Results 61 to 70 of about 4,842 (263)

Bio‐Inspired Mechanical Amplification Block on Implantable Tactile Sensors

Advanced Electronic Materials, EarlyView.
 . ABSTRACT Implantable strain sensors offer opportunities for continuous biomechanical monitoring, but their performance deteriorates severely once embedded in soft tissue due to mechanical shielding that suppresses strain transmission to the sensing layer.
Sungbin Choi, Yeong‐sinn Ye, Chanho Jeong, Yujin Mun, Suyoun Oh, Tae‐il Kim   +5 more
wiley   +1 more source

VSM-UNet: A Visual State Space Reconstruction Network for Anomaly Detection of Catenary Support Components

Sensors
Anomaly detection of catenary support components (CSCs) is an important component in railway condition monitoring systems. However, because the abnormal features of CSCs loosening are not obvious, and the current CNN models and visual Transformer models ...
Shuai Xu, Jiyou Fei, Haonan Yang, Xing Zhao, Xiaodong Liu, Hua Li   +5 more
doaj   +1 more source

Geometric SSM: LTI State Space Models for Selective Tasks

10 pages, 5 ...
Casti, Umberto, Baggio, Giacomo, Zampieri, Sandro, Pasqualetti, Fabio   +3 more
openaire   +2 more sources

Cortical-SSM: A Deep State Space Model for EEG and ECoG Motor Imagery Decoding

CoRR
Classification of electroencephalogram (EEG) and electrocorticogram (ECoG) signals obtained during motor imagery (MI) has substantial application potential, including for communication assistance and rehabilitation support for patients with motor impairments.
Shuntaro Suzuki, Shunya Nagashima, Masayuki Hirata, Komei Sugiura   +3 more
openaire   +2 more sources

Retinal Vessel Segmentation: A Comprehensive Review From Classical Methods to Deep Learning Advances (1982–2025)

Advanced Intelligent Systems, EarlyView.
Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal, Jan Kubíček, Jana Bahrová, Alice Varyšová, Martin Augustýnek, Marek Penhaker, Juraj Timkovič   +6 more
wiley   +1 more source

Enhancing Renewable Energy Integration via Robust Multi-Energy Dispatch: A Wind–PV–Hydrogen Storage Case Study with Spatiotemporal Uncertainty Quantification

Energies
This paper addresses the challenge of renewable energy curtailment, which stems from the inherent uncertainty and volatility of wind and photovoltaic (PV) generation, by developing a robust model predictive control (RMPC)-based scheduling strategy for an
Qilong Zhang, Guangming Li, Xiangping Chen, Anqian Yang, Kun Zhu   +4 more
doaj   +1 more source

TransMambaCNN: A Spatiotemporal Transformer Network Fusing State-Space Models and CNNs for Short-Term Precipitation Forecasting

Remote Sensing
Deep learning for precipitation forecasting remains constrained by complex meteorological factors affecting accuracy. To address this issue, this paper proposes TransMambaCNN, which is a spatiotemporal transformer network fusing state-space models and ...
Kai Zhang, Guojing Zhang, Xiaoying Wang
doaj   +1 more source

RS-SSM: Refining Forgotten Specifics in State Space Model for Video Semantic Segmentation

CoRR
Accepted by CVPR ...
Kai Zhu, Zhenyu Cui, Zehua Zang, Jiahuan Zhou   +3 more
openaire   +2 more sources

Remote Sensing Change Detection by Pyramid Sequential Processing With Mamba

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Change detection (CD) in remote sensing imagery is crucial for monitoring environmental variations over time. Recent advancements have introduced numerous effective algorithms, significantly enhancing the performance of CD tasks.
Jiancong Ma, Bo Li, Hanxi Li, Siying Meng, Ruitao Lu, Shaohui Mei   +5 more
doaj   +1 more source

Let SSMs be ConvNets: State-space Modeling with Optimal Tensor Contractions

CoRR
We introduce Centaurus, a class of networks composed of generalized state-space model (SSM) blocks, where the SSM operations can be treated as tensor contractions during training. The optimal order of tensor contractions can then be systematically determined for every SSM block to maximize training efficiency.
openaire   +3 more sources