A landslide monitoring method using data from unmanned aerial vehicle and terrestrial laser scanning with insufficient and inaccurate ground control points

Non-contact remote sensing techniques,such as terrestrial laser scanning(TLS)and unmanned aerial vehicle(UAV)photogrammetry,have been globally applied for landslide monitoring in high and steep mountainous areas.These techniques acquire terrain data and enable ground deformation monitoring.However,practical application of these technologies still faces many difficulties due to complex terrain,limited access and dense vegetation.For instance,monitoring high and steep slopes can obstruct the TLS sightline,and the accuracy of the UAV model may be compromised by absence of ground control points(GCPs).This paper proposes a TLS-and UAV-based method for monitoring landslide deformation in high mountain valleys using traditional real-time kinematics(RTK)-based control points(RCPs),low-precision TLS-based control points(TCPs)and assumed control points(ACPs)to achieve high-precision surface deformation analysis under obstructed vision and impassable conditions.The effects of GCP accuracy,GCP quantity and automatic tie point(ATP)quantity on the accuracy of UAV modeling and surface deformation analysis were comprehensively analyzed.The results show that,the proposed method allows for the monitoring accuracy of landslides to exceed the accuracy of the GCPs themselves by adding additional low-accuracy GCPs.The proposed method was implemented for monitoring the Xinhua landslide in Baoxing County,China,and was validated against data from multiple sources.

A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities.Additionally,it leverages inter-modal correlation to enhance recognition performance.Concurrently,the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features.Nevertheless,two issues persist in multi-modal feature fusion recognition:Firstly,the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities.Secondly,during modal fusion,improper weight selection diminishes the salience of crucial modal features,thereby diminishing the overall recognition performance.To address these two issues,we introduce an enhanced DenseNet multimodal recognition network founded on feature-level fusion.The information from the three modalities is fused akin to RGB,and the input network augments the correlation between modes through channel correlation.Within the enhanced DenseNet network,the Efficient Channel Attention Network(ECA-Net)dynamically adjusts the weight of each channel to amplify the salience of crucial information in each modal feature.Depthwise separable convolution markedly reduces the training parameters and further enhances the feature correlation.Experimental evaluations were conducted on four multimodal databases,comprising six unimodal databases,including multispectral palmprint and palm vein databases from the Chinese Academy of Sciences.The Equal Error Rates(EER)values were 0.0149%,0.0150%,0.0099%,and 0.0050%,correspondingly.In comparison to other network methods for palmprint,palm vein,and finger vein fusion recognition,this approach substantially enhances recognition performance,rendering it suitable for high-security environments with practical applicability.The experiments in this article utilized amodest sample database comprising 200 individuals.The subsequent phase involves preparing for the extension of the method to larger databases.

A Comprehensive Survey on Deep Learning Multi-Modal Fusion:Methods,Technologies and Applications

Multi-modal fusion technology gradually become a fundamental task in many fields,such as autonomous driving,smart healthcare,sentiment analysis,and human-computer interaction.It is rapidly becoming the dominant research due to its powerful perception and judgment capabilities.Under complex scenes,multi-modal fusion technology utilizes the complementary characteristics of multiple data streams to fuse different data types and achieve more accurate predictions.However,achieving outstanding performance is challenging because of equipment performance limitations,missing information,and data noise.This paper comprehensively reviews existing methods based onmulti-modal fusion techniques and completes a detailed and in-depth analysis.According to the data fusion stage,multi-modal fusion has four primary methods:early fusion,deep fusion,late fusion,and hybrid fusion.The paper surveys the three majormulti-modal fusion technologies that can significantly enhance the effect of data fusion and further explore the applications of multi-modal fusion technology in various fields.Finally,it discusses the challenges and explores potential research opportunities.Multi-modal tasks still need intensive study because of data heterogeneity and quality.Preserving complementary information and eliminating redundant information between modalities is critical in multi-modal technology.Invalid data fusion methods may introduce extra noise and lead to worse results.This paper provides a comprehensive and detailed summary in response to these challenges.

