Robot Operating System, or ROS, is poised to do the same for robots. Morgan Quigley programmed the first iteration of what grew into ROS as a graduate student in 2006, and today his opensource code is redefining the p...
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Robot Operating System, or ROS, is poised to do the same for robots. Morgan Quigley programmed the first iteration of what grew into ROS as a graduate student in 2006, and today his opensource code is redefining the practical limits of robotics. Since version 1.0 was released in 2010, ROS has become the de facto standard in robotics software. Unlike more conventional robotic technology, Quigley's four-fingered hand is not controlled by a central processor. Its fingers and palm distribute computing chores among 14 low-cost, low-power processors dedicated to controlling each joint directly. The masterstroke in Quigley's design is not strictly technical but social. Members of the community who produce a finished release can distribute it themselves, rather than having to house it on central servers.
Computational modeling has served a powerful tool for studying cross-situational word learning. Previous research has focused on convergence behaviors in a static environment, ignoring dynamic cognitive aspects of con...
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A graphic processing unit (GPU)-accelerated biological species recognition method using partially connected neural evolutionary network model is introduced in this paper. The partial connected neural evolutionary netw...
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A graphic processing unit (GPU)-accelerated biological species recognition method using partially connected neural evolutionary network model is introduced in this paper. The partial connected neural evolutionary network adopted in the paper can overcome the disadvantage of traditional neural network with small inputs. The whole image is considered as the input of the neural network, so the maximal features can be kept for recognition. To speed up the recognition process of the neural network, a fast implementation of the partially connected neural network was conducted on NVIDIA Tesla C1060 using the NVIDIA compute unified device architecture (CUDA) framework. Image sets of eight biological species were obtained to test the GPU implementation and counterpart serial CPU implementation, and experiment results showed GPU implementation works effectively on both recognition rate and speed, and gained 343 speedup over its counterpart CPU implementation. Comparing to feature-based recognition method on the same recognition task, the method also achieved an acceptable correct rate of 84.6% when testing on eight biological species.
The five articles in this special issue focus on human robot interactions. The papers bring together fields of study, such as cognitive architectures, computational neuroscience, developmental psychology, machine psyc...
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The five articles in this special issue focus on human robot interactions. The papers bring together fields of study, such as cognitive architectures, computational neuroscience, developmental psychology, machine psychology, and sociall affective robots.
The use of L1 regularisation for sparse learning has generated immense research interest, with many successful applications in diverse areas such as signal acquisition, image coding, genomics and collaborative filteri...
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ISBN:
(纸本)9781450312851
The use of L1 regularisation for sparse learning has generated immense research interest, with many successful applications in diverse areas such as signal acquisition, image coding, genomics and collaborative filtering. While existing work highlights the many advantages of L1 methods, in this paper we find that L1 regularisation often dramatically under-performs in terms of predictive performance when compared to other methods for inferring sparsity. We focus on unsupervised latent variable models, and develop L1 minimising factor models, Bayesian variants of "L1", and Bayesian models with a stronger L 0-like sparsity induced through spike-and-slab distributions. These spike-and-slab Bayesian factor models encourage sparsity while accounting for uncertainty in a principled manner, and avoid unnecessary shrinkage of non-zero values. We demonstrate on a number of data sets that in practice spike-and-slab Bayesian methods outperform L1 minimisation, even on a computational budget. We thus highlight the need to re-assess the wide use of L1 methods in sparsity-reliant applications, particularly when we care about generalising to previously unseen data, and provide an alternative that, over many varying conditions, provides improved generalisation performance. Copyright 2012 by the author(s)/owner(s).
Spatial selective attention pattern recognition plays a significant role in specific people's (e.g.: pilot's) state monitoring. Steady-State Visual Evoked Potentials (SSVEP) were recorded from the scalp of 6 s...
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ISBN:
(纸本)9781467361279
Spatial selective attention pattern recognition plays a significant role in specific people's (e.g.: pilot's) state monitoring. Steady-State Visual Evoked Potentials (SSVEP) were recorded from the scalp of 6 subjects who were cued to attend to a flickering sequence displayed in one visual field while ignoring a similar one with a different flickering rate in the opposite field. The SSVEP to either flickering stimulus was enhanced when attention was lead to the same direction rather than to the opposite direction. The most significant enlargement is generally located on the posterior scalp contralateral to the visual field of stimulation. This attention-caused amplitude enhancement of SSVEP can be used to measure the attention shifting. In this paper, we developed an algorithm to extract short SSVEP, selectively combine them to form a joint temporal spatial selective attention feature, and use Support Vector Machine (SVM) to classify different attention pattern joint features. By segmenting the long single trial SSVEP (12s) data into short pieces (1s), we are able to largely decrease the training time while still keeping a high recognition accuracy (>93%) for most subjects, which makes it possible to monitor spatial selective attention on time.
Performance of automatic face recognition algorithm has increased considerably over the past decades. However, face recognition under changes in lighting conditions remains a challenging issue for computers. In this p...
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ISBN:
(纸本)9781467317139
Performance of automatic face recognition algorithm has increased considerably over the past decades. However, face recognition under changes in lighting conditions remains a challenging issue for computers. In this paper, we propose a novel face recognition algorithm inspired by information taken from human fixation patterns. We augment a LGBP (Local Gabor Binary Pattern) algorithm - a well-known face recognition algorithm - to allocate different weights to each facial part during processing. For deriving the weights, we analyzed data from a human face recognition experiment using eye-tracking. Eye-tracking allows us to determine the facial parts during the recognition process which represent salient regions for human processing. Face images are pre-processed during the recognition step using a weight mask based on the salient regions from the eye-tracking data. A comparison with the standard non-weighted LGBP approach demonstrates the efficacy of our method with the weighted method performing better under lighting changes.
Worldwide, law enforcement agencies are encountering a substantial increase in the number of illicit drug pills being circulated in our society. Identifying the source and manufacturer of these illicit drugs will help...
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