A Cacophony of Signals: Woodpecker Sexbots, Squirrel NORAD, and Other Robotic Systems

Public ◽  
2019 ◽  
Vol 31 (59) ◽  
pp. 114-125
Author(s):  
Ian Ingram

This article discusses the author’s robotic artworks, which consider the human-made body's future as a willful entity and the nature of communication. The robots use computer vision or sound signal processing to search the world for the signals of target species and then attempt to respond through similar gestural and audible signalling. The robots are trying to communicate with the animals and, in part, allow human communion with those animals in ways that human bodies and umwelts don't allow. That human narrative stamps itself heavily onto the projects is confirmed by these becoming things like a hermaphroditic sexbot for Pileated Woodpeckers and a NORAD equivalent for Grey Squirrels.

Sensors ◽  
2014 ◽  
Vol 14 (4) ◽  
pp. 6247-6278 ◽  
Author(s):  
Gabriel García ◽  
Carlos Jara ◽  
Jorge Pomares ◽  
Aiman Alabdo ◽  
Lucas Poggi ◽  
...  

Author(s):  
Shiv Kumar ◽  
Agrima Yadav ◽  
Deepak Kumar Sharma

The exponential growth in the world population has led to an ever-increasing demand for food supplies. This has led to the realization that conventional and traditional methods alone might not be able to keep up with this demand. Smart agriculture is being regarded as one of the few realistic ways that, together with the traditional methods, can be used to close the gap between the demand and supply. Smart agriculture integrates the use of different technologies to better monitor, operate, and analyze different activities involved in different phases of the agricultural life cycle. Smart agriculture happens to be one of the many disciplines where deep learning and computer vision are being realized to be of major impact. This chapter gives a detailed explanation of different deep learning methods and tries to provide a basic understanding as to how these techniques are impacting different applications in smart agriculture.


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