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6 articles for “signal-to-noise”
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Enhancing Surface Roughness in the Taguchi Method for Turning Alloy Steel in Wet and Dry Environments
Abstract: The present investigation focuses on evaluating the performance of turning operations in alloy steel with particular emphasis on the effect of cutting parameters on surface roughness. In the machining of alloy steel, tool life and surface integrity are significantly influenced by parameters such as spindle speed, depth of cut, and feed rate. Among these, feed rate has been observed to exert the most prominent effect on surface roughness. To systematically …
Published in International Journal of Robotics and Automation in Mechanics · Vol. 3, Issue 2, 2025 · pp. 1–6 Read article
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Optimizing Manufacturing Processes with Taguchi Method in Production Engineering
Abstract: The Taguchi Method, pioneered by Genichi Taguchi, stands as a powerful optimization tool within the realm of production engineering. This paper delves into the principles, applications, and significance of the Taguchi Method in enhancing manufacturing processes. With a focus on minimizing variation and improving performance, this methodology plays a crucial role in addressing challenges faced by industries in their pursuit of operational excellence. The core components of the Taguchi Method, …
Published in International Journal of Robotics and Automation in Mechanics · Vol. 1, Issue 2, 2024 · pp. 1–8 Read article
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New Ideas in Quantum RF
Abstract: Quantum RF Innovations is leading the way in next-generation wireless technologies by connecting old radio frequency systems with new quantum- enabled solutions. Our goal is to change the way signals are processed, communicated, and sensed by using advanced quantum principles and cutting- edge RF engineering. We are creating a new class of devices and systems that can achieve ultra-low-noise signal amplification, unprecedented spectrum control, and quantum-secured communication through interdisciplinary research …
Published in International Journal of Radio Frequency Innovations · Vol. 3, Issue 2, 2025 · pp. 35–43 Read article
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Intelligent Electromagnetic Synthesis: An AI-Driven IoT Framework for Adaptive Antenna Design in Missile Navigation
Abstract: The rapid evolution of hypersonic and long-range tactical missile systems necessitates antenna architecture capable of maintaining robust communication links under extreme thermal, mechanical, and signal-jamming environments. Traditional antenna design methodologies often relying on iterative simulation cycles and static optimization are increasingly insufficient for the real-time requirements of modern aerospace navigation. This paper proposes an AI-driven, IoT- integrated framework that facilitates autonomous antenna design and performance optimization. By deploying a distributed …
Published in International Journal of Radio Frequency Innovations · Vol. 4, Issue 1, 2026 Read article
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Machine Learning–Guided Cognitive RF System with Dynamic FFT Resolution and Multiplier Reconfiguration for Adaptive Anti-Jamming Communication
Abstract: This paper presents a hierarchical adaptive RF communication system that integrates signal quality-based pre- processing with machine learning-driven signal classification to achieve robust and resource-efficient operation in dynamic, interference-prone environments. Unlike prior art that addresses adaptive RF, ML classification, or anti-jamming individually, this work uniquely combines real-time SNR/RSSI-based signal strength estimation with dynamic FFT size selection (64-, 256- , or 512-point) and arithmetic-level multiplier reconfiguration (CORDIC, Distributed Arithmetic, and hybrid …
Published in International Journal of Radio Frequency Innovations · Vol. 4, Issue 1, 2026 Read article
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AI Driven IoT Based Decision Making System for Brain wave study: KSK approach for Brain wave study
Abstract: The rapid convergence of Internet of Things (IoT) architectures and deep learning has unlocked unprecedented potential for real-time neuro-diagnostic monitoring. This paper presents a novel framework for an AI-driven IoT ecosystem designed to capture, transmit, and interpret human electroencephalography (EEG) signals with minimal latency. Traditional brain-computer interface (BCI) studies are often constrained by localized computing power and the high dimensionality of neural data. Our proposed architecture integrates low-power EEG sensors …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 Read article