The objectives of this research work are to investigate, design and implement active integrated antennas comprising active devices connected directly to the patch radiators, for various applications in high efficiency RF front-ends, integrated oscillator antennas, design and optimization of harmonic suppression antennas using a genetic algorithm (GA).
A computer-aided design approach to obtain a class F operation to optimizing the optimal fundamental load impedance and designing the input matching circuits for an active integrated antenna of the transmitting type is proposed and a case study of a design for 1.6 GHz is used to confirm the design principle. A study of active integrated oscillator antennas with a series feed back using a pseudomorphic high electronmobility transistor (PHEMT) confirms the design procedure in simulation and measurement for the oscillator circuit connected directly to the active antenna. Subsequently, another design of active oscillator antenna using bipolar junction transistor (BJT) improves the phase noise of the oscillation and in addition to achieve amplitude shift keying (ASK) and amplitude modulation (AM) modulation using the proposed design circuit. Moreover, the possibility of using a sensor patch technique to find the power accepted by the antenna at harmonic frequencies is studied.
A novel numerical solution, for designing and optimizing active patch antennas for harmonic suppression using GA in collaboration with numerical electromagnetic computation (NEC), is presented. A new FORTRAN program is developed and used for adaptively meshing any planar antenna structure in terms of wire grid surface structures. The program is subsequently implemented in harmonic suppression antenna design and optimization using GA. The simulation and measurement results for several surface structures show a good agreement.
Desalination technology provides fresh water to the arid regions around the world. Multi-Stage Flash (MSF) distillation process has been used for many years and is now the largest sector in the desalination industry. Top Brine Temperature (TBT) (boiling point temperature of the feed seawater in the first stage of the process) is one of the many important parameters that affect optimal design and operation of MSF processes. For a given pressure, TBT is a function of Boiling Point Temperature (BPT) at zero salinity and Temperature Elevation (TE) due to salinity. Modelling plays an important role in simulation, optimisation and control of MSF processes and within the model, calculation of TE is therefore important for each stages (including the first stage, which determines the TBT).
Firstly, in this work, several Neural Network (NN) based correlations for predicting TE are developed. It is found that the NN based correlations can predict the experimental TE very closely. Also predictions of TE by the NN based correlations were found to be good when compared to those obtained using the existing correlations from the literature.
Secondly, a hybrid steady state MSF process model is developed using gPROMS modelling tool embedding the NN based correlation. gPROMS provides an easy and flexible platform to build a process flowsheet graphically. Here a Master Model connecting (automatically) the individual unit model (brine heater, stages, etc.) equations is developed which is used repeatedly during simulation and optimisation. The model is validated against published results. Seawater is the main source raw material for MSF processes and is subject to seasonal temperature variation. With fixed design the model is then used to study the effect of a number of parameters (e.g. seawater and steam temperature) on the freshwater production rate. It is observed that, the variation in the parameters affect the rate of production of fresh water. How the design and operation are to be adjusted to maintain a fixed demand of fresh water through out the year (with changing seawater temperature) is also investigated via repetitive simulation.
Thirdly, with clear understanding of the interaction of design and operating parameters, simultaneous optimisation of design and operating parameters of MSF process is considered via the application MINLP technique within gPROMS. Two types of optimisation problems are considered: (a) For a fixed fresh water demand throughout the year, the external heat input (a measure of operating cost) to the process is minimised; (b) For different fresh water demand throughout the year and with seasonal variation of seawater temperature, the total annualised cost of desalination is minimised. It is found that seasonal variation in seawater temperature results in significant variation in design and some of the operating parameters but with minimum variation in process temperatures. The results also reveal the possibility of designing stand-alone flash stages which would offer flexible scheduling in terms of the connection of various units (to build up the process) and efficient maintenance of the units throughout the year as the weather condition changes. In addition, operation at low temperatures throughout the year will reduce design and operating costs in terms of low temperature materials of construction and reduced amount of anti-scaling and anti-corrosion agents. Finally, an attempt was made to develop a hybrid dynamic MSF process model incorporating NN based correlation for TE. The model was validated at steady state condition using the data from the literature. Dynamic simulation with step changes in seawater and steam temperature was carried out to match the predictions by the steady state model. Dynamic optimisation problem is then formulated for the MSF process, subjected to seawater temperature change (up and down) over a period of six hours, to maximise a performance ratio by optimising the brine heater steam temperature while maintaining a fixed water demand.
