Augmented Triplet Network for Individual Organism and Unique Object Classification for Reliable Monitoring of Ezoshika Deer

2021 ◽  
Author(s):  
Yojiro Harie ◽  
Sangam Babu Neupane ◽  
Bishnu Prasad Gautam ◽  
Shiratori Norio
Keyword(s):  
Object Classification ◽  
Individual Organism ◽  
Unique Object ◽  
Reliable Monitoring

DETECTION OF RESIDUAL LUNG CANCER CELLS BY PCR-BASED GENETIC TESTING FOR ROS1-TRANSLOCATION: CASE REPORT

2018 ◽  
Vol 64 (3) ◽  
pp. 331-334
Author(s):  
Fedor Moiseenko ◽  
Vladislav Tyurin ◽  
Nikita Levchenko ◽  
Yevgeniy Levchenko ◽  
Aglaya Ievleva ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Morphological Analysis ◽  
Pathologic Complete Response ◽  
Residual Tumor ◽  
Complete Response ◽  
Convincing Evidence ◽  
Pcr Analysis ◽  
Specific Gene ◽  
Highly Sensitive ◽  
Reliable Monitoring

A patient with lung cancer carrying ROS1 translocation was treated by crizotinib and then subjected to surgery. Morphological analysis revealed pathologic complete response in surgically removed tissues, while PCR test provided convincing evidence for the presence of residual tumor cells. PCR analysis of lung cancer specific gene translocations allows carrying out highly sensitive and reliable monitoring of tumor disease during the course of treatment.

Using Dempster-Shafer Methods for Object Classification in the Theater Ballistic Missile Environment

1999 ◽  
Author(s):  
Kimberly Coombs ◽  
Debra Freel ◽  
Douglas Lampert ◽  
Steven Brahm
Keyword(s):  
Object Classification ◽  
Ballistic Missile

scholarly journals Real-time 2D–3D door detection and state classification on a low-power device

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
João Gaspar Ramôa ◽  
Vasco Lopes ◽  
Luís A. Alexandre ◽  
S. Mogo
Keyword(s):  
Low Power ◽  
Real Time ◽  
Object Classification ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Power Device ◽  
Indoor Environments ◽  
State Classification ◽  
Segmentation Algorithms ◽  
Indoor Spaces

AbstractIn this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

scholarly journals IUE Studies of Luminous X-Ray Binaries

1983 ◽  
Vol 6 ◽  
pp. 648-648
Author(s):  
J.B. Hutchings
Keyword(s):  
Magellanic Clouds ◽  
Thick Shell ◽  
High Luminosity ◽  
Uv Spectrum ◽  
X Ray ◽  
Unique Object ◽  
The Future ◽  
Orbit Inclination ◽  
Supercritical Accretion ◽  
X Ray Binaries

IUE has been used to study 11 high luminosity X-ray binaries, of which 3 are in the Magellanic Clouds. In the supergiant systems, X-ray ionisation bubbles have been found in most cases, leading to a greater understanding of the winds and accretion processes. Further studies of precessing objects such as LMC X-4 with IUE and ST are clearly of considerable interest, relating to X-ray heating and blanketing. Detailed studies of the Cyg X-l ionisation bubble may resolve the long standing puzzle of its orbit inclination and masses. UV continua have furnished valuable information on extinction, temperatures and luminosities, and the presence of non-stellar (i.e. disk) luminosity. Here too, more detailed studies are clearly indicated for the future. A unique object of interest is the LMC transient 0538-66 whose UV spectrum has quasarlike lines and luminosity which varies oppositely to the visible. This may be a case of supercritical accretion generating an optically thick shell (“disk”) about the pulsar.

scholarly journals Analytical Methods for the Determination of Neuroactive Steroids

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 553
Author(s):  
Michal Kaleta ◽  
Jana Oklestkova ◽  
Ondřej Novák ◽  
Miroslav Strnad
Keyword(s):  
Nervous System ◽  
Steroid Hormone ◽  
Neuroactive Steroids ◽  
Physiological Changes ◽  
Biological Matrices ◽  
Pathological Conditions ◽  
Reliable Monitoring ◽  
Hormonal Steroids ◽  
Endocrine Tissues

Neuroactive steroids are a family of all steroid-based compounds, of both natural and synthetic origin, which can affect the nervous system functions. Their biosynthesis occurs directly in the nervous system (so-called neurosteroids) or in peripheral endocrine tissues (hormonal steroids). Steroid hormone levels may fluctuate due to physiological changes during life and various pathological conditions affecting individuals. A deeper understanding of neuroactive steroids’ production, in addition to reliable monitoring of their levels in various biological matrices, may be useful in the prevention, diagnosis, monitoring, and treatment of some neurodegenerative and psychiatric diseases. The aim of this review is to highlight the most relevant methods currently available for analysis of neuroactive steroids, with an emphasis on immunoanalytical methods and gas, or liquid chromatography combined with mass spectrometry.

scholarly journals Grasp to See—Object Classification Using Flexion Glove with Support Vector Machine

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1461
Author(s):  
Shun-Hsin Yu ◽  
Jen-Shuo Chang ◽  
Chia-Hung Dylan Tsai
Keyword(s):  
Support Vector Machine ◽  
Daily Life ◽  
Object Classification ◽  
Target Object ◽  
The Other ◽  
Support Vector ◽  
Wide Range ◽  
Dark Space ◽  
Flex Sensors ◽  
Shape And Size

This paper proposes an object classification method using a flexion glove and machine learning. The classification is performed based on the information obtained from a single grasp on a target object. The flexion glove is developed with five flex sensors mounted on five finger sleeves, and is used for measuring the flexion of individual fingers while grasping an object. Flexion signals are divided into three phases, and they are the phases of picking, holding and releasing, respectively. Grasping features are extracted from the phase of holding for training the support vector machine. Two sets of objects are prepared for the classification test. One is printed-object set and the other is daily-life object set. The printed-object set is for investigating the patterns of grasping with specified shape and size, while the daily-life object set includes nine objects randomly chosen from daily life for demonstrating that the proposed method can be used to identify a wide range of objects. According to the results, the accuracy of the classifications are achieved 95.56% and 88.89% for the sets of printed objects and daily-life objects, respectively. A flexion glove which can perform object classification is successfully developed in this work and is aimed at potential grasp-to-see applications, such as visual impairment aid and recognition in dark space.

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