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| 2. |
- Osipov, Evgeny, et al.
(författare)
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Holographic Graph Neuron
- 2014
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Ingår i: International Conference on Computer and Information Sciences. - : IEEE Communications Society. - 9781479943913 ; , s. 1-6
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Konferensbidrag (refereegranskat)abstract
- This article proposes the usage of Vector Symbolic Architectures for implementing Hierarchical Graph Neuron. The adoption of VSA representation maintains previously reported properties and performance characteristics of HGN and further makes it suitable for implementation in distributed wireless sensor networks of tiny devices.
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| 3. |
- Patil, Ajinkya H., et al.
(författare)
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Constraining the epoch of reionization with the variance statistic : simulations of the LOFAR case
- 2014
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Ingår i: Monthly notices of the Royal Astronomical Society. - : Oxford University Press (OUP). - 0035-8711 .- 1365-2966. ; 443:2, s. 1113-1124
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Tidskriftsartikel (refereegranskat)abstract
- Several experiments are underway to detect the cosmic-redshifted 21-cm signal from neutral hydrogen from the Epoch of Reionization (EoR). Due to their very low signal-to-noise ratio, these observations aim for a statistical detection of the signal by measuring its power spectrum. We investigate the extraction of the variance of the signal as a first step towards detecting and constraining the global history of the EoR. Signal variance is the integral of the signal's power spectrum, and it is expected to be measured with a high significance. We demonstrate this through results from a simulation and parameter estimation pipeline developed for the Low-Frequency Array (LOFAR)-EoR experiment. We show that LOFAR should be able to detect the EoR in 600 h of integration using the variance statistic. Additionally, the redshift (z(r)) and duration (Delta z) of reionization can be constrained assuming a parametrization. We use an EoR simulation of z(r) = 7.68 and Delta(z) = 0.43 to test the pipeline. We are able to detect the simulated signal with a significance of four standard deviations and extract the EoR parameters as z(r) = 7.72(-0.18)(+0.37) and Delta z = 0.53(-0.23)(+0.12) in 600 h, assuming that systematic errors can be adequately controlled. We further show that the significance of detection and constraints on EoR parameters can be improved by measuring the cross-variance of the signal by cross-correlating consecutive redshift bins.
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| 4. |
- Sadrollah, Ghazaleh Pour, et al.
(författare)
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A Distributed Framework for Supporting 3D Swarming Applications
- 2014
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Konferensbidrag (refereegranskat)abstract
- In-flight wireless sensor networks (WSN) are ofincreased interest owing to efficiency gains in weight and operationallifetime of IP-enabled computers. High impact 3Dswarming applications for such systems include autonomousmapping, surveying, servicing, environmental monitoring anddisaster site management. For distributed robotic applications,such as quad copter swarms, it is critical that the robots are ableto localise themselves autonomously with respect to other robotsand to share information. The importance of fast and reliabledissemination of localised information in these elastic threedimensionalnetworks provides us sufficient reason to presenta distributed framework and hardware settings for passing thisinformation pervasively through the swarm. The research field ofInternet of Things (IoT) have for several years been addressingissues around low-power, low-bandwidth wireless communication.By applying IoT technologies to the challenges around swarming,new opportunities are created. However, since IoT have beenprimarily used with stationary devices, the introduction of flyingsensors will add more challenges to address.
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| 5. |
- Sandin, Fredrik, et al.
(författare)
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Concept Learning in Neuromorphic Vision Systems: What Can We Learn from Insects?
- 2014
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Ingår i: Journal of Software Engineering and Applications. - : Scientific Research Publishing, Inc.. - 1945-3116 .- 1945-3124. ; 7:5, s. 387-395
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Tidskriftsartikel (refereegranskat)abstract
- Vision systems that enable collision avoidance, localization and navigation in complex and uncertain environments are common in biology, but are extremely challenging to mimic in artificial electronic systems, in particular when size and power limitations apply. The development of neuromorphic electronic systems implementing models of biological sensory-motor systems in silicon is one promising approach to addressing these challenges. Concept learning is a central part of animal cognition that enables appropriate motor response in novel situations by generalization of former experience, possibly from a few examples. These aspects make concept learning a challenging and important problem. Learning methods in computer vision are typically inspired by mammals, but recent studies of insects motivate an interesting complementary research direction. There are several remarkable results showing that honeybees can learn to master abstract concepts, providing a road map for future work to allow direct comparisons between bio-inspired computing architectures and information processing in miniaturized “real” brains. Considering that the brain of a bee has less than 0.01% as many neurons as a human brain, the task to infer a minimal architecture and mechanism of concept learning from studies of bees appears well motivated. The relatively low complexity of insect sensory-motor systems makes them an interesting model for the further development of bio-inspired computing architectures, in particular for resource-constrained applications such as miniature robots, wireless sensors and handheld or wearable devices. Work in that direction is a natural step towards understanding and making use of prototype circuits for concept learning, which eventually may also help us to understand the more complex learning circuits of the human brain. By adapting concept learning mechanisms to a polymorphic computing framework we could possibly create large-scale decentralized computer vision systems, for example in the form of wireless sensor networks.
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