Parametric Template Research Articles

Efficacy assessment of strategies for mechanical univentricular circulatory support

Univentricular palliation is performed in patients with congenital heart defects, such as single functional ventricle, hypoplasia, septal defects, valve defects, obstructive defects, etc. After the palliation, the single ventricle becomes systemic, and venae cavae are directly or indirectly connected to pulmonary arteries. Palliated patients may temporary lead a full life yet eventually need heart transplantation. Mechanical circulatory support (MCS) may be an alternative treatment for heart transplantation in patients having univentricular circulation. Personalized modeling of a biotechnical MCS system allows to forecast current and optimal system states and assess support advisability and efficacy before implanting a circulatory assist device. In this study, a mathematical model of univentricular circulation with possibility to connect a circulatory assist device is presented. An algorithm for efficacy forecast and assessment of personalized mechanical univenricular circulatory support was developed. The algorithm automatically determines circulatory model parameters based on patient’s hemodynamic data and a set of parameter templates for corresponding age groups and circulatory states. This first stage is based on genetic algorithm with model parameters used as a genotype. Criterion of obtaining personalized model parameters is a zero sum of relative deviations of patient’s hemodynamic data and simulated data after personalization given the acceptable ranges of deviations determined based on accuracy of clinical diagnostic devices. At the second stage, the developed algorithm simulates a personalized biotechnical system of mechanical univentricular circulatory support using personalized circulatory model and pressure head–flow rate characteristics of circulatory assist device. At the third stage, the algorithm assesses advisability of chosen control strategy with negative recommendation for inadvisable strategy. At the last stage, the algorithm forecasts optimal states of biotechnical system determined as sufficiently approximate to normal circulation. Verification of the developed algorithm was performed with patients’ hemodynamic data from literature. Distributions of hemodynamic parameters in simulated biotechnical systems for three patients are described along with forecast optimal states and corresponding values of control parameter for chosen methods of support. In these cases, pediatric rotary blood pump previously developed by our research group was used as circulatory assist device. Assessment of changes in biotechnical system after implantation of circulatory assist device revealed inefficacy of MCS in one of three considered patients.

A parametric approach for the identification of single-charged isotopes with AMS-02

Measurements of the isotopic composition of single-charged cosmic rays provide important insights in the propagation processes. However, the isotopic identification is challenging due to the one hundred times greater abundance of protons when compared to deuterons, the only stable isotope of hydrogen. Taking advantage of the precise measurements of the velocity and momentum in the Alpha Magnetic Spectrometer (AMS-02), a particle physics detector operating aboard the International Space Station since May 2011, we describe a parametric template fit method, which takes into account systematic uncertainties such as the fragmentation of particles inside AMS-02 and eventual differences between data and simulation through the use of nuisance parameters. With this method we are also able to assess the AMS-02 performance in terms of mass resolution, showing that it is able to separate the isotopes of hydrogen up to 10 GeV/n.

Transferring Well-Performing Collaborative Work Practices with Parameterized Templates and Guidebooks: Empowering Subject Matter Experts for an Adaptation to Slightly Different Contexts

Transferring Well-Performing Collaborative Work Practices with Parameterized Templates and Guidebooks: Empowering Subject Matter Experts for an Adaptation to Slightly Different Contexts

Computational Design of Knit Templates

We present an interactive design system for knitting that allows users to create template patterns that can be fabricated using an industrial knitting machine. Our interactive design tool is novel in that it allows direct control of key knitting design axes we have identified in our formative study and does so consistently across the variations of an input parametric template geometry. This is achieved with two key technical advances. First, we present an interactive meshing tool that lets users build a coarse quadrilateral mesh that adheres to their knit design guidelines. This solution ensures consistency across the parameter space for further customization over shape variations and avoids helices, promoting knittability. Second, we lift and formalize low-level machine knitting constraints to the level of this coarse quad mesh. This enables us to not only guarantee hand- and machine-knittability, but also provides automatic design assistance through auto-completion and suggestions. We show the capabilities through a set of fabricated examples that illustrate the effectiveness of our approach in creating a wide variety of objects and interactively exploring the space of design variations.

