Spot Extraction Research Articles

Grayscale Iterative Star Spot Extraction Algorithm Based on Image Entropy

Star trackers are susceptible to interference from stray light, such as sunlight, moonlight, and Earth atmosphere light, in the space environment, resulting in an overall improvement in the star image grayscale, poor background uniformity, low star extraction rate, and high number of false star spots. In response to these challenges, this paper proposes a grayscale iterative star spot extraction algorithm based on image entropy. The implementation of the algorithm is mainly divided into two steps: (1) The algorithm conducts multiple grayscale iterations, effectively utilizing the prior information on the local contrast of star spots to filter out stray light backgrounds to a certain extent. (2) By establishing an inner–outer template, the image entropy algorithm is employed to obtain the real star targets to be extracted, which further suppresses the background clutter and noise. Numerical simulations and experimental results demonstrate that, compared to traditional detection algorithms, this algorithm can effectively suppress background stray light, enhance star extraction rates, and reduce the number of false star spots, and it exhibits superior detection performance in complex backgrounds across various scenarios.

Maximum radial pattern matching for minimum star map identification

This paper proposes an all-sky star map identification algorithm that can simultaneously achieve high identification probability, low algorithm complexity, and small databases for well photometric and intrinsic parameters-calibrated star sensors. The proposed algorithm includes three main steps. First, a binary radial pattern table is constructed offline. Then, the maximum value matching of the radial pattern is performed between the star spots and the guide stars, and the star pairs (i.e., the minimum star map) after radial pattern matching undergo a coarse matching through angular distance cross-validation. Finally, a reference star map is designed based on the identified star pairs, and the matching of all the star spots in the field of view is realized. Simulation and analysis results show that the database required by the proposed algorithm for 5,000 guide stars is not larger than 200 KB. Also, when false and missing star spots account for 50% of all guide stars and the star spot extraction error is 0.5 pixel (the corresponding pointing error is 26″), the average star map identification time of the proposed algorithm is less than 2 ms, and its identification probability is higher than 98%. The results demonstrate that the proposed algorithm performs better than similar algorithms.

Detection of α-Galactosidase A Reaction in Samples Extracted from Dried Blood Spots Using Ion-Sensitive Field Effect Transistors.

Fabry disease is a lysosomal storage disorder caused by a significant decrease in the activity or absence of the enzyme α-galactosidase A. The diagnostics of Fabry disease during newborn screening are reasonable, due to the availability of enzyme replacement therapy. This paper presents an electrochemical method using complementary metal-oxide semiconductor (CMOS)-compatible ion-sensitive field effect transistors (ISFETs) with hafnium oxide-sensitive surfaces for the detection of α-galactosidase A activity in dried blood spot extracts. The capability of ISFETs to detect the reaction catalyzed by α-galactosidase A was demonstrated. The buffer composition was optimized to provide suitable conditions for both enzyme and ISFET performance. The use of ISFET structures as sensor elements allowed for the label-free detection of enzymatic reactions with melibiose, a natural substrate of α-galactosidase A, instead of a synthetic fluorogenic one. ISFET chips were packaged with printed circuit boards and microfluidic reaction chambers to enable long-term signal measurement using a custom device. The packaged sensors were demonstrated to discriminate between normal and inhibited GLA activity in dried blood spots extracts. The described method offers a promising solution for increasing the widespread distribution of newborn screening of Fabry disease.

Alignment measurement of immersed tunnels based on laser differential imaging

The precision of underwater alignment for tunnel elements directly impacts the closure state of immersed tunnels. The current global navigation satellite system measurement tower method provides centimeter-level precision; however, this is insufficient due to the tower deformation at great water depths, which fails to meet alignment requirements. In this study, a novel immersed tunnel alignment measurement method using laser differential imaging is developed. A unique sensor layout is designed to measure the axial angle and distance between tunnel elements. The system design is implemented, and its laser data processing scheme is optimized to improve performance base on this sensor layout. The experimental results demonstrate that the accuracy of underwater laser spot extraction is better than 1.16 pixels, and the precision of axial angle and axial distance alignment measurements is better than 25″ and 1.6 mm, with repeatability precision exceeding 10.55″ and 0.53 mm. The laser-based alignment measurement method is not affected by the depth of water, satisfying the required precision of immersed tunnel alignment. These results can be applied to future immersed tunnel installations without measuring towers, providing a novel method for achieving automated and unmanned intelligent construction of immersed tunnels.

