Terahertz photonic spectrum analyser

The volume of data transmitted via wireless communication will further increase in the upcoming years, eventually surpassing the bandwidth capabilities provided by existing technologies. Increasing the carrier frequency used for the data transmission also increases the available bandwidth and makes new, yet unused bands available. Current technologies use frequencies below 100 GHz but are said to expand into the terahertz (THz) frequencies (100 GHz - 10 THz) with 6G technologies and beyond. THz radiation is non-ionising and, therefore, interesting for medical applications on living organisms like cancer detection. THz fingerprinting detects materials based on their absorption which security applications use to identify substances like drugs or explosives. Most of the THz applications require a THz source in their measurement equipment. Current sources are still either bulky, expensive or low in output power requiring further research for improvements in all categories. For the spectral signal output characterisation, accurate and reliable spectrum analysers are required. The only currently commercially available spectrum analysers for the THz frequency range are electronic spectrum analysers with extender modules. Each of these covers a bandwidth of approximately 42 % of their centre frequency limited by the typically employed rectangular metallic hollow waveguides. Measuring harmonics of a signal already requires several extender modules. Covering the full frequency range from 100 GHz up to 1.5 THz, which is simultaneously the highest yet commercially reachable frequency, necessitates at least seven extender modules. The investment costs for the full frequency range easily exceed half a million Euros. An alternative to reaching the THz frequencies is photonic technology. Tuning a telecom-wavelength (1550 nm) laser by 1 THz equals a tuning by only 0.5 % and is easily accessible with commercial equipment. This thesis introduces several variants of a THz photonic spectrum analyser based on the difference frequency of two continuous-wave optical telecom-wavelength signals. A photoconductive mixer generates the optical difference frequency acting as a local oscillator and mixes it with the signal of a THz source. The mixing process transfers the spectral information of the source into the intermediate frequency, typically kHz or MHz, where an analog-digital converter acquires the data. The thesis introduces four measurement variants for the optical difference frequency generation and the data acquisition: 1.) The frequency sweep uses a continuously increasing difference frequency and measures all components falling within the bandwidth of a low-pass filter in the intermediate frequency chain. 2.) The offset frequency sweep employs a band-pass filter instead of the low-pass filter in order to reduce 1/f-noise. Each frequency component of the source is displayed twice. A deconvolution regains the original spectrum of the source. 3.) The Fourier transformation mode measures a time trace of the downconverted signal and transforms it to the frequency domain with a Fourier transformation. The resulting frequency information equals the spectrum surrounding the optical difference frequency. 4.) The final measurement mode is based on in-phase and quadrature demodulation. The signal is split into two paths and each downconverted within its respective photoconductive mixer. Both paths use the same optical difference frequency, yet with a relative phase difference of 90°. Experimentally, this thesis demonstrates three different implementations of the photonic THz spectrum analyser, each with its own way of generating the difference frequency. The first variant uses two free-running, temperature controlled distributed-feedback laser diodes and ErAs:InGaAs or low-temperature grown InGaAs:Be photoconductive mixers. We demonstrate operation between 100 GHz and 1.05 THz, yet may cover frequencies beyond 2.7 THz with a minimum resolution bandwidth of 1.2 MHz. The system reaches displayed average noise levels of -111.8 dBm/Hz at a frequency of 100 GHz and -98.0 dBm/Hz at a frequency of 1 THz. Using photoconductive mixers with planar, end-fire Vivaldi antennas, this photonic spectrum analyser variant extends to signals in dielectric waveguides and rectangular metallic hollow waveguides. The second photonic spectrum analyser implementation uses an electro-optical THz comb that generates the two required optical frequency components form a single laser with an electro-optical modulator. The electro-optical THz comb shows a phase noise of -108.6 dBc/Hz at an offset frequency of 1 MHz at a centre frequency of 40 GHz and a linewidth of 1.8 Hz at a frequency of 100 GHz. We used it for frequencies up to 110 GHz while frequencies up to 1 THz are currently possible. The third photonic spectrum analyser examined in this thesis utilises two continuous-wave lasers, both locked to the same frequency-comb and further to a global positioning system (GPS) signal, in combination with an InGaAs:Rh photoconductive mixer. This variant covers frequencies up to at least 6.5 THz with linewidths below 1 Hz at a frequency of 100 GHz and below 20 Hz at a frequency of 1 THz. Simultaneously, it offers a displayed average noise level of -145.6 dBm/Hz at a frequency of 100 GHz, -133.7 dBm/Hz at 1 THz and -111.5 dBm/Hz at a frequency of 4.5 THz

Perspectives on data-driven models and its potentials in metal forming and blanking technologies

