scholarly journals Kalman Filter for Noise Reducer on Sensor Readings

2019 ◽  
Vol 1 (2) ◽  
pp. 11-22
Author(s):  
Alfian Ma'arif ◽  
Iswanto Iswanto ◽  
Aninditya Anggari Nuryono ◽  
Rio Ikhsan Alfian
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
High Sensitivity ◽  
Sensor Data ◽  
Sensor Reading ◽  
Suitable Parameter ◽  
Noise Frequency ◽  
Filter Signal ◽  
Inertial Measurements ◽  
Environment Noise

Most systems nowadays require high-sensitivity sensors to increase its system performances. However, high-sensitivity sensors, i.e. accelerometer and gyro, are very vulnerable to noise when reading data from environment. Noise on data-readings can be fatal since the real measured-data contribute to the performance of a controller, or the augmented system in general. The paper will discuss about designing the required equation and the parameter of modified Standard Kalman Filter for filtering or reducing the noise, disturbance and extremely varying of sensor data. The Kalman Filter equation will be theoretically analyzed and designed based on its component of equation. Also, some values of measurement and variance constants will be simulated in MATLAB and then the filtered result will be analyzed to obtain the best suitable parameter value. Then, the design will be implemented in real-time on Arduino to reduce the noise of IMU (Inertial Measurements Unit) sensor reading. Based on the simulation and real-time implementation result, the proposed Kalman filter equation is able to filter signal with noises especially if there is any extreme variation of data without any information available of noise frequency that may happen to sensor- reading. The recommended ratio of constants in Kalman Filter is 100 with measurement constant should be greater than process variance constant.

scholarly journals Development of Liquid Capacity Measuring Algorithm Based on Data Integration from Multiple Sensors

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Kiwoong Park ◽  
Si-Kyoung Lee ◽  
Hyeon Cheol Kim
Keyword(s):  
Kalman Filter ◽  
Data Integration ◽  
Real Time ◽  
Rotation Angle ◽  
Sensor Data ◽  
Multiple Sensors ◽  
Complementary Filter ◽  
Level Sensor

This research proposes an algorithm using a process of integrating data from multiple sensors to measure the liquid capacity in real time regardless of the position of the liquid tank. The algorithm for measuring the capacity was created with a complementary filter using a Kalman filter in order to revise the level sensor data and IMU sensor data. The measuring precision of the proposed algorithm was assessed through repetitive experiments by varying the liquid capacity and the rotation angle of the liquid tank. The measurements of the capacity within the liquid tank were precise, even when the liquid tank was rotated. Using the proposed algorithm, one can obtain highly precise measurements, and it is affordable since an existing level sensor is used.

scholarly journals Real-Time Inverse Estimation of Ocean Wave Spectra from Vessel-Motion Sensors Using Adaptive Kalman Filter

2019 ◽  
Vol 9 (14) ◽  
pp. 2797 ◽  
Author(s):  
HanSung Kim ◽  
HeonYong Kang ◽  
Moo-Hyun Kim
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
High Frequency ◽  
Practical Importance ◽  
Ocean Wave ◽  
Sensor Data ◽  
Wave Spectra ◽  
Inverse Estimation ◽  
High Frequency Waves ◽  
Vessel Motion

The real-time inverse estimation of the ocean wave spectrum and elevation from a vessel-motion sensor is of significant practical importance, but it is still in the developing stage. The Kalman-filter method has the advantages of real-time estimation, cost reduction, and easy installation than other methods. Reasonable estimation of high-frequency waves is important in view of covering various sea states. However, if the vessel is less responsive for high-frequency waves, amplified noise may occur and cause overestimation problem there. In this paper, a configuration of Kalman filter with applying the principle of Wiener filter is proposed to suppress those over-estimations. Over-estimation is significantly reduced at high frequencies when the method is applied, and reliable real-time wave spectra and elevations can be obtained. The simulated sensor data was used, but the proposed algorithm has been proved to perform well for various sea states and different vessels. In addition, the proposed Kalman-filter technique is robust when it is applied to time-varying sea states.