Theoretical Calculations and Experimental Investigation of Separation of Dy3+, Er3+, Tm3+ and Yb3+ by Cascade Extraction in TOGDA–Kerosene/Hydrochloric Acid System

The hot spot extraction agent N,N,N′,N′-tetraoctyl-3-oxoglutaramide (TODGA) has the advantages of novelty, high efficiency and environmental protection in the separation of rare earths by tandem extraction. In this paper, the characteristic parameters for the separation of dysprosium, erbium, thulium and ytterbium ions in the TOGDA–kerosene/hydrochloric acid system for tandem extraction were calculated. Using a 10% TODGA–kerosene/5 mol·L−1 hydrochloric acid extraction system, the mixture containing Dy3+ 0.258 mol·L−1, Er3+ 0.252 mol·L−1, Tm3+ 0.248 mol·L−1 and Yb3+ 0.242 mol·L−1 can be separated by three separation steps, and four products of Dy3+, Er3+, Tm3+ and Yb3+ can be obtained. The purity of the four products was 99.80%, 99.20%, 99.70% and 99.70%, and the yields were 99.03%, 98.97%, 97.88% and 96.98%, in order. In this paper, the reasons were analyzed for the differences in the ability of the TODGA system to extract heavy rare earth ions (REEs) in terms of density functionality, complex structure and bond valence model. These will provide more basic data and guidance for the industrial application of TODGA.

Star Spot Extraction for Multi-FOV Star Sensors Under Extremely High Dynamic Conditions

Star Spot Extraction for Multi-FOV Star Sensors Under Extremely High Dynamic Conditions

Tandem mass spectrometric assay of N-acetylglucosamine-6-sulfatase for multiplex analysis of mucopolysaccharidosis-IIID in dried blood spots

Previously we developed a multiplex liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay using dried blood spots for all subtypes of mucopolysaccharidoses (MPS) except MPS-IIID. Here we show that the MPS-IIID enzyme N-acetylglucosamine-6-sulfatase (GNS) is inhibited in dried blood spot (DBS) extracts, but activity can be recovered if the extract is diluted to reduce the concentrations of endogenous inhibitors. The new GNS assay displays acceptable characteristics including linearity in product formation with incubation time and amount of enzyme, low variability, and ability to distinguish MPS-IIID-affected from healthy patients using DBS. The assay can be added to the LC-MS/MS multiplex panel for all MPS subtypes requiring ∼2 min per newborn for the LC-MS/MS run.

Position measurement technology of boom-type roadheader based on binocular vision

Positioning of underground mining equipment in coal mines is a primary challenge in the intelligent development of tunnel excavation. Accurate and reliable position measurement plays a crucial role in improving excavation efficiency. However, the stability and accuracy of traditional measurement methods are difficult to guarantee due to factors such as vibration, magnetic interference, and the absence of GPS signals in coal mine environments. To address the problem of unstable measurement of the boom-type roadheader‘s position, this paper proposes a binocular vision-based measurement technique that utilizes four light spots as characteristic points. By processing the target images captured by the binocular camera and combining the minimum bounding rectangle and ellipse fitting of the spot regions, the method successfully obtains the four light spot characteristics. Subsequently, precise matching and stereo distance measurement of the target in the left and right images enable the determination of the boom-type roadheader’s posi-tion information. A positioning platform based on binocular vision is built and experimentally evaluated. The results demonstrate that this method can achieve accurate spot extraction and stable measurement of the machine’s position, even in complex backgrounds such as mixed lighting and low illumination. The planar measurement errors within a distance range of 50 m are all within ±25 mm, which basically meets the required construction precision for tunnel excavation.

