Set inversion via interval analysis for nonlinear bounded-error estimation
Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Multidimensional scaling of interval-valued dissimilarity data
Pattern Recognition Letters
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Journal of Computational and Applied Mathematics - Special issue: Scientific computing, computer arithmetic, and validated numerics (SCAN 2004)
International Journal of Approximate Reasoning
On the granularity of summative kernels
Fuzzy Sets and Systems
Foreword: Special Issue on Choquet integration in honor of Gustave Choquet (1915--2006)
International Journal of Approximate Reasoning
Unifying practical uncertainty representations -- I: Generalized p-boxes
International Journal of Approximate Reasoning
Unifying practical uncertainty representations. II: Clouds
International Journal of Approximate Reasoning
Computing expectations with continuous p-boxes: Univariate case
International Journal of Approximate Reasoning
Computational algorithms for double bootstrap confidence intervals
Computational Statistics & Data Analysis
Possibility theory and statistical reasoning
Computational Statistics & Data Analysis
Nonparametric rank-based statistics and significance tests for fuzzy data
Fuzzy Sets and Systems
Computing best-possible bounds for the distribution of a sum of several variables is NP-hard
International Journal of Approximate Reasoning
B-spline signal processing. I. Theory
IEEE Transactions on Signal Processing
Optimal interval estimation fusion based on sensor interval estimates with confidence degrees
Automatica (Journal of IFAC)
Use of the domination property for interval valued digital signal processing
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
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In most sensor measure based applications, the raw sensor signal has to be processed by an appropriate filter to increase the signal-to-noise ratio or simply to recover the signal to be measured. In both cases, the filter output is obtained by convoluting the sensor signal with a supposedly known appropriate impulse response. However, in many real life situations, this impulse response cannot be precisely specified. The filtered value can thus be considered as biased by this arbitrary choice of one impulse response among all possible impulse responses considered in this specific context. In this paper, we propose a new approach to perform filtering that aims at computing an interval valued signal containing all outputs of filtering processes involving a coherent family of conventional linear filters. This approach is based on a very straightforward extension of the expectation operator involving appropriate concave capacities.