This paper deals with a state-space approach for adaptive second-order IIR notch digital filters with constrained poles and zeros. A simplified iterative algorithm is derived from the gradient-descent method to minimize the mean-squared output of an adaptive notch digital filter. Then, stability and parameter-estimation bias are analyzed for the simplified iterative algorithm. A numerical example is presented to demonstrate the validity and effectiveness of the proposed adaptive state-space notch digital filter and parameter-estimation bias analysis.

Recently, more and more people start investing. Understanding the factors affecting financial products is important for making investment decisions. However, it is difficult to understand factors for novices because various factors affect each other. Various technique has been studied, but conventional factor analysis methods focus on revealing the impact of factors over a certain period locally, and it is not easy to predict net asset values. As a reasonable solution for the prediction of net asset values, in this paper, we propose a trend shift model for the global analysis of factors by introducing trend change points as shift interference variables into state space models. In addition, to realize the trend shift model efficiently, we propose an effective trend detection method, TP-TBSM (two-phase TBSM), by extending TBSM (trend-based segmentation method). Comparing with TBSM, TP-TBSM could detect trends flexibly by reducing the dependence on parameters. We conduct experiments with eleven investment trust products and reveal the usefulness and effectiveness of the proposed model and method.

In this paper, we propose a CTRIL (Common Trend and Regression with Independent Loss) model to infer latent traffic demand in overloaded links as well as how much it is reduced due to QoS (Quality of Service) degradation. To appropriately provision link bandwidth for such overloaded links, we need to infer how much traffic will increase without QoS degradation. Because original latent traffic demand cannot be observed, we propose a method that compares the other traffic time series of an underloaded link, and by assuming that the latent traffic demands in both overloaded and underloaded are common, and actualized traffic demand in the overloaded link is decreased from common pattern due to the effect of QoS degradation. To realize the method, we developed a CTRIL model on the basis of a state-space model where observed traffic is generated from a latent trend but is decreased by the QoS degradation. By applying the CTRIL model to actual HTTP (Hypertext transfer protocol) traffic and QoS time series data, we reveal that 1% packet loss decreases traffic demand by 12.3%, and the estimated latent traffic demand is larger than the observed one by 23.0%.

A ripple-free dual-rate control system is designed for a single-input single-output dual-rate system, in which the sampling interval of a plant output is longer than the holding interval of a control input. The dual-rate system is converged to a multi-input single-output single-rate system using the lifting technique, and a control system is designed based on an error system using the steady-state variable. Because the proposed control law is designed so that the control input is constant in the steady state, the intersample output as well as the sampled output converges to the set-point without both steady-state error and intersample ripples when there is neither modeling nor disturbance. Furthermore, in the proposed method, a two-degree-of-freedom integral compensation is designed, and hence, the transient response is not deteriorated by the integral action because the integral action is canceled when there is neither modeling nor disturbance. Moreover, in the presence of the modeling error or disturbance, the integral compensation is revealed, and hence, the steady-state error is eliminated on both the intersample and sampled response.

This paper presents the formal analysis of the feature negotiation and connection management procedures of the Datagram Congestion Control Protocol (DCCP). Using state space analysis we discover an error in the DCCP specification, that result in both ends of the connection having different agreed feature values. The error occurs when the client ignores an unexpected Response packet in the OPEN state that carries a valid Confirm option. This provides an evidence that the connection management procedure and feature negotiation procedures interact. We also propose solutions to rectify the problem.

This paper proposes a novel scheme for sequential orthogonal frequency division multiplexing channel estimation on the receiver side.The scheme comprises two methods: one improves estimation accuracy and the other reduces computational complexity. Based on a state-space model, the first method appropriately considers frequency correlation in an approach that derives a narrow-band channel gain for multiple pilot subcarriers; such consideration of frequency correlation leads to an averaging effect in the frequency domain. The second method is based on the first one and forces the observation matrix into a sparse bidiagonal matrix in order to decrease the number of mathematical processes. The proposed scheme is verified by numerical analysis.

