Od swego początku ludzkość podziwia dzieła sztuki, ale prawie nigdy w swojej oryginalnej formie stworzonej przez artystów. Czas nieuchronnie zmienia wszystkie obiekty fizyczne – dzieła sztuki nie są jest wyjątkiem. Patrząc na obrazy widzimy kolory, kształty i teksturę ... ale także spękania, które nadają obiektom historyczny wygląd i wzbogacają odbiór estetyczny dzieła. Konserwatorzy potrafią rozpoznać i opisać różne sieci spękań - krakelury -, ale nie ma wiedzy na temat tego, jak powstaję rożne struktury różne wzory. To istotne ograniczenie w akademickim rozumieniu dzieł sztuki jest przedmiotem realizowanego projektu CRACQUELURE.

A new method for constructing a digital water-marking based on the Ateb-functions is proposed. This new approach belongs to methods of protecting images based on hiding data in the frequency domain. It is a generalization the well-known method using Fourier transform. The proposed method, based on the two dimension Discrete Ateb Transform is resistant to most types of attacks. Our experiments prove its efficiency and stability.

Machine learning and AI has been a trending topic for many years already. However, it is well known the challenges companies are facing to deploy robust solutions taking advantage of this kind of algorithms. The gap between R&D and IT-organisation is still latent, and until both of them don't get to a common point in which the cost and complexity of deploying AI-based solutions are well understood, the possibilities to extract all the value from data are diminished.

We call the bioelectric signal an electrical signal that arises in the tissues of living organisms. In particular, the signal generated in the complex functional system of the smooth muscle is interesting and described using EMG. The electromyogram reflects the bioelectrical activity of the smooth muscle of the reproductive system in the form of bioelectric activity recording preceding each systolic activity. At the same time, not all EMG activity triggers systolic activity.

The central limit theorem (CLT) and the law of the iterated logarithm (LIL) are, along the strong law of large numbers (SLLN), the most common limit theorems. Some well-known results concerning limit theorems, obtained mainly due to the martingale method, are gathered in [1]. Although the asymptotic behaviour of stationary and ergodic Markov chains is already well investigated, limit theorems for a wider class of Markov processes are still the subject of research.  Together with D. Czapla and K.