Dual Channel

This research area investigates silicon photonic multiplexers/demultiplexers designed to exploit dual optical streams for high-density on-chip communication. It focuses on integrating cascaded ring resonators with thermal tuning on silicon-on-insulator platforms to achieve dense wavelength division multiplexing (WDM) with large bandwidth, low crosstalk, and low power consumption. This is crucial for evolving optical interconnects in data centers and high-performance computing where ultra-high bandwidth and integration density are required.

This theme centers on understanding the consumer-driven dynamics in supply chains adopting dual distribution channels (offline traditional retail and online direct sales). It emphasizes identifying push (forces pushing consumers away from one channel), pull (attractors toward another channel), and mooring (personal and social facilitators or barriers) factors affecting channel switching behavior. Insights gained inform how firms may manage channel conflicts, optimize channel integration, and improve customer retention strategies.

Research under this theme addresses analytical and computational modeling of supply chain mechanisms involving simultaneous traditional retail and online direct sales channels. It explores the strategic interplay of pricing, inventory control, service quality (notably delivery lead times), and channel coordination under both vertically integrated and decentralized competitive scenarios. The focus is on characterizing equilibrium behaviors, supply chain efficiency, and profit implications for supply chain members.

This theme covers engineering approaches utilizing hybrid line coding schemes and multi-amplifier setups to improve performance metrics such as bit error rate, signal power amplitude, and total electrical power consumption in dual-channel free-space optics (FSO) and fiber optics (FO) communication systems. The goal is to extend reach and data rates while minimizing noise and power consumption in high-speed optical communication links.

This theme pertains to utilizing machine learning and feature engineering on multiplexed image data channels—specifically histological tissue staining images—to classify disease states such as prostate cancer. It involves extraction of texture-based features from dual imaging channels and applying advanced feature selection methods combined with AI classifiers to improve diagnostic accuracy in biomedical applications.