Automatic Alignment Research Articles

Plasmodium knowlesi circumsporozoite protein: genetic characterisation and predicted antigenicity of the central repeat region.

Circumsporozoite protein (CSP) central repeat region is one of the main target regions of the RTS,S/AS01 vaccine for falciparum infection as it consists of immunodominant B cell epitopes. However, there is a lack of study for P. knowlesi CSP central repeat region. This study aims to characterise the CSP repeat motifs of P. knowlesi isolates in Peninsular Malaysia. CSP repeat motifs of 64 P. knowlesi isolates were identified using Rapid Automatic Detection and Alignment of Repeats (RADAR). Antigenicity of the repeat motifs and linear B cell epitopes were predicted using VaxiJen 2.0, BepiPred-2.0 and BCPred, respectively. A total of 35 dominant repeat motifs were identified. The repeat motif "AGQPQAQGDGANAGQPQAQGDGAN" has the highest repeat frequency (n=15) and antigenicity index of 1.7986. All the repeat regions were predicted as B cell epitopes. In silico approaches revealed that all repeat motifs were antigenic and consisted of B cell epitopes which could be designed as knowlesi malaria vaccine.

Using the Corpus of North American Spoken English to explore regional variation in millions of eɪ diphthongs

Increases in the quality of automatic speech recognition (ASR) systems and forced alignment algorithms have opened up new horizons for the study of acoustic properties of naturalistic speech (Coto-Solano et al., 2021). This paper describes the procedures used for the automatic extraction and alignment of audio and ASR transcript excerpts containing eɪ diphthongs from the Corpus of North American Spoken English, a 1.3-billion-word corpus of geolocated ASR transcripts of videos uploaded to YouTube channels of local government entities. In total, more than 2 million eɪ tokens were extracted from naturalistic speech in over 2000 locations in the US and Canada. The pipeline utilizes python code to extract audio segments from the DASH manifest of YouTube videos, then feeds them together with the corresponding transcript excerpt to the Montreal Forced Aligner. Targeted segments are then filtered using functions in Parselmouth, a python port of Praat. Because the underlying corpus is searchable and words are annotated with timing information, the extraction procedure is suitable for retrieving acoustic data from a range of naturalistic speech configurations (for example from discussions of particular topics or in the context of specific utterance types such as questions or replies). The corpus contains a variety of speech genres, making it suitable for studies of geophonetic and sociophonetic variation, and pipelines can be constructed to target specific phenomenona in YouTube videos, depending on the underlying research question.

Interaction Multi-Agent Models' Automatic Alignment with MDA Higher Abstraction Level

With the massive growth of the software sector as well as the erratic needs of end users, agent-based information systems and Model Driven Architecture (MDA) approach are among the liveliest and significant fields of experimentation and improvement to emerge in the recent decade. In this vein, we suggest in this research an innovative method that automates the construction and the generation processes of the interaction multi-agent models from the business requirements engineering models at the MDA highest abstraction levels. So, our defiance is to align the Agent Modeling Language (AML) Communicative Interaction diagram with the E3value model dealing with the MDA approach. The ATLAS-Transformation Language (ATL) is applied to automate the model alignment process. The goal is to reduce project effort, time, and development costs as all alignment process is automatically done, boosting the chances of being more competitive in the software business.

GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D 1H-13C HSQC-Based Metabolomics.

