Template Generation Research Articles

Grid Interactive Solar PV-Based Water Pumping Using BLDC Motor Drive

This paper proposes bidirectional power flow control of a grid interactive solar photovoltaic (PV)-fed water pumping system. A brushless DC (BLDC) motor drive without phase current sensors is used to run a water pump. This system enables a consumer to operate the water pump at its full capacity for 24 hours regardless of the climatic condition and to feed a single-phase utility grid when water pumping is not required. The full utilization of a PV array and motor pump is made possible in addition to an enhanced reliability of the pumping system. A single-phase voltage source converter with a unit vector template generation technique accomplishes a bidirectional power flow control between the grid and the dc bus of the voltage source inverter (VSI), which feeds a BLDC motor. The VSI is operated at fundamental frequency, which minimizes the switching loss. The maximum power point operation of a PV array, and power quality improvements, such as power factor correction and reduction of total harmonic distortion of grid, are achieved in this system. Its applicability and reliability are demonstrated by various simulated results using MATLAB/Simulink platform and hardware implementation.

Efficient Cancelable Iris Template Generation for Wearable Sensors

When biometric authentication is performed on On-Body Wearable Wireless Networks, a cancelable template is useful to protect biometric information. A cancelable template generation method converts the original biometric information into irreversibly transformed information to protect the original biometric information. If a cancelable template is damaged or leaked, it can be replaced with another cancelable template. In 2017, Dwivedi et al. proposed a novel cancelable iris template generation scheme based on the randomized look-up table mapping. So far their scheme is the most accurate scheme with respect to EER compared to the previous cancelable iris template generation schemes. However, their scheme is not alignment-free and so is not efficient enough for wearable sensors. In the paper, we first suggest how to improve the accuracy of the Dwivedi et al.’s scheme using the partial sort technique. Our experiment result shows that our suggested scheme is more accurate than the Dwivedi et al.’s scheme under almost all parameter settings. More concretely, our scheme achieves EER 0.09%, whereas the Dwivedi et al.’s scheme achieves EER 0.43% in the best parameter settings for the CASIA-V3-Interval iris database. We also suggest how to improve the efficiency of the Dwivedi et al.’s scheme. Our second scheme is alignment-free by processing IrisCode column-wise, whereas the Dwivedi et al.’s scheme handles IrisCode row-wise. Our experiment shows that our second scheme is 15 times faster than the Dwivedi et al.’s scheme, so our scheme is efficient enough for wearable sensors. Though our second scheme has very high EER under some parameter settings, our second scheme achieves EER 0.53% in the best parameter settings for the CASIA-V3-Interval iris database.

Cost‐effective template development for the microfluidic device

Photolithography, typically used to create microchannel networks on silicon to fabricate the template for microfluid devices, has the drawback of requiring sophisticated instruments, available only in few premier fabrication units. Template fabrication thus was a privilege of few researchers. Through the years, researchers economised the process of device development using a three-dimensional (3D) printer which directly projected non-planar structures on to a photo-curable resin. Devices thus built lacked the versatility of polydimethylsiloxane (PDMS). The novelty of this work is to use the 3D printing resin for template fabrication and subsequent device development with PDMS. In this way, cost reduction and ease of template generation are substantially enhanced while retaining the advantages of a PDMS device. Unlike directly printed devices that are formed from ultraviolet curable photopolymer, this method fabricates the master with cured photopolymer used in 3D printing. The master pattern is transferred to PDMS for subsequent processing to construct the device. Compared to devices built on silicon templates, PDMS on polymer templates necessitate careful curing at a lower temperature. Low-temperature PDMS–substrate bonding has also been studied in this work. Fabricated device has channel dimensions in the order of 200–300 μm and has been used to study various oil–water emulsions.

Markerless tumor tracking using fast-kV switching dual-energy fluoroscopy on a benchtop system.

