When is (D,G)-scaling both necessary and sufficient

It is shown that the well-known (D,G)-scaling upper bound of the structured singular value is a nonconservative test for robust stability with respect to certain linear time-varying uncertainties     

A necessary and sufficient condition for robust asymptoticstability of time-variant discrete systems

A necessary and sufficient condition for the stability of time-variant interval matrices is presented. This condition allows stability to be tested by checking only products of vertex (extreme) matrices. The implementation of the test in the form of an algorithm and two illustrative examples are provided     

A necessary and sufficient condition for the stability ofnonnegative interval discrete systems

The stability of nonnegative discrete systems with interval uncertainty is discussed. For this class of system a necessary and sufficient condition for the stability is provided when all elements of the system matrix are assumed to be specified by intervals. An illustrative example is used as a guideline to demonstrate the effectiveness of the result. Simple computational tests are suggested, and their usefulness in this framework is discussed. The possible extension of this result to more general classes of systems is addressed     

A necessary and sufficient condition for diagnosability of stochastic discrete event systems

Stochastic discrete event systems (SDES) are systems whose evolution is described by the occurrence of a sequence of events, where each event has a defined probability of occurring from each state. The diagnosability problem for SDES is the problem of determining the conditions under which occurrences of a fault can be detected in finite time with arbitrarily high probability. (IEEE Trans Autom Control 50(4):476–492 2005) proposed a class of SDES and proposed two definitions of stochastic diagnosability for SDES called A- and A A-diagnosability and reported a necessary and sufficient condition for A-diagnosability, but only a sufficient condition for A A-diagnosability. In this paper, we provide a condition that is both necessary and sufficient for determining whether or not an SDES is A A-diagnosable. We also show that verification of A A-diagnosability is equivalent to verification of the termination of the cumulative sum (CUSUM) procedure for hidden Markov models, and that, for a specific class of SDES called fault-immediate systems, the sequential probability ratio test (SPRT) minimizes the expected number of observable events required to distinguish between the normal and faulty modes.     

An internal model principle is necessary and sufficient for linear output synchronization

Output synchronization of a network of heterogeneous linear state–space models under time-varying and directed interconnection structures is investigated. It is shown that, assuming stabilizability and detectability of the individual systems and imposing very mild connectedness assumptions on the interconnection structure, an internal model requirement is necessary and sufficient for synchronizability of the network to polynomially bounded trajectories. The resulting dynamic feedback couplings can be interpreted as a generalization of existing methods for identical linear systems.     

Convex necessary and sufficient conditions for frequency domain model (in)validation under SLTV structured uncertainty

This paper deals with the problem of model (in)validation of discrete time, causal, linear time-invariant (LTI) stable models subject to slowly linear time-varying structured uncertainty, using frequency domain data corrupted by additive noise. It is well known that in the case of structured LTI uncertainty the problem is NP hard in the number of uncertainty blocks. The main contribution of this paper shows that, on the other hand, if one considers arbitrarily slowly time varying uncertainty and noise in L/sub 2/, then tractable, convex necessary and sufficient conditions for (in)validation can be obtained. Additional results include a discussion of the case where the noise is characterized in terms of the L/sub /spl infin// norm.     

A necessary and sufficient condition for a picture to represent a polyhedral scene

As Shapira pointed out, a theorem by the author on line drawings of polyhedral scenes was not accurate. The present paper shows that the validity of the theorem is attained by a slight revision of the formulation.     

A new set of necessary and sufficient conditions for a system to be prime

In this note, we first generalize the notion of the interactor (Wolovich and Falb [1]), and modify the input-output structure algorithm of Silverman [2], in order to obtain simple relations between the dimension of supremal output nulling invariant subspace V^*and the degree of the determinant of the interactorpartial_{T}and the integers obtained from the structure algorithm. Then, we show that these relations can be used to derive a new set of necessary and sufficient conditions for a linear multivariable system to be prime (Morse [3]).     

Texture roughness analysis and synthesis via extended self-similar(ESS) model

The 2D fractional Brownian motion (FBM) model provides a useful tool to model textured surfaces whose roughness is scale-invariant. To represent textures whose roughness is scale-dependent, we generalize the FBM model to the extended self-similar (ESS) model in this research. We present an estimation algorithm to extract the model parameters from real texture data. Furthermore, a new incremental Fourier synthesis algorithm is proposed to generate the 2D realizations of the ESS model. Finally, the estimation and rendering methods are combined to synthesize real textured surfaces     

A necessary and sufficient condition for fundamental periods of cascade machines to be products of the fundamental periods of their constituent finite state machines

Suppose that a finite state machine α produces a periodic sequence (whose fundamental period is A) of nonnegative integer outputs which are used to drive another finite state machine β through a subsequence of its sequence of internal states in the following way. At the jth clock tick of α's clock its integer output a(j) tells β's clock to tick a(j) times (quickly) to drive β numerous steps down its sequence of internal states to the next internal state in this subsequence of its internal states. Suppose that the sequence of outputs of β is periodic with fundamental period B. Suppose that the sum (over any list of A successive entries of the output sequence of α) of α's outputs is S. Then the subsequence of outputs of the cascade machine β ← α consisting of β, driven by α's outputs as described above, is periodic. The fundamental period of the sequence of outputs of the cascade machine β ←a consisting of β driven by α's outputs is no larger than AB. If every output of α is smaller than B, and if S is relatively prime to B, then the fundamental period of the output sequence of this cascade machine is exactly AB. Moreover, every internal state of B occurs exactly A times in each block of AB successive internal states of the cascade machine β ← α. If, on the other hand, S is not relatively prime to B, then the fundamental period of the output sequence of β ← α is less than AB.     