A dynamic star spots extraction method based on pixel association

Star trackers are devices that determine high-accuracy attitude by observing stars. As aerospace technology develops, the dynamic performance of the star tracker becomes increasingly important. In the star images taken under high dynamic conditions, the star spots will be tailed or fractured. It will cause a sharp decrease in Signal-to-Noise Ratio (SNR) and make it a challenge to extract and position star spots. In this paper, an Object-to-Pixel star spots extraction and positioning method based on pixel association was proposed, which is a breakthrough compared to the existing Pixel-to-Object extraction method. In the proposed method, rough extraction of star spots is carried out by the Radon transform and image entropy. Precise positioning of star spots is carried out by Gaussian mixture model (GMM) and maximum likelihood estimation (MLE). Experimental results indicate that the proposed method can achieve reliable extraction for star spots with SNR less than 3 or even close to 1. Meanwhile, the positioning accuracy can be improved by at least twice compared with the conventional threshold method.

A Spatial–Temporal Star Spots Extraction for High Dynamics Star Sensors

In this article, we consider the problem of extracting star spots for traditional star sensors with short exposure time under high dynamic conditions (>5°/s). Under this setting, the signal-to-noise ratio (SNR) of star spots decreases visibly, resulting in less star information being presented, making most of star spots undetectable. We propose a spatial-temporal star spots extraction for this task. According to the constraints of angular distance and kinematic of stars in multi-frame images, a detection method based on spatial-temporal filtering is first proposed to detect star spots with low SNR. It establishes a star spot observation by using the correlation between its predicted image and measured image, which fuses the measurement information of the star and the motion information of the star sensor, and detects star spots based on the maximum a posteriori (MAP). Then, according to the imaging characteristics that the imaging region of a star spot can be regarded as a rectangle, an extraction method based on hypothesis-verification is developed to completely extract pixels of the star spot. It establishes the classification probability of a pixel by the multi-layer Gaussian mixture model, and extract pixels according to the maximum likelihood estimation (MLE) of the star image. Experiments demonstrate the extraction capability and accuracy of the method. In conclusion, there is a promising application value for the method to improve the attitude estimation accuracy and update rate of star sensor under high dynamic conditions.

Centroid Extraction of Laser Spots Captured by Infrared Detectors Combining Laser Footprint Images and Detector Observation Data

On-orbit geometric calibration of satellite-borne laser based on infrared detectors is the key tool to ensure the elevation measurement accuracy, and the accuracy of on-orbit geometric calibration is directly determined by laser spots captured by detectors. Mathematical methods, such as gray-scale barycenter, are widely applied for centroid extraction of spots captured by infrared detectors and completely depend on the energy values at points measured by detectors, which have low precision and are greatly affected by the consistency of the detectors and other factors at present. Based on the above question, considering the consistency between the real laser footprint shape and spot captured by detectors, a centroid extraction method of laser spots captured by infrared detectors combining laser footprint images and detector observation data is proposed for making up this defect to some extent. First, the self-adaptive “two-step method” is used to denoise footprint images hierarchically to obtain the real shape of footprints for constraining the spots captured by detectors, and then the centroids of spots are extracted by using the energy-weighted barycenter method based on regional blocks. In the experiment, Gaofen-7 (GF-7) satellite is taken as the research object, and the proposed method, as well as the other six methods, are used for the centroid extraction of laser spots captured by detectors, the calculation of calibration parameters based on the single-beam and dual-beam laser calibration models, the positioning of laser footprints, and cross verification. According to the results, the plane accuracy of centroid extraction using the proposed method is as follows: 0.34 grids for Beam 1 and 0.33 grids for Beam 2. In addition, on flat terrain, the elevation accuracy of Beam 1 and Beam 2 in 2021 is 5.2 cm and 5.0 cm, respectively, 0.6 cm and 4.2 cm higher than those in the most accurate one among other methods; the elevation accuracy in 2020 is 23.3 cm and 7.1 cm, respectively, 7.7 cm and 2.7 cm higher than those in the most accurate one among other methods. On slopes and gentle slopes, the method proposed is also superior to other methods. Since the change of pointing angle caused by satellite jitter, atmosphere, etc., between different years, the accuracy drops when laser footprints of 2020 are located using the parameters of 2021. In summary, under different terrains and years, the results fully demonstrate the effectiveness and accuracy of the proposed method, which has more significant advantages than other traditional methods.

