O projektiranju, mjerenjima i robusnoj regulaciji u bežičnim telekomunikacijskim mrežama

Over the years a lot of rigorous mathematical analysis has been done by systems and controls community for the optimization of multivariable systems with a mathematically rigorous systems theoretic approach. In parallel, wireless telecommunication industry was working on performance measurement analysis tools achieving demonstrable optimization. This paper will elaborate on the need for finding common grounds these two approaches. Some existing measurement tools are also presented. Decision on measurement tools in design of telecommunications networks implies tradeoffs between reliability, capacity, and the economics in meeting customer demands. The paper first identifies the parameters which can and should be measured to facilitate the optimization for performance of telecommunication networks. Secondly, there are given available resources on measurements tools for these parameters together with a comparative analysis. Thirdly, the hardware setup for some of these measurements will be explained. Finally, a method to find the robust controller for wireless network is introduced.Kroz godine je mnogo postignuto u optimizaciji multivarijabilnih sustava kroz rigorozan matematički teoretski pristup. U isto vijeme, u bežičnoj telekomunikacijskoj industriji radilo se na metodama za mjerenje i analizu kako se moglo optimirati rad telekomunikacijske mreže. Glavna tema ovog rada je uvod u analizu koja povezuje teoriju regulacijskih sustava i optimiranje rada bežičnih telekomunikacijskih mreža. Neke postojeće mjerne metode su također objašnjene. Izbor mjernih metoda i mjerenja potrebnih za uspostavljanje optimalne i pouzdane bežične telekomunikacijske mreže uključuje uspostavljanje ravnoteže između pouzdanosti, kapaciteta i cijene u zadovoljavanju trenutnih i budućih potreba korisnika. Uspostavljanje i održavanje efikasne bežične telekomunikacijske mreže jesvakodnevni izazov. Ovaj rad prvo identificira parametre koji mogu i trebaju biti mjereni kako bi se omogućilo optimiranje rada mreže. Zatim je data kratka analiza opreme za mjerenje uz objašnjenje hardwera za takva mjerenja. Na kraju je dana struktura robusnog regulatora za bežične telekomunikacijske mreže

Static output-feedback stabilization of discrete-time Markovian jump linear systems: a system augmentation approach

This paper studies the static output-feedback (SOF) stabilization problem for discrete-time Markovian jump systems from a novel perspective. The closed-loop system is represented in a system augmentation form, in which input and gain-output matrices are separated. By virtue of the system augmentation, a novel necessary and sufficient condition for the existence of desired controllers is established in terms of a set of nonlinear matrix inequalities, which possess a monotonic structure for a linearized computation, and a convergent iteration algorithm is given to solve such inequalities. In addition, a special property of the feasible solutions enables one to further improve the solvability via a simple D-K type optimization on the initial values. An extension to mode-independent SOF stabilization is provided as well. Compared with some existing approaches to SOF synthesis, the proposed one has several advantages that make it specific for Markovian jump systems. The effectiveness and merit of the theoretical results are shown through some numerical example

Strategic program management in a constrained product development environment

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, 2014.Cataloged from PDF version of thesis.Includes bibliographical references (page 64).Enterprises involved in research and development are often confronted with making decisions for allocating resources between multiple programs and projects. This work addresses the problem of managing research projects and programs in such organizations. This work presents how to allocate resources among the competing projects, whose properties are only partially known initially and better understood as time passes. This work presents a system theoretic program level management tool customized for research and product development organizations. Good practices from Systems Engineering, Product Development and Project Management are included in the analysis. This work also presents an implementation framework with a real case example with supporting quantitative results at an Engineering Product Development Organization.by Jyotirmay Gadewadikar.S.M. in Engineering and Managemen

Multilayer Neural Net Trajectory Tracking Control for Underwater Vehicle

An adaptive multilayer neural network controller for high precision maneuvering of underwater vehicles is presented. Maneuvering of underwater vehicles requires special attention to a number of factors, including thruster and vehicle’s nonlinearities, couplings which exist between various degrees of freedom as well as effects of the sea currents. The neuro control system for underwater vehicle maneuvering described in this paper is based on a conventional controller supported with the so-called adaptive neural network. The adaptive neural network has two tunable layers, thus the problem of selection of proper basis is avoided

Design of H∞ command and control loops for unmanned aerial vehicles using static output-feedback

10.1109/CDC.2007.4434065Proceedings of the IEEE Conference on Decision and Control5395-5400PCDC

Attitude control system design for unmanned aerial vehicles using H-infinity and loop-shaping methods

10.1109/ICCA.2007.43765452007 IEEE International Conference on Control and Automation, ICCA1174-117

Exploring Bayesian networks for medical decision support in breast cancer detection

The objective of this paper is to explore the implementation of a Bayesian Belief Network for an automated breast cancer detection support tool. It is intuitive that Bayesian networks are employed as one viable option for computer-aided detection by representing the relationships between diagnoses, physical findings, laboratory test results and imaging study findings. This work brings important entities such as Radiologists, Image Processing Scientists, Data Base Specialists and Applied Mathematicians on a common platform. A brief background concerning causal networks, probability theory and Bayesian networks is given; available computational tools and platforms are described. It is explained that, by exploiting conditional independencies entailed by influence chains, it is possible to represent a large instance in a Bayesian network using little space, and it is often possible to perform probabilistic inference among the features in an acceptable amount of time. The next steps towards realizing a Bayesian Belief Network Implementation are described. Bayesian networks have an unparallel advantage of being able to exploit the explicit structure of the domain model to derive a graphical representation for learning. The encoding of independencies in the network topology admits the design of efficient procedures for performing computations over the network. For the application of computer-aided detection in mammography, the researchers intend to design an interface between the project's Bayesian network learning algorithm and the radiologists, so that the radiologists can have interaction with the system by labeling only a small number of informative images presented by the active learning algorithm