Wind vector measurement based on ultrasonic sensors in the mixed noise of α and Gaussian noise

2021 ◽  
Vol 29 (11) ◽  
pp. 2734-2743
Author(s):  
Yi-ran SHI ◽  
◽  
Jin-wei QI ◽  
Si-ning QU ◽  
Yang ZHAO
Keyword(s):  
Gaussian Noise ◽  
Wind Vector ◽  
Ultrasonic Sensors ◽  
Vector Measurement ◽  
Mixed Noise

scholarly journals Reviewing Wind Measurement Approaches for Fixed-Wing Unmanned Aircraft

Atmosphere ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 422 ◽  
Author(s):  
Alexander Rautenberg ◽  
Martin Graf ◽  
Norman Wildmann ◽  
Andreas Platis ◽  
Jens Bange
Keyword(s):  
Wind Speed ◽  
Unmanned Aircraft ◽  
Flow Sensor ◽  
Navigation Satellite ◽  
Wind Vector ◽  
Data Set ◽  
Vector Measurement ◽  
Wide Range ◽  
Global Navigation ◽  
Global Navigation Satellite

One of the biggest challenges in probing the atmospheric boundary layer with small unmanned aerial vehicles is the turbulent 3D wind vector measurement. Several approaches have been developed to estimate the wind vector without using multi-hole flow probes. This study compares commonly used wind speed and direction estimation algorithms with the direct 3D wind vector measurement using multi-hole probes. This was done using the data of a fully equipped system and by applying several algorithms to the same data set. To cover as many aspects as possible, a wide range of meteorological conditions and common flight patterns were considered in this comparison. The results from the five-hole probe measurements were compared to the pitot tube algorithm, which only requires a pitot-static tube and a standard inertial navigation system measuring aircraft attitude (Euler angles), while the position is measured with global navigation satellite systems. Even less complex is the so-called no-flow-sensor algorithm, which only requires a global navigation satellite system to estimate wind speed and wind direction. These algorithms require temporal averaging. Two averaging periods were applied in order to see the influence and show the limitations of each algorithm. For a window of 4 min, both simplifications work well, especially with the pitot-static tube measurement. When reducing the averaging period to 1 min and thereby increasing the temporal resolution, it becomes evident that only circular flight patterns with full racetracks inside the averaging window are applicable for the no-flow-sensor algorithm and that the additional flow information from the pitot-static tube improves precision significantly.

Mixed Noise Image De-Noising Based on EM Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 4734-4741 ◽  
Author(s):  
Gui Cun Shi ◽  
Fei Xing Wang
Keyword(s):  
Applied Sciences ◽  
Wavelet Analysis ◽  
Em Algorithm ◽  
Gaussian Noise ◽  
Impulse Noise ◽  
Noise Source ◽  
High Quality ◽  
Noise Image ◽  
Mixed Noise ◽  
Non Gaussian

Obtaining high quality images is very important in many areas of applied sciences, but images are usually polluted by noise in the process of generation, transmission and acquisition. In recent years, wavelet analysis achieves significant results in the field of image de-noising. However, most of the studies of noise-induced phenomena assume that the noise source is Gaussian. The use of mixed Gaussian and impulse noise is rare, mainly because of the difficulties in handling them. In the process of image de-noising, the noise model’s parameter estimation is a key issue, because the accuracy of the noise model’s parameters could affect the de-noising quality. In the case of mixed Gaussian noises, EM algorithm is an iterative algorithm, which simplifies the maximum likelihood equation. This thesis takes wavelet analysis and statistics theory as tools, studies on mixed noise image de-noising, provides two classes of algorithms for dealing with a special type of non-Gaussian noise, mixed Gaussian and Pepper & Salt noise.

scholarly journals Novel strategy for wide-range wind vector measurement using the hybrid CP/CTD heating mode and sequential measuring and correcting

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254256
Author(s):  
Tian Wang ◽  
Yunbo Shi ◽  
Xiaoyu Yu ◽  
Guangdong Lan ◽  
Congning Liu
Keyword(s):  
Wind Velocity ◽  
Wind Direction ◽  
High Velocity ◽  
Measurement Instrument ◽  
Wind Vector ◽  
Velocity Range ◽  
Vector Measurement ◽  
Airflow Distribution ◽  
Wide Range ◽  
Novel Strategy

To improve the performance of wind sensors in the high velocity range, this paper proposes a wind measurement strategy for thermal wind velocity sensors that combines the constant power and constant temperature difference driving modes of the heating element. Based on the airflow distribution characteristics from fluid dynamics, sequential measurement and correction is proposed as a method of measuring wind direction. In addition, a wind velocity and direction measurement instrument was developed using the above-mentioned approaches. The test results showed that the proposed instrument can obtain large dynamic wind velocity measurements from 0 to 60 m/s. The wind velocity measurement accuracy was ±0.5 m/s in the common velocity range of 0–20 m/s and ±1 m/s in the high velocity range of 20–60 m/s. The wind direction accuracy was ±3° throughout the 360° range. The proposed approaches and instrument are not only practical but also capable of meeting the requirements of wide-range and large dynamic wind vector measurement applications.

scholarly journals Design of thermal wind sensor with constant power control and wind vector measurement method

PLoS ONE ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. e0231405
Author(s):  
Congning Liu ◽  
Yunbo Shi ◽  
Xiaoyu Yu ◽  
Tengxi Wang ◽  
Maria D. King
Keyword(s):  
Power Control ◽  
Measurement Method ◽  
Wind Vector ◽  
Constant Power ◽  
Thermal Wind ◽  
Vector Measurement

scholarly journals Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images

2019 ◽  
Vol 10 (1) ◽  
pp. 243 ◽  
Author(s):  
Josep Arnal ◽  
Luis Súcar
Keyword(s):  
Noise Reduction ◽  
Gaussian Noise ◽  
State Of The Art ◽  
Peer Group ◽  
Median Filter ◽  
Block Matching ◽  
Group Method ◽  
Hybrid Filter ◽  
Mixed Noise ◽  
Vector Median

