Impact of Tropical Convective conditions on Solar Irradiance Forecasting based on Cloud Motion Vectors

Cloud Motion Vector (CMV) estimation from consecutive satellite images is widely used commercially for providing hours-ahead intraday forecasts of solar irradiance and PV power production. The modelling assumptions in these methods are generally satisfied for advective motion which is common in mid-latitudes, but strained for tropical meteorological conditions dominated by convective clouds. The region under analysis in this study encompasses both tropical and sub-tropical climatic zones and is affected by seasonal strong convection, i.e., the South Asian Monsoon. The purpose of this study is to benchmark the monthly forecast error of three commonly used CMV estimation techniques - Block-match, Farneb&#228;ck (Optical flow) and TV-L1 (Optical flow), for analysing their performance on a seasonal basis. The main focus of this work is the analysis of the limitations of image processing based Block-match and Optical flow techniques in predicting irradiance during the Monsoon period, which presents frequent convective formation and dissipation. Forecasted Cloud Index (CI) maps are validated against reference analysis CI maps for the period 2018-2019 for forecast lead times from 0 to 5.5 hours ahead using the Peak Signal to Noise Ratio (PSNR) metric for estimating the accuracy. Persistence of analysis cloud index maps are used as the reference worst case scenario forecast. Site-level forecasts of irradiance for the same period are validated against ground measured irradiance from two BSRN stations - Gurgaon and Tiruvallur, located in Northern and Southern India respectively. From the Winter period in March to the Monsoon period in August, there is a marked reduction of the 30 minutes ahead forecast accuracy by 3 dB in terms of Peak Signal to Noise Ratio at the image-wide level. This can be observed for all the three methods and the worst-case persistence scenario. Both optical flow methods outperform Block-match by 0.5 dB for the entire period of analysis. The Gurgaon BSRN site is affected by Summer Monsoon and shows an increase in nRMSE by a factor of 3 for all the methods. This station shows a seasonal pattern of forecast error closely matching the image-wide forecast accuracy. The forecast error for the Tiruvallur BSRN station on the other hand reaches its peak in December (Data for October and November are absent), due to its location in the Winter Monsoon climatic zone. Again, the nRMSE for all methods increase by a factor of almost 3 from March to December. The inter-method difference in accuracy is not significant and a seasonal difference (20% nRMSE) dominates. This highlights the shortcomings of image processing techniques in extrapolating future cloud locations under convective situations, where there is rapid change in cloud content between consecutive images.

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Retinal Digital Image Quality Improvement as A Diabetes Retinopatic Disease Detection Effort

Journal of Electrical Technology UMY ◽

10.18196/jet.v4i2.8590 ◽

2020 ◽

Vol 4 (2) ◽

pp. 53-60

Author(s):

Latifah Listyalina ◽

Yudianingsih Yudianingsih ◽

Dhimas Arief Dharmawan

Keyword(s):

Image Processing ◽

Diabetic Retinopathy ◽

Image Quality ◽

Digital Image ◽

Signal To Noise Ratio ◽

Histogram Equalization ◽

Vital Role ◽

Retinal Images ◽

Signal To Noise ◽

Noise Ratio

Image processing is a technical term useful for modifying images in various ways. In medicine, image processing has a vital role. One example of images in the medical world, namely retinal images, can be obtained from a fundus camera. The retina image is useful in the detection of diabetic retinopathy. In general, direct observation of diabetic retinopathy is conducted by a doctor on the retinal image. The weakness of this method is the slow handling of the disease. For this reason, a computer system is required to help doctors detect diabetes retinopathy quickly and accurately. This system involves a series of digital image processing techniques that can process retinal images into good quality images. In this research, a method to improve the quality of retinal images was designed by comparing the methods for adjusting histogram equalization, contrast stretching, and increasing brightness. The performance of the three methods was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Signal to Noise Ratio (SNR). Low MSE values and high PSNR and SNR values indicated that the image had good quality. The results of the study revealed that the image was the best to use, as evidenced by the lowest MSE values and the highest SNR and PSNR values compared to other techniques. It indicated that adaptive histogram equalization techniques could improve image quality while maintaining its information.

