Formability analyses of uni-directional and textile reinforced thermoplastics Part A Applied science and manufacturing

Authentication of persons and objects is a crucial aspect of security. We experimentally demonstrate quantum-secure authentication (QSA) of a classical multiple-scattering key. The key is authenticated by illuminating it with a light pulse containing fewer photons than spatial degrees of freedom and verifying the spatial shape of the reflected light. Quantum-physical principles forbid an attacker to fully characterize the incident light pulse. Therefore, he cannot emulate the key by digitally constructing the expected optical response, even if all information about the key is publicly known. QSA uses a key that cannot be copied due to technological limitations and is quantum-secure against digital emulation. Moreover, QSA does not depend on secrecy of stored data, does not depend on unproven mathematical assumptions, and is straightforward to implement with current technology.

Quantum-secure authentication of a physical unclonable key

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Structural design toward functional materials by electrospinning: A review

The use of multimode fibers offers advantages in the field of communication technology in terms of transferable information density and information security. For applications using physical layer security or mode division multiplexing, the complex transmission matrix must be known. To measure the transmission matrix, the individual modes of the multimode fiber are excited sequentially at the input and a mode decomposition is performed at the output. Mode decomposition is usually performed using digital holography, which requires the provision of a reference wave and leads to high efforts. To overcome these drawbacks, a neural network is proposed, which performs mode decomposition with intensity-only camera recordings of the multimode fiber facet. Due to the high computational complexity of the problem, this approach was usually limited to a number of 6 modes. In this work, it could be shown for the first time that by using a DenseNet with 121 layers it is possible to break through the hurdle of 6 modes. The advancement is demonstrated by a mode decomposition with 10 modes experimentally. The training process is based on synthetic data. The proposed method is quantitatively compared to the conventional approach with digital holography. In addition, it is shown that the network can perform mode decomposition on a 55-mode fiber, which also supports modes unknown to the neural network. The smart detection using a DenseNet opens new ways for the application of multimode fibers in optical communication networks for physical layer security.

Intensity-only Mode Decomposition on Multimode Fibers using a Densely Connected Convolutional Network

Multimode fibers (MMFs) have the potential to carry complex images for endoscopy and related applications, but decoding the complex speckle patterns produced by mode-mixing and modal dispersion in MMFs is a serious challenge. Several groups have recently shown that convolutional neural networks (CNNs) can be trained to perform high-fidelity MMF image reconstruction. We find that a considerably simpler neural network architecture, the single hidden layer dense neural network, performs at least as well as previously-used CNNs in terms of image reconstruction fidelity, and is superior in terms of training time and computing resources required. The trained networks can accurately reconstruct MMF images collected over a week after the cessation of the training set, with the dense network performing as well as the CNN over the entire period.

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Image reconstruction through a multimode fiber with a simple neural network architecture

Multimode fibers (MMF) are promising candidates to increase the data rate while reducing the space required for optical fiber networks. However, their use is hampered by mode mixing and other effects, leading to speckled output patterns. This can be overcome by measuring the transmission matrix (TM) of a multimode fiber. In this contribution, a mode-selective excitation of complex amplitudes is performed with only one phase-only spatial light modulator. The light field propagating through the fiber is measured holographically and is analyzed by a rapid decomposition method. This technique requires a small amount of measurements N, which corresponds to the degree of freedom of the fiber. The TM determines the amplitude and phase relationships of the modes, which allows us to understand the mode scrambling processes in the MMF and can be used for mode division multiplexing.

https://www.mdpi.com/2076-3417/9/1/195/pdf

Transmission Matrix Measurement of Multimode Optical Fibers by Mode-Selective Excitation Using One Spatial Light Modulator

Inverse precoding algorithms in multimode fiber based communication networks are used to exploit mode dependent losses on the physical layer. This provides an asymmetry between legitimate (Bob) and unlegitimate (Eve) receiver of messages resulting in a significant SNR advantage for Bob. In combination with dynamic mode channel changes, Eve has no chance to reconstruct a sent message even in a worst case scenario in which she is almighty. This is the first time, Physical Layer Security in a fiber optical network is investigated on the basis of measured transmission matrices. These results show that messages can be sent securely with conventional communication techniques. Translating the task of securing data from software to hardware represents the potential of a scientific paradigm shift. The introduced technique is a step towards the development of cyber physical systems.

Physical Layer Security in Multimode Fiber Optical Networks.

The light propagation through a multimode fiber is used to increase information security during data transmission without the need for cryptographic approaches. The use of an inverse precoding method in a multimode fiber-optic communication network is based on mode-dependent losses on the physical layer. This leads to an asymmetry between legitimate (Bob) and illegitimate (Eve) recipients of messages, resulting in significant SNR advantage for Bob. In combination with dynamic mode channel changes, there are defined hurdles for Eve to reconstruct a sent message even in a worst-case scenario in which she knows the channel completely. This is the first time that physical layer security has been investigated in a fiber optical network based on measured transmission matrices. The results show that messages can be sent securely using traditional communication techniques. The technology introduced is a step towards the development of cyber physical systems with increased security.

Multimode fibers are regarded as the key technology for the steady increase in data rates in optical communication. However, light propagation in multimode fibers is complex and can lead to distortions in the transmission of information. Therefore, strategies to control the propagation of light should be developed. These strategies include the measurement of the amplitude and phase of the light field after propagation through the fiber. This is usually done with holographic approaches. In this paper, we discuss the use of a deep neural network to determine the amplitude and phase information from simple intensity-only camera images. A new type of training was developed, which is much more robust and precise than conventional training data designs. We show that the performance of the deep neural network is comparable to digital holography, but requires significantly smaller efforts. The fast characterization of multimode fibers is particularly suitable for high-performance applications like cyberphysical systems in the internet of things.

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Stefan Rothe

Papers

Intensity-only Mode Decomposition on Multimode Fibers using a Densely Connected Convolutional Network

Transmission Matrix Measurement of Multimode Optical Fibers by Mode-Selective Excitation Using One Spatial Light Modulator

Physical Layer Security in Multimode Fiber Optical Networks.

Physical Layer Security in Multimode Fiber Optical Networks.

Deep Learning for Computational Mode Decomposition in Optical Fibers