RESUMEN
In this paper, a scheme to realize unclonable physical-layer security key generation and distribution (PL-SKGD) based on historical fiber channel state information (HFCSI) is proposed. PL-SKGD schemes based on channel characteristics for enhancing the physical-layer security of optical networks have been proposed in recent years. However, there are potential disadvantages in these schemes, such as 1) low key generation rate (KGR): the slow frequency of the analog waveform change of the channel characteristic leading to low KGR; 2) incompatibility with existing infrastructure: active scrambling to increase the frequency of channel characteristic changes, or tracking changes of channel characteristics requires additional devices; 3) easy to be cloned: all of the optical channel state information is reflected in the signal transmitted inside the fiber, which makes it easy to reproduce by illegal eavesdropper through features analysis and other methods. In order to solve the above problems, a PL-SKGD scheme is designed which uses the chain structure composed of long short-term memory neural network (LSTM-NN) units to learn and store the unique mapping relationship between historical channel time series and provides unclonability based on the fundamental fact that the eavesdropper Eve can never obtain the full HFCSI. The simulation conducted in a quadrature phase shift keying point-to-point optical link system verified successfully that KGR = 0.82 Gbit/s error-free SKGD. The loss function of LSTM-NN drops sharply in the early stages of training and remains a small value. The security of the SKGD system is analyzed, which effectively improves the unclonability of the system. Finally, it is verified that the optimal fiber channel length for error-free SKGD of the proposed scheme is 150 km considering the error correction capability of information reconciliation and weighing key sequence error rate and valid bit generation rate.
RESUMEN
Underwater wireless optical communication (UWOC) has been widely studied as a key technology for ocean exploration and exploitation. However, current UWOC systems neglect semantic information of transmitted symbols, leading to unnecessary consumption of communication resources for transmitting non-essential data. In this paper, we propose and demonstrate a deep-learning-based underwater wireless optical semantic communication (UWOSC) system for image transmission. By utilizing a deep residual convolutional neural network, the semantic information can be extracted and mapped into the transmitted symbols. Moreover, we design a channel model based on long short-term memory network and employ a two-phase training strategy to ensure that the system matches the underwater channel. To evaluate the performance of the proposed UWOSC system, we conduct a series of experiments on an emulated UWOC experimental platform, in which the effects of different turbidity channel environments and bandwidth compression ratios are investigated. Experimental results show that the UWOSC system exhibits superior performance compared to the conventional communication schemes, particularly in challenging channel environments and low bandwidth compression ratios.
RESUMEN
The rapid detection of pyrosulfites in food chemistry is crucial to food safety and health. Here, a coumarin-type ratiometric fluorescent probe was developed based on the Michael addition reaction to detect sodium pyrosulfite (Na2S2O5). The probe exhibited high selectivity and fast response (t1/2 = 6 s) to Na2S2O5 and a low detection limit (26 nM). Because of its excellent ratiometric response performance, the probe was successfully applied to measure the amount of Na2S2O5 in preserved fruits. Colour information analysis and formula calculations were performed to quickly determine the sodium pyrosulfite amount in an actual sample by using a smartphone. Therefore, the intelligent strategy of combining the sensing process and smartphone provides a convenient and efficient method for the fast monitoring of sodium metabisulfite in actual food.