Today, design and operation of manufacturing processes heavily rely on the use of models, some analytical, empirical or numerical i.e. finite element simulations. Models do reflect reality as best as their design and structure may appear, but in many cases, they are based on simplifying assumptions and abstractions. Reality in production, i.e. reflected by measures such as forces, deflections, travels, vibrations etc. during the process execution, is tremendously characterised by noise and fluctuations revealing a stochastic nature. In metal forming such kind of impact on produced product today in detail is neither explainable nor supported by the aforementioned models. In industrial manufacturing the game to deal with process data changed completely and engineers learned to value the high significance of information included in such digital signals. It should be acknowledged that process data gained from real process environments in many cases contain plenty of technological information, which may lead to increase efficiency of production, to reduce downtime or to avoid scrap. For this reason, authors started to focus on process data gained from numerous metal forming technologies and sheet metal blanking in order to use them for process design objectives. The supporting idea was found in a potential combination of conventional process design strategies with new models purely based on digital signals captured by sensors, actuators and production equipment in general. To utilise established models combined with process data, the following obstacles have to be addressed: (1) acquired process data is biased by sensor artifacts and often lacks data quality requirements; (2) mathematical models such as neural networks heavily rely on high quantities of training data with good quality and sufficient context, but such quantities often are not available or impossible to gain; (3) data-driven black-box models often lack interpretability of containing results, further opposing difficulties to assess their plausibility and extract new knowledge. In this paper, an insight on usage of available data science methods like feature-engineering and clustering on metal forming and blanking process data is presented. Therefore, the paper is complemented with recent approaches of data-driven models and methods for capturing, revealing and explaining previously invisible process interactions. In addition, authors follow with descriptions about recent findings and current challenges of four practical use cases taken from different domains in metal forming and blanking. Finally, authors present and discuss a structure for data-driven process modelling as an approach to extent existing data-driven models and derive process knowledge from process data objecting a robust metal forming system design. The paper also aims to figure out future demands in research in this challenging field of increasing robustness for such kind of manufacturing processes

Die Rolle von Sprachmittlung/Mediation für das (Fremd-)Sprachenlernen und die Weiterentwicklung des (Fremd-)Sprachenunterrichts (an Schulen) im Kontext von Mehrsprachigkeit, Migration und Bildungsauftrag. Eine Stellungnahme

Dieser Beitrag postuliert eine stärkere Berücksichtigung von Mediation im (Fremd-)Sprachenunterricht der Zukunft. Denn Mediationskompetenzen sind genuiner Bestandteil der Interaktion in von Migration, Globalisierung und Mehrsprachigkeit geprägten Gesellschaften. Sie ermöglichen die Zusammenarbeit und das kommunikative Miteinander über kulturelle und sprachliche Grenzen hinweg. Der Begleitband des Gemeinsamen europäischen Referenzrahmens für Sprachen stellt nun auf der Basis eines erweiterten Mediationsbegriffs Skalen mit empirisch validierten Mediationsdeskriptoren zur Erfassung und Beobachtung von Mediationsaktivitäten zur Verfügung. Dieser Begleitband wird unseres Erachtens bei der Entwicklung von Curricula, die Mediationskompetenzen berücksichtigen, bei der Ausarbeitung entsprechender Lehr- und Lernmaterialien und bei der Konzipierung geeigneter Evaluationsformen eine wichtige Rolle spielen

(Meta-)Narrationen – Beschreibend-reflexive Zugänge zu lebensweltlicher Mehrsprachigkeit in der Lehramtsausbildung

Obwohl sprachliche Vielfalt in der Schweiz gesetzlich geregelt ist und gefördert wird, funktioniert ein Grossteil der Kantone einsprachig. Auch im schulischen Kontext dominieren nach wie vor monolingual geprägte Normvorstellungen. In diesem Zusammenhang interessiert uns, wie Menschen mit anderen Erstsprachen als Deutsch in biografisch-narrativen Interviews mit Lehramtsstudierenden über mehrsprachiges Aufwachsen in der Deutschschweiz berichten, und wie dieses ko-konstruierte Wissen von den Studierenden in Metatexten reflektiert wird. Mit Bezug auf narrativ-biographische Zugänge, die wir in der soziolinguistischen und diskursorientierten Erzählforschung verorten, ergründen wir zudem, inwiefern narrativ-sprachbiographische Zugänge in der Hochschullehre helfen, monolingual geprägte Normvorstellungen zu hinterfra-gen und Mehrsprachenkompetenz zu normalisieren

