Absolute Accuracy Research Articles

Metacomprehension in Multiple Document Reading

Abstract: Accurately assessing someone’s comprehension is central to learning from multiple documents. This study examines whether judgment accuracy can be increased if readers summarize texts before assessing their own comprehension. We asked 144 adults to read multiple documents on a historical controversy. They then produced Twitter-like summaries either (a) after a time delay or (b) immediately after reading; a control group (c) did not write summaries. Results showed that delaying writing summaries increased the absolute accuracy of self-assessments of undisputed historical content knowledge. We found negative effects for the accuracy of self-assessments of source–content links, intertextual conflicts, and integration performance in an essay. The relative accuracy was lower in the delayed summaries group than in the controls. In summary, the positive effect of writing summaries appears to be limited to the self-assessment of content knowledge, whereas it is reduced with respect to forms of knowledge specific to reading multiple texts.

Giving Historical Photographs a New Perspective: Introducing Camera Orientation Parameters as New Metadata in a Large-Scale 4D Application

The ongoing digitization of historical photographs in archives allows investigating the quality, quantity, and distribution of these images. However, the exact interior and exterior camera orientations of these photographs are usually lost during the digitization process. The proposed method uses content-based image retrieval (CBIR) to filter exterior images of single buildings in combination with metadata information. The retrieved photographs are automatically processed in an adapted structure-from-motion (SfM) pipeline to determine the camera parameters. In an interactive georeferencing process, the calculated camera positions are transferred into a global coordinate system. As all image and camera data are efficiently stored in the proposed 4D database, they can be conveniently accessed afterward to georeference newly digitized images by using photogrammetric triangulation and spatial resection. The results show that the CBIR and the subsequent SfM are robust methods for various kinds of buildings and different quantity of data. The absolute accuracy of the camera positions after georeferencing lies in the range of a few meters likely introduced by the inaccurate LOD2 models used for transformation. The proposed photogrammetric method, the database structure, and the 4D visualization interface enable adding historical urban photographs and 3D models from other locations.

Simple and inexpensive synovial fluid biomarkers for the diagnosis of prosthetic joint infection according to the new EBJIS definition.

Introduction: diagnosis of periprosthetic joint infection (PJI) is challenging, as no single test has absolute accuracy. The purpose of this study was to assess the utility of different simple synovial biomarkers in the diagnosis of PJI as defined by the European Bone and Joint Infection Society (EBJIS). Methods: we retrospectively identified all patients undergoing revision hip or knee arthroplasty from 2013 to 2019 on our prospectively maintained database. Only patients with minimum required infection diagnostic workup were included in the study. Patients with comorbidities that may influence the accuracy of synovial biomarkers were excluded. Receiver operator characteristic (ROC) curves were utilised to assess the diagnostic utility of synovial fluid white blood cell (WBC) count, polymorphonuclear leukocyte percentage (PMN %), C-reactive protein (CRP), adenosine deaminase (ADA), and alpha-2-microglobulin (A2M). Results: in total, 102 patients met the inclusion criteria. Of these, 58 were classified as infection unlikely, 8 as infection likely, and 36 as infection confirmed. Synovial WBC count (area under the curve (AUC) 0.94) demonstrated the best utility for the diagnosis of PJI, followed by PMN % (AUC 0.91), synovial CRP (AUC 0.90), ADA (AUC 0.82), and A2M (AUC 0.76). We found added value in the combined interpretation of different biomarkers. We calculated high sensitivity and negative predictive value if at least two of them are negative and high specificity and positive predictive value if at least two are elevated. Conclusion: current results show that synovial fluid investigation is a useful tool for the diagnosis of PJI, and the combined interpretation of simple and inexpensive biomarkers demonstrated improved diagnostic accuracy.

