Evolution towards Smart Optical Networking: Where Artificial Intelligence (AI) meets the World of Photonics

Smart optical networks are the next evolution of programmable networking and programmable automation of optical networks, with human-in-the-loop network control and management. The paper discusses this evolution and the role of Artificial Intelligence (AI)

Machine learning-based Raman amplifier design

A multi-layer neural network is employed to learn the mapping between Raman gain profile and pump powers and wavelengths. The learned model predicts with high-accuracy, low-latency and low-complexity the pumping setup for any gain profile.Comment: conferenc

Generalizirani inverzi

Tema diplomskog rada je pojam generaliziranog inverza matrice. Prvo poglavlje se bavi Moore--Penroseovim inverzom i njegovom ulogom u rješavanju sustava linearnih jednadžbi. Nadalje, promotrili smo i $\{i, j, k\}$-inverze koji zadovoljavaju samo neke od Penroseovih uvjeta, svojstva koja zadovoljavaju i njihovu primjenu. Pokazane su i dvije metode za njegovo računanje, jedna koja se bazira na particioniranju matrice po stupcima i druga koja se bazira na dekompoziciji singularnih vrijednosti matrice. U drugom poglavlju smo promotrili Drazinov inverz i njegov specijalan slučaj, grupni inverz. Spomenute su primjene u teoriji Markovljevih lanaca i u sustavima diferencijalnih jednadžbi. Detaljnije su proučena tri algoritma za njegovo računanje i njihova konvergencija, te njihovo ponašanje na odabranim primjerima.This thesis’ emphasis has been on the theory and application of generalized inverses. The first part is about the Moore–Penrose inverse and its role in finding solutions of linear systems. $\{i, j, k\}$-inverse satisfies some, but not all, of the Penrose equations. Its properties and applications are studied. Two methods of computing the Moore–Penrose inverse are mentioned, one relying on partitioning the matrix by columns, and the other on the singular value decomposition. The second part was about the Drazin inverse and its properties. A special case of the Drazin inverse, the group inverse, was introduced and its role in the theory of finite Markov chains examined. An application to linear systems of differential equations was briefly discussed. Finally, three algorithms for its computing have been studied in some detail along with the convergence and some examples

Optical Frequency Comb Noise Characterization Using Machine Learning

A novel tool, based on Bayesian filtering framework and expectation maximization algorithm, is numerically and experimentally demonstrated for accurate frequency comb noise characterization. The tool is statistically optimum in a mean-square-error-sense, works at wide range of SNRs and offers more accurate noise estimation compared to conventional methods

An ultra-fast method for gain and noise prediction of Raman amplifiers

A machine learning method for prediction of Raman gain and noise spectra is presented: it guarantees high-accuracy (RMSE < 0.4 dB) and low computational complexity making it suitable for real-time implementation in future optical networks controllers

Deep Learning of Geometric Constellation Shaping including Fiber Nonlinearities

A new geometric shaping method is proposed, leveraging unsupervised machine learning to optimize the constellation design. The learned constellation mitigates nonlinear effects with gains up to 0.13 bit/4D when trained with a simplified fiber channel model.Comment: 3 pages, 6 figures, submitted to ECOC 201

Employment in Patients With Renal Replacement Therapy

Introduction: To determine the prevalence and rate of employment of patients on renal replacement therapy (RRT) for end-stage renal disease (ESRD), to study the difference in the rate of employment between patients on hemodialysis (HD) and those with functioning kidney transplant (FKT) and to compare the rate of employment with patients’ opinions about their working ability and determine the possible reasons for the presumed disproportion.. Methods: 220 RRT patients (126 on HD and 94 with FKT) at the University Hospital Centre Osijek were surveyed. We created and used a questionnaire about the level of education, occupation, employment, professional timeline during the course of RRT, personal opinion about working ability and potential reasons for unemployment. Research was conducted during April and May 2017. The data were analyzed using SPSS (version 16.0. Inc., Chicago, IL, USA). Results: At the time of our research, 13.7% of patients on RRT were employed. Employment of FKT patients prevailed, without significant difference compared with dialyzed patients of working age (15 to 65 years old). 38.3% of patients in that age group felt capable of working. Transplantation did not improve access to employment. Highly educated people were employed more frequently. The main reasons for unemployment were poor health caused by CKD, advanced age, and employers’ unwillingness to hire chronically ill persons because of the potential need to adjust working hours. Conclusion: CKD reduced working ability and employment opportunities. Only a minority of patients on RRT were employed. Kidney transplantation did not increase the rate of employment. Patients should therefore be provided with education, appropriate guidelines and support for finding employment