We propose a noise suppression algorithm with the Kalman filter theory. The algorithm aims to achieve robust noise suppression for the additive white and colored disturbance from the canonical state space models with (i) a state equation composed of the speech signal and (ii) an observation equation composed of the speech signal and additive noise. The remarkable features of the proposed algorithm are (1) applied to adaptive white and colored noises where the additive colored noise uses babble noise, (2) realization of high performance noise suppression without sacrificing high quality of the speech signal despite simple noise suppression using only the Kalman filter algorithm, while many conventional methods based on the Kalman filter theory usually perform the noise suppression using the parameter estimation algorithm of AR (auto-regressive) system and the Kalman filter algorithm. We show the effectiveness of the proposed method, which utilizes the Kalman filter theory for the proposed canonical state space model with the colored driving source, using numerical results and subjective evaluation results.

This paper discusses a new structure of M-channel IIR perfect reconstruction filter banks. A novel building block defined as a cascade connection of some IIR building blocks and FIR building blocks is presented. An IIR building block is written by state space representation, where we easily obtain a stable filter bank by setting eigenvalues of the state transition matrix into the unit circle. Due to cascade connection of building blocks, we are able to design a system with a larger number of free parameters while keeping the stability. We introduce the condition which obtains the new building block without increasing of the filter order in spite of cascade connection. Additionally, by showing the simulation results, we show that this implementation has a better stopband attenuation than conventional methods.

The rapid growth of state space in a two-dimensional (2D) random walk model imposes heavy computational load on mobility analysis of personal communication services (PCS) networks. This letter presents a novel random walk model with a compact state space by exploiting the symmetry of cellular patterns in a K-layer cluster of cells. The size of the state space is reduced to (K+1)/2(K/2+1)+1, with an asymptotic compression ratio of 12.

Most conventional studies on self-stabilization have been indifferent to the vulnerability under convergence. This paper investigates how mutual exclusion property can be achieved in self-stabilizing rings even for illegitimate configurations. We present a new method which uses a state with a large state space to detect faults. If some faults are detected, every process is reset and not given a privilege. Even if the reset values are different between processes, our protocol mimics the behavior of Dijkstra's unidirectional K-state protocol. Then we have a fast and safe mutual exclusion protocol. Simulation study also examines its performance.

The legal firing sequence problem of Petri nets (LFS) is one of fundamental problems in the analysis of Petri nets, because it appears as a subproblem of various basic problems. Since LFS is shown to be NP-hard, various heuristics has been proposed to solve the problem of practical size in a reasonable time. In this paper, we propose a new algorithm for this problem. It is based on the partial order verification technique, and reduces redundant branches in the search tree. Moreover, the proposed algorithm can be combined with various types of heuristics.

Filtering and smoothing using a non-Gaussian state space model are proposed for motion trajectory of feature point in image sequence. A heavy-tailed non-Gaussian distribution is used for measurement noise to reduce the effect of outliers in motion trajectory. Experimental results are presented to show the usefulness of the proposed method.

Generating state spaces is one of important and general methods in the analysis of Petri nets. There are two reasons why state spaces of Petri nets become so large. One is concurrent occurring of transitions, and the other is periodic occurring of firing sequences. This paper focuses on the second problem, and proposes a new algorithm for exploring state spaces of finite capacity Petri nets with large capacities. In the proposed algorithm, the state space is represented in the form of a tree such that a set of markings generated by periodic occurrences of firing sequences is associated with each node, and it is much smaller than the reachability graph.

A hierarchical structure of the statistical models involving the parametric, state space, generalized state space, and self-organizing state space models is explained. It is shown that by considering higher level modeling, it is possible to develop models quite freely and then to extract essential information from data which has been difficult to obtain due to the use of restricted models. It is also shown that by rising the level of the model, the model selection procedure which has been realized with human expertise can be performed automatically and thus the automatic processing of huge time series data becomes realistic. In other words, the hierarchical statistical modeling facilitates both automatic processing of massive time series data and a new method for knowledge discovery.

