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. / Graduate

The speech of a person speaking in a noisy environment can be enhanced through electronic beamforming using spatially distributed microphones. As this approach demands precise information about the microphone locations, its application is limited in places where microphones must be placed quickly or changed on a regular basis. Highly precise calibration or measurement process can be tedious and time consuming. In order to understand tolerable limits on the calibration process, the impact of microphone position error on the intelligibility is examined. Analytical expressions are derived by modeling the microphone position errors as a zero mean uniform distribution. Experiments and simulations were performed to show relationships between precision of the microphone location measurement and loss in intelligibility. A variety of microphone array configurations and distracting sources (other interfering speech and white noise) are considered. For speech near the threshold of intelligibility, the results show that microphone position errors with standard deviations less than 1.5cm can limit losses in intelligibility to within 10% of the maximum (perfect microphone placement) for all the microphone distributions examined. Of different array distributions experimented, the linear array tends to be more vulnerable whereas the non-uniform 3D array showed a robust performance to positional errors.

This master's thesis is focused on the noise source identification using microphone arrays and beamforming as the signal processing method. It describes parts of such a system and provides a comparison with other systems that serve a similar purpose (eg. NAH). Various types of microphone arrays are mentioned with their influence on the resulting ability to identify the noise source. We are further focusing on Delay-And-Sum technology, on which we are explaining the basic principles and constraints of beamforming. The practical part describes the implementation of the DAS method in MATLAB and C language, the specific structures of built microphone arrays and assembly of complete systems capable of identifying sources of noise. These systems were tested by performing a practical experiment. Achievements in the form of distribution maps of acoustic energy in the focused space are interpreted in the last chapter.

Typical ultrafast plane-wave ultrasound imaging involves: 1) insonifying the medium with several plane-wave pulses emitted at different angles by a linear transducer array, 2) sampling the returning echo signals, after each plane-wave emission, with the same transducer array, 3) beamforming the recorded angle-specific raw data frames, and 4) compounding the beamformed data frames over all angles to form a final image. This thesis attempts to address the following question: Given a set of available plane-wave emission angles, which ones should we select for acquisition (i.e., which angle-specific raw data frames should we sample), to achieve adequate image quality at low cost associated with both sampling and computation? We propose a simple similarity-driven angle selection scheme and evaluate its several variants that rely on user-specified similarity measurement thresholds guiding the recursive angle selection process. Our results show that the proposed scheme has a low computational overhead and can yield significant savings in terms of the amount of sampled raw data. / Graduate

The goal of this master’s thesis is to explore the possibilities of multichannel localization of acoustic signal sources and their following application on a real signal localization and separation, using Beamforming methods. During this thesis two beamforming methods were selected, namely Delay and Sum a Constant Directivity Beamforming - Circular Arrays, and were applicated on real environment signals using two microphone arrays’ geometries ULA (Uniform linear array) and UCA (Uniform Circular array).

Biological signal processing can help us gain knowledge about biological complexity, as well as using this knowledge to engineer better systems. Three areas are identified as critical to understanding biology: 1) understanding DNA, 2) examining the overall biological function and 3) evaluating these systems in environmental (ie: turbulent) conditions. DNA is investigated for coding structure and redundancy, and a new tandem repeat region, an indicator of a neurodegenerative disease, is discovered. The linear algebraic framework can be used for further analysis and techniques. The work illustrates how signal processing is a tool to reverse engineer biological systems, and how our better understanding of biology can improve engineering designs. Then, the way a single-cell mobilizes in response to a chemical gradient, known as chemotaxis, is examined. Inspiration from receptor clustering in chemotaxis combined with a Hebbian learning method is shown to improve a gradient-source (chemical/thermal) localization algorithm. The algorithm is implemented, and its performance is evaluated in diffusive and turbulent environments. We then show that sensor cross-correlation can be used in solving chemical localization in difficult turbulent scenarios. This leads into future techniques which can be designed for gradient source tracking. These techniques pave the way for use of biologically-inspired sensor networks in chemical localization.