European Journal of Electrical Engineering and Computer Science

Hybrid Image Filtering Method Based on Wavelet Transform

2024-08-09T17:17:16-04:00

The image acquisition process often ends with obtaining an image tainted with parasitic information which alters its quality. In order to analyze the image effectively and thus make reliable decisions, it is essential to reduce or even eliminate the noise that corrupts it. This work proposes a method of filtering images in order to reduce the noise they contain. The proposed method is a combination of bilateral filter, bivariate shrinkage and Bayes Shrink thresholding in the wavelet domain. This method consists of applying the bilateral filter to the noisy image and then decomposing it into eight wavelet sub-bands. Subsequently, bivariate shrinkage is applied to the first-level detail sub-bands, and BayesShrink thresholding is applied to the second-level detail sub-bands. The filtered image is obtained after wavelet reconstruction. The experiments were carried out on two most used images in the literature (Cameraman and Lena) corrupted with Gaussian with noise variance σ² _N = {0.05, 10, 20, 30, 50}. The performance of the proposed method was evaluated in terms of PSNR. The results obtained compared to certain approaches in the literature show that the proposed method is efficient and considerably outperforms those of existing works with an optimal PSNR value.

Robust Fuzzy-PID Technique for the Automatic Generation Control of Interconnected Power System with Integrated Renewable Energy Sources

2024-07-24T17:07:35-04:00

This paper proposes a robust hybrid fuzzy PID controller, with the PID gain values tuned using the particle swarm optimization (PSO) technique for the automatic generation control (AGC) of the two-area hybrid power system. PSO Algorithm is applied to reduce the integral of time-weighted absolute error (ITAE) of the frequency and tie-line power variations of the two-area interconnected power system. The suggested method’s efficacy is examined on an interconnected two-area power system with Area 1 containing thermal reheat plants, hydropower plants, and diesel generating units, and Area 2 comprising thermal reheat plants and renewable energy sources (RES): wind energy conversion system, solar photovoltaic system, and electric vehicles. The thermal reheat power plants are modelled taking into consideration the governor dead band (GDB) and the generation rate constraints (GRC). The performance of the presented controller is compared with the genetic algorithm-optimized PID AGC and the PSO-optimised PID AGC. The simulation outcomes show superior results of the proposed controller against other controllers. Further, sensitivity analysis reflects that the proposed technique provides better dynamic response and robustness than the other techniques.

From Waste to Resource: How Standardized Health Metrics Can Accelerate the Circular Economy in Storage Media

2024-07-18T17:06:46-04:00

Current research into hard disk drives (HDD) and solid-state drives (SSD) focuses on predicting failure rates using SMART attributes to decide when to retire a drive and move to the end of life rather than identifying ways to increase product life. This paper looks instead at what attributes need to be present to reuse the drive with full confidence and which attributes have a material impact on reliability during second use.

To quantify which attributes had the highest impact we conducted a largescale erasure project of over 117,000 unique drives to identify reasons for the failure of erasure and what drive attributes led to the storage device’s failed sanitization.

These findings aim to inform the development of industry standards and practices prioritizing data security, device health and facilitating increased reuse of storage media and furthering the circular economy.

Optimizing Network Costs for NFV Solutions in Urban and Rural Indian Cellular Networks

2024-07-19T17:06:40-04:00

With the rapid growth of telecommunication infrastructure in India, there is a need for smarter, more innovative solutions to meet the demands of infrastructure expansion while keeping the costs optimized. Using Network Function Virtualization (NFV) promises a significant reduction in operation and setup costs. This paper aims to evaluate the economic impact of NFV implementation in urban and rural Indian contexts. The study conducts a thorough cost benefit analysis to assess the advantages of NFV adoption for Indian telecom operators, businesses and lawmakers. The research uses case studies to examine existing NFV implementations in India. In both the urban and rural contexts, the emphasis is on cost-saving, cost-effectiveness, services, quality of services, user satisfaction and coverage. Comparisons across all these scenarios are identified to understand the drivers and barriers to NFV adoption in India. Insight is offered into the impact of policies and the state of the existing technological infrastructure. The findings reveal that NFV can significantly optimized network costs and help improve service quality while providing a favorable return to investors within the calculated payback period. Additionally, best practices for effective NFV deployment are mentioned providing guidelines for telecom operators and businesses. This paper contributes to the general understanding of NFV as a potential tool to transform existing telecommunications infrastructure in India, emphasizing its impact via network efficiency and improvements in quality of service.

A Baseline Electroencephalography Motor Imagery Brain-Computer Interface System Using Artificial Intelligence and Deep Learning

2024-07-03T17:00:47-04:00

This paper presents a baseline or reference (single channel, single subject, single trial) electroencephalography (EEG) motor imagery (MI) brain computer interface (BCI) that harnesses deep learning artificial neural networks (ANNs) for brainwave signal classification. The EEG electrode or sensor is placed on the scalp within the frontal lobe of the right hemisphere of the brain and approximately above the motor cortex. Signal classification discriminates among three MI classes, namely, right first closed event, neutral event and left first closed event and the measured accuracy of the deep learning ANN was 83% which significantly outperforms chance classification. The effectiveness of the system is demonstrated by applying it to the navigation of a virtual environment, specifically, immersive 360-degree panoramas in equirectangular projection.