Digital Image Processing (DIP) is the ability to manipulate digital photographs via algorithms for pattern detection, segmentation, enhancement, and noise reduction. In addition, the Internet of Things (IoT) acts as the eye and system for all DIP in various applications. It can possess a camera or another image sensor in order to capture real-time data from its environment. All vital data is processed by image processing in such a way that it recognizes the object, detects an anomaly, and automatically decides in real-time. In addition, in an IoT system, the median filter is the technique used for noise reduction by substituting the value of the pixel with the central value of the surrounding pixels. It provides speed and efficiency for quick analysis in all IoT systems. However, the images can get corrupted, especially in resource-constrained IoT devices with small cameras, because of random glitches. Moreover, using new quantum technology like atomic-scale silicon dangling bond (DB) logic circuits, which have advanced in fabrication and become a strong contender for field-coupled nano-computing, can solve previous problems in IoT systems. In this paper, we propose a unique quantum CSM based on two new proposed Mux and De-mux. The proposed CSM can be used for computational circuits like median filter circuits (MFC) in a wide range of digital circuits, specifically IoT devices. The proposed design is verified and validated using the powerful SiQAD tool. When comparing CSM to the newest designs, the suggested quantum circuit uses 85% less energy and takes up 61% less area.

Electricity is a cornerstone of India’s development, yet the country faces significant challenges in its generation and distribution. Existing generation techniques encompass a variety of sources, including renewable (such as solar and wind) and non-renewable (such as coal and nuclear) sources. Among these, coal remains a predominant source of power generation. However, the efficiency of coal-based electricity generation is severely hampered by inefficiencies in the coal supply chain, particularly in transportation. This research paper proposes a blockchain-based solution to address these inefficiencies. By implementing a blockchain-based coal supply chain management system, we aim to introduce greater transparency, security, and efficiency into the process. This paper discusses the limitations of the existing coal supply chain systems and emphasizes the potential of blockchain technology. Our proposed model leverages the blockchain’s decentralized and immutable ledger to ensure real-time visibility, automated smart contracts for streamlined documentation, and robust cryptographic measures for data security. The implementation of this system could significantly enhance the resilience and sustainability of India’s energy sector. We have performed a performance analysis of the proposed application using Hyperledger Caliper to check its robustness.

In mathematical models and applications, evolutionary algorithms have proven indispensable for solving optimization problems. These algorithms may, however, eventually experience many issues, including diversity loss and stagnation. To overcome these challenges, a novel mutation operator has been introduced and incorporated into the differential evolution (DE) algorithm. This newly proposed mutation operator, based on the homeostasis factor, not only ensures global search and environmental balance but also promotes diversity within the problem.The effectiveness of the proposed method has been validated through two tests: experimentation and comparative studies on benchmark functions, as well as cost estimation for software applications. In the experimentation and comparative study, the proposed method achieved satisfactory results, with the minimum number of function evaluations and statistical functions reaching the target value of 108. Additionally, the proposed operator demonstrated favorable outcomes in terms of minimum error for each function, as confirmed by the Wilcoxon rank-sum test (p = 0.05). The experimental results indicate that the proposed method outperforms state-of-the-art algorithms, offering superior outcomes in terms of minimum error, accuracy prediction, mean magnitude of relative error (MMRE), and root mean square error (RMSE).