Image filtering by a scaled mean-square-error filter using a vector processing algorithm

1995 ◽  
Vol 31 (6) ◽  
pp. 443-444
Author(s):  
L. Guan ◽  
X.L. Zhou
Keyword(s):  
Mean Square Error ◽  
Image Filtering ◽  
Processing Algorithm ◽  
Mean Square ◽  
Vector Processing

Improved Canopy Characterization with Laser Scanning Sensor for Greenhouse Spray Applications

2021 ◽  
Vol 64 (6) ◽  
pp. 2125-2136
Author(s):  
Uchit Nair ◽  
Peter P. Ling ◽  
Heping Zhu
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Sensor Data ◽  
Processing Algorithm ◽  
Individual Plant ◽  
Laser Sensor ◽  
List Type ◽  
Mean Square ◽  
Processing Variable

HighlightsAn algorithm was developed to process laser sensor data to make more accurate measurements of canopy dimensions.The algorithm isolated individual canopies, removed distortion, and estimated the occluded portions of the dataset.The algorithm reduced measured error by 46% in terms of root mean square error (RMSE).The RMSE was higher for sensor heights below and above a calculated optimal sensor height.Abstract. Laser-guided intelligent spray technology for greenhouse applications requires sensors that can accurately measure plant dimensions. This study proposed a new method to overcome current limitations by introducing a processing algorithm that manipulates the noisy dataset and determines the optimal sensor height to produce better measurements of the canopy width. The processing algorithm involves a combination of registration, clustering, and mirroring. Registration aligns multiple scans of the same scene to improve resolution. Clustering isolates individual plant canopies from the dataset to enable further processing. Mirroring is used to resolve the problems of distortion and occlusion and predict missing information in the dataset. The performance of the processing algorithm was evaluated by calculating the root mean square error (RMSE) in the canopy width measurements. Its results were compared with the measurements reported in earlier research, where there was limited processing of the laser sensor data. The processing algorithm reduced RMSE values by 46% compared to the earlier research, and the largest improvements were seen for objects placed beyond 1.5 m from the sensor. The sensor height was observed to be inversely proportional to the RMSE values. The average RMSE of the processing algorithm was 25 mm, compared to 47 mm in the earlier research when the laser sensor was at a height of 1 m. Another experimental setup was used to test the limits of the relationship between sensor height and algorithm performance while using objects that were more representative of plant canopy shapes. The accuracy of the processing algorithm decreased when the sensor height was either above or below the optimal sensor height, which was derived from calculations made in earlier research. The processing algorithm has potential to improve spray efficiencies. Keywords: Automation, Clustering, LiDAR, Point cloud data processing, Variable-rate spray.

The Bordeaux Automatic Meridian Circle

1978 ◽  
Vol 48 ◽  
pp. 227-228
Author(s):  
Y. Requième
Keyword(s):  
Mean Square Error ◽  
Mean Square ◽  
Meridian Circle ◽  
The Mean ◽  
Full Automation

In spite of important delays in the initial planning, the full automation of the Bordeaux meridian circle is progressing well and will be ready for regular observations by the middle of the next year. It is expected that the mean square error for one observation will be about ±0.”10 in the two coordinates for declinations up to 87°.

Investigation of Accuracy of a Calibrated Estimator of a Ratio by Modelling

2005 ◽  
Vol 10 (4) ◽  
pp. 333-342
Author(s):  
V. Chadyšas ◽  
D. Krapavickaitė
Keyword(s):  
Mean Square Error ◽  
Taylor Series ◽  
Random Sampling ◽  
Finite Population ◽  
Taylor Series Expansion ◽  
Simple Random Sampling ◽  
Population Parameter ◽  
Mean Square ◽  
Parameter Ratio ◽  
Using Data

Estimator of finite population parameter – ratio of totals of two variables – is investigated by modelling in the case of simple random sampling. Traditional estimator of the ratio is compared with the calibrated estimator of the ratio introduced by Plikusas [1]. The Taylor series expansion of the estimators are used for the expressions of approximate biases and approximate variances [2]. Some estimator of bias is introduced in this paper. Using data of artificial population the accuracy of two estimators of the ratio is compared by modelling. Dependence of the estimates of mean square error of the estimators of the ratio on the correlation coefficient of variables which are used in the numerator and denominator, is also shown in the modelling.

