Smoothing Procedure Research Articles

On the ultrasonic characterization of the stacking sequence of CFRP laminates

Ultrasound in pulse-echo scan mode has already been demonstrated for the extraction of local fiber angle distribution in composite laminates. Depending on the ultrasonic (scan) parameters employed, different sensitivity, spatial resolution, and dynamic depth range can be reached.This study investigates the use of pulse-echo ultrasound testing in different frequency ranges to characterize the in-plane fiber angle distribution and ply stacking sequence in a multi-layer composite laminate. Minimization of the Mumford-Shah functional is performed as an edge-preserving smoothing procedure for the recorded ultrasonic dataset, followed by the application of a Gabor Filter-based Information Diagram approach to extract a 3D tomographic image of the fiber angles. The performance of the method is experimentally studied on ultrasonic datasets, recorded at various center frequencies, of a 5.5 mm thick 24-layer quasi-isotropic carbon fiber reinforced polymer laminate with stacking sequence [+45/0/−45/90]3s. For quantitative analysis, statistical metrics are employed to evaluate the accuracy and precision of the extracted angles over depth, and to better understand the interfering influence of adjacent plies. For the given case study, a 15 MHz ultrasound frequency is recommended as it provides a good balance between precision, accuracy, and depth range. To understand the influence of scan parameters on the fiber angle characterization, the 15 MHz ultrasonic dataset is analyzed for various noise conditions as well as different spatial step size of the scan.

UAV Path Planning in 3-D Constrained Environments Based on Layered Essential Visibility Graphs

In this paper, an optimal path search methodology for UAVs flying in 3D environments is presented, taking into account the presence of obstacles and other constraints deriving from flight dynamics. The so-called Essential Visibility Graph (EVG) is extended to the 3D case by describing the obstacles using a finite number of parallel planar layers at different altitudes. The resulting graph, called the Layered Essential Visibility Graph (LEVG), is based on an efficient branching algorithm and it is made up of a reduced number of nodes and edges thus assuring a limited computational burden. Once the optimal piece-wise linear path has been identified over the LEVG, aircraft performance related constraints, formulated in terms of turn and pull-up radii limits, can be taken into account via a smoothing procedure based on a 3D extension of Dubins' paradigm. This way an optimal flyable 3D path can be obtained. The implementation of a specific integer programming formulation within the graph search process ensures the full compliance of the optimal smoothed trajectory with the environmental constraints. The effectiveness of the proposed methodology is proved by means of numerical tests in complex operational scenarios over a real terrain morphology and an urban environment.

Clinical Observation of Non-Staining Circular Capsulorhexis for Hypermature Cataract

Objective: To explore the circular capsulorhexis method and its clinical effect in phacoemulsification of hypermature cataract without staining. Methods: In this prospective randomized case-control study, 22 patients (22 eyes) with hypermature cataract were randomly divided into a staining agent group (10 cases, 10 eyes) and a non-staining agent group (12 cases, 12 eyes). Both groups underwent phacoemulsification with foldable intraocular lens implantation under topical anesthesia. In the staining agent group, 0.5% indocyanine green was used to stain the anterior lens capsule to assist in circular capsulorhexis; in the non-staining agent group, after puncturing the anterior lens capsule and aspirating the liquefied cortical material, the capsular bag was filled with viscoelastic agent to increase the contrast prior to circular capsulorhexis. The operability of the anterior capsule without staining in capsulorhexis was observed, and the effect of capsulorhexis, diameter of capsulorhexis opening, and operative time were compared and analyzed between the two groups. Results: Both groups successfully underwent circular capsulorhexis, with a success rate of 100%. The diameters of capsulorhexis opening in the staining agent group and non-staining agent group were 5.68 ± 0.15 mm and 5.54 ± 0.16 mm, respectively, without any statistical difference between the groups (P > 0.05); the operative time of the staining agent group (40.40 ± 5.42 s) was shorter than that of the non-staining agent group (50.92 ± 3.97 s), and the difference was statistically significant (P < 0.001). In both groups, phacoemulsification was successful, and intraocular lens were implanted into the capsular bags; their visual acuity showed significant improvement during the 1-month follow-up period after surgery, without any surgical complications. Conclusion: In hypermature cataract, injecting viscoelastic agent into the capsular bag for volume replacement, given a lack of capsule staining agent, can increase the contrast for capsulorhexis and reduce the risk of large capsular bag collapse on the zonular ligament. Thus, it is considered safe and effective. Circular capsulorhexis is conducive to a smooth phacoemulsification procedure.