Towards trustworthy multi-modal motion prediction:Holistic evaluation and interpretability of outputs

Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning.This task is very complex,as the behaviour of road agents depends on many factors and the number of possible future trajectories can be consid-erable(multi-modal).Most prior approaches proposed to address multi-modal motion prediction are based on complex machine learning systems that have limited interpret-ability.Moreover,the metrics used in current benchmarks do not evaluate all aspects of the problem,such as the diversity and admissibility of the output.The authors aim to advance towards the design of trustworthy motion prediction systems,based on some of the re-quirements for the design of Trustworthy Artificial Intelligence.The focus is on evaluation criteria,robustness,and interpretability of outputs.First,the evaluation metrics are comprehensively analysed,the main gaps of current benchmarks are identified,and a new holistic evaluation framework is proposed.Then,a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system.To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework,an intent prediction layer that can be attached to multi-modal motion prediction models is proposed.The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi-modal trajectories and intentions.The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autono-mous vehicles,advancing the field towards greater safety and reliability.

Acousto-optic scanning multi-photon lithography with high printing rate

As a manufacturing method that is focused on end-users,3D printing has gained a lot of attention in recent years due to its unique advantages in fabricating complex three-dimensional structures.Various new micro-nano 3D printing methods have been developed to meet the demand for high-precision and high-yield manufacturing1-9.Among them,multi-photon-photon lithography(MPL) is a promising 3D nanofabrication technology due to its capability of true 3D digital processing and nanoscale processing resolution beyond the diffraction limit.It has been widely used to fabricate microoptics10,11,photonic crystals12,microfluidics13,meta-surfaces14,and mechanical metamaterials15.

Ultrasound Scanning Competencies in Midwifery Education in Zambia: Findings from a Desk Review and Needs Assessment

Introduction: Ultrasound is an essential component of antenatal care. Midwives provide most of the antenatal care but they do not perform ultrasound as it has been beyond their scope of practice. This leaves many women in Low and Middle-Income Countries without access to ultrasound scanning. The aim of this study was to identify competencies in ultrasound scanning in midwifery education. Methods: A desk review and needs assessment were conducted between July and October 2023. Articles and curricula on the internet, Google scholar and PubMed were searched for content on ultrasound scanning competencies. A Google form consisting of 20 questions was administered via email and WhatsApp to 135 participants. Descriptive statistics were used to analyse data. Results: The desk review showed that it is feasible to train midwives in ultrasound scanning. The training programs for midwives in obstetric ultrasound were conducted for 1 week to 3 months with most of them running for 4 weeks. Content included introduction to general principles of ultrasound, physics, basic knowledge in embryology, obstetrics, anatomy, measuring foetal biometry, estimating amniotic fluid and gestational age. Experts like sonographers trained midwives. Theory and hands on were the teaching methods used. Written and practical assessments were conducted. Needs assessment revealed that majority of participants 71 (53%) knew about basic ultrasound training for midwives. All participants (100%) said it is necessary to train midwives in basic ultrasound scan in Zambia. Some content should include, anatomy, measuring foetal biometry, assessing amniotic fluid level, and gestational age determination. Most participants 91 (67%) suggested that the appropriate duration of training is 4 - 6 weeks. Conclusion: Empowering every midwife with ultrasound scanning skills will enable early detection of any abnormality among pregnant women and prompt intervention to save lives.

Multi-dimension and multi-modal rolling mill vibration prediction model based on multi-level network fusion

Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.

Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.

Research on Multi-modal In-Vehicle Intelligent Personal Assistant Design

Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.

Generative Multi-Modal Mutual Enhancement Video Semantic Communications

Recently,there have been significant advancements in the study of semantic communication in single-modal scenarios.However,the ability to process information in multi-modal environments remains limited.Inspired by the research and applications of natural language processing across different modalities,our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos.Specifically,we propose a deep learning-basedMulti-ModalMutual Enhancement Video Semantic Communication system,called M3E-VSC.Built upon a VectorQuantized Generative AdversarialNetwork(VQGAN),our systemaims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission.With it,the semantic information can be extracted fromkey-frame images and audio of the video and performdifferential value to ensure that the extracted text conveys accurate semantic information with fewer bits,thus improving the capacity of the system.Furthermore,a multi-frame semantic detection module is designed to facilitate semantic transitions during video generation.Simulation results demonstrate that our proposed model maintains high robustness in complex noise environments,particularly in low signal-to-noise ratio conditions,significantly improving the accuracy and speed of semantic transmission in video communication by approximately 50 percent.