A 6DoF Pose Estimation Dataset and Network for Multiple Parametric Shapes in Stacked Scenarios

Most industrial parts are instantiated from different parametric templates. The 6DoF (6D) pose estimation tasks are challenging, since some part objects from a known template may be unseen before. This paper releases a new and well-annotated 6D pose estimation dataset for multiple parametric templates in stacked scenarios donated as Multi-Parametric Dataset, where a training set (50K scenes) and a test set (2K scenes) are obtained by automatical labeling techniques. In particular, the test set is further divided into a TEST-L dataset for learning evaluation and a TEST-G dataset for generalization evaluation. Since the part objects from the same template are regarded as a class in the Multi-Parametric Dataset and the number of part objects is infinite, we propose a new 6D pose estimation network as our baseline method, Multi-templates Parametric Pose Network (MPP-Net), aiming to have sufficient generalization ability for parametric part objects in stacked scenarios. To our best knowledge, our dataset and method are the first to jointly achieve 6D pose estimation and parameter values prediction for multiple parametric templates. Many experiments are conducted on the Multi-Parametric Dataset. The mIoU and Overall Accuracy of foreground segmentation and template segmentation on the two test datasets exceed 99.0%. Besides, MPP-Net achieves 92.9% and 90.8% on mAP under the threshold of 0.5cm for translation prediction, achieves 41.9% and 36.8% under the threshold of 5∘ for rotation prediction, and achieves 51.0% and 6.0% under the threshold of 5% for parameter values prediction, on the two test set, respectively. The results have shown that our dataset has exploratory value for 6D pose estimation and parameter values prediction tasks.

Non-parametric MRI Brain Atlas for the Polish Population.

Introduction: The application of magnetic resonance imaging (MRI) to acquire detailed descriptions of the brain morphology in vivo is a driving force in brain mapping research. Most atlases are based on parametric statistics, however, the empirical results indicate that the population brain tissue distributions do not exhibit exactly a Gaussian shape. Our aim was to verify the population voxel-wise distribution of three main tissue classes: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), and to construct the brain templates for the Polish (Upper Silesian) healthy population with the associated non-parametric tissue probability maps (TPMs) taking into account the sex and age influence.Material and Methods: The voxel-wise distributions of these tissues were analyzed using the Shapiro-Wilk test. The non-parametric atlases were generated from 96 brains of the ethnically homogeneous, neurologically healthy, and radiologically verified group examined in a 3-Tesla MRI system. The standard parametric tissue proportion maps were also calculated for the sake of comparison. The maps were compared using the Wilcoxon signed-rank test and Kolmogorov-Smirnov test. The volumetric results segmented with the parametric and non-parametric templates were also analyzed.Results: The results confirmed that in each brain structure (regardless of the studied sub-population) the data distribution is skewed and apparently not Gaussian. The determined non-parametric and parametric templates were statistically compared, and significant differences were found between the maps obtained using both measures (the maps of GM, WM, and CSF). The impacts of applying the parametric and non-parametric TPMs on the segmentation process were also compared. The GM volumes are significantly greater when using the non-parametric atlas in the segmentation procedure, while the CSF volumes are smaller.Discussion and Conclusion: To determine the population atlases the parametric measures are uncritically and widely used. However, our findings suggest that the mean and parametric measures of such skewed distribution may not be the most appropriate summary statistic to find the best spatial representations of the structures in a standard space. The non-parametric methodology is more relevant and universal than the parametric approach in constructing the MRI brain atlases.

