Sensor Fusion with Asynchronous Decentralized Processing for 3D Target Tracking with a Wireless Camera Network.

ABSTRACTUpdating of reservoir models by history matching of 4D seismic data along with production data gives us a better understanding of changes to the reservoir, reduces risk in forecasting and leads to better management decisions. This process of seismic history matching requires an accurate representation of predicted and observed data so that they can be compared quantitatively when using automated inversion. Observed seismic data is often obtained as a relative measure of the reservoir state or its change, however. The data, usually attribute maps, need to be calibrated to be compared to predictions. In this paper we describe an alternative approach where we normalize the data by scaling to the model data in regions where predictions are good. To remove measurements of high uncertainty and make normalization more effective, we use a measure of repeatability of the monitor surveys to filter the observed time‐lapse data.We apply this approach to the Nelson field. We normalize the 4D signature based on deriving a least squares regression equation between the observed and synthetic data which consist of attributes representing measured acoustic impedances and predictions from the model. Two regression equations are derived as part of the analysis. For one, the whole 4D signature map of the reservoir is used while in the second, 4D seismic data is used from the vicinity of wells with a good production match. The repeatability of time‐lapse seismic data is assessed using the normalized root mean square of measurements outside of the reservoir. Where normalized root mean square is high, observations and predictions are ignored. Net: gross and permeability are modified to improve the match.The best results are obtained by using the normalized root mean square filtered maps of the 4D signature which better constrain normalization. The misfit of the first six years of history data is reduced by 55 per cent while the forecast of the following three years is reduced by 29 per cent. The well based normalization uses fewer data when repeatability is used as a filter and the result is poorer. The value of seismic data is demonstrated from production matching only where the history and forecast misfit reductions are 45% and 20% respectively while the seismic misfit increases by 5%. In the best case using seismic data, it dropped by 6%. We conclude that normalization with repeatability based filtering is a useful approach in the absence of full calibration and improves the reliability of seismic data.

In this study, the linear and non‐linear multivariate relationships between 25 teleconnection indices (tele‐indices) as independent variables and annual P24max as the dependent variable were analyzed using multivariate linear regression (MLR) and decision tree regression models (M5), in selected synoptic weather stations of Iran over a statistical period of 30 years (1992–2021). No strong and statistically significant correlation between each tele‐index and P24max was observed. Therefore, it is not appropriate to attribute climate changes in the region to a single factor such as El Niño, but rather consider the combined influence of multiple factors. The M5 model demonstrated higher performance, indicating a non‐linear relationship between tele‐indices and P24max. The stepwise execution of the M5 model tree showed that the algorithm follows a greedy approach, and it is not necessary to use all variables to predict P24max. The normalized root mean square error (NRMSE) of P24max estimation was found to be 15%, 13%, 15%, 8%, 20%, 14%, and 12% with the coefficients of determination of 0.78, 0.79, 0.72, 0.85, 0.81, 0.82, and 0.84 in Hashemabad‐Gorgan, Rasht, Kermanshah, Ahvaz, Bandar Abbas, Isfahan, and Birjand, respectively. Finally, it is possible to forecast P24max using tele‐indices measured in the previous year.

In this paper, asynchronous complex histogram (ACH)-based multi-task artificial neural networks (MT-ANNs), are proposed to realize modulation format identification (MFI), optical signal-to-noise ratio (OSNR) estimation and fiber nonlinear (NL) noise power estimation simultaneously for coherent optical communication. Optical performance monitoring (OPM) is demonstrated with polarization mode multiplexing (PDM), 16 quadrature amplitude modulation (QAM), PDM-32QAM, as well as PDM-star 16QAM (S-16QAM) for the first time. The range of launched power is −3 to −2 dBm with a fiber link of 160–1600 km. Then, the accuracy of MFI reaches 100%. The average root mean square error (RMSE) of OSNR estimation can reach 0.37 dB. The average RMSE of NL noise power estimation can reach 0.25 dB. The results show that the monitoring scheme is robust to the increase of fiber length, and the solution can monitor more optical network parameters with better performance and fewer training data, simultaneously. The proposed ACH MT-ANN has certain reference significance for the future long-haul coherent OPM system.