There is an increasing need for accurate models describing the electrical behaviour of individual biological cells exposed to electromagnetic fields. In this area of solving linear problem, the most frequently used technique for computing the EM field is the Finite-Difference Time-Domain (FDTD) method. When modelling objects that are small compared with the wavelength, for example biological cells at radio frequencies, the standard Finite-Difference Time-Domain (FDTD) method requires extremely small time-step sizes, which may lead to excessive computation times. The problem can be overcome by implementing a quasi-static approximate version of FDTD, based on transferring the working frequency to a higher frequency and scaling back to the frequency of interest after the field has been computed.
An approach to modeling and analysis of biological cells, incorporating the Hodgkin and Huxley membrane model, is presented here. Since the external medium of the biological cell is lossy material, a modified Berenger absorbing boundary condition is used to truncate the computation grid. Linear assemblages of cells are investigated and then Floquet periodic boundary conditions are imposed to imitate the effect of periodic replication of the assemblages. Thus, the analysis of a large structure of cells is made more computationally efficient than the modeling of the entire structure. The total fields of the simulated structures are shown to give reasonable and stable results at 900MHz, 1800MHz and 2450MHz. This method will facilitate deeper investigation of the phenomena in the interaction between EM fields and biological systems.
Moreover, the nonlinear response of biological cell exposed to a 0.9GHz signal was discussed on observing the second harmonic at 1.8GHz. In this, an electrical circuit model has been proposed to calibrate the performance of nonlinear RF energy conversion inside a high quality factor resonant cavity with known nonlinear device. Meanwhile, the first and second harmonic responses of the cavity due to the loading of the cavity with the lossy material will also be demonstrated. The results from proposed mathematical model, give good indication of the input power required to detect the weakly effects of the second harmonic signal prior to perform the measurement. Hence, this proposed mathematical model will assist to determine how sensitivity of the second harmonic signal can be detected by placing the required specific input power.
The focus of this study is to examine the factors that lead to growth in
small firms in a Least Developed Country (LDC). The research is
based on the manufacturing sector in Ghana. The main objectives of
the research are to identify the key variables that lead to small firms'
growth and to ascertain the critical barriers that impede growth.
A research model which is developed out of an initial exploratory
research and existing literature focuses on how the characteristics of
the owner/manager, the characteristics of the firm and the business
strategy variables interact to affect growth in employment. In addition
factors that are perceived to have constrained the growth of the small
firms during the study period are ascertained and discussed.
To properly test the hypotheses developed a face to face interview
survey involving 122 owner/managers of small manufacturing firms is
conducted. This resulted in a range of variables that allowed for the
construction of a comprehensive multivariate model of small firm
A resulting regression model provides about 68 percent of the
explanation for the growth of the small firms sampled. It also indicates
that the owner/manager characteristics variables offer the most
powerful explanation to small firm growth. We find that the
owner/manager's growth aspiration is the most influential factor in
achieving growth. The other owner/manager characteristics variables
that have positive influence on growth are level of education, prior
industry experience and entrepreneurial family background.
Owner/managers with local experience and/or with other business
interests are less likely to achieve faster growth. Foreign
owned/managed firms grow faster.
Younger and smaller firms appear to grow faster. While firms with
multiple ownerships tend to grow at a slower rate than firms owned and
managed by one person.
Business planning, marketing and export have positive and significant
impacts on growth. Other business strategies such as innovations and
staff training also have direct relationships with growth but not
significant. Some of the main constraining factors to growth are cost of borrowing,
lack of access to credit, high cost of inputs, lack of trust within the
business community, high bureaucracy, late payments and lack of
efficient support system. While the external environment plays
important role in small firm growth and development, the behaviours,
response and strategies pursued by individual owner/manager are
significant factors that determine the rate at which a firm will grow.
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