An aerobic, Gram-stain-positive, rod-shaped, endospore-forming bacterial strain, designated BB3-R1
, was isolated from cow faeces sampled in Daejeon, Republic of Korea. Growth was observed at 25-45 ...°C (optimum, 35-40 °C) and pH 7.0-9.0 (optimum, pH 8.0), with up to 3 % (w/v) NaCl (optimum, 0 % NaCl). blast analysis of 16S rRNA gene sequences revealed the highest sequence similarity of strain BB3-R1
to
NRRL NRS-818
(98.8 %) followed by
JCM 15085
(97.5 %). According to 16S rRNA gene and whole-genome based phylogenetic trees, strain BB3-R1
clustered with
FJAT-54423
and
NRRL NRS-818
. OrthoANI and dDDH values of strain BB3-R1
with the closely related strains were lower than 77.5 and 26.8 %, respectively. The major menaquinones and polar lipids of the strain were MK-7 and phosphatidylmonomethylethanolamine, diphosphatidylglycerol, phosphatidylglycerol and phosphatidylethanolamine, respectively. The major fatty acids (>10 %) were C
iso, C
iso, C
anteiso and C
7
alcohol. The cell-wall peptidoglycan contained cross-linked
-diaminopimelic acid (type A1 gamma). The phenotypic, chemotaxonomic and genotypic data obtained in this study showed that the strain represents a novel species of the genus
, for which the name
sp. nov. (type strain BB3-R1
=KACC 22663
=NBRC 115962
) is proposed.
This paper proposes an algorithm that improves ship detection accuracy using preprocessing and post-processing. To achieve this, high-resolution electro-optical satellite images with a wide range of ...shape and texture information were considered. The developed algorithms display the problem of unreliable detection of ships owing to clouds, large waves, weather influences, and shadows from large terrains. False detections in land areas with image information similar to that of ships are observed frequently. Therefore, this study involves three algorithms: global feature enhancement pre-processing (GFEP), multiclass ship detector (MSD), and false detected ship exclusion by sea land segmentation image (FDSESI). First, GFEP enhances the image contrast of high-resolution electro-optical satellite images. Second, the MSD extracts many primary ship candidates. Third, falsely detected ships in the land region are excluded using the mask image that divides the sea and land. A series of experiments was performed using the proposed method on a database of 1984 images. The database includes five ship classes. Therefore, a method focused on improving the accuracy of various ships is proposed. The results show a mean average precision (mAP) improvement from 50.55% to 63.39% compared with other deep learning-based detection algorithms.
Strain BSF-3M
is a Gram-stain-positive, non-flagellated, facultative anaerobic and rod-shaped bacterium that was isolated from fermented feed collected at a cattle farm in the Daejeon region of the ...Republic of Korea. It was studied using polyphasic taxonomic methods. Using 16S rRNA gene sequences and the resulting phylogenetic tree, the strain was primarily identified as a member of the genus
. Strain BSF-3M
contained a chromosome of 2.5 Mbp and a plasmid of 33.4 kbp. The G+C content of genomic DNA was 51.3 mol%. Strain BSF-3M
had the highest ortho-average nucleotide identity value of 73.7 % with
7-19
, its closest relative in the phylogenetic tree based on the 16S rRNA gene sequences and the phylogenomic tree based on up-to-date bacterial core genes. Based on the results of a polyphasic taxonomic study, strain BSF-3M
represents a novel species in the genus
, for which the name
sp. nov. is proposed. The type strain is BSF-3M
(=KACC 23028
=NBRC 116014
).
A common countermeasure to detect threatening drones is the electro-optical infrared (EO/IR) system. However, its performance is drastically reduced in conditions of complex background, saturation ...and light reflection. 3D laser sensor LiDAR is used to overcome the problems of 2D sensors like EO/IR, but it is not enough to detect small drones at a very long distance because of low laser energy and resolution. To solve this problem, A 3D LADAR sensor is under development. In this work, we study the detection methodology adequate to the LADAR sensor which can detect small drones at up to 2 km. First, a data augmentation method is proposed to generate a virtual target considering the laser beam and scanning characteristics, and to augment it with the actual LADAR sensor data for various kinds of tests before full hardware system developed. Second, a detection algorithm is proposed to detect drones using voxel-based background subtraction and variable radially bounded nearest neighbor (V-RBNN) method. The results show that 0.2 m L2 distance and 60% expected average overlap (EAO) indexes are satisfied for the required specification to detect 0.3 m size of small drones.