Where is the optimal plane to mobilize the anterior rectal wall in female patients undergoing total mesorectal excision?

Since Heald proposed the total mesorectal excision (TME) procedure, the prognosis of patients with rectal cancer has been significantly improved. But Heald did not specifically describe the anterior surgical plane in female patients. And the surgical plane for mobilizing the anterior rectal wall during TME surgery in female patients remains controversial. To investigate the anatomy of the female pelvis and identify the optimal plane for mobilizing the anterior rectal wall. We retrospectively collected surgical procedure videos and clinical data of female patients diagnosed with middle or low rectal cancer who underwent the TME procedure between January 2020 and October 2022 across six hospitals. The patients were divided into two groups based on the surgical approach used to mobilize the anterior rectal wall: The experimental group was to open the peritoneum at the lowest point of the peritonea reflection and enter the plane for mobilizing, while the control group was cut at 0.5-1 cm above the peritoneal reflection and enter another plan. Then, we compared the preoperative and postoperative information between the two groups. We also dissected and observed ten adult female pelvises to analyze the anatomic structure and compare the entry plane between the two approaches. Finally, we researched the pathological structure between the rectum and the vagina. Finally, 77 cases that met the criteria were included in our study. Our observations revealed that the experimental group underwent a smooth procedure, entering the plane amidst the mesorectal fascia and adventitia of the vagina, whereas the control group entered the plane between the vaginal adventitia and muscle layers. Compared to the control group, the experimental group showed a significant decrease in intraoperative bleeding [22.5 (19.5-50) mL vs 17 (5-20) mL, P = 0.01], as well as a shorter duration of hospitalization [9 (7-11.25) d vs 7 (6-10) d, P = 0.03]. Through the examination of surgical videos and cadaveric studies, we discovered that Denonvilliers' fascia is absent in females. Additionally, pathological sections further revealed the absence of Denonvilliers' fascia in females, with only loose connective tissue present between the mesorectal fascia and adventitia of the vagina. The plane amidst the mesorectal fascia and vaginal adventitia is the optimal surgical plane to mobilize the anterior rectal wall for female patients undergoing the TME procedure.

Learning Block Structured Graphs in Gaussian Graphical Models

A prior distribution for the underlying graph is introduced in the framework of Gaussian graphical models. Such a prior distribution induces a block structure in the graph’s adjacency matrix, allowing learning relationships between fixed groups of variables. A novel sampling strategy named Double Reversible Jumps Markov chain Monte Carlo is developed for learning block structured graphs under the conjugate G-Wishart prior. The algorithm proposes moves that add or remove not just a single edge of the graph but an entire group of edges. The method is then applied to smooth functional data. The classical smoothing procedure is improved by placing a graphical model on the basis expansion coefficients, providing an estimate of their conditional dependence structure. Since the elements of a B-Spline basis have compact support, the conditional dependence structure is reflected on well-defined portions of the domain. A known partition of the functional domain is exploited to investigate relationships among portions of the domain and improve the interpretability of the results. Supplementary materials for this article are available online.

