Offenburg University of Applied Sciences
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Grazing incidence nanogap resonance in the prism-gap-ferromagnet magneto-plasmonic Otto configuration
No full textIn this Letter, we calculate the optical and magneto-optical reflectivity in a dielectric/gap/ferromagnet excited by a p-polarized monochromatic optical beam through the prism (Otto configuration) as a function of the angle of incidence θ and the gap thickness d. Besides the well-known surface plasmon polariton (SPP resonance at d ∼ λ), we find a new, to the best of our knowledge, resonance with a nanometric gap d ∼ 10 nm at a large θ ∼ 80°. Both resonances display pronounced resonant behavior in the transverse magneto-optical Kerr effect (T-MOKE)
Spurious Aeroacoustic Emissions in Lattice Boltzmann Simulations on Non-Uniform Grids
Get PDFAlthough there do exist a few aeroacoustic studies on harmful artificial phenomena related to the usage of non-uniform Cartesian grids in lattice Boltzmann methods (LBM), a thorough quantitative comparison between different categories of grid arrangement is still missing in the literature. In this paper, several established schemes for hierarchical grid refinement in lattice Boltzmann simulations are analyzed with respect to spurious aeroacoustic emissions using a weakly compressible model based on a D3Q19 athermal velocity set. In order to distinguish between various sources of spurious phenomena, we deploy both the classical Bhatnagar–Gross–Krook and other more recent collision models like the hybrid recursive-regularization operator, the latter of which is able to filter out detrimental non-hydrodynamic mode contributions, inherently present in the LBM dynamics. We show by means of various benchmark simulations that a cell-centered approach, either with a linear or uniform explosion procedure, as well as a vertex-centered direct-coupling method, proves to be the most suitable with regards to aeroacoustics, as they produce the least amount of spurious noise. Furthermore, it is demonstrated how simple modifications in the selection of distribution functions to be reconstructed during the communication step between fine and coarse grids affect spurious aeroacoustic artifacts in vertex-centered schemes and can thus be leveraged to positively influence stability and accuracy
Analysis and Prediction of Power Quality Anomalies in Power Electronic Dominated Industrial Systems
No full textThe increase in the use of sensitive Power Electronic dominated electrical devices has made the high visibility of their use for the consistent and high quality of electricity supply, as deviations in electricity supply can lead to malfunctions or failures, leading to loss of capital cost, loss of equipment, high maintenance cost and downtime. Despite technological advancements, issues in electricity persist due to factors like short circuits, voltage fluctuations, overloads, smart loads, unbalanced loads, and non-linear loads. This research focuses on forecasting anomalies in Power electronicdominated electrical devices in the institute by analyzing power system data to enhance the reliability of electricity supply.
Detecting and predicting anomalies in the electricity of a power grid is vital for ensuring the stability and reliability of power grids, especially with challenges from non-linear loads, renewable energy integration, and electric vehicles. Advanced Methods like curve fitting, Fast Fourier transform, and Cross-correlation were used to detect anomalies in the data. Machine learning models, such as ensemble methods, were used to forecast the occurrence of anomalies. An attempt was made to generate future synthetic voltage profiles to find the anomalies
Automatic Vulnerability Detection in Web Applications
No full textWeb applications play a crucial role in modern business operations but remain prime targets for cyberattacks due to the sensitive data they handle. Despite continuous advancements in cybersecurity, many applications are still susceptible to common vulnerabilities such as SQL Injection (SQLi), Cross-Site Scripting (XSS), Local File Inclusion (LFI), and Remote Code Execution (RCE), many of which are listed in the OWASP Top 10. Existing security tools often provide limited coverage, focusing on specific aspects like SSL validation or static code analysis, while failing to comprehensively detect and confirm exploitation attempts in real-world scenarios.
This thesis addresses these gaps by leveraging AI-driven attack automation for vulnerability detection and analysis. The system integrates automated reconnaissance, penetration testing, and AI-assisted exploitation validation to identify security flaws dynamically. Unlike conventional tools that rely on static analysis, this approach executes real attack scenarios, analyzes system responses, and determines whether an exploit truly succeeded. The research specifically evaluates the effectiveness of AI models in generating attack execution commands, constructing multi-stage attack chains, and assessing post-exploitation outcomes. The system is tested against a controlled vulnerable web environment, measuring its accuracy, efficiency, and reliability in detecting and validating real vulnerabilities.
