Technical University of Sofia

About: Technical University of Sofia is a education organization based out in Sofia, Bulgaria. It is known for research contribution in the topics: Computer science & Artificial neural network. The organization has 2087 authors who have published 4653 publications receiving 22523 citations.

A MHz-repetition-rate hard X-ray free-electron laser driven by a superconducting linear accelerator

Abstract: The European XFEL is a hard X-ray free-electron laser (FEL) based on a high-electron-energy superconducting linear accelerator. The superconducting technology allows for the acceleration of many electron bunches within one radio-frequency pulse of the accelerating voltage and, in turn, for the generation of a large number of hard X-ray pulses. We report on the performance of the European XFEL accelerator with up to 5,000 electron bunches per second and demonstrating a full energy of 17.5 GeV. Feedback mechanisms enable stabilization of the electron beam delivery at the FEL undulator in space and time. The measured FEL gain curve at 9.3 keV is in good agreement with predictions for saturated FEL radiation. Hard X-ray lasing was achieved between 7 keV and 14 keV with pulse energies of up to 2.0 mJ. Using the high repetition rate, an FEL beam with 6 W average power was created.

Risk identification – basic stage in risk management

Abstract: In this paper risk identification is investigated as a basic stage in risk management. The risk identification phase as the first stage in the risk management process is presented and its leading role for effective risk management is proved. The basic terms that are necessary for building of the frame approach for risk identification are defined: sources of risk‐hazard, factor‐peril‐resources exposed to risk. A classification of risk sources – physical, social, political, operational, economic, legal and cognitive environment – is proposed. It allows covering all types of risk facing the organisation. A grouping of the resources exposed to risk such as physical, human, and financial resources is introduced. It is based on a practical consideration of the risk situations in the organisations.

Removal of power-line interference from the ECG: a review of the subtraction procedure

Abstract: Modern biomedical amplifiers have a very high common mode rejection ratio. Nevertheless, recordings are often contaminated by residual power-line interference. Traditional analogue and digital filters are known to suppress ECG components near to the power-line frequency. Different types of digital notch filters are widely used despite their inherent contradiction: tolerable signal distortion needs a narrow frequency band, which leads to ineffective filtering in cases of larger frequency deviation of the interference. Adaptive filtering introduces unacceptable transient response time, especially after steep and large QRS complexes. Other available techniques such as Fourier transform do not work in real time. The subtraction procedure is found to cope better with this problem. The subtraction procedure was developed some two decades ago, and almost totally eliminates power-line interference from the ECG signal. This procedure does not affect the signal frequency components around the interfering frequency. Digital filtering is applied on linear segments of the signal to remove the interference components. These interference components are stored and further subtracted from the signal wherever non-linear segments are encountered. Modifications of the subtraction procedure have been used in thousands of ECG instruments and computer-aided systems. Other work has extended this procedure to almost all possible cases of sampling rate and interference frequency variation. Improved structure of the on-line procedure has worked successfully regardless of the multiplicity between the sampling rate and the interference frequency. Such flexibility is due to the use of specific filter modules. The subtraction procedure has largely proved advantageous over other methods for power-line interference cancellation in ECG signals.

Multilayer perceptron and neural networks

Abstract: The attempts for solving linear inseparable problems have led to different variations on the number of layers of neurons and activation functions used. The backpropagation algorithm is the most known and used supervised learning algorithm. Also called the generalized delta algorithm because it expands the training way of the adaline network, it is based on minimizing the difference between the desired output and the actual output, through the downward gradient method (the gradient tells us how a function varies in different directions). Training a multilayer perceptron is often quite slow, requiring thousands or tens of thousands of epochs for complex problems. The best known methods to accelerate learning are: the momentum method and applying a variable learning rate. The paper presents the possibility to control the induction driving using neural systems.