Complete sequence verification of plasmid DNA using the Oxford Nanopore Technologies’ MinION device

BackgroundSequence verification is essential for plasmids used as critical reagents or therapeutic products. Typically, high-quality plasmid sequence is achieved through capillary-based Sanger sequencing, requiring customized sets of primers for each plasmid. This process can become expensive, particularly for applications where the validated sequence needs to be produced within a regulated and quality-controlled environment for downstream clinical research applications.ResultsHere, we describe a cost-effective and accurate plasmid sequencing and consensus generation procedure using the Oxford Nanopore Technologies’ MinION device as an alternative to capillary-based plasmid sequencing options. This procedure can verify the identity of a pure population of plasmid, either confirming it matches the known and expected sequence, or identifying mutations present in the plasmid if any exist. We use a full MinION flow cell per plasmid, maximizing available data and allowing for stringent quality filters. Pseudopairing reads for consensus base calling reduces read error rates from 5.3 to 0.53%, and our pileup consensus approach provides per-base counts and confidence scores, allowing for interpretation of the certainty of the resulting consensus sequences. For pure plasmid samples, we demonstrate 100% accuracy in the resulting consensus sequence, and the sensitivity to detect small mutations such as insertions, deletions, and single nucleotide variants. In test cases where the sequenced pool of plasmids contains subclonal templates, detection sensitivity is similar to that of traditional capillary sequencing.ConclusionsOur pipeline can provide significant cost savings compared to outsourcing clinical-grade sequencing of plasmids, making generation of high-quality plasmid sequence for clinical sequence verification more accessible. While other long-read-based methods offer higher-throughput and less cost, our pipeline produces complete and accurate sequence verification for cases where absolute sequence accuracy is required.

Application of MobileNetV2 to waste classification.

Today, the topic of waste separation has been raised for a long time, and some waste separation devices have been installed in large communities. However, the vast majority of domestic waste is still not properly sorted and put out, and the disposal of domestic waste still relies mostly on manual classification. The research in this paper applies deep learning to this persistent problem, which has important significance and impact. The domestic waste is classified into four categories: recyclable waste, kitchen waste, hazardous waste and other waste. The garbage classification model trained based on MobileNetV2 deep neural network can classify domestic garbage quickly and accurately, which can save a lot of labor, material and time costs. The absolute accuracy of the trained network model is 82.92%. In comparison with CNN network model, the classification accuracy of MobileNetV2 model is 15.42% higher than that of CNN model. In addition, the trained model is light enough to be better applied to mobile.

New methods for the calibration of optical resonators: integrated calibration by means of optical modulation (ICOM) and narrow-band cavity ring-down (NB-CRD)

Abstract. Optical resonators are used in spectroscopic measurements of atmospheric trace gases to establish long optical path lengths L with enhanced absorption in compact instruments. In cavity-enhanced broad-band methods, the exact knowledge of both the magnitude of L and its spectral dependency on the wavelength λ is fundamental for the correct retrieval of trace gas concentrations. L(λ) is connected to the spectral mirror reflectivity R(λ), which is often referred to instead. L(λ) is also influenced by other quantities like broad-band absorbers or alignment of the optical resonator. The established calibration techniques to determine L(λ), e.g. introducing gases with known optical properties or measuring the ring-down time, all have limitations: limited spectral resolution, insufficient absolute accuracy and precision, inconvenience for field deployment, or high cost of implementation. Here, we present two new methods that aim to overcome these limitations: (1) the narrow-band cavity ring-down (NB-CRD) method uses cavity ring-down spectroscopy and a tunable filter to retrieve spectrally resolved path lengths L(λ); (2) integrated calibration by means of optical modulation (ICOM) allows the determination of the optical path length at the spectrometer resolution with high accuracy in a relatively simple setup. In a prototype setup we demonstrate the high accuracy and precision of the new approaches. The methods facilitate and improve the determination of L(λ), thereby simplifying the use of cavity-enhanced absorption spectroscopy.