Two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) has been increasingly applied to metabolomics studies because it can greatly improve the resolving capability compared with one-dimensional (1D) 1H NMR. However, preprocessing methods such as peak matching and alignment tools for 2D NMR-based metabolomics have lagged behind similar methods for 1D 1H NMR-based metabolomics. Correct matching and alignment of 2D NMR spectral features across multiple samples are particularly important for subsequent multivariate data analysis. Considering different intensity dynamic ranges of a variety of metabolites and the chemical shift variation across the spectra of multiple samples, here, we developed an efficient peak matching and alignment algorithm for 2D 1H-13C HSQC-based metabolomics, called global intensity-guided peak matching and alignment (GIPMA). In GIPMA, peaks identified in all spectra are pooled together and sorted by intensity. Chemical shift of a stronger peak is regarded to be more accurate and reliable than that of a weaker peak. The strongest undesignated peak is chosen as the reference of a new cluster if it is not located within the chemical shift tolerance of any existing peak cluster (PC), or otherwise it is matched to an existing PC and the aligned chemical shift of the PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the cluster. Setting an optimum chemical shift tolerance (Δδo) is critical for the peak matching and alignment across multiple samples. GIPMA dynamically searches for and intelligently selects the Δδo for peak matching to maximize the number of valid peak clusters (vPC), that is, spectral features, among multiple samples. By GIPMA, fully automatic peakwise matching and alignment do not require any spectrum as initial reference, while the chemical shift of each PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the same PC, which is warranted to be statistically more accurate. Accurate chemical shifts for each representative spectral feature will facilitate subsequent peak assignment and are essential for correct metabolite identification and result interpretation. The proposed method was demonstrated successfully on the spectra of six model mixtures consisting of seven typical metabolites, yielding correct matching of all known spectral features. The performance of GIPMA was also demonstrated on 2D 1H-13C HSQC spectra of 87 real extracts of 29 samples of five Dendrobium species. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of the 87 matched and aligned spectra by GIPMA generates correct classification of the 29 samples into five groups. In summary, the proposed algorithm of GIPMA provided a practical peak matching and alignment method to facilitate 2D NMR-based metabolomics studies.

Demonstration of efficient relativistic electron acceleration by surface plasmonics with sequential target processing using high repetition lasers

For high repetition ultrahigh-intensity laser system, automatic alignment of structured target is key to achieving consistent particle acceleration and plasma heating. In this work, we demonstrate efficient electron acceleration with two sequential steps of laser processing using a high repetition rate, 30-fs ultrahigh-intensity laser. The first pulse does laser machining and creates a steep cylindrical crater on the surface of a flat stainless-steel target. The crater is formed by the hydrodynamic expansion of the heated surface and by spallation of the inner, deeper material by nonthermal relativistic electrons. The crater shape is well controlled and reproducible with 200 \ensuremath{\mu}m width and 350 \ensuremath{\mu}m depth. The second pulse irradiates deeply inside the crater and interacts with the crater wall, efficiently accelerating electrons via surface plasmonic, without need for target realignment. The laser absorption efficiency increases from 32.5 to 97.5% by the process.

Automatic Alignment of an Orbital Angular Momentum Sorter in a Transmission Electron Microscope Using a Convolutional Neural Network.

We report on the automatic alignment of a transmission electron microscope equipped with an orbital angular momentum sorter using a convolutional neural network. The neural network is able to control all relevant parameters of both the electron-optical setup of the microscope and the external voltage source of the sorter without input from the user. It can compensate for mechanical and optical misalignments of the sorter, in order to optimize its spectral resolution. The alignment is completed over a few frames and can be kept stable by making use of the fast fitting time of the neural network.

Graphene/Ge Photoconductive Position-Sensitive Detectors Based on the Charge Injection Effect

Position-sensitive detectors (PSDs) are of great significance to optical communication, automatic alignment, and dislocation detection domains, by precisely obtaining the position information of infrared light spots which are invisible to human eyes. Herein, a kind of PSD based on graphene/germanium (Ge) heterojunction architecture is proposed and demonstrated, which exhibits amplified signals by unitizing the charge injection effect. Driven by the graphene/Ge heterojunction, a large number of photogenerated carriers diffuse from the incident position of the light spot and subsequently inject into graphene, which ultimately generates a photoresponse with high efficiency. The experimental results show that the device can exhibit a fast response speed of 3 μs, a high responsivity of ~40 A/W, and a detection distance of 3000 μm at the 1550 nm band, which hints that the graphene/Ge heterojunction can be used as an efficient platform for near-infrared light spot position sensing.

Non-local polarization alignment and control in fibers using feedback from correlated measurements of entangled photons.