Research on a method of creating digital shadow puppets based on parameterized templates

Shadow play is an ancient traditional folk art in China and a precious Chinese artistic treasure. However, with the continuous diversification of entertainment forms, traditional shadow play art is gradually fading out of sight and even facing the possibility of having no successor. A fast method of creating digital shadow puppets based on parameterized templates is proposed in this paper. Adopting this method can transform the materials of a shadow character designed by an artist into a digital shadow puppet that can be interactively controlled by hand gestures. This paper classifies digital shadow puppets based on an analysis and summary of traditional shadow play art and designs the corresponding parameterized template for each type. In addition, a control method for shadow puppets based on the Leap Motion device is designed and implemented in this paper in accordance with common human hand gestures and the requirements for controlling shadow puppets. Based on the above work, we design and implement a platform for digital shadow story creation and performance and test the proposed method on this platform. The experimental results show that the method proposed in this paper can quickly create digital shadow puppets that can be interactively controlled and allow shadow plays to be performed based on gesture control. The research ideas and achievements of this paper help promote the development of digital shadow play technology and have great significance to the inheritance and development of traditional shadow play art.

Modeling allelic analyte signals for aSTRs in NGS DNA profiles.

We describe an adaption of Bright et al.'s work modeling peak height variability in CE-DNA profiles to the modeling of allelic aSTR (autosomal short tandem repeats) read counts from NGS-DNA profiles, specifically for profiles generated from the ForenSeq™ DNA Signature Prep Kit, DNA Primer Mix B. Bright et al.'s model consists of three key components within the estimation of total allelic product-template, locus-specific amplification efficiencies, and degradation. In this work, we investigated the two mass parameters-template and locus-specific amplification efficiencies-and used MLE (maximum likelihood estimation) and MCMC (Markov chain Monte Carlo) methods to obtain point estimates to calculate the total allelic product. The expected read counts for alleles were then calculated after proportioning some of the expected stutter product from the total allelic product. Due to preferential amplicon selection introduced by the sample purification beads, degradation is difficult to model from the aSTR outputs alone. Improved modeling of the locus-specific amplification efficiencies may mask the effects of degradation. Whilst this model could be improved by introducing locus specific variances in addition to locus specific priors, our results demonstrate the suitability of adapting Bright et al.'s allele peak height model for NGS-DNA profiles. This model could be incorporated into continuous probabilistic interpretation approaches for mixed DNA profiles.

An extensive analysis of frequency and transient responses in S and C-shaped gears

ABSTRACT Gears are crucial mechanical components and considerably used in consumer and industrial machinery. Gears can be grouped into five types namely spur, helical, rack and pinion, worm and bevel. Spur gear has been selected as a case model in this study mainly because it is one of the basic types of gear which can be easily constructed and fabricated. Spur gear tooth is designed using S and C-shaped transition curves. These curves are constructed using the G2 parametric Said-Ball cubic curve and clothoid templates. The result demonstrates that the C-shaped gear model which consists of the natural frequency of0.7408 Hz and low onaverage and standard deviation of 1.00E-3 mm and 8.072E-3 mm offers the most affordable displacement when compared with S-shaped and existing gear models.

Application of intelligent automatic segmentation and 3D reconstruction of inferior turbinate and maxillary sinus from computed tomography and analyze the relationship between volume and nasal lesion

The nasal structure is closely related to nasal diseases. If an individual presents a discomfort in the nose for an extended period of time, the nasal tissue might have abnormal phenomena. Chronic sinusitis can cause nasal sinus shrinkage, especially the maxillary sinus, while allergic rhinitis can cause inferior turbinate pachynsis. Therefore, the volumes of the inferior turbinate and maxillary sinus are important indexes for otolaryngologists to judge nasal diseases. At present, the volumes of the inferior turbinate and maxillary sinus are estimated by radiologists manually contouring computed tomography (CT) images of the head and neck. The process consumes time and the results are not objective. This study aimed to propose an automatic recognition and volume calculation for the inferior turbinate and maxillary sinus by using image processing techniques. The back propagation neural network (BPNN) was used for automatic recognition of the inferior turbinate and maxillary sinus. Parametric template matching (PTM) and the sub-region similarity were used as feature inputs. The level set method (LSM) was applied to circle the contour of the inferior turbinate and maxillary sinus. The marching cubes algorithm was employed for 3D reconstruction and visualization. The volume information was obtained from the nonlinear regression curve. The accuracy and sensitivity of the automatic recognition results for inferior turbinate and maxillary sinus was 96.3 % and 95.1 %, respectively. The relationship between volume and nasal lesion has been analyzed as well.