Algorithm Stability and the Long‐Term Geospace Data Record From TIMED/SABER

AbstractThe ability of satellite instruments to accurately observe long‐term changes in atmospheric temperature depends on many factors including the absolute accuracy of the measurement, the stability of the calibration of the instrument, the stability of the satellite orbit, and the stability of the numerical algorithm that produces the temperature data. We present an example of algorithm instability recently discovered in the temperature data set from the SABER instrument on the NASA TIMED satellite. The instability resulted in derived temperatures that were substantially colder than anticipated from mid‐December 2019 to mid‐2022. This algorithm‐induced change in temperature over 1 to 2 years corresponded to the expected temperature change over several decades from increasing anthropogenic CO2. This paper highlights the importance of algorithm stability in developing Geospace Data Records (GDRs) for Earth’s mesosphere and lower thermosphere. A corrected version (Version 2.08) of the temperatures from SABER is described.

Forecasting of water consumption by integrating spatial and temporal characteristics of short-term water use in cities

Urban water consumption prediction is an important basis for urban water resource management and optimal scheduling of water supply. Most existing prediction methods disregard the spatial–temporal characteristics of water consumption data and utilize incomplete data mining, resulting in prediction models with low accuracy. To accurately predict short-term water demand, a data-cleaning algorithm based on statistical distributions and velocity constraints is proposed in this study. The long- and short-term time-series network (LSTNet), automated spatial–temporal graph prediction (AutoSTG), and attention-based spatial–temporal graphical convolutional neural network (ASTGCN) deep learning models were investigated for automatically capturing the spatial–temporal dynamic features. Additionally, the short-term predictions for 1, 3, 6, 12, and 24 h forecasting periods were investigated based on the water consumption data of water plants in Tianjin. The results revealed that the proposed data-cleaning algorithm can effectively manage single-point anomalies and continuous outliers, thus improving prediction accuracy. The LSTNet, AutoSTG, and ASTGCN deep learning models, particularly the ASTGCN model based on the spatial–temporal attention mechanism, significantly improved the prediction accuracy and adaptive spatial–temporal feature extraction. The final prediction model that combines data-cleaning algorithms with the ASTGCN deep learning method achieved root mean square error, mean absolute error, accuracy, and relative squared error values of 566.4 m3, 377.7 m3, 0.964, and 0.112, respectively. The proposed forecasting framework can improve short-term water use forecasting at multiple sites in the city and provide support for fine-grained water allocation.

FedMDS: An Efficient Model Discrepancy-Aware Semi-Asynchronous Clustered Federated Learning Framework

Federated learning (FL) is an emerging distributed machine learning paradigm that protects privacy and tackles the problem of isolated data islands. At present, there are two main communication strategies of FL: synchronous FL and asynchronous FL. The advantages of synchronous FL are the high precision and easy convergence of the model. However, this synchronous communication strategy has the risk of the straggler effect. Asynchronous FL has a natural advantage in mitigating the straggler effect, but there are threats of model quality degradation and server crash. In this paper, we propose a model discrepancy-aware semi-asynchronous clustered FL framework, <i>FedMDS</i> , which alleviates the straggler effect by 1) a clustered strategy based on the delay and direction of the model update and 2) a synchronous trigger mechanism that limits the model staleness. <i>FedMDS</i> leverages the clustered algorithm to reschedule the clients. Each group of clients performs asynchronous updates until the synchronous update mechanism based on the model discrepancy is triggered. We evaluate <i>FedMDS</i> based on four typical federated datasets in a non-IID setting and compare <i>FedMDS</i> to the baselines. The experimental results show that <i>FedMDS</i> significantly improves average test accuracy by more than <inline-formula><tex-math notation="LaTeX">$+9.2\%$</tex-math></inline-formula> on the four datasets compared to <i>TA-FedAvg</i> . In particular, <i>FedMDS</i> improves absolute Top-1 test accuracy by <inline-formula><tex-math notation="LaTeX">$+37.6\%$</tex-math></inline-formula> on FEMNIST compared to <i>TA-FedAvg</i> . The frequency of the average synchronization waiting time of <i>FedMDS</i> is significantly lower than that of <i>TA-FedAvg</i> on all datasets. Moreover, <i>FedMDS</i> can improve the accuracy and alleviate the straggler effect.

