Deep Learning-Based Wave Digital Modeling of Rate-Dependent Hysteretic Nonlinearities for Virtual Analog Applications

Electromagnetic components greatly contribute to the peculiar timbre of analog audio gear. Indeed, distortion effects due to the nonlinear behavior of magnetic materials are known to play an important role in enriching the harmonic content of an audio signal. However, despite the abundant research that has been devoted to the characterization of nonlinearities in the context of virtual analog modeling over the years, the discrete-time simulation of circuits exhibiting rate-dependent hysteretic phenomena remains an open challenge. In this article, we present a novel data-driven approach for the wave digital modeling of rate-dependent hysteresis using recurrent neural networks (RNNs). Thanks to the modularity of wave digital filters, we are able to locally characterize the wave scattering relations of a hysteretic reluctance by encapsulating an RNN-based model into a single one-port wave digital block. Hence, we successfully apply the proposed methodology to the emulation of the output stage of a vacuum-tube guitar amplifier featuring a nonlinear transformer

Behavioural simulation of mixed analogue/digital circuits.

Continuing improvements in integrated circuit technology have made possible the implementation of complex electronic systems on a single chip. This often requires both analogue and digital signal processing. It is essential to simulate such IC's during the design process to detect errors at an early stage. Unfortunately, the simulators that are currently available are not well-suited to large mixed-signal circuits. This thesis describes the design and development of a new methodology for simulating analogue and digital components in a single, integrated environment. The methodology represents components as behavioural models that are more efficient than the circuit models used in conventional simulators. The signals that flow between models are all represented as piecewise-linear (PWL) waveforms. Since models representing digital and analogue components use the same format to represent their signals, they can be directly connected together. An object-oriented approach was used to create a class hierarchy to implement the component models. This supports rapid development of new models since all models are derived from a common base class and inherit the methods and attributes defined in their parentc lassesT. he signal objectsa re implementedw ith a similar class hierarchy. The development and validation of models representing various digital, analogue and mixed-signal components are described. Comparisons are made between the accuracy and performance of the proposed methodology and several commercial simulators. The development of a Windows-based demonstrations imulation tool called POISE is also described. This permitted models to be tested independently and multiple models to be connected together to form structural models of complex circuits

Differential-Algebraic Equations and Beyond: From Smooth to Nonsmooth Constrained Dynamical Systems

The present article presents a summarizing view at differential-algebraic equations (DAEs) and analyzes how new application fields and corresponding mathematical models lead to innovations both in theory and in numerical analysis for this problem class. Recent numerical methods for nonsmooth dynamical systems subject to unilateral contact and friction illustrate the topicality of this development.Comment: Preprint of Book Chapte

The Investigation and Implementation of electrical Impedance Tomography Hardware System

Electrical impedance tomography (EIT) is a medical imaging technology that provides a tomographic representation of the distribution of electrical impedance within the body. As the electrical impedance varies for different body tissues, it is possible to characterize tissues from the images and to detect physiological events. EIT systems have been developed from applying a single signal frequency to a range of frequencies. Imaging at multiple frequencies significantly improves the ability to characterize and differentiate heterogeneity within the region of interest. Applications of EIT are limited by its poor resolution as a consequence of limited number of electrodes and lack of independently published measurements. In a practical EIT system design the parallel structure is normally adopted as it provides a real time monitoring structure. However, there is a difficulty in expanding to a 2-dimensitional or 3-dimensitional high resolution imaging system, as the number of electrodes increase. In this thesis, a serial structure spectrum EIT system has been investigated and developed. Modelling of the electrical circuit has shown that the system bandwidth is degraded primarily by the signal transmission in the coaxial cable and multiplexer. To remove the capacitive effect of these components, a distribute system concept has been developed. The concept uses active electrodes in which a current source and a front end amplifier are embedded in the electrode which makes direct contact with the tissue being measured. The active electrode is based on the Howland current source. The required high output impedance of Howland current source can be realised by matching the two resistor arms. However, from the electrical equivalent circuit analysis the actual output impedance of this circuit was found to be degraded by the op-amp' s limited open loop gain, especially at higher frequencies. To solve the problem, the author describes in detail a novel method of compensating for the above effects. Subsequent circuit tests showed significant improvement after the compensation. Further, to improve the small signal noise ratio a programmable gain amplifier to adapt the frame data measurement was developed. These developments have led to the feasibility of active electrodes. The thesis describes in detail the development, of the MK2 EIT system which is presented as the output of this research

Selected Papers from the 9th World Congress on Industrial Process Tomography

Industrial process tomography (IPT) is becoming an important tool for Industry 4.0. It consists of multidimensional sensor technologies and methods that aim to provide unparalleled internal information on industrial processes used in many sectors. This book showcases a selection of papers at the forefront of the latest developments in such technologies

Advanced electrode models and numerical modelling for high frequency Electrical Impedance Tomography systems

The thesis discusses various electrode models and finite element analysis methods for Electrical Impedance Tomography (EIT) systems. EIT is a technique for determining the distribution of the conductivity or admittivity in a volume by injecting electrical currents into the volume and measuring the corresponding potentials on the surface of the volume. Various electrode models were investigated for operating EIT systems at higher frequencies in the beta-dispersion band. Research has shown that EIT is potentially capable to distinguish malignant and benign tumours in this frequency band. My study concludes that instrumental effects of the electrodes and full Maxwell effects of EIT systems are the major issues, and they have to be addressed when the operating frequency increases. In the thesis, I proposed 1) an Instrumental Electrode Model (IEM) for the quasi-static EIT formula, based on the analysis of the hardware structures attached to electrodes; 2) a Complete Electrode Model based on Impedance Boundary Conditions (CEM-IBC) that introduces the contact impedances into the full Maxwell EIT formula; 3) a Transmission line Port Model (TPM) for electrode pairs with the instrumental effects, the contact impedance, and the full Maxwell effects considered for EIT systems. Circuit analysis, Partial Differential Equations (PDE) analysis, numerical analysis and finite element methods were used to develop the models. The results obtained by the proposed models are compared with widely used Commercial PDE solvers. This thesis addresses the two major problems (instrumental effects of the electrodes and full Maxwell effects of EIT systems) with the proposed advanced electrode models. Numerical experiments show that the proposed models are more accurate in the high frequency range of EIT systems. The proposed electrode models can be also applicable to inverse problems, and the results show promising. Simple hardware circuits for verifying the results experimentally have been also designed

Optimization Design Flow of Integrated Circuits based on Machine Learning Approaches

Nowadays, the increased complexity of analog/digital circuits and the extremelly wide range of specifications tend to change how an integrated-circuit designer addresses circuit optimization. A traditional analog engineer likes to use some intuition when designing circuits, as a second step following paper-pencil analysis. However, the numerous parameters that influence the circuit IV in modern transistors do not provide good guesses. Moreover, an optimization based on multiple parameter sweep helps only when the design space is reduced, which is not the case in modern designs. The present thesis, developed at INTEL (in Munich site, Germany), addresses new paradigms of circuit optimization. The proposed work relies on the use of machine learning techniques applied to the design of complex CMOS systems

SCEE 2008 book of abstracts : the 7th International Conference on Scientific Computing in Electrical Engineering (SCEE 2008), September 28 – October 3, 2008, Helsinki University of Technology, Espoo, Finland

This report contains abstracts of presentations given at the SCEE 2008 conference.reviewe