Optic disc (OD) detection is an important step in developing systems for automated diagnosis of various serious ophthalmic pathologies. This paper presents a new template-based methodology for ...segmenting the OD from digital retinal images. This methodology uses morphological and edge detection techniques followed by the Circular Hough Transform to obtain a circular OD boundary approximation. It requires a pixel located within the OD as initial information. For this purpose, a location methodology based on a voting-type algorithm is also proposed. The algorithms were evaluated on the 1200 images of the publicly available MESSIDOR database. The location procedure succeeded in 99% of cases, taking an average computational time of 1.67 s. with a standard deviation of 0.14 s. On the other hand, the segmentation algorithm rendered an average common area overlapping between automated segmentations and true OD regions of 86%. The average computational time was 5.69 s with a standard deviation of 0.54 s. Moreover, a discussion on advantages and disadvantages of the models more generally used for OD segmentation is also presented in this paper.
We study a method to simulate quantum many-body dynamics of spin ensembles using measurement-based feedback. By performing a weak collective measurement on a large ensemble of two-level quantum ...systems and applying global rotations conditioned on the measurement outcome, one can simulate the dynamics of a mean-field quantum kicked top, a standard paradigm of quantum chaos. We analytically show that there exists a regime in which individual quantum trajectories adequately recover the classical limit, and show the transition between noisy quantum dynamics to full deterministic chaos described by classical Lyapunov exponents. We also analyze the effects of decoherence, and show that the proposed scheme represents a robust method to explore the emergence of chaos from complex quantum dynamics in a realistic experimental platform based on an atom-light interface.
This paper presents a new supervised method for blood vessel detection in digital retinal images. This method uses a neural network (NN) scheme for pixel classification and computes a 7-D vector ...composed of gray-level and moment invariants-based features for pixel representation. The method was evaluated on the publicly available DRIVE and STARE databases, widely used for this purpose, since they contain retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. The method proves especially accurate for vessel detection in STARE images. Its application to this database (even when the NN was trained on the DRIVE database) outperforms all analyzed segmentation approaches. Its effectiveness and robustness with different image conditions, together with its simplicity and fast implementation, make this blood vessel segmentation proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.
•New methodology for instance segmentation based on Mask R-CNN.•A reduced architecture for the backbone and the Mask network.•Substitution of the neural network and the non-maximum suppression ...algorithm.•New open access database for strawberry instance segmentation.•New evaluation metrics for instance segmentation methodologies.
Fruit instance segmentation is a key element for autonomous picking systems. This paper proposes a methodology for instance segmentation of strawberries using deep learning techniques. This methodology presents the following changes in relation to the well-known Mask R-CNN network: a new architecture is designed for the backbone and the mask network; the object classifier and the bounding-box regressor are removed; the non-maximum suppression algorithm is replaced with a new region grouping and filtering algorithm without increasing the order of complexity.
For this research, a new high-resolution data set of 3100 strawberry images is obtained, along with the corresponding manually-segmented ground-truth images (StrawDI_Db1 database). In addition to using widely-used metrics based on Average Precision (AP), a new performance metric is proposed: the Instance Intersection Over Union (I2oU), to assess different options of instance segmentation techniques. This material, which is published and available to the research community, allows the rigorous performance comparison between future methodologies.
The results obtained in the 200 images included in the test set show that the proposed methodology significantly reduces the inference time (10 fps vs. 5 fps) while maintaining competitive results to the original Mask R-CNN for mean AP (43.85 vs. 45.36) and mean I2oU (87.27 vs. 87.70) metrics. Thus, the proposed method can be considered as the most promising one in view of its possible integration in automatic strawberry picking systems.
