Development of Novel Techniques to Study Nonlinear Active Noise Control

Active noise control has been a field of growing interest over the past few decades. The challenges thrown by active noise control have attracted the notice of the scientific community to engage them in intense level of research. Cancellation of acoustic noise electronically in a simple and efficient way is the vital merit of the active noise control system. A detailed study about existing strategies for active noise control has been undertaken in the present work. This study has given an insight regarding various factors influencing performance of modern active noise control systems. The development of new training algorithms and structures for active noise control are active fields of research which are exploiting the benefits of different signal processing and soft- computing techniques. The nonlinearity contributed by environment and various components of active noise control system greatly affects the ultimate performance of an active noise canceller. This fact motivated to pursue the research work in developing novel architectures and algorithms to address the issues of nonlinear active noise control. One of the primary focus of the work is the application of artificial neural network to effectively combat the problem of active noise control. This is because artificial neural networks are inherently nonlinear processors and possesses capabilities of universal approximation and thus are well suited to exhibit high performance when used in nonlinear active noise control. The present work contributed significantly in designing efficient nonlinear active noise canceller based on neural network platform. Novel neural filtered-x least mean square and neural filtered-e least mean square algorithms are proposed for nonlinear active noise control taking into consideration the nonlinear secondary path. Employing Legendre neural network led the development of a set new adaptive algorithms such as Legendre filtered-x least mean square, Legendre vi filtered-e least mean square, Legendre filtered-x recursive least square and fast Legendre filtered-x least mean square algorithms. The proposed algorithms outperformed the existing standard algorithms for nonlinear active noise control in terms of steady state mean square error with reduced computational complexity. Efficient frequency domain implementation of some the proposed algorithms have been undertaken to exploit its benefits. Exhaustive simulation studies carried out have established the efficacy of the proposed architectures and algorithms

Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area

Development of novel hybrid method and geometrical configuration-based active noise control system for circular cylinder and slat noise prediction and reduction applications

This thesis presents a study about the application of a geometrical configuration-based feedforward adaptive active noise control (ANC) system in the low-frequency range of flow-induced (aeroacoustics) noise cancellation and the investigation on the effects of different geometrical configurations on the cancellation performance in the presence of the residual noise signal magnitude (in decibel) or the average amount of cancellation (in decibel). The first motivation is that according to the literature review, the passive flow control is limited in the practical consideration and the active flow control performs better than the passive flow control, especially for the low-frequency range. Consider the principle of the active flow control is the same as the ANC technique, therefore, it is feasible to apply the ANC technique in cancelling the low-frequency range of the far-field (aeroacoustics) noise, which provides instructions on the future practical experiments. The second motivation is that we want to explore the effects of different geometrical configurations on the cancellation performance and it provides instructions on the implementation in future practical experiments. To predict the far-field (aeroacoustics) noise, the computational fluid dynamics (CFD) and the Ffowcs Williams and Hawkings (FW-H) equations are used separately for unsteady flow calculation and far-field (aeroacoustics) noise prediction. The proposed ANC system is used for the low-frequency range of the far-field (aeroacoustics) noise cancellation. Soft computing techniques and evolutionary-computing-based techniques are employed as the parameter adjustment mechanism to deal with nonlinearities existed in microphones and loudspeakers. The case study about the landing gear noise cancellation in the two-dimensional computational domain is completed. Simulation results validate the accuracy of the obtained acoustic spectrum with reasonable error because of the mesh resolution and computer capacity. It is observed that the two-dimensional approach can only predict discrete values of sound pressure level (SPL) associated with the fundamental frequency (Strouhal number) and its harmonics. Cancellation results demonstrate the cancellation capability of the proposed ANC system for the low-frequency range of far-field (aeroacoustics) noise and reflect that within the reasonable physical distance range, the cancellation performance will be better when the detector is placed closer to the secondary source in comparison with the primary source. This conclusion is the main innovative contribution of this thesis and it provides useful instructions on future practical experiments, but detailed physical distance values must be dependent on individual cases

