yes / The problem of parametric surface meshing for the purpose of design optimisation using finite element analysis is considered. Here the surface mesh is generated as a solution of a suitably posed boundary value problem implemented on a 2D parameter space. A robust meshing scheme is presented where an initial mesh is manipulated, with the aid of the 2D parameter space, so as to obtain a suitable surface triangulation. This meshing scheme can then be used to create suitable finite element meshes with which accurate design optimisations can be carried out.

With the advances in wireless communication systems, antennas with different shapes and design have achieved great demand and are desirable for many uses such as personal communication systems, and other applications involving wireless communication. This has resulted in different shapes and types of antenna design in order to achieve different antenna characteristic. One attractive approach to the design of antennas is to suppress or attenuate harmonic contents due to the non-linear operation of the Radio Frequency (RF) front end. The objectives of this work were to investigate, design and implement antennas for harmonic suppression with the aid of a genetic algorithm (GA). Several microstrip patch antennas were designed to operate at frequencies 1.0, 1.8 and 2.4 GHz respectively. The microstrip patch antenna with stub tuned microstrip lines was also employed at 1.0 and 1.8 GHz to meet the design objectives. A new sensing patch technique is introduced and applied in order to find the accepted power at harmonic frequencies. The evaluation of the measured power accepted at the antenna feed port was done using an electromagnetic (EM) simulator, Ansoft Designer, in terms of current distribution. A two sensors method is presented on one antenna prototype to estimate the accepted power at three frequencies. The computational method is based on an integral equation solver using adaptive surface meshing driven by a genetic algorithm. Several examples are demonstrated, including design of coaxially-fed, air-dielectric patch antennas implanted with shorting and folded walls. The characteristics of the antennas in terms of the impedance responses and far field radiation patterns are discussed. The results in terms of the radiation performance are addressed, and compared to measurements. The presented results of these antennas show a good impedance matching at the fundamental frequency with good suppression achieved at the second and third harmonic frequencies.

The main objective of this research is to design, model and implement several antenna geometries using electromagnetic band gap (EBG) material and a defected ground plane. Several antenna applications are addressed with the aim of improving performance, particularly the mutual coupling between the elements. The EBG structures have the unique capability to prevent or assist the propagation of electromagnetic waves in a specific band of frequencies, and have been incorporated here in antenna structures to improve patterns and reduce mutual coupling in multielement arrays. A neutralization technique and defected ground plane structures have also been investigated as alternative approaches, and may be more practical in real applications. A new Uni-planar Compact EBG (UC-EBG) formed from a compact unit cell was presented, giving a stop band in the 2.4 GHz WLAN range. Dual band forms of the neutralization and defected ground plane techniques have also been developed and measured. The recorded results for all antenna configurations show good improvement in terms of the mutual coupling effect. The MIMO antenna performance with EBG, neutralization and defected ground of several wireless communication applications were analysed and evaluated. The correlation coefficient, total active reflection coefficient (TARC), channel capacity and capacity loss of the array antenna were computed and the results compared to measurements with good agreement. In addition, a computational method combining Genetic Algorithm (GA) with surface meshing code for the analysis of a 2×2 antenna arrays on EBG was developed. Here the impedance matrix resulting from the meshing analysis is manipulated by the GA process in order to find the optimal antenna and EBG operated at 2.4 GHz with the goal of targeting a specific fitness function. Furthermore, an investigation of GA on 2×2 printed slot on DGS was also done.

Due to the natural, non-intrusive, easily collectible, widespread applicability, machine-based face recognition has received significant attention from the biometrics community over the past three decades. Compared with traditional appearance-based (2D) face recognition, shape-based (3D) face recognition is more stable to illumination variations, small head pose changes, and varying facial cosmetics. However, 3D face scans captured in unconstrained conditions may lead to various difficulties, such as non-rigid deformations caused by varying expressions, data missing due to self occlusions and external occlusions, as well as low-quality data as a result of some imperfections in the scanning technology. In order to deal with those difficulties and to be useful in real-world applications, in this thesis, we propose two 3D face recognition approaches: one is focusing on handling various expression changes, while the other one can recognize people in the presence of large facial expressions, occlusions and large pose various. In addition, we provide a provable and practical surface meshing algorithm for data-quality improvement. To deal with expression issue, we assume that different local facial region (e.g. nose, eyes) has different intra-expression/inter-expression shape variability, and thus has different importance. Based on this assumption, we design a learning strategy to find out the quantification importance of local facial regions in terms of their discriminating power. For facial description, we propose a novel shape descriptor by encoding the micro-structure of multi-channel facial normal information in multiple scales, namely, Multi-Scale and Multi-Component Local Normal Patterns (MSMC-LNP). It can comprehensively describe the local shape changes of 3D facial surfaces by a set of LNP histograms including both global and local cues. For face matching, Weighted Sparse Representation-based Classifier (W-SRC) is formulated based on the learned quantification importance and the LNP histograms. The proposed approach is evaluated on four databases: the FRGC v2.0, Bosphorus, BU-3DFE and 3D-TEC, including face scans in the presence of diverse expressions and action units, or several prototypical expressions with different intensities, or facial expression variations combine with strong facial similarities (i.e. identical twins). Extensive experimental results show that the proposed 3D face recognition approach with the use of discriminative facial descriptors can be able to deal with expression variations and perform quite accurately over all databases, and thereby has a good generalization ability. To deal with expression and data missing issues in an uniform framework, we propose a mesh-based registration free 3D face recognition approach based on a novel local facial shape descriptor and a multi-task sparse representation-based face matching process. [...]

