HYBRID NEURAL LUMPED ELEMENT APPROACH IN INVERSE MODELING OF RF MEMS SWITCHES

RF MEMS switches have been efficiently exploited in various applications in communication systems. As the dimensions of the switch bridge influence the switch behaviour, during the design of a switch it is necessary to perform inverse modeling, i.e. to determine the bridge dimensions to ensure the desired switch characteristics, such as the resonant frequency. In this paper a novel inverse modeling approach based on combination of artificial neural networks and a lumped element circuit model has been considered. This approach allows determination of the bridge fingered part length for the given resonant frequency and the bridge solid part length, generating at the same time values of the elements of the switch lumped element model. Validity of the model is demonstrated by appropriate numerical examples

COMPARATIVE ANALYSIS OF DIFFERENT CAD METHODS FOR EXTRACTION OF THE HEMT NOISE WAVE MODEL PARAMETERS

The noise wave model has appeared as a very appropriate model for the purpose of transistor noise modeling at microwave frequencies. The transistor noise wave model parameters are usually extracted from the measured transistor noise parameters by using time-consuming optimization procedures in microwave circuit simulators. Therefore, three different Computer-Aided Design methods that enable more efficient automatic determination of these parameters in the case of high electron-mobility transistors were developed. All of these extraction methods are based on different noise de-embedding procedures, which are described in detail within this paper. In order to validate the presented extraction methods, they were applied for the noise modeling of a specific GaAs high electron-mobility transistor. Finally, the obtained results were used for the comparative analysis of the presented extraction approaches in terms of accuracy, complexity and effectiveness

Zaoravanje žetvenih ostataka preduseva u cilju povećanja prinosa soje

Soybean yield depends on the choice of cultivar, soil fertility, cultivation practices, and weather conditions in different years. Ploughing down crop residues increases the content of soil organic matter, and thereby positively affects soil fertility. The use of crop residues as an energy source has been promoted in recent years. It would be wrong to refer to this as a renewable energy source as the removal of crop residues from agricultural fields reduces and ultimately damages soil fertility, which in turn leads to reduced yield and a crop residue decrease in the future. Due to the reduced application of manure and organic fertilisers, it is necessary to return crop residues to the soil to preserve soil structure and prevent soil fertility decline. The effect of ploughing down crop residues of preceding crops on soybean yield has been the focus of studies for eleven years. Ploughing down maize crop residues resulted in the soybean yield increase by about 11.69%, i.e. the annual yield increase ranged from 2.89% to 15.94%.Prinos soje zavisi od izbora sorte, plodnosti zemljišta, agrotehničkih mera, kao i od vremenskih uslova u pojedinim godinama. Zaoravanjem žetvenih ostataka preduseva povećava se sadržaj organske materije u zemljištu, što ima pozitivan uticaj na plodnost zemljišta. U jedanaestogodišnjim istraživanjima proučavan je uticaj zaoravanja žetvenih ostataka preduseva kukuruza na prinos soje. Poslednjih nekoliko godina sve više se promoviše korišćenje žetvenih ostataka za dobijanje energije. Pogrešno je nazivati ovaj vid dobijene energije kao obnovljivu energiju, pošto se na duži period odnošenjem žetvenih ostataka sa poljoprivrednih površina pogoršava i trajno narušava plodnost zemljišta, što će dovesti u budućnosti do smanjenja prinosa gajenih biljaka, a samim tim i do smanjenja žetvenih ostataka. Zbog sve manje primene stajnjaka i organskih đubriva, neophodno je bar deo žetvenih ostataka gajenih biljaka vratiti u zemljište, kako bi se sačuvala struktura zemljišta i usporilo opadanje njegove plodnosti. Zaoravanje žetvenih ostataka preduseva kukuruza dovelo je do povećanja prinosa soje u proseku za 11,69%, odnosno po pojedinim godinama povećanje prinosa je bilo od 2,89% do 15,94%

Application of low frequency electromagnetic waves (LFEV) and biological inputs in the production of soybean

Examination of the impact of electromagnetic fields on function of biological systems is more recent direction in agriculture. Application of electromagnetic waves on seed activates the enzyme complex which results in better germination and growing. The aim of this study was to investigate the use of different groups of microorganisms and low-frequency electromagnetic waves to achieve high yields. The study was conducted at the experimental field of the Institute of Field and Vegetable Crops in Novi Sad. Valjevka soybean variety was used. Necessary nutrients were provided by use of poultry manure in amount of: control, 750 and 1300 kg.ha-1 and incorporated mixture of useful microorganisms. Sub-plots include treatment of seeds with electromagnetic waves (15 Hz frequencies of exposure for 30 minutes) and the foliar treatment of plants with mixture of beneficial microorganisms in two growth stages of development. In the stage of development R3 were defined the basic parameters of the biological value of the soil, and achieved a high yield in the harvest. The results showed significant increase of soybean yield (21.4%) in the version with electromagnetic waves. The highest yield of 3179 kg.ha-1 was achieved in fertilization with 750 kg.ha-1 and use of electromagnetic waves, which was also in 4.44%, or 124 kg.ha-1 higher yield compared to the version without radiation. The results of investigated biogeny parameters of soil were compatible with soybean yield

