This paper proposes a new infrared and visible image fusion method based on the densely connected disentangled representation generative adversarial network (DCDR-GAN), which strips the content and the modal features of infrared and visible images through disentangled representation (DR) and fuses them separately. To deal with the mutually exclusive features in infrared and visible images, inject the modal features into the reconstruction of content features through adaptive instance normalization (AdaIN), reducing the interference. To reduce feature loss and ensure the expression of all-level features in the fused image, DCDR-GAN designs the densely connected content encoders and fusion decoder and constructs the multi-scale fusion structures between the enc-dec through long connections. Meanwhile, the content and the modal reconstruction losses are proposed to preserve the information of the source images. Finally, through the two-phase trained model, generate the fused image. The subjective and objective evaluation results of the TNO and INO datasets show that the proposed method has better visual effects and higher index values than other state-of-the-art methods.

Clock synchronization is a very important issue for the applications of wireless sensor networks. The sensors need to keep a strict clock so that users can know exactly what happens in the monitoring area at the same time. This paper proposes a novel internal distributed clock synchronization solution using group neighborhood average. Each sensor node collects the offset and skew rate of the neighbors. Group averaging of offset and skew rate value are calculated instead of conventional point-to-point averaging method. The sensor node then returns compensated value back to the neighbors. The propagation delay is considered and compensated. The analytical analysis of offset and skew compensation is presented. Simulation results validate the effectiveness of the protocol and reveal that the protocol allows sensor networks to quickly establish a consensus clock and maintain a small deviation from the consensus clock.

Multifocus image fusion has attracted considerable attention because it can overcome the physical limitations of optical imaging equipment and fuse multiple images with different depths of the field into one full-clear image. However, most existing deep learning-based fusion methods concentrate on the segmentation of focus–defocus regions, resulting in the loss of the details near the boundaries. To address the issue, this article proposes a novel generation adversarial network with dense connections (Fusion-UDCGAN) to fuse multifocus images. More specifically, the encoder and the decoder are first composed of dense modules with dense long connections to ensure the generated image’s quality. The content and clarity loss based on the $L1$ norm and the novel sum-modified-Laplacian (NSML) is further embedded to provide the fused images retaining more texture features. Considering that the previous dataset-making approaches may lose the relation between the overall structure and the information near the boundaries, a new dataset, which is uniformly distributed and can simulate natural focusing boundary conditions, is constructed for model training. Subjective and objective experimental results indicate that the proposed method significantly improves the sharpness, contrast, and detail richness compared to several state-of-the-art methods.

To address the problems of anisotropy and mode mixing in damage imaging on anisotropic composites laminates using ultrasonic Lamb wave, a sparse reconstruction imaging method based on S0 single-mode is proposed in this paper. In this approach, the sparsely arranged piezoelectric ceramic drivers sensor group on the surface of the measured composite laminate is employed to excite and receive Lamb wave. The received signal shows sparse under a pre-built damage dictionary with damage scattering signal waveform, and the damages can be localized and imaged using a sparse reconstruction method. Considering the velocity difference between S0 and A0 mode of the received Lamb wave signal, the S0 mode is extracted by intercepting the signal before the peak of the first wave packet to eliminate the influence of Lamb wave mode mixing. In order to minimize the influence of anisotropy of composites laminates on Lamb wave propagation, the Lamb wave propagation direction is divided evenly into several sectors, and Lamb wave propagation direction within a certain sector is considered as propagation in a quasi-isotropic material. The sparse reconstruction is solved by using the basis pursuit de-noising algorithm, which can find the optimal solution with a small number of iterations in noise interference. The experiments on composite fiber laminate specimens show that the proposed method can accurately locate simulated single delamination damage and has less artifact interference compared to the delay-and-sum method.