Linearization through state immersion of nonlinear systems admitting Lie symmetries

In this paper, it is shown that if a nonlinear system admits a Lie symmetry that can be transformed into its Poincaré-Dulac normal form by a state diffeomorphism, then, under some technical conditions, such a nonlinear system can be immersed into a linear one. This allows us to compute in closed-form the flow, all algebraic invariant curves (through semi-invariants) of the nonlinear system, and Lyapunov functions to study stability properties.     

Image segmentation by a contrario simulation

Segmenting an image into homogeneous regions generally involves a decision criterion to establish whether two adjacent regions are similar. Decisions should be adaptive to get robust and accurate segmentation algorithms, avoid hazardous a priori and have a clear interpretation. We propose a decision process based on a contrario reasoning: two regions are meaningfully different if the probability of observing such a difference in pure noise is very low. Since the existing analytical methods are intractable in our case, we extend them to allow a mixed use of analytical computations and Monte-Carlo simulations. The resulting decision criterion is tested experimentally through a simple merging algorithm, which can be used as a post-filtering and validation step for existing segmentation methods.     

Novel link adaptation for TETRA cellular systems

One of the main challenges for future wireless systems is to enhance the effective data throughput by exploiting the allocated bandwidth as much as possible. Among several approaches at different layers, one of the most important is constituted by the so‐called link adaptation (LA) techniques. They are characterized by the adaptation of a set of transmission parameters to the channel state in order to improve performance. In this context, this paper is focused on the analysis of a particular class of LA techniques called adaptive modulation and coding, where the modulation and coding rate of transmission can vary according to the channel behavior. In particular, a novel LA algorithm, namely the timed window (TW) method, suitable for time‐division Duplex systems is proposed here. The performance of the TW algorithm is evaluated by taking actual user mobility conditions, communication channel behavior, as well as the physical layer effects into account. Finally, it is important to stress that, even if the wireless bearer considered in this study is TETRA (TErrestrial Trunked RAdio), the approach is quite general and it can be of interest for other wireless networks and can be optimized for different channel models (e.g. TU50, HT200, etc.). Copyright © 2008 John Wiley & Sons, Ltd.     

Exploring the boundary region of tolerance rough sets for feature selection

Of all of the challenges which face the effective application of computational intelligence technologies for pattern recognition, dataset dimensionality is undoubtedly one of the primary impediments. In order for pattern classifiers to be efficient, a dimensionality reduction stage is usually performed prior to classification. Much use has been made of rough set theory for this purpose as it is completely data-driven and no other information is required; most other methods require some additional knowledge. However, traditional rough set-based methods in the literature are restricted to the requirement that all data must be discrete. It is therefore not possible to consider real-valued or noisy data. This is usually addressed by employing a discretisation method, which can result in information loss. This paper proposes a new approach based on the tolerance rough set model, which has the ability to deal with real-valued data whilst simultaneously retaining dataset semantics. More significantly, this paper describes the underlying mechanism for this new approach to utilise the information contained within the boundary region or region of uncertainty. The use of this information can result in the discovery of more compact feature subsets and improved classification accuracy. These results are supported by an experimental evaluation which compares the proposed approach with a number of existing feature selection techniques.     

Evaluation of incremental learning algorithms for HMM in the recognition of alphanumeric characters

We present an evaluation of incremental learning algorithms for the estimation of hidden Markov model (HMM) parameters. The main goal is to investigate incremental learning algorithms that can provide as good performances as traditional batch learning techniques, but incorporating the advantages of incremental learning for designing complex pattern recognition systems. Experiments on handwritten characters have shown that a proposed variant of the ensemble training algorithm, employing ensembles of HMMs, can lead to very promising performances. Furthermore, the use of a validation dataset demonstrated that it is possible to reach better performances than the ones presented by batch learning.     

Passive stability and synchronization of complex spatio-temporal switching networks with time delays

In this paper, a new model of complex spatio-temporal switching network is established. Each subsystem of the network at a different time interval corresponds to a different switching mode. Switching the signal depends on both the instant time t and the index of node i. Based on passivity property, the passive stability and synchronization of this kind of network with time delays are taken into account. An example and simulation results are included.     

Fast algorithms for nonparametric population modeling of large data sets

Population models are widely applied in biomedical data analysis since they characterize both the average and individual responses of a population of subjects. In the absence of a reliable mechanistic model, one can resort to the Bayesian nonparametric approach that models the individual curves as Gaussian processes. This paper develops an efficient computational scheme for estimating the average and individual curves from large data sets collected in standardized experiments, i.e. with a fixed sampling schedule. It is shown that the overall scheme exhibits a “client-server” architecture. The server is in charge of handling and processing the collective data base of past experiments. The clients ask the server for the information needed to reconstruct the individual curve in a single new experiment. This architecture allows the clients to take advantage of the overall data set without violating possible privacy and confidentiality constraints and with negligible computational effort.     

Robustness of quantized control systems with mismatch between coder/decoder initializations

This paper analyzes the stability of linear systems with quantized feedback in the presence of mismatch between the initial conditions at the coder and decoder. Under the assumption of the perfect channel, we show that using the scheme proposed in [Liberzon, D., & Neši?, D. (2007). Input-to-state stabilization of linear systems with quantized state measurements. Institute of Electrical and Electronic Engineers Transaction on Automatic Control, 52, 767-781] it is possible to achieve stability with exponential convergence of linear systems with quantized feedback when the coder and decoder are initialized at different initial conditions.