We present a novel approach to the problem of simultaneous localization and mapping (SLAM), that is not based on any of the three major SLAM paradigms: extended Kalman filters, particle filters and graph-based optimizer...
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We present a novel approach to the problem of simultaneous localization and mapping (SLAM), that is not based on any of the three major SLAM paradigms: extended Kalman filters, particle filters and graph-based optimizers. In this approach, the uncertain spatial constraints are represented as ordered sets of Monte Carlo samples drawn from the space of coordinate frame transformations. Such a representation enables fusion of two or more spatial constraints even if they are correlated, under certain assumptions. The spatial constraints are organised in a compact data structure which models the full posterior over the robot's pose and landmark locations. The number of Monte Carlo samples necessary to accurately represent the posterior does not grow exponentially with the number of state-space dimensions as in conventional particle filters; in fact, it is a constant parameter. This data structure provides a constant time access to marginal distributions and a newly observed spatial constraint can be accommodated in time linear to the number of landmarks tracked, regardless of the number of spatial constraints that have been observed previously. We provide an experimental evaluation of the method, and discuss its strengths and weaknesses with respect to the well-established SLAM approaches.
Activity recognition focuses on inferring current user activities by leveraging sensory data available on today's sensor rich environment. Supervised learning has been applied pervasively for activity recognition....
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The daily peaks and valleys in energy demand create inefficiencies and expense in the operation of the electricity grid. Valley periods force utilities to curtail renewable energy sources such as wind as their unpredi...
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The daily peaks and valleys in energy demand create inefficiencies and expense in the operation of the electricity grid. Valley periods force utilities to curtail renewable energy sources such as wind as their unpredictable nature makes it difficult to maintain line frequency across the network within target bounds. Peak periods require additional generators that remain dormant during other periods. Smoothing this demand cycle is one of the fundamental challenges of the Smart Grid, requiring flexibility and coordination between actors throughout the Grid. This paper describes the Smart Grid as a multi-layered system and proposes a cross-layered dynamic adaptation approach to facilitate this flexibility and coordination. This method uses a hierarchical taxonomy to identify appropriate adaptation actions in response to identified mismatches, supported by a run-time predictive statistical framework to predict mismatches, enabling timely adaptations to be triggered.
Activity recognition focuses on inferring current user activities by leveraging sensory data available on today's sensor rich environment. Supervised learning has been applied pervasively for activity recognition....
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ISBN:
(纸本)9781479902279
Activity recognition focuses on inferring current user activities by leveraging sensory data available on today's sensor rich environment. Supervised learning has been applied pervasively for activity recognition. Typical activity recognition techniques process sensory data based on point-by-point approaches. In this paper, we propose a novel cluster-based classification for activity recognition systems, termed StreamAR. The system incorporates incremental and active learning for mining user activities in data streams. The novel approach processes activities as clusters to build a robust classification framework. StreamAR integrates supervised, unsupervised and active learning and applies hybrid similarity measures technique for recognising activities. Extensive experimental results using real activity recognition datasets have evidenced that our new approach shows improved performance over other existing state-of-the-art learning methods.
Recently due to major changes in the structure of electricity industry and the rising costs of power generation, many countries have realized the potential and benefits of smart metering systems and demand response pr...
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There is a growing focus on 24/7 cardiac monitoring that leverages state of the art mobile phones and commercial-off-the-shelf (COTS) wearable bio-sensors. While many signal processing techniques for mobile ECG analys...
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Data classification has attracted considerable research attention in the field of computational statistics and data mining due to its wide range of applications. K Best Cluster Based Neighbour (KB-CB-N) is our novel c...
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There is a growing focus on 24/7 cardiac monitoring that leverages state of the art mobile phones and commercial-off-the-shelf (COTS) wearable bio-sensors. While many signal processing techniques for mobile ECG analys...
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There is a growing focus on 24/7 cardiac monitoring that leverages state of the art mobile phones and commercial-off-the-shelf (COTS) wearable bio-sensors. While many signal processing techniques for mobile ECG analysis have been developed, these techniques tend to be computationally intensive. In this paper, we propose, develop and evaluate a resource-aware and energy-efficient time series analysis technique for real-time ECG analysis on mobile devices based on the well-known SAX (Symbolic Aggregate Approximation) representation for time series termed RA-SAX.
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