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Bulk heterojunction (BHJ) organic solar cells have made remarkable inroads towards 20% efficiency, yet nonradiative recombination losses ({\Delta}Vnr) remain high compared to silicon and perovskite photovoltaics. Interfaces buried within BHJ blends hold the key to recombination losses but access to their energetic landscape underpinning charge transfer (CT) states and their disorder remain elusive. Here, we reveal the energetic landscape and CT state manifold of modern BHJs with both spatial and energetic resolutions and link the offset between singlet (ES1) and CT energy (ES1-CT) and interfacial energetic disorder with {\Delta}Vnr. We do so by locally mapping the energy distributions of modern PM6-based BHJs with IT4F, Y6 and PC71BM acceptors and combine it, for the first time, with sensitive EQE measurements, to visualize and quantify donor (D) and acceptor (A) energetics at interfaces and associated them with CT states within a modified Marcus framework. A key new ability is the identification of the specific BHJ interfaces associated with the CT manifold, including where the lowest energy CT states reside. Moreover, we quantify energy levels and electronic disorders directly at these and other interfaces and connect these contributions to the energy losses. We delineate the influences of S1-CT offset and interfacial energetic disorder on {\Delta}Vnr across morphologically varied BHJs. Our results show both factors influencing energy losses in different ways. We demonstrate that PM6:Y6 can achieve low {\Delta}Vnr by forming a nominally sharp D/A interface with exceptionally low interfacial disorder via judicious processing combined with a low S1 to CT offset. This provides a design rule to minimize {\Delta}Vnr for modern NFAs: sharp D/A interfaces with low S1 to CT offset exhibiting minimal interfacial disorder., Comment: Manuscript: 30 pages, 4 figures, 2 tables Supplementary information: 41 pages, 24 figures, 7 tables

Central to the efficacy of prognostics and health management methods is the acquisition and analysis of degradation data, which encapsulates the evolving health condition of engineering systems over time. Degradation data serves as a rich source of information, offering invaluable insights into the underlying degradation processes, failure modes, and performance trends of engineering systems. This paper provides an overview of publicly available degradation data sets.

Being able to predict the remaining useful life (RUL) of an engineering system is an important task in prognostics and health management. Recently, data-driven approaches to RUL predictions are becoming prevalent over model-based approaches since no underlying physical knowledge of the engineering system is required. Yet, this just replaces required expertise of the underlying physics with machine learning (ML) expertise, which is often also not available. Automated machine learning (AutoML) promises to build end-to-end ML pipelines automatically enabling domain experts without ML expertise to create their own models. This paper introduces AutoRUL, an AutoML-driven end-to-end approach for automatic RUL predictions. AutoRUL combines fine-tuned standard regression methods to an ensemble with high predictive power. By evaluating the proposed method on eight real-world and synthetic datasets against state-of-the-art hand-crafted models, we show that AutoML provides a viable alternative to hand-crafted data-driven RUL predictions. Consequently, creating RUL predictions can be made more accessible for domain experts using AutoML by eliminating ML expertise from data-driven model construction., Comment: Manuscript accepted at IEEE SMC 2023

Continual learning for Semantic Segmentation (CSS) is a rapidly emerging field, in which the capabilities of the segmentation model are incrementally improved by learning new classes or new domains. A central challenge in Continual Learning is overcoming the effects of catastrophic forgetting, which refers to the sudden drop in accuracy on previously learned tasks after the model is trained on new classes or domains. In continual classification this challenge is often overcome by replaying a small selection of samples from previous tasks, however replay is rarely considered in CSS. Therefore, we investigate the influences of various replay strategies for semantic segmentation and evaluate them in class- and domain-incremental settings. Our findings suggest that in a class-incremental setting, it is critical to achieve a uniform distribution for the different classes in the buffer to avoid a bias towards newly learned classes. In the domain-incremental setting, it is most effective to select buffer samples by uniformly sampling from the distribution of learned feature representations or by choosing samples with median entropy. Finally, we observe that the effective sampling methods help to decrease the representation shift significantly in early layers, which is a major cause of forgetting in domain-incremental learning., Comment: Accepted at 2022 IEEE Conference on Intelligent Transportation Systems (ITSC 2022)