Content yield of protein and oil in NS soybean varieties registered in 2021

U Institutu za ratarstvo i povrtarstvo do 2020. godine registrovano je 160 sorti soje i to 29 sorti soje grupe zrenja 000 i 00, 38 sorti 0 grupe zrenja, 50 sorti I grupe zrenja, 37 sorti II grupe zrenja i 6 sorti soje III grupe zrenja. Cilj ovoga rada je analiza prinosa, sadržaja proteina i ulja, kao i prinosa proteina i ulja po jedinici površine, najnovijih NS sorti soje priznatih u 2021. godini. Najviši prinos zrna ostvaren je sa sortom soje I grupe zrenja NS Deneris (4574 kg ha-1). Najviši sadržaj proteina imala je sorta soje NS Pavle, I grupe zrenja (40,53 %), dok je najviši sadržaj ulja zabeležen kod sorti soje I grupe zrenja NS Zmaj (23,04 %) i NS Deneris (23,01 %). Najviši prinos proteina po jedinici površine imala je sorta soje I grupe zrenja NS Deneris (1781 kg ha-1), a najviši prinos ulja sorta soje I grupe zrenja NS Zmaj (1051 kg ha-1).Until 2021, 160 soybean varieties were registered at the Institute of Field and Vegetable Crops, 29 soybean varieties of ripening group 000 and 00, 38 varieties of 0 ripening group, 50 varieties of ripening group I, 37 varieties of ripening group II and 6 soybean varieties of ripening group III. The aim of this paper is to analyse the yield, protein and oil content, as well as protein and oil yield per unit area, the latest NS varieties registered in 2021. The highest grain yield was achieved with the soybean variety of I ripening group NS Deneris (4574 kg ha-1). The highest protein content was in the soybean variety NS Pavle, I ripening group (40.53 %), while the highest oil content was recorded in the soybean variety I of the ripening group NS Zmaj (23.04 %) and NS Deneris (23.01 %). The highest protein yield per unit area was in the soybean variety of I ripening group NS Deneris (1781 kg ha-1), and the highest oil yield per unit area was in the soybean variety of I ripening group NS Zmaj (1051 kg ha-1)

Yield and quality of NS soybean varieties in the macro trials in 2021

Makroogledi se izvode radi rejonizacije sorti soje, odnosno da se za pojedine lokalitete gajenja odaberu one sorte koje će u datim agroekološkim uslovima ostvariti maksimalne prinose uz minimalna variranja u različitim godinama. Cilj ovoga rada je sagledavanje prinosa, sadržaja proteina i ulja, kao i prinosa proteina i ulja po jedinici površine NS sorti soje u mreži makroogleda u 2021. godini. Sorta soje NS Fantast ostvarila je najviši prinos zrna (2.725 kg ha-1), sorta Rubin najviši sadržaj proteina (40,1%), a sorte NS Atlas i NS Hogar najviši sadržaj ulja (21,5%), dok je najviši prinos proteina po jedinici površine (1.173 kg ha-1) ostvaren sa sortom soje NS Hogar, a najviši prinos ulja sa sortama NS Atlas i NS Hogar (624 kg ha-1).Macro-experiments are performed for the purpose of regionalization of soybean varieties, ie to select for individual cultivation sites those varieties that will achieve maximum yields in given agroecological conditions with minimal variations in different years. The aim of this paper is to consider the yield, protein and oil content, as well as protein and oil yield per unit area of NS soybean cultivars in the macro-experimental network in 2021. NS Fantast soybean variety had the highest grain yield (2725 kg ha-1), Rubin soybean highest protein content (40.1%), NS Atlas and NS Hogar cultivars the highest oil content (21.5%), while the highest protein yield per unit area (1173 kg ha-1) achieved with the soybean variety NS Hogar, and the highest oil yield with the cultivars NS Atlas and NS Hogar (624 kg ha-1)

Influence of year and soybean varieties on the number and weight of grain per plant