En raison des naturelle, non-intrusive, facilement percevable caractéristiques, et une large diffusive applicabilité pour la criminalistique et de la sécurité, reconnaissance faciale basée sur la machine a reçu beaucoup d’attention de la communauté biométrie au cours des trois dernières décennies. Par rapport à la traditionnelle reconnaissance faciale basée sur le visage 2D, la reconnaissance faciale basé sur la forme 3D est plus stable aux variations d’éclairage; petite changements de tête pose, et variant cosmétiques pour le visage. Cependant, le visage 3D numérise capturé dans des conditions non-contraintes peut conduire à des difficultés diverses, comme des déformations non rigides provoquées par la variant expressions, les données manquantes en raison de l’auto-occlusion et des occlusions externes, ainsi que des données de faible qualité en raison de certaines imperfections de la technologie de numérisation. Pour régler ces difficultés et d’améliorer les applications du monde réel, dans cette thèse, nous proposons deux approches de 3D reconnaissance faciale: l’un se concentre sur le handling de divers changements d’expression, l’autre peut reconnaître les gens à la situation de présence d’un grand les expressions facial, des occlusions et des grands pose divers. En outre, nous fournissons une surface prouvable et pratique algorithme de surface maillage pour l’amélioration de la qualité de données. Pour faire face aux problème d’expression, nous supposons que la variabilité des formes de intra-expression/inter-expression de la faciale local région différent (e. g., nez, yeux) est différent, et a donc une importance niveau différente. Sur la base de cette hypothèse, nous concevons une stratégie d’apprentissage pour découvrir l’importance de la quantification de régions faciales locales en fonction de leur énergie discriminant. Pour une description du visage, nous proposons un nouveau descripteur pour coder la microstructure du multi- canal d’information normale du visage dans multiples échelles, à savoir, Multi-Scale and Multi-Component Local Normal Patterns (MSMC-LNP). On peut globalement décrire les changements de forme locale de 3D surfaces faciales par un ensemble d’histogrammes LNP y compris les indices globaux et locaux. Pour le visage correspondant, Weighted Sparse Representation-based Classifier (W-SRC) est formulée sur la base de l’importance de la quantification appris et les histogrammes LNP. L’approche proposée est évaluée sur quatre bases de données: le FRGC v2. 0, Bosphore, BU- 3DFE et 3D -TEC, y compris les scans du visage en présence de diverses expressions et des unités d’action, ou de plusieurs expressions prototypiques avec des intensités différentes, ou des variations d’expression du visage combinée avec de fortes similitudes faciales (c.à.d. jumeaux identiques). Résultats expérimentaux étendus montrent que l’approche de reconnaissance de 3D visage proposé avec l’utilisation de descripteurs discriminants du visage peut régler les variations d’expression et d’effectuer avec assez de précision sur toutes les bases de données, et a ainsi une bonne capacité de généralisation. Pour faire face à l’expression et problème des données manquantes dans un cadre uniforme, nous proposons une approche sur le sans-enregistrement maillage-basé reconnaissance du 3D visage basé sur un nouveau local descripteur de la forme du visage et un correspondance processus d’clairsemée représentation du visage basée multi- tâche. [...] / Due to the natural, non-intrusive, easily collectible, widespread applicability, machine-based face recognition has received significant attention from the biometrics community over the past three decades. Compared with traditional appearance-based (2D) face recognition, shape-based (3D) face recognition is more stable to illumination variations, small head pose changes, and varying facial cosmetics. However, 3D face scans captured in unconstrained conditions may lead to various difficulties, such as non-rigid deformations caused by varying expressions, data missing due to self occlusions and external occlusions, as well as low-quality data as a result of some imperfections in the scanning technology. In order to deal with those difficulties and to be useful in real-world applications, in this thesis, we propose two 3D face recognition approaches: one is focusing on handling various expression changes, while the other one can recognize people in the presence of large facial expressions, occlusions and large pose various. In addition, we provide a provable and practical surface meshing algorithm for data-quality improvement. To deal with expression issue, we assume that different local facial region (e.g. nose, eyes) has different intra-expression/inter-expression shape variability, and thus has different importance. Based on this assumption, we design a learning strategy to find out the quantification importance of local facial regions in terms of their discriminating power. For facial description, we propose a novel shape descriptor by encoding the micro-structure of multi-channel facial normal information in multiple scales, namely, Multi-Scale and Multi-Component Local Normal Patterns (MSMC-LNP). It can comprehensively describe the local shape changes of 3D facial surfaces by a set of LNP histograms including both global and local cues. For face matching, Weighted Sparse Representation-based Classifier (W-SRC) is formulated based on the learned quantification importance and the LNP histograms. The proposed approach is evaluated on four databases: the FRGC v2.0, Bosphorus, BU-3DFE and 3D-TEC, including face scans in the presence of diverse expressions and action units, or several prototypical expressions with different intensities, or facial expression variations combine with strong facial similarities (i.e. identical twins). Extensive experimental results show that the proposed 3D face recognition approach with the use of discriminative facial descriptors can be able to deal with expression variations and perform quite accurately over all databases, and thereby has a good generalization ability. To deal with expression and data missing issues in an uniform framework, we propose a mesh-based registration free 3D face recognition approach based on a novel local facial shape descriptor and a multi-task sparse representation-based face matching process. [...]

