Alignment Process Research Articles

Translated object identification for efficient ghost imaging

Alignment of a single-pixel quantum ghost imaging setup is complex and requires extreme precision. Due to misalignment, easily created by human error in the alignment process, reconstructed images are often translated off the central imaging axis. This becomes problematic for intelligent object detection and identification in fast imaging cases, as these algorithms are unable to achieve early image identification. Here, we implemented a U-net algorithm to correctly recognize images in the early reconstruction stage regardless of any off-axis translation. The U-net was trained on a uniquely curated blurred, noised, and off-axis translated dataset. We achieved a 5× reduction in imaging speeds by early image identification in four different translation directions.

Biologically-informed Killer cell immunoglobulin-like receptor (KIR) gene annotation tool.

Natural killer (NK) cells are essential components of the innate immune system, with their activity significantly regulated by Killer cell Immunoglobulin-like Receptors (KIRs). The diversity and structural complexity of KIR genes present significant challenges for accurate genotyping, essential for understanding NK cell functions and their implications in health and disease. Traditional genotyping methods struggle with the variable nature of KIR genes, leading to inaccuracies that can impede immunogenetic research. These challenges extend to high-quality phased assemblies, which have been recently popularized by the Human Pangenome Consortium. This paper introduces BAKIR (Biologically-informed Annotator for KIR locus), a tailored computational tool designed to overcome the challenges of KIR genotyping and annotation on high-quality, phased genome assemblies. BAKIR aims to enhance the accuracy of KIR gene annotations by structuring its annotation pipeline around identifying key functional mutations, thereby improving the identification and subsequent relevance of gene and allele calls. It uses a multi-stage mapping, alignment, and variant calling process to ensure high-precision gene and allele identification, while also maintaining high recall for sequences that are significantly mutated or truncated relative to the known allele database. BAKIR has been evaluated on a subset of the HPRC assemblies, where BAKIR was able to improve many of the associated annotations and call novel variants. BAKIR is freely available on GitHub, offering ease of access and use through multiple installation methods, including pip, conda, and singularity container, and is equipped with a user-friendly command-line interface, thereby promoting its adoption in the scientific community. BAKIR is available at github.com/algo-cancer/bakir. Supplementary data are available at Bioinformatics online.

Neural Approach to Coordinate Transformation for LiDAR–Camera Data Fusion in Coastal Observation

The paper presents research related to coastal observation using a camera and LiDAR (Light Detection and Ranging) mounted on an unmanned surface vehicle (USV). Fusion of data from these two sensors can provide wider and more accurate information about shore features, utilizing the synergy effect and combining the advantages of both systems. Fusion is used in autonomous cars and robots, despite many challenges related to spatiotemporal alignment or sensor calibration. Measurements from various sensors with different timestamps have to be aligned, and the measurement systems need to be calibrated to avoid errors related to offsets. When using data from unstable, moving platforms, such as surface vehicles, it is more difficult to match sensors in time and space, and thus, data acquired from different devices will be subject to some misalignment. In this article, we try to overcome these problems by proposing the use of a point matching algorithm for coordinate transformation for data from both systems. The essence of the paper is to verify algorithms based on selected basic neural networks, namely the multilayer perceptron (MLP), the radial basis function network (RBF), and the general regression neural network (GRNN) for the alignment process. They are tested with real recorded data from the USV and verified against numerical methods commonly used for coordinate transformation. The results show that the proposed approach can be an effective solution as an alternative to numerical calculations, due to process improvement. The image data can provide information for identifying characteristic objects, and the obtained accuracies for platform dynamics in the water environment are satisfactory (root mean square error—RMSE—smaller than 1 m in many cases). The networks provided outstanding results for the training set; however, they did not perform as well as expected, in terms of the generalization capability of the model. This leads to the conclusion that processing algorithms cannot overcome the limitations of matching point accuracy. Further research will extend the approach to include information on the position and direction of the vessel.

