Nowadays, power analysis attacks are becoming more and more sophisticated. Through power analysis attacks, an attacker can obtain sensitive data stored in smart cards or other embedded devices more efficiently than wi...
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Nowadays, power analysis attacks are becoming more and more sophisticated. Through power analysis attacks, an attacker can obtain sensitive data stored in smart cards or other embedded devices more efficiently than with any other kind of physical attacks. Among power analysis, simple power analysis (SPA) is probably the most effective against elliptic curve cryptosystem, because an attacker can easily distinguish between point addition and point doubling in a single execution of scalar multiplication. To make elliptic curve scalar multiplication secure against SPA attacks, many methods have been proposed using special point representations. In this paper, a simple but efficient SPA-resistant multiscalar multiplication is proposed. The method is to convert the scalar into a nonadjacent form (NAF) representation at first and then constitute it in a new signed digit representation. This new representation is undertaken at a small precomputation cost, as each representation needs just one doubling and 1/2 additions for each bit. In addition, when combined with randomization techniques, the proposed method can also guard against differential power analysis (DPA) attack.
We apply the spatially and temporally periodic boundary conditions devised by Kraynik and Reinelt (Int. J. Multiphase Flow 18 (1992) 1045) to an atomic fluid undergoing planar elongational flow and describe several al...
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We apply the spatially and temporally periodic boundary conditions devised by Kraynik and Reinelt (Int. J. Multiphase Flow 18 (1992) 1045) to an atomic fluid undergoing planar elongational flow and describe several algorithms which efficiently apply the required nonstandard periodic boundary conditions. These periodic boundary conditions guarantee unrestricted simulation times, and are simply implemented if a rotational transformation is first applied to the coordinates of all atoms such that one of the cell boundaries aligns with the direction of elongation. While in the transformed frame one can apply either Lagrangian rhomboid (LR) or "deforming brick" (DB) periodic boundary conditions to all particle coordinates and relative distances. The latter (DB) scheme turns out to be very similar in form to standard Lees-Edwards periodic boundary conditions for planar shear flow, and both the LR and DB schemes are shown to be equivalent and numerically highly efficient. (C) 1999 Elsevier Science B.V.
Shadows cause a significant problem for automated systems which attempt to understand scenes, since shadow boundaries may be incorrectly recognised as a material change, and incorrectly recognised as an object. Shadow...
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Shadows cause a significant problem for automated systems which attempt to understand scenes, since shadow boundaries may be incorrectly recognised as a material change, and incorrectly recognised as an object. Shadow identification is therefore an important pre-processing step for applications such as shadow removal, shadow invariant object recognition and shadow invariant object tracking. Many existing shadow identification methods are often limited by the types of shadow boundaries (penumbra) which can be found, by the density (darkness) of the shadows and by the type of surface texture on which the shadows are cast. In addition many of these methods are limited to a specific type of scene, while others result in high levels of false positive (FP) shadow identification. To address these problems, a novel algorithm for automatic shadow identification is proposed, which makes use of a new tree-structured segmentation algorithm for candidate shadow region identification, as well as a combination of colour illumination invariance and texture analysis for shadow verification. The method is tested on a number of indoor and outdoor images exhibiting different types of shadows, surfaces and illumination sources. These results indicate that the proposed method performs well against the state of the art;in particular, the rate of FP identification is reduced from 26 to below 13% when compared with using illumination invariance alone.
Flight simulator systems generally use a separate image-generator component. The host is responsible for the positional data updates of the entities and the image generator is responsible for the rendering process. In...
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Flight simulator systems generally use a separate image-generator component. The host is responsible for the positional data updates of the entities and the image generator is responsible for the rendering process. In such systems, the sense of presence is decreased by model flickering. This study presents a method by which the host can minimize model flickering in the image-generator output. The method is based on preexisting algorithms, such as visibility culling and level of detail management of 3D models. The flickering is minimized for the visually important entities at the expense of increasing the flickering of the entities that are out of the user's focus using a new perception-based approach. It is shown through user studies that the new proposed approach increases the participants' sense of presence.
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.
This study addresses the trajectory tracking control problem of electrically driven wheeled mobile robots under non-holonomic constraints in the presence of model uncertainties without velocity measurement. By definin...
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This study addresses the trajectory tracking control problem of electrically driven wheeled mobile robots under non-holonomic constraints in the presence of model uncertainties without velocity measurement. By defining a suitable set of output equations, a new input-output model of wheeled mobile robots is developed, which helps the designer utilise the classic control algorithms of robot manipulators. An observer-based trajectory tracking controller is proposed for the new wheeled mobile robot (WMR) model. Then, in order to reduce the design complexity, the dynamic surface control approach is effectively exploited to propose a tracking controller considering the actuator dynamics. Adaptive robust techniques are also adopted to cope with the parametric and non-parametric uncertainties in the WMR model. A Lyapunov-based stability analysis is utilised to guarantee that tracking and state estimation errors are uniformly ultimately bounded. Simulation results are presented to illustrate the feasibility and efficiency of the proposed controller.
Selective encryption is a technique to save computational complexity or enable interesting new system functionality by only encrypting a portion of a compressed bitstream while still achieving adequate security. Altho...
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Selective encryption is a technique to save computational complexity or enable interesting new system functionality by only encrypting a portion of a compressed bitstream while still achieving adequate security. Although suggested in a number of specific cases, selective encryption could be much more widely used in consumer electronic applications ranging from mobile multimedia terminals through digital cameras were it subjected to a more thorough security analysis. We describe selective encryption and develop a simple scalar quantizer example to demonstrate the power of the concept, list a number of potential consumer electronics applications, and then describe an appropriate method for developing and analyzing selective encryption for particular compression algorithms. We summarize results from application of this method to the MPEG-2 video compression algorithm.
The article presents a discussion of topics in computer science related to Boolean satisfiability. It focuses on solutions which can be readily used in applications software, and their effectiveness for solving certai...
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The article presents a discussion of topics in computer science related to Boolean satisfiability. It focuses on solutions which can be readily used in applications software, and their effectiveness for solving certain types of problems. Topics addressed include the interaction between necessary values and constraints in generating computational complexity. Examples are provided using decision trees solved using various computer algorithms. The increasing commercial use of such programs in fields such as computer hardware and software testing and development is noted.
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