scholarly journals Image Processing Techniques For Improved Sun Sensor Performance

2021 ◽  
Author(s):  
Christopher. Li
Keyword(s):  
Random Noise ◽  
Super Resolution ◽  
Least Square ◽  
Bias Error ◽  
Calibration Data ◽  
Small Motion ◽  
Sun Sensor ◽  
Improved Performance ◽  
Relative Measurements ◽  
Processing Techniques

The behaviour of digital sun-sensors and associated super-resolution algorithms was explored. Using calibration data, a method was proposed to model the peak width of peaks across the image array. Using this with the non-linear least square algorithm gave improved performance across the field-of-view. A test was proposed that would measure precision for small sensor motions. Also, a method of accounting for local bias error was given. The small motion test defined limits at which the sensor detects motion, and the precision test gave metrics to measure how well the sensor renders motion. Finally, an extended kalman filter was developed that used sun-vector measurements, in addition to a new relative measurement. This was tested using a well-defined sensor as well as a generic sensor for which few error data were known. Results indicate that relative measurements only improve performance if random noise is low.

scholarly journals Image Processing Techniques For Improved Sun Sensor Performance

2021 ◽  
Author(s):  
Christopher. Li
Keyword(s):  
Random Noise ◽  
Super Resolution ◽  
Least Square ◽  
Bias Error ◽  
Calibration Data ◽  
Small Motion ◽  
Sun Sensor ◽  
Improved Performance ◽  
Relative Measurements ◽  
Processing Techniques

The behaviour of digital sun-sensors and associated super-resolution algorithms was explored. Using calibration data, a method was proposed to model the peak width of peaks across the image array. Using this with the non-linear least square algorithm gave improved performance across the field-of-view. A test was proposed that would measure precision for small sensor motions. Also, a method of accounting for local bias error was given. The small motion test defined limits at which the sensor detects motion, and the precision test gave metrics to measure how well the sensor renders motion. Finally, an extended kalman filter was developed that used sun-vector measurements, in addition to a new relative measurement. This was tested using a well-defined sensor as well as a generic sensor for which few error data were known. Results indicate that relative measurements only improve performance if random noise is low.

scholarly journals Atmospheric CO<sub>2</sub>, CH<sub>4</sub>, and CO with the CRDS technique at the Izaña Global GAW station: instrumental tests, developments, and first measurement results

2019 ◽  
Vol 12 (4) ◽  
pp. 2043-2066 ◽  
Author(s):  
Angel J. Gomez-Pelaez ◽  
Ramon Ramos ◽  
Emilio Cuevas ◽  
Vanessa Gomez-Trueba ◽  
Enrique Reyes
Keyword(s):  
Flow Rate ◽  
Random Noise ◽  
Least Square ◽  
Spatial Inhomogeneity ◽  
Inlet Pressure ◽  
Processing Scheme ◽  
Linear Relationships ◽  
Least Square Fit

Abstract. At the end of 2015, a CO2/CH4/CO cavity ring-down spectrometer (CRDS) was installed at the Izaña Global Atmosphere Watch (GAW) station (Tenerife, Spain) to improve the Izaña Greenhouse Gases GAW Measurement Programme, and to guarantee the renewal of the instrumentation and the long-term maintenance of this program. We present the results of the CRDS acceptance tests, the raw data processing scheme applied, and the response functions used. Also, the calibration results, the implemented water vapor correction, the target gas injection statistics, the ambient measurements performed from December 2015 to July 2017, and their comparison with other continuous in situ measurements are described. The agreement with other in situ continuous measurements is good most of the time for CO2 and CH4, but for CO it is just outside the GAW 2 ppb objective. It seems the disagreement is not produced by significant drifts in the CRDS CO World Meteorological Organization (WMO) tertiary standards. The more relevant contributions of the present article are (1) determination of linear relationships between flow rate, CRDS inlet pressure, and CRDS outlet valve aperture; (2) determination of a slight CO2 correction that takes into account changes in the inlet pressure/flow rate (as well as its stability over the years), and attributing it to the existence of a small spatial inhomogeneity in the pressure field inside the CRDS cavity due to the gas dynamics; (3) drift rate determination for the pressure and temperature sensors located inside the CRDS cavity from the CO2 and CH4 response function drift trends; (4) the determination of the H2O correction for CO has been performed using raw spectral peak data instead of the raw CO provided by the CRDS and using a running mean to smooth random noise in a long water-droplet test (12 h) before performing the least square fit; and (5) the existence of a small H2O dependence in the CRDS flow and of a small spatial inhomogeneity in the temperature field inside the CRDS cavity are pointed out and their origin discussed.

