Unmeasured Areas Research Articles

See the Unseen: Grid-Wise Drivable Area Detection Dataset and Network Using LiDAR

Drivable Area (DA) detection is crucial for autonomous driving. Camera-based methods rely heavily on illumination conditions and often fail to capture accurate 3D information, while LiDAR-based methods offer accurate 3D data and are less susceptible to illumination conditions. However, existing LiDAR-based methods focus on point-wise detection, so are prone to occlusion and limited by point cloud sparsity, which leads to decreased performance in motion planning and localization. We propose Argoverse-grid, a grid-wise DA detection dataset derived from Argoverse 1, comprising over 20K frames with fine-grained BEV DA labels across various scenarios. We also introduce Grid-DATrNet, a first grid-wise DA detection model utilizing global attention through transformers. Our experiments demonstrate the superiority of Grid-DATrNet over various methods, including both LiDAR and camera-based approaches, in detecting grid-wise DA on the proposed Argoverse-grid dataset. Grid-DATrNet achieves state-of-the-art results with an accuracy of 93.28% and an F1-score of 0.8328. We show that Grid-DATrNet can detect grids even in occluded and unmeasured areas by leveraging contextual and semantic information through global attention, unlike CNN-based DA detection methods. The preprocessing code for Argoverse-grid, experiment code, Grid-DATrNet implementation, and result visualization code are available at AVE Laboratory official git hub.

Partial reconstruction of sparse and incomplete point clouds applied to the generation of corneal topographic maps of healthy and diseased patients

The presence of unmeasured areas or areas with missing data in corneal topographies greatly affect the quality of digital corneal models. These are more frequent in posterior than in anterior corneal surface. This work seeks to complete and improve digital corneal models generated from incomplete point clouds obtained with commercial tomographic equipment, applying three interpolation/extrapolation techniques based on population data (Polynomial, Biharmonic Splines and Cubic Radial Base Functions). 43 personalized corneal models of patients with different grades of keratoconus were used to test each interpolation method. All interpolation results were compared using the Mean Average Percentage Error (MAPE) index, observing that it is possible to interpolate/extrapolate up to the radius 5.2 mm on the anterior face and up to 4.2 mm on the posterior face in general, but none of the methods stand out above the others for all the situations considered. Consequently, extrapolation method must be chosen carefully.

Geospatial determinants and spatio-temporal variation of early initiation of breastfeeding and exclusive breastfeeding in Ethiopia from 2011 to 2019, a multiscale geographically weighted regression analysis

BackgroundBreastfeeding offers numerous benefits for infants, mothers, and the community, making it the best intervention for reducing infant mortality and morbidity. The World Health Organization (WHO) recommends initiating breastfeeding within one hour after birth and exclusively breastfeeding for the first six months. This study investigated the trend, spatio-temporal variation, and determinants of spatial clustering of early initiation of breastfeeding (EIBF) and exclusive breastfeeding (EBF) in Ethiopia from 2011 to 2019.MethodsData from the Ethiopian Demographic and Health Survey (EDHS), which was conducted in 2011, 2016, and 2019, were analyzed utilizing a weighted sample of 10,616 children aged 0–23 years for EIBF and 2,881 children aged 0–5 months for EBF. Spatial autocorrelation analysis was used to measure whether EIBF and EBF were dispersed, clustered, or randomly distributed and Kriging interpolation was employed to predict the outcome variables in the unmeasured areas. Spatial scan statistics were used to identify spatial clusters with a high prevalence of cases. Both global and local regression modeling techniques were employed to examine the spatial relationships between the explanatory variables and the dependent variables.ResultsThe trend analysis revealed a notable increase in the prevalence of EIBF from 51.8% in 2011 to 71.9% in 2019. Similarly, the prevalence of EBF increased from 52.7% in 2011 to 58.9% in 2019. Spatial analysis demonstrated significant spatial variation in both EIBF and EBF throughout the country. Cold spots or clusters with a low prevalence of EIBF were observed consistently in the Tigray and Amhara regions, and significant cold spot areas of EBF were observed consistently in the Afar and Somali regions. Multiscale geographically weighted regression analysis revealed significant predictors of spatial variations in EIBF, including the religious affiliation of being a follower of the orthodox religion, parity of 1–2, absence of antenatal care visits, and delivery via cesarean section.ConclusionsDespite the increase in both EIBF and EBF rates over time in Ethiopia, these rates still fall below the national target. To address this issue, the government should prioritize public health programs aimed at improving maternal healthcare service utilization and maternal education. It is essential to integrate facility-level services with community-level services to achieve optimal breastfeeding practices. Specifically, efforts should be made to promote breastfeeding among mothers who have delivered via cesarean section. Additionally, there should be a focus on encouraging antenatal care service utilization and adapting maternal healthcare services to accommodate the mobile lifestyle of pastoralist communities. These steps will contribute to enhancing breastfeeding practices and achieving better outcomes for maternal and child health.