Performance analysis of global local mean square error criterion of stochastic linearization for nonlinear oscillator

2019 ◽  
Author(s):  
Nguyen Cao Thang ◽  
Luu Xuan Hung
Keyword(s):  
Performance Analysis ◽  
Mean Square Error ◽  
Nonlinear Oscillators ◽  
Degree Of Freedom ◽  
Equivalent Linearization ◽  
Mean Square ◽  
Error Criterion ◽  
Stochastic Linearization ◽  
Local Mean ◽  
Mean Square Error Criterion

The paper presents a performance analysis of global-local mean square error criterion of stochastic linearization for some nonlinear oscillators. This criterion of stochastic linearization for nonlinear oscillators bases on dual conception to the local mean square error criterion (LOMSEC). The algorithm is generally built to multi degree of freedom (MDOF) nonlinear oscillators. Then, the performance analysis is carried out for two applications which comprise a rolling ship oscillation and two degree of freedom one. The improvement on accuracy of the proposed criterion has been shown in comparison with the conventional Gaussian equivalent linearization (GEL).

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

Performance enhancement of Echo Cancellation Using a Combination of Partial Update ( PU) Methods and New Variable Length LMS (NVLLMS) Algorithm

2018 ◽  
Vol 24 (5) ◽  
pp. 66
Author(s):  
Thamer M. Jamel ◽  
Faez Fawzi Hammood
Keyword(s):  
Mean Square Error ◽  
Return Loss ◽  
Performance Enhancement ◽  
Variable Length ◽  
Echo Cancellation ◽  
Convergence Time ◽  
Length Variation ◽  
Mean Square ◽  
Step Size ◽  
Echo Return Loss Enhancement

In this paper, several combination algorithms between Partial Update LMS (PU LMS) methods and previously proposed algorithm (New Variable Length LMS (NVLLMS)) have been developed. Then, the new sets of proposed algorithms were applied to an Acoustic Echo Cancellation system (AEC) in order to decrease the filter coefficients, decrease the convergence time, and enhance its performance in terms of Mean Square Error (MSE) and Echo Return Loss Enhancement (ERLE). These proposed algorithms will use the Echo Return Loss Enhancement (ERLE) to control the operation of filter's coefficient length variation. In addition, the time-varying step size is used.The total number of coefficients required was reduced by about 18% , 10% , 6%, and 16% using Periodic, Sequential, Stochastic, and M-max PU NVLLMS algorithms respectively, compared to that used by a full update method which  is very important, especially in the application of mobile communication since the power consumption must be considered. In addition, the average ERLE and average Mean Square Error (MSE) for M-max PU NVLLMS are better than other proposed algorithms.  

Prediksi Daya Panel Surya Kapasitas 50 Wp Menggunakan Model Regresi Linier Majemuk

2020 ◽  
Vol 10 (2) ◽  
Author(s):  
Radian Indra Mukromin ◽  
Muhammad Khamim Asy'ari
Keyword(s):  
Mean Square Error ◽  
Mean Square

Sistem monitoring daya listrik pada panel surya penting dilakukan. Hal ini disebabkan daya listrik panel surya dapat mempengaruhi performansi pengisian baterai dan keandalan dari panel surya.  Sifat stokastik dari temperatur panel surya dan iradiasi surya mengakibatkan fluktuasi daya listrik, sehingga diperlukan sistem prediksi daya panel surya. Sistem prediksi dapat dirancang untuk mendapatkan model prediksi daya panel surya secara matematik menggunakan model regresi linier majemuk. Model dibangun untuk sistem prediksi dengan menggunakan data latih dari keluaran panel surya. Variasi yang diberikan adalah jenis variabel masukan untuk membangun model. Variabel masukan model terdiri dari temperatur panel surya, iradiasi surya, dan kombinasi dari keduanya. Pengujian data dilakukan dengan menggunakan uji korelasi majemuk, uji signifikasi regresi linier majemuk, dan uji signifikasi koefisien regresi. Hasil perancangan sistem prediksi terbaik adalah kombinasi temperatur panel surya dan iradiasi surya sebagai variabel masukan. Nilai MSE(mean square error) terkecil sebesar 9,83 untuk data latih dan 22,73 untuk data uji.

Cancelable Biometric System for face Based on linear minimum mean square error Model.

2019 ◽  
Vol 28 (1) ◽  
pp. 145-152
Author(s):  
Abd El-aziz Ebrahim Hsaneen ◽  
EL-Sayed M. El-Rabaei ◽  
Moawad I. Dessouky ◽  
Ghada El-bamby ◽  
Fathi E. Abd El-Samie ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Error Model ◽  
Mean Square ◽  
Biometric System
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