Investigation of reflection anisotropy induced by micropipe defects on the surface of a 4H-SiC single crystal using scanning anisotropy microscopy

Optical reflection anisotropy microscopy mappings of micropipe defects on the surface of a 4H-SiC single crystal are studied by the scanning anisotropy microscopy(SAM)system.The reflection anisotropy(RA)image with a'butterfly pattern'is obtained around the micropipes by SAM.The RA image of the edge dislocations is theoretically simulated based on dislocation theory and the photoelastic principle.By comparing with the Raman spectrum,it is verified that the micropipes consist of edge dislocations.The different patterns of the RA images are due to the different orientations of the Burgers vectors.Besides,the strain distribution of the micropipes is also deduced.One can identify the dislocation type,the direction of the Burgers vector and the optical anisotropy from the RA image by using SAM.Therefore,SAM is an ideal tool to measure the optical anisotropy induced by the strain field around a defect.

Prophylactic Pattern Scanning Laser Retinal Photocoagulation for Diabetic Retinopathy in Spontaneously Diabetic Torii Fatty Rats: Preliminary Experimental Results

Research Background and Purpose: The number of diabetic patients is rapidly increasing, making it crucial to find methods to prevent diabetic retinopathy (DR), a leading cause of blindness. We investigated the effects of prophylactic pattern scanning laser retinal photocoagulation on DR development in Spontaneously Diabetic Torii (SDT) fatty rats as a new prevention approach. Methods: Photocoagulation was applied to the right eyes of 8-week-old Spontaneously Diabetic Torii (SDT) fatty rats, with the left eyes serving as untreated controls. Electroretinography at 9 and 39 weeks of age and pathological examinations, including immunohistochemistry for vascular endothelial growth factor and glial fibrillary acidic protein at 24 and 40 weeks of age, were performed on both eyes. Results: There were no significant differences in amplitude and prolongation of the OP waves between the right and left eyes in SDT fatty rats at 39 weeks of age. Similarly, no significant differences in pathology and immunohistochemistry were observed between the right and left eyes in SDT fatty rats at 24 and 40 weeks of age. Conclusion: Prophylactic pattern scanning retinal laser photocoagulation did not affect the development of diabetic retinopathy in SDT fatty rats.

Accuracy of tree mapping based on hand-held laser scanning comparing leaf-on and leaf-off conditions in mixed forests

The use of mobile laser scanning to survey forest ecosystems is a promising,scalable technology to describe forest 3D structures at high resolution.To confirm the con-sistency in the retrieval of forest structural parameters using hand-held laser scanning(HLS),before operationalizing the method,confirming the data is crucial.We analyzed the per-formance of tree-level mapping based on HLS under differ-ent phenology conditions on a mixed forest in western Spain comprising Pinus pinaster and two deciduous species,Alnus glutinosa and Quercus pyrenaica.The area was surveyed twice during the growing season(July 2022)and once in the deciduous season(February 2022)using several scan-ning paths.Ground reference data(418 trees,15 snags)was used to calibrate the HLS data and to assess the influence of phenology when converting 3D data into tree-level attrib-utes(DBH,height and volume).The HLS-based workflow was robust at isolating tree positions and recognizing stems despite changes in phenology.Ninety-six percent of all pairs matched below 65 cm.For DBH,phenology barely altered estimates.We observed a strong agreement when comparing HLS-based tree height distributions.The values exceeded 2 m when comparing height measurements,confirming height data should be carefully used as reference in remote sensing-based inventories,especially for deciduous species.Tree volume was more precise for pines(r=0.95,and rela-tive RMSE=21.3–23.8%)compared to deciduous species(r=0.91–0.96,and relative RMSE=27.3–30.5%).HLS data and the forest structural complexity tool performed remark-ably,especially in tree positioning considering mixed forests and mixed phenology conditions.