To evaluate markerless tumor tracking (MTT) using fast-kV switching dual-energy (DE) fluoroscopy on a bench top system. Fast-kV switching DE fluoroscopy was implemented on a bench top which includes a turntable stand, flat panel detector, and x-ray tube. The customized generator firmware enables consecutive x-ray pulses that alternate between programmed high and low energies (e.g., 60 and 120kVp) with a maximum frame rate of 15Hz. In-house software was implemented to perform weighted DE subtraction of consecutive images to create an image sequence that removes bone and enhances soft tissues. The weighting factor was optimized based on gantry angle. To characterize this system, a phantom was used that simulates the chest anatomy and tumor motion in the lung. Five clinically relevant tumor sizes (5-25mm diameter) were considered. The targets were programmed to move in the inferior-superior direction of the phantom, perpendicular to the x-ray beam, using a cos4 waveform to mimic respiratory motion. Target inserts were then tracked with MTT software using a template matching method. The optimal computed tomography (CT) slice thickness for template generation was also evaluated. Tracking success rate and accuracy were calculated in regions of the phantom where the target overlapped ribs vs spine, to compare the performance of single energy (SE) and DE imaging methods. For the 5mm target, a CT slice thickness of 0.75mm resulted in the lowest tracking error. For the larger targets (≥10mm) a CT slice thickness ≤2mm resulted in comparable tracking errors for SE and DE images. Overall DE imaging improved MTT accuracy, relative to SE imaging, for all tumor targets in a rotational acquisition. Compared to SE, DE imaging increased tracking success rate of small target inserts (5 and 10mm). For fast motion tracking, success rates improved from 23% to 64% and 74% to 90% for 5 and 10mm targets inserts overlapping ribs, respectively. For slow moving targets success rates improved from 19% to 59% and 59% to 91% in 5 and 10mm targets overlapping the ribs, respectively. Similar results were observed when the targets overlapped the spine. For larger targets (≥15mm) tracking success rates were comparable using SE and DE imaging. This work presents the first results of MTT using fast-kV switching DE fluoroscopy. Using DE imaging has improved the tracking accuracy of MTT, especially for small targets. The results of this study will guide the future implementation of fast-kV switching DE imaging using the on-board imager of a linear accelerator.

Lightweight and Privacy-Preserving Template Generation for Palm-Vein-Based Human Recognition

The use of human biometrics is becoming widespread and its major application is human recognition for controlling unauthorized access to both digital services and physical localities. However, the practical deployment of human biometrics for recognition poses a number of challenges, such as template storage capacity, computational requirements, and privacy of biometric information. These challenges are important considerations, in addition to performance accuracy, especially for authentication systems with limited resources. In this paper, we propose a wave atom transform (WAT)-based palm-vein recognition scheme. The scheme computes, maintains, and matches palm-vein templates with less computational complexity and less storage requirements under a secure and privacy-preserving environment. First, we extract palm-vein traits in the WAT domain, which offers sparser expansion and better capability to extract texture features. Then, the randomization and quantization are applied to the extracted features to generate a compact, privacy-preserving palm-vein template. We analyze the proposed scheme for its performance and privacy-preservation. The proposed scheme obtains equal error rates (EERs) of 1.98%, 0%, 3.05%, and 1.49% for PolyU, PUT, VERA and our palm-vein datasets, respectively. The extensive experimental results demonstrate comparable matching accuracy of the proposed scheme with a minimum template size and computational time of 280 bytes and 0.43 s, respectively.

In situ template generation of silver nanoparticles as amplification tags for ultrasensitive surface plasmon resonance biosensing of microRNA

Surface plasmon resonance (SPR) biosensing strategies have drawn substantial attention due to their advantages of label free, real time, and high performance, but complicated modification procedures and strict reaction conditions of amplification tags associated with current SPR biosensors hinder their potential utilizations. Herein, an in situ prepared AgNPs-based SPR biosensor for MicroRNA (miRNA) sensitive detection was developed based on hybridization chain reaction (HCR) without the biomodification on amplification tags. The target miRNA initiated the HCR of the hairpin probes to generate long double strand DNA (dsDNA) chains that were immobilized on SPR disk. Thus, with Ag+ intercalating into dsDNA chains, large numbers of AgNPs generated after NaBH4 reduction, resulting in the significantly elevated SPR angle. Further, the SPR angle is positively proportional to target miRNA concentrations. As a result, the in situ generated AgNPs-based SPR biosensor realized exceptional let-7a detection with linear range of 0.001–0.1 pM and detection limit (LOD) of 0.35 fM, lower than that of other SPR biosensors that used modifiable amplification tags. Featured with the modification-free characteristic and excellent performance, the proposed strategy provides new way for ultrasensitive detection of miRNA, and allows to detect other biomarkers by simply verifying the target responsive substances, and thus has a great potential for health and early disease diagnosis.

