Template Pattern Research Articles

Kikuchi pattern simulations of backscattered and transmitted electrons

We discuss a refined simulation approach which treats Kikuchi diffraction patterns in electron backscatter diffraction (EBSD) and transmission Kikuchi diffraction (TKD). The model considers the result of two combined mechanisms: (a) the dynamical diffraction of electrons emitted coherently from point sources in a crystal and (b) diffraction effects on incoherent diffuse intensity distributions. Using suitable parameter settings, the refined simulation model allows to reproduce various thickness- and energy-dependent features which are observed in experimental Kikuchi diffraction patterns. Excess-deficiency features are treated by the effect of gradients in the incoherent background intensity. Based on the analytical two-beam approximation to dynamical electron diffraction, a phenomenological model of excess-deficiency features is derived, which can be used for pattern matching applications. The model allows to approximate the effect of the incident beam geometry as a correction signal for template patterns which can be reprojected from pre-calculated reference data. As an application, we find that the accuracy of fitted projection centre coordinates in EBSD and TKD can be affected by changes in the order of - if excess-deficiency features are not considered in the theoretical model underlying a best-fit pattern matching approach. Correspondingly, the absolute accuracy of simulation-based EBSD strain determination can suffer from biases of a similar order of magnitude if excess-deficiency effects are neglected in the simulationmodel.

Time Series Segmentation Using Neural Networks with Cross-Domain Transfer Learning

Searching for characteristic patterns in time series is a topic addressed for decades by the research community. Conventional subsequence matching techniques usually rely on the definition of a target template pattern and a searching method for detecting similar patterns. However, the intrinsic variability of time series introduces changes in patterns, either morphologically and temporally, making such techniques not as accurate as desired. Intending to improve segmentation performances, in this paper, we proposed a Mask-based Neural Network (NN) which is capable of extracting desired patterns of interest from long time series, without using any predefined template. The proposed NN has been validated, alongside a subsequence matching algorithm, in two datasets: clinical (electrocardiogram) and human activity (inertial sensors). Moreover, the reduced dimension of the data in the latter dataset led to the application of transfer learning and data augmentation techniques to reach model convergence. The results have shown the proposed model achieved better segmentation performances than the baseline one, in both domains, reaching average Precision and Recall scores of 99.0% and 97.5% (clinical domain), along with 77.0% and 71.4% (human activity domain), introducing Neural Networks and Transfer Learning as promising alternatives for pattern searching in time series.

Semantics Structure of Javanese Proverbs: A Natural Semantic Metalanguage Analysis

This study is aimed to investigate the explication of Javanese proverbs, and also to find the NSM semantic template pattern in Javanese language proverbs. This study integrates natural semantic metalanguage (NSM) into the semantic analysis to explain how the meaning of Javanese proverbs can be explained. In this study, the researcher uses a five-semantic template for English proverbs proposed by Goddard and Wierzbicka. The findings of the study indicate that not all of the semantic template is fulfilled in Javanese Proverbs. It means that there is not any pattern of NSM semantic template in Javanese language. However, the application of NSM sematic templates in explaining proverbs meaning is not only worthwhile but also practical in explicating the Javanese proverbs.

Identification of Hand Gestures Using the Inertial Measurement Unit of a Smartphone: A Proof-of-Concept Study

Assessing the hand postures is crucial to implement gesture-based user-computer interfaces for controlling robots and assistive devices. Apart from data gloves and optical tracking, techniques such as surface electromyography and force myography provide a straightforward, non-invasive way to estimate poses and intentions through the forearm muscles assessment. However, most of the myography systems rely on bulky, dedicated hardware with arrays of electrodes or force probes. Therefore, this work introduces the smartphone as an alternative for identifying gestures: with the mobile device attached to the forearm, the embedded inertial measurement unit detects muscular contractions produced during the transitions between postures, yielding signatures in acquired waveforms. After computing the correlation of measured and template patterns, a competitive layer votes the class with greater probability and identifies the gesture. Prior characterization studies evaluated the effect of smartphone placement and forearm orientation in the sensor response, revealing that the IMU signatures are repeatable and robust to positioning deviations. Next, using 10-fold cross-validation, the system discerned four gestures (fist, open hand, wave in, and wave out) performed by six volunteers in ten repetitions, providing 96.6% and 94.1% average accuracies for self-calibration and inter-participant analyses, respectively. The smartphone figures as a ubiquitous and straightforward alternative for assessing gestures, with further applications in human-robot interaction and assistive technologies.

