Journal of Electronic & Information Systems

Precise Spacecraft Attitude and Angular Velocity Estimates Using Cubature Kalman Filter on Intensely Distorted One-Axis Magnetometer Measurements

Tamer Mekky Ahmed Habib — 2025-08-20

Magnetometers are widely used spacecraft attitude sensors due to their numerous advantages. Typically, fully observing a spacecraft’s attitude requires the use of at least two distinct sensor types. Thus, relying exclusively on a magnetometer introduces major challenges for estimation algorithms. The problem of spacecraft attitude estimation based on magnetometer measurements is generally nonlinear. Cubature Kalman Filter (CKF) is considered as a newly developed filter that addresses the problem of state estimation for nonlinear systems. The current research article develops a CKF algorithm that utilizes magnetometer measurements as a sole spacecraft attitude sensor. The developed algorithm provides multiple benefits over traditional methods, offering exceptional accuracy comparable to other Extended Kalman Filter based (EKF-based) algorithms. The developed CKF has a resistance to significant initial estimation errors. The proposed CKF algorithm functions in every spacecraft operational mode, consistently delivering precise results. Even when measurements are severely noisy, CKF achieves an accuracy of better than 0.24 degree approximately in each axis. This accuracy enabled the magnetometer to serve as the sole source of spacecraft attitude information despite having one or two faulty channels out of three. A benchmarking for the proposed CKF is given against many other intensely verified EKF-based algorithms to present a quantitative comparison. This comparison could help the designer of the spacecraft Attitude and Orbit Control System (AOCS) to choose an appropriate algorithm according to mission specific key performance indices. A case study spacecraft is utilized which is subject to aerodynamics drag torques, solar radiation pressure torques, and residual magnetic torques.

Low – Power TSPC Flip-Flop with Auto-Gated Clock Gating, Power Gating and Redundant-Transition Suppression

Bairi. Rohith Kumar — 2025-08-13

An advanced low-power True Single Phase Clock (TSPC) flip-flop design leveraging a synergistic integration of three power-saving techniques: auto-gated clock gating, power gating, and redundant-transition suppression. The proposed architecture targets both dynamic and leakage power reduction in sequential circuits without sacrificing speed or timing integrity. Auto-gated clock gating dynamically disables the clock signal when input data remains stable, eliminating unnecessary switching activity. Power gating is employed to disconnect the power supply to idle flip-flop stages during prolonged inactivity, significantly reducing static leakage current. Additionally, redundant-transition suppression logic prevents internal node toggling in response to non-transitioning inputs, further minimizing dynamic power dissipation. These techniques are seamlessly embedded within the TSPC structure, preserving its inherent advantages such as single-phase clock operation and high-speed performance. The design is implemented and verified through post-layout simulations in a standard CMOS technology, demonstrating substantial improvements in energy efficiency compared to conventional TSPC and other existing low-power flip-flop designs. Results indicate significant reductions in both active and standby power consumption, achieving superior energy-delay product metrics. The proposed flip-flop is particularly well-suited for high-performance digital systems operating under stringent energy constraints, such as portable and battery-powered devices. By intelligently managing clock and power resources while maintaining robust functionality, this design offers a practical and scalable solution for next-generation energy-efficient integrated circuits.

P-CSNKS: Post-Quantum Collaborative Signature Scheme with Non-Linear Private Key Splitting Technique

Fei Long — 2025-07-17

Traditional collaborative signature schemes face significant challenges in resisting quantum computing attacks, securing private keys in distributed architectures, and balancing operational efficiency, which are critical requirements for modern electronic and information systems like IoT, blockchain, and federated learning. This paper proposes P-CSNKS, a novel post-quotum collaborative signature scheme featuring a non-linear private key splitting technique. Unlike linear secret sharing, P-CSNKS partitions the master private key into multiple interdependent subkeys using multiplicative inverses and modular arithmetic, ensuring algebraic interdependencies prevent full key reconstruction even if attackers compromise sufficient shares. Simultaneously, the scheme embeds hash-based post-quantum signature components directly into the collaborative ECDSA signing workflow. This hybrid design maintains backward compatibility with standard ECDSA verification while establishing dual security layers: one for classical security and another providing provable existential unforgeability against quantum adversaries in the quantum random oracle model. Crucially, P-CSNKS achieves this quantum resistance without incurring prohibitive computational costs. Rigorous experimental evaluations demonstrate that P-CSNKS significantly outperforms lattice-based while also showing efficiency gains against hash-based scheme. The optimized algorithms for key generation, signing, and verification ensure lightweight performance suitable for latency-sensitive applications. Thus, P-CSNKS delivers enhanced security against both classical and quantum threats while meeting the stringent efficiency demands of next-generation distributed systems.

