The knapsack cryptosystem invented by Merkle and Hellman and most of its variants have already been broken. The method by Lagarias and Odlyzko, known as the low density attack, is a powerful tool for attacking knapsack cryptosystems. No successful attack, however, has been reported against the matrix type knapsack system. We derive the critical density associated with this system and show some results of computer experiments of attack.

We propose a simple steganalytic scheme for least significant bit embedding. The new method is based on statistics of image difference signal, and enables us to estimate the relative message length with such a high accuracy as Sample Pairs Analysis. Experimental results show that our method detects a stego Work which is missed by Sample Pairs Analysis, and vice versa. This implies that a combination of several steganalytic techniques should be used to reduce the missing rate. We believe our method is one of useful tools for LSB steganalysis.

We introduce novel dissimilarity into a probabilistic clustering task to properly measure dissimilarity among multiple clusters when each cluster is characterized by a subpopulation in the mixture model. This measure of dissimilarity is called redundancy-based dissimilarity among probability distributions. From aspects of both source coding and a statistical hypothesis test, we shed light on several of the theoretical reasons for the redundancy-based dissimilarity among probability distributions being a reasonable measure of dissimilarity among clusters. We also elucidate a principle in common for the measures of redundancy-based dissimilarity and Ward's method in terms of hierarchical clustering criteria. Moreover, we show several related theorems that are significant for clustering tasks. In the experiments, properties of the measure of redundancy-based dissimilarity are examined in comparison with several other measures.

We challenge the human identification problem from the perspective of gait and body shape. Conventional methods depend on the camera viewing direction, and since they are based on matching image silhouettes or features their identification accuracy is low when there is a big difference between the camera viewing direction of the test and training data. Thus, if a person is walking in an arbitrary direction, they may not be accurately identified. In this paper, we propose a novel method that does not depend on the camera viewing direction. We develop a state space model called a "cyclic motion model" whose state variables are not only the phase of the motions but also the camera viewing direction. We learn model parameters for each candidate person, and represent their walking with the cyclic motion model. To identify a person from the observed image sequence, we first compute the model likelihoods for the sequence using a particle filter that represents a probability distribution by a set of weighted samples, We then identify the person from model likelihoods.

This paper proposes a method of making a local map of indoor environments by swiveling a camera and a sonar. By swiveling a camera, an omni-directional panoramic image of high azimuth angle solution can be obtained. Meanwhile, regions of constant depth (RCD) corresponding to boundary edges and walls can be obtained by swiveling a sonar. Since many vertical lines exist in indoor environments, a local map of indoor environments can be acquired based upon position of a vertical line in space. From a 2D image, we cannot know its depth in space. At the same time, we cannot also distinguish the RCDs of boundary vertical lines from those of walls and others. However, since a boundary vertical line in space not only appears in images but also has a corresponding RCD, a local map can be acquired by fusing the metrical information from a sonar and the texture information from a camera. This paper proposes the method of fusing and gives the experimental results in a real indoor environment.

This paper proposes a method of robot navigation in outdoor environments by using GPS (Global Positioning System) information and panoramic views. Since an outdoor environment is very large scaled, it is very difficult to determine the global position based on a visual sensor. Although a global position can be obtained from a GPS navigator, it is difficult to locate a robot accurately since it has a distance error about 30/spl sim/100 meter. Our system is equipped with a GPS navigator and a camera. A coarse-to-fine method is used to generate an outdoor environment map and locate a mobile robot. First, a robot finds its approximate position based on the GPS information. Then, it identifies its location from the image information. Experimental results in outdoor environments show the effectiveness of this method.

This paper proposes an idea of representing qualitatively a route in an outdoor environment in terms of landmarks. A robot continuously scans the side-view and yields an intermediate representation called panoramic representation. The objects in the scene are segmented based upon range and color information in the panoramic representation, and they are then arranged into a structure map which provides qualitative information on the environment structure. The objects which have distinctive features along the route are selected as the landmarks. Since their relative spatial management is invariant with the camera speed, the memorized landmark can be matched with the objects in the new structure map by a dynamic programming-based method. The correctness of matching is further verified by using the disparity and color cues. Results of experiments in, a real outdoor world show the effectiveness and and robustness of this method.

There is a need for highly redundant manipulators to work in complex, cluttered environments. We explore kinematics and path planning for highly redundant manipulators by means of a continuous manipulator model, which captures the macroscopic shape of highly redundant manipulators. A path for the continuous manipulator model can be found by finding a smooth path for its end-effector under a maximum curvature/torsion constraint. Free space decomposition into primary convex regions is suited to finding such smooth paths. On top of the free space representation, we have developed an algorithm to find a continuous curvature path in 2-D with a maximum curvature constraint. We report on an experiment conducted to measure the efficiency of the algorithm. The 2-D path planning algorithm could be used for path planning in 3-D space by restricting the manipulator movement. Alternatively, 3-D free space can be decomposed into primary convex regions. The path planning problem has been shown to be...

We investigate the mean-squared error (MSE) performance of a perfectly integrating delta modulator driven by an input sequence of independent and identically distributed (i.i.d.) increments having a rational characteristic function\phi Y^{(u)}. Although the input process is nonstationary, the mean-squared error (MSE) can be finite under some conditions. The limiting characteristic function of the error sequence is found by the method of Wiener-Hopf, and then a formula for the MSE is given in terms of the coefficients of the defining polynomials for\phi Y^{(u)}, roots of transcendental equations involving\phi Y^{(u)}, and certain quantization parameters. Curves are presented of normalized MSE versus the distribution parameter or quantization parameter, where the increments are one- or two-sided gamma distributed. The results obtained here give some insight to the asymptotic performance of delta modulation of stationary Markov processes having an amplitude distribution belonging to a wide class.

The performance of DPCM (Differential Pulse Code Modulation) with uniform quantization with the Wiener process input is analyzed. The approach taken is to establish an equation for the characteristic function of error distribution and then to solve its steady-state version. No use is made in the derivation of the approximating concepts of slope overload error and granular error. Exact formulas are derived which give the mean-squared-error in terms of the step size 2Δ and the number of levelsNof quantization. Curves are shown for two kinds of mean-squared-error versus Δ orN, and are compared with the rate distortion function.

This paper is concerned with an exact analysis of DPCM (Differential Pulse Code Modulation) systems with stationary Gaussian inputs. We consider a class of digital communication systems in which DPCM is included as a member. An integral equation for the joint probability of the input and the state of the system is derived first. Solution of the equation is sought in the form of a power series in the elements of the covariance matrix of the input that involves generalized Hermite functions. Then the integral equation is reduced to a set of algebraic equations for the coefficients in the series that are solved recursively. The steady-state distribution of the input and the state is thus found. We are interested particularly in the mean-squared error of the system as a function of step size, the sampling interval and the number of quantization levels. Numerical results are shown for the GaussMarkov input; the MSE is calculated in terms of system parameters and performance of DPCM and PCM is compared with reference to the theoretical bound.