Detection of stationary objects by the blind Cave FishAnoptichthys jordani (Characidae)

1981 ◽  
Vol 143 (3) ◽  
pp. 369-374 ◽  
Author(s):  
C. von Campenhausen ◽  
I. Riess ◽  
R. Weissert
Keyword(s):  
Stationary Objects ◽  
Blind Cave

Discrimination between stationary objects by the blind cave fishAnoptichthys jordani (Characidae)

1981 ◽  
Vol 143 (3) ◽  
pp. 375-381 ◽  
Author(s):  
R. Weissert ◽  
C. von Campenhausen
Keyword(s):  
Stationary Objects ◽  
Blind Cave

Moving Forward

2011 ◽  
Vol 42 (3) ◽  
pp. 225-230 ◽  
Author(s):  
Janet B. Ruscher
Keyword(s):  
Everyday Life ◽  
Interpersonal Communication ◽  
Spatial Metaphors ◽  
The People ◽  
Stationary Objects ◽  
Study Participants ◽  
Affective Forecasts

Two distinct spatial metaphors for the passage of time can produce disparate judgments about grieving. Under the object-moving metaphor, time seems to move past stationary people, like objects floating past people along a riverbank. Under the people-moving metaphor, time is stationary; people move through time as though they journey on a one-way street, past stationary objects. The people-moving metaphor should encourage the forecast of shorter grieving periods relative to the object-moving metaphor. In the present study, participants either received an object-moving or people-moving prime, then read a brief vignette about a mother whose young son died. Participants made affective forecasts about the mother’s grief intensity and duration, and provided open-ended inferences regarding a return to relative normalcy. Findings support predictions, and are discussed with respect to interpersonal communication and everyday life.

scholarly journals Collision Avoidance on Unmanned Aerial Vehicles Using Neural Network Pipelines and Flow Clustering Techniques

2021 ◽  
Vol 13 (13) ◽  
pp. 2643
Author(s):  
Dário Pedro ◽  
João P. Matos-Carvalho ◽  
José M. Fonseca ◽  
André Mora
Keyword(s):  
Collision Avoidance ◽  
Autonomous Vehicles ◽  
High Demand ◽  
Novel Approach ◽  
Vision Sensors ◽  
Learning Techniques ◽  
Stationary Objects ◽  
Computationally Intensive ◽  
Unmanned Autonomous Vehicles ◽  
Positive Results

Unmanned Autonomous Vehicles (UAV), while not a recent invention, have recently acquired a prominent position in many industries, and they are increasingly used not only by avid customers, but also in high-demand technical use-cases, and will have a significant societal effect in the coming years. However, the use of UAVs is fraught with significant safety threats, such as collisions with dynamic obstacles (other UAVs, birds, or randomly thrown objects). This research focuses on a safety problem that is often overlooked due to a lack of technology and solutions to address it: collisions with non-stationary objects. A novel approach is described that employs deep learning techniques to solve the computationally intensive problem of real-time collision avoidance with dynamic objects using off-the-shelf commercial vision sensors. The suggested approach’s viability was corroborated by multiple experiments, firstly in simulation, and afterward in a concrete real-world case, that consists of dodging a thrown ball. A novel video dataset was created and made available for this purpose, and transfer learning was also tested, with positive results.

Non visual discrimination of shapes in the blind cave cyprinidPhreatichthys andruzziiVinciguerra 1924

2010 ◽  
Vol 22 (4) ◽  
pp. 353-358 ◽  
Author(s):  
S. Sguanci ◽  
F. Ceccolini ◽  
R. Berti
Keyword(s):  
Visual Discrimination ◽  
Blind Cave

SFA-based ELM for remote detection of stationary objects

2021 ◽  
Author(s):  
Xiaolin Liang ◽  
Weifeng Zhu ◽  
Jiawen Sun ◽  
Jianqin Deng
Keyword(s):  
Remote Detection ◽  
Stationary Objects
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