Field repeatability of OBC 3D seismic data is investigated for evaluating feasibility of time-lapse (4D) seismic survey for monitoring water saturation in a carbonate reservoir in the Middle East. We use co-located data sets, which were acquired in a small part of a giant 3D survey by re-deployed shots/receivers at a short time interval, between which no reservoir change is supposed. Both pre- and post-stack seismic data are examined using two repeatability indices: normalized root mean square (NRMS) and predictability, each complementarily quantifying the similarity of two seismic traces, i.e., data repeatability. For pre-stack data, source-receiver mis-location along the offset orientation is found to control the repeatability. Post-stack repeatability NRMS evaluated after basic data processing including noise removal, stacking, and time shift, turns out to be around 10 %. The presence of noise and static shift severely degrades repeatability in post-stack. This data-driven repeatability evaluation takes account of the influences from shot/receiver mis-positioning, source-generated coherent and incoherent noise, and location-dependent near surface effect, which conventional approaches may under-estimate. The post-stack repeatability, however, is evaluated in a small area and can be laterally variable, since the 3D seismic data quality is strongly area-dependent. We therefore correlate the NRMS with "snapshot" 3D S/N (SN ratio of a 3D) of a single set of the repeated 3D and then use this relation for extrapolating the NRMS to the entire field through 3D S/N of the full field 3D. Combining this analysis with the result of our rock physics modeling, which reveals the magnitude of expected change in reservoir response, we conclude the feasibility of 4D seismic technique in this field is area-dependent. The proposed procedure provides more realistic area-dependent 4D repeatability than conventional approaches.

Road grade estimation for look-ahead vehicle control using multiple measurement runs

Computational modeling, such as finite element analysis, is employed in a range of biomechanics specialties, including impact biomechanics and surgical planning. These models rely on accurate material properties for skeletal muscle, which comprises roughly 40% of the human body. Due to surrounding tissues, compressed skeletal muscle in vivo likely experiences a semi-confined state. Nearly all previous studies investigating passively compressed muscle at the tissue level have focused on muscle in unconfined compression. The goals of this study were to (1) examine the stiffness and time-dependent material properties of skeletal muscle subjected to both confined and unconfined compression (2) develop a model that captures passive muscle mechanics under both conditions and (3) determine the extent to which different assumptions of volumetric behavior affect model results. Muscle in confined compression exhibited stiffer behavior, agreeing with previous assumptions of near-incompressibility. Stress relaxation was found to be faster under unconfined compression, suggesting there may be different mechanisms that support load these two conditions. Finite element calibration was achieved through nonlinear optimization (normalized root mean square error <6%) and model validation was strong (normalized root mean square error <17%). Comparisons to commonly employed assumptions of bulk behavior showed that a simple one parameter approach does not accurately simulate confined compression. We thus recommend the use of a properly calibrated, nonlinear bulk constitutive model for modeling of skeletal muscle in vivo. Future work to determine mechanisms of passive muscle stiffness would enhance the efforts presented here.

The first lactation weekly test day milk yield (WTDMY) records (57233) and monthly test day milk yield (MTDMY) records (11970) spread over a period of 23 years (1984– 2006) sired by 82 bulls of Karan Fries cattle were used to develop the best lactation curve model. The lactation curve parameters of Quadratic cum log model (QCLM), Gamma function (GF), Cobby Le Du model (CLDM), Polynomial regression function (PRF) and Multiphasic logistic function (MLF) were estimated. The average weekly test day milk yield was predicted with high degree of accuracy (R2 &gt; 85%) by all the models with the maximum accuracy (R2 = 99.50%) obtained by polynomial regression function (PRF) and the least fit (R2 = 87.90%) was obtained by Gamma function (GF). However Quadratic cum log model (R2 = 99.20%) was almost equal to polynomial regression function and was better than Cobby Le Du model (R2 = 92.80%). The average root mean square error (RMSE) was found to be minimum with PRF (0.0121 Kg) followed by MLF (Triphasic). Thus the best fit model was polynomial regression function, which was better than other functions for prediction of first lactation WTDMY. The average monthly test day milk yield was predicted with high degree of accuracy (R2 &gt; 75%) by all the models with the maximum accuracy (R2 &gt; 99.41%) obtained by polynomial regression function (PRF) and the least fit was obtained with gamma function (R2 = 79.05%). However, quadratic-cum-log model (R2 = 99.28%) was almost equal to polynomial regression function and was better than CLDM (R2 = 89.50%). The average root mean square error (RMSE) was found to be minimum with PRF (0.0061 kg) followed by QCLM (0.0620). Thus the best fit model was polynomial regression function for prediction of both weekly and monthly test day milk yield of first lactation in Karan Fries cows.

