Variational autoencoder-assisted unsupervised hardware fingerprint authentication in a fiber network.

Physical-layer authentication (PLA) based on hardware fingerprints can safeguard optical networks against large-scale masquerade or active injection attacks. However, traditional schemes rely on massive labeled close-set data. Here, we propose an unsupervised hardware fingerprint authentication based on a variational autoencoder (VAE). Specifically, the triplets are generated through variational inference on unlabeled optical spectra and then applied to train the feature extractor, which has an excellent generalization ability and enables fingerprint feature extraction from previously unknown optical transmitters. The feasibility of the proposed scheme is experimentally verified by the successful classification of eight optical transmitters after a 20 km standard single-mode fiber (SSMF) transmission, to distinguish efficiently the rogue from legal devices. A recognition accuracy of 99% and a miss alarm rate of 0% are achieved even under the interference of multiple rogue devices. Moreover, the proposed scheme is verified to have a comparable performance with the results obtained from supervised learning.

Flexible non-linear physical security coding scheme combined with chaotic neural network for OFDM-WDM-PON.

In this paper, a flexible physical security coding scheme integrating chaotic neural network (CNN) and non-linear encryption is proposed for orthogonal frequency division multiplexing wavelength division multiplexing passive optical network (OFDM-WDM-PON). The scheme improved the flexibility, adjustability and the key space of chaotic encryption system by introducing chaos into neural networks. The system will encrypt the bit series, probability shaping points, and subcarriers position of the OFDM signal through linear encryption and non-linear encryption concurrently. Results show that a key sensitivity of 10-15 and a key space of more than 10279 can be achieved. The encrypted system's Lyapunov is 5.2631, along with 12 parameters can be dynamically changed in the range of 0∼5. Furthermore, when the bit error rate (BER) is less than 3.8×10-3, probabilistic shaping (PS) technology decreases power loss by around 0.5 dB. A 20.454 Gb/s data transmission experiment was successfully verified for a span of 25 Km single-mode fiber. According to the experimental results, the proposed encryption scheme is likely to be used in future OFDM-WDM-PON transmission systems.

Multiple Stokes sectional plane image based modulation format recognition with a generative adversarial network.

A novel modulation format recognition (MFR) scheme based on multiple Stokes sectional planes images by generative adversarial network (GAN) is proposed and demonstrated to adapt to next-generation elastic optical network (EON). The application of the encoder, along with the suitable loss function, is able to achieve better performance with regards to MFR of GAN. Experimental verifications were performed for the polarization division multiplexing (PDM)-EON system at a symbol rate of 12.5GBaud. Five modulation formats, including PDM-BPSK, PDM-QPSK, PDM-8PSK, PDM-8QAM, PDM-16QAM, were recognized by our scheme under the condition of practical optical signal-to-noise ratio (OSNR) over both back-to-back transmission and 25km standard signal-mode fiber (SSMF). Specifically, the minimum required OSNR of PDM-16QAM signal to achieve 100% MFR success rate is 18 dB, which is lower than its corresponding 7% forward error correction (FEC) threshold. Results show that, compared with three other machine learning algorithms, the proposed scheme obtains the higher recognition accuracy in the case of the same OSNR. Moreover, the training data required by the proposed scheme is less than the traditional convolutional neural network (CNN) in MFR task, which means the training cost of the neural network is greatly reduced by using GAN.

Transfer learning assisted convolutional neural networks for modulation format recognition in few-mode fibers.

Few-mode fiber (FMF), a mode multiplex technique, has been a candidate to provide high transmission capability in next-generation elastic optical networks (EONs), where the probabilistic shaping (PS) technology is widely used to approach Shannon limit. In this paper, we investigate a fast and accurate method of modulation format recognition (MFR) of received signals based on a transfer learning network (TLN) in PS-based FMF-EONs. TLN can apply the feature extraction ability of convolutional neural networks to the analysis of the constellations. We conduct experiments to demonstrate the effectiveness of the proposed scheme in FMF transmissions. Six modulation formats, including 16QAM, PS-16QAM, 32QAM, PS-32QAM, 64QAM and PS-64QAM, and four propagating modes, including LP01, LP11a, LP11b and LP21, are involved. In addition, comparisons of TLN with different structures of convolutional neural networks backbones are presented. In the experiment, the iterations of the TLN are one-tenth that of conventional deep learning network (DLN), and the TLN overcomes the problem of overfitting and requires less data than that of DLN. The experimental results show that the TLN is an efficient and feasible method for MFR in the PS-based FMF communication system.

Effects of physicochemical and structural properties of single and double feedstuffs derived from different botanic sources on in vitro starch digestion and glucose release kinetics.

