PhD Student, Macquarie University
Design a compact and planar satellite antenna to replace the current parabolic antennas.
My PhD has been designed to have a real impact, as it is dealing with a real issue in the electromagnetics field. There is a crucial need for inexpensive and efficient passive devices that can provide desired EM properties in conjunction with high-gain antennas like Resonant Cavity antennas (RCAs). A full accommodation of this necessity would be a breakthrough in the field and will introduce applications which can be considered as 21st century technologies. Low-cost and reliable Meta-surfaces used with planar RCAs would make the bulky parabolic antennas redundant and satellite communication would be far more accessible, regardless of terrestrial position. Replacing reflector-shaped dishes with planar relatively small antennas composed of Meta-surfaces and RCAs will make a reliable satellite TV reception available even for moving vehicles, opening up a huge market. Such a mechanism can provide the internet through satellites, which is an emerging multibillion-dollar market which can remove inaccessibility to the internet in the remote or less-developed areas, specially in Africa, which is consistent with two of the United Nation's key issues (Development of Africa and Human rights). It should be noted that an specialized electromagnetic optimization algorithm based on particle swarm intelligence has been implemented for designing passive Meta-surfaces.
Abstract: The selection of swarm leaders (i.e., the personal best and global best), is important in the design of a multiobjective particle swarm optimization (MOPSO) algorithm. Such leaders are expected to effectively guide the swarm to approach the true Pareto optimal front. In this paper, we present a novel external archive-guided MOPSO algorithm (AgMOPSO), where the leaders for velocity update are all selected from the external archive. In our algorithm, multiobjective optimization problems (MOPs) are transformed into a set of subproblems using a decomposition approach, and then each particle is assigned accordingly to optimize each subproblem. A novel archive-guided velocity update method is designed to guide the swarm for exploration, and the external archive is also evolved using an immune-based evolutionary strategy. These proposed approaches speed up the convergence of AgMOPSO. The experimental results fully demonstrate the superiority of our proposed AgMOPSO in solving most of the test problems adopted, in terms of two commonly used performance measures. Moreover, the effectiveness of our proposed archive-guided velocity update method and immune-based evolutionary strategy is also experimentally validated on more than 30 test MOPs.
Pub.: 15 Jun '17, Pinned: 26 Aug '17
Abstract: Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.
Pub.: 21 Jun '17, Pinned: 26 Aug '17
Abstract: The number of patients with Alzheimer's disease is increasing rapidly every year. Scholars often use computer vision and machine learning methods to develop an automatic diagnosis system.In this study, we developed a novel machine learning system that can make diagnoses automatically from brain magnetic resonance images.First, the brain imaging was processed, including skull stripping and spatial normalization. Second, one axial slice was selected from the volumetric image, and stationary wavelet entropy (SWE) was done to extract the texture features. Third, a single-hidden-layer neural network was used as the classifier. Finally, a predator-prey particle swarm optimization was proposed to train the weights and biases of the classifier.Our method used 4-level decomposition and yielded 13 SWE features. The classification yielded an overall accuracy of 92.73±1.03%, a sensitivity of 92.69±1.29%, and a specificity of 92.78±1.51%. The area under the curve is 0.95±0.02. Additionally, this method only cost 0.88 s to identify a subject in online stage, after its volumetric image is preprocessed.In terms of classification performance, our method performs better than 10 state-of-the-art approaches and the performance of human observers. Therefore, this proposed method is effective in the detection of Alzheimer's disease.
Pub.: 22 Jul '17, Pinned: 26 Aug '17
Abstract: This article presents a new methodology allowing to design a planar electromagnetic band-pap antenna (choice of theebg material, sizing, feeding). This kind of antenna has already been presented.ebg antennas are very thin (compared with parabolic reflectors) high gain antennas. This article makes easier the realization ofebg antennas whatever the operating frequency, the gain, or still the bandwidth. A study about the sensitivity of the material properties on the performances is also presented. Finaly two exemples using this design method are described.
Pub.: 01 Mar '04, Pinned: 26 Aug '17
Abstract: A low profile dual band resonant cavity antenna that incorporates double sided partially reflective surface (PRS) with complementary layers is presented here. The PRS is formed by printing periodic array of complementary metallic square loops on the opposite sides of the dielectric material. The PRS has been used as a superstrate, placed above the radiating microstrip patch. This PRS acts as a dual band matching section between the microstrip patch and free space, hence resulting in dual resonance. The proposed structure has been analyzed using equivalent circuit model. Parametric analysis of the sensitive structural parameters has also been discussed. To validate the design, the simulation analysis and experimental results obtained from a prototype operating at 8.9 and 9.4 GHz are presented. The measured gain at the two frequencies is 10.2 and 8.5 dBi, respectively. The overall size of the antenna is 1.78λ × 1.78λ × 0.09 λ with λ corresponding to 8.9 GHz.
Pub.: 01 Nov '16, Pinned: 26 Aug '17
Abstract: The resonant cavity antenna (RCA) is a class of widely used high gain antennas, but usually suffers from narrow impedance bandwidth owing to its strong resonant property, as well as relatively low aperture efficiency because of its non-uniform electromagnetic (EM) field distribution on the aperture. This article explores enhancing the RCA's impedance bandwidth and aperture efficiency by designing a non-uniform metamaterial inspired superstrate, on which the metal patches vary their sizes with respect to their distances to the superstrate's center. After optimized by the Genetic Algorithm, the proposed RCA is designed, fabricated and tested. Measured results agree well with simulated ones and show that in comparison with a RCA with a uniform metamaterial inspired superstrate, this work significantly improves the |S11| < −10 dB impedance bandwidth from 2.1% to 6.1%, the gain at the working frequency 10 GHz from 19.07 dBi to 20.55 dBi, and correspondingly the aperture efficiency from 50.5% to 71%. A further analysis estimates that due to the non-uniform metamaterial inspired superstrate, a more homogeneous distribution for both the amplitude and phase of the EM field is observed on the superstrate's aperture.
Pub.: 12 Apr '17, Pinned: 26 Aug '17
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