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Updated: Apr 18, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

An adaptive handover prediction scheme for seamless mobility based wireless networks.

Ali Safa Sadiq¹, Norsheila Binti Fisal², Kayhan Zrar Ghafoor³

¹Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Lebuhraya Tun Razak, Gambang, 26300 Kuantan, Pahang, Malaysia.

Thescientificworldjournal

|January 10, 2015

Summary

We introduce an adaptive handover prediction (AHP) scheme using fuzzy logic for seamless wireless network mobility. This cognitive approach improves access point selection for better network performance.

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Last Updated: Apr 18, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Area of Science:

Computer Science
Electrical Engineering
Wireless Communication

Background:

Seamless mobility is crucial for wireless networks.
Current handover prediction methods lack adaptability.
Cognitive capabilities enhance network decision-making.

Purpose of the Study:

To propose an adaptive handover prediction (AHP) scheme for wireless networks.
To enhance handover decision-making using fuzzy logic and cognitive capabilities.
To improve the selection of the best access point (AP) for mobile nodes (MNs).

Main Methods:

Incorporating fuzzy logic into the AP prediction process for cognitive handover decisions.
Utilizing selection metrics: received signal strength (RSS), mobile node direction, and AP load.
Employing adaptable coefficients and adjustable weight vectors for input metrics.
Performing independent handover decisions at each MN based on collected data.

Main Results:

The proposed AHP scheme demonstrates superior performance compared to existing prediction approaches.
Adaptive coefficients and adjustable weights effectively handle variations in network metrics.
Fuzzy logic integration provides cognitive capabilities for intelligent handover decisions.

Conclusions:

The AHP scheme offers a robust and adaptive solution for seamless mobility in wireless networks.
The cognitive approach enhances the efficiency and reliability of handover processes.
This method significantly improves upon traditional handover prediction techniques.