Starch is the largest constituent in animal diets. The aims of this study were as follows: (a) to assess the variability of basic physicochemical properties and in vitro starch digestion of starchy feedstuffs and investigate relationship between physicochemical properties and starch digestion of the feedstuffs, and (b) to explore the effects of different sources of starchy feedstuffs on starch digestion and glucose release. In this study, we determined the inherent molecular structure and granular structure of starch and chemical compositions of seven starchy feedstuffs, as well as starch digestion in single feedstuff and different feedstuffs combined with corn. Scanning electron microscope (SEM) results revealed significant difference between granule shape and size of starch of different feedstuffs. Fourier transforms infrared (FTIR) spectra for barley and wheat had lower (p < 0.05) absorbance band at areas A_860 and A_928 than other feedstuffs, yet rice starch had the lowest value for ratio (R) (1047/1022). Moreover, digestion rate ranged from 0.0157/min for resistant starch (sorghum) to 0.029/min for rapidly starch (broken rice). The principle component analysis (PCA) showed that predicted glycaemic index (pGI) was positively related to A_1022, glucose and rapidly (RDS) content and negatively related to A_995, A_1047, R (1047/1022), resistant starch (RS) and amylose content. Most of the feedstufss with corn combination had no effect on rate of starch digestion. In addition, different starchy feeds and corn combination changed the rate of starch digestion, when barley, however, sorghum combined with corn seemed to affect rate of starch digestion. To sum up, different sources differed in basic physicochemical and structural properties, which would influence the digestion rate of starch and the release of glucose. Combination of different feedstuffs particular sorghum with corn has interactive effect on starch digestion and the release of glucose.

5D data iteration in a multi-wavelength OFDM-PON using the hyperchaotic system.

In this Letter, a novel five-dimensional (5D) data-iteration-based encryption model is proposed at physical layer for multi-wavelength optical frequency division multiplexing passive optical network (OFDM-PON) by using a hyperchaotic system. The proposed scheme can generate five chaotic sequences at a time. The sensitivity of 10-18 can be achieved, along with a key space of 1095. In addition, we use a multi-wavelength channel to transmit the information, and the optical network unit can freely choose the wavelength. The probability shaping technology has greatly improved the bit error rate performance. A 16Gb/s/λ data is successfully transmitted across 25 km standard single-mode fiber in the experimental verifications. Therefore, it will have a positive impact in the future security optical network.

Engineering hypoxia-responsive 6-aminonicotinamide prodrugs for on-demand NADPH depletion and redox manipulation.

Glucose-6-phosphate dehydrogenase (G6PD) is a promising target in cancer therapy. However, poor cellular uptake and off-target toxicity have impeded the clinical translation of a canonical G6PD inhibitor (6-aminonicotinamide/6AN). Here, we report a prodrug strategy to address this issue. The tailored 6AN prodrug contains an azo-bearing protection moiety. The hydrophobic prodrug showed increased cellular uptake than 6AN and was vulnerable to hypoxia, resulting in NAD(P)H quinone dehydrogenase 1 (NQO1)-triggered cleavage of azo bonds. Intriguingly, the prodrug showed configuration-dependent anti-cancer potency. Despite the lower thermodynamic stability, the cis isomer showed enhanced cellular uptake compared to the trans counterpart due to the increased aqueous solubility. Moreover, the boosted potency of the cis isomer compared to the trans isomer arose from the enhancement of NOQ1-catalyzed 6AN release under hypoxia, a hallmark of solid tumors. The discovery of hypoxia-responsive 6AN prodrugs in the current work opens up new avenues for G6PD-targeting cancer medicines.

Efficacy of Mobile Health Applications to Improve Physical Activity and Sedentary Behavior: A Systematic Review and Meta-Analysis for Physically Inactive Individuals.

Physical inactivity and sedentary behavior (SB) have attracted growing attention globally since they relate to noninfectious chronic diseases (NCDs) and could further result in the loss of life. This systematic literature review aimed to identify existing evidence on the efficacy of mobile health (mHealth) technology in inducing physical activity and reducing sedentary behavior for physically inactive people. Studies were included if they used a smartphone app in an intervention to improve physical activity and/or sedentary behavior for physically inactive individuals. Interventions could be stand-alone interventions or multi-component interventions, including an app as one of several intervention components. A total of nine studies were included, and all were randomized controlled trials. Two studies involved interventions delivered solely via a mobile application (stand-alone intervention) and seven studies involved interventions that used apps and other intervention strategies (multi-component intervention). Methodological quality was assessed, and the overall quality of the studies was ensured. The pooled data favored intervention in improving physical activity and reducing sedentary behavior. This review provided evidence that mobile health intervention improved physical activity and reduced sedentary behavior among inactive individuals. More beneficial effects can be guaranteed when interventions include multiple components. Further studies that maintain the effectiveness of such interventions are required to maximize user engagement and intervention efficacy.