Lightweight remote sensing image super-resolution (RSISR) aims to generate high-resolution remote sensing images (RSIs) with improved texture and structural details while keeping computational cost low. Most lightweight RSISR methods use convolutional neural networks (CNNs), which are excellent at capturing local spatial features but struggle to model long-range dependencies because their receptive fields are naturally limited. Even though Transformer-based methods address this problem by using global attention mechanisms, they aren’t very practical for lightweight, large-scale remote sensing applications because they are complex and require many parameters. Mamba, a state-space-based sequence modeling framework, has recently demonstrated that it can model long-range data effectively with linear computational complexity. This makes it a suitable choice for efficient global context modeling. These observations have led us to propose a hybrid convolutional network–Mamba architecture for RSISR. The proposed model includes a ConvNet branch for capturing local high-frequency details and a Spectral Mamba module for efficiently modeling long-range dependencies across spatial and spectral dimensions. We present ConvMambaB as the fundamental computational unit, integrating convolutional feature extraction, channel-mixing MLP enhancement, and Mamba-based state-space modeling within a progressive residual refinement framework. The ConvMambaGroup backbone consists of several ConvMambaB blocks stacked on top of each other. This enables effective feature integration at different levels. Extensive tests on three benchmark remote sensing datasets show that the proposed method achieves reconstruction performance that is either competitive with or better than existing lightweight models, while maintaining a low number of parameters and computational complexity.

Hyperspectral image (HSI) classification is essential in the remote sensing (RS) domain. Transformers have gained prominence in this domain in recent years owing to their capacity for global information modeling. However, the quadratic complexity constrains their efficacy when computer resources are restricted. A structured state space model (SSM) called Mamba has emerged. Like Transformers, it excels at modeling long-range dependencies in latent data. However, unlike Transformers, its complexity is linear. As a result, recent research has increasingly explored its efficacy in HSI classification. Nonetheless, the majority of them apply Mamba exclusively to HSIs without adequately considering the intrinsic properties of HSIs. This article introduces a novel, parameter-efficient modulated group Mamba layer designed to fully leverage Mamba’s capabilities in HSI classification. It segments the input channels into four groups and independently applies the proposed SSM-based efficient visual single selective scanning (VSSS) block to each group, with each VSSS block scanning in one of four spatial orientations. The Modulated Group Mamba layer (MGML) encapsulates the four VSSS. In addition to MGML, an efficient Multi-kernel depthwise convolution (MK-DeConv) is also used. The model’s backbone is an encoder block that uses MK-DeConv and MGML. By reducing unnecessary parameters and calculations, channel grouping and multi-kernel depthwise convolutions make things more efficient while still capturing both fine-grained and large-scale features. This combination makes it possible to create models that are both light and expressive while still being very accurate and not using too much memory or processing power. Extensive analyses of five benchmark hyperspectral imaging datasets (Pavia University, Houston, HanChuan, HongHu, and LongKou) reveal that GMambaHSI achieves enhanced classification accuracy while employing significantly fewer parameters compared to current CNN-, Transformer-, and Mamba-based models. In the Pavia University dataset, GMambaHSI, for example, raises the overall accuracy (OA) from 95.74% to 97.22% and the average accuracy (AA) from 95.8% to 97.87%.

Embedded arrhythmia classification is the first step towards heart diseases prevention in wearable applications. In this paper, a robust arrhythmia classification algorithm, NEO-CCNN, for wearables that can be implemented on a simple microcontroller is proposed. The NEO-CCNN algorithm not only detects QRS complex but also accurately locates R-peak with the help of the proposed adaptive time-dependent thresholding technique, improving the accuracy and sensitivity in arrhythmia classification. An optimized compact 1D-CNN network (CCNN) with 9,701 parameters is used for classification. A QRS complex augmentation method is introduced in the training process to cater for R-peak location error (RLE). A nested k1k2-fold cross-validation method is utilized to evaluate the robustness of the proposed algorithm. Simulation results show that the proposed algorithm has the ability to detect more than 99.79% of R peaks with an RLE of 7.94 ms for the MIT-BIH database. Implemented on the STM32F407 microcontroller, NEO-CNN attains a classification accuracy of 97.83% and sensitivity of 96.46% using only 8s window size.

In this paper, the standard hard C-means (HCM) clustering approach to image segmentation is modified by incorporating weighted membership Kullback–Leibler (KL) divergence and local data information into the HCM objective function. The membership KL divergence, used for fuzzification, measures the proximity between each cluster membership function of a pixel and the locally-smoothed value of the membership in the pixel vicinity. The fuzzification weight is a function of the pixel to cluster-centers distances. The used pixel to a cluster-center distance is composed of the original pixel data distance plus a fraction of the distance generated from the locally-smoothed pixel data. It is shown that the obtained membership function of a pixel is proportional to the locally-smoothed membership function of this pixel multiplied by an exponentially distributed function of the minus pixel distance relative to the minimum distance provided by the nearest cluster-center to the pixel. Therefore, since incorporating the locally-smoothed membership and data information in addition to the relative distance, which is more tolerant to additive noise than the absolute distance, the proposed algorithm has a threefold noise-handling process. The presented algorithm, named local data and membership KL divergence based fuzzy C-means (LDMKLFCM), is tested by synthetic and real-world noisy images and its results are compared with those of several FCM-based clustering algorithms.