Events

In this talk, the design dilemma of “What will be different between near-field communications (NFC) and far-field communications (FFC)?” is discussed from four perspectives. (1) From the channel modelling perspective, the differences between near-field and far-field channel models are discussed. (2) From the performance analysis perspective, analytical results for characterizing the degrees of freedom and the power scaling laws in the near-field region are provided. (3) From the beamforming perspective, the features of far-field beamsteering and near-field beamfocusing are compared. A couple of new beamforming structures for NFC are also introduced. (4) From the application perspective, several new designs are discussed in the context of promising next-generation technologies in NFC. Finally, research opportunities and problem are discussed.

ISTD PhD Oral Defense presented by Rulin Chen – Modeling and Design of Assemblies with Discrete Equivalence Classes

CONDENSATIONS – Singapore International Water Week 2024

In ARISE: Validate, teams will go out to the field and confirm if there is an actual need for their solution and discover other important requirements of their target market, to better shape the development roadmap of their solution.

ISTD PhD Oral Defense presented by Ms. Menglin Li – Leveraging Pre-trained Language Models for Social Geolocation

Congratulations to Associate Professor Desmond Loke’s Lab for receiving the 2024 Techconnect Innovation Award.

ARISE Program – Validate

ARISE Program – Validate

This talk aims to present the current research efforts on the development of implementing distributed machine learning algorithms in wireless systems. Specifically, we provide a comprehensive coverage of a distributed learning paradigm based on over-the-air computing, a.k.a. over-the-air federated learning (OTA-FL). We will present an analytical framework that quantifies the convergence rate of OTA-FL. Then, we discuss the system enhancements from an algorithmic perspective, i.e., adopting adaptive optimizations to accelerate the model training. We also introduce model pruning schemes that reduce the computation and communication overheads for OTA-FL. Finally, we will elaborate on the analysis of generalization error of the statistical models trained by OTA-FL, which shows that wireless interference has the positive potential of improving the generalization capability.

In ARISE: Validate, teams will go out to the field and confirm if there is an actual need for their solution and discover other important requirements of their target market, to better shape the development roadmap of their solution.