Mapping and classification of volcanic deposits using multi-sensor unoccupied aerial systems

2021 ◽  
Vol 264 ◽  
pp. 112581
Author(s):  
Brett B. Carr ◽  
Einat Lev ◽  
Theresa Sawi ◽  
Kristen A. Bennett ◽  
Christopher S. Edwards ◽  
...  
Keyword(s):  
Volcanic Deposits ◽  
Aerial Systems

Unmanned aerial system applications in construction: a systematic review

2018 ◽  
Vol 18 (4) ◽  
pp. 453-468 ◽  
Author(s):  
Shi Zhou ◽  
Masoud Gheisari
Keyword(s):  
Progress Monitoring ◽  
Unmanned Aerial Systems ◽  
Content Type ◽  
The Past ◽  
Building Maintenance ◽  
Current State ◽  
Aerial Systems ◽  
Rotary Wing ◽  
Uas Applications

PurposeOver the past decade, researchers have used unmanned aerial systems (UASs) in construction industry for various applications from site inspection to safety monitoring or building maintenance. This paper aims to assort academic studies on construction UAS applications, summarize logics behind using UAS in each application and extend understanding of current state of UAS research in the construction setting.Design/methodology/approachThis research follows a systematic literature assessment methodology to summarize the results of 54 research papers over the past ten years and outlines the research trends for applying UASs in construction.FindingsUASs are used in building inspection, damage assessment, site surveying, safety inspection, progress monitoring, building maintenance and other construction applications. Cost saving, time efficiency and improved accessibility are the primary reasons for choosing UAS in construction applications. Rotary-wing UASs are the most common types of UASs being used in construction. Cameras, LiDAR and Kinect are the most common onboard sensors integrated in construction UAS applications. The control styles used are manual, semi-autonomous and autonomous.Originality/valueThis paper contributes to classification of UAS applications in construction research and identification of UAS hardware and sensor types as well as their flying control systems in construction literature.

scholarly journals ADDRESSING THE CLASS IMBALANCE PROBLEM IN THE AUTOMATIC IMAGE CLASSIFICATION OF COASTAL LITTER FROM ORTHOPHOTOS DERIVED FROM UAS IMAGERY

2020 ◽  
Vol V-3-2020 ◽  
pp. 439-445
Author(s):  
D. Duarte ◽  
U. Andriolo ◽  
G. Gonçalves
Keyword(s):  
Machine Learning ◽  
Class Imbalance ◽  
Machine Learning Algorithms ◽  
Unmanned Aerial Systems ◽  
Class Imbalance Problem ◽  
Marine Litter ◽  
Imbalance Problem ◽  
Small Set ◽  
Aerial Systems

Abstract. Unmanned Aerial Systems (UAS) has been recently used for mapping marine litter on beach-dune environment. Machine learning algorithms have been applied on UAS-derived images and orthophotos for automated marine litter items detection. As sand and vegetation are much predominant on the orthophoto, marine litter items constitute a small set of data, thus a class much less represented on the image scene. This communication aims to analyse the class imbalance issue on orthophotos for automated marine litter items detection. In the used dataset, the percentage of patches containing marine litter is close to 1% of the total amount of patches, hence representing a clear class imbalance issue. This problem has been previously indicated as detrimental for machine learning frameworks. Three different approaches were tested to address this imbalance, namely class weighting, oversampling and classifier thresholding. Oversampling had the best performance with a f1-score of 0.68, while the other methods had f1-score value of 0.56 on average. The results indicate that future works devoted to UAS-based automated marine litter detection should take in consideration the use of the oversampling method, which helped to improve the results of about 7% in the specific case shown in this paper.

Aerial Systems For Aircraft

1956 ◽  
Vol 60 (542) ◽  
pp. 101-113 ◽  
Author(s):  
R. A. Burberry
Keyword(s):  
Wavelength Range ◽  
Operating Frequency ◽  
Aerial Systems

The range of frequency for which aerial systems for aircraft are required is immense: the highest used frequency is over one hundred thousand times the lowest. Very differing techniques are required throughout this range and this lecture will be chiefly concerned with the principles on which these techniques are based, rather than on individual aerial systems.One method of classification of aerials is by their operating frequency or wavelength range.

Note on the spectral classification of carbon stars in the photographic infrared region

1966 ◽  
Vol 24 ◽  
pp. 21-23
Author(s):  
Y. Fujita
Keyword(s):  
Infrared Region ◽  
Spectral Classification ◽  
Carbon Stars

We have investigated the spectrograms (dispersion: 8Å/mm) in the photographic infrared region fromλ7500 toλ9000 of some carbon stars obtained by the coudé spectrograph of the 74-inch reflector attached to the Okayama Astrophysical Observatory. The names of the stars investigated are listed in Table 1.

