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Dr. Anton Fokin The Svedberg Laboratory, Sweden

TSL DAQ r o o t i n s c i e n c e. Dr. Anton Fokin The Svedberg Laboratory, Sweden. R-Quant r o o t i n f i n a n c e. ROOT in Sweden. New ROOT customers The Svedberg Laboratory, Uppsala University SVEDAQ ++ Division of Cosmic and Subatomic Physics, Lund University PHENIX

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Dr. Anton Fokin The Svedberg Laboratory, Sweden

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  1. TSL DAQ r o o t i n s c i e n c e Dr. Anton Fokin The Svedberg Laboratory, Sweden R-Quant r o o t i n f i n a n c e

  2. ROOT in Sweden • New ROOT customers • The Svedberg Laboratory, Uppsala University • SVEDAQ ++ • Division of Cosmic and Subatomic Physics, Lund University • PHENIX • CHIC collaboration, CHICSi project

  3. CHICSi detector

  4. SVEDAQ ++ • Generic and expandable • Object-oriented (C++) • Lynx RTOS Event Builder • ROOT on the client side • On-line data analysis • Friendly for users • ROOT Win95 GUI • ROOT macro processing • Friendly for developers • ROOT class structure • ROOT documenting

  5. ROOT & DAQ Event Building? • Real Time Linux • Lynx RTOS • Unix compatible OS for real time applications. • gcc support with custom libs • Networking • Threads • ROOT for Lynx?

  6. ROOT and DAQ • Networking • UDP sockets and multicasting • Multithreading • Graphics in threads • Windows NT GUI • Lots of people use NT on office computers • Java support • Control experiments on the Internet

  7. What is inside?

  8. Time series and technical analysis R-Quant classes In finance people operate with either time series or cross-sectional data. A typical time series can contain several thousands of entries. Lots of specific statistical methods were developed for time series analysis. CINT seems to be a perfect macro processor to create new indicators.

  9. Optimization and portfolio management R-Quant classes • In finance people face (quadratic) optimization problems for sets of thousands variables with a number of constraints, therefore: • Stochastic optimization (Simulating annealing + Metropolis) • Genetic optimization

  10. Artificial Neural networks and genetic algorithms R-Quant classes • A set of generic classes supporting different network configurations • TNeuron • TInputNeuron • TThresholdNeuron • THiddenNeuron • TOutputNeuron • TNeuralLayer • TNeuralNetwork • TPerceptron • TKohonenMap • Optimization, visulaization and serialization

  11. Fuzzy logic and expert systems R-Quant classes • TFuzzyConstant • TFuzzyVariable • TFuzzyStatement • TFuzzyRule • TFuzzyExpert • Forward (conclusion) and backward (explanation) chain techniques. Fuzzy input for neural network applications. • CINT (C++) knowledge and action database even rate is low (sure) if event rate is low and experiment is fragmentation then beam is low (maybe) or detector is broken (maybe) if beam is low (maybe) and requested beam is high then wake up beam operators and ask to check if beam is high (sure) and event rate is low then wake up guy-who-knows and ask to come

  12. R-Quant is an open software project Welcome to use Welcome to join Contacts http://garbo.lucas.lu.se/~kosu_fokin/rquant.htm Email:fokin@tsl.uu.se Thanks to ROOT, but NT/Java GUI is deadly important for such applications! Conclusions

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