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A suturing including knot tying is one of the more difficult operations to learn in telesurgical systems. Apprentice surgeons commonly suffer from suture breakage or knot failure. The difficulty, generally, comes from the absence of feedback of interaction force cues in a medical device (e.g., a needle and a thread) . Even if there is haptic feedback to the operator, the operator may have a diffic

This paper investigates both positional dependence in systems with friction and the influence an increase in temperature has on the friction behavior. The positional dependence is modeled with a Radial Basis Function network and the temperature dependence is modeled as a first order system with the power loss due to friction as input, eliminating the need for temperature sensing. The proposed meth

Geometric reconstruction from image collections is a classical computer vision problem. The problem essentially consists of two steps; First, the identification of matches and assembling of point tracks, and second, multiple view geometry computations. In this paper we address the problem of constructing point tracks using graph theoretical algorithms. From standard descriptor matches between all

We propose a distributed learning algorithm for fair scheduling in common-pool games. Common-pool games are strategic-form games where multiple agents compete over utilizing a limited common resource. A characteristic example is the medium access control problem in wireless communications, where multiple users need to decide how to share a single communication channel so that there are no collisio

This note studies lumped-delay approximations of distributed-delay elements arising in various problems involving delay compensation or preview utilization. Several approaches to reduce implementation complexity, understood as the number of delays required to attain a required accuracy level, are put forward. First, we derive the H2-optimal coefficients for a given delay pattern. This can be seen

In a dispatchable wind power plant, turbines are free to continuously vary their power production as long as the sum of their productions meets the total power demand. Previous research has shown that this freedom can be used to reduce structural loads by allowing turbines in the plant to coordinate their power. This paper explains the mechanisms that make power coordination useful for reducing st

Cloud applications are often subject to unexpected events like flash crowds and hardware failures. Without a predictable behaviour, users may abandon an unresponsive application. This problem has been partially solved on two separate fronts: first, by adding a self-adaptive feature called brownout inside cloud applications to bound response times by modulating user experience, and, second, by intr

Husserlian phenomenological theory is offered as an integrated and integrating for an understanding of management decision making and related issues in work and organizational sciences. The aspects of meaning, meaning constitution, subjective processes, and perspectives, mutual understanding of each other´s perspective, individuals as horizons of possible worlds are central to this conception, mak

This paper describes how PID controllers can be designed by optimizing performance subject to robustness constraints. The optimization problem is solved using convex-concave programming. The method admits general process descriptions in terms of frequency response data and it can cope with many different constraints. Examples are presented and some pitfalls in optimization are discussed.

This article presents a linear, iterative method to solve the eye-to-hand calibration problem between a wrist-mounted laser scanner and the tool flange of a robot. Measurement data are acquired from a set of non parallel planes whereafter the plane equations and desired rigid transformation matrix are found in a two-step, iterative fashion. The method is shown to handle large error in the initial

We investigate the out-of-sequence measurements particle filtering problem for a set of conditionally linear Gaussian state-space models, known as mixed linear/nonlinear state-space models. Two different algorithms are proposed, which both exploit the conditionally linear substructure. The first approach is based on storing only a subset of the particles and their weights, which implies low memory

We focus on improving resilience of cloud services (e.g., e-commerce website), when correlated or cascading failures lead to computing capacity shortage. We study how to extend the classical cloud service architecture composed of a load-balancer and replicas with a recently proposed self-adaptive paradigm called brownout. Such services are able to reduce their capacity requirements by degrading us

To fulfill increasing requirements within the manufacturing sector, highly flexible and adaptable automation systems are needed. It is desirable to have one integrated approach that stretches from the process planning phase, through the implementation phase and all the way to the phase for execution of the process control logics. One promising approach is to use the concepts of service-oriented ar

Recently, several control systems for closed-loop anesthesia have been demonstrated both in simulation and clinical studies. A set of performance measures, proposed by Varvel et al., have constituted the standard means of comparing such systems. This paper debates the adequacy of the Varvel measures, as applied to closed-loop anesthesia, and proposes an alternative set of measures. Key features o

This work presents an event-based control structure using the generalized predictive control (GPC) algorithm with actuator deadband. The main objective of this work is to limit the number of controlled system updates. In this approach, the controlled process is sampled with a constant sampling time and is updated in an asynchronous way that depends on the obtained control signal value. To achieve

Gradient orientations are a common feature used in many computer vision algorithms. It is a good feature when the gradient magnitudes are high, but can be very noisy when the magnitudes are low. This means that some gradient orientations are matched with more confidence than others. By estimating this uncertainty, more weight can be put on the confident matches than those with higher uncertainty.

We analyze reinforcement learning under so-called "dynamic reinforcement." In reinforcement learning, each agent repeatedly interacts with an unknown environment (i.e., other agents), receives a reward, and updates the probabilities of its next action based on its own previous actions and received rewards. Unlike standard reinforcement learning, dynamic reinforcement uses a combination of long-ter

System-theoretical methods are already used for the control of computing systems, but much more can be done exploiting said methods for their design. This requires to express in control-theoretical terms desires and specifications that originate in the computer science do- main, which may not be immediate. It also requires to accept that part of the addressed system be modified, which may pose som

In digital control systems, the state is sampled at given sampling instants and the input is kept constant between two consecutive instants. With the optimal sampling problem, we mean the selection of sampling instants and control inputs, such that a given function of the state and input is minimized. In this paper, we formulate the optimal sampling problem and we derive a necessary condition of t