In this paper a method for estimating maximum ventricular elastance through an extended Kalman filter is proposed, based on measurement of ventricular volume and aortic pressure. The Kalman filter is particularly well suited to this task, since it produces an optimal estimate (in the sense that the error is statistically minimized) given noise corrupted data. The EKF model is derived from an electrical-analog model of the left ventricle and systemic load. An observability study was a priori conducted on the model, restricted to the ejection phase, to validate the estimation procedure. The method has been evaluated with simulated data and produced good results (the estimate error was 7.14%).
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http://dx.doi.org/10.1109/IEMBS.2004.1404024 | DOI Listing |
Sci Rep
January 2025
North University of China, School of Mechanical Engineering, Taiyuan, 030051, Shanxi, China.
To improve the efficiency of mobile robot movement, this paper investigates the fusion of the A* algorithm with the Dynamic Window Approach (DWA) algorithm (IA-DWA) to quickly search for globally optimal collision-free paths and avoid unknown obstacles in time. First, the data from the odometer and the inertial measurement unit (IMU) are fused using the extended Kalman filter (EKF) to reduce the error caused by wheel slippage on the mobile robot's positioning and improve the mobile robot's positioning accuracy. Second, the prediction function, weight coefficients, search neighborhood, and path smoothing processing of the A* algorithm are optimally designed to incorporate the critical point information in the global path into the DWA calculation framework.
View Article and Find Full Text PDFAm J Ophthalmol
January 2025
Department of Ophthalmology and Visual Sciences, University of Michigan Medical School, Ann Arbor, Michigan; Center for Eye Policy and Innovation, University of Michigan, Ann Arbor, Michigan; Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, Michigan. Electronic address:
Purpose: A previously developed machine-learning approach with Kalman-filtering technology accurately predicted disease trajectory for patients with various glaucoma types and severities using clinical trials data. This study assesses performance of the KF approach with real-world data.
Design: Retrospective cohort study.
Rev Sci Instrum
January 2025
State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei 230026, China.
Long-Time Coherent Integration (LTCI) utilizes digital integration to combine multiple coherent cycles, thereby improving the signal-to-noise ratio (SNR). Our previous work introduced single-bit LTCI, an approach optimized for FPGA implementation, but faced challenges of output saturation at high SNR levels and inherent limitations in SNR gain (SNRG), which are insufficient for certain applications. This paper presents a threshold tracking method that improves the performance of single-bit LTCI in high-SNR scenarios.
View Article and Find Full Text PDFChaos
January 2025
Classe di Scienze, Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy.
Modeling how a shock propagates in a temporal network and how the system relaxes back to equilibrium is challenging but important in many applications, such as financial systemic risk. Most studies, so far, have focused on shocks hitting a link of the network, while often it is the node and its propensity to be connected that are affected by a shock. Using the configuration model-a specific exponential random graph model-as a starting point, we propose a vector autoregressive (VAR) framework to analytically compute the Impulse Response Function (IRF) of a network metric conditional to a shock on a node.
View Article and Find Full Text PDFData Brief
February 2025
School of Engineering and Technology, University of New South Wales, Canberra, Australia.
This dataset is generated from real-time simulations conducted in MATLAB/Simscape, focusing on the impact of smart noise signals on battery energy storage systems (BESS). Using Deep Reinforcement Learning (DRL) agent known as Proximal Policy Optimization (PPO), noise signals in the form of subtle millivolt and milliampere variations are strategically created to represent realistic cases of False Data Injection Attacks (FDIA). These signals are designed to disrupt the State of Charge (SoC) and State of Health (SoH) estimation blocks within Unscented Kalman Filters (UKF).
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