Automakers are trying to make vehicles more intelligent and safe by embedding processors which can be used to implement ldquoby-wirerdquo applications for taking smart decisions on the road or assisting the driver in ...
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Automakers are trying to make vehicles more intelligent and safe by embedding processors which can be used to implement ldquoby-wirerdquo applications for taking smart decisions on the road or assisting the driver in doing the same. Given this proliferation, there is a need to minimize the computing power required without affecting the performance and safety of the applications. The latter is especially important since these by-wire applications are distributed and real-time in nature and involve deadline bound computations on critical data gathered from the environment. These applications have stringent requirements on the freshness of data items and completion time of the tasks. Our work studies one such safety-related automotive application namely, automatic merge control (AMC) which ensures safe vehicle maneuver in the region where two or more roads *** our contributions, we (i) propose two merge algorithms for amc: head of the lane (HoL) and all feasible sequences (AFS) (ii) demonstrate how DSRC-based wireless communication protocol can be leveraged for the development of AMC (iii) present a real-time approach towards designing AMC by integrating mode-change and real-time repository concepts for reducing the processing power requirements and (iv) provide a scheduling strategy to meet AMC taskspsila timing requirements. Simulations and implementation on robotic vehicular platforms demonstrate the advantages of using our approach for constructing merge-by-wire systems.
The paper describes the software architecture of an autonomous mobile outdoor robot (AMOR). The robot is intended for robust operation in heavy terrain under arbitrary weather condition. The software is implemented in...
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The paper describes the software architecture of an autonomous mobile outdoor robot (AMOR). The robot is intended for robust operation in heavy terrain under arbitrary weather condition. The software is implemented in a layered architecture, possessing behaviour based and model based components. The multitude of different sensors is unified by a virtualisation layer, which allows easy reconfiguration and utilisation by the application programmer. The higher levels of the software system realise localisation, navigation and obstacle avoidance. The overall performance of the system has been proven by winning the first prize in autonomous driving in the European land robot trial (ELROB 2007) for urban and non- urban terrain.
The interest to incorporate mobile devices into Grid systems has arisen with two main purposes. The first one is to enrich users of these devices while the other is that of enriching the own Grid infrastructure. Secur...
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ISBN:
(纸本)9789898111524
The interest to incorporate mobile devices into Grid systems has arisen with two main purposes. The first one is to enrich users of these devices while the other is that of enriching the own Grid infrastructure. Security of these systems, due to their distributed and open nature, is considered a topic of great interest. A formal approach to security in the software life cycle is essential to protect corporate resources. However, little attention has been paid to this aspect of software development. Due to its criticality, security should be integrated as a formal approach into the software life cycle. We are developing a methodology of development for secure mobile Grid computing based systems that helps to design and build secure Grid systems with support for mobile devices directed by use cases and security use cases and focused on service-oriented security architecture. In this paper, we will present one of the first steps of our methodology consisting of analyzing security requirements of mobile grid systems. This analysis will allow us to obtain a set of security requirements that our methodology must cover and implement.
This paper describes a solution to the problem of landing a helicopter autonomously. The methodology used poses the problem as a discrete time path following control problem where a conveniently defined error state sp...
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This paper describes a solution to the problem of landing a helicopter autonomously. The methodology used poses the problem as a discrete time path following control problem where a conveniently defined error state space model of the helicopter is modified to include the aerodynamic effects of flying within close proximity to the ground. An affine parameter-dependent model representation is adopted that describes the helicopter linearized error dynamics for the predefined landing operating region. A state feedback H{sub}2 control problem for affine parameter-dependent systems is posed and solved using Linear Matrix Inequalities (LMIs). The resulting nonlinear controller is implemented using the D-methodology and dynamic weights to enforce bandwidth constraints on the actuators. The effectiveness of the proposed controller with and without dynamic weights on the actuation is assessed in simulation using the full nonlinear dynamic model of a small-scale helicopter.
We propose a person-dependent, manifold-based approach for modeling and tracking rigid and nonrigid 3D facial deformations from a monocular video sequence. The rigid and nonrigid motions are analyzed simultaneously in...