Quantum measurements that use the entangled photons' polarization to encode quantum information require calibration and alignment of the measurement bases between spatially separate observers. Because of the changing birefringence in optical fibers arising from temperature fluctuations or external mechanical vibrations, the polarization state at the end of a fiber channel is unpredictable and time-varying. Polarization tracking and stabilization methods originally developed for classical optical communications cannot be applied to polarization-entangled photons, where the separately detected photons are statistically unpolarized, yet quantum mechanically correlated. We report here a fast method for automatic alignment and dynamic tracking of the polarization measurement bases between spatially separated detectors. The system uses the Nelder-Mead simplex method to minimize the observed coincidence rate between non-locally measured entangled photon pairs, without relying on classical wavelength-multiplexed pilot tones or temporally interleaved polarized photons. Alignment and control is demonstrated in a 7.1 km deployed fiber loop as well as in a controlled drifting scenario.

Automatic synchrotron tomographic alignment schemes based on genetic algorithms and human-in-the-loop software.

Tomography imaging methods at synchrotron light sources keep evolving, pushing multi-modal characterization capabilities at high spatial and temporal resolutions. To achieve this goal, small probe size and multi-dimensional scanning schemes are utilized more often in the beamlines, leading to rising complexities and challenges in the experimental setup process. To avoid spending a significant amount of human effort and beam time on aligning the X-ray probe, sample and detector for data acquisition, most attention has been drawn to realigning the systems at the data processing stages. However, post-processing cannot correct everything, and is not time efficient. Here we present automatic alignment schemes of the rotational axis and sample pre- and during the data acquisition process using a software approach which combines the advantages of genetic algorithms and human intelligence. Our approach shows excellent sub-pixel alignment efficiency for both tasks in a short time, and therefore holds great potential for application in the data acquisition systems of future scanning tomography experiments.

In Vivo Assessment of Shear Wave Propagation in Pennate Muscles Using an Automatic Ultrasound Probe Alignment System.

Goal: Skeletal muscle mechanics can be assessed in vivo using shear wave elastography. However, the impact of pennation angle on shear wave velocity (SWV) remains unclear. This study aims to quantify the effect by automatically aligning the ultrasound probe with muscle fiber orientation. Methods: We propose an automatic ultrasound probe alignment system and compare it to manual and no alignment. SWV of the gastrocnemius medialis muscle of ten volunteers was measured during rest and isometric contractions. Results: The SWV was different between the conditions (p = 0.008). The highest SWV was obtained during the automatic alignment and differences between the conditions were most pronounced during high-level contractions. The automatic system yielded more accurate alignment compared to a manual operator (p = 0.05). Conclusions: The present study indicates that pennation angle affects SWV, hence muscle fiber orientation must be considered to reliably interpret SWV. Using automatic alignment systems allows for more accurate alignment, improving the methodology of ultrasound elastography in skeletal muscles.

Automatic End Tool Alignment through Plane Detection with a RANSAC-Algorithm for Robotic Grasping

Automatic End Tool Alignment through Plane Detection with a RANSAC-Algorithm for Robotic Grasping

CNN with Embedding Transformers for Person Reidentification

For person reidentification (ReID), most slicing methods (such as part‐based convolutional baseline (PCB) and AlignedReID) introduce a lot of background devoid of pedestrian parts, resulting in the cross‐aliasing of features in the deep network. Besides, the resulting component features are not perfectly aligned with each other, thus affecting model performance. We propose a convolutional neural network (CNN) with embedding transformers (CET) person ReID network architecture based on the respective advantages of CNN and transformer. In CET, first, the residual transformer (RT) structure is first embedded in the backbone network of CNN to obtain a feature extractor, named transformers in CNN. The feature aliasing phenomenon is improved by utilizing transformer’s advantage in grasping the relevance of global information. Second, a feature fuse with learnable vector structure for fusing the output vector is added to the output of the transformer at the end of the network. A two branches loss structure is designed to balance the two different fusion strategies. Finally, the self‐attention mechanism in transformer is used for automatic part alignment of human body parts to solve the part alignment problem caused by inaccurate detection frames. The experimental results show that CET network architecture achieves better performance than PCB and some other block‐slicing methods.

