5 research outputs found
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Radio wave imaging using Ultra-Wide Band Spectrum Antennas for Near-Field Applications. Design, Development, and Measurements of Ultra-Wideband Antenna for Microwave Near-Field Imaging Applications by applying Optimisation Algorithms
The emergence of Ultra-wideband (UWB) technology application has yielded tremendous and vital impacts in the field of microwave wireless communications. These applications include military radar imaging, security screening, and tumour detection, especially for early detection of breast cancer. These indicators have stimulated and inspired many researchers to make the best use of this promising technology.
UWB technology challenges such as antenna design, the problem of imaging reconstruction techniques, challenges of severe signal attenuation and dispersion in high loss material. Others are lengthy computational time demand and large computer memory requirements are prevalent constraints that need to be tackled especially in a large scale and complex computational electromagnetic analysis. In this regard, it is necessary to find out recently developed optimisation techniques that can provide solutions to these problems.
In this thesis, designing, optimisation, development, measurement, and analysis of UWB antennas for near-field microwave imaging applications are considered. This technology emulates the same concept of surface penetrating radar operating in various forms of the UWB spectrum. The initial design of UWB monopole antennas, including T-slots, rectangular slots, and hexagonal slots on a circular radiating patch, was explicitly implemented for medical imaging applications to cover the UWB frequency ranging from 3.1 GHz to 10.6 GHz.
Based on this concept, a new bow-tie and Vivaldi UWB antennas were designed for a through-the-wall imaging application. The new antennas were designed to cover a spectrum on a lower frequency ranging from 1 GHz - 4 GHz to ease the high wall losses that will be encountered when using a higher frequency range and to guarantee deeper penetration of the electromagnetic wave. Finally, both simulated and calculated results of the designed, optimised antennas indicate excellent agreement with improved performance in terms of return loss, gain, radiation pattern, and fidelity over the entire UWB frequency. These breakthroughs provided reduced computational time and computer memory requirement for useful, efficient, reliable, and compact sensors for imaging applications, including security and breast cancer detection, thereby saving more lives.Tertiary Education Trust Fund (TET Fund)
Supported by the Nigerian Defence Academy (NDA
A Collective Neurodynamic Approach to Constrained Global Optimization
© 2016 IEEE. Global optimization is a long-lasting research topic in the field of optimization, posting many challenging theoretic and computational issues. This paper presents a novel collective neurodynamic method for solving constrained global optimization problems. At first, a one-layer recurrent neural network (RNN) is presented for searching the Karush-Kuhn-Tucker points of the optimization problem under study. Next, a collective neuroydnamic optimization approach is developed by emulating the paradigm of brainstorming. Multiple RNNs are exploited cooperatively to search for the global optimal solutions in a framework of particle swarm optimization. Each RNN carries out a precise local search and converges to a candidate solution according to its own neurodynamics. The neuronal state of each neural network is repetitively reset by exchanging historical information of each individual network and the entire group. Wavelet mutation is performed to avoid prematurity, add diversity, and promote global convergence. It is proved in the framework of stochastic optimization that the proposed collective neurodynamic approach is capable of computing the global optimal solutions with probability one provided that a sufficiently large number of neural networks are utilized. The essence of the collective neurodynamic optimization approach lies in its potential to solve constrained global optimization problems in real time. The effectiveness and characteristics of the proposed approach are illustrated by using benchmark optimization problems