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Simulation of the Photodetection in the PPS CMOS Sensors

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.8-9.611 ◽

2013 ◽

Vol 8-9 ◽

pp. 611-618

Author(s):

Florin Toadere ◽

Radu Arsinte

Keyword(s):

Image Processing ◽

Input Data ◽

Signal To Noise Ratio ◽

Image Processing Algorithm ◽

Signal To Noise ◽

Spectral Image ◽

Image Capture ◽

Visible Image ◽

Optical Part ◽

Electrical Part

The paper contains an analysis and simulation of passive pixel based sensors. The passive pixel CMOS image acquisition sensor (PPS) is the key part of a visible image capture systems. The PPS is a complex circuit composed by an optical part and an electrical part, both analog and digital. The goal of this paper is to simulate the functionality of the photodetection process that happens in the PPS sensor. The photodetector is responsible with the conversion from photons to electrical charges and then into current. In the optical part, the sensor is analyzed by a spectral image processing algorithm which uses as input data: the lenses array transmittance, the red, green and blue filters and the quantum efficiency of the PPS. In the electrical part of simulation, the program is computing the signal to noise ratio of the sensor taking into account the photon shot, white and fixed pattern noises. Our basic analysis is based on camera equation to which we add the noises.

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Forecasting of solar irradiance and ramp events with all-sky imagers

10.5194/ems2021-75 ◽

2021 ◽

Author(s):

Stavros-Andreas Logothetis ◽

Vasileios Salamalikis ◽

Stefan Wilbert ◽

Jan Remund ◽

Luis Zarzalejo ◽

...

Keyword(s):

Solar Irradiance ◽

Large Scale ◽

Forecast Error ◽

Forecast Accuracy ◽

Short Term ◽

Event Prediction ◽

High Penetration ◽

Spanish Research ◽

Short Term Forecasting ◽

Solar Resource

Cloud cameras (all sky imagers/ASIs) can be used for short-term (next 20 min) forecasts of solar irradiance. For this reason, several experimental and operational solutions emerged in the last decade with different approaches in terms of instrument types and forecast algorithms. Moreover, few commercial and semi-prototype systems are already available or being investigated. So far, the uncertainty of the predictions cannot be fully compared, as previously published tests were carried out during different periods and at different locations. In this study, the results from a benchmark exercise are presented in order to qualify the current ASI-based short-term forecasting solutions and examine their accuracy. This first comparative measurement campaign carried out as part of the IEA PVPS Task 16 (https://iea-pvps.org/research-tasks/solar-resource-for-high-penetration-and-large-scale-applications/). A 3-month observation campaign (from August to December 2019) took place at Plataforma Solar de Almeria of the Spanish research center CIEMAT including five different ASI systems and a network of high-quality measurements of solar irradiance and other atmospheric parameters. Forecasted time-series of global horizontal irradiance are compared with ground-based measurements and two persistence models to identify strengths and weaknesses of each approach and define best practices of ASI-based forecasts. The statistical analysis is divided into seven cloud classes to interpret the different cloud type effect on ASIs forecast accuracy. For every cloud cluster, at least three ASIs outperform persistence models, in terms of forecast error, highlighting their performance capabilities. The feasibility of ASIs on ramp event detection is also investigated, applying different approaches of ramp event prediction. The revealed findings are promising in terms of overall performance of ASIs as well as their forecasting capabilities in ramp detection. &#160;

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A method for presenting biomedical images based on a model of receptive fields of human visual perception

Journal of Physics Conference Series ◽

10.1088/1742-6596/2091/1/012027 ◽

2021 ◽

Vol 2091 (1) ◽

pp. 012027

Author(s):

V E Antsiperov ◽

V A Kershner

Keyword(s):

Image Processing ◽

Visual Perception ◽

Bayesian Learning ◽

Signal To Noise Ratio ◽

Receptive Fields ◽

New Method ◽

Human Visual Perception ◽

Signal To Noise ◽

Biomedical Images ◽

Local Characteristics

Abstract The paper is devoted to the development of a new method for presenting biomedical images based on local characteristics of the intensity of their shape. The proposed method of image processing is focused on images that have low indicators of the intensity of the recorded radiation, resolution, contrast and signal-to-noise ratio. The method is based on the principles of machine (Bayesian) learning and on samples of random photo reports. This paper presents the results of the method and its connection with modern approaches in the field of image processing.

