| 21. |
- Flärdh, Oscar, 1980-
(författare)
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Modelling, analysis and experimentation of a simple feedback scheme for error correction control
- 2007
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Data networks are an important part in an increasing number of applications with real-time and reliability requirements. To meet these demands a variety of approaches have been proposed. Forward error correction, which adds redundancy to the communicated data, is one of them. However, the redundancy occupies communication bandwidth, so it is desirable to control the amount of redundancy in order to achieve high reliability without adding excessive communication delay. The main contribution of the thesis is to formulate the problem of adjusting the redundancy in a control framework, which enables the dynamic properties of error correction control to be analyzed using control theory. The trade-off between application quality and resource usage is captured by introducing an optimal control problem. Its dependence on the knowledge of the network state at the transmission side is discussed. An error correction controller that optimizes the amount of redundancy without relying on network state information is presented. This is achieved by utilizing an extremum seeking control algorithm to optimize the cost function. Models with varying complexity of the resulting feedback system are presented and analyzed. Conditions for convergence are given. Multiple-input describing function analysis is used to examine periodic solutions. The results are illustrated through computer simulations and experiments on a wireless sensor network.
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| 22. |
- Henriksson, Roger, et al.
(författare)
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Brain Tumors - Prognostic and Predictive Markers
- 2009
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Ingår i: Histological and Serological Tumor Markers and Gene Expression and Their Clinical Usefulness in Cancers. - Hauppauge : Nova Science Publishers, Inc.. - 9781607413820 ; , s. 53-75
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Bokkapitel (refereegranskat)abstract
- This review summarizes the status of prognostic and predictive markers in brain tumors with a focus on the most frequent tumors, gliomas. Brain tumors are a heterogeneous group of different tumors with a huge variation in outcome. Although the most common tumor, high-grade malignant glioma, still has a dismal prognosis, the last years have seen a significant improvement in the management in this tumor as well as in most other brain tumors. Age, tumor grade and KPS are still the most reliable prognostic and predictive variables available for patients with brain tumors. Although chromosome 1p/19q co-deletion and methylation status of the promoter of the MGMT gene (encoding O6-methylguanine-DNA methyl transferase) have been identified as the most promising potential predictors of response to chemotherapy in malignant gliomas, there are as yet no reliable biomarkers for tumour grading or tumour monitoring in the clinical setting.
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| 23. |
- Jern, Mikael, 1946-, et al.
(författare)
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The GAV Toolkit for Multiple Linked Views
- 2007
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Ingår i: Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization, 2007. CMV '07.. - Los Alamitos, CA, USA : IEEE Computer Society. - 0769529038 ; , s. 85-97
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Konferensbidrag (refereegranskat)abstract
- Implementing InfoVis multivariate data tools, timelinked coordinated views and visual dynamic queries with conditioning from scratch is not a simple programming task. Our research objective is to develop a generic GeoAnalytics visualization (GAV) component toolkit, based on the principles behind visual analytics (VA), for dynamically exploring time-varying, geographically referenced and multivariate attributes simultaneously. GAV includes components based on a synergy of technologies from information visualization, geovisualization and scientific visualization. Our research concentrates on improving visual user interfaces (VUI) methods and trying to extend existing visual representation techniques. The effectiveness of our proposed component toolkit and framework is demonstrated in two customized applications GeoWizard analysing multivariate energy usage data for Swedish municipalities and MD-Explorer exploring multivariate data using novel interactive ternary diagrams. We use parallel coordinates with embedded visual inquiry methods that serves as a visual control panel for dynamically linked and coordinated views. Finally, discoveries made during the visual exploration process can be captured and organized in a format for later recall and communication to others.
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| 24. |
- Johansson, Ann, et al.
