We demonstrated a slotted silicon microring resonator with the waveguide gap filled by high mobility titanium-doped indium oxide (ITiO) MOS capacitor. It experimentally achieved an extremely large ...electro-optic wavelength tunability of 540 pm/V.
We demonstrated a silicon microring resonator driven by high mobility titanium-doped indium oxide MOS capacitor. It achieved an unprecedented wavelength tunability of 480 pm/V with field-effect ...mobility of 70 cm 2 V -1 s -1 in the gate.
We present an indoor free space, line-of-sight optical communication system with gigabit bandwidth using an ultra-low power VCSEL diode, which is enhanced by dynamic beam steering and beam shaping ...for rapid user acquisition.
We demonstrated a silicon microring resonator driven by a titanium-doped indium oxide capacitor with 10 nm hafnium oxide insulator, achieving a high quality-factor of 11,700 with a high electro-optic ...tunability of 120 pm/V.
We designed and demonstrated a high-speed plasmonic-conductive oxide-silicon modulator using epsilon-near-zero electro-absorption, achieving modulation bandwidth of 3.5GHz and 4.5Gb/s data rate. The ...electro-absorption modulator covers the entire C-band from 1515 nm to 1580 nm wavelength.
This paper presents a comprehensive and quality collection of functional human brain network data for potential research in the intersection of neuroscience, machine learning, and graph analytics. ...Anatomical and functional MRI images have been used to understand the functional connectivity of the human brain and are particularly important in identifying underlying neurodegenerative conditions such as Alzheimer's, Parkinson's, and Autism. Recently, the study of the brain in the form of brain networks using machine learning and graph analytics has become increasingly popular, especially to predict the early onset of these conditions. A brain network, represented as a graph, retains rich structural and positional information that traditional examination methods are unable to capture. However, the lack of publicly accessible brain network data prevents researchers from data-driven explorations. One of the main difficulties lies in the complicated domain-specific preprocessing steps and the exhaustive computation required to convert the data from MRI images into brain networks. We bridge this gap by collecting a large amount of MRI images from public databases and a private source, working with domain experts to make sensible design choices, and preprocessing the MRI images to produce a collection of brain network datasets. The datasets originate from 6 different sources, cover 4 brain conditions, and consist of a total of 2,702 subjects. We test our graph datasets on 12 machine learning models to provide baselines and validate the data quality on a recent graph analysis model. To lower the barrier to entry and promote the research in this interdisciplinary field, we release our brain network data and complete preprocessing details including codes at https://doi.org/10.17608/k6.auckland.21397377 and https://github.com/brainnetuoa/data_driven_network_neuroscience.
We designed a novel microring modulator based on hybrid transparent conductive oxide-silicon metal-oxide-semiconductor (MOS) capacitor. Experimental results achieved large wavelength tunability of ...100 pm/V and 3 dB modulation at 3V of V pp. Analysis indicates that 44 GHz sneed can be achieved by optimizing the electrode design.
We demonstrated a silicon microring resonator driven by titanium-doped indium oxide capacitor, achieving a tunability of 42 pm/V with a high quality factor above 3600. Analysis indicates that it ...potentially can achieve an extreme electro-optic tunability of 330 pm/V with a high quality factor above 5000.
We developed a facile route for separating and detecting phenethylamine from plasma using photonic crystal biosilica, which serves as a new lab-on-chip platform combining surface-enhanced Raman ...scattering sensing and thin layer chromatography.
