Tiberiu Letia

Near real-time monitoring of the ionosphere using dual frequency GPS data in a Kalman filter approach

The ionosphere is an important source of errors for the GPS signals that travel through the ionosphere on their way to the ground-based receivers by introducing a frequency dependent path delay proportional to the total electron content (TEC) along the signal path. For dual-frequency GPS receivers, the ionospheric effects can be accounted for by taking advantage of the dispersive nature of the ionosphere in the microwave region of the electromagnetic spectrum, while for the single frequency GPS receivers the ionospheric effects can be minimized by modeling them using, for example, empirical or physics-based ionospheric models. On the other hand, the errors imposed by the ionosphere on the GPS signals can provide important temporal and spatial information about the electron density distribution in the ionosphere. Besides the ionospheric errors, there are some other sources of errors that can affect the GPS signals, such as the satellite and receiver instrumental biases, carrier phase ambiguities, multipath effects, clock errors, orbital errors, tropospheric errors, but which can be compensated for, estimated, or neglected depending on the particular application. In this paper, we are only concerned with the ionospheric effects on the GPS signals, and describe a Kalman filter-based algorithm for near real-time estimation of the line-of-sight and vertical ionospheric TEC and of the combined satellite and receiver instrumental biases, using data from dual-frequency GPS receivers.

Controller synthesis method for Discrete Event Systems

Many applications contain plants that are Discrete Events Systems (DES). They have to be controlled such that DES fulfills some specifications like: avoid the deadlocks, reach or avoid the reaching of some given states, execute or avoid the execution of some given sequences of events, execute cyclically sequences of events with the shortest periods, etc. In the current study the plants are modeled by Delay Time Petri Nets (DTPN) and the controllers are Time Petri Nets (TPN) models. The controllers can be described by a particular Time Petri Net Language (TPNL). The TPNL descriptions can be transformed into Lisp descriptions. The latest are used by a Genetic Programming (GP) method for the controller synthesis such that DES meets most accurately the system requirements.

Distributed scheduling for real-time railway traffic control

The increase of railway traffic efficiency and flexibility requires new real-time scheduling and control methods. New charter trains have to be added continuously without disturbing the other (periodic) train moves or decreasing the safety conditions. A distributed method to schedule new trains such that their real-time constraints are fulfilled is presented. The trains have timelines to meet and hence the deadlines are extracted taking into account the included laxity. The trains have pre-established routes specifying the stations and required arrival times. The paths containing the block sections from one station to another are dynamically allocated without leading to deadlocks.

Enhancing the time Petri nets for automatic hybrid control synthesis

Hybrid control refers to applications having a discrete event part and a continuous one that involves the interactions of different model types. Their control synthesis is difficult due to the fact that they contain models that belong to different approaches. In the current study the Time Petri Nets (TPNs) are used for discrete event controlled part model. For the continuous plant model is used a discrete time system (DTS) and the Fuzzy Logic Control (FLC) for its control. A new proposed method links the TPN models with FLC models. The formal Time Petri Net based Language (TPNL) (used for TPNs description) is enhanced to comprise the FLC functions and thus the entire hybrid control system can be described. The hybrid control descriptions constructed with the extended TPNL are transformed into trees that are further processed by Genetic Programming for control structure synthesis. The controllers parameters are improved by Genetic Algorithm (GA). The evolutionary individuals are organized in species taking into account the isomorphic distance between them. The species evolution implemented by GP is alternated by its adaptation performed by GA. New methods are used for the bloat control leading to a higher speed of solution search.

Automatic Linear Robot Control Synthesis Using Genetic Programming

An automatic controller synthesis method for a single axe linear robot is considered. The robot motions are modeled by a Delay Time Petri Net (DTPN). The search refers to automatically finding a controller modeled by a Time Petri Net (TPN) that fulfills some specified requirements. The controller model is synthesized using a Genetic Programming (GP) method. The mapping between TPN model and the tree representation of individual genotypes is performed using a formal language named here TPNL (Time Petri Net based Language). This language is suited for formal description of the controller behavior traits like sequential, concurrent, selection, loop or input/output. The use of control traits guaranties the construction of individuals that are capable and useful to control the robot moves. To diminish the search durations, besides the usual genetic operators like mutation, permutation and crossover, a new atrophy operator was introduced.

Fuzzy logic decision in partial fingerprint recognition

Most of the fingerprint recognition systems rely on minutiae matching algorithms. Although minutiae based techniques are widely used because of their temporal performances, they do not perform so well on low quality images and in the case of partial fingerprint they might not be used at all. Therefore, when comparing partial input fingerprints to pre-stored templates, a different approach is needed. This paper proposes to discriminate between high quality inputs and partial ones and to apply different matching technique for each category. In the case of complete fingerprints a core-minutiae-based structure matching algorithm is proposed. On the other hand, a fuzzy logic algorithm based on correlating a minutiae set and regions between ridges is proposed for matching partial fingerprints.

Fingerprint recognition distributed system

The paper proposes a distributed identification and verification system which integrates fingerprint recognition and Global Positioning System methods. The system is composed of a number of mobile fingerprinting terminals which communicate with data processing units using GSM\GPRS technology. The motivation for integrating GPS technology in an identification system is to add a supplementary security level by granting access based on area localization. In order to further increase the system security a cryptographic module is used to encrypt / decrypt messages passed between the distributed components. The system architecture is presented and main modules are described.

Train Traffic Control Based on Distributed Resource Allocation

Abstract The railway traffic is characterized as a large and dynamic system with uncertain properties related to resource loading, train arrivals and failures. Despite these uncertainties, the control system is expected to guarantee that all the trains behave according to their timelines. The current approach solves the railway traffic control problem using the resource allocation. The trains are considered tasks with specified temporal behaviors that have to fulfill their deadlines. The solutions based on open loop, closed loop with independent, coordinated and heterarchical controllers are defined and compared. The control signals are implemented and verified using time Petri nets. Some algorithms for control system implementation are given. The method evaluations are performed using the meter functions: utility, utilization, reservation and efficiency. The results obtained through simulations show that the proposed distributed controllers solve adequately the control problems and can be used for large scale implementation.

Unified enhanced time Petri net models for development of the reactive applications

The reactive application development requires models that comprise continuous and logic variables, and reacts continuously and asynchronously to external and internal events. The newly proposed models called Unified Enhanced Time Petri Nets (UETPNs) endow the transitions of Time Petri Nets (enhanced with input and output channels) with mappings that include fuzzy logic features and arithmetic operations. The mappings are used to control the flow of the execution and to calculate the model states. The places have assigned real variables and so the contained tokens are real numbers. Extending the classical fuzzy logic sets with the empty set allows the construction of fuzzy logic rule sets that can model besides the fuzzy Petri nets capabilities the inhibitor arcs and reset arcs. The UETPN can model concurrency, selection, reaction to internal and external events and temporal behavior with fixed and variable delays. An algorithm to execute the UETPN models is provided with some examples of its use for application developments.

Fuzzy logic enhanced time Petri Net models for hybrid control systems

A Hybrid Control System (HCS) model has to implement the reaction to discrete events that occur in a controlled plant and as well as to control the continuous time parts according to required behaviors. A certain type of models that includes discrete event and discrete time features is proposed. More than this, the currently proposed models are capable of describing the synchronous and asynchronous reactions to discrete events and continuous modifications of plant outputs. The proposed models endow the Enhanced Time Petri Nets (ETPNs) with Fuzzy Logic (FL) rules leading to the Fuzzy Logic Enhanced Time Petri Net (FLETPN) model. An example of application to the control of the longitudinal move of a vehicle is used to show the model utilization and its benefits.