Due to the serious speckle noise in synthetic aperture radar (SAR) image, segmentation of SAR images is still a challenging problem. In this paper, a novel region merging method based on perceptual hashing is proposed...
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Due to the serious speckle noise in synthetic aperture radar (SAR) image, segmentation of SAR images is still a challenging problem. In this paper, a novel region merging method based on perceptual hashing is proposed for SAR image segmentation. In the proposed method, perceptual hash algorithm (PHA) is utilized to calculate the degree of similarity between different regions during region merging in SAR image segmentation. After reducing the speckle noise by Lee filter which maintains the sharpness of SAR image, a set of different homogeneous regions is constructed based on multi-thresholding and treated as the input data of region merging. The new contribution of this paper is the combination of multi-thresholding for initial segmentation and perceptual hash method for the adaptive process of region merging, which preserves the texture feature of input images and reduces the time complexity of the proposed method. The experimental results on synthetic and real SAR images show that the proposed algorithm is faster and attains higher-quality segmentation results than the three recent state-of-the-art image segmentation methods.
An economic user-centric WiFi offloading algorithm is proposed to satisfy the major concerns of wireless users, who wish to have better network performance with even less network expense. Thus in this paper both syste...
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An economic user-centric WiFi offloading algorithm is proposed to satisfy the major concerns of wireless users, who wish to have better network performance with even less network expense. Thus in this paper both system throughput and network expense are considered, and the goal of the proposed offloading algorithm is to obtain an optimal offloading ratio, which can both maximize the system throughput and minimize the network expense. Firstly, a practical system model is set up on the basis of a typical scenario of heterogeneous network. In this model, the average throughput of both cellular network and WiFi network is analyzed carefully. Then an economic user-centric WiFi offloading algorithm is proposed with an evaluation function to evaluate the system, and the optimal offloading ratio can be obtained by minimizing the evaluation function. At last, numerical results represent a direct calculating process of the optimal offloading ratio. These results in return validate the efficiency of the proposed offloading algorithm as well.
We study the optimal rates of convergence for estimating a prior distribution over a VC class from a sequence of independent data sets respectively labeled by independent target functions sampled from the prior. We sp...
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We study the optimal rates of convergence for estimating a prior distribution over a VC class from a sequence of independent data sets respectively labeled by independent target functions sampled from the prior. We specifically derive upper and lower bounds on the optimal rates under a smoothness condition on the correct prior, with the number of samples per data set equal the VC dimension. These results have implications for the improvements achievable via transfer learning. We additionally extend this setting to real valued function, where we establish consistency of an estimator for the prior, and discuss an additional application to a preference elicitation problem in algorithmic economics. (C) 2017 Published by Elsevier B.V.
Many recent games and applications target the interactive exploration of realistic large scale worlds. These worlds consist mostly of static terrain models, as the simulation of animated fluids in these virtual worlds...
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Many recent games and applications target the interactive exploration of realistic large scale worlds. These worlds consist mostly of static terrain models, as the simulation of animated fluids in these virtual worlds is computationally expensive. Adding flowing fluids, such as rivers, to these virtual worlds would greatly enhance their realism, but causes specific issues: as the user is usually observing the world at close range, small scale details such as waves and ripples are important. However, the large scale of the world makes classical methods impractical for simulating these effects. In this paper, we present an algorithm for the interactive simulation of realistic flowing fluids in large virtual worlds. Our method relies on two key contributions: the local computation of the velocity field of a steady flow given boundary conditions, and the advection of small scale details on a fluid, following the velocity field, and uniformly sampled in screen space.
We designed, implemented, and tested a real-time flexible controller for manipulating different types of robots and control algorithms. The robot-independent, IBM PC-based multiprocessor system contains a DSP56001 mas...
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We designed, implemented, and tested a real-time flexible controller for manipulating different types of robots and control algorithms. The robot-independent, IBM PC-based multiprocessor system contains a DSP56001 master controller, six independent HCTL-1100 joint processors for accurate robotic joint control, and an interface computer board for processor communication, The joint processors operate in four user-defined modes and can be connected directly to an incremental optical encoder, which accommodates specialized applications and eliminates extra hardware.
