In recent years, novel view synthesis from a monocular image has become a research hot-spot that attracts significant attention. Some recent work identifies latent vectors for high-quality view generation via iterative optimisation, which is a time-consuming process. In contrast, some others utilise an encoder learning a mapping function to approximately estimate optimal latent codes, which significantly reduces its processing time but sacrifices reconstruction quality. Consequently, how to balance synthesis quality and its generation efficiency still remains challenging. In this paper, we propose a residual-based encoder to incorporate with a 3D Generative Adversarial Networks (GAN), named ReE3D, for novel view synthesis. It applies an iterative prediction of latent codes to ensure much higher quality of novel view synthesis with an insignificant increase of processing time when compared to existing encoder-based 3D GAN inversion methods. Additionally, we enforce a novel geometric loss constraint on the encoder to predict view-invariant latent codes, thus effectively mitigating the trade-off between geometric and texture quality in 3D GAN inversion. Extensive experimental results demonstrate that our extended encoder-based method has achieved best trade-off performance in terms of novel view synthesis quality and its execution time. Our method has gained comparable synthesis quality with exponentially decreased processing time when compared to iterative optimisation methods, while improved synthesis performance of encoder-based methods significantly.

With the escalating demand for three-dimensional visual applications such as gaming, virtual reality, and autonomous driving, novel view synthesis has become a critical area of research. Current methods mainly depend on multiple views of the same subject to achieve satisfactory results, but there is often a significant lack of available data. Typically, only a single degraded image is available for reconstruction, which may be affected by occlusion, low resolution, or absence of color information. To overcome this limitation, we propose a two-stage feature matching approach designed specifically for single degraded images, leading to the synthesis of high-quality novel perspective images. This method involves the sequential use of an encoder for feature extraction followed by the fine-tuning of a generator for feature matching. Additionally, the integration of an information filtering module proposed by us during the GAN inversion process helps eliminate misleading information present in degraded images, thereby correcting the inversion direction. Extensive experimental results show that our method outperforms existing state-of-the-art single-view novel view synthesis techniques in handling challenges like occluded, grayscale, and low-resolution images. Moreover, the efficacy of our method remains unparalleled even when aforementioned method integrated with image restoration algorithms.

Sparse arrays play an important role in the widely used acoustic and electromagnetic applications such as sonar, radar, and satellite. In this paper, a recently proposed swarm intelligence algorithm, salp swarm algorithm (SSA), is introduced for design of both linear and planar sparse antenna arrays with optimum sidelobe level (SLL). The design of sparse antenna arrays can be regarded as a nonlinear optimization problem, which the main optimization goal is finding optimum excitations and positions for antenna elements to achieve antenna array beam pattern with desired SLL. The SSA is a new kind of swarm intelligence algorithm which inspired by the swarming behavior of salps. Compared to other evolutionary algorithms, the SSA requires few algorithm-specified tuning parameters, which makes it simple and easy to use for engineering. Illustrative examples of both linear and planar arrays are given to verify the validity of the SSA for SLL reduction of sparse arrays.

Owing to the high cross‐polarization discrimination (XPD) and high isolation characteristics, differential excitation is often employed in dual‐polarized microstrip antennas. However, the number of excitation ports will be doubled, and the power dividers in the differential circuit will introduce additional loss, the differential excitation scheme is thus not suitable for dual‐polarized large‐scale MIMO antenna arrays. In this work, a novel dual‐polarized stacked microstrip antenna with trident‐shaped baluns is proposed for base station MIMO array development. The trident‐shaped balun is proposed to replace the traditional differential excitation structures, while keeping the merits of high XPD and high isolation. The proposed antenna operates in the frequency band of 3.3‐3.8 GHz. The profile height of the antenna is only 0.09λ₀ (λ₀ is the free‐space wavelength at the central frequency), which is much lower than traditional base station antennas (0.25λ₀) in the sub‐6 GHz band. Moreover, a defective ground structure is implemented in a 2 × 2 MIMO antenna array to efficiently suppress the mutual coupling among antenna elements. Both simulated and measured results validate the design. Owing to its low‐profile height and low mutual coupling characteristic, the proposed design is a good candidate for sub‐6 GHz band base station MIMO array developments.