Wearable Antenna Design And Optimization Based On PSO

Stroke is caused by brain ischemic or brain injury, also known as cerebrovascular accident, and has a very high mortality and morbidity. It is one of the most lethal disease in the world. The mortality rate of stroke also increases with age. Due to the lack of effective treatment, the best measure is prevention. The detection of stroke is generally done using MRI, CT or other expensive medical equipment that are unaffordable to the rural residents and the small clinics. In recent years, microwave imaging technology has become the research focus of many universities and medical device companies. The stroke detection technology has become greatly advanced as in this detection system, the antenna transmission and reception module directly affects the imaging results. In order to make the image showing the stroke affected brain area clearer and easier to detect it is necessary to design a small size, light weight, and compact antenna for receiving and detection signal acquisition in imaging.However, in the antenna design optimization process, the time-consuming and complex structure of the ultra-wideband(UWB) antenna as well as multi-parameter and nonlinear problems present serious challenges. Therefore intelligent optimization algorithms for efficiently solving nonlinear problems, which are simple in principle,, robust, and suitable for a variety of complex optimization problems, are widely applied in the field of antenna optimization. With the help of intelligent algorithms to optimize the antenna, to meet the performance parameters of the antenna for specific technical applications, improved efficiency of the antenna design can be achieved. In this paper, particle swarm algorithm(PSO) and genetic algorithm(GA) were used to optimize antipodal Vivaldi antenna. Taking into account wearable research, the paper also researched on material characteristics, print technology, and insulation material properties by selecting the appropriate material and using the improved PSO and HFSS to design and optimize microstrip antenna. Finally, by modifying the structure of the antenna using HFSS to simulate the external environment changes and observing the performance of antenna the results of this study can be summarized as follows:(1) The introduction of HFSS-API; using this tool and MATLAB programming to model complex antenna structure, which it uses external algorithms to optimize antenna. In this paper, by successfully using this interface it was possible to design an ultra-wideband antenna antipodal Vivaldi.(2) Proposed the improved particle swarm optimization. It was used to optimize ultra wideband antipodal Vivaldi antenna. A comparison with genetic algorithm optimization that is integrated in HFSS was also done. The results showed that the proposed approach requires less than 20% simulation time while the antenna bandwidth ranges from 1.8GHz to 5.0 GHz with gain of 10 dB.(3) Fabricated an antenna according to the optimized result, and tested the performance of Vivaldi antenna. The results showed that the antenna gain was up to 9.8dB and beam width- reached 60 o at 5GHz. In addition, the antenna as a part of stroke detection system, can detect the location and size of the object, which confirmed the feasibility of improved particle swarm algorithm in practice.(4) Using particle swarm algorithm and HFSS to optimize and design microstrip antenna. Using the flexible material as the material of antenna in order to improve the wearable characteristic, and simulate bending strain, sweat or other external factors by changing the curved shape of the antenna and the dielectric constant to get the performance of antenna.