Subtraction Technique Research Articles

An alternating minimization algorithm with trajectory for direct exoplanet detection. The AMAT algorithm

Effective image post-processing algorithms are vital for the successful direct imaging of exoplanets. Standard point spread function (PSF) subtraction methods use techniques based on a low-rank approximation to separate the rotating planet signal from the quasi-static speckles and rely on signal-to-noise ratio maps to detect the planet. These steps do not interact or feed each other, leading to potential limitations in the accuracy and efficiency of exoplanet detection. We aim to develop a novel approach that iteratively finds the flux of the planet and the low-rank approximation of quasi-static signals in an attempt to improve upon current PSF subtraction techniques. In this study, we extend the standard L2 norm minimization paradigm to an L1 norm minimization framework in order to better account for noise statistics in the high contrast images. Then, we propose a new method, referred to as the alternating minimization algorithm with trajectory (AMAT), that makes more advanced use of estimating the low-rank approximation of the speckle field and the planet flux by alternating between them and utilizing both L1 and L2 norms. For the L1 norm minimization, we propose using L1 norm low-rank approximation (L1-LRA), a low-rank approximation computed using an exact block-cyclic coordinate descent method, while we use randomized singular value decomposition for the L2 norm minimization. Additionally, we enhance the visibility of the planet signal using a likelihood ratio as a post-processing step. Numerical experiments performed on a VLT/SPHERE-IRDIS dataset show the potential of AMAT to improve upon the existing approaches in terms of higher S/N, sensitivity limits (contrast curves), and receiver operating characteristic curves. Moreover, for a systematic comparison, we used datasets from the exoplanet data challenge to compare our algorithm with other algorithms in the challenge, and we find AMAT with a likelihood ratio map performs better than most algorithms tested on the exoplanet data challenge.

Locally finite two-loop amplitudes for electroweak production through gluon fusion

The computation of two-loop amplitudes for the production of multiple Higgs and electroweak gauge bosons via gluon fusion with exact dependence on quark masses relies primarily on numerical methods. We propose a framework that enables their numerical evaluation in momentum space. The method is inspired by the factorization of infrared divergences in QCD scattering amplitudes. It extends techniques introduced for electroweak gauge boson production from quark-antiquark annihilation to processes with external gluons. By combining diagrammatic integrands, we make use of local cancellations between diagrams that automatically eliminate most non-factoring infrared singularities. With a limited number of counterterms, we then derive two-loop integrands for which all soft and collinear singularities factorize locally. We hope that the local subtraction techniques presented in this article will play a useful role in extending the local factorization formalism to two-loop amplitudes for arbitrary processes.

Quotient quiver subtraction

We develop the diagrammatic technique of quiver subtraction to facilitate the identification and evaluation of the SU(n) hyper-Kähler quotient (HKQ) of the Coulomb branch of a 3dN=4 unitary quiver theory. The target quivers are drawn from a wide range of theories, typically classified as “good” or “ugly”, which satisfy identified selection criteria. Our subtraction procedure uses quotient quivers that are “bad”, differing thereby from quiver subtractions based on Kraft-Procesi transitions. The simple diagrammatic procedure identifies one or more resultant quivers, the union of whose Coulomb branches corresponds to the desired HKQ. Examples include quivers whose Coulomb branches are moduli spaces of free fields, closures of nilpotent orbits of classical and exceptional type, and slices in the affine Grassmanian. We calculate the Hilbert Series and Highest Weight Generating functions for HKQ examples of low rank. For certain families of quivers, we are able to conjecture HWGs for arbitrary rank. We examine the commutation relations between quotient quiver subtraction and other diagrammatic techniques, such as Kraft-Procesi transitions, quiver folding, and discrete quotients.

