Results 11 to 20 of about 5,581 (264)

SSM-FastICANet: a hybrid state space and FastICA model for economic growth forecasting in energy-economy-environment systems [PDF]

PeerJ Computer Science
This study examines the complex interactions between CO2 emissions, economic growth, and energy consumption across various classifications of countries.
Fahman Saeed
doaj   +4 more sources

EEG-SSM: Leveraging State-Space Model for Dementia Detection

CoRR
State-space models (SSMs) have garnered attention for effectively processing long data sequences, reducing the need to segment time series into shorter intervals for model training and inference.
Tran, Xuan-The, Nguyen, Quoc Toan, Lin, Chin-Teng, Do, Thomas, Le, Linh   +4 more
core   +2 more sources

MFBO-SSM: Multi-Fidelity Bayesian Optimization for Fast Inference in State-Space Models

Proceedings of the AAAI Conference on Artificial Intelligence, 2019
Nonlinear state-space models are ubiquitous in modeling real-world dynamical systems. Sequential Monte Carlo (SMC) techniques, also known as particle methods, are a well-known class of parameter estimation methods for this general class of state-space ...
Ghoreishi, Seyede Fatemeh, Imani, Mahdi, Allaire, Douglas, Braga-Neto, Ulisses M.   +3 more
core   +3 more sources

A state-space model to derive motorboat noise effects on fish movement from acoustic tracking data [PDF]

Scientific Reports, 2021
Motorboat noise is recognized as a major source of marine pollution, however little is known about its ecological consequences on coastal systems. We developed a State Space Model (SSM) that incorporates an explicit dependency on motorboat noise to ...
Margarida Barcelo-Serra, Sebastià Cabanellas, Miquel Palmer, Marta Bolgan, Josep Alós   +4 more
doaj   +2 more sources

Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting

CoRR
State Space Models (SSMs) have emerged as a potent tool in sequence modeling tasks in recent years. These models approximate continuous systems using a set of basis functions and discretize them to handle input data, making them well-suited for modeling ...
Liang, Yuxuan, Lan, Disen, Wen, Qingsong, Hu, Jiaxi, Zhou, Ziyu   +4 more
core   +2 more sources

LF-SSM: Lightweight HiPPO-Free State Space Model for Real-Time UAV Tracking

Drones
Visual object tracking from unmanned aerial vehicles (UAVs) demands both high accuracy and computational efficiency for real-time deployment on resource-constrained platforms.
Tianyu Wang, Xinghua Xu, Shaohua Qiu, Changchong Sheng, Di Wang, Hui Tian, Jiawei Yu   +6 more
doaj   +2 more sources

CIR-SSM: A Cross and Inter Resolution State-Space Model for Underwater Image Enhancement

Applied Sciences
Underwater images often suffer from strong color casts, low contrast, and blurred textures. It is observed that low resolution can provide globally correct color, so low-resolution priors can guide high-resolution correction.
Fengxian Liu, Ning Ye, Haitao Wang
doaj   +2 more sources

SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning

CoRR
Spiking neural networks (SNNs) provide an energy-efficient solution by utilizing the spike-based and sparse nature of biological systems. Since the advent of Transformers, SNNs have struggled to compete with artificial networks on long sequential tasks ...
Leng, Luziwei, Wang, Chao, Zhang, Jianguo, Zhong, Yan, Guo, Qinghai, Zhao, Ruoyu, Lu, Zhichao   +6 more
core   +2 more sources

TA-SSM net: tri-directional attention and structured state-space model for enhanced MRI-Based diagnosis of Alzheimer’s disease and mild cognitive impairment

BMC Medical Imaging
Early diagnosis of Alzheimer’s disease (AD) and its precursor, mild cognitive impairment (MCI), is critical for effective prevention and treatment. Computer-aided diagnosis using magnetic resonance imaging (MRI) provides a cost-effective and objective ...
Sichen Bao, Fengbo Zheng, Lifen Jiang, Qiuyuan Wang, Yong Lyu   +4 more
doaj   +3 more sources

A Rao-Blackwellized Particle Filter With Variational Inference for State Estimation With Measurement Model Uncertainties

IEEE Access, 2020
This paper develops a Rao-Blackwellized particle filter with variational inference for jointly estimating state and time-varying parameters in non-linear state-space models (SSM) with non-Gaussian measurement noise.
Cheng Cheng, Jean-Yves Tourneret, Xiaodong Lu   +2 more
doaj   +1 more source