New Approaches in Automation and Robotics

The book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book

Collaborative adaptive exponential linear-in-the-parameters nonlinear filters

by Vinal Patel, Somanath Pradhan and Nithin V. Georg

Feature Aggregation in Joint Sound Classification and Localization Neural Networks

This study addresses the application of deep learning techniques in joint sound signal classification and localization networks. Current state-of-the-art sound source localization deep learning networks lack feature aggregation within their architecture. Feature aggregation enhances model performance by enabling the consolidation of information from different feature scales, thereby improving feature robustness and invariance. This is particularly important in SSL networks, which must differentiate direct and indirect acoustic signals. To address this gap, we adapt feature aggregation techniques from computer vision neural networks to signal detection neural networks. Additionally, we propose the Scale Encoding Network (SEN) for feature aggregation to encode features from various scales, compressing the network for more computationally efficient aggregation. To evaluate the efficacy of feature aggregation in SSL networks, we integrated the following computer vision feature aggregation sub-architectures into a SSL control architecture: Path Aggregation Network (PANet), Weighted Bi-directional Feature Pyramid Network (BiFPN), and SEN. These sub-architectures were evaluated using two metrics for signal classification and two metrics for direction-of-arrival regression. PANet and BiFPN are established aggregators in computer vision models, while the proposed SEN is a more compact aggregator. The results suggest that models incorporating feature aggregations outperformed the control model, the Sound Event Localization and Detection network (SELDnet), in both sound signal classification and localization. The feature aggregation techniques enhance the performance of sound detection neural networks, particularly in direction-of-arrival regression

Nonlinear Active Noise Control Using Adaptive Wavelet Filters

This paper deals with nonlinear active noise control using adaptive wavelet filters. The ability of wavelets in signal reconstruction and function approximation make them appealing for black box system identification. Moreover, the intrinsic similarity between wavelet filters and noise/vibration signals implies that better approximation of these signals can be achieved by employing wavelet filters. Here, a new simple structure for using in active noise control system is proposed comprises a nonlinear static mapping cascaded with an IIR filter to take care of the dynamics of the system. With this strategy, one can avoid using multi-dimensional wavelet networks and thus eliminate curse of dimensionality. The performance of the proposed ANC system is examined for typical linear/nonlinear cases. The simulation results demonstrate superior performance of this method in terms of fast convergence rate and noise attenuation as well as computational complexity reduction while avoiding curse of dimensionality

Towards a Universal Modeling and Control Framework for Soft Robots

Traditional rigid-bodied robots are designed for speed, precision, and repeatability. These traits make them well suited for highly structured industrial environments, but poorly suited for the unstructured environments in which humans typically operate. Soft robots are well suited for unstructured human environments because they them to can safely interact with delicate objects, absorb impacts without damage, and passively adapt their shape to their surroundings. This makes them ideal for applications that require safe robot-human interaction, but also presents modeling and control challenges. Unlike rigid-bodied robots, soft robots exhibit continuous deformation and coupling between structure and actuation and these behaviors are not readily captured by traditional robot modeling and control techniques except under restrictive simplifying assumptions. The contribution of this work is a modeling and control framework tailored specifically to soft robots. It consists of two distinct modeling approaches. The first is a physics-based static modeling approach for systems of fluid-driven actuators. This approach leverages geometric relationships and conservation of energy to derive models that are simple and accurate enough to inform the design of soft robots, but not accurate enough to inform their control. The second is a data-driven dynamical modeling approach for arbitrary (soft) robotic systems. This approach leverages Koopman operator theory to construct models that are accurate and computationally efficient enough to be integrated into closed-loop optimal control schemes. The proposed framework is applied to several real-world soft robotic systems, enabling the successful completion of control tasks such as trajectory following and manipulating objects of unknown mass. Since the framework is not robot specific, it has the potential to become the dominant paradigm for the modeling and control of soft robots and lead to their more widespread adoption.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163062/1/bruderd_1.pd