Effect of Modulation Domain Coupled KalmanSpectral Filter on Speech Enhancement over Wireless Voiced Communication System

Noise suppression in speech signal has received great attention in signal processing research community. However, existing methods of noise suppression such as the time-domain Kalman filter suffers from the psychoacoustic and physiological characteristic feature while the spectral subtraction technique performed in the modulation domain is associated with remnant noise in the enhanced speech signal. Therefore, in this paper, a Modulation-Domain coupled Kalman-Spectral Filtering (MD-KSF) in a single channel system is proposed to address the aforementioned shortcomings in the two existing techniques. The simulation of the proposed coupled MD-KSF was performed in MATLAB software environment. The evaluation of the proposed coupled MD-KSF technique in terms of Spectral waveform, Mean Square Error (MSE) and Log Spectral Distance (LSD) was performed. Furthermore, Perceptual Evaluation of Speech Quality (PESQ) and Short-Time Objective Intelligibility (STOI) tests were employed to validate the quality and intelligibility of the proposed coupled MD- KSF using the NOIZEUS corpus data set. The proposed technique shows significant noise suppression over the existing techniques.

New astrometric positions for six Jovian irregular satellites using Gaia DR3 in 2016 — 2021

The Jovian system is like a miniature solar system, with the system being more than twice as massive as all the other planets combined and having many natural satellites. Its formation and early evolution had a profound influence on our knowledge of the sculpting of the architecture of the solar system. Astrometric observations are of importance, based on these observations, its orbital dynamics, as well as its origin sometimes, of a solar system object can be determined. We aimed to obtain good astrometric positions of some irregular satellites Elara, Pasiphae, Sinope, Lysithea, Carme and Ananke to refine their orbits and ephemerides, and to understand their dynamics. We have taken, processed and reduced 964 ground-based CCD frames obtained between 2016 and 2021 by the 2.4 m telescope at the Lijiang Station of Yunnan Observatory over 27 nights. Among CCD image processing, the image subtraction technique of ISIS is employed to eliminate star images that are close to or/and almost overlapped with those of our targets in 2019. The star catalog Gaia DR3 is utilized for astrometric calibration, in which a weight scheme is applied to solve more accurate plate model. For all targets, their theoretical positions are retrieved from the Institut de Mécanique Céleste et de Calcul des Éphémérides (IMCCE) ephemeris. Our results show the mean (O - C)s (observed minus computed) of the positional residuals of all targets are −0′′.007 and 0′′.011 in R.A. and Dec., respectively. Their corresponding standard deviations are about 0′′.05 in each direction, except for Ananke (the faintest satellite) with its standard deviation about 0′′.08 in each direction.

Simultaneous iodine and barium imaging with photon-counting CT

Photon-counting computed tomography (PCCT) could potentially offer significant improvements in image quality over conventional CT. This study leverages PCCT technology to achieve simultaneous multi-contrast imaging of barium (Z=56) and iodine (Z=53), addressing the limitations of conventional CT in differentiating materials with similar atomic numbers. Using a bench-top PCCT system with a cadmium zinc telluride (CZT) detector, experiments were conducted with various phantom setups and 1-5\% contrast agent concentrations and in a biological sample. Energy thresholds were adjusted to the K-edge absorption energies of barium (37.4 keV) and iodine (33.2 keV) to capture multi-energy CT images. K-edge decomposition was performed with K-edge subtraction and principal component analysis (PCA) techniques, demonstrating clear differentiation and accurate quantification of contrast agents in phantom combinations and the biological sample. The PCCT system successfully differentiated and quantified barium and iodine, with high correlations ($R^2 \approx 1$) between true and reconstructed concentrations. PCA outperformed K-edge subtraction by providing a superior capability to differentiate barium and iodine, particularly when calcium was present in the scanned object. These findings highlight the potential of PCCT for reliable, detailed imaging in clinical and research applications, particularly for contrast agents with close atomic numbers.

