| 1. |
- Hammerstrom, Dan
(författare)
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A Survey of Bio-Inspired and Other Alternative Architectures
- 2008
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Ingår i: Nanotechnology. Vol. 4, Information technology II. - Weinheim : Wiley-VCH. - 9783527317370 ; , s. 251-282
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In this chapter I have provided a brief and somewhat superficial survey of the specialized hardware developed over the last 20 years to support neurobiological models of computation. I then looked briefly at current efforts and speculated on how such hardware, especially when implemented in nanoscale electronics, could offer unprecedented compute density, possibly leading to new capabilities in computational intelligence. Also, biologically inspired models seem to be a better match to nano-scale circuits. The mix of continued Moore’s law scaling, models from computational neuroscience, and molecular scale technology portends a potential paradigm shift in how computing is done. Among other things the future of computing is probably not about discrete logic, but about the encoding, learning, and performing inference over stochastic variables. There could be a wide range of applications for devices like this in robotics, the reduction and compression of widely distributed sensor data, power management, etc. One of the leading lights of the first computer revolution saw this clearly. At the IEEE Centenary in 1984 (“The Next 100 Years,” IEEE Technical Convocation), Dr. Robert Noyce, co-founder of Intel and co-inventor of the Integrated Circuit, said: Until now we have been going the other way; that is, in order to understand the brain we have used the computer as a model for it. Perhaps it is time to reverse this reasoning: to understand where we should go with the computer, we should look to the brain for some clues.
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| 2. |
- Luk, Chiu-Hung, et al.
(författare)
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Vision-based hazard detection
- 2006
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Ingår i: Intelligent Engineering Systems Through Artificial Neural Networks - Volume 16. - New York : ASME Press (American Society of Mechanical Engineers). ; , s. 6-
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Konferensbidrag (refereegranskat)abstract
- Advances in sensor and GPS technologies make possible better guidance tools for vehicle/aerial navigation. In the work reported here we look at the detection of hazards, such as aircraft or other objects, on or near the target runway. Our system consists of two modules: 1) regions of interest (ROI) detection, and 2) hazard recognition. One of the harder problems in object recognition is to segment the target object from a cluttered background. In this system we use a “poor man’s” segmentation, by taking advantage of the fact that we have an approximate reference that we can differentiate from. The regions of interest are defined as significant differences between the input image and the reference image. Since this differencing can be complex due to the fact that the images may not be precisely registered, we employed a novel histogram method which is reasonably invariant to spatial transformations for ROI detection.
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