A new approach in solving illumination and facial expression problems for face recognition

In this paper, a novel dual optimal multiband features (DOMF) method is presented to increase the robustness of face recognition system to illumination and facial expression variations.The wavelet packet transform first decomposes image into low-, mid- and high-frequency subbands and the multiband feature fusion technique is incorporated to select the subbands that are invariant to illumination and expression variation separately.These subbands form the optimal feature sets.Parallel radial basis function neural networks are employed to classify these feature sets.The scores generated by the neural networks are combined by an adaptive fusion mechanism where the level of illumination variations of the testing image is estimated and the weights are assigned to the scores accordingly.The experimental results show that DOMF outperforms other algorithms and also achieves promising performance on illumination and facial expression variation conditions

Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems

Increasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300 GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including security sensing, industrial packaging, medical imaging, and non-destructive testing. Traditional methods for perception and imaging are challenged by novel data-driven algorithms that offer improved resolution, localization, and detection rates. Over the past decade, deep learning technology has garnered substantial popularity, particularly in perception and computer vision applications. Whereas conventional signal processing techniques are more easily generalized to various applications, hybrid approaches where signal processing and learning-based algorithms are interleaved pose a promising compromise between performance and generalizability. Furthermore, such hybrid algorithms improve model training by leveraging the known characteristics of radio frequency (RF) waveforms, thus yielding more efficiently trained deep learning algorithms and offering higher performance than conventional methods. This dissertation introduces novel hybrid-learning algorithms for improved mmWave imaging systems applicable to a host of problems in perception and sensing. Various problem spaces are explored, including static and dynamic gesture classification; precise hand localization for human computer interaction; high-resolution near-field mmWave imaging using forward synthetic aperture radar (SAR); SAR under irregular scanning geometries; mmWave image super-resolution using deep neural network (DNN) and Vision Transformer (ViT) architectures; and data-level multiband radar fusion using a novel hybrid-learning architecture. Furthermore, we introduce several novel approaches for deep learning model training and dataset synthesis.Comment: PhD Dissertation Submitted to UTD ECE Departmen

Vision Sensors and Edge Detection

Vision Sensors and Edge Detection book reflects a selection of recent developments within the area of vision sensors and edge detection. There are two sections in this book. The first section presents vision sensors with applications to panoramic vision sensors, wireless vision sensors, and automated vision sensor inspection, and the second one shows image processing techniques, such as, image measurements, image transformations, filtering, and parallel computing

Artificial neural network-statistical approach for PET volume analysis and classification

Copyright © 2012 The Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid the clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for oncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural networks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used in this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis by providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC image quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall cell lung cancer and pharyngolaryngeal squamous cell carcinoma. The proposed analysis methodology of clinical oncological PET data has shown promising results and can successfully classify and quantify malignant lesions.This study was supported by the Swiss National Science Foundation under Grant SNSF 31003A-125246, Geneva Cancer League, and the Indo Swiss Joint Research Programme ISJRP 138866. This article is made available through the Brunel Open Access Publishing Fund

The application of remote sensing techniques: Technical and methodological issues

Capabilities and limitations of modern imaging electromagnetic sensor systems are outlined, and the products of such systems are compared with those of the traditional aerial photographic system. Focus is given to the interface between the rapidly developing remote sensing technology and the information needs of operational agencies, and communication gaps are shown to retard early adoption of the technology by these agencies. An assessment is made of the current status of imaging remote sensors and their potential for the future. Public sources of remote sensor data and several cost comparisons are included

Study and miniaturisation of antennas for ultra wideband communication systems

PhDWireless communications have been growing with an astonishing rate over the past few years and wireless terminals for future applications are required to provide diverse services. This rising demand prompts the needs for antennas able to cover multiple bandwidths or an ultrawide bandwidth for various systems. Since the release by the Federal Communications Commission (FCC) of a bandwidth of 7.5 GHz (from 3.1 GHz to 10.6 GHz) for ultra wideband (UWB) wireless communications, UWB has been rapidly evolving as a potential wireless technology and UWB antennas have consequently drawn more and more attention from both academia and industries worldwide. Unlike traditional narrow band antennas, design and analysis of UWB antennas are facing more challenges and difficulties. A competent UWB antenna should be capable of operating over an ultra wide bandwidth as assigned by the FCC. At the same time, a small and compact antenna size is highly desired, due to the integration requirement of entire UWB systems. Another key requirement of UWB antennas is the good time domain behaviour, i.e. a good impulse response with minimal distortion. This thesis focuses on UWB antenna miniaturisation and analysis. Studies have been undertaken to cover the aspects of UWB fundamentals and antenna theory. Extensive investigations are also conducted on three different types of miniaturised UWB antennas. 5 The first type of miniaturised UWB antenna studied in this thesis is the loaded orthogonal half disc monopole antenna. An inductive load is introduced to broaden the impedance bandwidth as well as the pattern bandwidth, in other words, an equivalent size reduction is realised. The second type of miniaturised UWB antenna is the printed half disc monopole antenna. By simply halving the original antenna and tuning the width of the coplanar ground plane, a significant more than 50% size reduction is achieved. The third type of miniaturised UWB antenna is the printed quasi-self-complementary antenna. By exploiting a quasi-self-complementary structure and a built-in matching section, a small and compact antenna dimension is achieved. The performances and characteristics of the three types of miniaturised UWB antennas are studied both numerically and experimentally and the design parameters for achieving optimal operation of the antennas are also analysed extensively in order to understand the antenna operations. Also, time domain performance of the Coplanar Waveguide (CPW)-fed disc monopole antenna is examined in this thesis to demonstrate the importance of time domain study on UWB antennas. Over the past few years of my PhD study, I feel honoured and lucky to work with some of the most prestigious researchers in the Department of Electronic Engineering, Queen Mary, University of London. I would like to show my most cordial gratitude to those who have been helping me during the past few years. There would be no any progress without their generous and sincere support. First of all, I would like to thank my supervisors Professor Clive Parini and Professor Xiaodong Chen, for their kind supervision and encouragement. I am impressed by their notable academic background and profound understanding of the subjects, which have proved to be immense benefits to me. It has been my great pleasure and honour to be under their supervision and work with them. Second of all, I would like to thank Mr John Dupuy for his help in the fabrication and measurement of antennas I have designed during my PhD study. Also, a special acknowledgement goes to all of the staff for all the assistance throughout my graduate program