A Template Generation and Improvement Approach for Finger-Vein Recognition

Finger-vein biometrics have been extensively investigated for person verification. One of the open issues in finger-vein verification is the lack of robustness against variations of vein patterns due to the changes in physiological and imaging conditions during the acquisition process, which results in large intra-class variations among the finger-vein images captured from the same finger and may degrade the system performance. Despite recent advances in biometric template generation and improvement, current solutions mainly focus on the extrinsic biometrics (e.g., fingerprints, face, signature) instead of intrinsic biometrics (e.g., vein). This paper proposes a weighted least square regression based model to generate and improve enrollment template for finger-vein verification. Driven by the primary target of biometric template generation and improvement, i.e., verification error minimization, we assume that a good template has the smallest intra-class distance with respect to the images from the same class in a verification system. Based on this assumption, the finger-vein template generation is converted into an optimization problem. To improve the performance, the weights associated with similarity are computed for template generation. Then, the enrollment template is generated by solving the optimization problem. Subsequently, a template improvement model is proposed to gradually update vein features in the template. To the best of our knowledge, this is the first proposed work of template generation and improvement for finger-vein biometrics. The experimental results on two public finger-vein databases show that the proposed schemes minimize the intra-class variations among samples and significantly improve finger-vein recognition accuracy.

Design and analysis of different PLLs as load compensation techniques in 1-∅ grid-tied PV system

ABSTRACTThis paper discusses the design, modelling and analysis of a single-phase grid-tied photovoltaic (PV) system feeding a variety of loads. Normally, the phase-locked loop (PLL) circuits are used for synchronisation purposes and generation of in-phase and quadrature templates. However, this paper presents an interesting application of PLLs for achieving load compensation for power quality improvement as well as estimation of phase and frequency. Three PLL algorithms are considered which include the conventional Delay PLL, Enhanced PLL and Second-Order Generalised Integrator Frequency-Locked Loop (SOGI-FLL). Design and modelling of controllers using these PLLs for achieving power quality improvement is also presented in the paper. Suitable comparisons are drawn to investigate the performance of different PLLs for two purposes viz. grid synchronisation and achieving compensation in a single-phase grid-tied PV system. Finally, the application of SOGI-FLL for control of a 5 kW PV interfaced single-phase compensator for power quality improvement is presented. Appropriate experimental results are shown along with simulation results for suitable comparison.

Adaptive visual target tracking algorithm based on classified-patch kernel particle filter

We propose a high-performance visual target tracking (VTT) algorithm based on classified-patch kernel particle filter (CKPF). Novel features of this VTT algorithm include sparse representations of the target template using the label-consistent K-singular value decomposition (LC-KSVD) algorithm; Gaussian kernel density particle filter to facilitate candidate template generation and likelihood matching score evaluation; and an occlusion detection method using sparse coefficient histogram (ASCH). Experimental results validate superior performance of the proposed tracking algorithm over state-of-the-art visual target tracking algorithms in scenarios that include occlusion, background clutter, illumination change, target rotation, and scale changes.

Разработка методов семантического поиска в Интернете, основанных на древовидных лингвистических шаблонах

The article is devoted to development of methods of search of information on the Internet. Currently the issue of fast and automatic data extraction from different sources is very actual. The aim of this work is creation of instrument to solve this problem. The idea of the approach is to develop semi-automatic methods of linguistic templates construction for given fragment of atomic diagram. Algorithm of templates construction for the fragment of atomic diagram is performed in 2 stages. On the 1st stage generation of the simple templates is done, on the 2nd stage more seldom and complicated templates are found, using templates from 1st part of algorithm. Processing texts in natural language on the Internet using gotten templates, needed information may be found.

