As one of the carriers for human communication and interaction, images are prone to contamination by noise during transmission and reception, which is often uncontrollable and unknown. Therefore, how to denoise images...As one of the carriers for human communication and interaction, images are prone to contamination by noise during transmission and reception, which is often uncontrollable and unknown. Therefore, how to denoise images contaminated by unknown noise has gradually become one of the research focuses. In order to achieve blind denoising and separation to restore images, this paper proposes a method for image processing based on Root Mean Square Error (RMSE) by integrating multiple filtering methods for denoising. This method includes Wavelet Filtering, Gaussian Filtering, Median Filtering, Mean Filtering, Bilateral Filtering, Adaptive Bandpass Filtering, Non-local Means Filtering and Regularization Denoising suitable for different types of noise. We can apply this method to denoise images contaminated by blind noise sources and evaluate the denoising effects using RMSE. The smaller the RMSE, the better the denoising effect. The optimal denoising result is selected through comprehensively comparing the RMSE values of all methods. Experimental results demonstrate that the proposed method effectively denoises and restores images contaminated by blind noise sources.展开更多
In this paper, we study edge detection or segmentation, which is recognized as a rudiment innovation as it can evaluate sharpness and analyze object boundaries. That’s the reason it has been an influential figure in ...In this paper, we study edge detection or segmentation, which is recognized as a rudiment innovation as it can evaluate sharpness and analyze object boundaries. That’s the reason it has been an influential figure in the image-processing era. Because of this, it has a significant influence in the age of image processing. On the other hand, edge detection is the process of dividing an image into discontinuous regions. It specifies the intensity shift connected to the image’s edge. There are several methods for detecting edges. Four edge identification methods on satellite images and satellite images affected by Gaussian noise were examined. Known edge detection technologies such as Canny, Prewitt, Scharr, and Robert operators are included in this study. Additionally, the key feature of an image for evaluating its quality is the Image Quality Assessment (IQA) measure. We primarily take into account SSIM, MSE, PSNR, and RMSE when assessing image quality. Experimental validation has been obtained for the application of the Canny and Prewitt algorithms to the satellite dataset. However, when the Gaussian Noise effect is added to the same dataset, clever edge detection performs better.展开更多
Electric vehicle(EV)monitoring systems commonly depend on IoT-based sensormeasurements to track key performance parameters such as vehicle speed,state of charge(SoC),battery temperature,power consumption,motor RPM,and...Electric vehicle(EV)monitoring systems commonly depend on IoT-based sensormeasurements to track key performance parameters such as vehicle speed,state of charge(SoC),battery temperature,power consumption,motor RPM,and regenerative braking.While these systems enable real-time data acquisition,they are often hindered by sensor noise,communication delays,andmeasurement uncertainties,which compromise their reliability for critical decision-making.To overcome these limitations,this study introduces a comparative framework that integrates reference signals,a digital twin model emulating ideal system behavior,and real-time IoT measurements.The digital twin provides a predictive and noise-resilient representation of EV dynamics,enabling enhanced monitoring accuracy.Six critical parameters are evaluated using root mean square error(RMSE),mean absolute error(MAE),maximum deviation,and correlation coefficient(R^(2)).Results show that the digital twin significantly improves estimation fidelity,with RMSE for speed reduced from 2.5 km/h(IoT)to 1.2 km/h and R^(2) values generally exceeding 0.99,except for regenerative braking which achieved 0.982.These findings demonstrate the framework’s effectiveness in improving operational safety,energy management,and system reliability,offering a robust foundation for future advancements in adaptive calibration,predictive analytics,and fault detection in EV systems.展开更多
文摘As one of the carriers for human communication and interaction, images are prone to contamination by noise during transmission and reception, which is often uncontrollable and unknown. Therefore, how to denoise images contaminated by unknown noise has gradually become one of the research focuses. In order to achieve blind denoising and separation to restore images, this paper proposes a method for image processing based on Root Mean Square Error (RMSE) by integrating multiple filtering methods for denoising. This method includes Wavelet Filtering, Gaussian Filtering, Median Filtering, Mean Filtering, Bilateral Filtering, Adaptive Bandpass Filtering, Non-local Means Filtering and Regularization Denoising suitable for different types of noise. We can apply this method to denoise images contaminated by blind noise sources and evaluate the denoising effects using RMSE. The smaller the RMSE, the better the denoising effect. The optimal denoising result is selected through comprehensively comparing the RMSE values of all methods. Experimental results demonstrate that the proposed method effectively denoises and restores images contaminated by blind noise sources.
文摘In this paper, we study edge detection or segmentation, which is recognized as a rudiment innovation as it can evaluate sharpness and analyze object boundaries. That’s the reason it has been an influential figure in the image-processing era. Because of this, it has a significant influence in the age of image processing. On the other hand, edge detection is the process of dividing an image into discontinuous regions. It specifies the intensity shift connected to the image’s edge. There are several methods for detecting edges. Four edge identification methods on satellite images and satellite images affected by Gaussian noise were examined. Known edge detection technologies such as Canny, Prewitt, Scharr, and Robert operators are included in this study. Additionally, the key feature of an image for evaluating its quality is the Image Quality Assessment (IQA) measure. We primarily take into account SSIM, MSE, PSNR, and RMSE when assessing image quality. Experimental validation has been obtained for the application of the Canny and Prewitt algorithms to the satellite dataset. However, when the Gaussian Noise effect is added to the same dataset, clever edge detection performs better.
文摘Electric vehicle(EV)monitoring systems commonly depend on IoT-based sensormeasurements to track key performance parameters such as vehicle speed,state of charge(SoC),battery temperature,power consumption,motor RPM,and regenerative braking.While these systems enable real-time data acquisition,they are often hindered by sensor noise,communication delays,andmeasurement uncertainties,which compromise their reliability for critical decision-making.To overcome these limitations,this study introduces a comparative framework that integrates reference signals,a digital twin model emulating ideal system behavior,and real-time IoT measurements.The digital twin provides a predictive and noise-resilient representation of EV dynamics,enabling enhanced monitoring accuracy.Six critical parameters are evaluated using root mean square error(RMSE),mean absolute error(MAE),maximum deviation,and correlation coefficient(R^(2)).Results show that the digital twin significantly improves estimation fidelity,with RMSE for speed reduced from 2.5 km/h(IoT)to 1.2 km/h and R^(2) values generally exceeding 0.99,except for regenerative braking which achieved 0.982.These findings demonstrate the framework’s effectiveness in improving operational safety,energy management,and system reliability,offering a robust foundation for future advancements in adaptive calibration,predictive analytics,and fault detection in EV systems.
