern recognition method to measure the near-surface high temperature increase(NSHTI),one of the lesser-attended changes.First,raster window measurement was proposed to calculate the temperature lapse rate using MODIS land surface temperature and SRTM DEM data.It fully considers the terrain heights of two neighboring cells on opposite or adjacent slopes with a moving window of 3×3 cell size.Second,a threshold selection was performed to identify the NSHTI cells using a threshold of-0.65℃/100 m.Then,the NSHTI strips were parameterized through raster vectorization and spatial analysis.Taking Yunnan,a mountainous province in southwestern China,as the study area,the results indicate that the NSHTI cells concentrate in a strip-like pattern along the mountains and valleys,and the strips are almost parallel to the altitude contours with a slight northward uplift.Also,they are located mostly at a 3/5 height of high mountains or within 400 m from the valley floors,where the controlling topographic index is the altitude of the terrain trend surface but not the absolute elevation and the topographic uplift height and cutting depth.Additionally,the NSHTI intensity varies with the geographic locations and the proportions increase with an exponential trend,and the horizontal width has a mean of about 1000 m and a maximum of over 5000 m.The result demonstrates that the proposed method can effectively recognize NSHTI boundaries over mountains,providing support for the modeling of weather and climate systems and the development of mountain resources.

Overcoming the Limits of Cross-Sensitivity:Pattern Recognition Methods for Chemiresistive Gas Sensor Array

As information acquisition terminals for artificial olfaction,chemiresistive gas sensors are often troubled by their cross-sensitivity,and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area.Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors.It is crucial to choose an appropriate pattern recognition method for enhancing data analysis,reducing errors and improving system reliability,obtaining better classification or gas concentration prediction results.In this review,we analyze the sensing mechanism of crosssensitivity for chemiresistive gas sensors.We further examine the types,working principles,characteristics,and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays.Additionally,we report,summarize,and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification.At the same time,this work showcases the recent advancements in utilizing these methods for gas identification,particularly within three crucial domains:ensuring food safety,monitoring the environment,and aiding in medical diagnosis.In conclusion,this study anticipates future research prospects by considering the existing landscape and challenges.It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.

AI-Driven Pattern Recognition in Medicinal Plants: A Comprehensive Review and Comparative Analysis

The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern drugs.Throughout the extensive history of medicinal plant usage,various plant parts,including flowers,leaves,and roots,have been acknowledged for their healing properties and employed in plant identification.Leaf images,however,stand out as the preferred and easily accessible source of information.Manual plant identification by plant taxonomists is intricate,time-consuming,and prone to errors,relying heavily on human perception.Artificial intelligence(AI)techniques offer a solution by automating plant recognition processes.This study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification,drawing insights from literature across renowned repositories.This paper critically summarizes relevant literature based on AI algorithms,extracted features,and results achieved.Additionally,it analyzes extensively used datasets in automated plant classification research.It also offers deep insights into implemented techniques and methods employed for medicinal plant recognition.Moreover,this rigorous review study discusses opportunities and challenges in employing these AI-based approaches.Furthermore,in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research directions.This review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants.

Expression and clinical significance of pattern recognition receptor-associated genes in hand, foot and mouth disease

Objective:To explore which pattern recognition receptors(PRRs)play a key role in the development of hand,foot,and mouth disease(HFMD)by analyzing PRR-associated genes.Methods:We conducted a comparative analysis of PRR-associated gene expression in human peripheral blood mononuclear cells(PBMCs)infected with enterovirus 71(EV-A71)which were derived from patients with HFMD of different severities and at different stages.A total of 30 PRR-associated genes were identified as significantly upregulated both over time and across different EV-A71 isolates.Subsequently,ELISA was employed to quantify the expression of the six most prominent genes among these 30 identified genes,specifically,BST2,IRF7,IFI16,TRIM21,MX1,and DDX58.Results:Compared with those at the recovery stage,the expression levels of BST2(P=0.027),IFI16(P=0.016),MX1(P=0.046)and DDX58(P=0.008)in the acute stage of infection were significantly upregulated,while no significant difference in the expression levels of IRF7(P=0.495)and TRIM21(P=0.071)was found between different stages of the disease.The expression levels of BST2,IRF7,IFI16 and MX1 were significantly higher in children infected with single pathogen than those infected with mixed pathogens,and BST2,IRF7,IFI16 and MX1 expression levels were significantly lower in coxsackie B virus(COXB)positive patients than the negative patients.Expression levels of one or more of BST2,IRF7,IFI16,TRIM21,MX1 and DDX58 genes were correlated with PCT levels,various white blood cell counts,and serum antibody levels that reflect disease course of HFMD.Aspartate aminotransferase was correlated with BST2,MX1 and DDX58 expression levels.Conclusions:PRR-associated genes likely initiate the immune response in patients at the acute stage of HFMD.