АКТИВНОЕ ОБУЧЕНИЕ ДЛЯ ИЗВЛЕЧЕНИЯ ЗНАНИЙ ИЗ ОПИСАНИЙ ОБРАЗОВАТЕЛЬНЫХ КУРСОВ В УСЛОВИЯХ МАЛЫХ ОБЪЁМОВ ДАННЫХ

With the constant growth of volumes of available data, their manual processing stops being possible, giving way to various machine learning models. Modern algorithms do a good job of basic tasks, provided that there is a sufficient amount of training data. However, many modern tasks are much more complicated and are highly specialized, which limits the amount of training data available for training, hindering the performance of fully automatic systems. In this paper, an approach to the task of automated fact extraction from the collections of raw text documents adapted for the lack of training data is presented. The integration of rule-based approaches for specific knowledge domains with generalized, domain-independent machine learning models pre-trained on large volumes of data is discussed. The proposed approach based on the active learning methodology, seeks to reduce the expert’s labor costs required for the efficient generation of extractable fact templates without compromising the system’s performance. The paper also demonstrates the application of the proposed method of fact extraction based on the task of the target audience information search from the unstructured raw descriptions of online courses.

Rapid Rule Out of Culture-Negative Bloodstream Infections by Use of a Novel Approach to Universal Detection of Bacteria and Fungi.

Currently it can take up to 5 days to rule out bloodstream infection. With the low yield of blood cultures (approximately 10%), a significant number of patients are potentially exposed to inappropriate therapy that can lead to adverse events. More rapid rule out can accelerate deescalation or cessation of antimicrobial therapy, improving patient outcomes. A method is described, termed enzymatic template generation and amplification (ETGA), that universally and sensitively detects DNA polymerase activity liberated from viable bacteria and fungi isolated from blood culture samples as a measure of bloodstream infection. ETGA was applied in a diagnostic test format to identify negative blood cultures after an overnight incubation. Performance data for a prototype (Cognitor) and automated (Magnitor) version of the test are presented. The Cognitor manual assay displayed analytical reactivity for a panel of the 20 most prevalent causes of bloodstream infection, with a detection range of 28-9050 CFU/mL. Validation with 1457 clinical blood cultures showed a negative predictive value of 99.0% compared to blood culture incubation for 5 days. Magnitor showed an improved detection range of 1-67 CFU/mL, allowing for detection of bacteria-supplemented blood cultures after 2-8 h incubation, and Candida albicans-supplemented blood cultures at 16-22 h, 5-15 h faster than blood culture. Removing an aliquot from a blood culture bottle and replacing the bottle into the incubator was shown not to result in contaminating organisms being introduced. The described method displays excellent breadth and detection for microbial cells and demonstrates the capability of confirming negative blood cultures after an overnight incubation in a blood culture instrument.

Controller Design and Stability Analysis of Output Pressure Regulation in Electrohydrostatic Actuators

In this paper, a robust fixed-gain linear output pressure controller is designed for a double-rod electrohydrostatic actuator using quantitative feedback theory (QFT). First, the family of frequency responses of the system is identified by applying an advanced form of fast Fourier transform on the open-loop input–output experimental data. This approach results in realistic frequency responses of the system, which prevents the generation of unnecessary large QFT templates, and consequently contributes to the design of a low-order QFT controller. The designed controller provides desired transient responses, desired tracking bandwidth, robust stability, and disturbance rejection for the closed-loop system. Experimental results confirm the desired performance met by the QFT controller. Then, the nonlinear stability of the closed-loop system is analyzed considering the friction and leakage, and in the presence of parametric uncertainties. For this analysis, Takagi–Sugeno (T–S) fuzzy modeling and its stability theory are employed. The T–S fuzzy model is derived for the closed-loop system and the stability conditions are presented as linear matrix inequalities (LMIs). LMIs are found feasible and thus the stability of the closed-loop system is proven for a wide range of parametric uncertainties and in the presence of friction and leakages.