Cyber-Physical Systems (CPS) are formed through interconnected components capable of computation, communication, sensing and changing the physical world. The development of these systems poses a significant challenge since they have to be designed in a way to ensure cyber-security without impacting their performance. This article presents the Security Cost Modelling Framework (SCMF) and shows supported by an experimental study how it can be used to measure, normalise and aggregate the overall performance of a CPS. Unlike previous studies, our approach uses different metrics to measure the overall performance of a CPS and provides a methodology for normalising the measurement results of different units to a common Cost Unit. Moreover, we show how the Security Costs can be extracted from the overall performance measurements which allows to quantify the overhead imposed by performing security-related tasks. Furthermore, we describe the architecture of our experimental testbed and demonstrate the applicability of SCMF in an experimental study. Our results show that measuring the overall performance and extracting the security costs using SCMF can serve as basis to redesign interactions to achieve the same overall goal at less costs.

The importance of Cyber Physical Systems (CPS) and Internet of Things (IoT) applications is constantly increasing, especially in the context of Industry 4.0. Architectural decisions are crucial not just for performance, security and resilience reasons but also regarding costs and resource usage. In this paper we analyse two of the fundamental approaches to design control loops (i.e. time-driven and event-driven), show how they can be realised and evaluate their power requirements. Through this the design criteria can be extended also considering the optimization of energy related aspects., Comment: IEEE Link: https://ieeexplore.ieee.org/document/9464065

The seamless integration of III-V nanostructures on silicon is a long-standing goal and an important step towards integrated optical links. In the present work, we demonstrate scaled and waveguide coupled III-V photodiodes monolithically integrated on Si, implemented as InP/In0.5Ga0.5As/InP p-i-n structure. The waveguide coupled devices show a dark current down to 0.048 A/cm2 at -1 V and a responsivity up to 0.2 A/W at -2 V. Using grating couplers centered around 1320 nm, we observed a cutoff frequency f3dB exceeding 70 GHz and data reception at 50 GBd with OOK and 4PAM. When operated in forward bias as light emitting diode, the devices emit light centered at 1550 nm. Furthermore, we also investigate the self-heating of the devices using scanning thermal microscopy and find a temperature increase of only ~15 K during the device operation as emitter, in accordance with thermal simulation results.

Photonic crystal (PhC) cavities are promising candidates for Si photonics integrated circuits due to their ultrahigh quality (Q)-factors and small mode volumes. Here, we demonstrate a novel concept of a one-dimensional hybrid III-V/Si PhC cavity which exploits a combination of standard silicon-on-insulator technology and active III-V materials. Using template-assisted selective epitaxy, the central part of a Si PhC lattice is locally replaced with III-V gain material. The III-V material is placed to overlap with the maximum of the cavity mode field profile, while keeping the major part of the PhC in Si. The selective epitaxy process enables growth parallel to the substrate and hence, in-plane integration with Si, and in-situ in-plane homo- and heterojunctions. The fabricated hybrid III-V/Si PhCs show emission over the entire telecommunication band from 1.2 {\mu}m to 1.6 {\mu}m at room temperature validating the device concept and its potential towards fully integrated light sources on silicon., Comment: 10 pages, 5 figures

A key component for optical on-chip communication is an efficient light source. However, to enable low energy per bit communication and local integration with Si CMOS, devices need to be further scaled down. In this work, we fabricate micro- and nanolasers of different shapes in InP by direct wafer bonding on Si. Metal-clad cavities have been proposed as means to scale dimensions beyond the diffraction limit of light by exploiting hybrid photonic-plasmonic modes. Here, we explore the size scalability of whispering-gallery mode light sources by cladding the sidewalls of the device with Au. The metal clad cavities demonstrate room temperature lasing upon optical excitation for Au-clad devices with InP diameters down to 300 nm, while the purely photonic counterparts show lasing only down to 500 nm. Numerical thermal simulations support the experimental findings and confirm an improved heat-sinking capability of the Au-clad devices, suggesting a reduction in device temperature of 473 K for the metal-clad InP nanodisk laser, compared to the one without Au. This would provide substantial performance benefits even in the absence of a hybrid photonic-plasmonic mode. These results give us insight into the benefits of metal-clad designs to downscale integrated lasers on Si., Comment: 15 pages, 7 figures

