In this paper, a novel ultraviolet (UV) scatter communication scheme is presented, designed to dynamically adjust the signal duty cycle to optimize on–off keying (OOK) modulation and reduce the bit error rate (BER), particularly under varying rate settings. This approach addresses the significant challenge posed by LED tailing effects, which cause signal fluctuations and increase BER in high-speed communications. This BER suppression scheme is proposed for the first time in UV communication research, enhancing communication performance without the need for additional hardware or complex algorithms. A UV communication model that incorporates both path loss and LED tailing effects is introduced, with the probability density function of the signal from transmitter to receiver derived. By varying the signal duty cycle, tailing-induced BER is effectively minimized. Additionally, a closed-form expression for signal transmission BER using a single-scattering model is provided, and the proposed UV communication system is validated through comprehensive simulations and experimental tests. The results indicate that LED tailing has a pronounced impact on BER at higher communication speeds, while its effects are less significant at lower speeds. By optimizing the duty cycle parameters for various communication rates, findings demonstrate that lower duty cycle settings significantly reduce the BER at higher speeds. This further demonstrates the excellent performance of the proposed UV communication solution for OOK-modulated optical communication.

In recent years, a third-generation neural network, namely, spiking neural network, has received plethora of attention in the broad areas of Machine learning and Artificial Intelligence. In this paper, a novel differential-based encoding method is proposed and new spike-based learning rules for backpropagation is derived by constraining the addition of bias voltage in spiking neurons. The proposed differential encoding method can effectively exploit the correlation between the data and improve the performance of the proposed model, and the new learning rule can take complete advantage of the modulation properties of bias on the spike firing threshold. We experiment with the proposed model on the environmental sound dataset RWCP and the image dataset MNIST and Fashion-MNIST, respectively, and assign various conditions to test the learning ability and robustness of the proposed model. The experimental results demonstrate that the proposed model achieves near-optimal results with a smaller time step by maintaining the highest accuracy and robustness with less training data. Among them, in MNIST dataset, compared with the original spiking neural network with the same network structure, we achieved a 0.39% accuracy improvement.