Tailored for vehicle horn: A novel sound source capture method

The development of a horn sound capture system, capable of replacing manual control, offers a promising solution for alleviating horn noise. Existing horn sound systems lack localization algorithms that are capable of robustly resisting interference. To address this issue, this paper introduces an innovative horn sound capture system, including hardware and algorithms for sound recognition and localization. The pivotal innovation of the system is the introduction of the Sub-SRP algorithm, an advancement of the steered-response power phase transform (SRP-PHAT). Leveraging the frequency characteristics of horn sounds, it employs a series of filtering and spectral subtraction techniques to effectively filter out non-horn noise sources. In the interference experiment and field test, the Sub-SRP algorithm achieved DOA scores of 0.96 and 0.95, respectively, showing an approximately 10% improvement compared to the existing weighted SRP-PHAT, this improvement is even more pronounced under strong interference, increasing to 17%. This improvement significantly enhances the system’s ability to accurately capture honking vehicles and resist interference. Additionally, the horn sound recognition algorithm achieved accuracy rates of 95.75% and 97.65% in the two experiments, demonstrating the system’s effectiveness. In summary, this study contributes to the advancement of urban noise management and intelligent noise control systems.

Multiphase distribution in partly saturated hierarchical nonwoven fibre networks under applied load using X-ray computed tomography

In many industrial applications, nonwoven fibre networks are facilitated to operate under partly saturated conditions, allowing for filtration, liquid absorption and liquid transport. Resolving the governing liquid distribution in loaded polyamide-6 (PA6) fibre networks using X-ray computed micro-tomography is a challenge due to the similar X-ray attenuation coefficients of water and PA6 and limitations in using background subtraction techniques if the network is deformed, which will be the case if subjected to compression. In this work, we developed a method using a potassium iodide solution in water to enhance the liquid’s attenuation coefficient without modifying the water’s rheological properties. Therefore, we studied the evolving liquid distribution in loaded and partly saturated PA6 fibre networks on the microscale. Increasing the external load applied to the network, we observed an exponential decrease in air content while the liquid content was constant, increasing the overall saturation with increasing network strain. Furthermore, the microstructural properties created by the punch-needle process in the manufacturing of the network significantly influenced the out-of-plane liquid distribution. The method has been proven helpful in understanding the results of adaptions in both the fibre network design and manufacturing process, allowing for investigating the resulting liquid distribution on a microscale.

Empirical Analysis of the SAC-OCDMA-WDM System by Leveraging the AND Subtraction Technique

Empirical Analysis of the SAC-OCDMA-WDM System by Leveraging the AND Subtraction Technique

Application of posture estimation optimization algorithm in the analysis of college air volleyball teaching movements

The advent of computer technology and the modernization of sports education have led to an increasing reliance on intelligent technology in the field of sports education. To improve the application effect of intelligent technology in physical education, a volleyball motion analysis technology combined with pose estimation optimization algorithm is designed. During the process, Kinect collects the joint data of the research object, which is then combined with the commonly used background subtraction and frame difference optimization techniques. This results in the construction of a background model. The background update number in the initial frame is utilized as the reference value. The contour edge is smoothed through the application of a one-dimensional Gaussian kernel function, and a teaching action guidance system is designed. The experimental results showed that the average accuracy of the research method reached 79.7 % and the average recall rate reached 75.2 %. The average relative error of the method was 4.13 % when comparing the accuracy of human body model. The research method is validated to accurately capture and analyze volleyball motion, which can provide some technical help for sports teaching.

Feedback in Emerging Extragalactic Star Clusters, FEAST: The Relation between 3.3 μm Polycyclic Aromatic Hydrocarbon Emission and Star Formation Rate Traced by Ionized Gas in NGC 628

We present maps of ionized gas (traced by Paα and Brα) and 3.3 μm polycyclic aromatic hydrocarbon (PAH) emission in the nearby spiral galaxy NGC 628, derived from new JWST/NIRCam data from the Feedback in Emerging extrAgalactic Star clusTers (FEAST) survey. With this data, we investigate and calibrate the relation between 3.3 μm PAH emission and star formation rate (SFR) in and around emerging young star clusters (eYSCs) on a scale of ∼40 pc. We find a tight (correlation coefficient ρ ∼ 0.9) sublinear (power-law exponent α ∼ 0.75) relation between the 3.3 μm PAH luminosity surface density and SFR traced by Brα for compact, cospatial (within 0.″16 or ∼7 pc) peaks in Paα, Brα, and 3.3 μm (eYSC–I). The scatter in the relationship does not correlate well with variations in local interstellar medium metallicity, due to a radial metallicity gradient, but rather is likely due to stochastic sampling of the stellar initial mass function (IMF) and variations in the PAH heating and age of our sources. The deviation from a linear relation may be explained by PAH destruction in more intense ionizing environments, variations in age, and IMF stochasticity at intermediate to low luminosities. We test our results with various continuum subtraction techniques using combinations of NIRCam bands and find that they remain robust with only minor differences in the derived slope and intercept. An unexpected discrepancy is identified between the relations of hydrogen recombination lines (Paα versus Brα; Hα versus Brα).