Robust Accurate LiDAR-GNSS/IMU Self-Calibration Based on Iterative Refinement

Light detection and ranging (LiDAR) and global navigation satellite system (GNSS)/inertial measurement unit (IMU) have been widely used in autonomous driving systems. LiDAR-GNSS/IMU calibration directly affects the performance of vehicle localization and perception. Current calibration methods require specific vehicle movements or scenarios with artificial calibration markers to keep the problem well-constrained, which are empirical, time-consuming, and poorly automated. To solve this problem, this article proposes a novel self-calibration method based on both relative and absolute motion constraints. Initial calibration parameters are calculated with relative motion constraints derived from LiDAR odometry. To eliminate the impact of odometry drift and enhance the observability of translation parameters, calibration parameters are iteratively refined by tightly coupling both relative and absolute motion constraints derived from scan-global map matching. Tests on simulation and ground datasets show that the proposed method is robust and accurate with RMSEs of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{-{3^{\circ} }}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{-3}$ </tex-math></inline-formula> m for rotation and translation, respectively. Further mapping and localization experiments with calculated calibration parameters present a state-of-the-art absolute localization accuracy of about 3 cm.

High-Precision Gravimetric Monitoring Provides Insights into Subsurface Processes

Summary Monitoring of time-lapse changes in gravity has been carried out for decades, and measurement repeatability with relative spring gravimeters has in recent years improved to 0.6–2 µGal (standard deviation) for area surveys. This is better than often cited vendor specification. Gravimeters based on other physical principles offer high absolute accuracy or improved time-lapse sensitivity. All these may open possibilities for monitoring smaller and deeper subsurface changes and over shorter time intervals, with applications in hydrocarbon and geothermal production, gas and CO2 storage, hydrology, volcanology and more. Survey design and data processing are integral parts of a program. Sensor redundancy and repeat measurements provide opportunities for in-situ calibrations. An example from a gas accumulation shows a reduction in gravity of about 3 µGal and subsidence of 5–6 mm over 8 years. Both signals are clearly above the noise level and are likely to have been caused by production from a neighbouring structure. The information can quantify the amount of reservoir pore pressure drop, size of gas cap expansion and vertical movements of the gas-liquid contact to a precision well below 1 m.

Radiometric Re-Compensation of Sentinel-1 SAR Data Products for Artificial Biases due to Antenna Pattern Changes

SAR data products for Sentinel-1 have been freely available and delivered operationally on behalf of the European Space Agency since the routine operation of Sentinel-1A in 2014. These products were delivered using the best knowledge at their processing time, in particular with respect to the radiometric calibration. As reprocessing of SAR data products is not foreseen in the nominal processing chain of Sentinel-1, changes of applied processing parameters impact the SAR data quality and can be a disturbing factor for long-term monitoring of radiometric features. In particular, antenna pattern updates produce artificial radiometric steps which are visible in radar backscatter time series, especially in case of monitoring radiometric stable reference targets. This paper introduces a method for correcting changes due to such updates without the need of reprocessing SAR data products. The method was applied to long-lasting time series of data acquisitions which are used to monitor the radiometric performance with reference targets at the DLR calibration site. It has been shown that artificial steps due to antenna pattern updates disappear in backscatter timelines after correct application. Furthermore, the derived absolute radiometric accuracy was improved for the joint observation period of S1A and S1B for almost five years until December 2021.

