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The application of adaptive beamforming to biomedical ultrasound imaging has been an active research area in recent years. Adaptive beamforming techniques have the capability of achieving excellent resolution and sidelobe suppression, thus improving the quality of the ultrasound images. This quality improvement, however, comes at a high computational cost. The work presented in this thesis aims to answer the following basic question: Can we reduce the computational complexity of adaptive beamforming without a significant degradation of the image quality? Our objective is to explore a combination of low-complexity non-adaptive beamforming, such as the conventional Delay-and-Sum (DAS) method, with high-complexity adaptive beamforming, such as the standard Minimum-Variance Distortionless Response (MVDR) method implemented using the Generalized Sidelobe Canceller (GSC). Such a combination should have the lower computational complexity than adaptive beamforming, but it should also offer the image quality comparable to that obtained using adaptive beamforming. In addition to the adaptive GSC-based MVDR beamforming method, we also investigate the performance of the so-called Adaptive Single Snapshot Beamformer (ASSB), which is relatively unexplored in the ultrasound imaging literature. The main idea behind our approach to combining a non-adaptive beamformer with an adaptive one is based on the use of the data-dependent variable known as the coherence factor. The resulting hybrid beamforming method can be summarized as follows: For each input snapshot to be beamformed, calculate the corresponding coherence factor; if the coherence factor is below a certain threshold, use non-adaptive DAS beamforming, otherwise use adaptive (GSC-based or ASSB-based) beamforming. We have applied this simple switching scheme to the simulated B-mode ultrasound images of the 12-point and point-scatterer-cyst phantoms that are commonly used in the ultrasound imaging literature to evaluate the image quality. Our simulation results show that, in comparison to optimal high-complexity always-adaptive beamforming, our hybrid beamformer can yield significant computational savings that range from 59% to 99%, while maintaining the image quality (measured in terms of resolution and contrast) within a 5% degradation margin.
Adaptive heal-doming can significantly improve the image quality in biomedical ultrasound by reducing the clutter due to interfering signals arriving from undesired directions. Adaptive beamforming is computationally expensive, and the objective of this thesis is to expose and explore tradeoffs between computational complexity and quality of adaptive beamforming. We consider the conventional linearly constrained minimum variance (LCMV) adaptive beamformer, applied to B-mode ultrasound imaging, and study an alternative based on the well-known generalized sidelobe canceller (GSC) whose adaptation relies on unconstrained gradient-driven optimization. To our knowledge, this is the first time a GSC-based gradient-driven approach has been applied and evaluated in the context of ultrasound beamforming. As another alternative to the conventional LCMV method, we also propose and evaluate a simple idea of updating the beamformer's weight vector at a reduced rate. Both approaches have lead to significant computational savings, but they also sacrifice beamforming optimality. Our simulations show that despite suboptimal beamforming. the ultrasound image quality remains acceptable.
In the past decade, the application of adaptive beamforming methods to medical ultrasound imaging has become a field of increased interest, due to their ability to achieve superior ultrasound image quality. Such enhancements, however, come at a high computational cost. This thesis attempts to address the following simple question: Can we maintain a superior image quality while reducing the computational cost of adaptive beamforming? Our goal is to effectively combine low-complexity nonadaptive beamforming, such as the Delay-and-Sum (DAS) technique, with high-complexity adaptive beamforming, such as the Minimum variance Distortionless Response (MVDR) technique, implemented using the Generalized Sidelobe Canceller (GSC), to obtain high-quality images at low computational cost. We propose a simple two-pass beamforming scheme for that purpose. During the first pass, our scheme processes buffered input vectors using the inexpensive DAS method and computes the corresponding envelope. Based on that envelope information, selected outputs may be recomputed during the second pass (to improve beamforming performance) using the expensive GSC beamforming method. The purpose of the first pass is to identify which nonadaptively beamformed outputs can be spared from a heavy computational load of adaptive beamforming taking place in the second pass. We have evaluated our scheme using simulated ultrasound images of a 12-point phantom and a point-scatterer-cyst phantom, achieving substantial threshold-dependent computational savings without significant degradation in image resolution and contrast, compared to pure GSC beamforming.
