– Within the last 20 years, there has been witnessed a significant increase of the urban population of Sarajevo, as a result of economic and social migrations. Consequently, this has caused an increasing demand for new housing which is mainly profit-oriented without any beneficial social, environmental or cultural implication. Primary objective of this research is to analyze the current situation and to assess the quality of the buildings not only as a housing solution, but as a complex that unites the community who inhabits it. This research will be conducted in a qualitative manner in analysis and statistical approach over the data related to the urbanization, building standards and positive effects of the building. Newly built parts of settlements Otoka and Stup will be used as case studies, since these parts of the city are most influenced by the mass production of the new housing solutions. This paper stresses out the correlation between high demand for the new housing and decreased quality of the housing without respecting minimum spatial and environmental standards, without basic amenities, social infrastructure and recreational and cultural activities. There is a need for improvements in contemporary housing design that will reflect with positive impacts on social, environmental, economic and cultural aspects of urban living.

The choice of the appropriate variety of fruit is one of the most important factors in establishing new orchards. It is necessary to choose the variety that will give the best results in meeting the investment goals. This paper offered an innovative decision support model for plum variety selection, based on expert decision making and fuzzy logic. The fuzzy MARCOS (Measurement Alternatives and Ranking according to COmpromise Solution) method was used. The research was conducted with the aim of improving plum production in Bosnia and Herzegovina (BiH). To achieve this, the knowledge of experts from the Republic of Serbia was used, because this country is currently the third in the world in plum production and have branded many plum varieties. The results obtained using this model showed that two plum varieties stand out - Cacanska rodna and Stanley. These results were also confirmed by the performed sensitivity analysis. The worst results were obtained by the Sumadijka variety. These results will help in the selection of plum varieties when establishing new orchards in BiH to achieve the best results in Bosnian plum production.

For validation and demonstration of high accuracy ranging and positioning algorithms and systems, a wideband radio signal generation and acquisition testbed, tightly synchronized in time and frequency, is needed. The development of such a testbed requires solutions to several challenges. Tight time and frequency synchronization, derived from a centrally distributed time-frequency reference signal, needs to be maintained in the hardware of the transmitter and receiver nodes, and wideband signal acquisition requires sustainable data throughput between the receiver and host PC as well as data storage at GB level. This article presents a testbed for wideband radio signal acquisition, for validation and demonstration of high accuracy ranging and positioning. It consists of multiple Ettus X310 universal software radio peripherals (USRPs) and supports high accuracy (<100 ps) time-deterministic, sustainable signal transmission and acquisition, with a bandwidth up to 320 MHz (in dual channel mode) and frequencies up to 6 GHz. Generation and processing of wideband arbitrary signal waveforms is done offline. To realize these features, radio frequency on chip (RFNoC) compatible HDL units were developed for integration in the X310 SDR platform. Wideband transmission and signal acquisition at a lower duty cycle is applied to reduce the data offloading throughput to the host’s personal computer (PC). Benchmarking of the platform was performed to demonstrate sustainable long duration dual channel acquisition. Indoor range measurements with the synchronous operation of the testbed show a decimeter-level accuracy.

Bringing deep learning techniques to electromagnetic imaging is of interest considering its great success in various fields. Deep neural nets however are known for being data hungry machines, and in many practical cases, such as electromagnetic medical imaging, there is not enough to feed them. Scarcity of data necessitates reliance on simulations to generate a sufficiently large dataset for deep learning to perform any complicated task. Simulations however, can not perfectly represent real environments and therefore, any neural net trained on simulation data will invariably fail when evaluated on real data. This work customizes a deep domain adaptation technique for matching distributions of complex-valued electromagnetic data. We demonstrate the advantage of using complex-valued models over regular ones. An operational neural network trained on simulation data and adapted to practical data to perform brain injury localization is presented.