Patterned separator membranes with pillar surface microstructures for improved battery performance

In order to improve battery performance by tuning battery separator membranes, this work reports on porous poly(vinylidene fluoride-co-trifluoroethylene) - P(VDF-TrFE)- membranes with surface pillar microstructures. Separators with tailored pillar diameter, height and bulk thickness were fabricated by template patterning and computer simulations, allowing to evaluate the effect of the pillar microstructure characteristics on battery performance. It is shown that the different pillar microstructures of the separators affect the uptake value (150–325%), ionic conductivity value (0.8–1.6 mS·cm−1) and discharge capacity of the lithium ion batteries (LIB) when compared with the separator without pillars. The experimental charge-discharge behavior demonstrates that the pillar parameters affect battery performance and the best microstructure leading to 80 mAh·g−1 at 2C. Battery performance can be thus optimized by adjusting pillar diameter, height and bulk thickness of the separators keeping its volume constant, as demonstrated also by the simulation results. The parameter with most influence in battery performance is the bulk thickness of the separator, allowing to obtain a maximum discharge capacity value of 117.8 mAh·g−1 at 90C for a thickness of 0.01 mm. Thus, this work shows that the optimization of the pillar microstructure of the separator membranes allows increasing the capacity towards a new generation of high-performance LIBs.

Quantitative Precipitate Classification and Grain Boundary Property Control in Co/Ni-Base Superalloys

A correlative approach is employed to simultaneously assess structure and chemistry of (carbide and boride) precipitates in a set of novel Co/Ni-base superalloys. Structure is derived from electron backscatter diffraction (EBSD) with pattern template matching, and chemistry obtained with energy dispersive X-ray spectroscopy (EDS). It is found that the principal carbide in these alloys is Mo and W rich with the M6C structure. An M2B boride also exhibiting Mo and W segregation is observed at B levels above approximately 0.085 at. pct. These phases are challenging to distinguish in an SEM with chemical information (EDS or backscatter Z-contrast) alone, without the structural information provided by EBSD. Only correlative chemical and structural fingerprinting is necessary and sufficient to fully define a phase. The identified phases are dissimilar to those predicted using ThermoCalc. We additionally perform an assessment of the grain boundary serratability in these alloys, and observe that significant amplitude is only obtained in the absence of pinning intergranular precipitates.

Humor meets morality: Joke generation based on moral judgement

Although humor enriches human lives, some jokes fail to amuse people because of a lack of morality. In this paper, we propose a mechanism capable of selecting humor based on moral criteria. To this end, we first construct a model based on an N-gram corpus and generate joke candidates using various template patterns. We then employ a moral judgement classifier based on a recurrent neural network and utilize the trained model for humor selection. The experimental results obtained from best–worst scaling demonstrate that this scheme is able to generate jokes with moral category labels. We confirmed that jokes about the classifier categorized as Loyalty and Authority, which are regarded as good in our study, are funnier than jokes about Fairness, Purity, Harm, Cheating, and Degradation. Although we did not confirm that there was a difference in the funny level between good and bad moral jokes, the results demonstrate that moral categories of humor can affect the funny level.

Principled Design and Implementation of Steerable Detectors.

We provide a complete pipeline for the detection of patterns of interest in an image. In our approach, the patterns are assumed to be adequately modeled by a known template, and are located at unknown positions and orientations that we aim at retrieving. We propose a continuous-domain additive image model, where the analyzed image is the sum of the patterns to localize and a background with self-similar isotropic power-spectrum. We are then able to compute the optimal filter fulfilling the SNR criterion based on one single template and background pair: it strongly responds to the template while being optimally decoupled from the background model. In addition, we constrain our filter to be steerable, which allows for a fast template detection together with orientation estimation. In practice, the implementation requires to discretize a continuous-domain formulation on polar grids, which is performed using quadratic radial B-splines. We demonstrate the practical usefulness of our method on a variety of template approximation and pattern detection experiments. We show that the detection performance drastically improves when we exploit the statistics of the background via its power-spectrum decay, which we refer to as spectral-shaping. The proposed scheme outperforms state-of-the-art steerable methods by up to 50% of absolute detection performance.

Accelerating linear solvers for Stokes problems with C++ metaprogramming

The efficient solution of large sparse saddle point systems is very important in computational fluid mechanics. The discontinuous Galerkin finite element methods have become increasingly popular for incompressible flow problems but their application is limited due to high computational cost. We describe C++ programming techniques that may help to accelerate linear solvers for such problems. The approach is based on the policy-based design pattern and partial template specialization, and is implemented in the open source AMGCL library. The efficiency is demonstrated with the example of accelerating an iterative solver of a discontinuous Galerkin finite element method for the Stokes problem. The implementation allows selecting algorithmic components of the solver by adjusting template parameters without any changes to the codebase. It is possible to switch the system matrix to use small statically sized blocks to store the nonzero values, or use a mixed precision solution, which results in up to 4 times speedup, and reduces the memory footprint of the algorithm by about 40%. We evaluate both monolithic and composite preconditioning strategies for 3 benchmark problems. The performance of the proposed solution is compared with a multithreaded direct Pardiso solver and a parallel iterative PETSc solver.