Statistical models versus machine learning for competing risks: development and validation of prognostic models

BackgroundIn health research, several chronic diseases are susceptible to competing risks (CRs). Initially, statistical models (SM) were developed to estimate the cumulative incidence of an event in the presence of CRs. As recently there is a growing interest in applying machine learning (ML) for clinical prediction, these techniques have also been extended to model CRs but literature is limited. Here, our aim is to investigate the potential role of ML versus SM for CRs within non-complex data (small/medium sample size, low dimensional setting).MethodsA dataset with 3826 retrospectively collected patients with extremity soft-tissue sarcoma (eSTS) and nine predictors is used to evaluate model-predictive performance in terms of discrimination and calibration. Two SM (cause-specific Cox, Fine-Gray) and three ML techniques are compared for CRs in a simple clinical setting. ML models include an original partial logistic artificial neural network for CRs (PLANNCR original), a PLANNCR with novel specifications in terms of architecture (PLANNCR extended), and a random survival forest for CRs (RSFCR). The clinical endpoint is the time in years between surgery and disease progression (event of interest) or death (competing event). Time points of interest are 2, 5, and 10 years.ResultsBased on the original eSTS data, 100 bootstrapped training datasets are drawn. Performance of the final models is assessed on validation data (left out samples) by employing as measures the Brier score and the Area Under the Curve (AUC) with CRs. Miscalibration (absolute accuracy error) is also estimated. Results show that the ML models are able to reach a comparable performance versus the SM at 2, 5, and 10 years regarding both Brier score and AUC (95% confidence intervals overlapped). However, the SM are frequently better calibrated.ConclusionsOverall, ML techniques are less practical as they require substantial implementation time (data preprocessing, hyperparameter tuning, computational intensity), whereas regression methods can perform well without the additional workload of model training. As such, for non-complex real life survival data, these techniques should only be applied complementary to SM as exploratory tools of model’s performance. More attention to model calibration is urgently needed.

The influence of metacognition monitoring on L2 Chinese audiovisual reading comprehension.

Metacognition monitoring is the ability to evaluate the cognitive process actively. L2 learners with high metacognition monitoring ability can better monitor reading processes and outcomes consciously, thus facilitating self-regulated learning and improving reading efficiency. Previous studies mostly used offline self-reports to examine the metacognition monitoring in static text reading by L2 learners. This study investigated the effects of different indicators of metacognition monitoring on L2 Chinese audiovisual comprehension by online confidence judgment and audiovisual comprehension tasks. Target measures of metacognition monitoring included absolute calibration accuracy based on video or test and relative calibration accuracy measured by Gamma or Spearman correlation coefficient. 38 intermediate-advanced Chinese learners participated in the study. Multiple regression analysis showed three main results. First, absolute calibration accuracy can significantly predict L2 Chinese audiovisual comprehension, while relative calibration accuracy has no significant effect. Second, the predictive effect of video-based absolute calibration accuracy is affected by the video difficulty, that is, the greater the video difficulty, the greater the impact on the performance of audiovisual comprehension. Third, the predictive effect of test-based absolute calibration accuracy is influenced by the language proficiency, specifically, the higher the L2 Chinese proficiency, the stronger the prediction on the performance of audiovisual comprehension. These results support a multidimensional view of metacognition monitoring by specifying how different indicators of metacognition monitoring may predict L2 Chinese audiovisual comprehension. The findings have important pedagogical implications for strategy training of metacognition monitoring and point to the necessity to take task difficulty and individual differences among learners into full consideration.

Remote scoring models of rigidity and postural stability of Parkinson's disease based on indirect motions and a low-cost RGB algorithm.