A Secure IoT Service Architecture With an Efficient Balance Dynamics Based on Cloud and Edge Computing

The Internet of Things (IoT)-Cloud combines the IoT and cloud computing, which not only enhances the IoT's capability but also expands the scope of its applications. However, it exhibits significant security and efficiency problems that must be solved. Internal attacks account for a large fraction of the associated security problems, however, traditional security strategies are not capable of addressing these attacks effectively. Moreover, as repeated/similar service requirements become greater in number, the efficiency of IoT-Cloud services is seriously affected. In this paper, a novel architecture that integrates a trust evaluation mechanism and service template with a balance dynamics based on cloud and edge computing is proposed to overcome these problems. In this architecture, the edge network and the edge platform are designed in such a way as to reduce resource consumption and ensure the extensibility of trust evaluation mechanism, respectively. To improve the efficiency of IoT-Cloud services, the service parameter template is established in the cloud and the service parsing template is established in the edge platform. Moreover, the edge network can assist the edge platform in establishing service parsing templates based on the trust evaluation mechanism and meet special service requirements. The experimental results illustrate that this edge-based architecture can improve both the security and efficiency of IoT-Cloud systems.

Construction of restored model of fractured femurs based on anatomic features

To facilitate the construction of intact model of fractured bone and satisfy the representation of fracture information, a novel approach of constructing a restored model of fractured femur (RMFF) based on anatomic feature is put forward. First, the fracture mesh model is divided into major segments by removing the comminuted part. Next, through the constraint-based deformation of parameterized template with a few valid parameters of fracture segments, the intact reference surface model is constructed to represent normal anatomy of individual fractured femur, and the feature points of the reference model are extracted. Then, based on the solution of matrix equations of rigid registration between available feature points of fracture segments and the ones of the reference model, major fracture segments are piecewise adjusted to original proper positions respectively. Finally, the internal part of the reference model cut with the boundary curves of major segments is employed to represent the surface of the comminuted part, and the surface of the comminuted part is merged with adjusted segments to acquire the intact model. Simulation experimental results show that RMFFs can be reconstructed rapidly and accurately according to available information of the fractured femur.

A High-Efficiency Impeller Channel Quasi-Triangular Tool Path Planning Method Based on Template Trajectory Mapping

The manufacturing technology of an integral impeller is an important indicator for measuring the manufacturing capability of a country. Its manufacturing process involves complex free-form surface machining, a time consuming and error-prone process, and the tool path planning is considered as a critical issue of free-form surface machining but still lacks a systematic solution. In this paper, aiming at the tool path planning of the impeller channel, a quasi-triangular tool path planning method based on parametric domain template trajectory mapping is proposed. The main idea is to map the template trajectory to physical domain by using the mapping model of parametric domain to the physical domain to obtain the actual machining path. Firstly, the trajectory mapping model of parametric domain to physical domain is established using the morphing technique, and the template trajectory mapping method in the parametric domain is given. Secondly, the clean-up boundary of the impeller channel is determined in the parametric domain, and the quasi-triangular template trajectory of the impeller channel is defined. Finally, taking a certain type of impeller as an example, the quasi-triangular tool path of the impeller channel is calculated, and the tool path calculation time of this method is compared with that of the traditional isometric offset method. The result shows that the computational efficiency is improved by 45% with this method, which provides a new method for the rapid acquisition of NC machining tool path for impeller channels. In addition, the simulation and actual machining are carried out, the results show that the shape of actual cutting traces on the surface of the impeller channel is quasi-triangular, showing that this method is effective and feasible.