Ultrasound medical imaging stands out among the other diagnostic imaging modalities for its patient-friendliness, high temporal resolution, low cost, and absence of ionizing radiation. On the other hand, it may still suffer from limited detail level, low signal-to-noise ratio, and narrow field-of-view. In the last decade, new beamforming and image reconstruction techniques have emerged which aim at improving resolution, contrast, and clutter suppression, especially in difficult-to-image patients. Nevertheless, achieving a higher image quality is of the utmost importance in diagnostic ultrasound medical imaging, and further developments are still indispensable. From this point of view, a crucial role can be played by novel beamforming techniques as well as by non-conventional image formation techniques (e.g., advanced transmission strategies, and compounding, coded, and harmonic imaging). This Special Issue includes novel contributions on both ultrasound beamforming and image formation techniques, particularly addressed at improving B-mode image quality and related diagnostic content. This indeed represents a hot topic in the ultrasound imaging community, and further active research in this field is expected, where many challenges still persist.
Ultrasound medical imaging stands out among the other diagnostic imaging modalities for its patient-friendliness, high temporal resolution, low cost, and absence of ionizing radiation. On the other hand, it may still suffer from limited detail level, low signal-to-noise ratio, and narrow field-of-view. In the last decade, new beamforming and image reconstruction techniques have emerged which aim at improving resolution, contrast, and clutter suppression, especially in difficult-to-image patients. Nevertheless, achieving a higher image quality is of the utmost importance in diagnostic ultrasound medical imaging, and further developments are still indispensable. From this point of view, a crucial role can be played by novel beamforming techniques as well as by non-conventional image formation techniques (e.g., advanced transmission strategies, and compounding, coded, and harmonic imaging). This Special Issue includes novel contributions on both ultrasound beamforming and image formation techniques, particularly addressed at improving B-mode image quality and related diagnostic content. This indeed represents a hot topic in the ultrasound imaging community, and further active research in this field is expected, where many challenges still persist.
This book focuses on how machine learning techniques can be used to analyze and make use of one particular category of behavioral biometrics known as the gait biometric. A comprehensive Ground Reaction Force (GRF)-based Gait Biometrics Recognition framework is proposed and validated by experiments. In addition, an in-depth analysis of existing recognition techniques that are best suited for performing footstep GRF-based person recognition is also proposed, as well as a comparison of feature extractors, normalizers, and classifiers configurations that were never directly compared with one another in any previous GRF recognition research. Finally, a detailed theoretical overview of many existing machine learning techniques is presented, leading to a proposal of two novel data processing techniques developed specifically for the purpose of gait biometric recognition using GRF. This book · introduces novel machine-learning-based temporal normalization techniques · bridges research gaps concerning the effect of footwear and stepping speed on footstep GRF-based person recognition · provides detailed discussions of key research challenges and open research issues in gait biometrics recognition · compares biometrics systems trained and tested with the same footwear against those trained and tested with different footwear
Although there are many books available on WSNs, most are low-level, introductory books. The few available for advanced readers fail to convey the breadth of knowledge required for those aiming to develop next-generation solutions for WSNs. Filling this void, Wireless Sensor Networks: From Theory to Applications supplies comprehensive coverage of WSNs. In order to provide the wide-ranging guidance required, the book brings together the contributions of domain experts working in the various subfields of WSNs worldwide. This edited volume examines recent advances in WSN technologies and considers the theoretical problems in WSN, including issues with monitoring, routing, and power control. It also details methodologies that can provide solutions to these problems. The book’s 25 chapters are divided into seven parts: Data Collection Physical Layer and Interfacing Routing and Transport Protocols Energy-Saving Approaches Mobile and Multimedia WSN Data Storage and Monitoring Applications The book examines applications of WSN across a range of fields, including health, military, transportation, and mining. Addressing the main challenges in applying WSNs across all phases of our life, it explains how WSNs can assist in community development. Complete with a list of references at the end of each chapter, this book is ideal for senior undergraduate and postgraduate students, researchers, scholars, academics, industrial researchers, and practicing engineers working on WSNs. The text assumes that readers possess a foundation in computer networks, wireless communication, and basic electronics.
Understand design principles of key advanced transmission technologies by means of trade-off analysis using a range of mathematical tools.