Exploring the Archives: A Search for Novae in UVIT Snapshots of M31

Extensive multiwavelength studies of novae have been carried out in our galaxy and in M31 for decades. However, UV studies of extragalactic novae are limited, especially those in quiescence. For the first time, we present a UV catalog of novae in M31 using the archival AstroSat Ultraviolet Imaging Telescope (UVIT) imaging data. We used two image subtraction techniques to retrieve objects located deep into the M31 central region. We have found 42 novae in total in the UVIT images, 15 of which have been detected in multiple filters in the far-ultraviolet (FUV) and near-ultraviolet. The novae detected at quiescence show signatures of accretion disk from their UV spectral energy distributions, whereas those in the outburst phase show signatures of a pseudo-photosphere. A few novae were also detected in multiple epochs. Some show a near-constant FUV magnitude at quiescence, while others caught near the outburst reveal pre-eruption dips in their light curves. We conclude with a discussion on the significance of UV surveys in illuminating theoretical predictions for novae systems, including detecting the elusive early UV flash.

Dynamic Traffic Light Algorithm Incorporating Post-Intersection Space Allocation

This research presents a novel dynamic traffic light algorithm designed to optimize traffic flow and reduce traffic congestion by dynamically allocating green time based on post-intersection space availability. The algorithm employs a three-stage process: input generation, processing, and output. The input stage involves capturing traffic images using cameras strategically placed at intersections, which are then processed using background subtraction, edge detection, and object counting techniques. The processing phase includes vehicle counting using the YOLOv8 algorithm and open space calculation based on the maximum capacity of each road section. The output phase involves dynamically allocating green time to roads based on available post-intersection space and occupancy rates. The algorithm is designed to adapt to changing traffic conditions by continuously monitoring the post- intersection space and adjusting green times accordingly. It also incorporates a reset timer to ensure the algorithm loops back to the initial stage of gathering and processing traffic images. Simulation experiments using a physical model with toy vehicles and a camera setup demonstrated the benefits of this approach. Compared to the density-based approaches[1], this algorithm reduced average vehicle delay by 20-30%, increased overall intersection throughput by 15-25%, and decreased maximum queue lengths in each lane by 25-35%. It also adapted more effectively to fluctuations in traffic conditions, improving performance metrics by 20-30%. These results highlight the potential of incorporating downstream space considerations into traffic light control algorithms to enhance intersection efficiency, reduce traffic congestion, and enable more adaptive and fair traffic management.

Novel Deep Learning Domain Adaptation Approach for Object Detection Using Semi-Self Building Dataset and Modified YOLOv4

Moving object detection is a vital research area that plays an essential role in intelligent transportation systems (ITSs) and various applications in computer vision. Recently, researchers have utilized convolutional neural networks (CNNs) to develop new techniques in object detection and recognition. However, with the increasing number of machine learning strategies used for object detection, there has been a growing need for large datasets with accurate ground truth used for the training, usually demanding their manual labeling. Moreover, most of these deep strategies are supervised and only applicable for specific scenes with large computational resources needed. Alternatively, other object detection techniques such as classical background subtraction need low computational resources and can be used with general scenes. In this paper, we propose a new a reliable semi-automatic method that combines a modified version of the detection-based CNN You Only Look Once V4 (YOLOv4) technique and background subtraction technique to perform an unsupervised object detection for surveillance videos. In this proposed strategy, background subtraction-based low-rank decomposition is applied firstly to extract the moving objects. Then, a clustering method is adopted to refine the background subtraction (BS) result. Finally, the refined results are used to fine-tune the modified YOLO v4 before using it in the detection and classification of objects. The main contribution of this work is a new detection framework that overcomes manual labeling and creates an automatic labeler that can replace manual labeling using motion information to supply labeled training data (background and foreground) directly from the detection video. Extensive experiments using real-world object monitoring benchmarks indicate that the suggested framework obtains a considerable increase in mAP compared to state-of-the-art results on both the CDnet 2014 and UA-DETRAC datasets.