A Star Spot Extraction Method Based on SSA-UNet for Star Sensors Under Dynamic Conditions

Dynamic performance is an important index of star sensors. When the star sensor is under dynamic conditions, imaged star spots stretch to steaks as the energy is more dispersive than that of static conditions, which leads to a decrease in the signal-to-noise ratio (SNR) of the star spots. The threshold method is unable to detect enough stars under dynamic conditions. However, star sensors need sufficient stars to guarantee normal work. The image enhancement method enhances the star spots by integrating along with their movement directions. However, it still misses a lot of low-SNR stars as the determination of movement directions is sensitive to noise, which leads to the limited enhancement of low-SNR stars. To solve the problem, this study presented an improved UNet-based star spot extraction method for star sensors under dynamic conditions. The improved UNet, defined as SSA-UNet, used a star streak attention module (SSAM) to help it focus on the star spots and improve the extraction performance. The presented method used SSA-UNet for the rough extraction and modified the rough extraction in the subsequent process. Both simulation and ground experiments proved that the proposed method can accurately extract more low-SNR star spots than the image enhancement method, which had great significance for further improving the dynamic performance of star sensors.

Association between Vitamin B and Obesity in Middle-Aged and Older Chinese Adults.

Previous studies have found that obese people have lower levels of vitamin B, but most have focused on obesity as defined by body mass index (BMI), and its relationship with other types of obesity is unclear. The aim of this study was to explore the relationship between vitamin B levels and obesity assessed by different definitions among Chinese middle-aged and older community-dwelling adults. This cross-sectional study included 887 participants aged 45 years and older (45-82 years). The concentrations of vitamin B (B1, B2, B6, and B9) were measured by robotic dry blood spot extraction systems in combination with liquid chromatography-tandem mass spectrometry. BMI, body fat percentage (BF%), visceral fat area (VFA), and waist circumference (WC) were used to diagnose obesity. VFA and BF% were assessed by bioelectrical impedance analysis. The logistic regression model was used to assess the associations between vitamin B levels and the odds of obesity. The average age of all participants was 60.77 (SD 6.33) years. The prevalence of obesity varied from 8.6% to 52.4% depending on different diagnostic criteria. After adjusting for covariates, a negative correlation was observed between vitamin B1 level and obesity according to the criteria of WC, VFA, and BF%, and the adjusted odds ratio (OR) was 0.47, 0.52, and 0.46, respectively. When using WC and BF% to define obesity, higher quartiles of vitamin B2 were negatively associated with the odds of obesity (OR: 0.62 and 0.62, respectively). Vitamin B6 was inversely associated with VFA-defined and BF%-defined obesity (OR: 0.64 and 0.64, respectively). When using VFA and BF% to define obesity, a negative correlation was observed in vitamin B9 (OR: 0.61 and 0.67, respectively). Vitamin B (B1, B2, B6, and B9) level was negatively related to obesity (defined by WC, VFA, or BF%) in Chinese middle-aged and older adults.

