Stefan Bosse

Ultrasonic Testing (UT) is commonly used to detect damage in structures, e.g., metal plates. A sensor acquires Ultrasonic waves, e.g., by using PZT transducers. The time-resolved sensor signal must be processed with analog electronics, e.g., amplified and filtered. Commonly a digitalization follows using an Analog-to-Digital converter, finally processing the digital sensor signal, applying digital signal processing, feature extraction, and Machine Learning by using powerful microprocessor systems. The disadvantages of digital processing systems are their high number of transistors (microchip area), energy consumption, state-dependent processing and therefore sensitivity to energy supply interruption. Beyond silicon electronics, printed organic electronics gains interest. But printed electronics is still limited to low transistor and electronic component counts (typically 100). We will investigate and demonstrate a fully analog signal processing and feature extraction system consisting of an analog Hilbert transform deriving the signal envelope, simple analog arithmetic calculations for feature extraction, and finally damage classification and regression using an analog Artificial Neural Network. We expect a full damage detection system with less than 100 transistors. We will test our damage detection system with PZT transducer signals from Steel plates with circular defects. The focus of this work is the analog computation of the signal envelope (using all-pass filter networks for approximation of the Hilbert transform) and the analog feature extraction, forming an analog computer which can perform in-sensor computation.

Despite the rise and wide spread of digital technology, analog circuits are experiencing a resurgence for specific applications due to their efficiency in processing physical signals with fewer transistors and lower energy requirements. They offer advantages in explainability and stability, particularly in sensor systems. However, designing these circuits poses challenges, particularly in optimizing them against issues like component aging and environmental dependence. Current research aims to enhance automation in analog circuit design using optimization methods, including stochastic algorithms like Genetic Algorithms (GA), Simulated Annealing (SA), and Particle Swarm Optimization (PSO). These methods improve convergence and fault tolerance, while dealing with the high nonlinearity of analog circuits with transistors. The focus of this paper is on solving multivariate optimization problems with multiple objective functions for analog circuits. We evaluate and compare the component optimization of a small-signal amplifier, operational amplifier, and a sigmoid function circuit using gradient-based and stochastic methods for bipolar junction, field-effect, and organic electro-chemical transistors, differing in transistor transconductance and transfer curves. We show that PSO outperforms all other optimization algorithms in our experiments for all considered circuit classes, showing the highest convergence rates between 40–100% (depending on the circuit class) and the lowest optimization times. The OECT components were manufactured with a drop-on-demand ink-jet process and experimentally characterized, finally deriving an electronic functional digital twin model from measuring data. This is a simulation study but uses data and models from real devices.

Despite the rise and wide spread of digital technology, analog circuits are experiencing a resurgence for specific applications due to their efficiency in processing physical signals with fewer transistors and lower energy requirements. They offer advantages in explainability and stability, particularly in sensor systems. However, designing these circuits poses challenges, particularly in optimizing them against issues like component aging and environmental dependence. Current research aims to enhance automation in analog circuit design using optimization methods, including stochastic algorithms like Genetic Algorithms (GA), Simulated Annealing (SA), and Particle Swarm Optimization (PSO). These methods improve convergence and fault tolerance while dealing with the high nonlinearity of analog circuits with transistors. The project's focus is on solving multivariate optimization problems with multiple objective functions for analog circuit design to achieve high performance with minimal energy use, combining various algorithmic strategies for better outcomes. We will evaluate and compare the component optimization of a small signal amplifier circuit using gradient and the different stochastic methods for bipolar junction (BJT) and organic electro-chemical transistors (OECT), differing in transistor transconductance and transfer curves. We can show that PSO outperforms all other optimization algorithms. The OECT components were manufactured with a drop-on-demand ink-jet process, experimentally characterized, finally deriving an electronic functional digital twin model from measuring data. This is a simulation study, but using data and models from real devices.

Efficient Distributed Computing is still a major challenge, especially in networks composed of very-low-resource embedded systems, e.g., tiny microcontrollers deployed in sensor networks. This work will, firstly, address the design and implementation of event-driven and real-time capable low-resource Virtual Machines (VMs) tightly coupled to communication-centric systems, and secondly, address messaging and routing in mesh-grid networks. The distributed VM network herein forms one big virtual computer executing typically the same program on each node, but processing different data with different control states. The VM provides an integrated program code compiler and an optimized Bytecode processor. The programming language of the VM supports channel-based communication, multi-tasking, and event-based (asynchronous) data processing following the CSP model. The VM fits in microcontrollers with only a few kB of RAM and ROM. A major part of this work is dedicated to network messaging (supported by the VM, too) and routing in two-dimensional mesh-grid networks with a varying degree k of communication ports per node (connectivity degree k), and especially considering the odd but technical relevant case, k = 3, which introduces challenges in message routing that are solved herein. This study demonstrates the performance and suitability of our VM approach for distributed sensor networks performing distributed Machine Learning and clustering by using local sensor data only.

This issue of Engineering Proceedings gathers the papers presented at the 12th International Electronic Conference on Sensors and Applications (ECSA-12), held online from 12 to 14 November 2025, through the sciforum

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