scholarly journals Spatial Enhancement of AWiFS along Wider Swath using NSCT

2018 ◽  
Vol 7 (3.12) ◽  
pp. 474
Author(s):  
K S. R. Radhika ◽  
C V. Rao ◽  
V Kamakshi Prasad
Keyword(s):  
Temporal Resolution ◽  
Accuracy Assessment ◽  
Super Resolution ◽  
Computation Time ◽  
Processing Technique ◽  
Sensor Data ◽  
Liss Iii ◽  
Novel Processing ◽  
Single Image Super Resolution ◽  
Processing Techniques

Image acquisition in a wider swath, cannot assess the best spatial resolution (SR) and temporal resolution (TR) simultaneously, due to inherent limitations of space borne sensors. But any of the information extraction from remote sensed (RS) images demands the above characteristics. As this is not possible onboard, suitable ground processing techniques need to be evolved to realise the requirements through advanced image processing techniques. The proposed work deals with processing of two onboard sensor data viz., Resourcesat-1 (RS1): LISS-III, which has medium swath combined with AWiFS, which has wider swath data to provide high spatial and temporal resolution at the same instant. LISS-III at 23m and 24 days, AWiFS at 56m and 5 days spatial and temporal revisits acquire the data at different swaths. In the process of acquisition at the same time, the 140km swath of LISS-III coincides at the exact centre line 740km swath of AWiFS. If the non-overlapping area of AWiFS has same features of earth’s surface as of LISS-III overlapping area, it then provides a way to increase the SR of AWiFS to SR of LISS-III in the same non-overlapping area. Using this knowledge, a novel processing technique Fast One Pair Learning and Prediction (FOPLP) is developed in which time is optimized against the existing methods. FOPLP improves the SR of LISS-III in non-overlapping area using technique Single Image Super Resolution (SISR) with Non Sub sampled Contourlet Transforms (NSCT) method and is applied on different sets of images. The proposed technique resulting into an image having TR of 5 days, 740km swath at SR of 23m. Results have shown the strength of the proposed method in terms of computation time and prediction accuracy assessment.  

scholarly journals Calibration of MEMS Triaxial Accelerometers Based on the Maximum Likelihood Estimation Method

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yifan Sun ◽  
Xiang Xu
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Random Noise ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Least Square ◽  
Triaxial Accelerometer ◽  
Zero Bias ◽  
Estimation Function ◽  
Maximum Likelihood Estimation Method

As a widely used inertial device, a MEMS triaxial accelerometer has zero-bias error, nonorthogonal error, and scale-factor error due to technical defects. Raw readings without calibration might seriously affect the accuracy of inertial navigation system. Therefore, it is necessary to conduct calibration processing before using a MEMS triaxial accelerometer. This paper presents a MEMS triaxial accelerometer calibration method based on the maximum likelihood estimation method. The error of the MEMS triaxial accelerometer comes into question, and the optimal estimation function is established. The calibration parameters are obtained by the Newton iteration method, which is more efficient and accurate. Compared with the least square method, which estimates the parameters of the suboptimal estimation function established under the condition of assuming that the mean of the random noise is zero, the parameters calibrated by the maximum likelihood estimation method are more accurate and stable. Moreover, the proposed method has low computation, which is more functional. Simulation and experimental results using the consumer low-cost MEMS triaxial accelerometer are presented to support the abovementioned superiorities of the maximum likelihood estimation method. The proposed method has the potential to be applied to other triaxial inertial sensors.

