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The delay-range-dependent stochastic stability for uncertain neutral Markovian jump systems with interval time-varying delays and nonlinear perturbations is investigated. The perturbations under consideration are time-varying and norm-bounded. By delay interval dividing, a novel augmented Lyapunov functional which contains triple-integral terms to reduce the conservativeness is introduced. Based on the Lyapunov functional approach and the nature of convex combination, some improved delay-range-dependent stochastic stability criteria are obtained in terms of linear matrix inequalities without introducing any free-weighting matrices. Finally, numerical examples are given to illustrate the effectiveness of the developed techniques. [ABSTRACT FROM AUTHOR]

Abstract: The synchronization problem of a neutral complex dynamical network (NCDN) with distributed delay, Markovian jump parameters and partially unknown transition rates via sampled-data controller is investigated in this paper. The retarded, neutral and distributed delays are considered to be interval mode-dependent and time-varying, while the sampling period is assumed to be time-varying and bounded. By the interval dividing approach, a new augmented stochastic Lyapunov functional is constructed, which contains some triple-integral terms to reduce the conservativeness. Then the delay-range-dependent and rate-dependent exponential stability conditions for the closed-loop error system are obtained by the Lyapunov–Krasovskii stability theory, integral matrix inequalities and reciprocally convex lemma. Based on these new stability conditions, the sampled-data exponential synchronization controllers are found in terms of the solutions to linear matrix inequalities. Finally, numerical examples are given to demonstrate the feasibility and effectiveness of the proposed theoretic result. [Copyright &y& Elsevier]

Readahead is an important technique to deal with the huge gap between disk drives and applications. It has become a standard in modern operating systems and advanced storage systems. However, it is difficult to develop the common kernel readahead and to achieve full testing coverage of all cases. In this paper, we formulate the kernel read handling, caching and readahead behavior as an absorbing Markov decision process, and present performance evaluations to compare or verify various readaheads. We also introduce algorithms to find optimal prefetching policies for specific read pattern and present the convergence analysis. By exploiting sample-path-based methods, it becomes much easier to evaluate and optimize prefetching policies, which can provide valuable informations to help the design and improvement of practical readaheads. For illustration and verification, we present two examples and some experiments on the readaheads in Linux kernel. The results show that the model-based evaluations agree with the practice and the improved prefetching policy significantly outperforms the original one in Linux kernel for the specified workloads. [ABSTRACT FROM AUTHOR]

Minimizing the number of bandwidth changes of individual stream and enabling the transmission schedule flexible can benefit increasing the number of concurrent multiplexed streams in periodic network without degrading quality of video. However, minimum change bandwidth allocation (MCBA) only provides a fixed transmission schedule for given video and client buffer although it achieves the minimum number of rate changes and the minimum peak rate. So the MCBA schedule cannot be adjusted when multiple streams are multiplexed over a limited bandwidth shared link. In this paper, we propose a relaxing transmission schedule that provides a range of rates for each interval and preserves the minimum number of rate changes without increasing any rate. As a result, the individual schedule can be adjusted by network service to try to satisfy bandwidth constraint when the total required bandwidth of multiplexed streams exceeds the available network capacity. First, we underline that the key of MCBA is to segment video with making each linear trajectory as long as possible. Then we use linear separability and support vector machine under the classification model to compute the longest interval and the required rate, respectively. Due to our proposed decoupled form between segmentation and rate computation, finally, we construct the relaxing schedule bounded by the original MCBA rate and the minimum rate. The simulation using real MPEG4 and H.264 video traces confirms the philosophy and evaluates the relaxing performance of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, hidden semi-Markov model (HSMM) is introduced into intrusion detection. Hidden Markov model (HMM) has been applied in intrusion detection systems several years, but it has a major weakness: the inherent duration probability density of a state in HMM is exponential, which may be inappropriate for the modeling of audit data of computer systems. We can handle this problem well by developing an HSMM for perfect normal processes of computer systems. Based on this HSMM, an algorithm of anomaly detection is presented in this paper, which computes the distance between the processes monitored by intrusion detection system and the perfect normal processes. In this algorithm, we use the average information entropy (AIE) of fixed-length observed sequence as the anomaly detection metric based on maximum entropy principle (MEP). To improve accuracy, the segmental K-means algorithm is applied as training algorithm for the HSMM. By comparing the accurate rate with the experimental results of previous research, it shows that our method can perform a more accurate detection. [Copyright &y& Elsevier]

Robust Kalman filtering problem for a class of continuous-time Markov jump linear systems with uncertain second-order statistical properties is investigated. Uncertainty is modeled by allowing process and observation noises spectral density matrices to vary arbitrarily within given classes. The upper bounds of the perturbation to the noise covariance matrices are given based on the estimation error performance, and a steady-state estimator is therefore adopted under the worst situation. Not only can this method minimize the worst performance function of the uncertainty, but the error performance can be guaranteed to be within a given bound. Finally the developed theory is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