The problem of estimating the state of a nonlinear stochastic plant is considered. Unlike classical approaches such as the extended Kalman filter, which are based on the linearization of the plant and the measurement ...
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The problem of estimating the state of a nonlinear stochastic plant is considered. Unlike classical approaches such as the extended Kalman filter, which are based on the linearization of the plant and the measurement model, we concentrate on the nonlinear filter equations such as the Zakai equation. The numerical approximation of the conditional probability density function (pdf) using ordinary grids suffers from the "curse of dimension" and is therefore not applicable in higher dimensions. It is demonstrated that sparse grids are an appropriate tool to represent the pdf and to solve the filtering equations numerically. The basic algorithm is presented. Using some enhancements it is shown that problems in higher dimensions can be solved with an acceptable computational effort. As an example a six-dimensional, highly nonlinear problem, which is solved in real-time using a standard PC, is investigated.
A self-stabilizing algorithm cannot detect by itself that stabilization has been reached. For overcoming this drawback Lin and Simon introduced the notion of an external observer, i.e., a set of processes, one being l...
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A self-stabilizing algorithm cannot detect by itself that stabilization has been reached. For overcoming this drawback Lin and Simon introduced the notion of an external observer, i.e., a set of processes, one being located at each node, whose role is to detect stabilization. We propose here a less expensive approach, where there is a single observing process located at a unique node. This process is not allowed to detect false stabilization and it must eventually detect that stabilization is reached. Moreover it must not interfere with the observed self-stabilizing algorithm. Our result is that there exists such an observer for any problem on a distinguished network having a synchronous self-stabilizing solution. Note that our proof is constructive.
In semi-online scheduling problems, we always assume that some partial additional information is exactly known in advance. This may not be true in some application. This paper considers semi-online problems on identic...
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In semi-online scheduling problems, we always assume that some partial additional information is exactly known in advance. This may not be true in some application. This paper considers semi-online problems on identical machines with inexact partial information. Three problems are considered, where we know in advance that the optimal value, or the largest job size are in given intervals, respectively, while their exact values are unknown. We give both lower bounds of the problems and competitive ratios of algorithms as functions of a so-called disturbance parameter r is an element of [1, infinity). We establish for which r the inexact partial information is useful to improve the performance of a semi-online algorithm with respect to its pure online problem. Optimal preemptive semi-online algorithms are then obtained.
Texture and color information as important characters of video objects have been widely used in the detection of moving objects. A detection algorithm based on texture may cause detection errors in regions of blank te...
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Texture and color information as important characters of video objects have been widely used in the detection of moving objects. A detection algorithm based on texture may cause detection errors in regions of blank texture and heterogeneous texture, and a detection algorithm based on color is easily influenced by illumination changes and shadows. In this paper, a new detection and fusion algorithm is proposed. At the detection stage based on texture, the background texture is classified according to the steering kernel. At the fusion stage, for the moving objects detected on the basis of texture and color respectively, a scheme based on a boundary selection strategy is proposed for combining the different detection objects. A relatively smooth boundary is selected as the true boundary, and the shadow detection is carried out to assist the boundary selection. Experimental results verify the advantages of the proposed algorithm as compared to the existing state-of-the-art algorithms. (C) 2012 Elsevier Ltd. All rights reserved.
Service-oriented computing is revolutionizing the modern computing paradigms with its aim to boost software reuse and enable business agility. Under this paradigm, new services are fabricated by composing available se...
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Service-oriented computing is revolutionizing the modern computing paradigms with its aim to boost software reuse and enable business agility. Under this paradigm, new services are fabricated by composing available services. The problem arises as how to effectively and efficiently compose heterogeneous services facing the high complexity of service composition. Based on environment ontology, this paper introduces a requirement-driven service composition approach. We propose the algorithms to decompose the requirement, the rules to deduct the relation between services, and the algorithm for composing service. The empirical results and the comparison with other services' composition methodologies show that this approach is feasible and efficient.
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