Dynamic Signature Verification Using Pattern Recognition

Dynamic signature is a biometric modality that recognizes an individual’s anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key motivating factor for embarking on this study. This study was necessitated by the damages and dangers posed by signature forgery coupled with the intractable nature of the problem. The aim and objectives of this study is to design a proactive and responsive system that could compare two signature samples and detect the correct signature against the forged one. Dynamic Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. In this research work, Convolutional Neural Networks (CNNsor ConvNet) which is a class of deep, feed forward artificial neural networks that has successfully been applied to analysing visual imagery was used to train the model. The signature images are stored in a file directory structure which the Keras Python library can work with. Then the CNN was implemented in python using the Keras with the TensorFlow backend to learn the patterns associated with the signature. The result showed that for the same CNNs-based network experimental result of average accuracy, the larger the training dataset, the higher the test accuracy. However, when the training dataset are insufficient, better results can be obtained. The paper concluded that by training datasets using CNNs network, 98% accuracy in the result was recorded, in the experimental part, the model achieved a high degree of accuracy in the classification of the biometric parameters used.

Wafer Defect Map Pattern Recognition Based on Improved ResNet

The defect detection of wafers is an important part of semiconductor manufacturing.The wafer defect map formed from the defects can be used to trace back the problems in the production process and make improvements in the yield of wafer manufacturing.Therefore,for the pattern recognition of wafer defects,this paper uses an improved ResNet convolutional neural network for automatic pattern recognition of seven common wafer defects.On the basis of the original ResNet,the squeeze-and-excitation(SE)attention mechanism is embedded into the network,through which the feature extraction ability of the network can be improved,key features can be found,and useless features can be suppressed.In addition,the residual structure is improved,and the depth separable convolution is added to replace the traditional convolution to reduce the computational and parametric quantities of the network.In addition,the network structure is improved and the activation function is changed.Comprehensive experiments show that the precision of the improved ResNet in this paper reaches 98.5%,while the number of parameters is greatly reduced compared with the original model,and has well results compared with the common convolutional neural network.Comprehensively,the method in this paper can be very good for pattern recognition of common wafer defect types,and has certain application value.

Crack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the Radial Basis Function Network and Pattern Recognition Neural Network

The crack fault is one of the most common faults in the rotor system,and researchers have paid close attention to its fault diagnosis.However,most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration signals.In this paper,a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the Radial basis function(RBF)network and Pattern recognition neural network(PRNN)is presented.Firstly,a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method,where the crack's periodic opening and closing pattern and different degrees of crack depth are considered.Then,the dynamic response is obtained by the harmonic balance method.By adjusting the crack parameters,the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall plots.The analysis results show that the first critical speed,first subcritical speed,first critical speed amplitude,and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault diagnosis.Based on this,the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as input.Test results show that the proposed method has high fault diagnosis accuracy.This research proposes a crack detection method adequate for the hollow shaft rotor system,where the crack depth and position are both unknown.

Numerical Comparison of Shapeless Radial Basis Function Networks in Pattern Recognition

This work focuses on radial basis functions containing no parameters with themain objective being to comparatively explore more of their effectiveness.For this,a total of sixteen forms of shapeless radial basis functions are gathered and investigated under the context of the pattern recognition problem through the structure of radial basis function neural networks,with the use of the Representational Capability(RC)algorithm.Different sizes of datasets are disturbed with noise before being imported into the algorithm as‘training/testing’datasets.Each shapeless radial basis function is monitored carefully with effectiveness criteria including accuracy,condition number(of the interpolation matrix),CPU time,CPU-storage requirement,underfitting and overfitting aspects,and the number of centres being generated.For the sake of comparison,the well-known Multiquadric-radial basis function is included as a representative of shape-contained radial basis functions.The numerical results have revealed that some forms of shapeless radial basis functions show good potential and are even better than Multiquadric itself indicating strongly that the future use of radial basis function may no longer face the pain of choosing a proper shape when shapeless forms may be equally(or even better)effective.

Hierarchical pattern recognition of landform elements considering scale adaptation

Landform elements with varying morphologies and spatial arrangements are recognized as feature indicator of landform classification and play a critical role in geomorphological studies.Differential geometry method has been extensively applied in prior landform element research,while its efficacy in differentiating similar morphological characteristics remains inadequate to date.To reduce reliance on geomorphometric variables and increase awareness of landform patterns,geomorphons method was generated in previous study corresponding to specific landform reclassification map based on lookup table.Besides,to address the problem of feature similarity,hierarchical classification was proposed and effectively utilized for terrain recognition through the analytical strategy of fuzzy gradient features.Thus,combining the advantages of these two aspects,a hierarchical framework was proposed in this study for landform element pattern recognition considering the morphology and hierarchy factors.First,the local triplet patterns derived from geomorphons were enhanced by setting the flatness threshold,and subsequently adopted for the primary landform element recognition.Then,as geomorphic units with the same morphology possess different spatial analytical scales,the unidentified landform elements under the principle of scale adaptation were determined by calculating the spatial correlation and entropy information.To ensure the effectiveness of this proposed method,the sampling points were randomly selected from NASADEM data and then validated against a real 3D terrain model.Quantitative results of landform element pattern recognition demonstrate that our approach can reach above 77%average accuracy.Additionally,it delineates local details more effectively than geomorphons in visual assessment,resulting in a 7%accuracy improvement in overall scale.