Novel Algorithm for Hand Gesture Recognition Utilizing a Wrist-Worn Inertial Sensor

In this paper, we propose a dynamic time warping (DTW)-based template matching method with a novel template generation algorithm for hand gesture recognition utilizing a wrist-worn inertial sensor. Hand orientation measurements are utilized to reconstruct gesture trajectories. The DTW technique with the Riemannian distance is employed to perform similarity measurements between gesture orientation trajectories. The proposed gesture recognition algorithm comprises three stages: a data preprocessing stage, a training stage, and a recognition stage. First, a moving intrinsic average filter is introduced to suppress the effects of measurement noise and unconscious hand shaking. Next, a double-threshold segmentation scheme is applied to extract individual gesture segments. In the training stage, to cope with temporal and spatial variations in gestures, an adaptive DTW barycenter averaging algorithm combined with an intrinsic averaging method is developed to generate gesture templates. A suitable rejection threshold is determined according to intra-class DTW distances. In the recognition stage, the rejection percentage between the input gesture and each gesture template is calculated. Finally, the nearest neighbor decision rule is applied to determine the recognition result. Experiments performed on a database of 3600 gesture samples illustrate that the proposed DTW-based gesture template generation and classification algorithm outperforms existing methods.

Design of Optimal QFT Controller and Prefilter for Buck Converter Using Metaheuristic Algorithms

A buck converter is a step-down switching regulator. Buck convertors are being widely used in industrial applications that rely on regulated output voltage under fluctuating input voltage. A buck convertor works in the following modes: (a) current-controlled or (b) voltage-controlled mode. But these convertors manifest several nonlinearites because of the switching operation. Hence, in order to generate a quality output of the convertor, the design of a controller becomes crucial. In this paper, the synthesis of a QFT-based robust controller and prefilter has been carried out for an uncertain buck converter with varying input voltage and varying load. The controller synthesis problem has been posed as an optimization problem, and metaheuristic algorithms have been used for obtaining the optimal gains for the QFT controller and prefilter. By doing this, the QFT synthesis can be carried out in a single step instead of following the sequential classical QFT process on Nichols charts and the need for the generation of templates and bounds has be eliminated. The designed 2-degree-of-freedom QFT control system offers a robust behavior and efficiently handles the parametric uncertainties. The robustness of the designed controller has been confirmed through simulation results for large input voltage and load fluctuations.

Discrete Wavelet Transform Based Cancelable Biometric System for Speaker Recognition

The biometric template characteristics and privacy conquest are challenging issues. To resolve such limitations, the cancelable biometric systems have been briefed. In this paper, the efficient cancelable biometric system based on the cryptosystem is introduced. It depends on permutation using a chaotic Baker map and substitution using masks in various transform domains. The proposed cancelable system features extraction phase is based on the Cepstral analysis from the encrypted speech signal in the time domain combined with the encrypted speech signal in the discrete wavelet transform (DWT). Then, the resultant features are applied to the artificial neural network for classification. Furthermore, wavelet denoising is used at the receiver side to enhance the proposed system. The cryptosystem provides a robust protection level of the speech template. This speech template can be replaced and recertified if it is breached. Our proposed system enables the generation of various templates from the same speech signal under the constraint of linkability between them. The simulation results confirmed that the proposed cancelable biometric system achieved higher a level of performance than traditional biometric systems, which achieved 97.5% recognition rate at low signal to noise ratio (SNR) of -25dB and 100% with -15dB and above.

PAVOOC: designing CRISPR sgRNAs using 3D protein structures and functional domain annotations.

SummarySingle-guide RNAs (sgRNAs) targeting the same gene can significantly vary in terms of efficacy and specificity. PAVOOC (Prediction And Visualization of On- and Off-targets for CRISPR) is a web-based CRISPR sgRNA design tool that employs state of the art machine learning models to prioritize most effective candidate sgRNAs. In contrast to other tools, it maps sgRNAs to functional domains and protein structures and visualizes cut sites on corresponding protein crystal structures. Furthermore, PAVOOC supports homology-directed repair template generation for genome editing experiments and the visualization of the mutated amino acids in 3D. Availability and implementationPAVOOC is available under https://pavooc.me and accessible using modern browsers (Chrome/Chromium recommended). The source code is hosted at github.com/moritzschaefer/pavooc under the MIT License. The backend, including data processing steps, and the frontend are implemented in Python 3 and ReactJS, respectively. All components run in a simple Docker environment.Supplementary information Supplementary data are available at Bioinformatics online.

