Recently, the growing number of Autonomous Underwater Vehicles (AUVs) can be seen. These vehicles are power supplied and controlled from the sources located on their boards. To operate autonomously underwater robots have to be equipped with the diff erent sensors and software for making decision based on the signals from these sensors. The goal of the paper is to show initial research carried out for underwater objects recognition based on video images. Based on several examples included in the literature, the object recognition algorithm proposed in the paper is based on the deep neural network. In the research, the network and training algorithms accessible in the Matlab have been used. The fi nal software will be implemented on board of the Biomimetic Autonomous Underwater Vehicle (BAUV), driven by undulating propulsion imitating oscillating motion of fi ns, e.g. of a fi sh. Sažetak U posljednje vrijeme može se uočiti sve veći broj autonomnih podvodnih plovila (AUV). Ova plovila imaju pogon i kontroliraju ih izvori koji se nalaze na njima. Da bi radili, autonomno podvodni roboti moraju biti opremljeni raznim senzorima i softverom kako bi se donosile odluke na temelju signala primljenih s pomoću senzora. Cilj je ovoga rada prikazati početno istraživanje provedeno na prepoznavanju podvodnih objekata putem videa. Na temelju nekoliko primjera koji se mogu naći u literaturi, algoritam za prepoznavanje objekata koji se predlaže u ovome radu temelji se na dubokim neuronskim mrežama. U istraživanju koristili su se dostupna mreža i algoritmi za obuku u Matlabu. Konačno dobiveni softver primijenit će se na biomimetrijskom autonomnom podvodnom plovilu (BAUV), pogonjenom valovitim pogonom koji imitira oscilirajuće gibanje peraja, npr. ribljih peraja.

In this paper, a hydroacoustic system designed for a biomimetic underwater vehicle (BUV) is presented. The Biomimetic Underwater Vehicle No. 2 (BUV2) is a next-generation BUV built within the ambit of SABUVIS, a European Defense Agency project (category B). Our main efforts were devoted to designing the system so that it will avoid collisions with vessels with low-speed propellers, e.g., submarines. Verification measurements were taken in a lake using a propeller-driven pontoon with a spectrum similar to that produced by a submarine propulsion system. Here, we describe the hydroacoustic signal used, with careful consideration of the filter and method of estimation for the bearings of the moving obstacle. Two algorithms for passive obstacle detection were used, and the results are discussed herein.

Signal processing in hydroacoustic system will be presented in this paper. The research results, depicted in this article, were achieved during realization one of the stages of the project for the development of an biomimetic underwater vehicle (BUV). The hydroacoustic system is installed inside Biomimetic Underwater Vehicle no. 2 (BUV2) and is designed for passive obstacle detection system. The passive measurement system was based on two hydrophones mounted on the upper part of the BUV2. The results of the hydroacoustic module testing were made in a real environment. The signals from the hydrophones were converted from analog to digital form and then fi ltered and analyzed by using algorithms implemented in the Texas Instruments C2000 series microcontroller. Sažetak U ovome će se članku prikazati procesuiranje hidroakustičnoga signala. Rezultati istraživanja opisani u ovome članku postignuti su tijekom realizacije jedne od faza projekta za razvoj biomimetičkoga podvodnoga vozila (BUV). Hidroakustički sustav instalira se unutar biomimetičkoga podvodnoga vozila broj 2 (BUV2) i predviđen je za sustav detekcije pasivne prepreke. Sustav pasivne izmjene temeljen je na dva hidrofona postavljena na gornji dio BUV". Rezultati hidroakustičnog modula testiranja napravljeni su u stvarnom okruženju. Signali s hidrofona konvertirani su iz analognoga u digitalni format i potom fi ltrirani i analizirani korištenjem algoritama koji su implementirani u mikrokontrolni instrument serijeTexas C2000. KEY WORDS digital signal processing, hydrophone passive detection of obstacles Biomimetic Underwater Vehicle KLJUČNE RIJEČI digitalno procesuiranje hidrofon pasivna detekcija prepreke biomimetičko podvodno vozilo

This article undertakes the subject matter of applying artificial neural networks to analyze optical reflectance spectra of objects exhibiting a change of optical properties in the domain of time. A compact Digital Light Projection NIRscan Nano Evaluation Module spectrometer was used to record spectra. Due to the miniature spectrometer’s size and its simplicity of measurement, it can be used to conduct tests outside of a laboratory. A series of plant-derived objects were used as test subjects with rapidly changing optical properties in the presented research cycle. The application of artificial neural networks made it possible to determine the aging time of plants with a relatively low mean squared error, reaching 0.56 h for the Levenberg–Marquardt backpropagation training method. The results of the other ten training methods for artificial neural networks have been included in the paper.

In recent years, there has been a dynamic development of photovoltaic materials based on perovskite structures. Solar cells based on perovskite materials are characterised by a relatively high price/performance ratio. Achieving stability at elevated temperatures has remained one of the greatest challenges in the perovskite solar cell research community. However, significant progress in this field has been made by utilising different compositional engineering routes for the fabrication of perovskite semiconductors such as triple cation-based perovskite structures. In this work, a new approach for the rapid analysis of the changes occurring in time in perovskite structures was developed. We implemented a quick and inexpensive method of estimating the ageing of perovskite structures based on an express diagnosis of light reflection in the near-infrared region. The possibility of using optical reflectance in the NIR range (900–1700 nm) to observe the ageing of perovskite structures over time was investigated, and changes in optical reflectance spectra of original perovskite solar cell structures during one month after PSC production were monitored. The ratio of characteristic pikes in the reflection spectra was determined, and statistical analysis by the two-dimensional correlation spectroscopy (2D-COS) method was performed. This method allowed correctly detecting critical points in thermal ageing over time.