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Towards the Interpretability of Deep Learning Models for Multi-Modal Neuroimaging: Finding Structural Changes of the Ageing Brain
Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear. We combined ensembles of convolutional neural networks with...
Optical Generation and Transmission of mmWave Signals in 5G ERA: Experimental Evaluation Paradigm
We demonstrate the generation, of a mmWave signal via the injection of an optical frequency comb (OFC) into an integrated tunable dual distributed Bragg reflector (DBR) laser as well as the fiber transmission and the processing of this signal by...
Ultrawideband Systems and Networks: Beyond C+L -Band
In the evolution of optical networks, enhancement of spectral efficiency (SE) enhancement has been the most cost-efficient and thus the main driver for capacity enhancementincrease for decades. As a result, the development of optical transport...
Hyperspectral Demosaicing of Snapshot Camera Images Using Deep Learning
This work proposes a parallel neural network based demosaicing procedure for single-camera-one-shot-for hyperspectral imaging trained on a new ground truth dataset captured in a controlled environment by a hyperspectral snapshot camera with a 4×4...
Experimental Investigation of Information Bit Scrambling for Physical-Layer Security in Coherent Fiber-Optic Systems
We experimentally demonstrate tap-proof coherent optical 640-Gb/s transmission based on encryption-less physical layer security. Information bit scrambling combined with soft-decision error-correction coding yields favorably small security gaps,...
Perfusion Assessment via Local Remote Photoplethysmography (rPPG)
We present an approach to assess the perfusion of visible human tissue from RGB video files. We show that locally resolved rPPG-signals can be used for intraoperative perfusion analysis and visualization during skin and organ transplantation as...
Customizing the Appearance of Sparks with Binary Metal Alloys
Alloys consisting of >65 at. % of a brightly emitting and low-boiling-point metal and a carrier metal allow achieving long-flying deeply colored sparks. Besides the color, branching of sparks is crucial for the visual appearance. Rare-earth...
Explaint to not Forget: Defending Against Catastrophic Forgetting with XAI
The ability to continuously process and retain new information like we do naturally as humans is a feat that is highly sought after when training neural networks. Unfortunately, the traditional optimization algorithms often require large amounts...
Towards the Interpretability of Deep Learning Models for Human Neuroimaging
Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear. We combined ensembles of convolutional neural networks with...
Characterization of Dispersion-Tailored Silicon Strip Waveguide for Wideband Wavelength Conversion
In view of application to wideband wavelength conversion, an SOI waveguide was fabricated and characterized. Conversion of C- band WDM test signals into S- and L- bands in a single waveguide is demonstrated.
DSP-Based Link Tomography for Amplifier Gain Estimation and Anomaly Detection in C+L-Band Systems
In this work, we propose a spatially-resolved and wavelength-dependent DSP-based monitoring scheme to accurately estimate the spectral gain profile of C+L-band in-line Erbium-doped fiber amplifiers deployed in a 280-km single mode fiber link.
Bayesian Optimization for Nonlinear System Identification and Pre-distortion in Cognitive Transmitters
We present a digital signal processing (DSP) scheme that performs hyperparameter tuning (HT) via Bayesian optimization (BO) to autonomously optimize memory tap distribution of Volterra series and adapt parameters used in the synthetization of a...
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Innovationen für die digitale Gesellschaft
Innovationen für die digitale Gesellschaft von morgen stehen im Mittelpunkt der Forschungs- und Entwicklungsarbeit des Fraunhofer-Instituts für Nachrichtentechnik, Heinrich-Hertz-Institut, HHI. Dabei ist das Fraunhofer HHI weltweit führend in der Erforschung von mobilen und optischen Kommunikationsnetzen und -systemen, faseroptischen Sensorsystemen sowie der Kodierung von Videosignalen und der Bilddatenverarbeitung.
Gemeinsam mit internationalen Partnern aus Forschung und Industrie arbeitet das Fraunhofer HHI im gesamten Spektrum der digitalen Infrastruktur – von der grundlegenden Forschung bis hin zur Entwicklung von Prototypen und Lösungen. Das Institut trägt signifikant zu den Standards für Informations- und Kommunikationstechnologien bei und schafft neue Anwendungen als Partner der Industrie.
Innovationen für die digitale Gesellschaft von morgen stehen im Mittelpunkt der Forschungs- und Entwicklungsarbeit des Fraunhofer Heinrich-Hertz-Instituts HHI. Dabei ist das Institut weltweit führend in der Erforschung von mobilen und optischen Kommunikationsnetzen und -systemen, faseroptischen Sensorsystemen sowie der Kodierung von Videosignalen und der Bilddatenverarbeitung.