Polar alignment of a dusty circumbinary disc–II. Application to 99 Herculis

ABSTRACT We investigate the formation of dust traffic jams in polar-aligning circumbinary discs. In our first paper, we found as the circumbinary disc evolves towards a polar configuration perpendicular to the binary orbital plane, the differential precession between the gas and dust components leads to multiple dust traffic jams. These dust traffic jams evolve to form a coherent dust ring. In part two, we use 3D smoothed particle hydrodynamical simulations of gas and dust to model an initially highly misaligned circumbinary disc around the 99 Herculis (99 Her) binary system. Our results reveal that the formation of these dust rings is observed across various disc parameters, including the disc aspect ratio, viscosity, surface density power-law index, and temperature power-law index. The dust traffic jams are long-lived and persist even when the disc is fully aligned polar. The midplane dust-to-gas ratio within the rings can surpass unity, which may be a favourable environment for planetesimal formation. Using 2D inviscid shearing box calculations with parameters from our 3D simulations, we find streaming instability modes with significant growth rates. The streaming instability growth time-scale is less than the tilt oscillation time-scale during the alignment process. Therefore, the dust ring will survive once the gas disc aligns polar, suggesting that the streaming instability may aid in forming polar planets around 99 Her.

Modeling fiber alignment in 3D printed ultra-high-performance concrete based on stereology theory

This paper introduces a theoretical model for forecasting fiber orientation in 3D-printed ultra-high-performance concrete (3DP-UHPC). Initially, the dynamic evolution process of fiber alignment in 3DP-UHPC was characterized using X-ray computed tomography (X-CT) and image analysis. The results indicated that fiber alignment during extrusion process was primarily constrained by the rigid boundary of nozzle. Leveraging stereology theory, the regularity of fiber alignment affected by boundary effects was elucidated. Following layer deposition, the flattening effect resulting from the nozzle's extrusion force and gravity of upper layers influenced fiber alignment along printing direction. To quantify this impact, a flattening correction coefficient was introduced to modify fiber orientation coefficient in an ideal state. Finally, considering the overlapping effect of boundary and flattening on fiber orientation in 3DP-UHPC, a theoretical model was developed to predict fiber orientation. The model demonstrated robust adaptability, providing valuable insights into the design of 3DP-UHPC with improved fiber reinforcement efficiency.

Magnetic-field-controllable elasticity of helical spring magnets composed of magnetic-particle-polymer composites

Abstract We experimentally demonstrated the ability to control the spring constant of helical mechanical springs using a magnetic field. These springs were composed of magnetic nanoparticles embedded within an elastic polymer matrix. The composite material, in its gel form, was injected into a 3D-printed mold featuring a helical-spring-shaped cavity. An external magnetic field applied perpendicular to the coil axis of the spring allows the aligmment of the magnetic nanoparticle assemblies (chain axis) in the field direction. This alignment process determines the preferred magnetization orientation of the particle assembly chain, thereby balancing the magnetic force between the magnetic anisotropy field and the Zeeman field under a given external field. When the spring is subjected to compression or stretching loads under an externally applied magnetic field, these two magnetic fields modify the effective spring constant of the magnetic helical spring by ~ 31 %, incresing it from 8.7 N/m (under no field) to 11.5 N/m at 300 mT. Analytical modeling using a simplified rod geometry aptly explains the experimental results, demonstrating that the spring constant linearly increases with the field strength up to 300 mT. Such composite magnetic helical springs could be utilized as active vibration absorbers or isolators due to their field-controllable elasticity.

AUTOMATIC MACHINE TOOL ALIGNMENT ON ITS FOUNDATION

This article deals with the issue of the automatic setting of machines on their base. The focus is on machines with a self-supporting frame, freestanding, on more than three supports. The alignment uses the previously presented method of precise and repeatable machine placement on the foundation. As a result, the machine has the necessary parameters in terms of its geometry, and at the same time a defined weight distribution on the individual building elements is guaranteed. The paper presents a new method for automatic machine alignment using so-called “smart feet”. Due to this, the process of machine alignment is clearly defined, repeatable and does not depend on the experience of the personnel performing the alignment. The presented algorithm makes it possible to automate the entire machine-setting process. The article includes the presentation of a machine placement on its base, which is used for testing and verification of the functionality of the alignment algorithm.

