2026 ASIAN Conference on Communication and Networks (ASIANComNet 2026)

Artificial Intelligence (AI) is rapidly transforming the field of signal processing by enabling machines to learn complex patterns directly from data. The integration of AI and signal processing is creating powerful solutions for analysing speech, images, biomedical signals, radar signals, audio, and communication signals. Traditional signal-processing techniques rely heavily on mathematical models and expert-designed features. AI-based methods can automatically learn meaningful features and representations from large and complex datasets.Machine learning and deep learning have significantly enhanced signal detection, classification, prediction, and reconstruction. Convolutional Neural Networks are widely used for image, speech, and time-frequency signal analysis. Recurrent and transformer-based architectures provide powerful approaches for modelling temporal and sequential signals. AI also enables intelligent noise reduction, signal enhancement, anomaly detection, and source separation. In wireless communications, AI can support channel estimation, spectrum sensing, modulation recognition, and resource optimization. In healthcare, AI-driven signal processing assists in analysing ECG, EEG, medical imaging, and other physiological signals. In radar and sensing applications, intelligent algorithms improve target detection, tracking, and recognition. The emergence of edge AI enables real-time signal processing with low latency and reduced communication requirements. Explainable AI is becoming increasingly important for understanding and validating intelligent signal-processing decisions. Privacy, data quality, computational complexity, robustness, and security remain important challenges. Future signal-processing systems are expected to combine model-based approaches with data-driven AI techniques. Generative AI and foundation models are opening new possibilities for signal generation, restoration, interpretation, and multimodal analysis. The convergence of AI, signal processing, and advanced hardware will enable increasingly autonomous sensing and communication systems. This keynote will explore emerging methodologies, applications, challenges, and future directions in AI-enabled signal processing. It will highlight how intelligent signal processing can bridge the gap between raw data and actionable knowledge. Ultimately, AI-powered signal processing is poised to become a key foundation for next-generation intelligent systems.