scholarly journals Multicomponent MR Image Denoising

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
José V. Manjón ◽  
Neil A. Thacker ◽  
Juan J. Lull ◽  
Gracian Garcia-Martí ◽  
Luís Martí-Bonmatí ◽  
...  
Keyword(s):  
Random Noise ◽  
Principal Component ◽  
Magnetic Resonance Images ◽  
Image Resolution ◽  
Single Image ◽  
Mr Images ◽  
Mr Image ◽  
Automatic Feature Extraction ◽  
Improved Performance ◽  
Multicomponent Nature

Magnetic Resonance images are normally corrupted by random noise from the measurement process complicating the automatic feature extraction and analysis of clinical data. It is because of this reason that denoising methods have been traditionally applied to improve MR image quality. Many of these methods use the information of a single image without taking into consideration the intrinsic multicomponent nature of MR images. In this paper we propose a new filter to reduce random noise in multicomponent MR images by spatially averaging similar pixels using information from all available image components to perform the denoising process. The proposed algorithm also uses a local Principal Component Analysis decomposition as a postprocessing step to remove more noise by using information not only in the spatial domain but also in the intercomponent domain dealing in a higher noise reduction without significantly affecting the original image resolution. The proposed method has been compared with similar state-of-art methods over synthetic and real clinical multicomponent MR images showing an improved performance in all cases analyzed.

Parameter Estimation of the Mechanical Behavior of the Artery Using a Nonlinear Least Square Method With a Noise Model

2008 ◽  
Author(s):  
Shahrokh Zeinali ◽  
Jongeun Choi ◽  
Seungik Baek
Keyword(s):  
Parameter Estimation ◽  
Blood Vessel ◽  
Mechanical Behavior ◽  
Constitutive Relation ◽  
Environmental Changes ◽  
Constitutive Relations ◽  
Estimation Method ◽  
Least Square ◽  
Noise Model ◽  
Bias Error

Although it is well known that blood vessels adapt and remodel in response to various biomechanical stimuli, quantifying changes in constitutive relation corresponding to environmental changes is still challenging. Especially, when the dimension of blood vessel is small, the uncertainties in experimental measurements become significant and make it difficult to precisely estimate parameters of constitutive relations for mechanical behavior of the blood vessel. Hence without considering measurement error in displacement, a conventional nonlinear least square (NLS) method results in a biased parameter estimation. In this paper, we propose a new parameter estimation method to eliminate such bias error and provide more accurate estimated parameters for a constitutive relation using a weighted nonlinear least square (WNLS) method with a noise model. We first applied the proposed technique to a set of synthesized data with computer generated white noises and compared the fitting results to those of the NLS method without the noise model. We also applied our method to experimental data sets from mechanical tests of rabbit basilar and mouse carotid arteries and studied parameter sensitivity of the constitutive model.

The importance of policy to effective IM use and improved performance

2019 ◽  
Vol 33 (1) ◽  
pp. 180-197
Author(s):  
Lin Jia ◽  
Lijuan Huang ◽  
Zhijun Yan ◽  
Dianne Hall ◽  
Jiahe Song ◽  
...  
Keyword(s):  
Instant Messaging ◽  
Partial Least Square ◽  
Least Square ◽  
Communication Quality ◽  
Content Type ◽  
Adaptation And Learning ◽  
Antecedents And Consequences ◽  
Improved Performance ◽  
Effective Use ◽  
The Impact