Reassessment of the Shirley and model functions within X-ray photoelectron spectroscopy analysis utilizing comprehensive search methodologies of Bayesian inference

The analysis of X-ray photoelectron spectra often faces challenges due to the lack of standardization in modeling approaches, background subtraction methods, and computational algorithms within the field of computer science. The interpretation of XPS data significantly relies on the unique expertise and judgment of individual researchers. Therefore, the objective of this study is to highlight the difficulties associated with analytical methods that depend heavily on the discretion of individual scientists, to elucidate the prevailing models and background subtraction techniques, and to suggest improvements to these methodologies. This endeavor aims to enhance the reliability and reproducibility of XPS analysis, thereby contributing to the advancement of research in this area. By utilizing the information criterion as part of a thorough search methodology in Bayesian inference, we show that our sophisticated analytical techniques significantly outperform others in the analysis of actual X-ray photoelectron spectroscopy (XPS) spectra. This improvement is evidenced through enhanced accuracy and reliability in spectral interpretation, underscoring the efficacy of our methods in practical applications of XPS.

A case for using methods from natural science in advancing the field of cognitive neuroscience.

Cognitive neuroscience seeks to pinpoint the neural basis of cognitive function. Application of scientific methods can be credited for its advancement within the field of psychology. Past approaches such as phrenology, that linked bumps on the skull to mental capabilities, initially gained popularity, but the lack of experimental testing contributed to its demise. Research in neuropsychology and the use of the double dissociation experimental technique subsequently emerged. Objective measurements of behaviour following selective damage within the brain led to a paradigm shift. More recently, application of the subtraction technique, coupled with the emergence of cognitive neuroimaging tools, has allowed psychologists to isolate and measure specific functions such as language, vision, memory, and recognition of emotion. Importantly, these approaches enable reliable prediction of behaviours, given parameters of brain integrity, a key goal within the field of psychology.

Mitigating the Blurring Effect of CryoEM Averaging on a Flexible and Highly Symmetric Protein Complex through Sub-Particle Reconstruction.

Many macromolecules are inherently flexible as a feature of their structure and function. During single-particle CryoEM processing, flexible protein regions can be detrimental to high-resolution reconstruction as signals from thousands of particles are averaged together. This "blurring" effect can be difficult to overcome and is possibly more pronounced when averaging highly symmetric complexes. Approaches to mitigating flexibility during CryoEM processing are becoming increasingly critical as the technique advances and is applied to more dynamic proteins and complexes. Here, we detail the use of sub-particle averaging and signal subtraction techniques to precisely target and resolve flexible DARPin protein attachments on a designed tetrahedrally symmetric protein scaffold called DARP14. Particles are first aligned as full complexes, and then the symmetry is reduced by alignment and focused refinement of the constituent subunits. The final reconstructions we obtained were vastly improved over the fully symmetric reconstructions, with observable secondary structure and side-chain placement. Additionally, we were also able to reconstruct the core region of the scaffold to 2.7 Å. The data processing protocol outlined here is applicable to other dynamic and symmetric protein complexes, and our improved maps could allow for new structure-guided variant designs of DARP14.

