Drought is considered as one of the most important abiotic stress factor, which can cause a severe impact on agriculture. The development of new cultivars of barley is therefore more focused on improving of abiotic stress tolerance to drought, as well as on maintaining of good health status, appropriate yield levels and quality parameters. This work was aimed at evaluation of barley cv. Tadmor, which is specifically adapted to drought and can be potentially used as a source of drought tolerance in barley breeding programmes. F5 generation lines derived from reciprocal crosses between cv. Tadmor and cv. Jersey (advanced European spring malting barley) together with parent cultivars were cultivated at two sites (Brno, Žabčice). The assessment was aimed at traits associated with yield (thousand grain weight - TGW, percentage of sieving fraction over 2.0 mm, grain yield per plant) and quality (protein and starch content). The effect of water-limited conditions at Žabčice site was demonstrated particularly in the TGW and the percentage of sieving fraction over 2.0 mm, while the lines from Tadmor x Jersey cross sustained the stress significantly better than those from Jersey x Tadmor cross. The lines grown in Žabčice reached in average a higher level of protein content and a lower level of starch content. The significant effect of genotype was detected only in protein content and was non-significant for starch. The reaction of selected lines on osmotic stress was studied in and independent experiment under controlled conditions. The plant stress reaction was evaluated according to changes in osmotic potential in leaves and accumulation of proline. Lines with Tadmor as a female parent showed higher values of osmotic potential in comparison with those derived from the reciprocal cross. The increase in osmotic potential under stress was strongly associated with the content of proline. The last part was focused on evaluation of susceptibility to powdery mildew caused by Blumeria graminis. The symptoms of powdery mildew on experimental lines were assessed in field and the lines, which were classified as resistant/susceptible, were further analysed for the presence of mlo allele. According to the results all resistant lines carried mlo9. With regard to all evaluated parameters, positive reaction on physiological drought stress and genetically controlled resistance against powdery mildew two lines can be recommended for further work.

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Array antennas are used extensively in remote sensing applications, where a highly directive beam is needed to scan a particular area of interest on the surface of the earth. The research presented here focuses on the design of different microstrip patch antenna arrays to be used in environmental sensing applications in the X and Ka frequency bands, such as measurements in Snow and Cold Land Processes (SCLP) to detect snow accumulation, snow melt, etc. The goal of this research is to produce highly integrated, low loss, and compact size antenna arrays, while maintaining low power consumption. Multilayer organic (MLO) System-on-a-Package (SOP) technology, using laminates such as Liquid Crystal Polymer (LCP) and RT/Duroid®, provides a lightweight and low cost 3D solution for the fabrication of the antenna arrays. The elements of the antenna arrays are rectangular patches. Two feeding mechanisms, aperture coupling and via feed, were implemented and compared. For the RF distribution network and interconnects, a corporate feed approach was used with reactive T-junctions, Wilkinson dividers, or both, for power division. The feed networks were designed using microstrip. The basic multilayer antenna array design consists of 3 layers of cladded laminate material. The metal layers are as follows: 1) patch antennas, 2) ground plane, 3) feed network, and 4) surface-mount components. The surface mount components would include LNA, PA, TR switch and phase shifter.

Mammography screenings are performed regularly on women in order to detect early signs of breast cancer, which is the most common form of cancer. During an exam, X-ray images (called mammograms) are taken from two different angles and reviewed by a radiologist. If they find a suspicious lesion in one of the views, they confirm it by finding the corresponding region in the other view. Finding the corresponding region is a non-trivial task, due to the different image projections of the breast and different angles of compression needed during the exam. This thesis explores the possibility of using deep learning, a data-driven approach, to solve the corresponding regions problem. Specifically, a convolutional neural network (CNN) called U-net is developed and trained on scanned mammograms, and evaluated on both scanned and digital mammograms. A model based method called the arc model is developed for comparison. Results show that the best U-net produced better results than the arc model on all evaluated metrics, and succeeded in finding the corresponding area 83.9% of times, compared to 72.6%. Generalization to digital images was excellent, achieving an even higher score of 87.6%, compared to 83.5% for the arc model.

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