A sufficient condition for a graph to be an (a,b, k)-critical graph

Let G be a graph, and let a, b, k be integers with 0≤a≤b, k≥0. An [a, b]-factor of graph G is defined as a spanning subgraph F of G such that a≤d _F (x)≤b for each x∈V(G). Then a graph G is called an (a, b, k)-critical graph if after deleting any k vertices of G the remaining graph of G has an [a, b]-factor. In this article, a sufficient condition is given, which is a neighborhood condition for a graph G to be an (a, b, k)-critical graph.     

Hierarchical time-extended Petri nets (H-EPNs) based erroridentification and recovery for multilevel systems

In this paper, hierarchical time-extended Petri nets (H-EPNs), an extended Petri net based modeling and analysis tool, are used to derive the coordination level model of hierarchically decomposable systems, viewed from a three-level hierarchical structure of organization, coordination and execution of tasks. A two-layer (vertical) coordination level framework, consisting of the dispatcher/analyzer and the H-EPN controller is presented. A detailed two sub-level (horizontal) H-EPN controller model is derived to model system operations (including system soft failures). Error classification based on the interaction between the various system coordinators is derived from the H-EPN model. The H-EPN approach preserves multi-resolutional system details as well as effective communication flows between the various subsystems. A simple example illustrates the proposed approach. The important H-EPN properties of boundedness, safeness and reversibility are verified.     

Hierarchical WSN protocol with fuzzy multi-criteria clustering and bio-inspired energy-efficient routing (FMCB-ER)

Multimedia Tools and Applications - Wireless Sensors Network (WSN) is used for monitoring and recording the physical environment and to transfer the sensed data to a central location by means of...     

Concurrent modeling language (CML) for discrete process modeling,simulation and control

Modern manufacturing systems can be viewed as distributed concurrent discrete processes, using extensive communication internally and with the environment. The concurrency and reactive nature of the system make it difficult to model and analyze correctly. This paper presents a genuine high level and concurrent modeling language, CML, that can be used for modeling and control of manufacturing processes. In addition, it can be used for simulation and analysis of the system's operation. The CML language is based on flat concurrent prolog which is a declarative concurrent logic programming language, therefore, highly expressive, and theoretically sound.     

Hierarchical associative classifier (HAC) for malware detection from the large and imbalanced gray list

Nowadays, numerous attacks made by the malware (e.g., viruses, backdoors, spyware, trojans and worms) have presented a major security threat to computer users. Currently, the most significant line of defense against malware is anti-virus products which focus on authenticating valid software from a whitelist, blocking invalid software from a blacklist, and running any unknown software (i.e., the gray list) in a controlled manner. The gray list, containing unknown software programs which could be either normal or malicious, is usually authenticated or rejected manually by virus analysts. Unfortunately, along with the development of the malware writing techniques, the number of file samples in the gray list that need to be analyzed by virus analysts on a daily basis is constantly increasing. The gray list is not only large in size, but also has an imbalanced class distribution where malware is the minority class. In this paper, we describe our research effort on building automatic, effective, and interpretable classifiers resting on the analysis of Application Programming Interfaces (APIs) called by Windows Portable Executable (PE) files for detecting malware from the large and imbalanced gray list. Our effort is based on associative classifiers due to their high interpretability as well as their capability of discovering interesting relationships among API calls. We first adapt several different post-processing techniques of associative classification, including rule pruning and rule re-ordering, for building effective associative classifiers from large collections of training data. In order to help the virus analysts detect malware from the imbalanced gray list, we then develop the Hierarchical Associative Classifier (HAC). HAC constructs a two-level associative classifier to maximize precision and recall of the minority (malware) class: in the first level, it uses high precision rules of majority (benign file samples) class and low precision rules of minority class to achieve high recall; and in the second level, it ranks the minority class files and optimizes the precision. Finally, since our case studies are based on a large and real data collection obtained from the Anti-virus Lab of Kingsoft corporation, including 8,000,000 malware, 8,000,000 benign files, and 100,000 file samples from the gray list, we empirically examine the sampling strategy to build the classifiers for such a large data collection to avoid over-fitting and achieve great effectiveness as well as high efficiency. Promising experimental results demonstrate the effectiveness and efficiency of the HAC classifier. HAC has already been incorporated into the scanning tool of Kingsoft’s Anti-Virus software.