We introduce a knitted inductive flex sensor which seamlessly integrates a coil and a capacitor into a soft and flexible tubular knit. By knitting enameled copper wires, we form a self-supporting coil, whose inductance changes with stretching and bending. Knitting both a coil and a parallel-wire capacitor, we create a textile resonant LC circuit, while preserving the softness, elasticity, and breathability of knitted textiles. In this paper, we present the fabrication process using an industrial knitting machine, evaluate sensor sensitivity and hysteresis over 100 bending cycles, and demonstrate the sensors versatility across joints of different radii. Our results show that knitted inductive sensors combine the wearability of soft textiles with the stability of inductive sensing, opening new sensing opportunities in healthcare, rehabilitation, and interactive electronic garments.

We introduce SqueezeMe, a soft and flexible inductive pressure sensor with high sensitivity made from ferromagnetic elastomers for wearable and embedded applications. Constructed with silicone polymers and ferromagnetic particles, this biocompatible sensor responds to pressure and deformation by varying inductance through ferromagnetic particle density changes, enabling precise measurements. We detail the fabrication process and demonstrate how silicones with varying Shore hardness and different ferromagnetic fillers affect the sensor’s sensitivity. Applications like weight, air pressure, and pulse measurements showcase the sensor’s versatility for integration into soft robotics and flexible electronics.

Software systems that process structured inputs often lack complete and up-to-date specifications, which specify the input syntax and the semantics of input processing. While grammar mining techniques have focused on recovering syntactic structures, the semantics of input processing remains largely unexplored. In this work, we introduce a novel approach for inferring attributed grammars from parser implementations. Given an input grammar, our technique dynamically analyzes the implementation of recursive descent parsers to reconstruct the semantic aspects of input handling, resulting in specifications in the form of attributed grammars. By observing program executions and mapping the program's runtime behavior to the grammar, we systematically extract and embed semantic actions into the grammar rules. This enables comprehensive specification recovery. We demonstrate the feasibility of our approach using an initial set of programs, showing that it can accurately reproduce program behavior through the generated attributed grammars.

Undetected errors in software systems are a common cause of vulnerabilities and security holes. Grammar Fuzzing is an effective method for testing these systems, but it has limitations such as lack of knowledge about the semantics of the program and difficulty obtaining grammar for these systems. To address these limitations, we propose an approach to automatically mine grammars, and enhance it with semantic rules and contextual constraints to create attribute grammars. These attribute grammars can then be used for fuzzing. Our preliminary results show that this automated extraction process is feasible, as we successfully applied it to an expression parser and were able to extract an attribute grammar representing the parser’s functionality.

The specification of program inputs is a requirement for many software engineering tasks, but often does not exist or is out of date. To tackle this problem, software engineers may apply program analysis techniques to extract parts of a specification from the source code that processes the program input. Today there are analysis techniques for the extraction of constraints (mathematical formulas) for individual program inputs (e.g. function parameters) as well as emerging techniques for inferring context-free grammars that specify the syntax of program input strings. However, such techniques focus on a single aspect (e.g., constraints or grammars) of the specification only and neglect the other one. We propose to integrate such analysis techniques by extending existing approaches for mining input grammars with the extraction of constraints. Constraints are integrated with a grammar in the form of attributes and context constraints on grammar symbols, resulting in an attribute grammar as specification format. To achieve this goal, we choose the analysis method dynamic symbolic execution (DSE), which is already an established technique for the extraction of constraints and beneficial for grammar mining (e.g., through automatic input generation) as well. Thus, DSE not only covers both aspects but also—as a single analysis method—should facilitate the integration of these two aspects. In this paper, we describe the basic idea of the proposed integration and report the first results on DSE-based grammar extraction.

In this paper, we present TexYZ, a method for rapid and effortless manufacturing of textile mutual capacitive sensors using a commodity embroidery machine. We use enameled wire as a bobbin thread to yield textile capacitors with high quality and consistency. As a consequence, we are able to leverage the precision and expressiveness of projected mutual capacitance for textile electronics, even when size is limited. Harnessing the assets of machine embroidery, we implement and analyze five distinct electrode patterns, examine the resulting electrical features with respect to geometrical attributes, and demonstrate the feasibility of two promising candidates for small-scale matrix layouts. The resulting sensor patches are further evaluated in terms of capacitance homogeneity, signal-to-noise ratio, sensing range, and washability. Finally, we demonstrate two use case scenarios, primarily focusing on continuous input with up to three degrees-of-freedom.

We present a novel method for augmenting arbitrary fabrics with textile-based pressure sensors using an off-the-shelf embroidery machine. We apply resistive textiles and conductive yarns on top of a base fabric, to yield a flexible and versatile continuous sensing device, which is based on the widespread principle of force sensitive resistors. The patches can easily be attached to measurement and/or computing devices, e.g. for controlling accessories. In this paper, we investigate the impacts of related design and fabrication parameters, introduce five different pattern designs, and discuss their pros and cons. We present crucial insights and recommendations for design and manufacturing of embroidered pressure sensors. Our sensors show a very low activation threshold, as well as good dynamic range, signal-to-noise ratio, and part-to-part repeatability.

We present Sonoflex, a thin-form, embroidered dynamic speaker made without using a permanent magnet. Our design consists of two flat spiral coils, stacked on top of each other, and is based on an isolated, thin (0.15 mm) enameled copper wire. Our approach allows for thin, lightweight, and textile speakers and does not require high voltage as in electrostatic speakers. We show how the speaker can be designed and fabricated and evaluate its acoustic properties as a function of manufacturing parameters (size, turn counts, turn spacing, and substrate materials). The experiment results revealed that we can produce audible sound with a broad frequency range (1.5 kHz - 20 kHz) with the embroidered speaker with a diameter of 50 mm. We conclude the paper by presenting several applications such as audible notifications and near-ultrasound communication.