A novel approach for apple leaf disease image segmentation in complex scenes based on two-stage DeepLabv3+ with adaptive loss

In complex environments, overlapping leaves and uneven light can make pixels of leaf edges difficult to identify, resulting in a poor segmentation performance of the target leaf. In addition, the pixel ratio imbalance between the background area and the target area is the main reason that undermines the accuracy of spot extraction. To address these problems, a novel two-stage DeepLabv3+ with adaptive loss is proposed for the segmentation of apple leaf disease images in complex scenes. The proposed adaptive loss adds a modulation factor to the cross-entropy (CE) loss that can reduce the weight of losses generated by easily classified pixels. Therefore, it allows the model to focus more on hard-to-classify pixels during learning, thus improving segmentation accuracy. The novel two-stage model, consisting of Leaf-DeepLabv3+ and Disease-DeepLabv3+, is named LD-DeepLabv3+. In the first stage of the proposed model, Leaf-DeepLabv3+ is employed to extract the leaves from the complex environment. At this stage, the receptive field block (RFB) and the reverse attention (RA) module are introduced to improve the perception ability of the model for different sizes of blades and their edges. Then, the Disease-DeepLabv3+ is designed to segment disease spots from the erased background leaf images in the second stage of the proposed model. In the Disease-DeepLabv3+, the rates of the dilated convolution in atrous spatial pyramid pooling (ASPP) are adjusted to make it more suitable for extracting smaller targets, and the channel attention block (CAB) is introduced to highlight significant spot information and suppress unimportant information. The experimental results show that the proposed method, which combines LD-DeepLabv3+ with the adaptive loss, reaches 98.70% intersection over union (IoU) for leaf segmentation and 86.56% IoU for spot extraction. Compared with the two-stage model DUNet, the proposed method improves the segmentation accuracy of leaves and spots by 0.93% and 4.27%, respectively. Moreover, the total number of parameters and floating points of operations of the proposed method are only 16.96% and 18.25% of those of DUNet, respectively. Hence, the proposed method can provide an effective solution to extract leaves and disease spots in complex environments and has lower computational costs. This makes it suitable for deployment on mobile devices for applications in agriculture.

Development of the dried blood spot preparation protocol for comprehensive evaluation of the hematocrit effect

The application of dried blood spots (DBS) has gradually increased in different fields because of its several advantages. The hematocrit (Hct) effect is one major analytical challenge that may affect the quantification accuracy of DBS samples and should be investigated when developing a novel DBS method. However, previous studies usually overlooked the Hct-related distribution bias when evaluating the Hct effect. This study aimed to propose an effective DBS preparation protocol for the comprehensive evaluation of the Hct effect. We selected voriconazole and posaconazole as the demonstration drugs. Fifteen microliters of the blood samples were spotted on DBS cards followed by whole spot extraction. An LC-MS/MS method was first developed to quantify voriconazole and posaconazole in DBS samples. The quantitation accuracy for both azole drugs was within 93.5%–111.7%, except for the accuracies of posaconazole at the LLOQ, which were less than 119%. The intra- and interday precision were below 11%. The validated LC-MS/MS method was used to develop the DBS preparation protocol for evaluating the Hct effect. Three critical parameters that may affect the observed Hct effect were investigated. The results showed that using the solid-state of the target analytes, spiking the target analytes before preparing different Hct levels, and allowing enough equilibrium time after spiking target analytes can provide a more holistic Hct effect evaluation. The validity of the proposed new protocol was verified by conversion factors obtained from 71 paired DBS and plasma samples. Conversion factors calculated by clinical samples were consistent with the Hct effect evaluated by manually prepared DBS samples. This new DBS preparation protocol eliminated the common pitfalls in studying the Hct effect and offered a comprehensive strategy to assess the Hct effect for further DBS studies.

Improving Harmonization and Standardization of Expanded Newborn Screening Results by Optimization of the Legacy Flow Injection Analysis Tandem Mass Spectrometry Methods and Application of a Standardized Calibration Approach.

