Applying SMT Solvers to the Test Template Framework

The Test Template Framework (TTF) is a model-based testing method for the Z notation. In the TTF, test cases are generated from test specifications, which are predicates written in Z. In turn, the Z notation is based on first-order logic with equality and Zermelo-Fraenkel set theory. In this way, a test case is a witness satisfying a formula in that theory. Satisfiability Modulo Theory (SMT) solvers are software tools that decide the satisfiability of arbitrary formulas in a large number of built-in logical theories and their combination. In this paper, we present the first results of applying two SMT solvers, Yices and CVC3, as the engines to find test cases from TTF's test specifications. In doing so, shallow embeddings of a significant portion of the Z notation into the input languages of Yices and CVC3 are provided, given that they do not directly support Zermelo-Fraenkel set theory as defined in Z. Finally, the results of applying these embeddings to a number of test specifications of eight cases studies are analysed.Comment: In Proceedings MBT 2012, arXiv:1202.582

An open-source human-in-the-loop BCI research framework: method and design

Brain-computer interfaces (BCIs) translate brain activity into digital commands for interaction with the physical world. The technology has great potential in several applied areas, ranging from medical applications to entertainment industry, and creates new conditions for basic research in cognitive neuroscience. The BCIs of today, however, offer only crude online classification of the user's current state of mind, and more sophisticated decoding of mental states depends on time-consuming offline data analysis. The present paper addresses this limitation directly by leveraging a set of improvements to the analytical pipeline to pave the way for the next generation of online BCIs. Specifically, we introduce an open-source research framework that features a modular and customizable hardware-independent design. This framework facilitates human-in-the-loop (HIL) model training and retraining, real-time stimulus control, and enables transfer learning and cloud computing for the online classification of electroencephalography (EEG) data. Stimuli for the subject and diagnostics for the researcher are shown on separate displays using web browser technologies. Messages are sent using the Lab Streaming Layer standard and websockets. Real-time signal processing and classification, as well as training of machine learning models, is facilitated by the open-source Python package Timeflux. The framework runs on Linux, MacOS, and Windows. While online analysis is the main target of the BCI-HIL framework, offline analysis of the EEG data can be performed with Python, MATLAB, and Julia through packages like MNE, EEGLAB, or FieldTrip. The paper describes and discusses desirable properties of a human-in-the-loop BCI research platform. The BCI-HIL framework is released under MIT license with examples at: bci.lu.se/bci-hil (or at: github.com/bci-hil/bci-hil)

Real-Time Brain-Computer Interfaces

A brain-computer interface (BCI) is a real-time system that converts a user’s brain activity into commands, enabling control over applications such as moving a cursor on the screen. This conversion is made possible by machine learning techniques and other algorithms. Historically, BCI research has focused on the analysis of brain signals offline. However, increasing computational power and algorithmic sophistication now allow real-time interaction, enabling systems to interpret brain activity and translate it into commands instantaneously. This is critical for neuroprosthetic applications where even millisecond-level improvements can produce more natural movement. Even in more playful areas of use, such as brain-controlled computer games, response time is important for creating an experience that can engage the user: "It’s no fun if it lags". A key driver of these advances is machine learning. Unlike earlier BCIs that relied on fixed models, machine learning allows systems to adapt and continuously learn from user signals and behavior, fostering a more intuitive human-machine interface through ongoing feedback and mutual learning. Despite these promising developments, significant challenges remain: real-time BCI systems demand high-performance hardware and software, require efficient wireless communication, robust processing capabilities, and adaptive algorithms. Furthermore, integrating these systems into real-world applications raises questions about usability and safety. This thesis addresses some of these challenges, particularly in the context of reducing cognitive load, addressing latency uncertainties and developing a modular BCI research framework. This work presents new insights and pathways toward functional BCI systems. Through experimentation and novel system architecture, this research lays a foundation for the future of BCI technology

Taking Your Online Lectures to the Next Level

This paper introduces P-expo, a do-it-yourself open-source software solution for enhancing online lectures. P-expo seamlessly integrates text, images, videos, webcams, and audio, with support for text-based tools like git for collaboration. Additionally, the paper introduces affordable DIY hardware components to enrich online education seminars, and presents a curated selection of accessible open-source software tools to complement P-expo, empowering educators to deliver engaging lectures and improve remote learning experiences

Vergleichende Untersuchungen an Rind und Schaf zum Futterwert verschiedener Maissilagen

Bei Grassilage wurden keine tierartspezifischen Unterschiede in der Verdaulichkeit der organischen Substanz bei Verdaulichkeitsquotienten von 75.8 bzw. 75.4% fuer Kalbinnen und Hammel festgestellt, bei Maissilage betrugen die Werte 76.5 bzw. 73.5% (Kalbinnen verdauten vor allem die Rohfaser besser). Bei Zulage staerkereicher Einzelkomponenten in Kombination mit Maissilage wurde dieser Unterschied aufgehoben (78.1 bzw. 79.1%). Zwischen der Staerketraegerart und der Grundfutterart koennen Wechselwirkungen im Hinblick auf den Futterwert einer Ration bestehen. Der Energiegehalt von Maissilage erhoehte sich mit ansteigendem Reifegrad, bei Maissilagen ist ein TS-Bereich von 30-35% anzustreben. Sorten mit lang gruenbleibender Restpflanze und hohem Kolbenanteil sind wuenschenswert. Bei der Schaetzung des energetischen Futterwertes von Maissilage lohnt sich die Durchfuehrung von in-vitro-Verfahren nur bei zusaetzlicher Hereinnahme einfacher Kenndaten (TS-Gehalt, Kolbenanteil, Rohnaehrstoffgehalt) in die Regressionsgleichungen. Keine wesentlichen Unterschiede zwischen den 3 in-vitro-Verfahren Hohenheimer Futterwerttest, Cellulase-Methode und 2-Stufen-Methode nach TILLEY u. TERRYSIGLEAvailable from: D-80333 Muenchen Technische Univ. (Germany). Universitaetsbibliothek, Arcisstr. 21 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Automatic Control of a Wheelchair Using a Brain Computer Interface and Real-Time Decision-Making

In this study, we simulate the automatic control of an electric wheelchair for indoor Pac-Man-style navigation using solely thought commands. We delve into the decision-making mechanisms of an operational EEG-based brain computer interface that employs a visual oddball paradigm. We investigate strategies to enhance the efficiency of decision-making processes, aiming to accelerate response times while maintaining a defined error rate. Furthermore, we explore methodologies to decrease the user's cognitive load by reducing the number of stimuli needed before an action