The Movement Disorder Society's Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III) is mostly common used for assessing the motor symptoms of Parkinson's disease (PD). In remote circumstances, vision-based techniques have many strengths over wearable sensors. However, rigidity (item 3.3) and postural stability (item 3.12) in the MDS-UPDRS III cannot be assessed remotely since participants need to be touched by a trained examiner during testing. We developed the four scoring models of rigidity of the neck, rigidity of the lower extremities, rigidity of the upper extremities, and postural stability based on features extracted from other available and touchless motions. The red, green, and blue (RGB) computer vision algorithm and machine learning were combined with other available motions from the MDS-UPDRS III evaluation. A total of 104 patients with PD were split into a train set (89 individuals) and a test set (15 individuals). The light gradient boosting machine (LightGBM) multiclassification model was trained. Weighted kappa (k), absolute accuracy (ACC ± 0), and Spearman's correlation coefficient (rho) were used to evaluate the performance of model. For model of rigidity of the upper extremities, k = 0.58 (moderate), ACC ± 0 = 0.73, and rho = 0.64 (moderate). For model of rigidity of the lower extremities, k = 0.66 (substantial), ACC ± 0 = 0.70, and rho = 0.76 (strong). For model of rigidity of the neck, k = 0.60 (moderate), ACC ± 0 = 0.73, and rho = 0.60 (moderate). For model of postural stability, k = 0.66 (substantial), ACC ± 0 = 0.73, and rho = 0.68 (moderate). Our study can be meaningful for remote assessments, especially when people have to maintain social distance, e.g., in situations such as the coronavirus disease-2019 (COVID-19) pandemic.

Evaluation of NMR predictors for accuracy and ability to reveal trends in 1 H NMR spectra of fatty acids.

Four different nuclear magnetic resonance (NMR) predictors have been evaluated for their ability to predict 600-MHz 1 H spectra of free fatty acids and fatty acid methyl esters of 20 common fatty acids. The predictors were evaluated on two main criteria: (1) their accuracy in direct prediction of the spectra (absolute accuracy) and (2) the ability to reveal trends or predict the change that occurs in the spectra as a result of a change in the fatty acid carbon chain, or by esterification of the free fatty acids to methyl esters (relative accuracy). The absolute accuracy in chemical shift prediction for fatty acids was good, compared with previous reports on a broader range of compounds. All four predictors had median prediction errors for chemical shifts of the signals in fatty acid methyl esters well below 0.1ppm and as low as 0.015 ppm for one of the predictors. However, all predictors also had outliers with errors far above the upper interquartile range. In general, they also fail to reproduce trends of diagnostic value that were observed in the experimental data or properly predict the result of a minor change in molecular structure. All four predictors depend on experimental data from different origins. This may be a limiting factor for the relative accuracy of the predictors.

Improving the upper atmospheric temperature accuracy of the ground-based instrument by eliminating noise ways.

In view of the special properties of the upper atmosphere at the altitude of 80-120km, a ground-based passive remote sensing instrument ground-based airglow volume emission rate and temperature imaging interferometer (GBAVTII) is built to detect the atmospheric temperature used O 2(0-1) spectral line of night airglow at the altitude of 94km. In the process of photographing the upper atmosphere airglow with the GBAVTII, the stray light (white noise) such as moonlight, city lights, and starlight will be affected. In this paper, the theoretical expression of denoising is derived based on the rotational line temperature measurement of diatomic O 2(0-1) airglow. Through a slight adjustment of different parameters in the forward equation of the GBAVTII and noise reduction in laboratory flat-field fine calibration, and other denoising methods in the GBAVTII image processing process, the maximum accuracy of the GBAVTII detection of the upper atmospheric temperature is enhanced to 2.4K. Also, the minimum error of the GBAVTII detecting data with the satellite instrument sounding of atmosphere using broadband emission radiometry is 0.4K. Thus, the absolute accuracy of the GBAVTII in detecting the upper atmospheric temperature can be improved to ±(0.4-2.4)K through the theory and method studied in this paper.

Plasma D-Dimer Is Noninferior to Serum C-Reactive Protein in the Diagnosis of Periprosthetic Joint Infection.