The optimization problems of Markov control processes (MCPs) with exact knowledge of system parameters, in the form of transition probabilities or infinitesimal transition rates, can be solved by using the concept of Markov performance potential which plays an important role in the sensitivity analysis of MCPs. In this paper, by using an equivalent infinitesimal generator, we first introduce a definition of discounted Poisson equations for semi-Markov control processes (SMCPs), which is similar to that for MCPs, and the performance potentials of SMCPs are defined as solution of the equation. Some related optimization techniques based on performance potentials for MCPs may be extended to the optimization of SMCPs if the system parameters are known with certainty. Unfortunately, exact values of the distributions of the sojourn times at some states or the transition probabilities of the embedded Markov chain for a large-scale SMCP are generally difficult or impossible to obtain, which leads to the uncertainty of the semi-Markov kernel, and thereby to the uncertainty of equivalent infinitesimal transition rates. Similar to the optimization of uncertain MCPs, a potential-based policy iteration method is proposed in this work to search for the optimal robust control policy for SMCPs with uncertain infinitesimal transition rates that are represented as compact sets. In addition, convergence of the algorithm is discussed. [ABSTRACT FROM AUTHOR]

Average-cost optimization problems for a class of continuous-time Markov control processes with a compact action set have been studied. The definition of a generalized average-cost Poisson equation, which can be viewed as an extension to the standard one is first given. Markov performance potentials are defined as its unique solution. Based on the formula of performance potentials, an average-cost optimality equation is derived and the existence of its solution is established. Then, policy iteration and value iteration algorithms are proposed and their convergence discussed. A numerical example for controlled closed queuing networks illustrates the application of the proposed value iteration algorithm. [ABSTRACT FROM AUTHOR]

The replication of popular data objects can effectively reduce the access time and bandwidth requirements of network services. We study the replication problem in the model of distributed replication groups and propose two distributed algorithms: an approximation optimal replication algorithm, which is an asynchronous distributed algorithm as it takes more time to be completed. However its performance approaches the optimal algorithm, and a fast replication algorithm that is very suitable as the initial algorithm of the approximation optimal algorithm. We give a proof of the complexity of the algorithms, and show that the time and communication complexities of the algorithms are polynomial with respect to the number of objects and the maximum storage capacities of the servers. Finally, simulation experiments are performed to investigate the performance of the algorithms, and the results show that the two algorithms can effectively solve the replication problem. [ABSTRACT FROM AUTHOR]

The finite-time synchronization problem is investigated for a class of neutral complex dynamical networks with Markovian switching, partly unknown transition rates and time-varying, mode-dependent delays. By utilizing the pinning control technique and constructing the appropriate stochastic Lyapunov–Krasovskii functional, several sufficient conditions are proposed to ensure the finite-time synchronization for the neutral complex dynamical networks with Markovian switching, on the basis of Kronecker product, inequality techniques and finite-time stability theorem. Finally, numerical examples and simulations are given to illustrate the feasibility and effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

The distributed H∞-consensus filtering problem is investigated for a class of discrete time-varying non-linear systems on a finite horizon. The topology of the sensor networks is assumed to be Markovian switching and the missing measurements (packet dropouts) problem is also considered. Based on the recursive linear matrix inequalities, an effective distributed H∞-consensus filter is designed, which is suitable for online computation. A numerical example is given to verify the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

The highly scalable infrastructure of large-scale distributed systems is very attractive for network services. However, data access is unpredictable in this environment for the reasons of loosely coupled nature and large-scale data storage of such systems. Today, an increasing number of network applications require not only considerations of computation capacity of servers but also accessibility for adequate job allocations. An effective and adaptive mechanism of access control is important in this environment. In our study, the client clustering is used to describe the behaviors of clients and the adaptive server clustering is used to divide the large-scale distributed system into relevant small-scale systems. Since the clients which are assigned to one server cluster have the similar behaviors, we can use the stochastic control and passive measurement to do reliable and adaptive accessibility estimation and client allocation in such a small-scale system. We call this adaptive mechanism of access control based on accessibility estimation and client clustering as ACEC, and the experimental results show that ACEC can significantly reduce the data access cost and guarantee the load balance and controllability of large-scale distributed systems. [ABSTRACT FROM PUBLISHER]

