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Tianben Ding

Publications and source records attributed to Tianben Ding.

2 recordsLinked to original sources

STEPIC: High-Speed Imaging via Spatio-Temporal Encoding in Photonic Integrated Circuits

High-speed imaging of cells in flow is essential for probing cellular heterogeneity in large populations. Existing imaging approaches based on single-pixel detection and spatio-temporal encoding provide exceptional speed, but typically rely on bulky free-space optics, long dispersive elements, and are prone to alignment instabilities. Here, we introduce STEPIC Microscopy, the first fully integrated on-chip system for high-speed imaging via spatio-temporal encoding in photonic integrated circuits. Our platform leverages waveguides, splitters, fiber delay-lines, and 3D optical remappers to encode spatial information into the temporal domain, enabling robust image reconstruction of cells flowing through microchannels. The monolithic architecture provides a compact and robust platform for high-throughput bioimaging, enabling scalable and practical implementations of ultrafast imaging systems.

physics.optics

Online regularization of complex-valued neural networks for structure optimization in wireless-communication channel prediction

This paper proposes online-learning complex-valued neural networks (CVNNs) to predict future channel states in fast-fading multipath mobile communications. CVNN is suitable for dealing with a fading communication channel as a single complex-valued entity. This framework makes it possible to realize accurate channel prediction by utilizing its high generalization ability in the complex domain. However, actual communication environments are marked by rapid and irregular changes, thus causing fluctuation of communication channel states. Hence, an empirically selected stationary network gives only limited prediction accuracy. In this paper, we introduce regularization in the update of the CVNN weights to develop online dynamics that can self-optimize its effective network size by responding to such channel-state changes. It realizes online adaptive, highly accurate and robust channel prediction with dynamical adjustment of the network size. We demonstrate its online adaptability in simulations and real wireless-propagation experiments.

eess.SP