Ice Thickness Measurement and Volume Modeling of Muztagh Ata Glacier No.16, Eastern Pamir

As a heavily glaciated region, the Eastern Pamir plays a crucial role in regional water supply. However, considerable ambiguity surrounds the distribution of glacier ice thickness and the details of ice volume. Accurate data at the local scale are largely insufficient. In this study, ground-penetrating radar (GPR) was applied to assess the ice thickness at Muztagh Glacier No.16 (MG16) in Muztagh Ata, Eastern Pamir, for the first time, detailing findings from four distinct profiles, bridging the gap in regional measurements. We utilized a total of five different methods based on basic shear stress, surface velocity, and mass conservation, aimed at accurately delineating the ice volume and distribution for MG16. Verification was conducted using measured data, and an aggregated model outcome provided a unified view of ice distribution. The different models showed good agreement with the measurements, but there were differences in the unmeasured areas. The composite findings indicated the maximum ice thickness of MG16 stands at 115.87 ± 4.55 m, with an ice volume calculated at 0.27 ± 0.04 km3. This result is relatively low compared to the findings of other studies, which lies in the fact that the GPR measurements somewhat constrain the model. However, the model parameters remain the primary source of uncertainty. The results from this study can be used to enhance water resource assessments for future glacier change models.

A Voronoi Diagram and Q-Learning based Relay Node Placement Method Subject to Radio Irregularity

Industrial Wireless Sensor Networks (IWSNs) have been widely used in industrial applications that require highly reliable and real-time wireless transmission. A lot of works have been done to optimize the Relay Node Placement (RNP), which determines the underlying topology of IWSNs and hence impacts the network performance. However, existing RNP algorithms use a fixed communication radius to compute the deployment result at once offline, while ignoring that the radio environment may vary drastically across different locations, also known as radio irregularity. To address this limitation, we propose a Voronoi diagram and Q-learning based RNP (VQRNP) method in this article. Instead of using a fixed communication radius, VQRNP employs the Q-learning algorithm to dynamically update the radio environment of measured areas, uses a Voronoi diagram based method to estimate the radio environment of unmeasured areas, and proposes a coverage extension location selection algorithm to place RNs so as to extend the coverage of the deployed network based on the results estimated by Voronoi diagram based Graph Generating (VGG). In this way, the VQRPN method can adapt itself well to the variation of radio environment and largely speed up the deployment process. Extensive simulations verify that VQRNP significantly outperforms existing RNP algorithms in terms of reliability.

Pixel Perfect: Using Vision Transformers to Improve Road Quality Predictions from Medium Resolution and Heterogeneous Satellite Imagery

Critical infrastructure, such as roads and electricity, are core systems that enable economic development. However, these crucial systems are frequently under-monitored in developing regions, resulting in lost opportunities for growth. Recent advances in remote sensing and machine learning have enabled monitoring and measurement of infrastructure faster and more frequently than traditional methods. However, ground data are often unavailable, resulting in a disconnect between labels and remotely sensed data. Furthermore, data from industrialized regions can only sometimes be transferred to regions with sparse data due to differences in the concept of quality between regions. Additionally, inconsistency in data and the complexity of ML models can introduce bias due to learned characteristics across diverse regions, leading to inaccurate predictions and recommendations for action. In this study, we train and compare traditional neural networks and vision transformers to predict road quality from medium-resolution satellite imagery and apply them to realistic data conditions: heterogeneous temporal-spatial resolutions. The best models (vision transformers) achieve AUROC scores of 0.934 and 0.685 for binary and five-class classification tasks, respectively, exhibiting results appealing for inference in otherwise unmeasured areas. Furthermore, these experiments and results showed that proper training techniques could produce accurate models from limited, heterogeneous, and low-resolution data.