Calorimetry of extracellular vesicles fusion to single phospholipid membrane

Extracellular vesicles (EVs)-mediated communication relies not only on the delivery of complex molecular cargoes as lipids, proteins, genetic material, and metabolites to their target cells but also on the modification of the cell surface local properties induced by the eventual fusion of EVs’ membranes with the cells’ plasma membrane. Here we applied scanning calorimetry to study the phase transition of single phospholipid (DMPC) monolamellar vesicles, investigating the thermodynamical effects caused by the fusion of doping amounts of mesenchymal stem cells-derived EVs. Specifically, we studied EVs-induced consequences on the lipids distributed in the differently curved membrane leaflets, having different density and order. The effect of EV components was found to be not homogeneous in the two leaflets, the inner (more disordered one) being mainly affected. Fusion resulted in phospholipid membrane flattening associated with lipid ordering, while the transition cooperativity, linked to membrane domains’ coexistence during the transition process, was decreased. Our results open new horizons for the investigation of the peculiar effects of EVs of different origins on target cell membrane properties and functionality

Balancing the Affinity and Tumor Cell Binding of a Two-in-One Antibody Simultaneously Targeting EGFR and PD-L1

The optimization of the affinity of monoclonal antibodies is crucial for the development of drug candidates, as it can impact the efficacy of the drug and, thus, the dose and dosing regimen, limit adverse effects, and reduce therapy costs. Here, we present the affinity maturation of an EGFR×PD-L1 Two-in-One antibody for EGFR binding utilizing site-directed mutagenesis and yeast surface display. The isolated antibody variants target EGFR with a 60-fold-improved affinity due to the replacement of a single amino acid in the CDR3 region of the light chain. The binding properties of the Two-in-One variants were confirmed using various methods, including BLI measurements, real-time antigen binding measurements on surfaces with a mixture of both recombinant proteins and cellular binding experiments using flow cytometry as well as real-time interaction cytometry. An AlphaFold-based model predicted that the amino acid exchange of tyrosine to glutamic acid enables the formation of a salt bridge to an arginine at EGFR position 165. This easily adaptable approach provides a strategy for the affinity maturation of bispecific antibodies with respect to the binding of one of the two antigens

Generative AI in the context of assistive technologies: Trends, limitations and future directions

With the tremendous successes of Large Language Models (LLMs) like ChatGPT for text generation and Dall-E for high-quality image generation, generative Artificial Intelligence (AI) models have shown a hype in our society. Generative AI seamlessly delved into different aspects of society ranging from economy, education, legislation, computer science, finance, and even healthcare. This article provides a comprehensive survey on the increased and promising use of generative AI in assistive technologies benefiting different parties, ranging from the assistive system developers, medical practitioners, care workforce, to the people who need the care and the comfort. Ethical concerns, biases, lack of transparency, insufficient explainability, and limited trustworthiness are major challenges when using generative AI in assistive technologies, particularly in systems that impact people directly. Key future research directions to address these issues include creating standardized rules, establishing commonly accepted evaluation metrics and benchmarks for explainability and reasoning processes, and making further advancements in understanding and reducing bias and its potential harms. Beyond showing the current trends of applying generative AI in the scope of assistive technologies in four identified key domains, which include care sectors, medical sectors, helping people in need, and co-working, the survey also discusses the current limitations and provides promising future research directions to foster better integration of generative AI in assistive technologies

UAV-based person re-identification: A survey of UAV datasets, approaches, and challenges

Person re-identification (ReID) has gained significant interest due to growing public safety concerns that require advanced surveillance and identification mechanisms. While most existing ReID research relies on static surveillance cameras, the use of Unmanned Aerial Vehicles (UAVs) for surveillance has recently gained popularity. Noting the promising application of UAVs in ReID, this paper presents a comprehensive overview of UAV-based ReID, highlighting publicly available datasets, key challenges, and methodologies. We summarize and consolidate evaluations conducted across multiple studies, providing a unified perspective on the state of UAV-based ReID research. Despite their limited size and diversity, We underscore current datasets’ importance in advancing UAV-based ReID research. The survey also presents a list of all available approaches for UAV-based ReID. The survey presents challenges associated with UAV-based ReID, including environmental conditions, image quality issues, and privacy concerns. We discuss dynamic adaptation techniques, multi-model fusion, and lightweight algorithms to leverage ground-based person ReID datasets for UAV applications. Finally, we explore potential research directions, highlighting the need for diverse datasets, lightweight algorithms, and innovative approaches to tackle the unique challenges of UAV-based person ReID

Adjoint variable method for transient nonlinear electroquasistatic problems

Many optimization problems in electrical engineering consider a large number of design parameters. A sensitivity analysis identifies the design parameters with the strongest influence on the problem of interest. This paper introduces the adjoint variable method as an efficient approach to study sensitivities of nonlinear electroquasistatic problems in time domain. In contrast to the more common direct sensitivity method, the adjoint variable method has a computational cost nearly independent of the number of parameters. The method is applied to study the sensitivity of the field grading material parameters on the performance of a 320 kV cable joint specimen, which is modeled as a finite element nonlinear transient electroquasistatic problem. Special attention is paid to the treatment of quantities of interest, which are evaluated at specific points in time or space. It is shown that the method is a valuable tool to study this strongly nonlinear and highly transient technical example