Investigating the Role of Inward Alfvén Waves and Structures in the Slow Solar Wind

The solar wind dynamics are thought to be governed by counter-propagating Alfvén waves. Alfvén waves generate a turbulent cascade through nonlinear couplings between shearing wave packets. However, imbalances, structures, and intermittency complicate the solar wind’s magnetohydrodynamic (MHD) turbulence. Simulations and theories have suggested that dynamic alignment, or the tendency for velocity and magnetic fluctuations to become more correlated as a function of scale, may dictate the turbulent cascade. Observations have hinted at dynamic alignment at large scales but remain inconclusive at small scales. To investigate the nature of dynamically aligning fluctuations in the solar wind, we examine slow wind intervals from the Helios 2 spacecraft. We develop a two-component model of solar wind turbulence consisting of Alfvénic and non-Alfvénic contributions. We assume that only counter-propagating Alfvén waves experience dynamic alignment, and the non-Alfvénic structures do not participate in the alignment process. Our model allows us to constrain the relative contribution of inward Alfvén waves and non-Alfvénic structures with respect to the amplitude of outward Alfvén waves in the slow solar wind as a function of scale under the assumption of dynamic alignment. We show that the power ratio of inward to outward Alfvén fluctuations decreases as a function of scale. At the same time, the non-Alfvénic structures to outward Alfvén fluctuations increase with decreasing scale. Increasing structures provide a possible explanation for no dynamic alignment at small scales. Our study implies the need for new theoretical models to fully account for the solar wind’s compressibility, intermittency, and imbalanced nature.

Accelerating DNA Sequence Alignment using Altera DE2-115

DNA sequence alignment is a technique for discovering information between two base sequences which the Smith-Waterman algorithm is the accurate method that provides a precise result for alignment compared to others. However, the performance was influence by size of dataset and a long DNA base sequence which resulted the time required for the alignment process is much longer in relation to the number of DNA sequence samples. There are many ways to accelerate DNA sequence alignment, and Field Programmable Gate Array (FPGA) is a good choice due to its parallel processing and cost efficiency. Although FPGA acceleration approaches are not new, this work investigates a purely software-based FPGA acceleration using the Altera Cyclone IV EP4CE115F29C7N FPGA as the target device. The SW algorithm was developed using the C language in Quartus II version 18.1 and the Nios II software build tools for Eclipse. The development starts with setting up the Qsys architecture before developing the code in Eclipse to determine the computational performance. The result shows the computational timing and speed of the implementation, with the highest speed achieved being 198.76 cells per millisecond. To summarise, the computational performance ultimately depends on the maximum matrix size of the FPGA, which is also influenced by the DNA-based pair length and able to complete using low-cost FPGA.

Modeling of Fabrication Errors Caused by Operator Habits in Substrate Alignment Process

Modeling of Fabrication Errors Caused by Operator Habits in Substrate Alignment Process

Principle of TEM alignment using convolutional neural networks: Case study on condenser aperture alignment

The possibility of automatically aligning the transmission electron microscope (TEM) is explored using an approach based on artificial intelligence (AI). After presenting the general concept, we test the method on the first step of the alignment process which involves centering the condenser aperture. We propose using a convolutional neural network (CNN) that learns to predict the x and y-shifts needed to realign the aperture in one step. The learning data sets were acquired automatically on the microscope by using a simplified digital twin. Different models were tested and analysed to choose the optimal design. We have developed a human-level estimator and intend to use it safely on all apertures. A similar process could be used for most steps of the alignment process with minimal changes, allowing microscopists to reduce the time and training required to perform this task. The method is also compatible with continuous correction of alignment drift during lengthy experiments or to ensure uniformity of illumination conditions during data acquisition.