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A Hybrid Image Processing Approach to Enhance Signal to Noise Ratio of Carbonate Micro CT-Images

10.3997/2214-4609.202011220 ◽

2020 ◽

Author(s):

A. Moslemipour ◽

S. Sadeghnejad ◽

J. Gostick

Keyword(s):

Image Processing ◽

Signal To Noise Ratio ◽

Ct Images ◽

Micro Ct ◽

Signal To Noise ◽

Processing Approach ◽

Noise Ratio

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Improvement of lesion detection by echographic image processing: Signal-to-noise-ratio imaging

Ultrasonic Imaging ◽

10.1016/0161-7346(91)90074-r ◽

1991 ◽

Vol 13 (3) ◽

pp. 238-251 ◽

Cited By ~ 22

Author(s):

J Verhoeven

Keyword(s):

Image Processing ◽

Signal To Noise Ratio ◽

Lesion Detection ◽

Signal To Noise ◽

Ratio Imaging ◽

Processing Signal ◽

Noise Ratio

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A low-PDAP and high-PSNR approximate 4:2 compressor cell in CNFET technology

Circuit World ◽

10.1108/cw-01-2019-0009 ◽

2019 ◽

Vol 45 (3) ◽

pp. 156-168 ◽

Cited By ~ 2

Author(s):

Yavar Safaei Mehrabani ◽

Mehdi Bagherizadeh ◽

Mohammad Hossein Shafiabadi ◽

Abolghasem Ghasempour

Keyword(s):

Image Processing ◽

Design Methodology ◽

Figure Of Merit ◽

Signal To Noise Ratio ◽

Monte Carlo Analysis ◽

The Other ◽

Threshold Logic ◽

Signal To Noise ◽

Content Type ◽

Power Delay Product

Purpose This paper aims to present an inexact 4:2 compressor cell using carbon nanotube filed effect transistors (CNFETs). Design/methodology/approach To design this cell, the capacitive threshold logic (CTL) has been used. Findings To evaluate the proposed cell, comprehensive simulations are carried out at two levels of the circuit and image processing. At the circuit level, the HSPICE software has been used and the power consumption, delay, and power-delay product are calculated. Also, the power-delaytransistor count product (PDAP) is used to make a compromise between all metrics. On the other hand, the Monte Carlo analysis has been used to scrutinize the robustness of the proposed cell against the variations in the manufacturing process. The results of simulations at this level of abstraction indicate the superiority of the proposed cell to other circuits. At the application level, the MATLAB software is also used to evaluate the peak signal-to-noise ratio (PSNR) figure of merit. At this level, the two primary images are multiplied by a multiplier circuit consisting of 4:2 compressors. The results of this simulation also show the superiority of the proposed cell to others. Originality/value This cell significantly reduces the number of transistors and only consists of NOT gates.

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Perona-Malik Model with Diffusion Coefficient Depending on Fractional Gradient via Caputo-Fabrizio Derivative

Abstract and Applied Analysis ◽

10.1155/2020/7624829 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Gustavo Asumu Mboro Nchama ◽

Angela Leon Mecias ◽

Mariano Rodriguez Ricard

Keyword(s):

Image Processing ◽

Mean Absolute Error ◽

Signal To Noise Ratio ◽

Visual Quality ◽

Absolute Error ◽

Natural Images ◽

Mean Square ◽

Signal To Noise ◽

Major Drawback ◽

Diffusivity Function

The Perona-Malik (PM) model is used successfully in image processing to eliminate noise while preserving edges; however, this model has a major drawback: it tends to make the image look blocky. This work proposes to modify the PM model by introducing the Caputo-Fabrizio fractional gradient inside the diffusivity function. Experiments with natural images show that our model can suppress efficiently the blocky effect. Also, our model has good performance in visual quality, high peak signal-to-noise ratio (PSNR), and lower value of mean absolute error (MAE) and mean square error (MSE).

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