scholarly journals A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 803 ◽  
Author(s):  
Ákos Odry ◽  
Istvan Kecskes ◽  
Peter Sarcevic ◽  
Zoltan Vizvari ◽  
Attila Toth ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Attitude Estimation ◽  
Sensor Data ◽  
External Disturbances ◽  
Filter Performance ◽  
Filter Structure ◽  
Adaptive Extended Kalman Filter ◽  
Fuzzy Adaptive

This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which novel measurement methods are used to calculate the magnitudes of system vibrations, external accelerations, and magnetic distortions. These magnitudes, as external disturbances, are incorporated into a sophisticated fuzzy inference machine, which executes fuzzy IF-THEN rules-based adaption laws to consistently modify the noise covariance matrices of the filter, thereby providing accurate and robust attitude results. A six-degrees of freedom (6 DOF) test bench is designed for filter performance evaluation, which executes various dynamic behaviors and enables measurement of the true attitude angles (ground truth) along with the raw MARG sensor data. The tuning of filter parameters is performed with numerical optimization based on the collected measurements from the test environment. A comprehensive analysis highlights that the proposed adaptive strategy significantly improves the attitude estimation quality. Moreover, the filter structure successfully rejects the effects of both slow and fast external perturbations. The FAEKF can be applied to any mobile system in which attitude estimation is necessary for localization and external disturbances greatly influence the filter accuracy.

scholarly journals A novel method for visualizing and tracking endogenous mRNA in a specific cell population in pathological neovascularization

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Md Imam Uddin ◽  
Tyler C. Kilburn ◽  
Sara Z. Jamal ◽  
Craig L. Duvall ◽  
John S. Penn
Keyword(s):  
Real Time ◽  
Disease Burden ◽  
Ex Vivo ◽  
Expression Patterns ◽  
Fluorescence Emission ◽  
High Sensitivity ◽  
Living Systems ◽  
Specific Cell ◽  
Potential Marker

AbstractDiabetic retinopathy, retinopathy of prematurity and retinal vein occlusion are potentially blinding conditions largely due to their respective neovascular components. The development of real-time in vivo molecular imaging methods, to assess levels of retinal neovascularization (NV), would greatly benefit patients afflicted with these conditions. mRNA hybridization techniques offer a potential method to image retinal NV. The success of these techniques hinges on the selection of a target mRNA whose tissue levels and spatial expression patterns correlate closely with disease burden. Using a model of oxygen-induced retinopathy (OIR), we previously observed dramatic increases in retinal endoglin that localized to neovascular structures (NV), directly correlating with levels of neovascular pathology. Based on these findings, we have investigated Endoglin mRNA as a potential marker for imaging retinal NV in OIR mice. Also of critical importance, is the application of innovative technologies capable of detecting mRNAs in living systems with high sensitivity and specificity. To detect and visualize endoglin mRNA in OIR mice, we have designed and synthesized a novel imaging probe composed of short-hairpin anti-sense (AS) endoglin RNA coupled to a fluorophore and black hole quencher (AS-Eng shRNA). This assembly allows highly sensitive fluorescence emission upon hybridization of the AS-Eng shRNA to cellular endoglin mRNA. The AS-Eng shRNA is further conjugated to a diacyl-lipid (AS-Eng shRNA–lipid referred to as probe). The lipid moiety binds to serum albumin facilitating enhanced systemic circulation of the probe. OIR mice received intraperitoneal injections of AS-Eng shRNA–lipid. Ex vivo imaging of their retinas revealed specific endoglin mRNA dependent fluorescence superimposed on neovascular structures. Room air mice receiving AS-Eng shRNA–lipid and OIR mice receiving a non-sense control probe showed little fluorescence activity. In addition, we found that cells in neovascular lesions labelled with endoglin mRNA dependent fluorescence, co-labelled with the macrophage/microglia-associated marker IBA1. Others have shown that cells expressing macrophage/microglia markers associate with retinal neovascular structures in proportion to disease burden. Hence we propose that our probe may be used to image and to estimate the levels of retinal neovascular disease in real-time in living systems.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

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