7771298
Robust stability of a class of nonlinear time-varying discrete systems
This paper studies the problem of robust absolute stability of a class of nonlinear discrete-time systems with time-varying matrix uncertainties of polyhedral type and multiple time-varying sector nonlinearities. By using the variational method and the Lyapunov Second Method, criteria for robust absolute stability are obtained in different forms for the given class of systems. Specifically, we determine the parametric class of Lyapunov functions which defines the necessary and sufficient conditions of robust absolute stability. We apply these Lyapunov functions to derive an algebraic criterion for robust absolute stability in the form of solvability conditions of a set of matrix equations.
35821065
Criteria for asymptotic stability of a class of discrete systems with multiple independent variables
This paper investigates the problem of asymptotic stability for a class of linear shift-invariant discrete systems with multiple independent variables. We first establish the equivalence of this problem and the problem of robust stability for a class of ordinary linear shift-varying discrete systems with the matrix uncertainty set defined by the coefficient matrices of the original system. On the basis of this equivalence, by using the variational method and the Lyapunov Second Method, necessary and sufficient conditions for asymptotic stability are obtained in different forms for the class of systems considered in the present paper. The parametric classes of Lyapunov functions which define the necessary and sufficient conditions of asymptotic stability are determined. We use the piecewise linear polyhedral Lyapunov functions of the infinity vector norm type to derive an algebraic criterion for asymptotic stability of the given class of discrete systems in the form of solvability conditions of a set of matrix equations.
38675279
On-Line Learning Control for Discrete Nonlinear Systems Via an Improved ADDHP Method
This paper mainly discusses a generic scheme for on-line adaptive critic design for nonlinear system based on neural dynamic programming (NDP), more exactly, an improved action-depended dual heuristic dynamic programming (ADDHP) method. The principal merit of the proposed method is to avoid the model neural network which predicts the state of next time step, and only use current and previous states in the method, as makes the algorithm more suitable for real-time or on-line application for process control. In this paper, convergence proof of the method will also be given to guarantee the control to reach the optimal. At last, simulation result verifies the performance.
31887438
Robust Stability Analysis of a Class of Hopfield Neural Networks with Multiple Delays
The robust stability of a class of Hopfield neural networks with multiple delays is analyzed. Sufficient conditions for the global robust stability of the equilibrium point are established through constructing a suitable Lyapunov-Krasovskii functional. The present results take the form of linear matrix inequalities, and are computationally efficient. In addition, the results are independent of delays and established without assuming differentiability and monotonicity of the activation function.
7951709
Design of bidirectional associative memories based on the perceptron training technique
Bidirectional associative memories are being used extensively for solving a variety of problems related to pattern recognition. In the present paper, a new synthesis approach is developed for bidirectional associative memories using feedback neural networks. The synthesis problem of bidirectional associative memories is formulated as a set of linear inequalities which can be solved using the perceptron training algorithm. To demonstrate the applicability of the present results a specific example is considered.
31045346
A New Fuzzy Identification Method Based on Adaptive Critic Designs
A new fuzzy identification method for unknown nonlinear discrete systems is presented by introducing adaptive critic designs into the generalized fuzzy hyperbolic model (GFHM). This method minimizes the long-time error other than the immediate error to improve the identification effect. We first represent the GFHM with a neural network structure, and then utilize the adaptive critic designs (ACDs) to get the optimal parameters of the network so that the long-time identification error is minimized. Finally, we give a simulation example to verify the effectiveness of this identification method.
769378
Efficient Distribution of Key Chain Commitments for Broadcast Authentication in Distributed Sensor Networks
Broadcast authentication is a fundamental security service in distributed sensor networks. A scheme named $\mu$TESLA has been proposed for efficient broadcast authentication in such networks. However, $\mu$TESLA requires initial distribution of certain information based on unicast between the base station and each sensor node before the actual authentication of broadcast messages. Due to the limited bandwidth in wireless sensor networks, this initial unicast-based distribution severely limits the application of $\mu$TESLA in large sensor networks. This paper presents a novel technique to replace the unicast-based initialization with a broadcast-based one. As a result, $\mu$TESLA can be used in a sensor network with a large amount of sensors, as long as the message from the base station can reach these sensor nodes. This paper further explores several techniques that improve the performance, the robustness, as well as the security of the proposed method. The resulting protocol satisfies several nice properties, including low overhead, tolerance of message loss, scalability to large networks, and resistance to replay attacks as well as some known Denial of Service (DOS) attacks.
1435851
Soft shape context for iterative closest point registration
This paper introduces a shape descriptor, the soft shape context, motivated by the shape context method. Unlike the original shape context method, where each image point was hard assigned into a single histogram bin, we instead allow each image point to contribute to multiple bins, hence more robust to distortions. The soft shape context can easily be integrated into the iterative closest point (ICP) method as an auxiliary feature vector, enriching the representation of an image point from spatial information only, to spatial and shape information. This yields a registration method more robust than the original ICP method. The method is general for 2D shapes. It does not calculate derivatives, hence being able to handle shapes with junctions and discontinuities. We present experimental results to demonstrate the robustness compared with the standard ICP method.
14012169
Efficient k nearest neighbor queries on remote spatial databases using range estimation
We propose a k-NN query processing algorithm that uses one or more window queries to retrieve the nearest neighbors of a given query point. We also propose two different methods to estimate the ranges to be used by the window queries. Each range estimation method requires different statistical knowledge about the spatial databases. Our experiments on the TIGER data allow us to study the behavior of the proposed algorithm using different range estimation methods. Apart from not requiring direct access to the spatial indices, the window queries used in the proposed algorithm can be easily supported by non-spatial database systems containing spatial objects.
53748903
A Framework for Designing Reusable Analog Circuits
8564766
Location-based pairwise key establishments for static sensor networks
Sensor networks are ideal candidates for applications such as target tracking and environment monitoring. Security in sensor networks is critical when there are potential adversaries. Establishment of pairwise keys is a fundamental security service, which forms the basis of other security services such as authentication and encryption. However, establishing pairwise keys in sensor networks is not a trivial task, particularly due to the resource constraints on sensors. This paper presents several techniques for establishing pairwise keys in static sensor networks. These techniques take advantage of the observation that in static sensor networks, although it is difficult to precisely pinpoint sensors' positions, it is often possible to approximately determine their locations. This paper presents a simple location-aware deployment model, and develops two pairwise key predistribution schemes, a closest pairwise keys predistribution scheme and a location-based pairwise keys scheme using bivariate polynomials, by taking advantage of sensors' expected locations. The analysis in this paper indicates that these schemes can achieve better performance if such location information is available and that the smaller the deployment error (i.e., the difference between a sensor's actual location and its expected location) is, the better performance they can achieve.