Image Process Optimization in Laser Beam Far-Field Automatic Alignment

Image Process Optimization in Laser Beam Far-Field Automatic Alignment

Multimedia Imaging System of Data Collection and Antenna Alignment for Unmanned Aerial Vehicles Based Internet of Things

Because network of sensors gives a more accurate representation of remotely sensed environments, a network of wirelessly connected sensors is essential. Data packets must be routed to the base station hop by hop, which causes conventional network data collecting to use a lot of power. Unmanned aerial vehicles (UAV) were employed for hovering over the detected environment and gather data to solve this issue. The paper also aims to provide an automatic alignment for UAV antennas for tracking by utilising computer vision technologies. A directional antenna with high gain is used by a ground station that can operate by a pan-tilt to point towards the low-gain omnidirectional antenna carried by the UAV. To center the UAV's antenna's image in the frame, the antenna is equipped with a camera, and a computer detects the video and controls the pan-tilt. The antennas are aligned if there are no more than a few pixels between the UAV image center and the image center. The proposed imaging system exhibits fast data collection, thus attaining a high packet delivery rate and the minimum use of energy. With the suggested antenna auto-alignment approach, the antennas can be accurately aligned with an angle error of under one. UAVs must take the smoothest and shortest pathways possible to accommodate their motion and time constraints. As a result, the Traveling Sales Problem (TSP) is utilized to determine the shortest route, and Bezier curves are then employed to turn paths into a flyable path.

Saturated Nonsingular Fast Sliding Mode Control for the Crane-Form Pipeline System.

The crane-form pipeline (CFP) system is a kind of petrochemical mechanical equipment composed of multiple rotating joints and rigid pipelines. It is often used to transport chemical fluid products in the factory to tank trucks. In order to realize the automatic alignment of the CFP and the tank mouth, the trajectory tracking control problem of the CFP must be solved. Therefore, a saturated nonsingular fast terminal sliding mode (SNFTSM) algorithm is proposed in this paper. The new sliding mode manifold is constructed by the nonsingular fast terminal sliding mode (NFTSM) manifold, saturation functions and signum functions. Further, according to the sliding mode control algorithm and the dynamic model of the CFP system, the SNFTSM controller is designed. Owing to the existence of saturation functions in the controller, the stability analysis using the Lyapunov equation needs to be discussed in different cases. The results show that the system states can converge to the equilibrium point in finite time no matter where they are on the state's phase plane. However, due to the existence of signum functions, the control signal will produce chattering. In order to eliminate the chattering problem, the form of the controller is improved by using the boundary layer function. Finally, the control effect of the algorithm is verified by simulation and compared with the NTSM, NFTSM and SNTSM algorithms. From the comparison results, it is obvious that the controller based on the SNFTSM algorithm can effectively reduce the amplitude of the control torque while guaranteeing the fast convergence of the CFP system state error. Specifically, compared with the NFTSM algorithm, the maximum input torque can even be reduced by more than half.

An automatic alignment method for discharge arm of mobile crushing station based on binocular vision and fuzzy control

The mobile crushing station is one of the main equipment of the semi-continuous open-pit mining system. The discharge arm and the receiving equipment are manually aligned, which has the problems of long alignment time and low alignment accuracy, which affects the working efficiency of the mining system. According to the development and application of space rendezvous and docking technology at home and abroad, the advantages and disadvantages of different measurement methods are compared and analysed, and the method of applying binocular measurement technology to system positioning in the automatic alignment system of the discharge arm is determined. There are three movements in the mechanical part of the designed discharge arm alignment control system, including the rotary motion of the visual measurement mechanism and the horizontal rotation and telescopic motion of the discharge arm. According to the kinematic analysis and binocular vision measurement theory, the deviation model of the alignment control of the discharge arm is established. A binocular vision measurement and localization method based on the combination of stereo calibration and template matching is proposed, which achieves surprising measurement accuracy. An automatic alignment method of the discharge arm of the mobile crushing station is proposed based on the binocular vision and fuzzy control method. Its validity is verified by simulation and experiment. The strategy of motion decomposition is applied to the alignment system to avoid unnecessary motion of the discharge arm. The research results all show that the alignment method can achieve the angle deviation within ±0.5 degrees, the distance deviation within ±15 mm, and the test alignment time is about 5 minutes, which is better than other alignment control models; the alignment accuracy and the alignment time are improved by more than 50%. The method can control the discharge arm to complete the alignment task quickly and smoothly, which lays a foundation for the further automatic research of the discharge arm.