Fuzzy pattern recognition model of geological sweetspot for coalbed methane development

From the perspective of geological zone selection for coalbed methane(CBM) development, the evaluation parameters(covering geological conditions and production conditions) of geological sweetspot for CBM development are determined, and the evaluation index system of geological sweetspot for CBM development is established. On this basis, the fuzzy pattern recognition(FPR) model of geological sweetspot for CBM development is built. The model is applied to evaluate four units of No.3 Coal Seam in the Fanzhuang Block, southern Qinshui Basin, China. The evaluation results are consistent with the actual development effect and the existing research results, which verifies the rationality and reliability of the FPR model. The research shows that the proposed FPR model of geological sweetspot for CBM development does not involve parameter weighting which leads to uncertainties in the results of the conventional models such as analytic hierarchy process and multi-level fuzzy synthesis judgment, and features a simple computation without the construction of multi-level judgment matrix. The FPR model provides reliable results to support the efficient development of CBM.

Human Personality Assessment Based on Gait Pattern Recognition Using Smartphone Sensors

Human personality assessment using gait pattern recognition is one of the most recent and exciting research domains.Gait is a person’s identity that can reflect reliable information about his mood,emotions,and substantial personality traits under scrutiny.This research focuses on recognizing key personality traits,including neuroticism,extraversion,openness to experience,agreeableness,and conscientiousness,in line with the bigfive model of personality.We inferred personality traits based on the gait pattern recognition of individuals utilizing built-in smartphone sensors.For experimentation,we collected a novel dataset of 22 participants using an android application and further segmented it into six data chunks for a critical evaluation.After data pre-processing,we extracted selected features from each data segment and then applied four multiclass machine learning algorithms for training and classifying the dataset corresponding to the users’Big-Five Personality Traits Profiles(BFPT).Experimental results and performance evaluation of the classifiers revealed the efficacy of the proposed scheme for all big-five traits.

Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.

Robust Human Interaction Recognition Using Extended Kalman Filter

In the field of computer vision and pattern recognition,knowledge based on images of human activity has gained popularity as a research topic.Activity recognition is the process of determining human behavior based on an image.We implemented an Extended Kalman filter to create an activity recognition system here.The proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the image.To minimize noise,we use Gaussian filters.Extraction of silhouette using the statistical method.We use Binary Robust Invariant Scalable Keypoints(BRISK)and SIFT for feature extraction.The next step is to perform feature discrimination using Gray Wolf.After that,the features are input into the Extended Kalman filter and classified into relevant human activities according to their definitive characteristics.The experimental procedure uses the SUB-Interaction and HMDB51 datasets to a 0.88%and 0.86%recognition rate.

Corrigendum to“Migration pattern of a population of Barn Swallows(Hirundo rustica)breeding in East Asian tropical region”[Avian Res.15(2024)100192]

The authors regret that the caption of Fig.4 should be replaced as below.

Clinical manifestation,lifestyle,and treatment patterns of chronic erosive gastritis:A multicenter real-world study in China

BACKGROUND Although chronic erosive gastritis(CEG)is common,its clinical characteristics have not been fully elucidated.The lack of consensus regarding its treatment has resulted in varied treatment regimens.AIM To explore the clinical characteristics,treatment patterns,and short-term outcomes in CEG patients in China.METHODS We recruited patients with chronic non-atrophic or mild-to-moderate atrophic gastritis with erosion based on endoscopy and pathology.Patients and treating physicians completed a questionnaire regarding history,endoscopic findings,and treatment plans as well as a follow-up questionnaire to investigate changes in symptoms after 4 wk of treatment.RESULTS Three thousand five hundred sixty-three patients from 42 centers across 24 cities in China were included.Epigastric pain(68.0%),abdominal distension(62.6%),and postprandial fullness(47.5%)were the most common presenting symptoms.Gastritis was classified as chronic non-atrophic in 69.9%of patients.Among those with erosive lesions,72.1%of patients had lesions in the antrum,51.0%had multiple lesions,and 67.3%had superficial flat lesions.In patients with epigastric pain,the combination of a mucosal protective agent(MPA)and proton pump inhibitor was more effective.For those with postprandial fullness,acid regurgitation,early satiety,or nausea,a MPA appeared more promising.CONCLUSION CEG is a multifactorial disease which is common in Asian patients and has non-specific symptoms.Gastroscopy may play a major role in its detection and diagnosis.Treatment should be individualized based on symptom profile.

