Multimodal biometric technology relatively is a technology developed to overcome those limitations imposed by unimodal biometric systems. The paradigm consolidates evidence from multiple biometric sources offering considerable improvements in reliability with reasonably overall performance in many applications. Meanwhile, the issue of efficient and effective information fusion of these evidence...

Dempster-Shafer theory provides a sensor fusion framework that autonomously accounts for obstacle occlusion in dynamic, urban environments. However, to discern static and moving obstacles, the Dempster-Shafer approach requires manual tuning of parameters dependent on the situation and sensor types. The proposed methodology utilizes a deep fully convolutional neural network to improve the robust...

This paper develops multiple-prior Bayesian inference for a set-identi…ed parameter whose identi…ed set is constructed by an intersection of two identi…ed sets. We formulate an econometrician’s practice of "adding an assumption" as "updating ambiguous beliefs." Among several ways to update ambiguous beliefs proposed in the literature, we consider the DempsterShafer updating rule (Dempster (1968...

The solution of the prediction problem is presented for the finite possibilistic modelling [3,4,6,7]. A recurrent variant of finite possibilistic models is considered. In this variant, we define the regularization condition for constructing a quasi-optimal estimator of fuzzy transition operator (FTO). We construct the discrete recurrent extremal fuzzy process with possibilistic uncertainty, the...

This paper introduces an evidential reasoning-based approach for recognizing and extracting manufacturing features from solid model description of objects. A major di4culty faced by previously proposed methods for feature extraction has been the interaction between features due to non-uniqueness and ambiguousness in feature representation. To overcome this di4culty, we introduce a Dempster–Shaf...

In real applications, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. In this paper, in the frame of Dempster-Shafer evidence theory, a weighted belief entropy based on Deng entropy is proposed to quantify the uncertainty of uncertain information. The weight of the proposed belief entropy is based on the relative scale of a propositio...

This paper addresses a soft computing approach of fusion of signals from different independent sources. The signals may be from different types of primary classifiers. The Dempster Shafer Evidence Accumulation (DSEA) theory provides a robust platform for evidence fusion and it incorporates uncertainty, imprecision and conflicting situations in the process of decision making into a mathematical ...

We present intrusion detection algorithms to detect physical layer jamming attacks in wireless networks. We compare the performance of local algorithms on the basis of the signal-to-interference-plus-noise ratio (SINR) executing independently at several monitors, with a collaborative detection algorithm that fuses the outputs provided by these algorithms. The local algorithms fall into two cate...

The aim of this paper is to show that Dempster–Shafer evidence theory may be successfully applied to unsupervised classification in multisource remote sensing. Dempster–Shafer formulation allows to consider unions of classes, and to represent both imprecision and uncertainty, through the definition of belief and plausibility functions. These two functions, derived from mass function, are genera...