Real-time Fusion of Stereo Vision and Hyperspectral Imaging for Objective Decision Support during Surgery
We present a real-time stereo hyperspectral imaging system that combines 3D reconstruction with spectral tissue analysis, enabling live visualization of oxygenation and tissue composition during surgery.
Live Demonstration of Modulation Format Identification Using a Photonic Neural Network
We demonstrate modulation format identification on a micro ROADM ring using a photonic neural network with low-speed photodiodes.
Design and Validation of a Time Domain Correlation based Channel Sounder up to 500 GHz
In this paper, we introduce a novel time domain correlation based channel sounder that operates at 485 GHz.
Agile Sub-Terahertz to Terahertz Broadband Time-Domain Photonic Channel Sounder
This paper presents the design and implementation of an agile sub-terahertz (sub-THz) broadband time-domain photonic channel sounder, capable of characterizing wireless propagation channels within the 100 GHz to 500 GHz frequency range.
RIPE: Reinforcement Learning on Unlabeled Image Pairs for Robust Keypoint Extraction
We introduce RIPE, an innovative reinforcement learning-based framework for weakly-supervised training of a keypoint extractor that excels in both detection and description tasks. In contrast to conventional training regimes that depend heavily...
Waveform Design for Simultaneous MIMO Radar Sensing and Multi-User Communication
This work introduces a two-stage waveform design framework for multi-antenna integrated sensing and communication systems. By optimizing radar beampatterns and communication signals under practical constraints, the proposed method achieves...
Digital Post-Distortion Architectures for Nonlinear Power Amplifiers: Volterra and Kernel Methods
In modern 5G UEs, the PA consumes large amounts of power. However, there is a trade-off between power efficiency and nonlinear distortion. This study explores digital post-distortion to address PA nonlinearities at the base station. We take a...
Batch-Aware Active Learning for Object Detection
Active Learning is a powerful way to cut down the time and effort needed to annotate data, but its use in object detection remains underexplored. We introduce a Batch-Aware Active Learning (BAAL) framework that combines uncertainty with diversity...
Resilience Enhancement of Optical Network-Cloud Ecosystems with Dataspace Framework and Multi-entity Cooperation (Invited)
To enhance the resilience of network-cloud ecosystems, we establish a data governance framework for sharing optical testbed data across organizations and fostering machine learning research of optical networks. We further introduce multientity...
Data Sovereign LLM-Assisted Automation Platform for Open Optical and Packet Transport Networks
The Open Optical Transport Network (OOPT) architecture, defined by the Telecom Infra Project (TIP), is currently being deployed by major operators. The OOPT ecosystem, with its multi-stakeholder and multi-vendor environment, faces challenges like...