A innovative wavelet transformation method optimization in the noise-canceling application within intelligent building occupancy detection monitoring

The study deals with detection of the occupation of Intelligent Building (IB) using data obtained from indirect methods with Big Data Analysis within IoT. In the area of daily living activity monitoring, one of the most challenging tasks is occupancy prediction, giving us information about people's mobility in the building. This task can be done via monitoring of CO2 as a reliable method, which has the ambition to predict the presence of the people in specific areas. In this paper, we propose a novel hybrid system, which is based on the Support Vector Machine (SVM) prediction of the CO2 waveform with the use of sensors that measure indoor/outdoor temperature and relative humidity. For each such prediction, we also record the gold standard CO2 signal to objectively compare and evaluate the quality of the proposed system. Unfortunately, this prediction is often linked with a presence of predicted signal activities in the form of glitches, often having an oscillating character, which inaccurately approximates the real CO2 signals. Thus, the difference between the gold standard and the prediction results from SVM is increasing. Therefore, we employed as the second part of the proposed system a smoothing procedure based on Wavelet transformation, which has ambitions to reduce inaccuracies in predicted signal via smoothing and increase the accuracy of the whole prediction system. The whole system is completed with an optimization procedure based on the Artificial Bee Colony (ABC) algorithm, which finally classifies the wavelet's response to recommend the most suitable wavelet settings to be used for data smoothing.

Learning to segment fetal brain tissue from noisy annotations.

Automatic fetal brain tissue segmentation can enhance the quantitative assessment of brain development at this critical stage. Deep learning methods represent the state of the art in medical image segmentation and have also achieved impressive results in brain segmentation. However, effective training of a deep learning model to perform this task requires a large number of training images to represent the rapid development of the transient fetal brain structures. On the other hand, manual multi-label segmentation of a large number of 3D images is prohibitive. To address this challenge, we segmented 272 training images, covering 19-39 gestational weeks, using an automatic multi-atlas segmentation strategy based on deformable registration and probabilistic atlas fusion, and manually corrected large errors in those segmentations. Since this process generated a large training dataset with noisy segmentations, we developed a novel label smoothing procedure and a loss function to train a deep learning model with smoothed noisy segmentations. Our proposed methods properly account for the uncertainty in tissue boundaries. We evaluated our method on 23 manually-segmented test images of a separate set of fetuses. Results show that our method achieves an average Dice similarity coefficient of 0.893 and 0.916 for the transient structures of younger and older fetuses, respectively. Our method generated results that were significantly more accurate than several state-of-the-art methods including nnU-Net that achieved the closest results to our method. Our trained model can serve as a valuable tool to enhance the accuracy and reproducibility of fetal brain analysis in MRI.

A Localized Version of the Method of Fundamental Solutions in a Multi-level Context

The Method of Fundamental Solutions is applied to the Laplace equation. Instead of using the traditional approach with external source points and boundary collocation points, the original domain decomposed into a lot of smaller, overlapping subdomains, and the Method of Fundamental Solutions is used to the individual local subdomains. After eliminating the local source points, local schemes are obtained. Instead of constructing a global scheme, the local subproblems are solved sequentially, in an iterative way. This mimics a multiplicative Schwarz method with overlapping subdomains, which assures the convergence of the method. Combining the iteration with a simple Seidel-type method, the resulting iteration is used as a smoothing procedure of a multi-level method. The points belonging to the coarse and fine levels are defined by a quadtree-generated cell system controlled by the boundary of the original domain. The multi-level character of the obtained method makes it possible to reduce the necessary number of iterations, that is, the overall computational cost can be significantly reduced. Moreover, the solution of large and ill-conditioned systems is completely avoided. The method is illustrated through several numerical test examples.

An EM Algorithm for Lebesgue-sampled State-space Continuous-time System Identification*

This paper concerns the identification of continuous-time systems in state-space form that are subject to Lebesgue sampling. Contrary to equidistant (Riemann) sampling, Lebesgue sampling consists of taking measurements of a continuous-time signal whenever it crosses fixed and regularly partitioned thresholds. The knowledge of the intersample behavior of the output data is exploited in this work to derive an expectation-maximization (EM) algorithm for parameter estimation of the state-space and noise covariance matrices. For this purpose, we use the incremental discrete-time equivalent of the system, which leads to EM iterations of the continuous-time state-space matrices that can be computed by standard filtering and smoothing procedures. The effectiveness of the identification method is tested via Monte Carlo simulations.