This paper proposes an algorithm for analyzing the reachability property of Petri nets by the use of unfoldings. It is known that analyzing the reachability by using unfoldings requires exponential time and space to the size of unfolding. The algorithm is based on the branch and bound technique, and experimental results show efficiency of the algorithm.

Unfolding originally introduced by McMillan is gaining ground as a partial-order based method for the verification of concurrent systems without state space explosion. However, it can be exposed to redundancy which may increase its size exponentially. So far, there have been trials to reduce such redundancy resulting from conflicts by improving McMillan's cut-off criterion. In this paper, we show that concurrency is also another cause of redundancy in unfolding, and present an algorithm to reduce such redundancy in live, bounded and reversible Petri nets which is independent of any cut-off algorithm.

In this paper, we present a new state space-based approach for the two-dimensional (2-D) frequency estimation problem which occurs in various areas of signal processing and communication problems. The proposed method begins with the construction of a state space model associated with the noiseless data which contains a summation of 2-D harmonics. Two auxiliary Hankel-block-Hankel-like matrices are then introduced and from which the two frequency components can be derived via matrix factorizations along with frequency shifting properties. Although the algorithm can render high resolution frequency estimates, it also calls for lots of computations. To alleviate the high computational overhead required, a highly parallelizable implementation of it via the principle subband component (PSC) of some appropriately chosen transforms have been addressed as well. Such a PSC-based transform domain implementation not only reduces the size of data needed to be processed, but it also suppresses the contaminated noise outside the subband of interest. To reduce the computational complexity induced in the transformation process, we also suggest that either the transform of the discrete Fourier transform (DFT) or the Haar wavelet transform (HWT) be employed. As a consequence, such an approach of implementation can achieve substantial computational savings; meanwhile, as demonstrated by the provided simulation results, it still retains roughly the same performance as that of the original algorithm.

Signal Transition Graphs (STG's) are Petri nets, which were introduced to represent a behavior of asynchronous circuits. To derive logic functions from an STG, the reachability graph should be constructed. In the verification of STG's some method based on an Occurrence net (OCN) and its prefix, called an unfolding, has been proposed. OCN's can represent both causality and concurrency between two nodes by net structure. In this paper, we propose a method to derive a logic function by generating sub state space of a given STG using the structural properties of OCN.

In this paper we present an efficient method to solve reachability problems for Petri nets based on genetic algorithms and a kind of random search which is called postpone search. Genetic algorithm is one of algorithms developed for solving several problems of optimization. We apply GAs and postpone search to approximately solving reachability problems. This approach can not determine exact solutions, however, from applicability points of view, does not directly face state space explosion problems and can extend class of Petri nets to deal with very large state space in reasonable time. First we describe how to represent reachability problems on each of GAs and postpone search. We suppose the existence of a nonnegative parickh vector which satisfies the necessary reachability condition. Possible firing sequences of transitions induced by the parickh vector is encoded on GAs. We also define fitness function to solve reachability problems. Reachability problems can be interpreted as an optimization ones on GAs. Next we introduce random reachability problems which are capable of handling state space and the number of firing sequences which enable to reach a target marking from an initial marking. State space and the number of firing sequences are considered as factors which effect on the hardness of reachability problems to solve with stochastic methods. Furthermore, by using those random reachability problems and well known dining philosophers problems as benchmark problems, we compare GAs' performance with the performance of postpone search. Finally we present empirical results that GAa is more useful method than postpone search for solving more harder reachability problems from the both points of view; reliability and efficiency.

Signal Transition Graphs (STG'S) [1] are Petrinets [2], which were introduced to represent a behavior of asynchronous circuits. To derive logic functions from an STG, the reachability graph should be constructed. In the verification of STG's some method based on Occurrence nets (OCN) and its prefix, called unfollding, has been proposed [3], [4]. OCN's can represent both causality and concurrency between two nodes by net stryctyre. In this paper, we propose an efficient algorithm to derive a logic function by generating sub-state space of a given STG using the structural properties of OCN. The proposed algorithm can be seem as a parallel algorithm for deriving a logic function.