14 research outputs found
Transgenerationale Schuld in Nora Krugs graphic novel Heimat
No full textIn the graphic novel Heimat. Ein deutsches Familienalbum (2018), the German author Nora Krug documents the analysis of her family’s history. As a member of the third generation after the Holocaust, Krug wants to find out if and in which way her grandparents were involved in the Holocaust. In addition, her research shows whether her own family’s history corresponds to Germany’s cultural history. Krug seeks to improve her understanding of her family’s relations and her ‘Heimat’. The archive is the starting point of her investigations and defines the design of the story. Heimat connects information from the archives with testimonies and memorabilia. In this fashion, Krug creates a creative treatment of the family history. Not only the archive, but also photography is significant in the design of the graphic novel. Hirsch’ theory on postmemory explains the self-representation of the family in photographs. Krugs plurimedial design of the story creates a multilayered narrative that processes the complex themes of Heimat in a sensible way
A comparative study of nonlinear companding schemes for CO-OFDM transmissions
No full textInternational audienceWang’s nonlinear companding scheme is investigatedhere for the first time for CO-OFDM transmissions. Asa result of an optimal predistortion function design, wedemonstrate a 3 dB BER advantage for a systememploying a Semiconductor Optical Amplifier (SOA)
A Parametric Network for the Global Compensation of Physical Layer Linear Impairments in Coherent Optical Communications
No full textInternational audienceThis paper proposes a parametric network for the joint compensation of multiple linear impairments in coherent optical communication systems. The considered linear impairments include both static and time-variant effects such as in-phase/quadrature (IQ) imbalance, laser phase noise (PN), chromatic dispersion (CD), polarization mode dispersion (PMD), and carrier frequency offset (CFO). To jointly compensate for these considered impairments, the proposed network is composed of parametric layers that exploit the particular signal model of each impairment. The layers’ parameters are jointly learned during a training stage. This stage uses a supervised step that exploits the knowledge of some transmitted data (preamble and/or pilots) and a self-labeling step that uses the knowledge of the symbol constellation. In addition, a new validation technique that does not require a different dataset is developed to avoid overfitting. The parametric network performance is compared to classical digital signal processing (DSP), and Deep Learning (DL) approaches using simulated data. Simulation results show that the proposed network outperforms the competing approaches in terms of Bit Error Rate (BER) while maintaining a relatively reduced computational complexity. In the scenarios considered, compared to the parametric network, the DSP approach introduces an OSNR penalty between 0.2 dB and 1.7 dB at a BER of 4×10^−3 . Furthermore, simulation results demonstrate that the proposed network is way more flexible than other approaches since it can easily be adapted to a different scenario and coupled with other techniques
ParamNet: A Multi-Layer Parametric Network for Joint Channel Estimation and Symbol Detection
No full textThis paper proposes a parametric-based network architecture for joint channel
estimation and data detection in communications systems with hardware
impairments. This architecture is composed of a data-augmented layer, a custom
soft thresholding function, and several linear layers modeling the effect of
channel effects and hardware impairments. In the proposed network, the soft
thresholding function softly constrains the detected data to be within the
considered constellation. The latter depends only on one one parameter that is
optimized during training. The benefit of the proposed approach is illustrated
through a communication chain corrupted by multiple impairments and noises
An IR-UWB multi-sensor approach for collision avoidance in indoor environments
No full textThe content of this paper reflects only the authors' view and the Research Executive Agency is not responsible for any use that may be made of the information it contains.International audienceThis paper aims to propose new techniques to detect and distinguish humans from moving machines in indoor environments. Although many research efforts have been already dedicated to humans' indoor detection, most of the work has been focused on counting people and crowd measurement for consumer business applications. Our objective is to develop a reliable approach for humans' indoor detection and localization aiming at avoiding collisions inside a mixed Industry 4.0 manned and unmanned environment, so that to enhance the personal and equipment safety and to prevent unwanted intrusions. An original aspect of our research is that we have worked on the real time estimation of humans' and moving machines' positions, while addressing the problems of multipath components and noise clutter detection. A multi-pulse constant false alarm rate detection algorithm is also proposed for removing the misdetections due to heavy clutter components in the indoor environment. Four impulse radio ultrawideband transceivers are placed in a specific geometry and data fusion is performed to reduce the influence of multipath and noise on the detection process. A convolutional neural network (CNN) is then used to extract the patterns corresponding to a moving machine and humans and classify them accordingly. Experiments have been carried out in two different indoor environments to demonstrate the performance of the proposed algorithms
Digital Predistortion for CO-OFDM Systems Using Generalized Memory Polynomials
No full textInternational audienceGeneralized memory polynomials have recently been proposed as an efficient means of linearization in wireless radio systems. Their unique structure provides an excellent compromise between complexity and accuracy. In the present paper, we show that the approach can successfully be transposed to optical systems. The investigated scenario is that of a CO-OFDM solution for metropolitan/access networks
Tensor-network-based predistorter design for multiple-input multiple-output nonlinear systems
No full textThe recent development in tensor algorithms has shed new light on many design problems previously doomed by the curse of dimensionality. In particular, latest advances are seen in the tensor-network-based multiple-input multiple-output (MIMO) Volterra series modeling of nonlinear systems whereby the Volterra kernels can now be efficiently identified at an unprecedented order and memory length. Subsequent to nonlinear system identification, this paper studies the nonlinear MIMO predistorter design that is crucial for linearizing the response of nonlinear modules such as power amplifiers in mixed-signal applications. Two tensor-network-based predistorter design schemes are presented for the first time, whose effectiveness are validated through practical examples
Joint Estimation and Compensation of Transmitter IQ Imbalance and Laser Phase Noise in Coherent Optical Systems
No full textInternational audienc
An Improved Envelope Tracking SOA Amplifier for CO-OFDM Transmission by Using PSO
No full textInternational audienc
