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Feature quantization is a crucial component for efficient large scale image retrieval and object recognition. By quantizing local features into visual words, one hopes that features that match each other obtain the same word ID. Then, similarities between images can be measured with respect to the corresponding histograms of visual words. Given the appearance variations of local features, traditio

This paper presents a combined experiment and identification procedure, well suited to obtain low-order dynamic models of a patients’ response to continuous drug administration. The experiment requires no a priori information and is of very short duration. The identification method provides both a parametric low-order model, and an estimate of the parameter error covariance. It has been demonstrat

A general framework for analysing robust synchronization in large-scale heterogenous networks is proposed based on the theory of integral quadratic constraints (IQCs). Dynamic agents are represented as linear time-invariant single-input-single-output systems. The agents exchange information according to a sparse dynamical interconnection operator in order to achieve synchronization, where their ou

A novel automatic tuning strategy is proposed. It is based on an experiment of very short duration, followed by simultaneous identification of LTI model parameters and an estimate of their error covariance. The parametric uncertainty model is subsequently exploited to design linear controllers with magnitude bounds on some closed-loop transfer function of interest, such as the sensitivity function

A novel autotuning procedure is presented through application to an industrial in-line pH control system. The procedure has three advantages over classical relay auto-tuners: experiment duration is very short (no need for limit-cycle convergence); all data is used for identification (instead of only peaks and switch instances); a parameter uncertainty model is identified and utilized for robust co

We derive frequency-domain criteria for the convergence of linear iterative learning control (ILC) on finite-time intervals that are less restrictive than existing ones in the literature. In particular, the former can be used to establish the convergence of ILC in certain cases where the latter are violated. The results cover ILC with non-causal filters and provide insights into the transient beha

We examine the capacity of artificial biomolecular networks to respond to perturbations with structurally signed steady-state changes. We consider network architectures designed to balance their output production as a function of downstream demand: the species producing the output, called a source, up- or down-regulates its production rate as a function of the demand. Using an exact algorithm we s

We study diffusion of cooperation in a two-population game in continuous time. At each instant, the game involves two random individuals, one from each population. The game has the structure of a Prisoner's dilemma where each player can choose either to cooperate (c) or to defect (d), and is reframed within the field of approachability in two-player repeated game with vector payoffs. We turn the g

The clinical success of targeted cancer therapies is limited by the emergence of drug resistance often due to pre-existing tumor genetic heterogeneity and acquired, therapy-induced resistance. Targeted therapies have varied success in addressing metastatic disease, due to their ability to penetrate certain physiological compartments. This paper considers an evolutionary cancer model that incorpora

Estimating Cartesian contact forces and torques enables external force supervision for robotic manipulators and even force-controlled applications while avoiding the need for additional external sensing. Redundant manipulators facilitate the problem of Cartesian contact force and torque estimation (CCFE) at the TCP, since an increased amount of joint level information is available for estimating t

In many system identification methods, process model parameters are considered stochastic variables. Several methods do not only yield expectations of these, but in addition their variance, and sometimes higher moments. This paper proposes a method for robust synthesis of the proportional-integral-derivative (PID) controller, taking parametric process model uncertainty explicitly into account. The

In this paper we describe our NLP supported programming-by-demonstration approach to high-level robot programming that allows users to generate skills and robot program primitives for later refinement and re-use. Our ideas incorporate the identification of common user strategies (interaction patterns) in the programming process, which can be exploited to support a human user in establishing common

In this paper we explain our concept of Interaction and Task Patterns, and discuss how such patterns can be applied to support mixed-initiative in symbiotic human-robot interaction both with service and industrial robotic systems.

The goal of the GHG-CCI project (http://www.esa-ghg-cci.org/) of ESA's Climate Change Initiative (CCI) is to generate global atmospheric satellite-derived carbon dioxide (CO2) and methane (CH4) data sets as needed to improve our understanding of the regional sources and sinks of these important greenhouse gases (GHG). Here we present an overview about the latest data set called Climate Research Da

This thesis addresses problems which require low-rank solutions under convex constraints. In particular, the focus lies on model reduction of positive systems, as well as finite dimensional optimization problems that are convex, apart from a low-rank constraint. Traditional model reduction techniques try to minimize the error between the original and the reduced system. Typically, the resulting re

Event-based sensing and communication holds the promise of lower resource utilization and/or better performance for remote state estimation applications found in e.g. networked control systems. Recently, stochastic event-triggering rules have been proposed as a means to avoid the complexity of the problem that normally arises in event-based estimator design. By using a scaled Gaussian function in

Carbon dioxide (CO 2) is the most important anthropogenic greenhouse gas contributing to about half of the total anthropogenic change in the Earth's radiation budget. And about half of the anthropogenic CO2 emissions stay in the atmosphere, the remainder is taken up by the biosphere. It is of paramount importance to better understand CO2 sources and sinks and their spatio-temporal distribution. In

This paper presents a study on iconic programming support for mainly position-based lead-through programming of an ABB YuMi collaborative robot. A prototype tool supporting a hybrid programming and execution mode was developed and evaluated with 21 non-expert users with varying programming and robotics experience. We also present a comparison of the programming times for an expert robot programmer

High-precision analyses of the isotopic composition of methane in ambient air can potentially be used to discriminate between different source categories. Due to the complexity of isotope ratio measurements, such analyses have generally been performed in the laboratory on air samples collected in the field. This poses a limitation on the temporal resolution at which the isotopic composition can be

Cloud computing technology provides the means to share physical resources among multiple users and data center tenants by exposing them as virtual resources. There is a strong industrial drive to use similar technology and concepts to provide timing sensitive services. One such is virtual networking services, so called services chains, which consist of several interconnected virtual network functi