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This book constitutes the refereed proceedings of the 15th EAI International Conference on Bio-inspired Information and Communications Technologies, BICT 2024, held in Quzhou, China, during August 15-16, 2024.
The 16 full papers included in this book were carefully reviewed and selected from 52 submissions. They were organized in topical sections as follows: Asia-Pacific Workshop on Molecular Communications; Bio-inspired ICT; and ICT-inspired Biomedicine.
.- Asia-Pacific Workshop on Molecular Communications. .- Industrial Perspectives for Molecular Communication in Future Networks. .- Optimizing Drug Delivery Strategies by Pathway Analysis for Waveform Modulation-based Molecular Communication. .- Enhancing DNA-based IoBNT Throughput and Reducing Congestion with Yin-Yang Coding. .- A Molecular Communication Model Driven by Magnetic Field Force. .- CDM Based on Izhikevich Neuron Model. .- Multi-user Diffusive Molecular Communication Systems with A Passive Relay node: Receiver Design and Performance Analysis. .- Machine Learning-Based Detection Time Estimation for Molecular Communication. .- Taming Signal-dependent Counting Noise with Machine Learning for Molecular Communication. .- Energy-Efficient Transmitter Creation in Molecular Communication. .- A Method for Determining Relay Node location in Molecular Communication. .- Glycemic Oscillation Decomposition-based Personalized Blood Glucose Prediction with Continuous Glucose Monitoring. .- Bio-inspired ICT. .- Bio-inspired Microstrip Antenna (Bi-MPA) for Medical Microwave Imaging Applications. .- Image Encryption and Decryption Algorithm based on DNA Sequence: Performance Analysis of Channel Fusion Processing. .- ICT-inspired Biomedicine. .- Optimizing the Classification of SSVEP Signals in Brain-computer Interfaces: A Novel Sliding Window Data Segmentation Method Based on Weighted Voting Mechanism. .- Spontaneous Motion of Nanorobots Inspired Computational Technology for Tumor Boundary Exploration. .- Light-Driven Aggregation of Nanorobot Swarms for Precision Tumor Targeting in Manhattan-Geometry Vasculature.
