Weizheng Ren

A deep-learning-based antenna array synthesis method for varied aperture lengths is proposed in this article. Inspired by Natural Language Processing (NLP), a tri-branched Recurrent Neural Network (RNN) based on Long Short Term Memory (LSTM) layers and “encoder-decoder” structure is designed as the backbone architecture of the proposed model. To further enhance deep models' comprehension of physical scenarios, this article also proposes an improved word embedding method that allows continuous real-valued parameters to be mapped into high-dimensional vectors in a manner similar to individual vocabulary terms, while preserving their original “semantic” relationships. Based the approaches mentioned above, the proposed model is built and operates through an interactive way, similar to a chatbot. Once receiving main lobe direction and array element positions as inputs, it automatically generates amplitude-phase synthesis results that meet specified requirements while ensuring sidelobe levels are minimized to the greatest extent possible. In the numerical experiments, it shows that this method can generate patterns whose main lobe covers the range from 45° to 135° on non-uniform linear arrays with 15-30 isotropic elements, while controlling the side lobe level below 15 dB, effectively improving the directivity of the antenna array. Compared to genetic algorithms, particle swarm optimization and DNN, this method demonstrates significant advantages in both synthesis performance and computational efficiency, significantly reducing the training time while achieving better radiation pattern characteristics.

A dual-branched Convolutional Neural Network integrated with hybrid training criteria is proposed for real-time multi-beam synthesis in planar uniform antenna arrays. For higher speed in dataset generation, pattern superposition method is introduced in this article, which significantly improves data generation speed (~0.7ms per each). With a new hybrid training strategy integrating both “Data-Driven Approximation” and “Physics-Informed Approximation” criteria, flexible training while maintaining model synthesis efficiency is achieved simultaneously. Based on approaches mentioned above, the proposed dual-branched convolutional neural network enables dynamic multi-objective balance between its two branch outputs, accomplishing amplitude-phase synthesis for antenna array design. In the numerical simulation, consistent sidelobe suppression (20~40dB) is demonstrated for dual-beam steering across 10°~20° off-axis angles, with high synthesis speeds (~0.05s per each) on suitable computing platforms. In comparison experiments, the proposed method outperforms typical population-based stochastic optimization algorithms and Deep Neuron Network structure in both synthesis efficiency and pattern regularity. The model's real-time capability is verified for beam reconfiguration scenarios requiring sub-second responses, suggesting a new pathway for physics-embedded deep learning in antenna array synthesis.

In this paper, the tracking control problem is investigated for time-varying nonlinear mechanical systems with and without frictions. A reduced-order dynamic and a traditional time-invariant Lyapunov design method is utilized for the case without frictions. Based on the traditional results, a new class of time-varying Lyapunov functions is provided for the case with time-varying frictions. It is shown that the tracking errors of the closed-loop system will converge to the neighborhood of the origin, which is determined by the feedback parameters. Numerical examples are presented for illustration.

Scalable video coding (SVC) addresses the application need of a more flexible format of coded video in heterogeneous and time-varying environments. Rate control (RC) plays an important role in allocating H.264/SVC bits to adapt to the actual channel bandwidth and meanwhile to achieve high-quality reconstructed images. A macroblock level rate control algorithm based on layer level and MB level bit-rate allocation is proposed for SVC spatial and MGS scalability in this paper. At the same time, layer level buffer control is used. The coding process of each layer is controlled by a respective buffer controller and a train of RC parameters. The accurate bit-rate can be achieved and the maximum mismatch between actual bit-rate and target bit-rate can be maintained, using the proposed algorithm. Comparing with FixQpEncoder adopted in the joint scalable video model (JSVM), high peak signal to noise ratio (PSNR) can be obtained without multiple iterative and the complexity is reduced. The proposed algorithm can meet the real-time demand.