Sucrose solution for alleviating needle pain during inferior alveolar nerve block in children aged 7-10 years: a randomized clinical trial.

Intraoral local anesthesia is essential for delivering dental care; however, injection of this local anesthetic is perceived as the most painful and distressing agent for children, parents, and healthcare providers. Reducing pain as much as possible is essential to ensure smooth subsequent treatment procedures, especially in pediatric dentistry. In clinical practice, oral sucrose administration has been reported to decrease the pain during heel lance and cold pressor tests in neonates and children. This study aimed to determine whether the prior administration of a 30% sucrose solution reduced the pain related to inferior alveolar nerve block in children. A total of 42 healthy children aged 7-10 years requiring dental treatment of mandibular molars involving inferior alveolar nerve block were recruited. The participants' demographic details were recorded, height and weight were measured, and the anesthetic injection was delivered after receiving the respective intraoral sucrose solution and distilled water by the intervention (group 1) and control (group 2) group participants for 2 min. The subjective pain perceived during injection was measured using an animated emoji scale. The pain scores between the groups were compared using the Mann-Whitney U test. The median pain score and range for the intervention and control groups were 4 (2 - 6) and 6 (4 - 8), respectively, and statistically significant differences (P < 0.001) were observed in the intervention group. Age, sex, height, and weight did not influence the analgesic effect of the sucrose solution. Oral administration of sucrose may relieve pain associated with inferior alveolar nerve block in children.

Faculty Turnover: It's Preventable

Faculty Turnover: It's Preventable

On the Canonization of the Founders of Religious Orders in Early Modern Times

Abstract Neither before nor after the multiple canonization processes of 1622 have so many founders of religious orders been canonized at the same time. When examining in detail the historic processes of the founders, however, it is not generally possible to identify smooth and rapid procedures leading to their canonization. In the history of the Congregation of Rites, there were multiple hurdles and forms of resistance in the Congregation itself. The reason for the swift canonizations of 1622 was not in reality the founding of an order, but the ecclesiastical interests of the Pope and the Curia in highlighting the important protagonists of Catholic Reform. In addition, the new saints were associated with the struggle against heresies and the defence of the Apostolic See.

Intrinsic Image Decomposition Using Paradigms.

Intrinsic image decomposition is the task of mapping image to albedo and shading. Classical approaches derive methods from spatial models. The modern literature stresses evaluation, by comparing predictions to human judgements ("lighter", "same as", "darker"). The best modern intrinsic image methods train a map from image to albedo using images rendered from computer graphics models and example human judgements. This approach yields practical methods, but obtaining rendered images can be inconvenient. Furthermore, the approach cannot explain how a one could learn to recover intrinsic images without geometric, surface and illumination models, as people and animals appear to do. This paper describes a method that learns intrinsic image decomposition without seeing human annotations, rendered data, or ground truth data. Instead, the method relies on paradigms - spatial models of albedo and of shading. Rather than finding the "best" albedo and shading for an image via optimization, our approach trains a neural network on synthetic images. The synthetic images are constructed by multiplying albedos and shading fields sampled from our models. The network is subject to a novel smoothing procedure that ensures good behavior at short scales on real images. An averaging procedure ensures that reported albedo and shading are largely equivariant - different crops and scalings of an image will report the same albedo and shading at shared points. This averaging procedure controls long scale error. The standard evaluation for an intrinsic image method is a WHDR score. Our method achieves WHDR scores competitive with those of strong recent methods allowed to see training WHDR annotations, rendered data, and ground truth data. Our method produces albedo and shading maps with attractive qualitative properties - for example, albedo fields do not suppress wood grain and represent narrow grooves in surfaces well. Because our method is unsupervised, we can compute estimates of the test/train variance of WHDR scores; these are quite large, and suggest is unsafe to rely small differences in reported WHDR.