A review of understanding electrocatalytic reactions in energy conversion and energy storage systems via scanning electrochemical microscopy

To address climate change and promote environmental sustainability,electrochemical energy conversion and storage systems emerge as promising alternative to fossil fuels,catering to the escalating demand for energy.Achieving optimal energy efficiency and cost competitiveness in these systems requires the strategic design of electrocatalysts,coupled with a thorough comprehension of the underlying mechanisms and degradation behavior occurring during the electrocatalysis processes.Scanning electrochemical microscopy(SECM),an analytical technique for studying surface electrochemically,stands out as a powerful tool offering electrochemical insights.It possesses remarkable spatiotemporal resolution,enabling the visualization of the localized electrochemical activity and surface topography.This review compiles crucial research findings and recent breakthroughs in electrocatalytic processes utilizing the SECM methodology,specifically focusing on applications in electrolysis,fuel cells,and metal–oxygen batteries within the realm of energy conversion and storage systems.Commencing with an overview of each energy system,the review introduces the fundamental principles of SECM,and aiming to provide new perspectives and broadening the scope of applied research by describing the major research categories within SECM.

Imaging of human parafoveal area with large field of view in adaptive optics line scanning ophthalmoscope

The parafoveal area,with its high concentration of photoreceptors andfine retinal capillaries,is crucial for central vision and often exhibits early signs of pathological changes.The current adaptive optics scanning laser ophthalmoscope(AOSLO)provides an excellent tool to acquire accurate and detailed information about the parafoveal area with cellular resolution.However,limited by the scanning speed of two-dimensional scanning,thefield of view(FOV)in the AOSLO system was usually less than or equal to 2,and the stitching for the parafoveal area required dozens of images,which was time-consuming and laborious.Unfortunately,almost half of patients are unable to obtain stitched images because of their poorfixation.To solve this problem,we integrate AO technology with the line-scan imaging method to build an adaptive optics line scanning ophthalmoscope(AOLSO)system with a larger FOV.In the AOLSO,afocal spherical mirrors in pairs are nonplanar arranged and the distance and angle between optical elements are optimized to minimize the aberrations,two cylinder lenses are orthogonally placed before the imaging sensor to stretch the point spread function(PSF)for sufficiently digitizing light energy.Captured human retinal images show the whole parafoveal area with 55FOV,60 Hz frame rate and cellular resolutions.Take advantage of the 5FOV of the AOLSO,only 9 frames of the retina are captured with several minutes to stitch a montage image with an FOV of 99,in which photoreceptor counting is performed within approximately 5eccentricity.The AOLSO system not only provides cellular resolution but also has the capability to capture the parafoveal region in a single frame,which offers great potential for noninvasive studying of the parafoveal area.

Influence of pore structure heterogeneity on channeling channels during hot water flooding in heavy oil reservoir based on CT scanning

Hot water flooding is an effective way to develop heavy oil reservoirs.However,local channeling channels may form,possibly leading to a low thermal utilization efficiency and high water cut in the reservoir.The pore structure heterogeneity is an important factor in forming these channels.This study proposes a method that mixes quartz sand with different particle sizes to prepare weakly heterogeneous and strongly heterogeneous models through which hot water flooding experiments are conducted.During the experiments,computer tomography(CT)scanning identifies the pore structure and micro remaining oil saturation distribution to analyze the influence of the pore structure heterogeneity on the channeling channels.The oil saturation reduction and average pore size are divided into three levels to quantitatively describe the relationship between the channeling channel distribution and pore structure heterogeneity.The zone where oil saturation reduction exceeds 20%is defined as a channeling channel.The scanning area is divided into 180 equally sized zones based on the CT scanning images,and threedimensional(3D)distributions of the channeling channels are developed.Four micro remaining oil distribution patterns are proposed,and the morphology characteristics of micro remaining oil inside and outside the channeling channels are analyzed.The results show that hot water flooding is more balanced in the weakly heterogeneous model,and the oil saturation decreases by more than 20%in most zones without narrow channeling channels forming.In the strongly heterogeneous model,hot water flooding is unbalanced,and three narrow channeling channels of different lengths form.In the weakly heterogeneous model,the oil saturation reduction is greater in zones with larger pores.The distribution range of the average pore size is larger in the strongly heterogeneous model.The network remaining oil inside the channeling channels is less than outside the channeling channels,and the hot water converts the network remaining oil into cluster,film,and droplet remaining oil.