A structured methodology is followed, beginning with a comprehensive literature review of web vulnerabilities and attack automation techniques, followed by the design, development, and experimental evaluation of the AI-driven penetration testing framework. The results indicate significant challenges in AI-assisted exploitation validation, with both models exhibiting high false positive rates and misclassification of vulnerabilities. However, the study highlights key areas for improvement, including enhancing AI’s exploit validation mechanisms and reducing false positives through contextual analysis.
By bridging the gap between automated attack execution and intelligent exploit validation, this research contributes to the advancement of AI-driven penetration testing methodologies. The findings underscore the potential and limitations of current AI models in cybersecurity, paving the way for future enhancements in AI-assisted vulnerability assessment and exploitation validation techniques
C++ for Embedded Systems: Enhancing Real-Time Performance through Efficient Code and Resource Management
No full textWith the advancement of technology in 21st century, the marketspace of various categories like Consumer electronics, Automotive, Healthcare & medical devices, Industrial Automation, Telecommunication and Défense & Aerospace is being disrupted with the fast-paced changing technology. Companies aiming on faster real-time processing, AI integration and better energy efficiency. Embedded systems have played a major role in the growth of such industries. An Embedded system is a computer system designed for a specific function within a larger mechanical or electrical system [1]. With an aim to perform a specific task, the devices are designed using a microprocessor or microcontroller with required memory, Input/Output interface and an integrated development environment for software coding. With the advent of technology, Embedded systems gained popularity for performing specific task at a faster rate, slowly and steadily the manual labour/machines are reduced/replaced with the addition of technology. Sensors/electronic devices have also played a pivotal role as a ground for an Embedded system. With the development of devices, the researchers have also stress on finding more efficiency, memory, affordability, reliability, complexity and reusability of the devices. IoT devices rely on embedded systems for data processing and control. With the popularity of IoT devices like Smart home devices (Amazon Echo, Philips Smart bulbs), wearables devices, smart agricultural devices, connected vehicles and transport devices, the embedded system has gained grounds in the technological space and transforming every day’s life.
With the success of embedded system in various categories the demand and the requirements grew from customers and complex device requirement for multitasking with efficiency became a necessity. Traditionally, C was used for most of the embedded devices due to its efficiency, portability and hardware access as C provided control to low level hardware using pointers and allowed manipulation of peripheral settings. C also provided the advantage of saving space as the code size is not large and uses embedded compilers like GCC, Keil etc. Furthermore, it is compatible with Real time operating system.
With the enhancement of embedded hardware from 1960s [2], it provided room to perform complex tasks. With C, the code modularity and its reusability, code maintainability, bugs detection was not very efficient for complex embedded system, hence it allowed programming languages like C++ which supports object-oriented programming concept to gain grounds as it allows the code to use features like Encapsulation, Inheritance and Polymorphism. Additionally, it allowed better bug detection & correction along with code maintainability. The work will reflect the advantages and disadvantages of C++ in resource restricted environment and provide better code optimization across the real time application
Untersuchung zu den Erfolgsdeterminanten für die Nutzung von Künstlicher Intelligenz bei kleinen und mittelständischen Unternehmen der Region Schwarzwald-Baar-Heuberg
No full textDiese Arbeit untersucht die Erfolgsfaktoren für die KI-Implementierung in KMUs der ländlich-industriell geprägten Region Schwarzwald-Baar-Heuberg (SBH). Trotz hohem Innovationspotenzial und Herausforderungen wie Fachkräftemangel nutzen KMUs KI-Chancen zur Effizienzsteigerung und Automatisierung nur zögerlich. Die zentrale Leitfrage lautet:
„Welche Erfolgsdeterminanten und deren Wechselwirkungen sind für die KI-Implementation in KMUs ländlicher Räume maßgeblich und wie lassen sich diese am Beispiel der Region Schwarzwald-Baar-Heuberg empirisch validieren?“
Basierend auf Literaturanalyse wurde das TOE-Framework um die regionale Dimension zum TOER-Framework (Technologie, Organisation, Wirtschaft, Region) erweitert. Ein Mixed-Methods-Ansatz kombinierte eine quantitative Umfrage mit qualitativen Interviews. Die geringe Stichprobengröße (trotz umfangreicher Bewerbung durch die IHK) schränkt die Repräsentativität stark ein; die Ergebnisse sind explorativ. Die niedrige Teilnahme deutet selbst auf Hürden hin (z.B. „KI-Müdigkeit“, Unsicherheit).
