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IEEE Internet of Things Journal

A New Median Filter Circuit Design Based on Atomic Silicon Quantum-Dot for Digital Image Processing and IoT Applications

Seyed‐Sajad Ahmadpour, Danial Bakhshayeshi Avval, Nima Jafari Navimipour, Hadi Rasmi, Arash Heidari, Sankit Ramkrishna Kassa, Neeraj Kumar Misra, Ahmad Habibizad Navin, Mohammad Mosleh, Mehdi Hosseinzadeh, Mukesh Patidar

Published Apr 8, 2025• 31 citations
Publisher

Abstract

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.

DOI
10.1109/jiot.2025.3553706

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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.
The fastest growing Internet of Things (IoT) application field necessitates efficient signal processing frameworks with high computational portions that have an incomparably low power consumption, especially among embedded devices subject to very limited resources. In this paper, we present a hybrid quantum-inspired signal processing paradigm combining both theoretical aspects of a quantum-inspired computation we have used along our theoretical aspects of classical DSP, e.g., moving between amplitude encoding, quantum superposition, quantum optimization of rotation gates, adaptive FIR/IIR filtering and discrete wavelet transforms, and fixed-point optimisation. Such theoretical combination solves some of the most important signal processing issues that involve lowered computational complexity, enhanced convergence characteristics of adaptive filters, and heightened noise resilience in non-stationary settings. The hardware-algorithm co-design in the framework uses a combination of fixed-point arithmetic, loop unrolling and memory blocking to facilitate successful real-time deployment on an ARM Cortex-M4 microcontroller. Up to 43 percent reduction of energy, 31 percent improvement on execution speed and 25 percent better in the classification rate compared to baseline DSP methods are also shown through experimental validation. Demonstration of the generalizability of the strategy by utilizing it in the area of biomedical signal denoising, environmental sound analysis, and structural vibration monitoring creates a scalable methodology of realizing elegant signal processing theory under resource-limited embedded IoT devices.
The proliferation of Internet of Things (IoT) devices has created a critical demand for highly energy-efficient and timing-accurate Very Large-Scale Integration (VLSI) circuits. Traditional simulation and modeling techniques often fall short in capturing real-time performance metrics, particularly under dynamic operating conditions and process variations. Digital Twin (DT) technology has emerged as a transformative approach, enabling real-time mirroring and prediction of physical systems. However, classical DT frameworks are computationally limited when simulating complex VLSI behaviors involving multiple non-linear interactions and stringent power constraints. To address these challenges, this research proposes a novel quantum computing-enhanced digital twin framework designed for low-power VLSI circuits in IoT applications. The proposed system leverages the computational parallelism and probabilistic modeling capabilities of quantum algorithms to accelerate the simulation and optimization of power and timing metrics. A hybrid quantum-classical architecture is employed, where a quantum variational circuit models the probabilistic switching behavior of logic gates, while classical components handle data acquisition and control operations. Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) are integrated to minimize energy dissipation and optimize timing paths, respectively. Real-time telemetry from IoT hardware is used to continuously update the twin, ensuring fidelity with the physical circuit. Experimental evaluations on industry-standard benchmark circuits (e.g., ISCAS-85, ITC-99) demonstrate significant improvements in dynamic power estimation accuracy and critical path timing prediction when compared to classical simulation methods. Energy consumption was reduced by up to 17%, and timing deviations were minimized by 22% under varying operating conditions. Moreover, the quantum-DT framework proved robust in the presence of noise and variations, exhibiting scalability for large-scale circuit designs. This study highlights the potential of integrating quantum computing with digital twin technology to meet the stringent design goals of modern VLSI systems, especially in resource-constrained IoT environments. The proposed framework not only enhances modeling precision but also enables real-time adaptive optimization, paving the way for next-generation intelligent circuit design and autonomous system calibration.
Quantum image processing is a new branch of quantum computing that aims to adapt traditional image processing tasks and operations to the quantum computing field. Obtaining images with high accuracy is a very difficult task. Filters are used to enhance the efficiency of the image and modify digital images. There are many filtering techniques, such as median filtering, Laplacing filtering, Qaussin filtering, neighborhood average filtering, and box filtering. Currently, there are limitations in quantum median filtering techniques due to either incomplete noise recognition or computational complexity. We propose the design of an estimated image median filter with a noise tolerance threshold to remove salt and pepper noise, and finally, we design a quantum circuit that decreases the computational complexity to O( . Furthermore, the simulation results demonstrate that the proposed median filtering is feasible.
This paper addresses the problem of digital image denoising using a median filtering method based on concepts inspired by quantum computing. In the proposed approach, pixel values are encoded in a manner analogous to quantum states, and their distribution is processed using the Quantum Fourier Transform. The median value estimation is performed based on a probabilistic distribution, which enables a direct comparison with the classical median filtering technique. Artificial noise is added to the images, and the quality of the images before and after filtering is evaluated using objective quality metrics. The experimental results demonstrate that the proposed quantum median filtering model provides a positive effect in restoring image quality and effectively reduces the impact of noise. This approach highlights the potential of applying quantum-inspired computational methods in the field of digital image processing.
The significance of using quantum computing in a variety of applications is illustrated by qubits, coherence time, and gate error rate, respectively, in all of the measurements. In quantum information, the fundamental units are called qubits, which are comparable to bits in classical information. The complicated computation can be simplified by employing qubits, which can be used in a wide variety of contexts. Data can be analyzed without producing any decoherence errors, which is particularly useful for image processing and real-time applications. The concept of “noisy intermediate scale quantum” (NISA) can be addressed and used in real-time data collecting through the Internet of Things as well as in picture processing. The NISA has a primary emphasis on factorization and optimization techniques for the purpose of data analysis. Therefore, this progress will be essential in unlocking the full potential of quantum computing and enabling it to tackle a wider variety of difficult challenges like the Internet of Things (IoT) and Image Processing
This paper explores the potential of dynamic quantum circuits with fault tolerance in enhancing Internet of Things (IoT) gateways. By allowing real-time adaptability and incorporating robust error correction mechanisms, dynamic quantum circuits can significantly improve the security, efficiency, and reliability of IoT systems. The adaptability of these circuits enables them to respond to changing conditions and errors, making them well-suited for the complex and often unpredictable nature of IoT environments. However, the implementation of dynamic quantum circuits presents several challenges. These include the complexity of circuit design, the need to manage high error rates, increased resource requirements, and the integration of quantum systems with existing classical architectures. Additionally, developing effective real-time feedback mechanisms and ensuring robust security measures are critical for maintaining the integrity of these advanced systems. Despite these challenges, the potential benefits of dynamic quantum circuits in IoT applications are substantial. As research progresses and technology advances, overcoming these obstacles could lead to the realization of smarter, more resilient IoT environments that leverage the unique capabilities of quantum computing. This integration has the potential to drive innovation across various sectors, ultimately enhancing everyday life and transforming how we interact with technology.