With the proliferation of consumer virtual reality (VR) headsets and creative tools, content creators have started to experiment with new forms of interactive audience experience using immersive media. Understanding user attention and behaviours in virtual environment can greatly inform creative processes in VR. We developed an abstract VR painting and an experimentation system to study audience encounters through eye gaze and movement tracking. The data from a user experiment with 35 participants reveal a range of user activity patterns in art exploration. Deep learning models are used to study the connections between behavioural data and audience background. New integrated methods to visualise user attention as part of the artwork are also developed as a feedback loop to the content creator.

Fixed edge processing has become a key feature of 5G networks, while playing a key role in reducing latency, improving energy efficiency and introducing flexible compute resource utilization on-demand with added cost savings. Autonomous vehicles are expected to possess significantly more on-board processing capabilities and with improved connectivity. Vehicles continue to be used for a fraction of the day, and as such there is a potential to increase processing capacity by utilizing these resources while vehicles are in short-term and long-term car parks, in roads and at road intersections. Such car parks and road segments can be transformed, through 6G networks, into vehicular fog clusters, or Fogbanks, that can provide processing, storage and sensing capabilities, making use of underutilized vehicular resources. We introduce the Fogbanks concept, outline current research efforts underway in vehicular clouds, and suggest promising directions for 6G in a world where autonomous driving will become commonplace. Moreover, we study the processing allocation problem in cloud-based Fogbank architecture. We solve this problem using Mixed Integer Programming (MILP) to minimize the total power consumption of the proposed architecture, taking into account two allocation strategies, single allocation of tasks and distributed allocation. Finally, we describe additional future directions needed to establish reliability, security, virtualisation, energy efficiency, business models and standardization.

Metastable wurtzite crystal phases of semiconductors comprise enormous potential for high-performance electro-optical devices, owed to their extended tunable direct band gap range. However, synthesizing these materials in good quality and beyond nanowire size constraints has remained elusive. In this work, the epitaxy of wurtzite InP microdisks and related geometries on insulator for optical applications is explored. This is achieved by an elaborate combination of selective area growth of fins and a zipper-induced epitaxial lateral overgrowth, which enables co-integration of diversely shaped crystals at precise position. The grown material possesses high phase purity and excellent optical quality characterized by STEM and $\mu$-PL. Optically pumped lasing at room temperature is achieved in microdisks with a lasing threshold of 365 $\mu J/cm^2$, thus demonstrating promise for a wide range of photonic applications., Comment: 8 pages, 4 figures

In Industry 4.0, Cyber-Physical Systems (CPS) are formed by components, which are interconnected with each other over the Internet of Things (IoT). The resulting capabilities of sensing and affecting the physical world offer a vast range of opportunities, yet, at the same time pose new security challenges. To address these challenges there are various IoT Frameworks, which offer solutions for managing and controlling IoT-components and their interactions. In this regard, providing security for an interaction usually requires performing additional security-related tasks (e.g. authorisation, encryption, etc.) to prevent possible security risks. Research currently focuses more on designing and developing these frameworks and does not satisfactorily provide methodologies for evaluating the resulting costs of providing security. In this paper we propose an initial approach for measuring the resulting costs of providing security for interacting IoT-components by using a Security Cost Modelling Framework. Furthermore, we describe the necessary building blocks of the framework and provide an experimental design showing how it could be used to measure security costs at runtime., Comment: https://www.scitepress.org/PublicationsDetail.aspx?ID=X/mD2ocAMT0=&t=1

In times of Industry 4.0 and cyber-physical systems (CPS) providing security is one of the biggest challenges. A cyber attack launched at a CPS poses a huge threat, since a security incident may affect both the cyber and the physical world. Since CPS are very flexible systems, which are capable of adapting to environmental changes, it is important to keep an overview of the resulting costs of providing security. However, research regarding CPS currently focuses more on engineering secure systems and does not satisfactorily provide approaches for evaluating the resulting costs. This paper presents an interaction-based model for evaluating security costs in a CPS. Furthermore, the paper demonstrates in a use case driven study, how this approach could be used to model the resulting costs for guaranteeing security.