A Computer-aided Mold Design for Transfer Molding Process in Semiconductor Packaging Industry

This article presents a novel method for the development of a computer-aided mold design for transfer molding process in semiconductor packaging industry. The method developed will standardize mold design process and reduce lead time and costs significantly. In this method, highly robust 3D parametric templates for mold shot for different types of packages are created and stored to constitute a library for computer-aided mold design process. Mold design terms and all related design rules are standardized and streamlined for each mold shot. The mold shot is used to cut out mold parts during the next design stage. Leadframe configurations, customer input as well as technical specifications and design inputs are standardized to create the robust mold shot. Some critical features of mold parts like clamping area, location, and gate dimensions are calculated on mold shot. The mold shot is used as a positive cut-out for the top and bottom cavities as well as cull and sleeve strips. After completion of the cut-out process based on mold shot, air venting for packages and runners, clamping areas, shutoff areas, side-rail areas, relief areas, locating pins holes and their relief, misalignment pins holes and their relief are created by the rules and thumbs and mathematical formulae incorporated into the program. Finally ejection pins, support plugs, springs and other parts are created using parametric parts library and database. A prototype system “PMOLD” is developed based on is new method. This research would make a significant contribution in transfer molding and semiconductor packaging industries.

What is the Direction of Land Change? A New Approach to Land-Change Analysis

Accurate characterization of the direction of land change is a neglected aspect of land dynamics. Knowledge on direction of historical land change can be useful information when understanding relative influence of different land-change drivers is of interest. In this study, we present a novel perspective on land-change analysis by focusing on directionality of change. To this end, we employed Maximum Cross-Correlation (MCC) approach to estimate the directional change in land cover in a dynamic river floodplain environment using Landsat 5 Thematic Mapper (TM) images. This approach has previously been used for detecting and measuring fluid and ice motions but not to study directional changes in land cover. We applied the MCC approach on land-cover class membership layers derived from fuzzy remote-sensing image classification. We tested the sensitivity of the resulting displacement vectors to three user-defined parameters—template size, search window size, and a threshold parameter to determine valid (non-noisy) displacement vectors—that directly affect the generation of change, or displacement, vectors; this has not previously been thoroughly investigated in any application domain. The results demonstrate that it is possible to quantitatively measure the rate of directional change in land cover in this floodplain environment using this particular approach. Sensitivity analyses indicate that template size and MCC threshold parameter are more influential on the displacement vectors than search window size. The results vary by land-cover class, suggesting that spatial configuration of land-cover classes should be taken into consideration in the implementation of the method.

Research on wind power ramp events prediction based on strongly convective weather classification

In this study a forecasting model for wind power ramps based on strongly convective weather classification is presented. First, the dynamics and thermodynamic behaviours of strongly convective weather are characterised by the predictors in the selected region. Then the support vector domain description for ramps scenario classification is introduced to establish an initialised extremum model, and the parameter templates method is used to identify the ramps weather in strongly convective weather library. Meanwhile, the original wind speed data is modified to obtain more accurate wind speed, and a new wind power ramps definition is proposed based on the ramp character itself and its impact on the power grid. Thus the catastrophe detection method (Bernaola Galvan algorithm) used for strongly convective weather forecasting. Finally, the wind power ramps forecasting method based on the discrimination of convective weather is developed. Comparing with the existing wind power ramps forecasting algorithms, the proposed prediction method here gets into meteorologic essence of triggering great fluctuation of wind speed.

Parametric design-based multi-objective optimisation for high-pressure turbine disc

Mass and radial deformation are of great importance for a high-pressure turbine disc (HPTD). However, computational cost of computer-aided engineering (CAE) is too high to optimise the mutually restricted objectives. A parameterisation-based method is proposed to speed the optimisation process of HPTD: ‘body-flange’-based parametric template is used to generate CAE samples; noise-based virtual samples are implemented to enlarge the training set, a cost-effective neural network is used as fitness function of non-dominated sorting genetic algorithm-II for optimisation whose initial population is the combination of different sample sets. Experiment results show that the proposed data-driven framework reduces the engineering difficulty of multi-objective optimisation, and it has high popularisation value for optimisation of other complex products.