(författare)
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Interstitial photodynamic therapy for primary prostate cancer incorporating realtime treatment dosimetry
- 2007
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Ingår i: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. - : SPIE. - 1042-4687 .- 1605-7422. ; 6427, s. 4270-4270
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Konferensbidrag (refereegranskat)abstract
- Photodynamic therapy (PDT) for the treatment of prostate cancer has been demonstrated to be a safe treatment option capable of inducing tissue necrosis and decrease in prostate specific antigen (PSA). Research groups report on large variations in treatment response, possibly due to biological variations in tissue composition and shortterm response to the therapeutic irradiation. Within our group, an instrument for interstitial PDT on prostate tissue that incorporates realtime treatment feedback is being developed. The treatment protocol consists of two parts. The first part incorporates the pre-treatment plan with ultrasound investigations, providing the geometry for the prostate gland and surrounding risk organs, an iterative random-search algorithm to determine near-optimal fiber positions within the reconstructed geometry and a Block-Cimmino optimization algorithm for predicting individual fiber irradiation times. During the second part, the therapeutic light delivery is combined with measurements of the light transmission signals between the optical fibers, thus monitoring the tissue effective attenuation coefficient by means of spatially resolved spectroscopy. These data are then used as input for repeated runs of the Block-Cimmino optimization algorithm. Thus, the irradiation times for individual fibers are updated throughout the treatment in order to compensate for the influence of changes in tissue composition on the light distribution at the therapeutic wavelength.
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| 25. |
- Johansson, Björn, et al.
(författare)
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A RANDOMIZED INCREMENTAL SUBGRADIENT METHOD FOR DISTRIBUTED OPTIMIZATION IN NETWORKED SYSTEMS
- 2009
-
Ingår i: SIAM Journal on Optimization. - 1052-6234 .- 1095-7189. ; 20:3, s. 1157-1170
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Tidskriftsartikel (refereegranskat)abstract
- We present an algorithm that generalizes the randomized incremental subgradient method with fixed stepsize due to Nedic and Bertsekas [SIAM J. Optim., 12 (2001), pp. 109-138]. Our novel algorithm is particularly suitable for distributed implementation and execution, and possible applications include distributed optimization, e.g., parameter estimation in networks of tiny wireless sensors. The stochastic component in the algorithm is described by a Markov chain, which can be constructed in a distributed fashion using only local information. We provide a detailed convergence analysis of the proposed algorithm and compare it with existing, both deterministic and randomized, incremental subgradient methods.
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| 26. |
- Johansson, Björn, et al.
(författare)
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A simple peer-to-peer algorithm for distributed optimization in sensor networks
- 2007
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Ingår i: PROCEEDINGS OF THE 46TH IEEE CONFERENCE ON DECISION AND CONTROL. ; , s. 5402-5407
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Konferensbidrag (refereegranskat)abstract
- We propose a distributed algorithm that solves a special class of optimization problems using only peer-to-peer communication. One application is parameter estimation problems in sensor networks. Current decentralized algorithms for solving this class of optimization problems typically rely on passing around a parameter estimate in a ring consisting of all network nodes. In our algorithm, which extends the randomized incremental subgradient method with fixed stepsize due to Nedic and Bertsekas, nodes maintain individual estimates and need to exchange information only with their neighbors. We establish approach of the solution to an interval around the optimum value. We illustrate the algorithm's performance, in terms of convergence rate and communication cost relative to alternative schemes, through several numerical examples.
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| 27. |
- Johansson, Björn, et al.
(författare)
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Distributed non-smooth resource allocation over a network
- 2009
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Ingår i: IEEE Conference on Decision and Control. ; , s. 1678-1683
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Konferensbidrag (refereegranskat)abstract
- Networked systems are common and crucial. Oneof the canonical problems in such systems is distributed resourceallocation. From this rather broad class of problems, weconsider a convex non-smooth resource allocation problem witha global resource constraint. Specifically, the objective functionis separable and consists of a sum of convex functions, eachassociated with a node in a given network. Each component ofthe objective depends on a single variable local to the associatednode and the sum of all local variables must remain constantat all times. For scalability, we constrain the nodes to onlycommunicate and exchange resources with their immediateneighbors. We propose an algorithm that combines subgradientoptimization with distributed averaging. Starting the algorithmfrom a feasible point, the nodes iteratively exchange resourceswith their neighbors to get close to the optimal set whilesatisfying the total resource constraint at all times.We show thatunder mild technical conditions the algorithm converges in anepsilon-sense, as long as the stepsize is chosen sufficiently smalland the distributed averaging process is sufficiently accurate.We derive expressions for how the stepsize and the number ofconsensus iterations affect the accuracy of the final result.