In this brief, we consider extracting generalized eigenvectors in parallel for the generalized eigendecomposition problem. The problem is formulated as an optimization problem of minimizing an unconstrained quartic cost function based on the weighted rule. It is shown that the proposed weighted cost function has a unique global minimum, which corresponds to the principal generalized eigenvectors. In order to estimate the principal generalized eigenvector matrix efficiently, we simplify the quartic cost function as a quadric one by making an appropriate approximation, and then derive a fast algorithm for extracting the principal generalized eigenvector in parallel. We also show the application of the proposed algorithm in blind source separation. Numerical simulations are performed, and the results demonstrate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Cao's work shows that, by defining an α-dependent equivalent infinitesimal generator Aα, a semi-Markov decision process (SMDP) with both average- and discounted-cost criteria can be treated as an α-equivalent Markov decision process (MDP), and the performance potential theory can also be developed for SMDPs. In this work, we focus on establishing error bounds for potential and Aα-based iterative optimization methods. First, we introduce an α-uniformized Markov chain (UMC) for a SMDP via Aα and a uniformized parameter, and show their relations. Especially, we obtain that their performance potentials, as solutions of corresponding Poisson equations, are proportional, so that the studies of a SMDP and the α-UMC based on potentials are unified. Using these relations, we derive the error bounds for a potential-based policy-iteration algorithm and a value-iteration algorithm, respectively, when there exist various calculation errors. The obtained results can be applied directly to the special models, i.e., continuous-time MDPs and Markov chains, and can be extended to some simulation-based optimization methods such as reinforcement learning and neuro-dynamic programming, where estimation errors or approximation errors are common cases. Finally, we give an application example on the look-ahead control of a conveyor-serviced production station (CSPS), and show the corresponding error bounds. [ABSTRACT FROM AUTHOR]

Counting on-road vehicles in the highway is fundamental for intelligent transportation management. This paper presents the first highway vehicle counting method in compressed domain, aiming at achieving comparable estimation performance with the pixel-domain methods. Counting in compressed domain is rather challenging due to limited information about vehicles and large variance in vehicle numbers. To address this problem, we develop new low-level features to mitigate the challenge from insufficient information in compressed videos. The new proposed features can be easily extracted from the coding-related metadata. Then, we propose a hierarchical classification-based regression (HCR) model to estimate the number of vehicles from the compressed-domain low-level features for individual frame. HCR hierarchically divides the traffic scenes into different cases according to the density of vehicles such that the large variance of traffic scenes can be effectively captured. Beside the spatial regression in each frame, we propose a locally temporal regression model to further refine the counting results, which exploits the continuous variation characteristics of the traffic flow. We extensively evaluate the proposed method on real highway surveillance videos. The experimental results consistently show that the proposed method is very competitive compared with the pixel-domain methods, which can reach similar performance with much lower computational cost. [ABSTRACT FROM AUTHOR]

We consider the point-to-point adaptive transmission of scalable video coding streams over a fading channel in energy harvesting (EH) aided communication systems, where the transmitter is equipped with an EH device and a rechargeable battery. The aim of this paper is to enable the transmitter having the ability of adaptively adjusting the number of video layers to be transmitted according to the available energy. Since the depletion of the energy in the battery acts as a trigger for the interruption of the video transmission, we define the energy starvation probability for equivalently characterizing a quality-of-experience (QoE) metric. We formulate the problem of EH-aided video transmission as a constrained optimization problem, maximizing the number of video layers for transmission while keeping the energy starvation probability below a threshold. Classic large deviation theory is invoked for estimating the energy starvation probability from online measurements. Consequently, our dynamic layer switch algorithm operates with no prior statistical knowledge of both the EH process and of the channel quality. Furthermore, the technique of perturbation analysis that constructs a perturbed sample path from an original sample path is invoked for improving the performance of the proposed method. Our experimental results verify that the algorithms proposed have the adaptation capability to accommodate both the energy-dynamics and the channel-dynamics for improving the video quality subject to an unobjectionable level of transmission interruption rate. [ABSTRACT FROM AUTHOR]

Machine-to-machine (M2M) communications have many applications, such as in smart grid, health monitoring/alerting, and intelligent transportation. Due to unique characteristics that are different from the traditional human-to-human communications, numerous design challenges have to be tackled in M2M. A critical issue is that of dealing with the massive access of machine-type communication (MTC) devices while guaranteeing the desired quality of service (QoS). In this paper, we group MTC devices into clusters based on their QoS requirement. By utilizing the time-controlled M2M feature, we formulate the problem of massive access with QoS guarantee as a queuing problem. We derive an overflow probability estimation model by applying a large deviation principle and then propose an online-measurement-based adaptive massive access management for deciding the amount of resources allocated for each cluster. The proposed method bases the decision on the observed traffic workload with no prior knowledge of its statistics. We conduct the simulations in multicluster scenarios with distinct maximum tolerable delay and corresponding acceptable probability in a long-term evolution system. The simulation results show the adaptability of the proposed massive access management regime to the variations in the traffic workload; thus, it is capable of accommodating dynamic propagation conditions and unknown traffic characteristics, particularly in burst-by-burst communications. [ABSTRACT FROM PUBLISHER]