Newborn screening (NBS) laboratories in the United Kingdom adhere to common protocols based on single analyte cutoff values (COVs); therefore, interlaboratory harmonization is of paramount importance. Interlaboratory variation for screening analytes in UK NBS laboratories ranges from 17% to 59%. While using common stable isotope internal standards has been shown to significantly reduce interlaboratory variation, instrument set-up, sample extraction, and calibration approach are also key factors. Dried blood spot (DBS) extraction processes, instrument set-up, mobile-phase composition, sample introduction technique, and calibration approach of flow injection analysis-tandem mass spectrometry (FIA-MS/MS) methods were optimized. Inter- and intralaboratory variation of methionine, leucine, phenylalanine, tyrosine, isovaleryl-carnitine, glutaryl-carnitine, octanoyl-carnitine, and decanoyl-carnitine were determined pre- and postoptimization, using 3 different calibration approaches. Optimal recovery of analytes from DBS was achieved with a 35-min extraction time and 80% methanol (150 μL). Optimized methodology decreased the mean intralaboratory percentage relative SD (%RSD) for the 8 analytes from 20.7% (range 4.1-46.0) to 5.4% (range 3.0-8.5). The alternative calibration approach reduced the mean interlaboratory %RSD for all analytes from 16.8% (range 4.1-25.0) to 7.1% (range 4.1-11.0). Nuclear magnetic resonance analysis of the calibration material highlighted the need for standardization. The purities of isovaleryl-carnitine and glutaryl-carnitine were 85.13% and 69.94% respectively, below the manufacturer's stated values of ≥98%. For NBS programs provided by multiple laboratories using single analyte COVs, harmonization and standardization of results can be achieved by optimizing legacy FIA-MS/MS methods, adopting a common analytical protocol, and using standardized calibration material rather than internal calibration.

Spot extraction and analysis using an automatic detection method of tourist spots using SNS

Spot extraction and analysis using an automatic detection method of tourist spots using SNS

Spot extraction and analysis using an automatic detection method of tourist spots using SNS

Spot extraction and analysis using an automatic detection method of tourist spots using SNS

Prediction of Oil Spill Trajectory on the Ocean Surface Using Mathematical Modeling

A simplistic mathematical model based on the law of motion, for predicting oil spill trajectory on the ocean surface utilizing random walk technique is proposed in this paper. Validation of the proposed model is performed by comparing results with the General NOAA Operational Modeling Environment (GNOME) model and the available set of periodic Sentinel -1 Synthetic Aperture Radar (SAR) images of the contingency location (Corsica oil spill incident in the Mediterranean Sea). SAR images are processed for speckle noise removal, dark spot detection, feature extraction and classification of dark spots as oil spills and look-alikes for suitability of comparison. The accuracy of prediction is evaluated using Centroid Skill Score (CSS) metric and is compared with that of the prediction results from MEDSLIK – II model. The results of proposed model are found to be in good agreement with available SAR images. The simulation also showed that using an hourly wind and ocean current data on the study region, more accurate prediction of the trajectory is possible.

Detecting Lung Cancer Using Machine Learning Techniques

In recent days, Internet of Things (IoT) based image classification technique in the healthcare services is becoming a familiar concept that supports the process of detecting cancers with Computer Tomography (CT) images. Lung cancer is one of the perilous diseases that increases the mortality rate exponentially. IoT based image classifiers have the ability to detect cancer at an early stage and increases the life span of a patient. It supports oncologist to monitor and evaluate the health condition of a patient. Also, it can decipher cancer risk marker and act upon them. The process of feature extraction and selection from CT images plays a key role in identifying cancer hot spots. Convolutional Neural Network (CNN) is one of the efficient feature extraction techniques that improves the performance of image classifier by reducing the entropy of image data sets. A Random Forest (RF) classifier is a machine learning technique that can improve its efficiency with the support of CNN. This paper presents an RF classifier with CNN based technique to improve the percentage of accuracy in detecting cancer hot spots with CT images. The experimentation of the proposed approach is based on three dimensions: Feature extraction, selection, and prediction of cancer hot spots. To evaluate the performance of the proposed approach, benchmark image repositories which consists of 3954 images and 50 low dose whole lungs CT scan images are employed. The proposed method achieves an effective result on all test images under different aspects. Consequently, it obtains an average accuracy of 93.25% and an F-measure of 91.75% which is higher than the other methods, comparatively.