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| 28. |
- Johansson, Björn, 1978-
(författare)
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Distributed resource allocation in networked systems using decomposition techniques
- 2006
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The Internet and power distribution grids are examples of ubiquitous systems that are composed of subsystems that cooperate using a communication network. We loosely define such systems as networked systems. These systems are usually designed by using trial and error. With this thesis, we aim to fill some of the many gaps in the diverse theory of networked systems. Therefore, we cast resource allocation in networked systems as optimization problems, and we investigate a versatile class of optimization problems. We then use decomposition methods to devise decentralized algorithms that solve these optimization problems. The thesis consists of four main contributions: First, we review decomposition methods that can be used to devise decentralized algorithms for solving the posed optimization problems. Second, we consider cross-layer optimization of communication networks. Network performance can be increased if the traditionally separated network layers are jointly optimized. We investigate the interplay between the data sending rates and the allocation of resources for the communication links. The communication networks we consider have links where the data transferring capacity can be controlled. Decomposition methods are applied to the design of fully distributed protocols for two wireless network technologies: networks with orthogonal channels and network-wide resource constraints, as well as wireless networks using spatial-reuse time division multiple access. Third, we consider the problem of designing a distributed control strategy such that a linear combination of the states of a number of vehicles coincide at a given time. The vehicles are described by linear difference equations and are subject to convex input constraints. It is demonstrated how primal decomposition techniques and incremental subgradient methods allow us to find a solution in which each vehicle performs individual planning of its trajectory and exchanges critical information with neighbors only. We explore various communication, computation, and control structures. Fourth, we investigate the resource allocation problem for large-scale server clusters with quality-of-service objectives, in which key functions are decentralized. Specifically, the problem of selecting which services the servers should provide is posed as a discrete utility maximization problem. We develop an efficient centralized algorithm that solves this problem, and we propose three suboptimal schemes that operate with local information.
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| 29. |
- Johansson, Björn, et al.
(författare)
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Distributed resource allocation strategies for achieving quality of service in server clusters
- 2006
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Ingår i: PROCEEDINGS OF THE 45TH IEEE CONFERENCE ON DECISION AND CONTROL. ; , s. 1990-1995
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Konferensbidrag (refereegranskat)abstract
- We investigate the resource allocation problem for large-scale server clusters with quality-of-service objectives, where key functions are decentralized. Specifically, the optimal service selection is posed as a discrete utility maximization problem that reflects management objectives and resource constraints. We develop an efficient centralized algorithm that solves this problem, and we propose three suboptimal schemes that operate with local information. The performance of the suboptimal schemes is evaluated in simulations, both under idealized conditions and in a full-scale system simulator.
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| 30. |
- Johansson, Björn, et al.
(författare)
-
Faster Linear Iterations for Distributed Averaging
- 2008
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Ingår i: Proceedings of the 17th IFAC World Congress, 2008.
-
Konferensbidrag (refereegranskat)abstract
- Distributed averaging problems are a subclass of distributed consensus problems,which have received substantial attention from several research communities. Although many ofthe proposed algorithms are linear iterations, they vary both in structure and state dimension.In this paper, we investigate the performance benefits of adding extra states to distributedaveraging iterations. We establish conditions for convergence and discuss possible ways ofoptimizing the convergence rates. By numerical examples, it is shown that the performance canbe significantly increased by adding extra states. Finally, we provide necessary and sufficientconditions for convergence of a more general version of distributed averaging iterations.
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