A key enabler of intelligent maintenance systems is the ability to predict the remaining useful lifetime (RUL) of its components, i.e., prognostics. The development of data-driven prognostics models ...requires datasets with run-to-failure trajectories. However, large representative run-to-failure datasets are often unavailable in real applications because failures are rare in many safety-critical systems. To foster the development of prognostics methods, we develop a new realistic dataset of run-to-failure trajectories for a fleet of aircraft engines under real flight conditions. The dataset was generated with the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) model developed at NASA. The damage propagation modelling used in this dataset builds on the modelling strategy from previous work and incorporates two new levels of fidelity. First, it considers real flight conditions as recorded on board of a commercial jet. Second, it extends the degradation modelling by relating the degradation process to its operation history. This dataset also provides the health respectively fault class. Therefore, besides its applicability to prognostics problems, the dataset can be used for fault diagnostics.
In this work the content of seven heavy metals (Cd, Cr, Cu, Hg, Ni, Pb and Zn) and other parameters (the pH, organic matter, carbonates and granulometric fraction) in agricultural topsoil in the Ebro ...basin are quantified, based on 624 samples collected according to an 8 by 8
km square mesh. The average concentrations (mg/kg) obtained were: Cd 0.415
±
0.163, Cr 20.27
±
13.21, Cu 17.33
±
14.97, Ni 20.50
±
22.71, Pb 17.54
±
10.41, Zn 17.53
±
24.19 and Hg 35.6
±
42.05
μg/kg. The concentration levels are relatively low in areas of high pH and low organic matter content concentration. The results of factor analysis group Cd, Cu, Hg, Pb and Zn in F1 and Cr y Ni in F2. The spatial heavy metals component maps based on geostatistical analysis, show definite association of these factors with the soil parent material. The local anomalies (found in Cu, Zn and Pb) are attributed to anthropogenic influence.
Geostatistical analyses showed definite association of metals with soil parent materials.
Physics-based and data-driven models for remaining useful lifetime (RUL) prediction typically suffer from two major challenges that limit their applicability to complex real-world domains: (1) the ...incompleteness of physics-based models and (2) the limited representativeness of the training dataset for data-driven models. Combining the advantages of these two approaches while overcoming some of their limitations, we propose a novel hybrid framework for fusing the information from physics-based performance models with deep learning algorithms for prognostics of complex safety-critical systems. In the proposed framework, we use physics-based performance models to infer unobservable model parameters related to a system’s components health by solving a calibration problem. These parameters are subsequently combined with sensor readings and used as input to a deep neural network, thereby generating a data-driven prognostics model with physics-augmented features. The performance of the hybrid framework is evaluated on an extensive case study comprising run-to-failure degradation trajectories from a fleet of nine turbofan engines under real flight conditions. The experimental results show that the hybrid framework outperforms purely data-driven approaches by extending the prediction horizon by nearly 127%. Furthermore, it requires less training data and is less sensitive to the limited representativeness of the dataset as compared to purely data-driven approaches. Furthermore, we demonstrated the feasibility of the proposed framework on the original CMAPSS dataset, thereby confirming its superior performance.
•Novel hybrid framework for prognostics of complex safety-critical systems proposed.•The framework combines deep learning and physics-based performance models.•Deep neural networks are trained with physics-augmented features for RUL prediction.•Framework evaluated on the new CMPASS aero-engine degradation dataset.•The proposed framework outperforms equivalent purely data-driven approaches.