No single test has demonstrated absolute accuracy in the diagnosis of periprosthetic joint infection (PJI). Serological markers are often used as screening tools in the workup of patients with suspected PJI. This study aimed to determine the diagnostic utility of plasma D-dimer for PJI in a variety of clinical scenarios. This prospective study enrolled 502 patients undergoing revision hip or knee arthroplasty. PJI was defined per a modified version of the 2018 International Consensus Meeting (ICM) criteria. Plasma D-dimer, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and fibrinogen were measured preoperatively. Receiver operating characteristic curves were used to assess the utility of each biomarker in the diagnosis of PJI. Pairwise comparison with Bonferroni correction was performed to determine whether the differences in areas under the curve (AUCs) between the markers were significant. Of the 412 patients included, 317 (76.9%) did not have an infection (aseptic group) and 95 (23.1%) had an infection (PJI group). All 4 serological markers, D-dimer (AUC, 0.860; sensitivity, 81.3%; specificity, 81.7%), CRP (AUC, 0.862; sensitivity, 90.4%; specificity, 70.0%), ESR (AUC, 0.833; sensitivity, 73.9%; specificity, 85.2%), and fibrinogen (AUC, 0.798; sensitivity, 74.7%; specificity, 75.4%), demonstrated comparable accuracy for the diagnosis of PJI (all p > 0.05). When examining the performance of the different inflammatory markers in diagnosing infection caused by indolent organisms, D-dimer demonstrated the highest sensitivity at 93.8%. We found that plasma D-dimer was noninferior to serum CRP and ESR in the diagnosis of PJI and may be a useful adjunct when screening patients undergoing revision total joint arthroplasty. Diagnostic Level II . See Instructions for Authors for a complete description of levels of evidence.

A Dynamic Scene Vision SLAM Method Incorporating Object Detection and Object Characterization

Simultaneous localization and mapping (SLAM) based on RGB-D cameras has been widely used for robot localization and navigation in unknown environments. Most current SLAM methods are constrained by static environment assumptions and perform poorly in real-world dynamic scenarios. To improve the robustness and performance of SLAM systems in dynamic environments, this paper proposes a new RGB-D SLAM method for indoor dynamic scenes based on object detection. The method presented in this paper improves on the ORB-SLAM3 framework. First, we designed an object detection module based on YOLO v5 and relied on it to improve the tracking module of ORB-SLAM3 and the localization accuracy of ORB-SLAM3 in dynamic environments. The dense point cloud map building module was also included, which excludes dynamic objects from the environment map to create a static environment point cloud map with high readability and reusability. Full comparison experiments with the original ORB-SLAM3 and two representative semantic SLAM methods on the TUM RGB-D dataset show that: the method in this paper can run at 30+fps, the localization accuracy improved to varying degrees compared to ORB-SLAM3 in all four image sequences, and the absolute trajectory accuracy can be improved by up to 91.10%. The localization accuracy of the method in this paper is comparable to that of DS-SLAM, DynaSLAM and the two recent target detection-based SLAM algorithms, but it runs faster. The RGB-D SLAM method proposed in this paper, which combines the most advanced object detection method and visual SLAM framework, outperforms other methods in terms of localization accuracy and map construction in a dynamic indoor environment and has a certain reference value for navigation, localization, and 3D reconstruction.

Polarized angle-resolved spectral reflectometry for real-time ultra-thin film measurement.

We propose a polarized, angle-resolved spectral (PARS) reflectometry for simultaneous thickness and refractive-index measurement of ultra-thin films in real time. This technology acquires a two-dimensional, angle-resolved spectrum through a dual-angle analyzer in a single shot by radially filtering the back-focal-plane image of a high-NA objective for dispersion analysis. Thus, film parameters, including thickness and refractive indices, are precisely fitted from the hyper-spectrum in angular and wavelength domains. Through a high-accuracy spectral calibration, a primary PARS system was built. Its accuracy was carefully verified by testing a set of SiO2 thin films of thicknesses within two µm grown on monocrystalline-Si substrates against a commercial spectroscopic ellipsometer. Results show that the single-shot PARS reflectometry results in a root-mean-square absolute accuracy error of ∼1 nm in film thickness measurement without knowing its refractive indices.