Diagnostic Value of Electron Microscopy in Soft Tissue Tumors

1970 ◽  
Vol 28 ◽  
pp. 220-221
Author(s):  
Gerald Fine ◽  
Azorides R. Morales
Keyword(s):  
Cardiac Myxoma ◽  
Osteogenic Sarcoma ◽  
Diagnostic Value ◽  
Soft Tissue Tumors ◽  
Amelanotic Melanoma ◽  
Growth Patterns ◽  
Light Microscopic Level ◽  
Mesenchymal Tumors ◽  
Good Agreement

For years the separation of carcinoma and sarcoma and the subclassification of sarcomas has been based on the appearance of the tumor cells and their microscopic growth pattern and information derived from certain histochemical and special stains. Although this method of study has produced good agreement among pathologists in the separation of carcinoma from sarcoma, it has given less uniform results in the subclassification of sarcomas. There remain examples of neoplasms of different histogenesis, the classification of which is questionable because of similar cytologic and growth patterns at the light microscopic level; i.e. amelanotic melanoma versus carcinoma and occasionally sarcoma, sarcomas with an epithelial pattern of growth simulating carcinoma, histologically similar mesenchymal tumors of different histogenesis (histiocytoma versus rhabdomyosarcoma, lytic osteogenic sarcoma versus rhabdomyosarcoma), and myxomatous mesenchymal tumors of diverse histogenesis (myxoid rhabdo and liposarcomas, cardiac myxoma, myxoid neurofibroma, etc.)

Ultrastructure of mesotheliomas

1984 ◽  
Vol 42 ◽  
pp. 98-101
Author(s):  
Irving Dardick
Keyword(s):  
Electron Microscopy ◽  
Latent Period ◽  
Spindle Cell ◽  
Spindle Cell Sarcoma ◽  
Metastatic Adenocarcinoma ◽  
Cell Sarcoma ◽  
Cytological Features ◽  
Industrial Use ◽  
Degree Of Differentiation

With the extensive industrial use of asbestos in this century and the long latent period (20-50 years) between exposure and tumor presentation, the incidence of malignant mesothelioma is now increasing. Thus, surgical pathologists are more frequently faced with the dilemma of differentiating mesothelioma from metastatic adenocarcinoma and spindle-cell sarcoma involving serosal surfaces. Electron microscopy is amodality useful in clarifying this problem.In utilizing ultrastructural features in the diagnosis of mesothelioma, it is essential to appreciate that the classification of this tumor reflects a variety of morphologic forms of differing biologic behavior (Table 1). Furthermore, with the variable histology and degree of differentiation in mesotheliomas it might be expected that the ultrastructure of such tumors also reflects a range of cytological features. Such is the case.

Interpreting HVEM of muscle-cell impulse networks

1992 ◽  
Vol 50 (1) ◽  
pp. 126-127
Author(s):  
Paul DeCosta ◽  
Kyugon Cho ◽  
Stephen Shemlon ◽  
Heesung Jun ◽  
Stanley M. Dunn
Keyword(s):  
Structural Analysis ◽  
Muscle Cell ◽  
Electron Micrographs ◽  
Relative Size ◽  
Automatic Classification ◽  
Nerve Fibers ◽  
Analysis Algorithm ◽  
Structural Networks

Introduction: The analysis and interpretation of electron micrographs of cells and tissues, often requires the accurate extraction of structural networks, which either provide immediate 2D or 3D information, or from which the desired information can be inferred. The images of these structures contain lines and/or curves whose orientation, lengths, and intersections characterize the overall network.Some examples exist of studies that have been done in the analysis of networks of natural structures. In, Sebok and Roemer determine the complexity of nerve structures in an EM formed slide. Here the number of nodes that exist in the image describes how dense nerve fibers are in a particular region of the skin. Hildith proposes a network structural analysis algorithm for the automatic classification of chromosome spreads (type, relative size and orientation).

Dual stain kit for the microscopic gram classification of ocular infection bacteria

1993 ◽  
Vol 51 ◽  
pp. 248-249
Author(s):  
Jacob S. Hanker ◽  
Dale N. Holdren ◽  
Kenneth L. Cohen ◽  
Beverly L. Giammara
Keyword(s):  
Contact Lenses ◽  
Ocular Infection ◽  
Gram Negative Bacteria ◽  
Gram Negative ◽  
Gram Staining ◽  
Ocular Infections ◽  
Different Types ◽  
Culture Positive ◽  
Increased Susceptibility

Keratitis and conjunctivitis (infections of the cornea or conjunctiva) are ocular infections caused by various bacteria, fungi, viruses or parasites; bacteria, however, are usually prominent. Systemic conditions such as alcoholism, diabetes, debilitating disease, AIDS and immunosuppressive therapy can lead to increased susceptibility but trauma and contact lens use are very important factors. Gram-negative bacteria are most frequently cultured in these situations and Pseudomonas aeruginosa is most usually isolated from culture-positive ulcers of patients using contact lenses. Smears for staining can be obtained with a special swab or spatula and Gram staining frequently guides choice of a therapeutic rinse prior to the report of the culture results upon which specific antibiotic therapy is based. In some cases staining of the direct smear may be diagnostic in situations where the culture will not grow. In these cases different types of stains occasionally assist in guiding therapy.

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