Assessing the dynamics of O_(2) desorption from cobalt phthalocyanine by in situ electrochemical scanning tunneling microscopy

We report here the in situ electrochemical scanning tunneling microscopy(ECSTM) study of cobalt phthalocyanine(CoPc)-catalyzed O_(2) evolution reaction(OER) and the dynamics of CoPc-O_(2) dissociation.The self-assembled CoPc monolayer is fabricated on Au(111) substrate and resolved by ECSTM in 0.1 M KOH electrolyte.The OH^(-)adsorption on CoPc prior to OER is observed in ECSTM images.During OER,the generated O_(2) adsorbed on Co Pc is observed in the CoPc monolayer.Potential step experiment is employed to monitor the desorption of OER-generated O_(2) from CoPc,which results in the decreasing surface coverage of CoPc-O_(2) with time.The rate constant of O_(2) desorption is evaluated through data fitting.The insights into the dynamics of Co-O_(2) dissociation at the molecular level via in situ imaging help understand the role of Co-O_(2) in oxygen reduction reaction(ORR) and OER.

Fatigue properties and damage constitutive model of salt rock based on CT scanning

To investigate the macroscopic fatigue properties and the mesoscopic pore evolution characteristics of salt rock under cyclic loading,fatigue tests under different upper-limit stresses were carried out on salt rock,and the mesoscopic pore structures of salt rock before and after fatigue tests and under different cycle numbers were measured using CT scanning instrument.Based on the test results,the effects of the cycle number and the upper-limit stress on the evolution of cracks,pore morphology,pore number,pore volume,pore size,plane porosity,and volume porosity of salt rock were analyzed.The failure path of salt rock specimens under cyclic loading was analyzed using the distribution law of plane porosity.The damage variable of salt rock under cyclic loading was defined on basis of the variation of volume porosity with cycle number.In order to describe the fatigue deformation behavior of salt rock under cyclic loading,the nonlinear Burgers damage constitutive model was further established.The results show that the model established can better reflect the whole development process of fatigue deformation of salt rock under cyclic loading.

Contribution of CT Scanning in the Investigation of Cranioencephalic Lesions in Bangui

Introduction: Cranioencephalic exploration has always played a major role in CT scans. In the Central Africa Republic (CAR), the lack of cross-sectional imaging before the year 2020 meant that no study had focused on cranioencephalic lesions. The aim of this study was to contribute to improving the management of cranioencephalic pathologies in CAR. Patients and Method: The study took place at the Bangui National Medical Imaging Centre (CNIMB). It was a retrospective study over a two-year period (March 1, 2021 to February 30, 2023). All patients referred for cranioencephalic CT scans were included, regardless of age or sex. Results: 1745 CT scans were performed, 575 of which were cranioencephalic CT scans. The majority of patients were male (53%). Most lived in the capital Bangui (90.9%). Patients aged 61 and over were the most representative. The distribution of patients by requesting department showed that the reception and emergency department was one of the least requesting departments. The main abnormalities observed were strokes, 82.1% of which were ischaemic strokes and 17.9% haemorrhagic strokes. Strokes were followed by degenerative lesions. Post-traumatic injuries included haemorrhagic contusions (38.3%), subdural haematomas in 20.5% of cases, and extradural haematomas (9.3%). Craniofacial lesions (fractures) were observed in 45.8% of cases. Conclusion: Cranioencephalic scans accounted for 1/3 of CT examinations performed during the study period. It revealed pathologies that could not be detected by conventional means. All in all, CT scans contributed to the diagnosis of cerebral pathologies.

Study on Joint Method of 3D Acoustic Emission Source Localization Simplex and Grid Search Scanning

Acoustic emission(AE)source localization is a fundamental element of rock fracture damage imaging.To improve the efficiency and accuracy of AE source localization,this paper proposes a joint method comprising a three-dimensional(3D)AE source localization simplex method and grid search scanning.Using the concept of the geometry of simplexes,tetrahedral iterations were first conducted to narrow down the suspected source region.This is followed by a process of meshing the region and node searching to scan for optimal solutions,until the source location is determined.The resulting algorithm was tested using the artificial excitation source localization and uniaxial compression tests,after which the localization results were compared with the simplex and exhaustive methods.The results revealed that the localization obtained using the proposed method is more stable and can be effectively avoided compared with the simplex localization method.Furthermore,compared with the global scanning method,the proposed method is more efficient,with an average time of 10%–20%of the global scanning localization algorithm.Thus,the proposed algorithm is of great significance for laboratory research focused on locating rupture damages sustained by large-sized rock masses or test blocks.