싱글포인트 라이다 기반 실내지도 생성시간 감소 기법

Indoor mapping technology is utilized as a digital twin-based technology in various fields that require information about indoor spaces, such as building and facility management services, smart factories, smart yards, etc. Sensors utilized for mapping include LiDAR. For certain services using constraint hardware such as small robots, it is appropriate to utilize single-point LiDAR, which is relatively cost-effective compared to high-performance LiDAR, but at the same time, the processing time is quite large to generate indoor maps using such sensors that identify only one point. Therefore, in this study, we propose a scheme to reduce the time for generating indoor maps based on single-point lidar. The proposed method consists of measurement, feature detection and partial map generation, partial map matching, and navigation to the next measurement point. By searching for points that can increase the measurement rate of unmeasured areas in the next measurement, the number of measurements is reduced, thereby reducing the total processing time. The proposed method is evaluated through experiments in a testbed environment by fabricating a mobile measurement device.

Validation of an Uncertainty Propagation Method for Moving‐Boat Acoustic Doppler Current Profiler Discharge Measurements

AbstractThe moving‐boat Acoustic Doppler Current Profiler (ADCP) gauging method is extensively used to measure the discharge of rivers and canals. Informed decisions related to water management require reliable estimates of the uncertainty of such measurements. The evaluation of the uncertainty is however a difficult task because of the complexity of the ADCP data workflow and the lack of reference discharges in rivers and canals. This study presents an evolution of the OURSIN method, which follows the framework of the Guide to the expression of Uncertainty in Measurement. The method has been implemented in the QRevInt software which provides an ADCP data quality review prior to the uncertainty analysis. As a computationally efficient alternative to the Monte Carlo approach, the uncertainty propagation combines elemental measurement uncertainty sources, transect‐to‐transect discharge variability, and estimation of the uncertainty of discharges in unmeasured areas by sensitivity analysis. The OURSIN uncertainty results are validated using empirical uncertainty estimates from two large‐scale repeated measures experiments with variable site and flow conditions. The mean and the 95% quantiles of the uncertainty differences are −0.4% [−4.4%; +2.5%]. The OURSIN method provides a comprehensive uncertainty budget that is helpful for understanding the relative impacts of error sources and defining strategies to minimize them. The method should be further evaluated with experiments in different conditions, and additional uncertainty components and options such as GPS reference for boat velocity could be implemented.

Research on Phase Current Reconstruction for DPWM2 of Induction Motor Drive System Based on DC-Link Current Sampling

In order to reduce the switching loss and cost, as well as improve the reliability of the induction motor (IM) drive system, the technology involving the three-phase current reconstruction of 60° discontinuous pulse width modulation (DPWM2) is studied in this paper. According to the analysis of the switching state for DPWM2 in different sectors, the three-phase current can be constructed by sampling the voltage of the DC-link resistor. When the target voltage vector is located near the sector boundary or in the low-modulation area, the duration of the active vector in the sampling period is less than the voltage sampling time, which leads to measurement errors of the DC-link current. Therefore, on the basis of the switching state in different unmeasured areas, a time compensation method combining phase shifting and frequency reduction is proposed, and the expressions for comparing values are derived. Lastly, a simulation model and an experimental platform are established to validate the accuracy of the proposed method.

