scholarly journals Processing Centroids of Smearing Star Image of Star Sensor

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yufu Liao ◽  
Enhai Liu ◽  
Jianyong Zhong ◽  
Hui Zhang
Keyword(s):  
Angular Rate ◽  
Analytical Form ◽  
Integration Time ◽  
Star Sensor ◽  
Centre Of Mass ◽  
Image Model ◽  
Star Image ◽  
Centroid Estimation ◽  
Novel Method ◽  
Better Than

A novel method was presented for increasing the accuracy of subpixel centroid estimation for smearing star image. Model of the smearing trajectory of smearing star was built. It helped to study the analytical form of the errors, caused by image smearing, for centroid estimation. In the algorithm, the errors were estimated with accuracy and used to revise the centroid processed by CoM (centre of mass). Simulations have been run to study the effect of angular rates, integration time, and actual position of star on the accuracy of centroid estimation. Results were presented which suggested that the proposed algorithm had a precision better than 1/10 of a pixel when the angular rate was up to 3.0 deg/s.

scholarly journals Star Sensor Denoising Algorithm Based on Edge Protection

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5255
Author(s):  
Kaili Lu ◽  
Enhai Liu ◽  
Rujin Zhao ◽  
Hui Zhang ◽  
Hong Tian
Keyword(s):  
Image Denoising ◽  
Estimation Error ◽  
Experimental Results ◽  
Star Sensor ◽  
Side Window ◽  
Star Image ◽  
Centroid Estimation ◽  
Single Pixel

Single-pixel noise commonly appearing in a star sensor can cause an unexpected error in centroid extraction. To overcome this problem, this paper proposes a star image denoising algorithm, named Improved Gaussian Side Window Filtering (IGSWF). Firstly, the IGSWF algorithm uses four special triangular Gaussian subtemplates for edge protection. Secondly, it exploits a reconstruction function based on the characteristic of stars and noise. The proposed IGSWF algorithm was successfully verified through simulations and evaluated in a star sensor. The experimental results indicated that the IGSWF algorithm performed better in preserving the shape of stars and eliminating the single-pixel noise and the centroid estimation error (CEE) value after using the IGSWF algorithm was eight times smaller than the original value, six times smaller than that after traditional window filtering, and three times smaller than that after the side window filtering.

A New Restoration Algorithm for the Smeared Image of a SINS-aided Star Sensor

2014 ◽  
Vol 67 (5) ◽  
pp. 881-898 ◽  
Author(s):  
Kedong Wang ◽  
Chao Zhang ◽  
Yong Li ◽  
Xin Kan
Keyword(s):  
Inertial Navigation System ◽  
Attitude Determination ◽  
Angular Rate ◽  
Wiener Filter ◽  
Star Sensor ◽  
Tracking Method ◽  
Trace Length ◽  
Star Image ◽  
Restoration Algorithm ◽  
The Impact

The accuracy of attitude determination using a star sensor tends to be degraded if the host vehicle's manoeuvring smears the star image. In this paper, a new restoration algorithm with the aid of a Strap-down Inertial Navigation System (SINS) is proposed to reduce the effect of the smeared image. The smeared trace length is estimated with aid of the SINS angular rate. The restoration algorithm based on a Wiener filter is designed after the smeared zone is derived with the aid of the SINS coarse attitude. A tracking method is proposed to reject the stars of low centroid extraction accuracy. Simulations demonstrate that the success rate and the accuracy of attitude determination are improved significantly by the restoration algorithm even under a very fast rotation. The impact of the SINS error on the restoration is evaluated in the simulations.

scholarly journals SPAN512: A new mid-latitude pulsar survey with the Nançay Radio Telescope

2012 ◽  
Vol 8 (S291) ◽  
pp. 375-377 ◽  
Author(s):  
Gregory Desvignes ◽  
Ismaël Cognard ◽  
David Champion ◽  
Patrick Lazarus ◽  
Patrice Lespagnol ◽  
...  
Keyword(s):  
Radio Telescope ◽  
Large Scale ◽  
High Time ◽  
Frequency Resolution ◽  
Integration Time ◽  
Sensitivity Limit ◽  
Long Period ◽  
Large Bandwidth ◽  
L Band ◽  
Better Than

AbstractWe present an ongoing survey with the Nançay Radio Telescope at L-band. The targeted area is 74° ≲ l < 150° and 3.5° < |b| < 5°. This survey is characterized by a long integration time (18 min), large bandwidth (512 MHz) and high time and frequency resolution (64 μs and 0.5 MHz) giving a nominal sensitivity limit of 0.055 mJy for long period pulsars. This is about 2 times better than the mid-latitude HTRU survey, and is designed to be complementary with current large scale surveys. This survey will be more sensitive to transients (RRATs, intermittent pulsars), distant and faint millisecond pulsars as well as scintillating sources (or any other kind of radio faint sources) than all previous short-integration surveys.

scholarly journals An approximate method for 1-D simulation of pollution transport in streams with dead zones