Broj zrna i masa zrna po biljci su morfološke osobine koje direktno utiču na ostvareni prinos soje. Cilj ovih istraživanja je ispitivanje uticaja godine i različitih sorti na broj zrna i masu zrna po biljci. U 2017. godini i 2021. godini broj i masa zrna po biljci bili su statistički veoma značajno niži u odnosu na ostale godine istraživanja. Posmatrane morfološke osobine imaju najviše vrednosti kod sorte soje sa najdužim vegetacionim periodom, broj zrna po biljci bio je statistički veoma značajno viši kod sorte Rubin u odnosu na sorte Sava i Galina, dok je masa zrna po biljci statistički veoma značajno viša kod sorte Rubin u odnosu na sortu Galina i statistički značajno viša u odnosu na sortu Sava.The number of grains and the mass of grains per plant are morphological characteristics that directly affect the achieved soybean yield. The aim of this research is to examine the influence of year and different varieties on the number of grains and grain weight per plant. In 2017 and 2021, the number and weight of grains per plant were statistically significantly lower compared to other years of research. The observed morphological characteristics have the highest values in the soybean variety with the longest vegetation period, the number of grains per plant was statistically significantly higher in the Rubin variety compared to the Sava and Galina varieties, while the grain weight per plant was statistically significantly higher in the Rubin variety on the Galina variety and statistically significantly higher in relation to the Sava variety

Content yield of protein and oil in NS soybean varieties registered in 2021

U Institutu za ratarstvo i povrtarstvo do 2020. godine registrovano je 160 sorti soje i to 29 sorti soje grupe zrenja 000 i 00, 38 sorti 0 grupe zrenja, 50 sorti I grupe zrenja, 37 sorti II grupe zrenja i 6 sorti soje III grupe zrenja. Cilj ovoga rada je analiza prinosa, sadržaja proteina i ulja, kao i prinosa proteina i ulja po jedinici površine, najnovijih NS sorti soje priznatih u 2021. godini. Najviši prinos zrna ostvaren je sa sortom soje I grupe zrenja NS Deneris (4574 kg ha-1). Najviši sadržaj proteina imala je sorta soje NS Pavle, I grupe zrenja (40,53 %), dok je najviši sadržaj ulja zabeležen kod sorti soje I grupe zrenja NS Zmaj (23,04 %) i NS Deneris (23,01 %). Najviši prinos proteina po jedinici površine imala je sorta soje I grupe zrenja NS Deneris (1781 kg ha-1), a najviši prinos ulja sorta soje I grupe zrenja NS Zmaj (1051 kg ha-1).Until 2021, 160 soybean varieties were registered at the Institute of Field and Vegetable Crops, 29 soybean varieties of ripening group 000 and 00, 38 varieties of 0 ripening group, 50 varieties of ripening group I, 37 varieties of ripening group II and 6 soybean varieties of ripening group III. The aim of this paper is to analyse the yield, protein and oil content, as well as protein and oil yield per unit area, the latest NS varieties registered in 2021. The highest grain yield was achieved with the soybean variety of I ripening group NS Deneris (4574 kg ha-1). The highest protein content was in the soybean variety NS Pavle, I ripening group (40.53 %), while the highest oil content was recorded in the soybean variety I of the ripening group NS Zmaj (23.04 %) and NS Deneris (23.01 %). The highest protein yield per unit area was in the soybean variety of I ripening group NS Deneris (1781 kg ha-1), and the highest oil yield per unit area was in the soybean variety of I ripening group NS Zmaj (1051 kg ha-1)

Soja u 2015. godini

Protekla godina nije bila povoljna za proizvodnju soje zbog pojave suše praćene visokim temperaturama. Prema nezvaničnim podacima soja je u 2015. godini bila zasejana na blizu 200.000 ha, a ostvareni prosečan prinos je ispod višegodišnjeg proseka (2,5 t/ha). Prinosi su veoma varirali, ne samo u odnosu na različite regione gajenja, već i u istim regionima, zavisno od parcele. Veoma niski prinosi zabeleženi su na parcelama sa lošijim zemljištem, usled propusta u primeni agrotehničkih mera i pojave grinja koje su na pojedinim parcelama izazvale značajne štete na usevima soje

Characterization and Neural Modeling of a Microwave Gas Sensor for Oxygen Detection Aimed at Healthcare Applications

The studied sensor consists of a microstrip interdigital capacitor covered by a gas sensing layer made of titanium dioxide (TiO2). To explore the gas sensing properties of the developed sensor, oxygen detection is considered as a case study. The sensor is electrically characterized using the complex scattering parameters measured with a vector network analyzer (VNA). The experimental investigation is performed over a frequency range of 1.5 GHz to 2.9 GHz by placing the sensor inside a polytetrafluoroethylene (PTFE) test chamber with a binary gas mixture composed of oxygen and nitrogen. The frequency-dependent response of the sensor is investigated in detail and further modelled using an artificial neural network (ANN) approach. The proposed modelling procedure allows mimicking the measured sensor performance over the whole range of oxygen concentration, going from 0% to 100%, and predicting the behavior of the resonant frequencies that can be used as sensing parameters