Improving Fast Density Peak Clustering Using Mass Distance: m-FDPC

Mouloud Merbouche — 2025-08-30

In this work, we introduce m-FDPC, a mass-based variant of the Fast Density Peak Clustering (FDPC) algorithm, aimed at improving both performance and ease of use in unsupervised learning tasks. Traditional FDPC relies on Euclidean distance and requires careful parameter tuning and data normalization, which can significantly affect clustering outcomes—especially for heterogeneous or high-dimensional datasets. To address these challenges, m-FDPC replaces the conventional Euclidean metric with a mass-based distance measure derived from isolation forests, a method originally designed for anomaly detection. This substitution allows the algorithm to capture local data density and structure more naturally, while eliminating the need for normalization and simplifying the choice of key parameters such as cutoff distance and density thresholds. Comprehensive experiments on synthetic and real-world datasets demonstrate that m-FDPC not only matches or surpasses the performance of well-established clustering techniques such as DBSCAN, K-means, and Euclidean FDPC, but also offers greater robustness, scalability, and interpretability, particularly in high-dimensional or unevenly distributed data scenarios. Results evaluated through metrics like Matching Score and Silhouette Score confirm the algorithm’s superior ability to detect meaningful cluster structures with minimal user intervention. Overall, m-FDPC provides a more efficient, adaptive, and user-friendly framework for density-based clustering, making it a promising tool for diverse applications in data mining, anomaly detection, and exploratory data analysis.

Fuzzy Inference System for Analysing Physical Fitness Metrics of Male-Female Trainee Athletes: De-Fuzzification via Hull and Sigma Scale Analysis

Rita Rani — 2025-07-25

A simulation-based and deterministic approach was employed to assess the health-related fitness of upper primary school students through a Fuzzy Inference System (FIS) implemented in MATLAB. Standardized physical assessments were used to gather fitness data, which were then systematically categorized by gender and grade. While statistical metrics such as mean and standard deviation were extracted, inconsistencies and data ambiguities reduced the effectiveness of a strictly deterministic analysis. To overcome these limitations, fuzzy logic was introduced to better manage uncertainty and overlapping patterns in the data. Linguistic variables derived from the Hull and Sigma Scales were incorporated as signal descriptors within the fuzzy framework, improving system interpretability. A triangular membership function was chosen for its balance of computational simplicity and accuracy in classifying fitness levels. Simulation outcomes revealed that the Hull Scale achieved 18% higher consistency in classification compared to the Sigma Scale, highlighting its superior diagnostic performance and potential for identifying health-related fitness trends across diverse student populations. Additionally, optimal input parameters were identified, further enhancing the functionality of decision support systems in school health monitoring. Results confirmed that integrating fuzzy logic with deterministic models leads to a more adaptable and reliable method for assessing student fitness across genders. This hybrid approach can support educators, health professionals, and policymakers in developing more effective, targeted physical wellness interventions. Thus, upper primary students' health-related fitness can be accurately evaluated using a FIS-based system in MATLAB, enhancing performance in youth-focused decision support applications.

Energy-Efficient Federated Learning at the Wireless Edge: A Survey

Christopher Aseer J Albert — 2025-09-09

The recent surge in edge computing and wireless connectivity has accelerated the adoption of Federated Learning (FL), a paradigm that enables privacy-preserving distributed intelligence across resource-constrained devices. However, implementing FL in practical wireless edge networks introduces significant challenges, particularly excessive energy consumption and communication overhead. This survey provides a system-level exploration of energy-efficient FL strategies, examining algorithmic advances and deployment challenges. Core techniques—such as model compression, update sparsification, and adaptive client scheduling—are analyzed with respect to their trade-offs in scalability, convergence, and long-term energy sustainability, especially under non-IID data distributions and heterogeneous device conditions. Practical insights are drawn from case studies in the Internet of Things (IoT), 5G/6G wireless ecosystems, and ultra-low-power device deployments, highlighting both limitations and optimization opportunities for real-world implementations. In addition, the survey explores emerging enablers, including blockchain-based trust frameworks, neuromorphic processors, and reinforcement learning-driven orchestration, which hold potential for achieving robust, sustainable FL in dynamic edge environments. By integrating perspectives from communication theory, distributed systems, and sustainable computing, this work delivers an interdisciplinary roadmap for the realistic deployment of energy-efficient FL in next-generation wireless systems, aiming to guide future research toward scalable, fair, and sustainable federated intelligence at the wireless edges.