Sensitivity of Remote Sensing Floodwater Depth Calculation to Boundary Filtering and Digital Elevation Model Selections

The Floodwater Depth Estimation Tool (FwDET) calculates water depth from a remote sensing-based inundation extent layer and a Digital Elevation Model (DEM). FwDET’s low data requirement and high computational efficiency allow rapid and large-scale calculation of floodwater depth. Local biases in FwDET predictions, often manifested as sharp transitions or stripes in the water depth raster, can be attributed to spatial or resolution mismatches between the inundation map and the DEM. To alleviate these artifacts, we are introducing a boundary cell smoothing and slope filtering procedure in version 2.1 of FwDET (FwDET2.1). We present an optimization analysis that quantifies the effect of differing parameterization on the resulting water depth map. We then present an extensive intercomparison analysis in which 16 DEMs are used as input for FwDET Google Earth Engine (FwDET-GEE) implementation. We compare FwDET2.1 to FwDET2.0 using a simulated flood and a large remote sensing derived flood map (Irrawaddy River in Myanmar). The results show that FwDET2.1 results are sensitive to the smoothing and filtering values for medium and coarse resolution DEMs, but much less sensitive when using a finer resolution DEM (e.g., 10 m NED). A combination of ten smoothing iterations and a slope threshold of 0.5% was found to be optimal for most DEMs. The accuracy of FwDET2.1 improved when using finer resolution DEMs except for the MERIT DEM (90 m), which was found to be superior to all the 30 m global DEMs used.

An efficient low-pass-filtering algorithm to de-noise global GRACE data

The global monthly equivalent water height (EWH) anomaly (ΔEWH), derived from the Gravity Recovery and Climate Experiment (GRACE) level-2 data, helps understand the terrestrial water storage variation. Unfortunately, the ΔEWH data consist of stripes or noise. Numerous de-noising approaches have been studied. However, the following issues exist, possible overly smoothed results, two-step (de-striping and Gaussian-based smoothing) procedures introducing additional uncertainty and error propagation, requiring multi-temporal datasets or prior knowledge, and possible computational inefficiency. Thus, after analyzing the spectrum of the ΔEWH data, a novel low-pass-filtering algorithm is proposed to remove the noise and resolve the issues. Furthermore, without the global in situ measurements, an alternative assessment method is studied based on the additive characteristics of the signal and noise in the GRACE data. The method consists of the residual analysis and root mean square (RMS) value of the de-noised signal. Then, the proposed algorithm was applied to de-noising the ΔEWH datasets between 2002 and 2015. De-noised results are satisfactory qualitatively and quantitatively. Compared with well-known two-step de-noising methods, data blurring does not occur after the proposed algorithm. The algorithm removes noise the most assessed by the residual analysis and preserves the signal the most evaluated by large RMS values.

Path algorithms for fused lasso signal approximator with application to COVID-19 spread in Korea.

The fused lasso signal approximator (FLSA) is a smoothing procedure for noisy observations that uses fused lasso penalty on unobserved mean levels to find sparse signal blocks. Several path algorithms have been developed to obtain the whole solution path of the FLSA. However, it is known that the FLSA has model selection inconsistency when the underlying signals have a stair-case block, where three consecutive signal blocks are either strictly increasing or decreasing. Modified path algorithms for the FLSA have been proposed to guarantee model selection consistency regardless of the stair-case block. In this paper, we provide a comprehensive review of the path algorithms for the FLSA and prove the properties of the recently modified path algorithms' hitting times. Specifically, we reinterpret the modified path algorithm as the path algorithm for local FLSA problems and reveal the condition that the hitting time for the fusion of the modified path algorithm is not monotone in a tuning parameter. To recover the monotonicity of the solution path, we propose a pathwise adaptive FLSA having monotonicity with similar performance as the modified solution path algorithm. Finally, we apply the proposed method to the number of daily-confirmed cases of COVID-19 in Korea to identify the change points of its spread.