Free‐space quantum key distribution transmitter system using WDM filter for channel integration

AbstractIn this study, we report a transmitter system for free‐space quantum key distribution (QKD) using the BB84 protocol, which does not require an internal alignment process, by using a wavelength‐division multiplexing (WDM) filter and polarization‐encoding module. With a custom‐made WDM filter, the signals required for QKD can be integrated by simply connecting fibers, thus avoiding the laborious internal alignment required for free‐space QKD systems using conventional bulk‐optic setups. The WDM filter is designed to multiplex the single‐mode signals from 785‐nm quantum and 1550‐nm synchronization channels for spatial‐mode matching while maintaining the polarization relations. The measured insertion loss and isolation are 1.8 dB and 32.6 dB for 785 nm and 0.7 dB and 28.3 dB for 1550 nm, respectively. We also evaluate the QKD performance of the proposed system. The sifted key rate and quantum bit error rate are 1.6 Mbps and 0.62%, respectively, at an operating speed of 100 MHz, rendering our system comparable to conventional systems using bulk‐optic devices for channel integration.

A direction-adaptive ultra-low frequency energy harvester with an aligning turntable

Ultra-low frequency vibrations in the ambient environment contain abundant sustainable energy. However, their time-varying and random direction nature poses a prominent challenge for the design of effective energy harvesters. This paper proposes a direction-adaptive ultra-low frequency energy harvester with an aligning turntable to ensure a systematic alignment between the pendulum swinging plane and the excitation direction. The aligning turntable is easily rotated by overcoming friction, thus reducing the deviation angle till zero. Experimental results verify the output power enhancement from several milliwatts to over 1 W owing to the alignment process. Moreover, setting resonance frequency with large amplitude vibration can significantly shorten the alignment time. Decreasing the pendulum swinging natural frequency contributes to a slower alignment process but enhances the system stability.

In-motion fine alignment algorithm for AUV based on improved extended state observer and Kalman filter

Abstract Achieving fast initial alignment has always been a major challenge for autonomous underwater vehicle navigation, especially in motion. During the fine alignment process, traditional algorithms often face the slow convergence of the azimuth misalignment angle. To solve this problem, this paper proposes a fast alignment algorithm based on the improved extended state observer (ESO) and Kalman filter. Specifically, since the application potential of ESO in fine alignment has not been fully explored by previous studies, this paper introduces the improved ESO through the in-depth research of traditional ESO. The obtained improved ESO is then combined with Kalman filter to constitute the proposed fast alignment algorithm. The algorithm first uses Kalman filter to estimate the two horizontal misalignment angles. Once the horizontal angles estimation of Kalman filter tends to be stable, the improved ESO is used to estimate the azimuth misalignment angle. Simulation and lake test show the proposed algorithm has excellent performance of in-motion alignment. The comparative analysis shows that the algorithm significantly improves the convergence speed of azimuth alignment angle.

Alignment of a 360° image with posed color images for locally accurate texturing of 3D mesh

With the popularity of 3D content like virtual tours, the challenges of 3D data registration have become increasingly significant. The registration of heterogeneous data obtained from 2D and 3D sensors is required to create photo-realistic 3D models. However, the alignment of 2D images with 3D models introduces a significant challenge due to their inherent differences. This article introduces a rigorous mathematical approach to align a 360° image with its corresponding 3D model generated from images with known camera poses followed by texture projection on the model. We use Scale-Invariant Feature Transform (SIFT) feature descriptors enhanced with a homography-based metric to establish correspondences between the faces of a cubemap and the posed images. To achieve optimal alignment, we use a non-linear least squares optimization technique with a custom objective function. Subsequently, the outcomes of the alignment process are evaluated through texturing using a customized raytracing algorithm. The resulting projections are compared against the original textures, with a comprehensive assessment of the alignment's fidelity and precision.

Enhancing surgical navigation: a robust hand-eye calibration method for the Microsoft HoloLens 2.