Introduction to the templatic verb morphology of Birhor (Birhoɽ), a Kherwarian Munda language

AbstractBirhor (Birhoɽ) is a Kherwarian Munda language spoken in small enclaves in India, primarily in Hazaribagh, Ranchi, and Singhbhum districts and other small pockets in Jharkhand state. Birhor has to date been poorly documented, and even the basic properties of its core grammatical systems remain largely undescribed. All data used in this study come from field notes collected in several trips dating back to 2015. This paper is a preliminary attempt to identify the basic templatic structures of positive and negative finite conjugations in Birhor of both monovalent and polyvalent predicates. We discuss here two basic intersecting inflectional oppositions in the grammar of Birhor: (i) between perfective and imperfective tense-aspect forms (the imperfective includes imperfective and imperfect forms, and the perfective includes the past, the anterior and the perfect); and (ii) between monovalent predicates and polyvalent ones. Like all Kherwarian languages, Birhor has a nominative-accusative alignment of argument indexing and a complex templatic verb structure. It encodes subjects with monovalent stems. Polyvalent predicates encode two arguments, a first argument/syntactic subject and a second argument/syntactic ‘object’ following a primary object pattern. A complex array of different templates is thus found across positive and negative conjugations that contrast polyvalent vs. monovalent imperfective, perfective, and imperative forms. Many different formal templatic patterns are attested within each of the paradigmatic oppositional sets in Birhor. There are two formal subtypes of monovalent predicates. They contrast in both positive and negative conjugations, for both the imperfective and the perfective series of inflections. Polyvalent predicates also contrast the imperfective and the perfective series. Lastly, there are distinct templates for imperative and prohibitive of monovalent and polyvalent predicates as well.

Carbon nanotube/polydimethylsiloxane composite micropillar arrays using non-lithographic silicon nanowires as a template for performance enhancement of triboelectric nanogenerators

Carbon nanotube/polydimethylsiloxane composite micropillar (CNT/PDMS MP) arrays were successfully fabricated using non-lithographic silicon nanowire (SiNW) arrays as a template for performance enhancement of triboelectric nanogenerators (TENG). The CNT/PDMS MP arrays were obtained by pouring CNT/PDMS composites on the SiNW arrays and peeled off. Surface topology of CNT/PDMS composites directly depends on morphology of SiNW arrays, which can be varied by the etching time of the typical metal-assisted chemical etching process. The micropatterned CNT/PDMS composites was mostly depicted to the SiNW array template pattern when the morphologies of the SiNW were optimized with a length of approximately 10 mm. Next, the CNT/PDMS MP arrays were utilized as a triboelectric layer of TENGs, generating the maximum output voltage of 22.84 ± 0.85 V, enabling an approximately 18-fold improvement in an electrical output compared to the flat PDMS-based TENG. The performance enhancement of TENGs based on CNT/PDMS MP arrays are attributed to synergic effects of (1) an enhancement of electrostatic induction by CNT composites, increasing dielectric constant, and (2) an enhancement of electrification by surface texturing using non-lithographic pattern and CNT composites.

Semi-automated analysis of dot blots using ImageJ/Fiji

Commercially available dot blots provide a set of specific antibodies spotted on membranes in a given pattern. If the target analyte is present in the solution that the membrane is incubated with, the detection reaction will result in a chemiluminescence signal which is recorded by film or scanner. In order to know which analytes were detected, the analysis consists of measuring the intensity of the recorded signal on each spot. Manually measuring the entire array (typically ~200 spots) is unreliable and tedious. Fully automatic registration of the blot membrane to the template pattern often fails since there might be only very few positive spots on the membrane. This article presents an ImageJ/Fiji macro that requires minimal user input to perform a robust iterative registration of an adjustable template mask representing the spot pattern to the recorded blot. Once the template mask is matched to the dot blot, the spot intensity of each dot is measured and reported in a results table.

Efficient intensity-based fringe projection profilometry method resistant to global illumination.

