GPS Meteorology: Sliding-Window Analysis*

2005 ◽  
Vol 22 (6) ◽  
pp. 687-695 ◽  
Author(s):  
James Foster ◽  
Michael Bevis ◽  
Steven Businger
Keyword(s):  
Real Time ◽  
Time Window ◽  
Sliding Window ◽  
Precipitable Water ◽  
Fine Tuning ◽  
Window Analysis ◽  
Time Estimates ◽  
Gps Meteorology ◽  
Overlapping Data ◽  
Window Method

Abstract The sliding-window technique uses a moving time window to select GPS data for processing. This makes it possible to routinely incorporate the most recently collected data and generate estimates for atmospheric delay or precipitable water in (near) real time. As a consequence of the technique several estimates may be generated for each time epoch, and these multiple estimates can be used to explore and analyze the characteristics of the atmospheric estimates and the effect of the processing model and parameters. Examples of some of the analyses that can be undertaken are presented. Insights into the phenomenology of the atmospheric estimates provided by sliding-window analysis permit the fine-tuning of the GPS processing as well as the possibility of both improving the accuracy of the near-real-time estimates themselves and constraining the errors associated with them. The overlapping data windows and the multiple estimates that characterize the sliding-window method can lead to ambiguity in the meaning of many terms and expressions commonly used in GPS meteorology. In order to prevent confusion in discussions of sliding-window processing, a nomenclature is proposed that formalizes the meaning of the primary terms and defines the geometric and physical relationships between them.

scholarly journals Simulation and Detection of Small Drones/Suspicious UAVs in Drone Grid

2021 ◽  
Vol 9 (VI) ◽  
pp. 5452-5458
Author(s):  
Arpit Gupta
Keyword(s):  
Real Time ◽  
High Speed ◽  
Visual Detection ◽  
Autonomous Systems ◽  
Sliding Window ◽  
The Past ◽  
Detection And Tracking ◽  
Window Method ◽  
Sliding Window Method ◽  
State Of Art

Today’s technology is evolving towards autonomous systems and the demand in autonomous drones, cars, robots, etc. has increased drastically in the past years. This project presents a solution for autonomous real-time visual detection and tracking of hostile drones by moving cameras equipped on surveillance drones. The algorithm developed in this project, based on state-of-art machine learning and computer vision methods, succeeds at autonomously detecting and tracking a single drone by moving a camera and can run at real-time. The project can be divided into two main parts: the detection and the tracking. The detection is based on the YOLOv3 (You Only Look Once v3) algorithm and a sliding window method. The tracking is based on the GOTURN (Generic Object Tracking Using Regression Networks) algorithm, which allows the tracking of generic objects at high speed. In order to allow autonomous tracking and enhance the accuracy, a combination of GOTURN and tracking by detection using YOLOv3 was developed.

An online identification approach for a nonlinear ship motion model based on a receding horizon

2021 ◽  
pp. 014233122110196
Author(s):  
Jian Zheng ◽  
Ming Yan ◽  
Yun Li ◽  
Changhai Huang ◽  
Yiping Ma ◽  
...  
Keyword(s):  
Real Time ◽  
Time Window ◽  
Control Object ◽  
Ship Motion ◽  
Identification Algorithm ◽  
Time Varying ◽  
Ocean Engineering ◽  
Online Identification ◽  
Motion System ◽  
Window Method

The ship motion system is a nonlinear control object, and its system parameters exhibit time-varying characteristics with the ship motion state, which increases the difficulty of control. Therefore, parameter identification has an important significance for the stability of ship motion control. Aiming at the real-time identification problem of the nonlinear and time-varying ship motion system during movement, this paper reconstructs the ship motion system with the propeller speed and rudder angle as control variables and designs an online identification algorithm with the sliding time window method based on the extended Kalman filter algorithm. In addition, to solve the problem of noise in ship motion data collected in real-time, a real-time wavelet filter is developed to perform online preprocessing of the input data of the identification algorithm. The applicability of the method is further demonstrated via a model-scale Korea Research Institute of Ships and Ocean Engineering container ship free-running experiments in a basin.

scholarly journals Evaluation of Optimal Vibrotactile Feedback for Force-Controlled Upper Limb Myoelectric Prostheses

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5209 ◽  
Author(s):  
Andrea Gonzalez-Rodriguez ◽  
Jose L. Ramon ◽  
Vicente Morell ◽  
Gabriel J. Garcia ◽  
Jorge Pomares ◽  
...  
Keyword(s):  
Real Time ◽  
Upper Limb ◽  
Closed Loop ◽  
Fine Tuning ◽  
Vibrotactile Feedback ◽  
Loop Control ◽  
Myoelectric Prostheses ◽  
Amplitude Level ◽  
Real Time Application ◽  
Selection Of