Remote sensing is a promising tool for the detection of floating marine plastics offering extensive area coverage and frequent observations. While floating plastics are reported in high ...concentrations in many places around the globe, no referencing dataset exists either for understanding the spectral behavior of floating plastics in a real environment, or for calibrating remote sensing algorithms and validating their results. To tackle this problem, we initiated the Plastic Litter Projects (PLPs), where large artificial plastic targets were constructed and deployed on the sea surface. The first such experiment was realised in the summer of 2018 (PLP2018) with three large targets of 10 × 10 m. Hereafter, we present the second Plastic Litter Project (PLP2019), where smaller 5 × 5 m targets were constructed to better simulate near-real conditions and examine the limitations of the detection with Sentinel-2 images. The smaller targets and the multiple acquisition dates allowed for several observations, with the targets being connected in a modular way to create different configurations of various sizes, material composition and coverage. A spectral signature for the PET (polyethylene terephthalate) targets was produced through modifying the U.S. Geological Survey PET signature using an inverse spectral unmixing calculation, and the resulting signature was used to perform a matched filtering processing on the Sentinel-2 images. The results provide evidence that under suitable conditions, pixels with a PET abundance fraction of at least as low as 25% can be successfully detected, while pinpointing several factors that significantly impact the detection capabilities. To the best of our knowledge, the 2018 and 2019 Plastic Litter Projects are to date the only large-scale field experiments on the remote detection of floating marine litter in a near-real environment and can be used as a reference for more extensive validation/calibration campaigns.
Surface treatments for textile substrates have received significant attention from researchers around the world. Ozone and plasma treatments trigger a series of surface alterations in textile ...substrates that can improve the anchoring of other molecules or particles on these substrates. This work aims to evaluate the effect of ozone and plasma treatments on the impregnation of polymeric microcapsules containing lavender oil in polyester fabrics (PES). Microcapsules with walls of chitosan and gum arabic were prepared by complex coacervation and impregnated in PES, plasma-treated PES, and ozone-treated PES by padding. The microcapsules were characterized for their size and morphology and the surface-treated PES was evaluated by FTIR, TGA, SEM, and lavender release. The microcapsules were spherical in shape, with smooth surfaces. The FTIR analyses of the textile substrates with microcapsules showed bands referring to the polymers of the microcapsules, but not to the lavender; this was most likely because the smooth surface of the outer wall did not retain the lavender. The mass loss and the degradation temperatures measured by TGA were similar for all the ozone-treated and plasma-treated polyester samples. In the SEM images, spherical microcapsules and the impregnation of the microcapsules of larger sizes were perceived. Through the lavender release, it was observed that the plasma and ozone treatments interfered both with the amount of lavender delivered and with the control of the delivery.
•A deep learning-based vessel segmentation method in fundus images is presented.•It uses a convolutional neural network based on a UNet model simplified version.•The method is evaluated on DRIVE, ...STARE and CHASE_Db1 retinal image databases.•It was capable of working with the highest level of accuracy and robustness.•The method reaches better performance than the rest of state-of-art methods.
Background and Objective: Automatic monitoring of retinal blood vessels proves very useful for the clinical assessment of ocular vascular anomalies or retinopathies. This paper presents an efficient and accurate deep learning-based method for vessel segmentation in eye fundus images.
Methods: The approach consists of a convolutional neural network based on a simplified version of the U-Net architecture that combines residual blocks and batch normalization in the up- and downscaling phases. The network receives patches extracted from the original image as input and is trained with a novel loss function that considers the distance of each pixel to the vascular tree. At its output, it generates the probability of each pixel of the input patch belonging to the vascular structure. The application of the network to the patches in which a retinal image can be divided allows obtaining the pixel-wise probability map of the complete image. This probability map is then binarized with a certain threshold to generate the blood vessel segmentation provided by the method.
Results: The method has been developed and evaluated in the DRIVE, STARE and CHASE_Db1 databases, which offer a manual segmentation of the vascular tree by each of its images. Using this set of images as ground truth, the accuracy of the vessel segmentations obtained for an operating point proposal (established by a single threshold value for each database) was quantified. The overall performance was measured using the area of its receiver operating characteristic curve. The method demonstrated robustness in the face of the variability of the fundus images of diverse origin, being capable of working with the highest level of accuracy in the entire set of possible points of operation, compared to those provided by the most accurate methods found in literature.
Conclusions: The analysis of results concludes that the proposed method reaches better performance than the rest of state-of-art methods and can be considered the most promising for integration into a real tool for vascular structure segmentation.
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