Simultaneous Photometric and Spectroscopic Analysis of V505 Lacertae, a Photospherically and Chromospherically Active Contact Binary Star

New BVR photometric and high-resolution spectroscopic observations of V505 Lac are presented with Transiting Exoplanet Survey Satellite (TESS) photometric data. The orbital period has experienced a secular decrease during the past 16 yr. A clear anticorrelation in the primary and secondary eclipse timing variation (PSETV) obtained from the TESS data is also identified. A double-lined radial velocity (RV) curve is secured, and the effective temperatures of the less- and more-massive stars (Stars 1 and 2, respectively) are measured. Using a spectral subtraction technique, excess emissions are detected in the time-series Ca ii H and K and Hα lines for Star 2. Simultaneous analysis of the light and RV curves using the Wilson–Devinney (WD) code reveals that V505 Lac is a photospherically and chromospherically active W-subtype contact binary system. The component-star masses and radii are determined to an accuracy of approximately 1%. The WD spot model is individually applied to 221 light curves segmented from the TESS data so as to derive the spot parameters of a cool spot on Star 2. The combined variations in both longitude and colatitude among the spot parameters appear to be strongly associated with those of both the anticorrelation in the PSETV and the O’Connell effect in the TESS light curves. Robust negative linear relationships between the PSETV anticorrelation size and the O’Connell effect magnitude are found for the first time. Mass–radius, mass–luminosity, and mass ratio–mass diagrams of contact binaries, along with the mass ratio frequency distribution, are presented in an attempt to elucidate the evolutionary characteristics of these systems.

Hybrid Semiconductor Wafer Inspection Framework via Autonomous Data Annotation

Abstract In smart manufacturing, semiconductors play an indispensable role in collecting, processing, and analyzing data, ultimately enabling more agile and productive operations. Given the foundational importance of wafers, the purity of a wafer is essential to maintain the integrity of the overall semiconductor fabrication. This study proposes a novel automated visual inspection (AVI) framework for scrutinizing semiconductor wafers from scratch, capable of identifying defective wafers and pinpointing the location of defects through autonomous data annotation. Initially, this proposed methodology leveraged a texture analysis method known as gray-level co-occurrence matrix (GLCM) that categorized wafer images—captured via a stroboscopic imaging system—into distinct scenarios for high- and low-resolution wafer images. GLCM approaches further allowed for a complete separation of low-resolution wafer images into defective and normal wafer images, as well as the extraction of defect images from defective low-resolution wafer images, which were used for training a convolutional neural network (CNN) model. Consequently, the CNN model excelled in localizing defects on defective low-resolution wafer images, achieving an F1 score—the harmonic mean of precision and recall metrics—exceeding 90.1%. In high-resolution wafer images, a background subtraction technique represented defects as clusters of white points. The quantity of these white points determined the defectiveness and pinpointed locations of defects on high-resolution wafer images. Lastly, the CNN implementation further enhanced performance, robustness, and consistency irrespective of variations in the ratio of white point clusters. This technique demonstrated accuracy in localizing defects on high-resolution wafer images, yielding an F1 score greater than 99.3%.

BSEO-Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic Devices.

Microfluidic models have become essential instruments for studying enhanced oil recovery techniques through fluid and chemical injection into micromodels to observe interactions with pore structures and resident fluids. The widespread use of cost-effective lab-on-a-chip devices, known for efficient data extraction and minimal reagent usage, has driven demand for efficient data management methods crucial for high-performance data and image analyses. This article introduces a semiautomatic method for calculating oil recovery in polymeric nanofluid flooding experiments based on the background subtraction (BSEO). It employs the background subtraction technique, generating a foreground binary mask to detect injected fluids represented as pixel areas. The pixel difference is then compared to a threshold value to determine whether the given pixel is foreground or background. Moreover, the proposed method compares its performance with two other representative methods: the ground truth (manual segmentation) and Fiji-ImageJ software. The experiments yielded promising results. Low values of mean-squared error (MSE), mean absolute error (MAE), and root-mean-squared error (RMSE) indicate minimal prediction errors, while a substantial coefficient of determination (R2) of 98% highlights the strong correlation between the method's predictions and the observed outcomes. In conclusion, the presented method emphasizes the viability of BSEO as a robust alternative, offering the advantages of reduced computational resource usage and faster processing times.