Abstract 747: Deciphering tumor ecosystems at super-resolution from spatial transcriptomics

Abstract The tumor microenvironment (TME) contains networks of cells and structures that surround tumor cells. Cell populations in the TME, including their abundance, composition, and spatial location are critical determinants of the occurrence, growth, and metastasis of a tumor. A comprehensive analysis of the multiple exchanges between tumor cells and their TME is essential for understanding the underlying mechanisms of tumor growth and response to anti-cancer therapies. Recent advances in spatially resolved transcriptomics (SRT) techniques have enabled gene expression profiling while preserving location information in tissues, which innovates a promising avenue to study the TME in a spatial context. With the power of SRT, we aim to provide a detailed annotation of tumor structure and different lymphocytes by integrating gene expression information and cell morphology features in the complemented high-resolution histology image obtained from the same tissue section. A major challenge that hinders gene expression and histology integration in SRT data is the relatively low resolution of gene expression data compared to pixel-resolution histology images. As gene expression is only measured in discrete spots that are separated by tissue gaps, a large proportion of the tissue area remains unmeasured (e.g., >50% in 10x Visium). The incomplete coverage of gene expression in ST has prevented the deciphering of detailed TME structures such as the tertiary lymphoid structure (TLS).To overcome this challenge, we present TESLA (Tumor Edge Structure and Lymphocyte multi-level Annotation), a machine learning framework that integrates gene expression and histology image in SRT to investigate the detailed strictures in TME. TESLA first fills in the gene expression for unmeasured areas and generates gene expression images in the same resolution as the histology image. Next, TESLA integrates the gene images with the histology image to annotate different tumor/TME cell types at pixel resolution. Some specific tumor-infiltrating lymphocytes structures, such as TLSs, can also be detected by colocalization analysis of different lymphocytes. TESLA is also able to characterize high-resolution cellular and molecular spatial structures of tumor by separating tumor into different subtype regions and elucidating differential transcriptome programs. The detailed multi-level annotations performed by TESLA provide a comprehensive understanding of the spatial context and the nature of cellular heterogeneity of the TME. Citation Format: Jian Hu, Kyle Coleman, Edward B. Lee, Humam Kadara, Linghua Wang, Mingyao Li. Deciphering tumor ecosystems at super-resolution from spatial transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 747.

Development and Assessment of Methods for Predicting Rainfall in Unmeasured Watersheds Using the Relation Between Observed and Sensor-Measured Rainfall

Recently, with the development of information and communication technology and the Internet of Things (IoT), observation technology using sensors is being applied in a variety of ways, such as using a sensor to observe rainfall in an unmeasured area. In this study, the relationship between the rainfall sensor signal (S) and the amount of rainfall (R) was developed through an experiment in an artificial rainfall generator, and the applicability was evaluated through outdoor observation. The coefficient of determination of the relational expression developed through the indoor experiment was 0.95, the mean absolute error was 2.66 mm/hr, the root mean square error was 3.87 mm/hr, the efficiency coefficient was 0.89, and the concordance index was 0.97, showing very high reliability. In the outdoor test results, the error rate was 7.96% when comparing the data from the rainfall sensors in vehicles and the precipitation station, which were not observed at the same location. Despite such errors, it is judged that accurate rainfall observation using a rainfall sensor is possible in an area where a precipitation station is not installed.

Spatial variation of ambient PM2.5 and PM10 in the industrial city of Arak, Iran: A land-use regression

Land-use regression models (LUR) were developed to estimate the spatial patterns and seasonal variability of particulate matter with aerodynamic diameter ≤2.5 mm and ≤10 mm (PM2.5 and PM10) in the industrial city of Arak, Iran. PM2.5 and PM10 data were obtained from the air monitoring stations belonging to the Department of Environment from 2015 to 2020. Spatial interpolation of particles by ordinary kriging method was conducted to estimate pollutant variability in unmeasured areas. The predicted particles concentrations (PM2.5 and PM10) of the three hundred points were subsequently collected from the kriging map and accepted as a response variable in final LUR models. All available Geographic Information System (GIS) predictors in vector format were applied as potentially independent variables. The leave-one-out cross-validation (LOOCV) method was used to evaluate the performance of the LUR models. The annual mean PM2.5 and PM10 concentrations were 22.92 and 49.78 μg/m3 across the city. LUR models explained 88.5% of the variances (R2) for PM2.5 and 54.5% for PM10. For the PM2.5 and PM10 models, the mean LOOCV R2 values were 0.83 and 0.45, respectively. There were some variations between both LUR models for PM2.5 and PM10, but the heating and non-heating season models were nearly similar to the annual LUR models. These LUR models provide a spatial distribution of particles at the home addresses and predict concentrations for no-monitored areas. High cross-validation of this method confirms the robustness of the suggested approach to forecasting particle concentrations in another place.