2018 ◽  
Vol 66 (4) ◽  
pp. 437-447 ◽  
Author(s):  
Marek Sokáč ◽  
Yvetta Velísková ◽  
Carlo Gualtieri
Keyword(s):  
Approximate Method ◽  
Statistical Evaluation ◽  
Pollution Transport ◽  
Dead Zones ◽  
Advection Diffusion Equation ◽  
Gumbel’S Distribution ◽  
Novel Method ◽  
Substance Transport ◽  
Better Than ◽  
Tracer Experiments

Abstract Analytical solutions describing the 1D substance transport in streams have many limitations and factors, which determine their accuracy. One of the very important factors is the presence of the transient storage (dead zones), that deform the concentration distribution of the transported substance. For better adaptation to such real conditions, a simple 1D approximation method is presented in this paper. The proposed approximate method is based on the asymmetric probability distribution (Gumbel’s distribution) and was verified on three streams in southern Slovakia. Tracer experiments on these streams confirmed the presence of dead zones to various extents, depending mainly on the vegetation extent in each stream. Statistical evaluation confirms that the proposed method approximates the measured concentrations significantly better than methods based upon the Gaussian distribution. The results achieved by this novel method are also comparable with the solution of the 1D advection-diffusion equation (ADE), whereas the proposed method is faster and easier to apply and thus suitable for iterative (inverse) tasks.

Experimental study of the adaptive gain feature for improved position-sensitive ion spectroscopy with Timepix2

2022 ◽  
Vol 17 (01) ◽  
pp. C01025
Author(s):  
B. Bergmann ◽  
P. Smolyanskiy ◽  
P. Burian ◽  
S. Pospisil
Keyword(s):  
Energy Content ◽  
High Energy ◽  
Relative Energy ◽  
Ion Spectroscopy ◽  
Spatially Resolved ◽  
Deposited Energy ◽  
Novel Method ◽  
Thickness Variations ◽  
Sigma E ◽  
Better Than

Abstract In the present work, we study the Timepix2 pixels’ high energy response in the so-called adaptive gain mode. Therefore, Timepix2 with a 500 μm thick silicon sensor was irradiated with protons of energies in the range from 400 keV to 2 MeV and α-particles of 5.5 MeV from 241Am. A novel method was developed to determine the energy deposit in single pixels of particle imprints, which are spread out over a set of neighbor pixels (cluster). We show that each pixel is capable of measuring the deposited energy from 4 keV up to ∼3.2 MeV. Reconstructing the full energy content of the clusters, we found relative energy resolutions ( σ E ) better than 2.7% and better than 4% for proton and α-particle data, respectively. In a simple experiment with a 5.5 MeV α-particle source, we demonstrate that energy losses in thin (organic) specimen can be spatially resolved, mapping out sample thickness variations, with a resolution around 1–2 μm, across the sensor area. The inherent spatial resolution of the device was determined to be 350 nm in the best case.

Distance Based Pattern Driven Mining for Outlier Detection in High Dimensional Big Dataset

2022 ◽  
Vol 13 (1) ◽  
pp. 1-17
Author(s):  
Ankit Kumar ◽  
Abhishek Kumar ◽  
Ali Kashif Bashir ◽  
Mamoon Rashid ◽  
V. D. Ambeth Kumar ◽  
...  
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Outlier Detection ◽  
Credit Card ◽  
High Dimensional ◽  
Work Efficiency ◽  
Average Value ◽  
Novel Method ◽  
Detection Of Outliers ◽  
Better Than

Detection of outliers or anomalies is one of the vital issues in pattern-driven data mining. Outlier detection detects the inconsistent behavior of individual objects. It is an important sector in the data mining field with several different applications such as detecting credit card fraud, hacking discovery and discovering criminal activities. It is necessary to develop tools used to uncover the critical information established in the extensive data. This paper investigated a novel method for detecting cluster outliers in a multidimensional dataset, capable of identifying the clusters and outliers for datasets containing noise. The proposed method can detect the groups and outliers left by the clustering process, like instant irregular sets of clusters (C) and outliers (O), to boost the results. The results obtained after applying the algorithm to the dataset improved in terms of several parameters. For the comparative analysis, the accurate average value and the recall value parameters are computed. The accurate average value is 74.05% of the existing COID algorithm, and our proposed algorithm has 77.21%. The average recall value is 81.19% and 89.51% of the existing and proposed algorithm, which shows that the proposed work efficiency is better than the existing COID algorithm.