Automatic marking point positioning of printed circuit boards based on template matching technique

The traditional global template matching is time consuming, has low accuracy, and cannot be adapted to rotation and scale change. The template matching technique proposed in this study improves the time, accuracy and robustness for printed circuit boards (PCB). In order to shorten the image positioning time, the image preprocessing is implemented on PCB image and the image blocks are labeled to obtain the tagged image, and the feature vector is extracted and the marking point region image is selected. The feature vector with rotation change and scale change robustness is extracted from the tagged image after labeling in the PCB image by using artificial neural network, combined with image moments for training. The marking point region image in the PCB image is selected. The scale value of the marking point region image is estimated by parametric template vector matching. The deflection angle of marking point region image is calculated by Hough transform. The obtained scale value and deflection angle value are used for fast template matching to determine the marking point positioning. The three-dimensional (3D) parabolic curve fitting is implemented in marking point positioning and adjacent pixel position to reach the sub-pixel level accuracy. The experiment showed that the proposed template matching technique for the PCB image with or without noise or angle rotation, the average position accuracy error of each translated image is lower than 7 \(\upmu \)m, and the error standard deviation is lower than 5 \(\upmu \)m. The rotation angle error average and standard deviation of angular error of Hough transform are lower than 0.2\(^{\circ }\), more accurate than orientation code (OC) method. The scale value estimation, relative error average and error standard deviation are lower than 0.004 and 0.006 for the image with or without noise. The average complete positioning time of PCB image at resolution of \(2500\times 2500\) is only 0.55 s, which is better than the 3.97 s of traditional global template matching. The results prove that the template matching technique of this study not only has sub-pixel level high accuracy and short computing time, but also has the robustness of rotation change and scale change interference. It can implement rapid, efficient and accurate positioning.

Automatic generation of efficient accelerators for reconfigurable hardware

Acceleration in the form of customized datapaths offer large performance and energy improvements over general purpose processors. Reconfigurable fabrics such as FPGAs are gaining popularity for use in implementing application-specific accelerators, thereby increasing the importance of having good high-level FPGA design tools. However, current tools for targeting FPGAs offer inadequate support for high-level programming, resource estimation, and rapid and automatic design space exploration. We describe a design framework that addresses these challenges. We introduce a new representation of hardware using parameterized templates that captures locality and parallelism information at multiple levels of nesting. This representation is designed to be automatically generated from high-level languages based on parallel patterns. We describe a hybrid area estimation technique which uses template-level models and design-level artificial neural networks to account for effects from hardware place-and-route tools, including routing overheads, register and block RAM duplication, and LUT packing. Our runtime estimation accounts for off-chip memory accesses. We use our estimation capabilities to rapidly explore a large space of designs across tile sizes, parallelization factors, and optional coarse-grained pipelining, all at multiple loop levels. We show that estimates average 4.8% error for logic resources, 6.1% error for runtimes, and are 279 to 6533 times faster than a commercial high-level synthesis tool. We compare the best-performing designs to optimized CPU code running on a server-grade 6 core processor and show speedups of up to 16.7×.

SOCIAL SENSOR: AN ANALYSIS TOOL FOR SOCIAL MEDIA

In this research, we propose a new concept for social media analysis called Sensor, which is an innovative design attempting to transform the concept of a physical sensor in the real world into the world of social media with three design features: manageability, modularity, and reusability. The system is a case-centered design that allows analysts to select the type of social media (such as Twitter), the target data sets, and appropriate social sensors for analysis. By adopting parameter templates, one can quickly apply the experience of other experts at the beginning of a new case or even create one’s own templates. We have also modularized the analysis tools into two social sensors: Language Sensor and Text Sensor. A user evaluation was conducted and the results showed that usefulness, modularity, reusability, and manageability of the system were all very positive. The results also show that this tool can greatly reduce the time needed to perform data analysis, solve the problems encountered in traditional analysis process, and obtained useful results. The experimental results reveal that the concept of social sensor and the proposed system design are useful for big data analysis of social media. To cite this document: Chun-Hsiao Wu and Tsai-Yen Li, Social sensor: an analysis tool for social media, International Journal of Electronic Commerce Studies, Vol.7, No.1, pp.77-94, 2016. Permanent link to this document: http://dx.doi.org/10.7903/ijecs.1411