In motion capture systems, markers are often seen by multiple cameras. All cameras do not measure the position of the markers with the same reliability because of environmental factors such as the position of the marker in the field of view or the light intensity received by the cameras. Kalman filters offer a general framework to take the reliability of the various cameras into account and consequently improve the estimation of the marker position. The proposed process can be applied to both passive and active systems. Several reliability models of the cameras are compared for the Codamotion active system, which is considered as a specific illustration. The proposed method significantly reduces the noise in the signal, especially at long-range distances. Therefore, it improves the confidence of the positions at the limits of the field of view.
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http://dx.doi.org/10.1080/10255842.2013.864640 | DOI Listing |
ACS Sens
March 2025
State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China.
Electronic noses have been widely used in industrial production, food preservation, agricultural product storage, environmental monitoring, and other fields. However, due to the cross-sensitivity of gas-sensing responses, accurately measuring the concentration of mixed gases remains challenging. To address this issue, this study attempts to determine the number of state variables that produce the cross-influence based on the experimental data, establish the state space model from the equivalent circuit model, and obtain model parameters through parameter correlation iterative algorithms and a Kalman filter.
View Article and Find Full Text PDFRev Sci Instrum
March 2025
School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China.
Rotor attitude detection (RAD) is one of the key technologies to control permanent magnet spherical motors (PMSpM). This paper proposes an improved you only look once v8n (YOLOv8n) based RAD method for a PMSpM. The visual image datasets collection and annotation method are described, and three different visual feature objects are set for the RAD.
View Article and Find Full Text PDFNetwork
March 2025
Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, India.
The neurodegenerative disorder called Parkinson's disease (PD) is one of the most common diseases now a day. In this research, PD is detected and severity classification is done using the proposed Jaccard LeNet (JLeNet) with the help of voice signal in the IoT environment. Here, the IoT simulation is done.
View Article and Find Full Text PDFSci Rep
March 2025
School of Mechatronic Engineering, Xi'an Technological University, Xi'an, 710021, China.
This paper addresses the challenge of reconstructing the motion process of the safety and arming (S&A) mechanism in fuze by transforming the problem into a target detection and tracking problem. A novel tracking method, which fuses an improved Kalman filter with a temporal scale-adaptive KCF (AKF-CF), is proposed. The methodology introduces key innovations: (1) Extraction of grayscale images and directional gradient histogram (HOG) features of the target, followed by the use of an Adaptive Wave PCA-Autoencoder (AWPA) method to accurately capture multi-modal and multi-scale features of the target; (2) Application of bilinear interpolation and hybrid filtering techniques to generate a spatial and temporal scale-adaptive bounding box for the filtered target, enabling dynamic adjustment of the tracking box size; (3) Integration of an occlusion-aware mechanism using average peak correlation energy (APCE) to trigger Kalman-based position prediction when the target is occluded, thus mitigating tracking drift.
View Article and Find Full Text PDFSci Rep
March 2025
School of Electrical Engineering and Automation, Anhui University, Hefei, 230601, Anhui, China.
With the rapid advancements in artificial intelligence (AI), 5G technology, and robotics, multi-sensor fusion technologies have emerged as a critical solution for achieving high-precision localization in mobile robots operating within dynamic and unstructured environments. This study proposes a novel hybrid fusion framework that combines the Extended Kalman Filter (EKF) and Recurrent Neural Network (RNN) to address challenges such as sensor frequency asynchrony, drift accumulation, and measurement noise. The EKF provides real-time statistical estimation for initial data fusion, while the RNN effectively models temporal dependencies, further reducing errors and enhancing data accuracy.
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