Phenological shifts in plant species are one of the most conspicuous signs of climate change impact on the biosphere. Modeling phenological variations of plant species over broad regions is challenging because of the varied climatic requirements of geographic populations due to local adaptation. In this study, we developed an empirical method to calibrate phenological models of temperate trees using latitude as a predictor to account for local adaptation of populations to a N-S temperature gradient. Fourteen widely distributed tree species in the eastern U.S.A. were investigated using data from the USA-National Phenology Network. We implemented the method in a basic thermal time bud break model to introduce the algorithm of the method and test its effectiveness. For each species, dates of breaking leaf buds were first predicted using a traditional non-spatial model and then with a spatial model that has the critical thermal forcing requirements calibrated for different populations at varied latitudes. As anticipated, non-spatial model predictions that assumed a uniform forcing requirement across latitudes showed consistent and systematic biases at both higher (overestimation-predictions being later) and lower (underestimation-predictions being earlier) latitudes. Spatial models that have been calibrated using our method removed the geographic biases and yielded latitudinal gradients that more closely matched those of the observations. The spatial models also reduced the overall prediction errors from an average root mean square error (RMSE) of 32.2 days to 20.4 days for the training dataset and an average root mean square error for prediction (RMSEP) of 32.2 days to 19.9 days for the testing dataset. This paper is focused on introducing the new calibration method as a preparatory step toward developing operational models that may potentially predict large-scale and range-wide phenological responses of various plant species to climatic changes with improved local accuracy.

To retrospectively compare the rapidity, efficiency, and accuracy of three-dimensional (3D) and two-dimensional (2D) ultrasonography (US) for complete anatomic survey in fetuses at 17-21 weeks of gestation. Institutional review board approval was obtained, informed consent was waived, and the study was HIPAA compliant. Fifty consecutive women undergoing fetal anatomic survey at 17-21 weeks of gestation formed the study cohort. After standard 2D US was performed by one of eight sonographers, the same sonographer also obtained five 3D volumes to encompass the entire fetal anatomy. Three physicians interpreting the scans independently evaluated the completeness of the examination and time needed to read the scans, comparing the standard 2D method with the 3D volume reconstruction technique. The paired t test was used to compare biparietal diameter (BPD), femur length, and performance times between the 3D measurements and the 2D measurement. The t test was used to compare fetal anatomy according to volume angle. Differences were significant when P < .05. Mean time to perform 2D US was 19.6 minutes per examination, whereas mean time to perform complete 3D volume acquisition was 1.8 minutes. Mean times needed to interpret 3D images and measure the BPD and femur were 5.53, 4.79, and 5.34 minutes for the three interpreting physicians. Compared with complete fetal surveys performed with 2D US, individual fetal anatomic landmarks (except for fetal arms and cavum septum pellucidum) were identified more than 94% of the time by using 3D US. Grouping anatomic views by region, the heart, head, extremities, and abdominal views were completely seen in 88%, 90%, 90%, and 95% of patients, respectively. No significant difference was seen between the three physicians regarding completeness of the 3D examinations (P = .7). One fetus had multiple anomalies, with 3D volumes identified as abnormal by all three physicians. Overall, 74% of 3D BPD measurements were within 1 mm of the 2D measurements, and 64% of 3D femur measurements were within 1 mm of the 2D measurements. The standard fetal anatomic survey can be performed in less than 2 minutes with 3D volume US, and the volumes can be interpreted in 6-7 minutes, compared with a mean of 19.6 minutes to perform standard 2D US.

This study aims to expand a measuring range of stereovision system. In the previous paper, the authors achieved a 3D motion capture system by using one camera with triangle markers, named as Mono-MoCap (MMC). MMC has two features. One is that MMC can measure 3D positions of subjects by using one camera on the basis of the perspective n point (PnP) problem. Another is that MMC does not need to recalibrate the camera parameters. In this paper, the authors apply MMC to binocular stereovision system for expanding its measurement range. MMC will solve the problem that stereovision system can not measure 3D positions of objects when even one camera does not capture the objects. In this study, 3D positions of three points where a geometrical relation each other is already-known will be measured by a stereovision. 3D position measurement by the stereovision is enabled by secondarily using MMC when a part of the object is hidden with one camera of the stereovision. Simulation and experimental results will show the effectiveness of the proposed method for expanding the measuring range of stereovision system.

Chapter 9 - Repeatability

In order to support real-time face recognition using a wireless camera network, we design a data acquisition service to quickly and reliably acquire face images of human subjects from multiple views and to simultaneously index each acquired image into its corresponding pose. In comparison with detection of frontal faces, the detection of non-frontal faces with unknown pose is a much more challenging problem that involves significant image processing. In this paper, we describe a collaborative approach in which multi-view camera geometry and inter-camera communication is utilized at run time to significantly reduce the required processing time. By doing so, we are able to achieve a high capture rate for both frontal and non-frontal faces and at the same time maintain a high detection accuracy. We implement our face acquisition system on a 1.6 GHz Intel Atom Processor based embedded camera network and show that we can reliably acquire frontal faces at 11 fps and non-frontal faces at 10 fps on images captured at a resolution of 640 × 480 pixels.

