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The human sensory system has a fascinating stimulus-detection capability attributed to the fact that the feature (pattern) of an input stimulus can be extracted through perceptual learning. Therefore, sensory information can be organized and identified efficiently based on iterative experiences, whereby the sensing ability is improved. Specifically, the distributed network of receptors, neurons, and synapses in the somatosensory system efficiently processes complex tactile information. Herein, we demonstrate an artificial tactile sensor system with a sensory neuron and a perceptual synaptic network composed of a single device: a semivolatile carbon nanotube transistor. The system can differentiate the temporal features of tactile patterns, and its recognition accuracy can be improved by an iterative learning process. Furthermore, the developed circuit model of the system provides quantitative analytical and product-level feasibility. This work is a step toward the design and use of a neuromorphic sensory system with a learning capability for potential applications in robotics and prosthetics. Neuromorphic computing: simultaneously simulating neurons and synapses An array of electronic components that can mimic information processing in the brain has been developed by scientists in South Korea. Digital electronic devices operate using only ones and zeros, but neurons in the brain and the synapses that connect them work on an analog basis. Neuromorphic devices attempt to mimic this analog behavior to achieve low-power information processing. Previous neuromorphic networks have been created using different types of structures to mimic neurons and synapses. Sung-Jin Choi from Kookmin University in Seoul and co-workers have now demonstrated a device that can simulate both synaptic and neuronal functions simultaneously. The researchers used an array of carbon nanotube transistors to create a neuromorphic system that can recognize different tactile patterns and learn in the process. The system has potential applications in robotics and prosthetics. We demonstrate an artificial tactile sensor system with a sensory neuron and a perceptual synaptic network that is composed of a single device type, i.e., a semivolatile carbon nanotube transistor. The system can differentiate the temporal features of tactile patterns, and its recognition accuracy can be improved by an iterative learning process. [ABSTRACT FROM AUTHOR]

Identifying zones and movement patterns of people is crucial to understanding adjacent regions and the relationship in urban areas. Most previous studies addressed zones or movement patterns separately without analysing simultaneously the two issues. In this article, we propose an integrated approach to discover directly both zones and movement patterns among the zones, referred to asmovement patterns between zones(MZPs), from historical boarding behaviours of passengers in subway networks by using an agglomerative clustering method. In addition, evaluation measures of MZPs are suggested in terms of coverage and accuracy. The effectiveness of the proposed approach is finally demonstrated through a real-world data set obtained from smart cards on a subway network in Seoul, Korea. [ABSTRACT FROM AUTHOR]