Wireless sensor network (WSN) has been already widely used in many fields in terms of industry, agriculture, and military, and so forth. The basic composition is WSN nodes that are capable of performing processing, ga...
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Wireless sensor network (WSN) has been already widely used in many fields in terms of industry, agriculture, and military, and so forth. The basic composition is WSN nodes that are capable of performing processing, gathering information, and communicating with other connected nodes in the network. The main components of a WSN node are microcontroller, transceiver, and some sensors. Undoubtedly, it also can be added with some actuators to form a tiny mechanical system. Under this case, the existence of task preemption while executing operating system will not only cost more energy for WSN nodes themselves, but also bring unacceptable system states caused by vibrations. However for these nodes, task I/O delays are inevitable due to the existence of task preemption, which will bring extra overhead for the whole system, and even bring unacceptable system states caused by vibrations. This paper mainly considers the earliest deadline first (EDF) task preemption algorithm executed in WSN OS and proposes an improved task preemption algorithm so as to lower the preemption overhead and I/O delay and then improve the system performance. The experimental results show that the improved task preemption algorithm can reduce the I/O delay effectively, so the real-time processing ability of the system is enhanced.
A relatively fast pursuit algorithm in face recognition is proposed, compared to existing pursuit algorithms. More stopping rules have been put forward to solve the problem of slow response of OMP, which can fully dev...
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A relatively fast pursuit algorithm in face recognition is proposed, compared to existing pursuit algorithms. More stopping rules have been put forward to solve the problem of slow response of OMP, which can fully develop the superiority of pursuit algorithm - avoiding to process useless information in the training dictionary. For the test samples that are affected by partial occlusion, corruption, and facial disguise, recognition rates of most algorithms fall rapidly. The robust version of this algorithm can identify these samples automatically and process them accordingly. The recognition rates on ORL database, Yale database, and FERET database are 95.5%, 93.87%, and 92.29%, respectively. The recognition performance under various levels of occlusion and corruption is also experimentally proved to be significantly enhanced.
This paper develops new methods for vision-based satellite attitude control aimed at space-based optical tracking of resident space objects (RSOs). An Earth-orbiting chaser satellite equipped with either one or two bo...
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This paper develops new methods for vision-based satellite attitude control aimed at space-based optical tracking of resident space objects (RSOs). An Earth-orbiting chaser satellite equipped with either one or two body-fixed cameras can successfully track an RSO provided that the target is kept within the camera field of view. Because the cameras are body fixed, the attitude of the satellite needs to be controlled to maintain target lock. Novel vision-based control algorithms are developed to align the chaser camera's optical axis with the chaser-target line of sight. Two control architectures are presented for the cases of monocular and stereoscopic vision. In the case of the monocular architecture, relative chaser-target acceleration information is not available. Moreover, in both cases unknown perturbations can impair the tracking performance. To increase the tracking algorithm's robustness to these effects, a variable structure attitude control technique is employed. The stability of the developed control laws are substantiated based on Lyapunov's direct method and demonstrated using Monte Carlo simulations. The results clearly show that using stereoscopic vision yields faster target tracking, increased robustness to noise and field-of-view limits, and reduced fuel consumption compared with monocular vision-based attitude tracking.
The problem of reconstruction of inclusions in rough diamonds occupies an important place in the entire technological process of extraction and cutting precious stones. Inclusions are foreign objects that are naturall...
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The problem of reconstruction of inclusions in rough diamonds occupies an important place in the entire technological process of extraction and cutting precious stones. Inclusions are foreign objects that are naturally formed in raw stock during the formation of diamond (see Fig. 1). The algorithm proposed in the present paper performs a full reconstruction of three-dimensional models of inclusions from photo images. Three-dimensional models of inclusions are based on a voxel representation. The algorithm performs the coloring of voxels and the segmentation of a voxel grid and constructs polygonal models of inclusions. Experiments carried out on real and synthetic data show that the algorithm can reconstruct three-dimensional models of medium- and large-size inclusions, which may serve as a first approximation for the methods of refinement of the shape of inclusions.
We present five new cloud detection algorithms over land based on dynamic threshold or Bayesian techniques, applicable to the Advanced Along-Track Scanning Radiometer (AATSR) instrument and compare these to the standa...
