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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

This paper considers switching output feedback control of linear systems and variable structure systems. Theory for stability analysis and design for a class of observer-based feedback control systems is presented. A circle-criterion approach can be used to design an observer-based state feedback control which yields a closed-loop system with specified robustness characteristics. The approach is r

We are concerned with software that can self-adapt to satisfy certain reliability requirements, in spite of adverse changes affecting the environment in which it is embedded. Self-adapting software architectures are heavily based on dynamic binding. The bindings among components are dynamically set as the conditions that require a self-adaptation are discovered during the system's lifetime. By ado

With the advent of massive online encyclopedic corpora such as Wikipedia, it has become possible to apply a systematic analysis to a wide range of documents covering a significant part of human knowledge. Using semantic parsers, it has become possible to extract such knowledge in the form of propositions (predicate―argument structures) and build large proposition databases from these documents. Th

In control theory there exist two convenient representations of a model, which are transfer functions and state-space matrices. However, structure in the transfer functions is not clearly seen in the state-space form, and on the other hand structure in the state-space form is not clearly seen in the transfer functions. The main reason for this is that Laplace transformation destroys most types of

Industrial robots are important when the degree of automation in industry is increased. To enable the use of robots also when the products change rapidly, the programming must be quick and easy to perform. One way to accomplish this is to use lead-through programming, i.e., the user manually guides the robot. This paper presents a sensorless approach, and thus avoids the need for a typically expen

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

Embedded systems are becoming increasingly complex. At the same time, the components that make up the system grow more uncertain in their properties. For example, current developments in CPU design focuses on optimizing for average performance rather than better worst case performance. This, combined with presence of 3rd party software components with unknown properties, makes resource management

Production plants need to be set up and reconfigured faster to fulfill increasing market demands. Highly flexible automation systems are needed and a promising approach is Service Oriented Architecture (SOA) which has recently received much attention in both academia and industry. OPC Unified Architecture (OPC UA), the next generation of the de facto standard for interoperability in the automation

We consider distributed model predictive control (DMPC) where a sparse centralized optimization problem without a terminal cost or a terminal constraint set is solved in distributed fashion. Distribution of the optimization algorithm is enabled by dual decomposition. Gradient methods are usually used to solve the dual problem resulting from dual decomposition. However, gradient methods are known f

Addressing the challenges of extreme scale computing re- quires holistic design of new programming models and sys- tems that support those models. This paper discusses the Angstrom processor, which is designed to support a new Self-aware Computing (SEEC) model. In SEEC, applications explicitly state goals, while other systems components provide actions that the SEEC runtime system can use to meet

We consider the classical 3-state Moore-Greitzer model, which is commonly used for approximating dynamics of deviations of flow and pressure variables in an axial compressor from their nominal steady-state values. The linearization of the nonlinear system is not controllable and, therefore, even local asymptotic stability cannot be achieved using methods of linear control theory. We propose a fami

Inresource-constrained systems, the interference generated by the concurrent execution of multiple controller tasks leads to extra delay and jitter, which degrade control performance and may even jeopardize the stability of the controlled system. This work presents a general methodology that integrates control issues and real-time schedulability analysis to improve the control performance in embed

Autonomic computing systems are capable of adapting their behavior and resources thousands of times a second to automatically decide the best way to accomplish a given goal despite changing environmental conditions and demands. Different decision mechanisms are considered in the literature, but in the vast majority of the cases a single technique is applied to a given instance of the problem. This