Most model-based approaches to fault diagnosis of discrete-event systems require a complete and accurate model of the system to be diagnosed. However, the discrete-event model may have arisen from abstraction and simplification of a continuous time system, or through model building from input-output data. As such, it may not capture the dynamic behavior of the system completely. In a previous paper, we addressed the problem of diagnosing faults given an incomplete model of the discrete-event system. We presented the learning diagnoser which not only diagnoses faults, but also attempts to learn missing model information through parsimonious hypothesis generation. In this paper, we study the properties of learnability and diagnosability. Learnability deals with the issue of whether the missing model information can be learned, while diagnosability corresponds to the ability to detect and isolate a fault after it has occurred. We provide conditions under which the learning diagnoser can learn missing model information. We define the notions of weak and strong diagnosability and also give conditions under which they hold.

Most model-based approaches to fault diagnosis of discrete-event systems (DESs) require a complete and accurate model of the system to be diagnosed. However, the discrete-event model may have arisen from abstraction and simplification of a continuous time system or through model building from input-output data. As such, it may not capture the dynamic behavior of the system completely. In this paper, we address the problem of diagnosing faults, given an incomplete model of the discrete-event system. When the model is incomplete, discrepancies will arise between the actual output and the output predicted by the model. We introduce learning into the diagnoser construction by forming hypotheses that explain these discrepancies. We view the process of generating and evaluating hypotheses about the model of the system as an instance of the set-cover problem, which we formalize using parsimonious covering theory. We describe in detail the construction of the learning diagnoser, which not only performs fault diagnosis but also attempts to learn the missing model information. If the model is complete, the learning diagnoser reduces to the standard state-based diagnoser. Examples are provided to illustrate how learning and diagnosis can be simultaneously achieved through the learning diagnoser.

This paper studies the problem of achieving watermark semifragility in watermark-based authentication systems through a composite hypothesis testing approach. Embedding a semifragile watermark serves to distinguish legitimate distortions caused by signal-processing manipulations from illegitimate ones caused by malicious tampering. This leads us to consider authentication verification as a composite hypothesis testing problem with the watermark as side information. Based on the hypothesis testing model, we investigate effective embedding strategies to assist the watermark verifier to make correct decisions. Our results demonstrate that quantization-based watermarking is more appropriate than spread-spectrum-based methods to achieve the semifragility tradeoff between two error probabilities. This observation is confirmed by a case study of an additive Gaussian white noise channel with a Gaussian source using two figures of merit: 1) relative entropy of the two hypothesis distributions and 2) the receiver operating characteristic. Finally, we focus on common signal-processing distortions, such as JPEG compression and image filtering, and investigate the discrimination statistic and optimal decision regions to distinguish legitimate and illegitimate distortions. The results of this paper show that our approach provides insights for authentication watermarking and allows for better control of semifragility in specific applications.

This paper focuses on a coding approach for effective analysis and design of secure watermark-based multimedia authentication systems. We provide a design framework for semi-fragile watermark-based authentication such that both objectives of robustness and fragility are effectively controlled and achieved. Robustness and fragility are characterized as two types of authentication errors. The authentication embedding and verification structures of the semi-fragile schemes are derived and implemented using lattice codes to minimize these errors. Based on the specific security requirements of authentication, cryptographic techniques are incorporated to design a secure authentication code structure. Using nested lattice codes, a new approach, called MSB-LSB decomposition, is proposed which we show to be more secure than previous methods. Tradeoffs between authentication distortion and implementation efficiency of the secure authentication code are also investigated. Simulations of semi-fragile authentication methods on real images demonstrate the effectiveness of the MSB-LSB approach in simultaneously achieving security, robustness, and fragility objectives.

We study the performance of robust digital watermarking approaches in the presence of lossy compression by introducing practical analysis methodologies. Correlation expressions between the embedded watermark and the extracted watermark are derived to determine the optimal watermarking domain to maximize data hiding rates for spread spectrum and quantization watermarking. It is determined both theoretically and through simulations that the embedding strategy, in addition to the transform used for lossy compression, dictate the optimal transform for watermarking. Through analytic comparisons, we develop a novel hybrid watermarking algorithm that exploits the best of both approaches for greater resilience to JPEG compression.

We approach the problem of characterizing an optimal FIR filter bank from an algebraic point of view. We introduce the concept of majorization ordering to compare the performance of various filter banks in an admissible set /spl Lscr/. Using the properties of this ordering, we show that a principal component filter bank is associated with the greatest element in /spl Lscr/. A greatest element does not necessarily exist in /spl Lscr/ hence one has to deal with the closely related notion of a maximal element. We show by construction that a maximal element always exist in /spl Lscr/. An interesting result of the presented algebraic theory is that the connection between principal component filter banks and filter banks with maximum coding gain is clearly revealed. In fact, we show that coding gain is a Schur (1973) convex function preserving the order of majorization.

This paper presents an approximation method for estimating the average number of data packets in a SENET-concept integrated voice and data system. The method is simple to use and its estimates are found to be in good agreement with simulation results. In general, the method applies to any system that integrates packet traffic with a more slowly varying traffic class that can preempt a portion of the packet transmission capacity.