HighlightsSoftware was developed for estimation of DSSAT CSM-CROPGRO-Soybean cultivar coefficients.Phenology-related coefficients were estimated based on observed phenological events.Growth-related cultivar coefficients were estimated based on time-series observations.Cultivar coefficients were optimized based on single- and multiple-experiment data sets.Abstract. The Decision Support System for Agrotechnology Transfer (DSSAT) is one of the most popular software solutions for predicting crop growth and yield while capturing the effects of management practices and interactions between the crop and the environment. Accurate estimation of the crop cultivar coefficients that govern in-season growth and development is critical for correct yield estimates. The manual cultivar coefficient estimation process is time-consuming and results in user-dependent, subjective optimums that are difficult to reproduce. Typically, end-of-season observations (point-based) are used for estimating dynamic in-season biomass accumulation rates. The objective of this study was to develop a time-series estimator (TSE) capable of using multiple in-season observations for estimating the coefficients that define in-season growth and biomass partitioning. Using the TSE, cultivar coefficients were estimated based on multiple in-season observations of leaf area index (LAI) and shoot, leaf, and grain dry matter weights. The cultivar coefficients were estimated from single- and multiple-treatment (seasons and locations) in-season observations. This was done for two cultivars for six management × environment combinations. Estimated multiple-treatment based cultivar coefficients were evaluated with an independent data set and compared to DSSAT standard (manual) coefficients and the cultivar coefficients estimated with the GLUE method. The average normalized root mean squared error (nRSME) for LAI and shoot, leaf, and grain weights was 26% lower for one cultivar and about the same for the other cultivar when compared to the DSSAT standard. Because GLUE uses end-of-season point-based cultivar coefficient estimation, the grain weight over time was underestimated in earlier phases and more accurate toward harvest. The TSE-estimated cultivar coefficients based on 346 in-season observations across multiple target variables and six experiments more accurately reflected in-season growth and grain weight without compromising final grain weight predictions. Keywords: . CROPGRO-Soybean, DSSAT, Genetic coefficients, Normalized root mean square error minimization, Time-seris observations.

Effects of Speed on Spatiotemporal Gluteus Maximus Activation During a Chair-Rise Following Chronic Stroke

A total of 110 patients with obstructive sleep apnea hypoventilation syndrome were selected from January 2017 to December 2018, including 76 cases of men and 34 females, aged 40-50 y and according to the apnea hyponea index, patients were classified into 3 groups of moderate to moderate and 5 apnea hyponea index 20 was divided into group A (36 cases), the 40 of 20 to apnea hyponea index is divided into group B (45 cases) and apnea hyponea index the 40 of 20 to 40 is divided into group B (45 cases) and apnea hyponea index to 40 is divided into group C (29 cases). In the same period in the health check-up center to extract 40 cases without obstructive sleep apnea hypopnea syndrome, arrhythmia and other medical history of health check-up objects. 24 h Holter-monitoring and polysomography monitoring of 4 groups of people, observed and recorded heart rate variability indicators for each group, including all standard deviations for sinus cardiac arrest, standard deviation per 5 min sinus cardiac interstitial period, the average root mean square of successive differences of the total sinus intercardial period difference is divided by the number of individuals of the sinus cardiac interstitial period of 50 ms the average root mean square of successive differences of the total sinus intercardial period difference is divided by the number of individuals of the sinus cardiac inner struggle period of 50 ms divided by the percentage of the total sinus cardiac inner struggle period (proportion of NN50), low frequency and high frequency. Standard deviation of all sinus beats, standard deviation of the mean value of sinus, root mean square of successive differences, proportion of NN50, low frequency and high frequency were lower than those in the control group (p<0.05) in patients with obstructive sleep apnea hypopnea syndrome. The proportion of NN50 and high frequency in group C were lower than A and B (p<0.05) and the low frequency was higher than the proportion of NN50 and high frequency in group C were lower than A and B (p<0.05) and the low frequency was higher than group A, group B and control group (p<0.05). Holter-monitoring is a non-invasive test, the price is not high and it can be a regular test for testing the autonomic nervous function in patients with obstructive sleep apnea hypopnea syndrome.