The main goal of this study is to evaluate how to optimally select the best vibrotactile pattern to be used in a closed loop control of upper limb myoelectric prostheses as a feedback of the exerted force. To that end, we assessed both the selection of actuation patterns and the effects of the selection of frequency and amplitude parameters to discriminate between different feedback levels. A single vibrotactile actuator has been used to deliver the vibrations to subjects participating in the experiments. The results show no difference between pattern shapes in terms of feedback perception. Similarly, changes in amplitude level do not reflect significant improvement compared to changes in frequency. However, decreasing the number of feedback levels increases the accuracy of feedback perception and subject-specific variations are high for particular participants, showing that a fine-tuning of the parameters is necessary in a real-time application to upper limb prosthetics. In future works, the effects of training, location, and number of actuators will be assessed. This optimized selection will be tested in a real-time proportional myocontrol of a prosthetic hand.

scholarly journals Real-Time Musculoskeletal Kinematics and Dynamics Analysis Using Marker- and IMU-Based Solutions in Rehabilitation

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1804
Author(s):  
Dimitar Stanev ◽  
Konstantinos Filip ◽  
Dimitrios Bitzas ◽  
Sokratis Zouras ◽  
Georgios Giarmatzis ◽  
...  
Keyword(s):  
Real Time ◽  
Small Error ◽  
Grand Challenge ◽  
Muscle Forces ◽  
Time Estimates ◽  
Musculoskeletal Models ◽  
Open Source Framework ◽  
Measurement Units ◽  
Technical Details ◽  
Significant Milestone

This study aims to explore the possibility of estimating a multitude of kinematic and dynamic quantities using subject-specific musculoskeletal models in real-time. The framework was designed to operate with marker-based and inertial measurement units enabling extensions far beyond dedicated motion capture laboratories. We present the technical details for calculating the kinematics, generalized forces, muscle forces, joint reaction loads, and predicting ground reaction wrenches during walking. Emphasis was given to reduce computational latency while maintaining accuracy as compared to the offline counterpart. Notably, we highlight the influence of adequate filtering and differentiation under noisy conditions and its importance for consequent dynamic calculations. Real-time estimates of the joint moments, muscle forces, and reaction loads closely resemble OpenSim’s offline analyses. Model-based estimation of ground reaction wrenches demonstrates that even a small error can negatively affect other estimated quantities. An application of the developed system is demonstrated in the context of rehabilitation and gait retraining. We expect that such a system will find numerous applications in laboratory settings and outdoor conditions with the advent of predicting or sensing environment interactions. Therefore, we hope that this open-source framework will be a significant milestone for solving this grand challenge.

scholarly journals HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

2021 ◽  
Vol 13 (11) ◽  
pp. 2179
Author(s):  
Pedro Mateus ◽  
Virgílio B. Mendes ◽  
Sandra M. Plecha
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Real Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

scholarly journals Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Simon Tam ◽  
Mounir Boukadoum ◽  
Alexandre Campeau-Lecours ◽  
Benoit Gosselin
Keyword(s):  
Real Time ◽  
Transfer Learning ◽  
Control Strategy ◽  
High Density ◽  
Fine Tuning ◽  
Mode Switching ◽  
Correct Prediction ◽  
Learning Approach ◽  
Predictive Values ◽  
Hand Prosthesis

AbstractMyoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.

Effects of the SNAP25 on Integration Ability of Brain Functions in Children With ADHD

2020 ◽  
pp. 108705472096456
Author(s):  
Yue Yang ◽  
Gang Peng ◽  
Hongwu Zeng ◽  
Diangang Fang ◽  
Linlin Zhang ◽  
...  
Keyword(s):  
Time Window ◽  
Precentral Gyrus ◽  
Children With Adhd ◽  
Brain Functions ◽  
Lingual Gyrus ◽  
Window Method ◽  
Central Gyrus ◽  
The Right ◽  
The Brain ◽  
Insight Into

Objective: The present study aimed to examine the effects of SNAP25 on the integration ability of intrinsic brain functions in children with ADHD, and whether the integration ability was associated with working memory (WM). Methods: A sliding time window method was used to calculate the spatial and temporal concordance among five rs-fMRI regional indices in 55 children with ADHD and 20 healthy controls. Results: The SNAP25 exhibited significant interaction effects with ADHD diagnosis on the voxel-wise concordance in the right posterior central gyrus, fusiform gyrus and lingual gyrus. Specifically, for children with ADHD, G-carriers showed increased voxel-wise concordance in comparison to TT homozygotes in the right precentral gyrus, superior frontal gyrus, postcentral gyrus, and middle frontal gyrus. The voxel-wise concordance was also found to be related to WM. Conclusion: Our findings provided a new insight into the neural mechanisms of the brain function of ADHD children.

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