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We propose a person-dependent, manifold-based approach for modeling and tracking rigid and nonrigid 3D facial deformations from a monocular video sequence. The rigid and nonrigid motions are analyzed simultaneously in 3D, by automatically fitting and tracking a set of landmarks. We do not represent all nonrigid facial deformations as a simple complex manifold, but instead decompose them on a basis of eight 1D manifolds. Each 1D manifold is learned offline from sequences of labeled expressions, such as smile, surprise, etc. Any expression is then a linear combination of values along these 8 axes, with coefficient representing the level of activation. We experimentally verify that expressions can indeed be represented this way, and that individual manifolds are indeed 1D. The manifold dimensionality estimation, manifold learning, and manifold traversal operation are all implemented in the N-D Tensor Voting framework. Using simple local operations, this framework gives an estimate of the tangent and normal spaces at every sample, and provides excellent robustness to noise and outliers. The output of our system, besides the tracked landmarks in 3D, is a labeled annotation of the expression. We demonstrate results on a number of challenging sequences.
This paper addresses the problem of stabilizing a fully-actuated rigid body. The problem is formulated considering the natural space for rigid body configurations, the Special Euclidean group SE(3). The proposed solut...
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ISBN:
(纸本)9781424431236
This paper addresses the problem of stabilizing a fully-actuated rigid body. The problem is formulated considering the natural space for rigid body configurations, the Special Euclidean group SE(3). The proposed solution consists of an output-feedback controller for force and torque actuation that guarantees almost global asymptotic stability of the desired equilibrium point. As such the equilibrium point is a stable attractor for all initial conditions except for those in a nowhere dense set of measure zero. As an additional feature, the controller is required to verify prescribed bounds on the actuation.
Spatial-temporal local motion features have shown promising results in complex human action classification. Most of the previous works [6],[16],[21] treat these spatial- temporal features as a bag of video words, omit...
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Spatial-temporal local motion features have shown promising results in complex human action classification. Most of the previous works [6],[16],[21] treat these spatial- temporal features as a bag of video words, omitting any long range, global information in either the spatial or temporal domain. Other ways of learning temporal signature of motion tend to impose a fixed trajectory of the features or parts of human body returned by tracking algorithms. This leaves little flexibility for the algorithm to learn the optimal temporal pattern describing these motions. In this paper, we propose the usage of spatial-temporal correlograms to encode flexible long range temporal information into the spatial-temporal motion features. This results into a much richer description of human actions. We then apply an unsupervised generative model to learn different classes of human actions from these ST-correlograms. KTH dataset, one of the most challenging and popular human action dataset, is used for experimental evaluation. Our algorithm achieves the highest classification accuracy reported for this dataset under an unsupervised learning scheme.
We propose and evaluate a novel method for enhancing performance of lips contour tracking, which is based on the concept of statistic shape models (ASM) and multi features. On the first image of the video sequence, li...
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We propose and evaluate a novel method for enhancing performance of lips contour tracking, which is based on the concept of statistic shape models (ASM) and multi features. On the first image of the video sequence, lip region is detected using the Bayesian's rule in which lip color information is modeled by using the Gaussian mixture model (GMM) and the GMM is trained by expectation-maximization (EM) algorithm. The lip region is then used to initialize the lip shape model. A single feature-based ASM presents good performance only in particular conditions but gets stuck in local minima for noisy conditions (like beard, wrinkle, poor texture, low contrast between lip and skin, etc). To enhance the convergence, we propose to use 2 features: normal profile and grey level patches, and combine them by using a voting approach. The standard ASM is not able to take into account temporal information from previous frames therefore the lip contours are tracked by replacing the standard ASM with a hybrid active shape model (HASM) which is capable to take advantage of the temporal information. Initial experimental results on video sequences show that MF-HASM is more robust to local minimum problem and gives a higher accuracy than traditional single feature-based method in lip tracking problem.
We propose and evaluate a novel method for enhancing performance of lips contour tracking, which is based on the concept of active shape models (ASM) and multi features. On the first image of the video sequence, lip r...
详细信息
We propose and evaluate a novel method for enhancing performance of lips contour tracking, which is based on the concept of active shape models (ASM) and multi features. On the first image of the video sequence, lip region is detected using the Bayesian's rule in which lip color information is modeled by a Gaussian mixture model (GMM) which is trained by expectation-maximization (EM) algorithm. The lip region is then used to initialize the lip shape model. A single feature-based ASM presents good performance only in particular conditions but gets stuck in local minima for noisy conditions (like beard, wrinkle, poor texture, low contrast between lip and skin, etc). To enhance the convergence, we propose to use 2 features: normal profile and grey level patches, and combine them with a voting approach. The standard ASM is not able to take into account temporal information from previous frames therefore the lip contours are tracked by replacing the standard ASM with a hybrid active shape model (HASM) which is capable to take advantage of the temporal information. Initial experimental results on video sequences show that MF-HASM is more robust to local minimum problem and gives a higher accuracy than traditional single feature-based method in lip tracking problem.
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