TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks.

Spiking neural networks (SNNs) are attracting widespread interest due to their biological plausibility, energy efficiency, and powerful spatiotemporal information representation ability. Given the critical role of attention mechanisms in enhancing neural network performance, the integration of SNNs and attention mechanisms exhibits tremendous potential to deliver energy-efficient and high-performance computing paradigms. In this article, we present a novel temporal-channel joint attention mechanism for SNNs, referred to as TCJA-SNN.

Interfacial Engineering of InSe/h-BN/CsPb(Br/I) Heterostructure Photodetector and Its Application in Automatic Obstacle Avoidance System.

Driven by the rapid development of autonomous vehicles, ultrasensitive photodetectors with high signal-to-noise ratio and ultraweak light detection capability are urgently needed. Due to its intriguing attributes, the emerging van der Waals material, indium selenide (InSe), has attracted extensive attention as an ultrasensitive photoactive material. However, the lack of an effective photoconductive gain mechanism in individual InSe inhibits its further application.

DLPNet: A deep manifold network for feature extraction of hyperspectral imagery.

Deep learning has received increasing attention in recent years and it has been successfully applied for feature extraction (FE) of hyperspectral images. However, most deep learning methods fail to explore the manifold structure in hyperspectral image (HSI). To tackle this issue, a novel graph-based deep learning model, termed deep locality preserving neural network (DLPNet), was proposed in this paper.

Local Manifold-Based Sparse Discriminant Learning for Feature Extraction of Hyperspectral Image.

Hyperspectral image (HSI) generally contains a complex manifold structure and strong sparse correlation in its nonlinear high-dimensional data space. However, the existing manifold learning and sparse learning methods usually consider the manifold structure and sparse relationship separately rather than combining manifold and sparse properties to discover the intrinsic information in the original data. To simultaneously reveal the complex sparse relation and manifold structure of HSI, a novel feature extraction (FE) method, called local manifold-based sparse discriminant learning (LMSDL), has been proposed on the basis of manifold learning and sparse representation (SR).

Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-Spectral Manifold Learning.

The graph embedding (GE) methods have been widely applied for dimensionality reduction of hyperspectral imagery (HSI). However, a major challenge of GE is how to choose the proper neighbors for graph construction and explore the spatial information of HSI data. In this paper, we proposed an unsupervised dimensionality reduction algorithm called spatial-spectral manifold reconstruction preserving embedding (SSMRPE) for HSI classification.