Signal template generation from acquired images for model observer-based image quality analysis in mammography.

Mammography images undergo vendor-specific processing, which may be nonlinear, before radiologist interpretation. Therefore, to test the entire imaging chain, the effect of image processing should be included in the assessment of image quality, which is not current practice. For this purpose, model observers (MOs), in combination with anthropomorphic breast phantoms, are proposed to evaluate image quality in mammography. In this study, the nonprewhitening MO with eye filter and the channelized Hotelling observer were investigated. The goal of this study was to optimize the efficiency of the procedure to obtain the expected signal template from acquired images for the detection of a 0.25-mm diameter disk. Two approaches were followed: using acquired images with homogeneous backgrounds (approach 1) and images from an anthropomorphic breast phantom (approach 2). For quality control purposes, a straightforward procedure using a single exposure of a single disk was found adequate for both approaches. However, only approach 2 can yield templates from processed images since, due to its nonlinearity, image postprocessing cannot be evaluated using images of homogeneous phantoms. Based on the results of the current study, a phantom should be designed, which can be used for the objective assessment of image quality.

Convolutional Neural Networks with Template-Based Data Augmentation for Functional Lung Image Quantification

We propose an automated segmentation pipeline based on deep learning for proton lung MRI segmentation and ventilation-based quantification which improves on our previously reported methodologies in terms of computational efficiency while demonstrating accuracy and robustness. The large data requirement for the proposed framework is made possible by a novel template-based data augmentation strategy. Supporting this work is the open-source ANTsRNet-a growing repository of well-known deep learning architectures first introduced here. Deep convolutional neural network (CNN) models were constructed and trained using a custom multilabel Dice metric loss function and a novel template-based data augmentation strategy. Training (including template generation and data augmentation) employed 205 proton MR images and 73 functional lung MRI. Evaluation was performed using data sets of size 63 and 40 images, respectively. Accuracy for CNN-based proton lung MRI segmentation (in terms of Dice overlap) was left lung: 0.93 ± 0.03, right lung: 0.94 ± 0.02, and whole lung: 0.94 ± 0.02. Although slightly less accurate than our previously reported joint label fusion approach (left lung: 0.95 ± 0.02, right lung: 0.96 ± 0.01, and whole lung: 0.96 ± 0.01), processing time is <1 second per subject for the proposed approach versus ∼30 minutes per subject using joint label fusion. Accuracy for quantifying ventilation defects was determined based on a consensus labeling where average accuracy (Dice multilabel overlap of ventilation defect regions plus normal region) was 0.94 for the CNN method; 0.92 for our previously reported method; and 0.90, 0.92, and 0.94 for expert readers. The proposed framework yields accurate automated quantification in near real time. CNNs drastically reduce processing time after offline model construction and demonstrate significant future potential for facilitating quantitative analysis of functional lung MRI.

Simultaneous joint and object trajectory templates for human activity recognition from 3-D data

Availability of low-cost range sensors and the development of relatively robust algorithms for the extraction of skeleton joint locations have inspired many researchers to develop human activity recognition methods using 3-D data. In this paper, an effective method for the recognition of human activities from the normalized joint trajectories is proposed. We represent the actions as multidimensional signals and introduce a novel method for generating action templates by averaging the samples in a “dynamic time” sense. Then, in order to deal with the variations in speed and style of performing actions, we warp the samples with action templates by an efficient algorithm and employ wavelet filters to extract meaningful spatiotemporal features. The proposed method is also capable of modeling the human-object interactions, by performing the template generation and temporal warping procedure via the joint and object trajectories simultaneously. Experimental evaluations on several challenging datasets demonstrates the effectiveness of our method compared to the state-of-the-arts as well as its robustness against different sources of noise.