Unmanned aerial vehicles (UAVs) are equipped with optical systems including an infrared (IR) camera such as electro-optical IR (EO/IR), target acquisition and designation sights (TADS), or forward ...looking IR (FLIR). However, images obtained from IR cameras are subject to noise such as dead pixels, lines, and fixed pattern noise. Nonuniformity correction (NUC) is a widely employed method to reduce noise in IR images, but it has limitations in removing noise that occurs during operation. Methods have been proposed to overcome the limitations of the NUC method, such as two-point correction (TPC) and scene-based NUC (SBNUC). However, these methods still suffer from unfixed pattern noise. In this paper, a background registration-based adaptive noise filtering (BRANF) method is proposed to overcome the limitations of conventional methods. The proposed BRANF method utilizes background registration processing and robust principle component analysis (RPCA). In addition, image quality verification methods are proposed that can measure the noise filtering performance quantitatively without ground truth images. Experiments were performed for performance verification with middle wave infrared (MWIR) and long wave infrared (LWIR) images obtained from practical military optical systems. As a result, it is found that the image quality improvement rate of BRANF is 30% higher than that of conventional NUC.
Automatic segmentation of intracellular compartments is a powerful technique, which provides quantitative data about presence, spatial distribution, structure and consequently the function of cells. ...With the recent development of high throughput volumetric data acquisition techniques in electron microscopy (EM), manual segmentation is becoming a major bottleneck of the process. To aid the cell research, we propose a technique for automatic segmentation of mitochondria and endolysosomes obtained from urinary bladder urothelial cells by the dual beam EM technique. We present a novel publicly available volumetric EM dataset – the first of urothelial cells, evaluate several state-of-the-art segmentation methods on the new dataset and present a novel segmentation pipeline, which is based on supervised deep learning and includes mechanisms that reduce the impact of dependencies in the input data, artefacts and annotation errors. We show that our approach outperforms the compared methods on the proposed dataset.
•A novel public volumetric data-set of cellular ultra-structure in electron microscopy volumes.•A new state-of-the-art pipeline for segmentation of mitochondria and endo-lysosomes.•Contrast enhancement with transfer learning improves segmentation of unbalanced EM data.
This paper proposes an automatic cast product surface defect detection system based on deep learning artificial intelligence technology. Application of deep learning is difficult because of the ...uneven surface and small defects of the cast product which are easily affected by the lighting position and angle. Therefore, three channel fusion data from an optical system that simultaneously acquires a 2D surface image and 3D shape information of the target object were obtained and used for deep learning. The mean average precision (mAP) of the proposed defect detection model using the three-channel fusion data is about 77%. And this result is greater than the 60% mAP of a defect detection model that uses single-channel data. For further optimization, we investigate a deep learning model that employs a deep learning network with multiple models, where each model trains and detects only a single type of defect. The experimental results demonstrate that the mAP of the model was improved to 88%.
To enhance the light out coupling and stabilize the angular spectrum of white organic light emitting diodes (WOLEDs), we formed a randomly distributed corrugation directly on the external glass ...surface. Using a metallic master, which was prepared using laser ablation, we have imprinted the corrugation pattern directly on the glass surface. Our method yields 32% increase in the external quantum efficiency, widened luminance distribution and low angular spectra variation. Considering WOLED as a lighting source, this feature is particularly appealing. Besides the implications of the spectrally stable efficient WOLEDs through random corrugation, our work suggests a new structural approach for various light applications in which efficiency and spectral stability matter.
Display omitted
Although recently developed trackers have shown excellent performance even when tracking fast moving and shape changing objects with variable scale and orientation, the trackers for the ...electro-optical targeting systems (EOTS) still suffer from abrupt scene changes due to frequent and fast camera motions by pan-tilt motor control or dynamic distortions in field environments. Conventional context aware (CA) and deep learning based trackers have been studied to tackle these problems, but they have the drawbacks of not fully overcoming the problems and dealing with their computational burden. In this paper, a global motion aware method is proposed to address the fast camera motion issue. The proposed method consists of two modules: (i) a motion detection module, which is based on the change in image entropy value, and (ii) a background tracking module, used to track a set of features in consecutive images to find correspondences between them and estimate global camera movement. A series of experiments is conducted on thermal infrared images, and the results show that the proposed method can significantly improve the robustness of all trackers with a minimal computational overhead. We show that the proposed method can be easily integrated into any visual tracking framework and can be applied to improve the performance of EOTS applications.
Compared to the side view, a top-view is robust against occlusion generated by objects located indoors. It offers a better wide view angle and much visibility of a scene. However, there are still ...problems to be handled. The top-view image shows asymmetrical features and radially distorted scenes around the corners, such as omnidirectional view images and self-occlusion. Conventional human detection methods are suitable for finding moving objects in front view imaging systems. And there are some limitations, such as slow execution speed due to computational complexity. In this paper, we propose an efficient method. A static saliency map with low activity and a dynamic saliency map with a lot of movement are respectively detected. These two models were fused to create a multi-saliency map, and both characteristics were used simultaneously to improve detection rates. To handle problems such as asymmetry, a rotation matrix was calculated around the center, and Histogram of Oriented Gradient (HOG) features descriptor were extracted from the multi-saliency map to create an image patch (a small image region of interest containing human candidates). For the classification of image patches, we used machine learning-based supervised learning models support-vector machine (SVM) algorithm to improve performance. As a result of the proposed algorithm, it showed low resource occupancy and achieved Average Precision of 92.3% and 96.12% when Intersection over Union were 50% and 45% respectively.
PDF
