scholarly journals Fully automatic identification and discrimination of sperm’s parts in microscopic images of stained human semen smear

2012 ◽  
Vol 05 (07) ◽  
pp. 384-395 ◽  
Author(s):  
Ahmad Bijar ◽  
Antonio Peñalver Benavent ◽  
Mohammad Mikaeili ◽  
Rasoul khayati
Keyword(s):  
Automatic Identification ◽  
Human Semen ◽  
Microscopic Images ◽  
Fully Automatic

scholarly journals A NEW FULLY-AUTOMATIC PROCEDURE FOR THE IDENTIFICATION AND THE COUPLING OF THE OVERTOPPING WAVES

2018 ◽  
pp. 36 ◽  
Author(s):  
Sara Mizar Formentin ◽  
Barbara Zanuttigh
Keyword(s):  
Flow Velocity ◽  
Water Surface ◽  
Automatic Identification ◽  
Practical Relevance ◽  
Threshold Values ◽  
Flow Conditions ◽  
Fully Automatic ◽  
The Individual ◽  
Coupling Algorithm ◽  
Submerged Conditions

This contribution presents a new procedure for the automatic identification of the individual overtopping events. The procedure is based on a zero-down-crossing analysis of the water-surface-elevation signals and, based on two threshold values, can be applied to any structure crest level, i.e. to emerged, zero-freeboard, over-washed and submerged conditions. The results of the procedure are characterized by a level of accuracy comparable to the human-supervised analysis of the wave signals. The procedure includes a second algorithm for the coupling of the overtopping events registered at two consecutive gauges. This coupling algorithm offers a series of original applications of practical relevance, a.o. the possibility to estimate the wave celerities, i.e. the velocities of propagation of the single waves, which could be used as an approximation of the flow velocity in shallow water and broken flow conditions.

Fully automatic identification of AC and PC landmarks on brain MRI using scene analysis

1997 ◽  
Vol 16 (5) ◽  
pp. 610-616 ◽  
Author(s):  
L. Verard ◽  
P. Allain ◽  
J.M. Travere ◽  
J.C. Baron ◽  
D. Bloyet
Keyword(s):  
Brain Mri ◽  
Scene Analysis ◽  
Automatic Identification ◽  
Fully Automatic

Automatic Identification and Elastic Properties of Deformed Objects Using their Microscopic Images

2009 ◽  
pp. 351-372
Author(s):  
C. Papaodysseus ◽  
P. Rousopoulos ◽  
D. Arabadjis ◽  
M. Panagopoulos ◽  
P. Loumou
Keyword(s):  
Elastic Properties ◽  
State Of The Art ◽  
The State ◽  
Automatic Identification ◽  
Clustering Methods ◽  
Microscopic Images ◽  
High Deformation ◽  
Third Stage ◽  
Exact Evaluation

In this chapter the state of the art is presented in the domain of automatic identification and classification of bodies on the basis of their deformed images obtained via microscope. The approach is illustrated by means of the case of automatic recognition of third-stage larvae from microscopic images of them in high deformation instances. The introduced methodology incorporates elements of elasticity theory, image processing, curve fitting and clustering methods; a concise presentation of the state of the art in these fields is given. Combining proper elements of these disciplines, we first evaluate the undeformed shape of a parasite given a digital image of a random parasite deformation instance. It is demonstrated that different orientations and deformations of the same parasite give rise to practically the same undeformed shape when the methodology is applied to the corresponding images, thus confirming the consistency of the approach. Next, a pattern recognition method is introduced to classify the unwrapped parasites into four families, with a high success rate. In addition, the methodology presented here is a powerful tool for the exact evaluation of the mechano-elastic properties of bodies from images of their deformation instances.

scholarly journals Fully automatic and robust 3D registration of serial-section microscopic images

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Ching-Wei Wang ◽  
Eric Budiman Gosno ◽  
Yen-Sheng Li
Keyword(s):  
Serial Section ◽  
3D Registration ◽  
Microscopic Images ◽  
Fully Automatic

scholarly journals Deep reinforcement learning for efficient measurement of quantum devices

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
V. Nguyen ◽  
S. B. Orbell ◽  
D. T. Lennon ◽  
H. Moon ◽  
F. Vigneau ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Double Quantum ◽  
Automatic Identification ◽  
Large Parameter ◽  
Quantum Devices ◽  
Parameter Spaces ◽  
Machine Learning Approach ◽  
Efficient Measurement ◽  
Fully Automatic ◽  
Mean Time

AbstractDeep reinforcement learning is an emerging machine-learning approach that can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision processes to navigate large parameter spaces. This paper proposes an approach to the efficient measurement of quantum devices based on deep reinforcement learning. We focus on double quantum dot devices, demonstrating the fully automatic identification of specific transport features called bias triangles. Measurements targeting these features are difficult to automate, since bias triangles are found in otherwise featureless regions of the parameter space. Our algorithm identifies bias triangles in a mean time of <30 min, and sometimes as little as 1 min. This approach, based on dueling deep Q-networks, can be adapted to a broad range of devices and target transport features. This is a crucial demonstration of the utility of deep reinforcement learning for decision making in the measurement and operation of quantum devices.

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