Subnational mapping for targeting anaemia prevention in women of reproductive age in Ethiopia: A coverage-equity paradox.

Anaemia in women of reproductive age (WRA) can be effectively addressed if supported by a better understanding of the spatial variations, magnitude, severity and distribution of anaemia. This study aimed to map the subnational spatial distribution of anaemia (any, moderate and severe forms) among WRA in Ethiopia. We identified and mapped (any, moderate and severe) anaemia hotspots in WRA (n = 14,923) at the subnational level and identified risk factors using multilevel logistic regression. Kulldorff scan statistics were used to identify hotspot regions. Ordinary kringing was used to predict the anaemia prevalence in unmeasured areas. The overall anaemia prevalence increased from 16.6% in 2011 to 23.6% in 2016, a rise that was mostly related to the widening of existing hotspot areas. The primary clusters of (any) anaemia were in Somali and Afar regions. The horn of the Somali region represented a cluster of 330 km where 10% of WRA were severely anaemic. The Oromia-Somali border represented a significant cluster covering 247 km, with 9% severe anaemia. Population-dense areas with low anaemia prevalence had high absolute number of cases. Women education, taking iron-folic-acid tablets during pregnancy and birth-delivery in health facilities reduced the risk of any anaemia (P < 0.05). The local-level mapping of anaemia helped identify clusters that require attention but also highlighted the urgent need to study the aetiology of anaemia to improve the effectiveness and safety of interventions. Both relative and absolute anaemia estimates are critical to determine where additional attention is needed.

Mapping of suspended sediment transport using acoustic methods in a Pantanal tributary.

Generally, fluvial systems are used for different objectives including energy production, water supply, recreation, and navigation. Thus, many impacts must be considered with their use. An understanding of sediment dynamics in fluvial systems is often of value for a variety of objectives, given that erosion and depositional processes can change the fluvial system morphology and can substantially alter the fluvial environment. In this sense, sediment monitoring is important because it helps to explain and quantify sediment dynamics in the environment. Hence, this study presents an innovative sediment monitoring technique: the use of the acoustic Doppler current profiler, commonly used to obtain discharge measurements, to obtain suspended sediment concentration (SSC). This paper aims to describe the application of additional corrections to the ADP-M9 signal to obtain SSC from measurement campaigns that used the ADP only for discharge measurements. The analyses were based on traditional sediment sampling methods and discharge measurements, with the ADP-M9, from 7 field campaigns at the Taquari River, a major tributary from the Alto Paraguay Basin, in the Pantanal Biome, known as the largest freshwater wetland system in the world. The correlation was assessed considering the following: (a) the equipment frequency operation mode (Smart Pulse or Fixed Frequency) and (b) by checking the influence of the sediment attenuation coefficient. Furthermore, extrapolation was conducted in filtered and unmeasured areas of the ADP to map the suspended sediment concentration over the entire cross section. Results indicate that ADP correlations can be an effective tool for estimating SSC in the Taquari River when samples cannot be collected. Correlations could be applied to past and future ADP measurements made at the location where the correlation was created, as long as similar environmental conditions are present as when the correlation was developed. The described technique can expand the amount of sediment data available at a monitoring site even with reduced traditional sampling and by leveraging instruments used for other monitoring purposes.

Efficient Evaluation of Remaining Wall Thickness in Corroded Water Pipes Using Pulsed Eddy Current Data

In order to analyse failures of an ageing water pipe, some methods such as the loss-of-section require remaining wall thickness (RWT) along the pipe to be fully known, which can be measured by the magnetism based non-destructive evaluation sensors though they are practically slow due to the magnetic penetrating process. That is, fully measuring RWT at every location in a water pipe is not really practical if RWT inspection causes disruption of water supply to customers. Thus, this paper proposes a new data prediction approach that can increase amount of RWT data of a corroded water pipe collected in a given period of time by only measuring RWT on a part (e.g. 20%) of the total pipe surface area and then employing the measurements to predict RWT at unmeasured area. It is proposed to utilize a marginal distribution to convert the non-Gaussian RWT measurements to the standard normally distributed data, which can then be input into a 3-dimensional Gaussian process model for efficiently predicting RWT at unmeasured locations on the pipe. The proposed approach was implemented in two real-life in-service pipes, and the obtained results demonstrate its practicality.