To decrease contamination from a mixed combination of impulse and Gaussian noise on color digital images, a novel hybrid filter is proposed. The new technique is composed of two stages. A filter based on a fuzzy metric is used for the reduction of impulse noise at the first stage. At the second stage, to remove Gaussian noise, a fuzzy peer group method is applied on the image generated from the previous stage. The performance of the introduced algorithm was evaluated on standard test images employing widely used objective quality metrics. The new approach can efficiently reduce both impulse and Gaussian noise, as much as mixed noise. The proposed filtering method was compared to the state-of-the-art methodologies: adaptive nearest neighbor filter, alternating projections filter, color block-matching 3D filter, fuzzy peer group averaging filter, partition-based trimmed vector median filter, trilateral filter, fuzzy wavelet shrinkage denoising filter, graph regularization filter, iterative peer group switching vector filter, peer group method, and the fuzzy vector median method. The experiments demonstrated that the introduced noise reduction technique outperforms those state-of-the-art filters with respect to the metrics peak signal to noise ratio (PSNR), the mean absolute error (MAE), and the normalized color difference (NCD).

Fourth-order partial differential equations based anisotropic diffusion model for low-dose CT images

2018 ◽  
Vol 32 (25) ◽  
pp. 1850300 ◽  
Author(s):  
Yadunath Pathak ◽  
K. V. Arya ◽  
Shailendra Tiwari
Keyword(s):  
Partial Differential Equations ◽  
Differential Equations ◽  
Fourth Order ◽  
Gaussian Noise ◽  
Anisotropic Diffusion ◽  
Low Dose ◽  
X Ray ◽  
Mixed Noise ◽  
Data Fidelity ◽  
Fidelity Term

The low-dose X-ray Computed Tomography (CT) is one of the most effective and indispensable imaging tools for clinical diagnosis. The reduced number of photons in low-dose X-ray CT imaging introduces the vulnerability towards Poisson and Gaussian noise. The majority of research till date focuses on reconstructing the images by reducing the effect of either Poisson or Gaussian noise. Thus, there is a need for a reconstruction framework that can counter the effects of both types of noises simultaneously. In this paper, an approach is proposed to handle the mixed noise (i.e. Poisson and Gaussian noises). Variational framework is utilized as energy minimization function. Minimizing the log likelihood gives data-fidelity term which portrays the distribution of noise in low-dose X-ray CT images. The problem of data-fidelity term as well as mixed noise issue in the sinogram data is resolved simultaneously by proposing a novel filter. The proposed filter modifies the Anisotropic Diffusion (AD) model based on Convolution Virtual Electric Field AD called as MADC. The modification in AD is achieved by applying fourth-order partial differential equations. To evaluate the effectiveness of the proposed MADC technique, both qualitative and quantitative evaluations are performed on three simulated test phantoms and one real standard thorax phantom of size [Formula: see text]. Afterwards, the performance of the proposed technique is compared with competitive denoising techniques. The experimental results reveal that the proposed framework significantly preserves the edges of reconstructed images and introduces lesser number of gradient reversal artifacts.

An Image Filtering Algorithm for Removing Mixed Noise

2013 ◽  
Vol 433-435 ◽  
pp. 383-388 ◽  
Author(s):  
Mao Xiang Chu ◽  
An Na Wang ◽  
Rong Fen Gong
Keyword(s):  
Gaussian Noise ◽  
Weighting Function ◽  
Median Filter ◽  
Bilateral Filter ◽  
Salt And Pepper Noise ◽  
Filtering Algorithm ◽  
Noise Simulation ◽  
Mixed Noise ◽  
Local Edge ◽  
Salt And Pepper

In order to remove salt-and-pepper noise and Gaussian noise in image, a novel filtering algorithm is proposed in this paper. The novel algorithm can preserve image edge details as much as possible. Firstly, five-median-binary code (FMBC) is proposed and used to describe local edge type of image. Secondly, median filter algorithm is improved to remove salt-and-pepper noise by using FMBC. Then, local enhanced bilateral filter with FMBC and a new type of exponential weighting function is used to remove Gaussian noise. Simulation results show that the algorithm proposed in this paper is very effective not only in filtering mixed noise but also in preserving edge details.

scholarly journals Hyperspectral Unmixing with Bandwise Generalized Bilinear Model

2018 ◽  
Vol 10 (10) ◽  
pp. 1600 ◽  
Author(s):  
Chang Li ◽  
Yu Liu ◽  
Juan Cheng ◽  
Rencheng Song ◽  
Hu Peng ◽  
...  
Keyword(s):  
Gaussian Noise ◽  
Impulse Noise ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Additive White Gaussian Noise ◽  
Bilinear Model ◽  
Hyperspectral Unmixing ◽  
Noise Impulse ◽  
Mixed Noise ◽  
Alternative Direction Method

Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of AWGN in each band of hyperspectral image (HSI) is assumed to be the same. However, the real HSIs are usually degraded by mixture of various kinds of noise, which include Gaussian noise, impulse noise, dead pixels or lines, stripes, and so on. Besides, the intensity of AWGN is usually different for each band of HSI. To address the above mentioned issues, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM), which can be adapted to the presence of complex mixed noise in real HSI. Besides, the alternative direction method of multipliers (ADMM) is adopted to solve the proposed NU-BGBM. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed NU-BGBM compared with some other state-of-the-art unmixing methods.

Sign in / Sign up

Export Citation Format

Share Document