Abstract Monitoring the variables of dynamic systems is a difficult task due to their inherent complexity and uncertainty. Particle Filters (PF) perform that task, yielding probability distribution over the unobserved variables. However, they suffer from the curse of dimensionality problem: the number of particles grows exponentially with the dimensionality of the hidden state space. The proble...

For model-based 3D human pose estimation, even simple models of the human body lead to high-dimensional state spaces. Where the class of activity is known a priori, lowdimensional activity models learned from training data make possible a thorough and efficient search for the best pose. Conversely, searching for solutions in the full state space places no restriction on the class of motion to b...

A Bayesian ensemble learning method is introduced for unsupervised extraction of dynamic processes from noisy data. The data are assumed to be generated by an unknown nonlinear mapping from unknown factors. The dynamics of the factors are modeled using a nonlinear statespace model. The nonlinear mappings in the model are represented using multilayer perceptron networks. The proposed method is c...

Background: With increasing age, some changes appeared in specifications of vessels which including dimensions and elasticity in their. The changes in parameters such as resistance, inertance and compliance vessels appear and eventually changes in the environmental pulse releases are in circulation. These changes clearly appear in specification of photoplethysmogram particularly in the size and...

Measure-adaptive state-space construction is the process of exploiting symmetry in high-level model and performance measure specifications to automatically construct reduced state-space Markov models that support the evaluation of the performance measure. This paper describes a new reward variable specification technique, which, combined with recently developed state-space construction techniqu...

Verification of software systems is a very hard problem due to the large size of program state-space. The traditional techniques (like model checking) do not scale; since they include the whole state-space by inlining the library function codes. Current research avoids these problem by creating a lightweight representation of the library in form of an interface graph (call sequence graph). In t...

the development of the dynamical core of a potential-vorticity-based atmospheric general circulation model is explored. there are some advantages of using potential vorticity (pv) as a prognostic variable in that the resulting model can give more accurate simulation of the evolution of pv, as arguably the most fundamental dynamical quantity. further, there is possibility of explicit representat...