Mitochondrial Genome of Aphelinus mali (Haldeman) (Hymenoptera: Aphelinidae) with a Higher at Percent and the Highest AT-Skew in Hymenoptera

Aphelinus mali (Haldeman) (Hymenoptera: Aphelinidae) is an important specific endoparasitoid of Eriosoma lanigerum (Hausmann) (Hymenoptera: Aphididae) with high natural parasitism in the field. By studying the COI gene of the A. mali mitochondrion, two distinct clades, Shandong and Liaoning clades, were found in China. In this paper, the mitochondrial genome of A. mali populations from Shandong province was sequenced and analyzed. In total, 14415 bp were sequenced, representing 93.5% of the total length of the A. mali mitochondrial genome, with six tRNA, trnY, trnM, trnI, trnQ, trnW, trnS1, not sequenced. The unmeasured area was located in an A+T-rich region of approximately 1000 bp. The AT content in the A+T-rich region was as high as 91.3% and this may be why this region has not been detected until now. The A+T content was 84.4% in the measured region, which is higher than that of other Hymenoptera. The AT skew was 0.1291, the highest AT skew known among Hymenoptera, which might be related to the higher AT content of protein-coding genes, rRNA and RSCU AT content. Transport genes (tRNA) had a secondary clover shaped structure, compared with other Hymenoptera; there is a more incomplete secondary structure, and less base mismatch. The mitochondrial genome of A. mali showed considerable rearrangement when compared with the mitochondrial genome of the most primitive arrangement, mainly concentrated in six tRNA genes and the gene region from cob to trnE and from trnL to nad1. The mitochondrial genome of A. mali is quite different from that of other Hymenoptera as can be demonstrated by constructing a phylogenetic tree. This paper provides a basis for further study of phylogeny, genetic structure, and hybridization of A. mali populations.

인공신경망을 활용한 미계측지역 지하수 함양량 추정 기법의 적용성 평가

국내의 경우 다양한 방법의 지하수 함양량 산정 기법이 적용되고 있으나, 표준화된 방법이 마련되어 있지 않은 실정이다. 지하수위 시계열 자료를 활용한 TSM-hWTF 방법이 개발되어 있으나 지하수 관측소의 개수가 한정적이어서 미계측 지역에 대한 지하수 함양량 산정은 어려웠다. 이를 해결하고자, 207개 지하수 관측 지점의 지형 표고, 경사, 인근 하천, 지질, 토양 등과 같은 각 관측소의 지점 특성 15개 항목을 입력 인자, 각 지점의 지하수 함양율을 출력 인자로 설정한 후 다층퍼셉트론 기법에 의한 인공신경망 모델을 개발하였다. 지하수 함양율의 모델 예측값과 실제값의 상관계수는 0.8 이상의 높은 값을 보였으며, 표토특성과 심토특성이 가장 큰 영향을 미치는 것으로 분석되었다. 홍성군 서부면 양곡리 지역에 대하여 본 모델을 적용하여 추정한 지하수 함양량과 9개 지하수 관측 자료로부터 TSM-hWTF로 추정한 지하수 함양량은 유사한 경향을 보이는 것으로 평가되어 본 인공신경망 모델에 기반한 지하수 함양량 추정이 효과적인 것으로 나타났다.