ON THE QUASI-STATIONARY DISTRIBUTION OF SIS MODELS

2016 ◽  
Vol 30 (4) ◽  
pp. 622-639 ◽  
Author(s):  
Gaofeng Da ◽  
Maochao Xu ◽  
Shouhuai Xu
Keyword(s):  
Stationary Distribution ◽  
Upper Bound ◽  
Hazard Rate ◽  
State Of The Art ◽  
Upper Bounds ◽  
Hazard Rate Order ◽  
Reversed Hazard Rate ◽  
Novel Method ◽  
Better Than ◽  
Quasi Stationary Distribution

In this paper, we propose a novel method for constructing upper bounds of the quasi-stationary distribution of SIS processes. Using this method, we obtain an upper bound that is better than the state-of-the-art upper bound. Moreover, we prove that the fixed point map Φ [7] actually preserves the equilibrium reversed hazard rate order under a certain condition. This allows us to further improve the upper bound. Some numerical results are presented to illustrate the results.

scholarly journals Star Image Prediction and Restoration under Dynamic Conditions

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1890 ◽  
Author(s):  
Liu ◽  
Chen ◽  
Liu ◽  
Shi
Keyword(s):  
Attitude Control ◽  
Attitude Determination ◽  
Back Propagation ◽  
Prediction Method ◽  
Back Propagation Neural Network ◽  
Attitude Measurement ◽  
Star Sensor ◽  
Dynamic Conditions ◽  
Star Image ◽  
Image Prediction

The star sensor is widely used in attitude control systems of spacecraft for attitude measurement. However, under high dynamic conditions, frame loss and smearing of the star image may appear and result in decreased accuracy or even failure of the star centroid extraction and attitude determination. To improve the performance of the star sensor under dynamic conditions, a gyroscope-assisted star image prediction method and an improved Richardson-Lucy (RL) algorithm based on the ensemble back-propagation neural network (EBPNN) are proposed. First, for the frame loss problem of the star sensor, considering the distortion of the star sensor lens, a prediction model of the star spot position is obtained by the angular rates of the gyroscope. Second, to restore the smearing star image, the point spread function (PSF) is calculated by the angular velocity of the gyroscope. Then, we use the EBPNN to predict the number of iterations required by the RL algorithm to complete the star image deblurring. Finally, simulation experiments are performed to verify the effectiveness and real-time of the proposed algorithm.

scholarly journals Dictionary Learning Based on Nonnegative Matrix Factorization Using Parallel Coordinate Descent

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Zunyi Tang ◽  
Shuxue Ding ◽  
Zhenni Li ◽  
Linlin Jiang
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Coordinate Descent ◽  
Multispectral Data ◽  
Overcomplete Dictionary ◽  
Novel Method ◽  
Better Than

Sparse representation of signals via an overcomplete dictionary has recently received much attention as it has produced promising results in various applications. Since the nonnegativities of the signals and the dictionary are required in some applications, for example, multispectral data analysis, the conventional dictionary learning methods imposed simply with nonnegativity may become inapplicable. In this paper, we propose a novel method for learning a nonnegative, overcomplete dictionary for such a case. This is accomplished by posing the sparse representation of nonnegative signals as a problem of nonnegative matrix factorization (NMF) with a sparsity constraint. By employing the coordinate descent strategy for optimization and extending it to multivariable case for processing in parallel, we develop a so-called parallel coordinate descent dictionary learning (PCDDL) algorithm, which is structured by iteratively solving the two optimal problems, the learning process of the dictionary and the estimating process of the coefficients for constructing the signals. Numerical experiments demonstrate that the proposed algorithm performs better than the conventional nonnegative K-SVD (NN-KSVD) algorithm and several other algorithms for comparison. What is more, its computational consumption is remarkably lower than that of the compared algorithms.

scholarly journals Application of Game-Based Learning in the Teaching Process of Disaster Medicine for Medical Students

2019 ◽  
Vol 34 (s1) ◽  
pp. s101-s101
Author(s):  
Hai Hu
Keyword(s):  
Medical Students ◽  
Computer Game ◽  
Disaster Medicine ◽  
Board Game ◽  
Game Based Learning ◽  
Teaching Process ◽  
Novel Method ◽  
Great Possibility ◽  
Injury Classification ◽  
Better Than

Introduction:Classroom instruction of disaster medicine for medical students is complicated and lacks attraction. Nowadays a novel method, which is named Game-Based Learning (GBL), has been used in other fields and received good feedback.Aim:To apply GBL to the teaching process of disaster medicine and discuss the effect of its application.Methods:A computer game was devised based on a syllabus of disaster medicine and employed it in classes of disaster medicine for medical students. Then a questionnaire about the application of GBL in education was used inquiring the demands of medical students for the designing of GBL in disaster medicine, including their platform and game mode preferences. Feedback was collected and data was analyzed after the class.Results:201 questionnaires were issued, and the valid rate was 100%. From the responses, 77% of medical students considered the application of GBL in education on disaster medicine was necessary, and 73% of the respondents thought it was practical. Furthermore, over 90% of medical students expressed their expectation for the adoption of GBL. According to another survey of 51 medical students we conducted, after attending a class about knowledge of injury classification with one board game adopted, most of the students believed GBL was better than traditional methods of teaching.Discussion:There is a high approbation degree among medical students to the adoption of GBL in the teaching process of disaster medicine, which suggests a great possibility for the application of GBL in medical education. It is concluded that GBL can be used in the teaching process of disaster medicine.

Sign in / Sign up

Export Citation Format

Share Document