Intensity-based fringe projection profilometry (IBFPP) is used widely because of its simple structure, high robustness, and noise resilience. Most IBFPP methods assume that any scene point is illuminated by direct illumination only, but global illumination effects introduce strong biases in the reconstruction result for many real-world scenes. To solve this problem, this paper describes an efficient IBFPP method for reconstructing three-dimensional geometry in the presence of global illumination. First, the average intensity of two sinusoidal patterns is used as a pixel-wise threshold to binarize the codeword patterns. The binarized template pattern is then used to convert other binarized fringe patterns into traditional Gray-code patterns. A proprietary compensation algorithm is then applied to eliminate fringe errors caused by environmental noise and lens defocusing. Finally, simple, efficient, and robust phase unwrapping can be achieved despite the effects of subsurface scattering and interreflection. Experimental results obtained in different environments show that the proposed method can obtain three-dimensional information reliably when influenced by global illumination.

Ultra-high sensitivity pH-sensors using silicon nanowire channel dual-gate field-effect transistors fabricated by electrospun polyvinylpyrrolidone nanofibers pattern template transfer

In this study, we fabricated a silicon nanowire (SiNW)-based ultra-high-sensitivity dual-gate (DG) pH ion-sensitive field-effect transistor (pH-ISFET) using polyvinylpyrrolidone (PVP) nanofibers (NFs) as a template for SiNW fabrication. By comparing the electrical properties, capacitive coupling, pH sensitivity, and non-ideal effects, such as drift or hysteresis voltages with a silicon film channel-type DG pH-ISFET, the effectiveness of the SiNW channel was verified. The SiNW-type DG FET exhibited better electrical properties, such as higher mobility, smaller sub-threshold slopes, and larger on-off current ratios, than the conventional silicon film-type DG FET. It also showed an increased capacitive-coupling ratio between the top and bottom gates. Through pH-ISFETs, which have an extended gate connected to the fabricated DG FETs, the sensing performance of the SiNW channel and silicon film channel can be compared. In the single-gate sensing mode, the film-type and SiNW-type devices showed a sensitivity of 56.6 mV/pH and 56.3 mV/pH, respectively. Meanwhile, in the DG sensing mode, the film-type device showed a sensitivity of 938.4 mV/pH, and the SiNW-type device has a sensitivity of 1438.8 mV/pH, indicating that the pH sensitivity in the SiNW channel was significantly amplified. Non-ideal effects were also evaluated, and the SiNW-type device showed more stable performance with a smaller hysteresis voltage and drift rate than the film-type device. Therefore, pH-ISFETs based on the SiNW-type DG FET using PVP nanofiber pattern template transfer, which involve a simple and low-cost process, show their potential in future bio-sensing applications owing to their high sensitivity and excellent stability.

Ontology Fixing by Using Software Engineering Technology

This paper presents OntologyFixer, a web-based tool that supports a methodology to build, assess, and improve the quality of ontology web language (OWL) ontologies. Using our software, knowledge engineers are able to fix low-quality OWL ontologies (such as those created from natural language documents using ontology learning processes). The fixing process is guided by a set of metrics and fixing mechanisms provided by the tool, and executed primarily through automated changes (inspired by quick fix actions used in the software engineering domain). To evaluate the quality, the tool supports numerical and graphical quality assessments, focusing on ontology content and structure attributes. This tool follows principles, and provides features, typical of scientific software, including user parameter requests, logging, multithreading execution, and experiment repeatability, among others. OntologyFixer architecture takes advantage of model view controller (MVC), strategy, template, and factory design patterns; and decouples graphical user interfaces (GUI) from ontology quality metrics, ontology fixing, and REST (REpresentational State Transfer) API (Application Programming Interface) components (used for pitfall identification, and ontology evaluation). We also separate part of the OntologyFixer functionality into a new package called OntoMetrics, which focuses on the identification of symptoms and the evaluation of the quality of ontologies. Finally, OntologyFixer provides mechanisms to easily develop and integrate new quick fix methods.

Shearing with solvent vapor annealing on block copolymer thin films for templates with macroscopically aligned nanodomains

A template with macroscopically aligned nanopatterns can be an effective vehicle for arranging nanoscale particles or rods in a particular orientation to achieve their anisotropic properties. A room-temperature process is also desirable for nanoscale patterning of heat-sensitive functional molecules such as organic fluorophores. Here, large-area orientation of nanodomains of block copolymers is demonstrated by simultaneous shearing and solvent vapor annealing at room temperature. The shear-aligned nanodomains are applied to a chemical template for nanoscale patterning of green fluorescent molecules. In addition, the grooved nanochannels obtained from the macroscopically aligned nanodomains work as a physical template for guiding Au nanorods to end-to-end assemblies which exhibit the polarization-dependent plasmonic extinction.

