8,861 research outputs found

    Analysing imperfect temporal information in GIS using the Triangular Model

    Get PDF
    Rough set and fuzzy set are two frequently used approaches for modelling and reasoning about imperfect time intervals. In this paper, we focus on imperfect time intervals that can be modelled by rough sets and use an innovative graphic model [i.e. the triangular model (TM)] to represent this kind of imperfect time intervals. This work shows that TM is potentially advantageous in visualizing and querying imperfect time intervals, and its analytical power can be better exploited when it is implemented in a computer application with graphical user interfaces and interactive functions. Moreover, a probabilistic framework is proposed to handle the uncertainty issues in temporal queries. We use a case study to illustrate how the unique insights gained by TM can assist a geographical information system for exploratory spatio-temporal analysis

    Modeling urban evolution by identifying spatiotemporal patterns and applying methods of artificial intelligence.Case study: Athens, Greece.

    Get PDF
    While during the past decades, urban areas experience constant slow population growth, the spatial patterns they form, by means of their limits and borders, are rapidly changing in a complex way. Furthermore, urban areas continue to expand to the expense of "rural” intensifying urban sprawl. The main aim of this paper is the definition of the evolution of urban areas and more specifically, the specification of an urban model, which deals simultaneously with the modification of population and building use patterns. Classical theories define city geographic border, with the Aristotelian division of 0 or 1 and are called fiat geographic boundaries. But the edge of a city and the urbanization "degree" is something not easily distinguishable. Actually, the line that city ends and rural starts is vague. In this respect a synthetic spatio - temporal methodology is described which, through the adaptation of different computational methods aims to assist planners and decision makers to gain an insight in urban - rural transition. Fuzzy Logic and Neural Networks are recruited to provide a precise image of spatial entities, further exploited in a twofold way. First for analysis and interpretation of up - to - date urban evolution and second, for the formulation of a robust spatial simulation model, the theoretical background of which is that the spatial contiguity between members of the same or different groups is one of the key factors in their evolution. The paper finally presents the results of the model application in the prefecture of Attica in Greece, unveiling the role of the Athens Metropolitan Area to its current and future evolution, by illustrating maps of urban growth dynamics.urban growth; urban dynamics; neural networks; fuzzy logic; Greece; Athens

    Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

    Full text link
    Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at Elsevier Neural Network
    • 

    corecore