An explicit particle method for simulation of multiphase flows

Weakly-compressible Moving particle simulation (WCMPS) is a Lagrange meshless method that is advantageous in solving complex fluid problems. Although the accuracy can be addressed in WCMPS, instability arises due to pressure fluctuations from particle clustering. Weakly Compressible Moving Particle Explicit (WCMPE) method improved the pressure calculation and stability of WCMPS while preserving its coding system with high computational efficiency. In this study, the multiphase WCMPE method is developed with a simple density smooth technique and interface detection procedure. Several numerical cases were investigated by the multiphase WCMPE method, including Rayleigh-Taylor (R-T) instability cases with various density and viscosity ratio conditions, oil spill and oil-water sloshing experiments. In general, good agreements with analytical solutions or from experimental data were obtained in multiphase WCMPE results with negligible unphysical pressure oscillation and pressure noise points.

On a frictional unilateral contact problem in nonlinear elasticity-existence and smoothing procedure.

This note is devoted to a novel frictional unilateral contact problem in finite-strain elasticity. Here, we adopt the Tresca friction model from linear elasticity. Our analysis relies on the polyconvexity approach to nonlinear elasticity due to J. Ball. We include the delicate case where the elastic body neither is fixed nor has a deformation prescribed along some part of its boundary, but rests on a rigid foundation with a free boundary and is only submitted to forces and loads acting in the interior (like gravity) and at the boundary, respectively. This leads to a loss of coercivity and necessitates an extra condition that prevents the body from escaping by the geometry of the obstacle. This new condition extends a similar condition of Ciarlet and Nečas from the frictionless case to the case of Tresca friction. In addition, as a first step towards a numerical treatment of such nonlinear problems, we present a smoothing procedure that tackles the non-smooth term from Tresca friction and provide a convergence result for the novel smoothing method. This article is part of the theme issue 'Non-smooth variational problems and applications'.

Forecasting Hourly Intermittent Rainfall by Deep Belief Networks with Simple Exponential Smoothing

Accurate rainfall forecasting is essential in planning and managing water resource systems efficiently. However, intermittent rainfall patterns increase the difficulty of accurately forecasting rainfall values. Deep learning techniques have recently been popular and powerful in forecasting. Thus, this study employed deep belief networks with a simple exponential smoothing procedure (DBNSES) to forecast hourly intermittent rainfall values in Taiwan. Weather factors were used as independent variables to forecast rainfall volume. The simple exponential smoothing data preprocessing procedure was used to deal with the intermittent data patterns. The other three forecasting models, namely the least squares support vector regression (LSSVR), the generalized regression neural network (GRNN), and the backpropagation neural network (BPNN), were employed to forecast rainfall using the same data sets. In addition, genetic algorithms were utilized to determine the parameters of four forecasting models. The empirical results indicate that the developed DBNSES models are superior to the other forecasting models in terms of forecasting accuracy. In addition, the DBNSES can obtain smaller values of RMSE than those in the previous studies. Therefore, the DBNSES model is a suitable and effective way of forecasting rainfall with intermittent data patterns.

A coarray processing technique for nested vector-sensor arrays with improved resolution capabilities

In this paper, we propose a new coarray processing technique for nested electromagnetic vector-sensor (EMVS) arrays. Vector-sensors by providing multi-component measurements of the incident waves offer additional information over the common single-output scalar sensors. The current paper takes the advantage of the multi-component measurements of vector-sensors combined with the elegant nested array strategy to design a new coarray processing technique. In this way, we utilize the auto/cross-correlation matrices of the underlying subarrays of the nested EMVS array to serve as snapshots in the coarray domain. Then, we apply two levels of spatial and temporal smoothing procedure to recover the rank of the model and also improve the covariance matrix estimation in the difference coarray domain. The proposed method can provide O(N2) degrees of freedom (DOFs) with only N physical EMVSs. The effectiveness of the proposed coarray processing technique is demonstrated through several simulation examples. Simulation results show that the proposed technique provides superior resolution capabilities and more accurate DOA estimates in the asymptotic region compared to the other counterparts. Moreover, the proposed method requires relatively less computational efforts among the other methods.