Citation:

Madesto Agnus, 2025. "Intelligent Sensor Fusion Using Deep Learning for Next-Generation Electronic Applications", ESP Journal of Engineering & Technology Advancements  5(2): 231-239.

Abstract:

Innovations in artificial intelligence together with the fast development of sensor technology have driven the creation of intelligent sensor fusion systems capable of efficiently interpreting and merging several data sources. Deep learning methods including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and graph neural networks (GNNs) have radically changed how sensor data is analysed, therefore enabling more exact and context-aware decision-making. From raw data aggregation to high-level semantic awareness, these techniques enable fusion at several levels. The combination of artificial intelligence and sensor fusion is opening hitherto unheard-of prospects in several fields. The architectures, models, and deep learning techniques used in intelligent sensor fusion are systematically reviewed in this work. Along with smart wearables that use biosensors and motion detectors to enable continuous health monitoring, key application domains include autonomous vehicles—where data from LiDAR, radar, ultrasonic sensors, and cameras is fused for environment perception. While in industrial automation it allows predictive maintenance and adaptive process control, in robotics sensor fusion improves object detection, localisation, and path planning. Furthermore helping smart homes, augmented reality systems, unmanned aerial vehicles (UAVs) are strong, multimodal sensor data interpretation. The paper also covers issues including computing costs, real-time restrictions, synchronising several sensors, and the need of labelled information. Potential solutions are suggested to be techniques including self-supervised learning, attention processes, and sensor calibration. Moreover, scalable, low-latency, and privacy-preserving sensor fusion uses are being enabled by the combination of edge computing, neuromorphic hardware, and federated learning. This study highlights the future possibilities of intelligent sensor fusion in promoting invention in next-generation electronic systems, so preparing the foundation for smarter, more autonomous, and linked technologies.

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Keywords:

Sensor Fusion, Deep Learning, Edge Computing, Neural Networks, IoT, Context-Aware Systems, Smart Electronics, Real-Time Decision Making.