Induced Start Dynamic Sampling for Wafer Metrology Optimization

Metrology, which plays an important role in ensuring production quality in modern manufacturing industries, incurs substantial costs both in terms of the infrastructure required and the time needed to perform measurements. In particular, in the semiconductor manufacturing industry, measuring fundamental quantities on different sites of a wafer surface is associated with increased production time. To increase metrology efficiency, a typical strategy is to limit the number of sites measured and to exploit statistical models (soft sensing) to reconstruct the wafer profile. Moreover, for quality reasons, spatial dynamic sampling strategies may be employed to ensure that all regions of a wafer surface are checked periodically during production. In this paper, we propose a new sampling strategy, called induced start dynamic sampling (ISDS), which adapts greedy feature selection algorithms to the spatial dynamic sampling problem, such that the number of measured sites at each process run is minimized while achieving good wafer profile reconstruction accuracy and process visibility. The superiority of the proposed strategy with respect to the state of the art is demonstrated using both simulated data and an industrial chemical vapor deposition case study. Note to Practitioners —In this paper, we tackle a practical metrology problem encountered in semiconductor manufacturing, namely, the design of a dynamic wafer measurement plan to monitor the accuracy of a process across the whole wafer surface. The measurement plan is called “dynamic,” since the measurement locations, which are drawn from a candidate set that provides coverage of the whole wafer, change at each process iteration. Our methodology addresses the challenge of finding an optimized tradeoff between the number of measurements performed on each wafer and the reconstruction accuracy that can be achieved for the unmeasured areas on the wafer while, at the same time, for quality assurance purposes, ensuring that all locations on a wafer are visited in a finite number of process runs. The major benefit of the methodology is that it can significantly reduce the number of sites that need to be measured on each wafer enabling greater throughput on metrology tools without sacrificing process monitoring and anomaly detection capability.

Simulation of optical vortex measurement for out-of-plane displacement by using fringe-based optical-flow

Fringe-based optical flow is a novel method for measuring the deformation of an object with a high sensitivity. When it is used for out-of-plane displacement measurement, there would be greatly error in low fringe-frequency area. To solve the problem, a new method is proposed, in which an optical vortex beam is used as the object light and a spherical wave as the reference light in double-optical-path interferometry. The interference system and principle are introduced. Some simulations are completed. The results show that there is an unmeasurable area with the circular closed fringe patterns formed by two plane waves. But with the non-closed circular fringe patterns formed by the OV object light and the spherical wave reference light, the measured phase differences are in good agreement with the standard values. And then the measurement errors would be decreased greatly. Besides, the method by using patterns obtained by the interference of an OV light and a spherical wave have a certain degree of anti-noise performance.

Urban land use regression models: can temporal deconvolution of traffic pollution measurements extend the urban LUR to suburban areas?

Land use regression (LUR) models that predict intra-urban variability of traffic-related air pollution are frequently used to inform exposure assessments in epidemiological studies. Spatially extending models built in city centres to unmeasured surrounding areas could be beneficial as it increases the size of area available for exposure assessment, which can improve the statistical power of health studies. However, past studies have shown poor performance when these models were transferred to unmeasured areas. In this study, ambient concentrations of black carbon (BC), ultrafine particles (UFP), nitric oxide (NO) and nitrogen dioxide (NO2) with response times ranging from 1 s to 20 s were measured in the summer using a mobile laboratory. These ambient concentrations were temporally deconvolved into local, neighbourhood- and regional-background time-series signals. The temporally resolved signals were used to develop resolved LUR models (i.e. local, neighbourhood-background and regional-background models) while unresolved LUR models were developed from total ambient concentrations (i.e., measurements that were not temporally resolved). Both resolved and unresolved LUR models displayed modest R2 values when compared to models developed from fixed sites which may be due to the mobile nature of our data. External validation of the resolved and unresolved models within the models' original geographic domain showed comparable performance for BC (R2 for resolved vs. unresolved: 0.44 vs. 0.47), UFP (0.34 vs. 0.35), NO (0.18 vs. 0.19) and NO2 (0.42 vs.0.40). However, the resolved models were better able to assess exposure than the unresolved models when they were spatially extended to suburban areas bordering the urban area: UFP (R2: 0.42 vs. 0.29), NO2 (0.36 vs 0.26), NO (0.21 vs 0.14) and BC (0.32 vs 0.30). Furthermore, the resolved models were better able to predict the observed variability in suburban areas with both similar and different land uses to the urban area.