The feature coding-pooling framework is shown to perform well in image classification tasks, because it can generate discriminative and robust image representations. The unavoidable information loss incurred by feature quantization in the coding process and the undesired dependence of pooling on the image spatial layout, however, may severely limit the classification. In this paper, we propose a linear distance coding (LDC) method to capture the discriminative information lost in traditional coding methods while simultaneously alleviating the dependence of pooling on the image spatial layout. The core of the LDC lies in transforming local features of an image into more discriminative distance vectors, where the robust image-to-class distance is employed. These distance vectors are further encoded into sparse codes to capture the salient features of the image. The LDC is theoretically and experimentally shown to be complementary to the traditional coding methods, and thus their combination can achieve higher classification accuracy. We demonstrate the effectiveness of LDC on six data sets, two of each of three types (specific object, scene, and general object), i.e., Flower 102 and PFID 61, Scene 15 and Indoor 67, Caltech 101 and Caltech 256. The results show that our method generally outperforms the traditional coding methods, and achieves or is comparable to the state-of-the-art performance on these data sets. [ABSTRACT FROM PUBLISHER]

This paper considers the problem of dynamic cluster reconfiguration for computation intensive services. In order to provide a quality-of-service in terms of overload probability, we formulate the problem of energy consumption as a constrained optimization problem, i.e., minimizing the number of active servers to reduce the energy consumption while keeping the overload probability below a desired threshold. An overload probability estimation model is derived by applying large deviation principle, and an online measurement based algorithm is developed to decide the number of servers to power on/off, which makes decision based on current workload without any prior knowledge of the workload statistics. Moreover, the proposed dynamic cluster reconfiguration algorithm iteratively adjusts the number of the active servers, instead of directly determining the number of active servers that is hard to guarantee optimality for the nonstationary workloads. Since the distribution of the workloads among the servers has an impact on potential active servers to turn off, a server scheduling strategy is proposed to collaborate with the proposed decision algorithm to achieve better energy conservation. In order to provide an integrated solution, we present an event model-based implementation to demonstrate the practical application of the proposed approach. Finally, we evaluate the performance of the scheme by using real workloads. The experimental results show the adaptability of the proposed approach to the variations in the workload and robustness of quality-of-service for nonstationary workloads. [ABSTRACT FROM AUTHOR]

This article is concerned with a class of continuous-time Markovian jump linear systems where the regime transition rates are determined through an initial policy. The purpose is to study the jump linear quadratic regulator for such class of systems. A two-level regulating method is adopted to design the state-feedback controller and the policy separately. The problem of tuning the existing policy with respect to a prescribed quadratic performance criterion is formulated as a gradient projection-based iterative optimisation. This method leads to a near-optimal policy whose performance is better than that of the initial one. Useful characteristics of such a policy are also developed. The efficiency of the main results is elucidated by two numerical examples. [ABSTRACT FROM PUBLISHER]

String extraction and matching techniques have been widely used in generating signatures for worm detection, but how to generate effective worm signatures in an adversarial environment still remains a challenging problem. For example, attackers can freely manipulate byte distributions within the attack payloads and thus inject well-crafted noisy packets to contaminate the suspicious flow pool. To address these attacks, we propose SAS, a novel S emantics A ware S tatistical algorithm for automatic signature generation. When SAS processes packets in a suspicious flow pool, it uses data flow analysis techniques to remove non-critical bytes. We then apply a hidden Markov model (HMM) to the refined data to generate state-transition-graph-based signatures. To our best knowledge, this is the first work combining semantic analysis with statistical analysis to automatically generate worm signatures. Our experiments show that the proposed technique can accurately detect worms with concise signatures. Moreover, our results indicate that SAS is more robust to the byte distribution changes and noise injection attacks compared to Polygraph and Hamsa. [ABSTRACT FROM AUTHOR]

Wireless Multimedia service faces the difficulty of QoS decrease caused by the cache underflow, due to both the unreliable and time-varying nature of the wireless channel. In order to reduce the probability of the cache outage, a statistical model is established to estimate the underflow time of the cache. Based on the estimation of the underflow time, an adaptive playout algorithm with dualthreshold to adjust the frame rate is presented. This approach adjusts the threshold dynamically according to the channel conditions and the status of the cache, and thus regulates the frame rate timely. Moreover, the algorithm possesses scene-aware characteristics, since it adjusts the playout rate according to the motion intensity of the playout scenes to reduce the underflow probability of the cache and provide better QoS. Simulation results show that as compared to other adaptive media playout algorithms, the proposed algorithm efficiently reduces the probability of the cache underflow, especially in low rate channel and timevarying rate environment, and provides better visual quality1. [ABSTRACT FROM AUTHOR]