Finger Knuckle Print Recognition using MMDA with Fuzzy Vault

Currently frequent biometric scientific research such as with biometric applications like face, iris, voice, hand-based biometrics traits like palm print and fingerprint technique are utilized for spotting out the persons. These specific biometrics habits have their own improvement and weakness so that no particular biometrics can adequately opt for all terms like the accuracy and cost of all applications. In recent times, in addition, to distinct with the hand-based biometrics technique, Finger Knuckle Print (FKP) has been appealed to boom the attention among biometric researchers. The image template pattern formation of FKP embraces the report that is suitable for spotting the uniqueness of individuality. This FKP trait observes a person based on the knuckle print and the framework in the outer finger surface. This FKP feature determines the line anatomy and finger structures which are well established and persistent throughout the life of an individual. In this paper, a novel method for personal identification will be introduced, along with that data to be stored in a secure way has also been proposed. The authentication process includes the transformation of features using 2D Log Gabor filter and Eigen value representation of Multi-Manifold Discriminant Analysis (MMDA) of FKP. Finally, these features are grouped using k-means clustering for both identification and verification process. This proposed system is initialized based on the FKP framework without a template based on the fuzzy vault. The key idea of fuzzy vault storing is utilized to safeguard the secret key in the existence of random numbers as chaff pints

Epithelial tissue geometry directs emergence of bioelectric field and pattern of proliferation.

Patterns of proliferation are templated by both gradients of mechanical stress as well as by gradients in membrane voltage (Vm), which is defined as the electric potential difference between the cytoplasm and the extracellular medium. Either gradient could regulate the emergence of the other, or they could arise independently and synergistically affect proliferation within a tissue. Here, we examined the relationship between endogenous patterns of mechanical stress and the generation of bioelectric gradients in mammary epithelial tissues. We observed that the mechanical stress gradients in the tissues presaged gradients in both proliferation and depolarization, consistent with previous reports correlating depolarization with proliferation. Furthermore, disrupting the Vm gradient blocked the emergence of patterned proliferation. We found that the bioelectric gradient formed downstream of mechanical stresses within the tissues and depended on connexin-43 (Cx43) hemichannels, which opened preferentially in cells located in regions of high mechanical stress. Activation of Cx43 hemichannels was necessary for nuclear localization of Yap/Taz and induction of proliferation. Together, these results suggest that mechanotransduction triggers the formation of bioelectric gradients across a tissue, which are further translated into transcriptional changes that template patterns of growth.

A situation refinement model for complex event processing

Complex Event Processing (CEP) systems aim at processing large flows of events to discover situations of interest (SOI). CEP uses predefined pattern templates to detect occurrences of complex events in an event stream. CEP systems rely on domain experts to define complex patterns rules to recognize SOI. The task of identifying complex patterns faces with several challenges, such as the complexity of writing the pattern rules, and the need to acquire and process background information considering event stream’s real-time constraints. Developing an efficient rule mining algorithm to fine-tune the CEP pattern to recognize SOI requires the tackling of three main obstacles. First, the CEP pattern rules must be inferred by utilizing the user’s preferred context and the history of the event stream. Second, to avoid the issue of pattern complexity, the minimum number of rules must be used in the refinement process. Finally, to respond to emerging situations, the refinement task must be fulfilled in near real-time. In this work, we present a rule mining model to refine the CEP pattern rules by considering these obstacles while providing the ability to adjust the level of refinement to fit the applied scenario. This paper aims to: (1) Review the challenges associated with incorporating domain knowledge in CEP systems to improve awareness of real-world situations; (2) Present a Situation Refinement model to extract the minimal set of rules from external knowledge required to identify Situations Of Interest; (3) Demonstrate the summary update process of the event stream; and (4) Evaluate the derived rules with respect to their coverage and complexity.

Guiding assurance of architectural design patterns for critical applications

Abstract Development of critical systems nowadays is hardly achievable without reuse of previous knowledge. Design patterns have an important role in the design of such systems as they define and document common solutions to recurring design problems. However, critical systems such as those that are safety or security related, often require specific assurances that the system is adequate to operate in a given environment. Just as with any other reused knowledge in such systems, the reuse via application of design patterns needs to be assured every time. In this paper, we present a methodology for assuring the application of design patterns in critical domains. In particular, we enrich the design patterns template to support their further assurance. We define the aspects that should be tackled during the assurance of a design pattern application. We use the information specified in the design pattern template to guide the automated instantiation of the argumentation for each design pattern application in the system. We provide tool-support for our methodology in the context of the AMASS tool-platform and evaluate it in an automotive case study.