Wichtigste Ergebnisse (TOER):
• T: IT-Basis oft vorhanden, aber KI-spezifische Lücken (Datenmanagement, Hardware). Bedarf an passgenauen, sicheren, nutzerfreundlichen Lösungen; Kosten und Unsicherheit hemmen. Generative KI am populärsten.
• O: Führung oft unterstützend, aber Mitarbeiterakzeptanz, Kompetenzen und Change-Management sind Hauptbarrieren. Strategieanpassung langsam.
• E: Fokus auf Effizienz, kurze Amortisation (1-2 Jahre) erwartet, dennoch Investitionszurückhaltung. Systematische Erfolgsmessung fehlt meist.
• R: Fachkräftemangel kritischstes regionales Hemmnis. Unterstützungsangebote oft
unbekannt/unpassend. Netzwerke wichtig (v.a. für größere KMUs), aber wenig genutzt. Hoher Bedarf an Best Practices, individueller Beratung und Förderung. Hohe Kosten für externe Expertise als Hürde wahrgenommen.
Die Faktoren interagieren; Führung und regionale Netzwerke sind zentral, aber oft ungenutzt. Grundlegende Erfolgsfaktoren (Netzwerke, maßgeschneiderte Unterstützung, Führung) sind tendenziell auf andere Regionen übertragbar, erfordern ggf. regionale Anpassungen.
Zentrale Handlungsempfehlung:
Zur Überwindung der Hürden (v.a. Mangel an vertrauenswürdiger Expertise) wird die Initiierung eines regionalen „AI-ExpertHub SBH“ vorgeschlagen. Als Verbundprojekt (IHK, Hochschulen, Unternehmen) soll er Wissen bündeln, Unterstützung bieten, Qualität sichern, Best Practices fördern und Akteure vernetzen, um die KI-Adaption zu beschleunigen
Datenbasierte Auswertung der technischen Kenndaten von zertifizierten Wärmepumpen
No full textWärmepumpen stehen im Zentrum der Wärmewende und sind eine Schlüsseltechnologie für die nachhaltige Gebäudetechnik. Doch wie schneiden die verfügbaren Modelle auf dem Markt hinsichtlich Effizienz, Einsatzbereich und Umweltverträglichkeit ab
Autodesk® Fusion – kurz und bündig
No full textDieses Lehrbuch ermöglicht Anfängern und Anfängerinnen in der 3D-Modellierung einen schnellen Einstieg in die Arbeit mit dem cloudbasierten CAD-System Autodesk Fusion, ehemals Fusion 360. Der Schwerpunkt liegt dabei auf den grundlegenden Funktionen zur Modellierung von Einzelteilen und dem Zusammenbau von Produkten, sowie in der Erstellung von einfachen technischen Zeichnungen. Dabei werden bei jedem Schritt die besonderen Anforderungen an eine 3D-Druck-gerechte Gestaltung erläutert und umgesetzt. Somit ist das Ergebnis dieser „Schritt für Schritt“-Anleitung die vollständige Modellierung eines Miniatur-Automobils, das am 3D-Drucker in ein reales Modell umgesetzt werden kann. Das didaktische Konzept ist so ausgelegt, dass alle Schritte für ein Selbststudium geeignet sind.
Die vorliegende Auflage enthält eine Übersicht der 3D-Druckwerkstoffe und geht auf die aktuellen Weiterentwicklungen von Autodesk Fusion ein. Dabei werden neue Funktionen in den Bereichen Konstruktion und Zeichnung demonstriert, wie z. B. die automatische Modellierung von Bauteilen
Reducing the Risk of Perceived Overload and Fatigue on Social Media: The Role of Cognitive Social Media Literacy
No full textSocial media has become an integral part of daily life for many individuals, serving as a platform for communication, information sharing, and entertainment. However, the extensive use of social media has led to issues such as information overload and social media fatigue, where users feel overwhelmed and emotionally exhausted by constant interaction and content. This paper investigates cognitive social media literacy skills—appraisal, comprehension, curation, and interaction—and their ability to mitigate perceived overload. Based on a quantitative study of 335 respondents, the results confirm that higher social media literacy reduces perceived information overload, but only the skill “appraisal” significantly lowers communication overload. The study highlights the critical role of social media literacy in reducing negative social media effects, offering practical insights for policymakers, educators, and platform developers to address social media fatigue