Automatic Point Clouds Alignment for the Reconstruction of Upper Limb Anatomy

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.

Alignment of code switching varies with proficiency in second language learning dialogue

Speakers in dialogue tend to adopt the language patterns of the other, aligning their language to their interlocutor. This can happen at many levels of communication, including the tendency to code switch (CS), or change to another language. Alignment has often been considered the result of an unconscious automatic process that facilitates speakers' mutual understanding. In dialogues with a second language (L2) learner, alignment is constrained by the proficiency of the learner, and additional non-automatic processes will be at play, namely the individual pedagogical goals of learner and tutor. In this study, we investigate alignment in dialogues between Spanish/Catalan learners of English and their tutors. We analyse CS incidence, whether code switching can be explained as automatic alignment between speakers, and whether this is independent of other, non-automatic factors related to speakers’ goals. We find that alignment of code switching is present, varies with learner proficiency, and that code switching can additionally be triggered by lexical overlap and turn taking asymmetry, which we attribute to conscious pedagogical choices on the part of both tutor, at lower levels, and learner, at higher levels of student proficiency.

Automatic alignment of standard views for transesophageal echocardiographic images.

3D transesophageal echocardiography (TEE) has become an important modality for pre- and peri-operative imaging of valvular heart disease. TEE can give excellent visualization of valve morphology in 3D rendering. As a convention, 3D TEE images are reformatted in three standard views. We describe a method for automatic calculation of parameters needed to define the standard views from 3D TEE images using no manual input. An algorithm was designed to find the center of the mitral valve and the left ventricular outflow tract (OT). These parameters defined the three-chamber view. The problem was modeled as a state estimation problem in which a 3D model was deformed based on shape priors and edge detection using a Kalman filter. This algorithm is capable of running in real time after initialization. The algorithm was validated by comparing the automatic alignments of 106 TEE images against manually placed landmarks. The median error for determining the mitral valve center was 7.1mm, and the median error for determining the left ventricular OT orientation was 13.5deg. The algorithm is an accurate tool for automating the process of finding standard views for TEE images of the mitral valve.

Alignment of vaccine codes using an ontology of vaccine descriptions

BackgroundVaccine information in European electronic health record (EHR) databases is represented using various clinical and database-specific coding systems and drug vocabularies. The lack of harmonization constitutes a challenge in reusing EHR data in collaborative benefit-risk studies about vaccines.MethodsWe designed an ontology of the properties that are commonly used in vaccine descriptions, called Ontology of Vaccine Descriptions (VaccO), with a dictionary for the analysis of multilingual vaccine descriptions. We implemented five algorithms for the alignment of vaccine coding systems, i.e., the identification of corresponding codes from different coding ystems, based on an analysis of the code descriptors. The algorithms were evaluated by comparing their results with manually created alignments in two reference sets including clinical and database-specific coding systems with multilingual code descriptors.ResultsThe best-performing algorithm represented code descriptors as logical statements about entities in the VaccO ontology and used an ontology reasoner to infer common properties and identify corresponding vaccine codes. The evaluation demonstrated excellent performance of the approach (F-scores 0.91 and 0.96).ConclusionThe VaccO ontology allows the identification, representation, and comparison of heterogeneous descriptions of vaccines. The automatic alignment of vaccine coding systems can accelerate the readiness of EHR databases in collaborative vaccine studies.