Intelligent Recognition Using Ultralight Multifunctional Nano‑Layered Carbon Aerogel Sensors with Human‑Like Tactile Perception

Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence.

Venous Doppler flow patterns,venous congestion,heart disease and renal dysfunction:A complex liaison

The World Journal of Cardiology published an article written by Kuwahara et al that we take the pleasure to comment on.We focused our attention on venous congestion.In intensive care settings,it is now widely accepted that venous congestion is an important clinical feature worthy of investigation.Evaluating venous Doppler profile abnormalities at multiple sites could suggest adequate treatment and monitor its efficacy.Renal dysfunction could trigger or worsen fluid overload in heart disease,and cardio-renal syndrome is a well-characterized spectrum of disorders describing the complex interactions between heart and kidney diseases.Fluid overload and venous congestion,including renal venous hypertension,are major determinants of acute and chronic renal dysfunction arising in heart disease.Organ congestion from venous hypertension could be involved in the development of organ injury in several clinical situations,such as critical diseases,congestive heart failure,and chronic kidney disease.Ultrasonography and abnormal Doppler flow patterns diagnose clinically significant systemic venous congestion.Cardiologists and nephrologists might use this valuable,noninvasive,bedside diagnostic tool to establish fluid status and guide clinical choices.

Coupling Analysis of Multiple Machine Learning Models for Human Activity Recognition

Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in HAR.The analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the models.Additionally,the MHEALTH database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and adaptability.The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data training.In contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature extraction.Furthermore,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model performance.The best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,respectively.Comparative analysis using the MHEALTH dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,respectively.Finally,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research findings.In conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.

Automatic modulation recognition of radio fuzes using a DR2D-based adaptive denoising method and textural feature extraction

The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems.However,the electromagnetic environment of modern battlefields is complex,and the signal-to-noise ratio(SNR)of such environments is usually low,which makes it difficult to implement accurate recognition of radio fuzes.To solve the above problem,a radio fuze automatic modulation recognition(AMR)method for low-SNR environments is proposed.First,an adaptive denoising algorithm based on data rearrangement and the two-dimensional(2D)fast Fourier transform(FFT)(DR2D)is used to reduce the noise of the intercepted radio fuze intermediate frequency(IF)signal.Then,the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices(GLCMs),and support vector machines(SVMs)are used for classification.The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of-10 d B and above,which is higher than that of other typical algorithms.The trained SVM classification model achieves an average recognition accuracy of more than 96%on seven modulation types and recognition accuracies of more than 94%on each modulation type under SNRs of-12 d B and above,which represents a good AMR performance of radio fuzes under low SNRs.

Fine-Grained Ship Recognition Based on Visible and Near-Infrared Multimodal Remote Sensing Images: Dataset,Methodology and Evaluation

Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national security.Currently,with the emergence of massive high-resolution multi-modality images,the use of multi-modality images for fine-grained recognition has become a promising technology.Fine-grained recognition of multi-modality images imposes higher requirements on the dataset samples.The key to the problem is how to extract and fuse the complementary features of multi-modality images to obtain more discriminative fusion features.The attention mechanism helps the model to pinpoint the key information in the image,resulting in a significant improvement in the model’s performance.In this paper,a dataset for fine-grained recognition of ships based on visible and near-infrared multi-modality remote sensing images has been proposed first,named Dataset for Multimodal Fine-grained Recognition of Ships(DMFGRS).It includes 1,635 pairs of visible and near-infrared remote sensing images divided into 20 categories,collated from digital orthophotos model provided by commercial remote sensing satellites.DMFGRS provides two types of annotation format files,as well as segmentation mask images corresponding to the ship targets.Then,a Multimodal Information Cross-Enhancement Network(MICE-Net)fusing features of visible and near-infrared remote sensing images,has been proposed.In the network,a dual-branch feature extraction and fusion module has been designed to obtain more expressive features.The Feature Cross Enhancement Module(FCEM)achieves the fusion enhancement of the two modal features by making the channel attention and spatial attention work cross-functionally on the feature map.A benchmark is established by evaluating state-of-the-art object recognition algorithms on DMFGRS.MICE-Net conducted experiments on DMFGRS,and the precision,recall,mAP0.5 and mAP0.5:0.95 reached 87%,77.1%,83.8%and 63.9%,respectively.Extensive experiments demonstrate that the proposed MICE-Net has more excellent performance on DMFGRS.Built on lightweight network YOLO,the model has excellent generalizability,and thus has good potential for application in real-life scenarios.