Purpose Although the use of instant messaging (IM) at work has been studied in the IS field, its effective use and impact on performance have not been adequately addressed. The purpose of this paper is to explore the antecedents and consequences of the effective use of IM at work by adapting Burton-Jones and Grange’s theory of effective use. Design/methodology/approach The authors introduce “Comprehensive IM policy” as a facilitator of adaptation and learning actions to improve the effective use of IM, which will improve communication quality and productivity. The impact of IM competence on effective use is also discussed. Based on a survey of 215 managers, this study applies the partial least square technique to test the research model. Findings The results indicate that comprehensive IM policy encourages adaptation and learning actions, which improve the effective use of IM and thereafter improve communication quality and productivity. Meanwhile, IM competence has a substitutive interaction effect with IM reconfiguration and self-learning on effective use. Originality/value The results refine the general theory of effective use and provide managers with an approach to leverage IM use at work for performance gains.

scholarly journals Sparsity Based Full Rank Polarimetric Reconstruction of Coherence Matrix T

2019 ◽  
Vol 11 (11) ◽  
pp. 1288 ◽  
Author(s):  
Hossein Aghababaee ◽  
Giampaolo Ferraioli ◽  
Laurent Ferro-Famil ◽  
Gilda Schirinzi ◽  
Yue Huang
Keyword(s):  
Super Resolution ◽  
Full Rank ◽  
Real Data ◽  
Natural Environments ◽  
Data Sets ◽  
Scattering Pattern ◽  
Component Decomposition ◽  
Resolution Imaging ◽  
Processing Techniques ◽  
Physical Features

In the frame of polarimetric synthetic aperture radar (SAR) tomography, full-ranks reconstruction framework has been recognized as a significant technique for fully characterization of superimposed scatterers in a resolution cell. The technique, mainly is characterized by the advantages of polarimetric scattering pattern reconstruction, allows physical feature extraction of the scatterers. In this paper, to overcome the limitations of conventional full-rank tomographic techniques in natural environments, a polarimetric estimator with advantages of super-resolution imaging is proposed. Under the frame of compressive sensing (CS) and sparsity based reconstruction, the profile of second order polarimetric coherence matrix T is recovered. Once the polarimetric coherence matrices of the scatterers are available, the physical features can be extracted using classical polarimetric processing techniques. The objective of this study is to evaluate the performance of the proposed full-rank polarimetric reconstruction by means of conventional three-component decomposition of T, and focusing on the consistency of vertical resolution and polarimetric scattering pattern of the scatterers. The outcomes from simulated and two different real data sets confirm that significant improvement can be achieved in the reconstruction quality with respect to conventional approaches.

Random noise attenuation using f-x regularized nonstationary autoregression

Geophysics ◽  
2012 ◽  
Vol 77 (2) ◽  
pp. V61-V69 ◽  
Author(s):  
Guochang Liu ◽  
Xiaohong Chen ◽  
Jing Du ◽  
Kailong Wu
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Geological Structure ◽  
Least Square ◽  
Noise Attenuation ◽  
Spatial Direction ◽  
Random Noise Attenuation ◽  
Time Space ◽  
Prediction Technique ◽  
Domain Prediction

We have developed a novel method for random noise attenuation in seismic data by applying regularized nonstationary autoregression (RNA) in the frequency-space ([Formula: see text]) domain. The method adaptively predicts the signal with spatial changes in dip or amplitude using [Formula: see text] RNA. The key idea is to overcome the assumption of linearity and stationarity of the signal in conventional [Formula: see text] domain prediction technique. The conventional [Formula: see text] domain prediction technique uses short temporal and spatial analysis windows to cope with the nonstationary of the seismic data. The new method does not require windowing strategies in spatial direction. We implement the algorithm by an iterated scheme using the conjugate-gradient method. We constrain the coefficients of nonstationary autoregression (NA) to be smooth along space and frequency in the [Formula: see text] domain. The shaping regularization in least-square inversion controls the smoothness of the coefficients of [Formula: see text] RNA. There are two key parameters in the proposed method: filter length and radius of shaping operator. Tests on synthetic and field data examples showed that, compared with [Formula: see text] domain and time-space domain prediction methods, [Formula: see text] RNA can be more effective in suppressing random noise and preserving the signals, especially for complex geological structure.

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