Optical-see-through head-mounted displays have the ability to seamlessly integrate virtual content with the real world through a transparent lens and an optical combiner. Although their potential for use in surgical settings has been explored, their clinical translation is sparse in the current literature, largely due to their limited tracking capabilities and the need for manual alignment of virtual representations of objects with their real-world counterparts. We propose a simple and robust hand-eye calibration process for the depth camera of the Microsoft HoloLens2, utilizing a tracked surgical stylus fitted with infrared reflective spheres as the calibration tool. Using a Monte Carlo simulation and a paired-fiducial registration algorithm, we show that a calibration accuracy of 1.65mm can be achieved with as little as 6 fiducial points. We also present heuristics for optimizing the accuracy of the calibration. The ability to use our calibration method in a clinical setting is validated through a user study, with users achieving a mean calibration accuracy of 1.67mm in an average time of 42s. This work enables real-time hand-eye calibration for the Microsoft HoloLens2, without any need for a manual alignment process. Using this framework, existing surgical navigation systems employing optical or electromagnetic tracking can easily be incorporated into an augmented reality environment with a high degree of accuracy.

Electrically tunable metasurface in TN LC configuration with enhanced quality factor and metaatom-induced alignment.

Dynamic metasurface with subwavelength dimensions has emerged as a key optical technology in recent years. Although various active tuning mechanisms are being proposed, liquid crystal-based dynamic metasurface remains attractive due to its large index variation, electrical tunability, reliability, and mass fabricability. In this work, we report a dynamic metasurface for amplitude modulation in reflection, with twisted nematic liquid crystal configuration to reduce broadband reflection in the off state. The metasurface consists of coupled subwavelength grating fingers, which provide alignment for the liquid crystal without the need for an additional alignment material or process. The alignment of liquid crystal materials was examined between crossed polarizers, and the twist of nematic liquid crystal was confirmed. The coupled grating fingers exhibit a resonance quality factor of 27 at telecommunication wavelength and an amplitude modulation depth of 8 times of the minimum at 1630 nm. This work highlights the potential of liquid crystal-based tunable metasurface, combining polarization control via liquid crystal and spectrum control via metasurface. Furthermore, it also shows a way in which the interaction between liquid crystal and metasurface is used for an alignment layer-free cell assembly process.

A practical guide to light-sheet microscopy for nanoscale imaging: Looking beyond the cell.

We present a comprehensive guide to light-sheet microscopy (LSM) to assist scientists in navigating the practical implementation of this microscopy technique. Emphasizing the applicability of LSM to image both static microscale and nanoscale features, as well as diffusion dynamics, we present the fundamental concepts of microscopy, progressing through beam profile considerations, to image reconstruction. We outline key practical decisions in constructing a home-built system and provide insight into the alignment and calibration processes. We briefly discuss the conditions necessary for constructing a continuous 3D image and introduce our home-built code for data analysis. By providing this guide, we aim to alleviate the challenges associated with designing and constructing LSM systems and offer scientists new to LSM a valuable resource in navigating this complex field.

Ultrasonic visualization of destruction processes in nonwoven polymeric matrix for regenerative medicine

Abstract The paper depicts the tensile behaviour of a random oriented nonwoven polymer matrix based on poly-3-hydroxybutyrate. The process of alignment, orientation and stretching of polymer fibers in a water filled condition mimicked presence in a living organism was revealed using acoustic microscopy. To describe the micromechanics of electrospun mats the mechanism of ultrasound contrast of small-sized single cylindrical scatterer was described.

Data science and business value: Insight into the alignment process

We develop a comprehensive business - data science alignment model for deriving business value from data science initiatives, thus assisting business organizations in transforming digitally. With a purposive sample of 13 data science leaders in the capacities of C-level executives, semi-structured interviews were conducted to unfold their data science journey. The study extends the understanding from the business - IT/IS alignment literature to data science initiatives. The alignment dimensions and their components are extracted through the interview transcripts using a general inductive approach. We propose a business - data science alignment model as a dynamic and iterative process in which the data science initiatives are shaped while aligning with intellectual, operational, social, cultural, and societal dimensions to derive business value. The five most relevant components represent each of these alignment dimensions. The study emphasizes the groundwork to be done before investing in data science endeavors by capturing the learnings from the literature and practitioners. With the proposed data science alignment framework, our study offers to craft data science projects by guiding business executives in achieving business value.