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We present five new cloud detection algorithms over land based on dynamic threshold or Bayesian techniques, applicable to the Advanced Along-Track Scanning Radiometer (AATSR) instrument and compare these to the standard threshold-based SADIST cloud detection scheme. We use a manually classified dataset as a reference to assess algorithm performance and quantify the impact of each cloud detection scheme on land-surface temperature (LST) retrieval. The use of probabilistic Bayesian cloud detection methods improves algorithm true skill scores by 8-9% over SADIST (maximum score of 77.93% compared with 69.27%). We present an assessment of the impact of imperfect cloud masking, in relation to the reference cloud mask, on the retrieved AATSR LST imposing a 2K tolerance over a 3x3 pixel domain. We find an increase of 5-7% in the observations falling within this tolerance when using Bayesian methods (maximum of 92.02% compared with 85.69%). We also demonstrate that the use of dynamic thresholds in the tests employed by SADIST can significantly improve performance, applicable to cloud-test data to be provided by the Sea and Land Surface Temperature Radiometer (SLSTR) due to be launched on the Sentinel 3 mission (estimated 2014).
In the context of real-time, GPU-based rendering of animated skinned meshes, we propose a new algorithm to compute surface normals with minimal overhead both in terms of the memory footprint and the required per-verte...
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In the context of real-time, GPU-based rendering of animated skinned meshes, we propose a new algorithm to compute surface normals with minimal overhead both in terms of the memory footprint and the required per-vertex operations. By accounting for the variation of the skinning weights over the surface, we achieve a higher visual quality compared to the standard approximation ubiquitously used in video-game engines and other real-time applications. Our method supports Linear Blend Skinning and Dual Quaternion Skinning. We demonstrate the advantages of our technique on a variety of datasets and provide a complete open-source implementation, including GLSL shaders.
We present a novel framework to compute geodesics on implicit surfaces and point clouds. Our framework consists of three parts, particle based approximate geodesics on implicit surfaces, Cartesian grid based approxima...
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We present a novel framework to compute geodesics on implicit surfaces and point clouds. Our framework consists of three parts, particle based approximate geodesics on implicit surfaces, Cartesian grid based approximate geodesics on point clouds, and geodesic correction. The first two parts can effectively generate approximate geodesics on implicit surfaces and point clouds, respectively. By introducing the geodesic curvature flow, the third part produces smooth and accurate geodesic solutions. Differing from most of the existing methods, our algorithms can converge to a given tolerance. The presented computational framework is suitable for arbitrary implicit hypersurfaces or point clouds with high genus or high curvature.
An algorithm for machine recognition of human gender by face images based on machine learning methods is described. The synthesized algorithm consists of two stages, viz. extraction of adaptive features and support ve...
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An algorithm for machine recognition of human gender by face images based on machine learning methods is described. The synthesized algorithm consists of two stages, viz. extraction of adaptive features and support vector machine classification. Comparative analysis of operation of the proposed algorithm is performed, and the training and testing technique is given.
A mathematical model must be established to study the motions of ships in order to control them effectively. An assessment of the model depends on the accuracy of hydrodynamic parameters. An algorithm for the paramete...
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A mathematical model must be established to study the motions of ships in order to control them effectively. An assessment of the model depends on the accuracy of hydrodynamic parameters. An algorithm for the parameter identification of the coupled pitch and heave motions in ships is, thus, put forward in this paper. The algorithm proposed is based on particle swarm optimization (PSO) and the opposition-based learning theory known as opposition-based particle swarm optimization (OPSO). A definition of the opposition-based learning algorithm is given first of all, with ideas on how to improve this algorithm and its process being presented next. Secondly, the design of the parameter identification algorithm is put forward, modeling the disturbing force and disturbing moment of the identification system and the output parameters of the identification system. Then, the problem involving the hydrodynamic parameters of motions is identified and the coupled pitch and heave motions of a ship described as an optimization problem with constraints. Finally, the numerical simulations of different sea conditions with unknown parameters are carried out using the PSO and OPSO algorithms. The simulation results show that the OPSO algorithm is relatively stable in terms of the hydrodynamic parameters identification of the coupled pitch and heave motions.
The higher goal of rehabilitation robot is to aid a person to achieve a desired functional task (e. g., tracking trajectory) based on assisted-as-needed principle. To this goal, a new adaptive inverse optimal hybrid c...
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The higher goal of rehabilitation robot is to aid a person to achieve a desired functional task (e. g., tracking trajectory) based on assisted-as-needed principle. To this goal, a new adaptive inverse optimal hybrid control (AHC) combining inverse optimal control and actor-critic learning is proposed. Specifically, an uncertain nonlinear rehabilitation robot model is firstly developed that includes human motor behavior dynamics. Then, based on this model, an open-loop error system is formed;thereafter, an inverse optimal control input is designed to minimize the cost functional and a NN-based actor-critic feedforward signal is responsible for the nonlinear dynamic part contaminated by uncertainties. Finally, the AHC controller is proven (through a Lyapunov-based stability analysis) to yield a global uniformly ultimately bounded stability result, and the resulting cost functional is meaningful. Simulation and experiment on rehabilitation robot demonstrate the effectiveness of the proposed control scheme.
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