With the advances in wireless communication systems, antennas with different shapes and design have achieved great demand and are desirable for many uses such as personal communication systems, and other applications involving wireless communication. This has resulted in different shapes and types of antenna design in order to achieve different antenna characteristic. One attractive approach to the design of antennas is to suppress or attenuate harmonic contents due to the non-linear operation of the Radio Frequency (RF) front end. The objectives of this work were to investigate, design and implement antennas for harmonic suppression with the aid of a genetic algorithm (GA). Several microstrip patch antennas were designed to operate at frequencies 1.0, 1.8 and 2.4 GHz respectively. The microstrip patch antenna with stub tuned microstrip lines was also employed at 1.0 and 1.8 GHz to meet the design objectives. A new sensing patch technique is introduced and applied in order to find the accepted power at harmonic frequencies. The evaluation of the measured power accepted at the antenna feed port was done using an electromagnetic (EM) simulator, Ansoft Designer, in terms of current distribution. A two sensors method is presented on one antenna prototype to estimate the accepted power at three frequencies. The computational method is based on an integral equation solver using adaptive surface meshing driven by a genetic algorithm. Several examples are demonstrated, including design of coaxially-fed, air-dielectric patch antennas implanted with shorting and folded walls. The characteristics of the antennas in terms of the impedance responses and far field radiation patterns are discussed. The results in terms of the radiation performance are addressed, and compared to measurements. The presented results of these antennas show a good impedance matching at the fundamental frequency with good suppression achieved at the second and third harmonic frequencies. / Home government

The main objective of this research is to design, model and implement several antenna geometries using electromagnetic band gap (EBG) material and a defected ground plane. Several antenna applications are addressed with the aim of improving performance, particularly the mutual coupling between the elements. The EBG structures have the unique capability to prevent or assist the propagation of electromagnetic waves in a specific band of frequencies, and have been incorporated here in antenna structures to improve patterns and reduce mutual coupling in multielement arrays. A neutralization technique and defected ground plane structures have also been investigated as alternative approaches, and may be more practical in real applications. A new Uni-planar Compact EBG (UC-EBG) formed from a compact unit cell was presented, giving a stop band in the 2.4 GHz WLAN range. Dual band forms of the neutralization and defected ground plane techniques have also been developed and measured. The recorded results for all antenna configurations show good improvement in terms of the mutual coupling effect. The MIMO antenna performance with EBG, neutralization and defected ground of several wireless communication applications were analysed and evaluated. The correlation coefficient, total active reflection coefficient (TARC), channel capacity and capacity loss of the array antenna were computed and the results compared to measurements with good agreement. In addition, a computational method combining Genetic Algorithm (GA) with surface meshing code for the analysis of a 2×2 antenna arrays on EBG was developed. Here the impedance matrix resulting from the meshing analysis is manipulated by the GA process in order to find the optimal antenna and EBG operated at 2.4 GHz with the goal of targeting a specific fitness function. Furthermore, an investigation of GA on 2×2 printed slot on DGS was also done. / Ministry of Higher Education Malaysia and Universiti Tun Hussein Onn Malaysia (UTHM)