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.

While attention-based approaches have shown considerable progress in enhancing image fusion and addressing the challenges posed by long-range feature dependencies, their efficacy in capturing local features is compromised by the lack of diverse receptive field extraction techniques. To overcome the shortcomings of existing fusion methods in extracting multi-scale local features and preserving global features, this paper proposes a novel cross-modal image fusion approach based on a multi-scale convolutional neural network with attention Transformer (MATCNN). MATCNN utilizes the multi-scale fusion module (MSFM) to extract local features at different scales and employs the global feature extraction module (GFEM) to extract global features. Combining the two reduces the loss of detail features and improves the ability of global feature representation. Simultaneously, an information mask is used to label pertinent details within the images, aiming to enhance the proportion of preserving significant information in infrared images and background textures in visible images in fused images. Subsequently, a novel optimization algorithm is developed, leveraging the mask to guide feature extraction through the integration of content, structural similarity index measurement, and global feature loss. Quantitative and qualitative evaluations are conducted across various datasets, revealing that MATCNN effectively highlights infrared salient targets, preserves additional details in visible images, and achieves better fusion results for cross-modal images. The code of MATCNN will be available at https://github.com/zhang3849/MATCNN.git.

This brief presents a tutorial on multifaceted techniques for high efficiency piezoelectric energy harvesting. For the purpose of helping design piezoelectric energy harvesting system according to different application scenarios, we summarize and discuss the recent design trends and challenges. We divide the design focus into the following three categories, namely, (1) AC-DC rectifiers, (2) C_P compensation circuits, (3) maximum power point tracking (MPPT) circuits. The features, problems encountered, and suitable systems of various AC-DC rectifier topologies are introduced and compared. The important role of non-linear methods for piezoelectric energy harvesting is illustrated from the perspective of impedance matching. Energy extraction techniques and voltage flipping techniques based on inductors, capacitors, and hybrid structures are analyzed. MPPT techniques with different features and targets are discussed.

In over-complicated machine vision, target tracking within deep learning paradigms yields inaccurate and energy-intensive outputs. Although spiking neural networks excel at processing dynamic information, challenging tracking environments demand further enhancement in feature correlation learning for efficient target tracking. Distinct from Paired-spike-timing-dependent-plasticity-based architectures, we demonstrate a visual sensor based on van der Waals phototransistors, leveraging Triplet-spike-timing-dependent plasticity to extract bioinspired high-order correlation information, through tunable light-electric cooperation and competition effect on synaptic plasticity originating from interfacial defects-dominated persistent photoconductance phenomena. The universal Triplet-spike-timing-dependent plasticity with enhanced spatiotemporal correlation learning characteristic renders spiking neural networks with better processing capabilities for confusing object classification and dynamic tracking (90.44%) tasks, excelling particularly in seamless tracking post-occlusion, furthermore experimentally validated through hardware implementation on a 6\(\times\)6 van der Waals phototransistor array. The offers a bottom-up methodology employing device physics to guide mapping of biorational learning for high-performance dynamic tracking towards advanced machine visual technologies.

Sparse linear discriminant analysis (LDA) is a popular machine learning method that improves the accuracy of data classification by introducing sparsity. However, its performance often degrades seriously when encountering noise. To address this issue, this paper proposes a new method called efficient and robust sparse linear discriminant analysis (ERSLDA). The core idea is to characterize the local pixel corruptions by integrating $L_{p}$-norm $(0< p< 1)$, and to describe the global structured sparsity by enforcing $L_{2,p}$-norm $(0< p< 1)$, thereby improving the ability of feature selection. Compared with existing $L_{1}$-norm and $L_{2,1}$-norm, the involved $L_{p}$-norm and $L_{2,p}$-norm can bring higher robustness and better accuracy. Moreover, an additional matrix with Frobenius norm is embedded to represent Gaussian noise, which further enhances the robustness in different scenarios. In algorithms, an iterative optimization scheme is developed based on the alternating direction method of multipliers (ADMM) to solve the proposed ERSLDA, and the resulting subproblems can be calculated efficiently. Extensive numerical comparisons are performed with nine state-of-the-art LDA-based methods on seven benchmark image datasets. The experimental results validate that the proposed method is efficient for data classification and robust to noise.

This paper introduces a high-performance flipping-input synchronized switch harvesting on inductor and capacitors (FI-SSHIC) piezoelectric energy harvesting interface circuit. The unique design approach involves segmenting the flipping stages, enabling the FI-SSHIC to execute 17 phase flips utilizing merely a diminutive inductor of 5μH and 8 compact capacitors. By circumventing the traditional full bridge rectifier (FBR) and streamlining the switch count, this study’s flipping efficiency soars to an impressive 96.3%. When juxtaposed with SSHC and SSHI, utilizing identical capacitor and inductor dimensions, our design yields a 15.1% and 59.3% enhancement in output power, respectively. Furthermore, the pinnacle of power efficiency exhibits a staggering 648% augmentation when compared to the ideal FBR, underscoring the advancements proffered by our proposed circuit.